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Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases
Open accessAbstract
Complex and dynamic networks of molecules are involved in human diseases. High-throughput technologies enable omics studies interrogating thousands to millions of makers with similar biochemical properties (eg, transcriptomics for RNA transcripts). However, a single layer of "omics" can only provide limited insights into the biological mechanisms of a disease. In the case of genome-wide association studies, although thousands of single nucleotide polymorphisms have been identified for complex diseases and traits, the functional implications and mechanisms of the associated loci are largely unknown. Additionally, the genomic variants alone are not able to explain the changing disease risk across the life span. DNA, RNA, protein, and metabolite often have complementary roles to jointly perform a certain biological function. Such complementary effects and synergistic interactions between omic layers in the life course can only be captured by integrative study of multiple molecular layers. Building upon the success in single-omics discovery research, population studies started adopting the multi-omics approach to better understanding the molecular function and disease etiology. Multi-omics approaches integrate data obtained from different omic levels to understand their interrelation and combined influence on the disease processes. Here, we summarize major omics approaches available in population research, and review integrative approaches and methodologies interrogating multiple omic layers, which enhance the gene discovery and functional analysis of human diseases. We seek to provide analytical recommendations for different types of multi-omics data and study designs to guide the emerging multi-omic research, and to suggest improvement of the existing analytical methods.
Verified 2026-10-05 · 1 reference not checked yet
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In the paper
“Incorporating multi-omic measures of population samples into multidimensional network and system analyses (Figure 1), will address the gaps in our current knowledge of molecular mediation mechanisms, gene-environment interactions, and longitudinal effects during the development of chronic diseases”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 1. Introduction
What the sources say
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Thus, emphasizing only one omic layer can miss important information, particularly the complementary effects and interactions between omic layers.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies
plainconditional- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
In the paper
“However, a single layer of ‘omics’ can only provide limited insights into the biological mechanisms of a disease.”
- Also stated in: § 1. Introduction · plain
“the cross-talk between multiple molecular layers cannot be assessed by such simplified reduction approach”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > Abstract
What the sources say
- Not checked yetcites Chen et al., 2012 source
plain- Asserted: passes — The authors put it forward as true.
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- Also stated in: § 1. Introduction · plain
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In the paper
“Studying the epigenome in the well-characterized samples may enable us to discover novel genes and pathways through which genetic factors and environmental exposures influence disease initiation and development, and thereby provide new targets for prevention and treatment”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.2 Epigenome-wide association studies
What the sources say
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“The group of correlated epigenetic and metabolic markers influenced by common environmental exposures suggest their potential utility in studying the environmental risk and gene-environmental interaction for human diseases.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.2 Epigenome-wide association studies
What the sources say
- Not checked yetcites Petersen et al., 2014 source
hedgedcorrelationaln = 1,814- Asserted: passes — The authors put it forward as true.
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In the paper
“the relationship between DNA methylation and Gene expression levels have been integrated at the genome-wide scale (Gibbs et al., 2010; Liu, Ding, et al., 2013).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.2 Epigenome-wide association studies
What the sources say
- Not checked yetcites Gibbs et al., 2010 source
- Not checked yetcites Liu, Ding, et al., 2013
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Using hundreds to thousands of subjects, these EWAS identified DNAm sites with much larger effect sizes compared to a typical GWAS of the same trait (Demerath et al., 2015; Dick et al., 2014).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.2 Epigenome-wide association studies
What the sources say
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Therefore, lack of correlation between epigenetic variation and gene expression levels does not mean that the epigenetic modifications have no functional consequence in gene expression.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.2 Epigenome-wide association studies
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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In the paper
“In a recent study, the associations between 649 metabolic traits and over 400,000 DNA methylation markers were examined using blood samples from 1,814 European participants. The epigenome-wide association approach revealed strong associations with metabolic traits driven by either genetic effects or possible environmental factors (Petersen et al., 2014).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.2 Epigenome-wide association studies
What the sources say
- Not checked yetcites Petersen et al., 2014 source
hedgedcorrelationaln = 1,814- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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In the paper
“numerous studies have successfully identified leukocyte-based DNAm markers associated with disease traits (Demerath et al., 2015; Dick et al., 2014; Hidalgo et al., 2014; Irvin et al., 2014; Liang et al., 2015; Liu, Aryee, et al., 2013).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.2 Epigenome-wide association studies
What the sources say
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Such heterogeneity can shift the summary level of omic markers which are cell-type specific, and leads to associations unrelated to the direct omic changes (e.g. modifications of RNA and peptide expression levels).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.5 Common issues and limitations of omics approaches
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“There are reports showing that some blood-based omic makers share similar association as in other tissues (e.g. smoking-related DNA methylation (Sun, 2014))”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.5 Common issues and limitations of omics approaches
What the sources say
- Not checked yetcites Sun, 2014 source
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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In the paper
“The contributing cell type(s) of the associated markers among the mixture remain unidentified (Abbas, Wolslegel, Seshasayee, Modrusan, & Clark, 2009; Houseman et al., 2012; Houseman, Molitor, & Marsit, 2014).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.5 Common issues and limitations of omics approaches
What the sources say
- Not checked yetcites Abbas, Wolslegel, Seshasayee, Modrusan, & Clark, 2009
- Not checked yetcites Houseman et al., 2012 source
- Not checked yetcites Houseman, Molitor, & Marsit, 2014
plain- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“Statistical methods have been developed to adjust for potential confounding effects due to cell type heterogeneity.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.5 Common issues and limitations of omics approaches
What the sources say
- Not checked yetcites Abbas, Wolslegel, Seshasayee, Modrusan, & Clark, 2009
- Not checked yetcites Houseman et al., 2012 source
- Not checked yetcites Houseman, Molitor, & Marsit, 2014
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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In the paper
“On the contrary, consortia studies have demonstrated distinct patterns of omic profiles across tissues and cell types (Bernstein et al., 2012; Roadmap Epigenomics et al., 2015).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.5 Common issues and limitations of omics approaches
What the sources say
boostedcorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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In the paper
“Except for genetic profile being identical across tissues and cell types, all other omic profiles (e.g. transcriptome, epigenome, proteome and metabolome) vary across tissues and cell types.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.5 Common issues and limitations of omics approaches
What the sources say
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“but there is no clear evidence linking global omic mechanisms to disease development and environmental exposures across tissues.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.5 Common issues and limitations of omics approaches
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Since then, numerous human studies have generated eQTLs maps in multiple tissues such as brain, liver, skin, immune cells, and lymphoblastoid cell lines (Ding et al., 2010; Fairfax et al., 2012; Myers et al., 2007; Pickrell et al., 2010; Schadt et al., 2008; Veyrieras et al., 2008; W. Zhang et al., 2008).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
What the sources say
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“Genetics is one of the primary determinants of epigenetic variation including DNA methylation (Bjornsson, Fallin, & Feinberg, 2004).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
What the sources say
- Not checked yetcites Bjornsson, Fallin, & Feinberg, 2004
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Studies have revealed that over 30% of gene transcripts are substantially influenced by eQTLs (Romanoski et al., 2010).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
What the sources say
- Not checked yetcites Romanoski et al., 2010 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“GTEx collects and analyzes the genome-wide genetic variation and tissue-specific gene expression data to understand the genetic basis for gene expression variation across multiple human tissues (43 up to date).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
What the sources say
- Not matchedcites G. T. Consortium, 2013, 2015
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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- Not matched
In the paper
“Mapping eQTLs, particularly cis-eQTLs (i.e. eQTLs colocated with the gene encoding the RNA transcript), facilitated the understanding of functional linkage between disease-associated loci and the disease phenotype via gene expression regulation (Schadt et al., 2005).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
What the sources say
- Not checked yetcites Schadt et al., 2005 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The overlap between eQTL and disease-associated loci may indicate the putative functional role.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“Ten years ago, Schadt et al. demonstrated that integration of genetic variation and gene expression data with phenotypic data may identify key genes of complex traits in segregating mouse populations.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
What the sources say
- Not checked yetcites Schadt et al., 2005 source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Therefore, the QTLs of each omic layer hold the key functional information linking the observed genetic association and the disease phenotypes.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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None of the sources cited for this claim have been checked yet.
In the paper
“Over a dozen of meQTL studies of numerous tissues (e.g. peripheral blood, lung, brain, adipose tissue and tumor tissues) have been reported by correlating the genome-wide SNP data with tissue-specific DNA methylome data (Bell et al., 2011; Drong et al., 2013; Gibbs et al., 2010; Grundberg et al., 2013; Gutierrez-Arcelus et al., 2013; Heyn et al., 2013; Heyn et al., 2014; Shi et al., 2014; A. K. SM”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
What the sources say
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“A non-targeted genome-metabolome-wide study analyzed more than 250 metabolites from 60 known pathways in human serum samples. Combined with genome-wide SNP data from the same 2,820 individuals with metabolomic data, 37 mQTLs were significant at a stringent genome-wide threshold.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
What the sources say
- Not checked yetcites Suhre et al., 2011 source
plaincorrelationaln = 2,820- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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None of the sources cited for this claim have been checked yet.
In the paper
“Because of tissue and cell type specificity of DNA methylation, the genome-wide meQTLs can distribute differently from tissue to tissue (Grundberg et al., 2013; A. K. Smith et al., 2014), as well as from cell type to cell type (Gutierrez-Arcelus et al., 2013).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
What the sources say
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“The most recent large scale mQTL study investigated 529 metabolites of plasma or serum samples from 7,824 adult Europeans using MS technology. A total of 299 SNP-metabolite associations (i.e. mQTLs) at 145 independent loci were genome-wide significant after correction for the number of SNP-metabolite association tests (Shin et al., 2014).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
What the sources say
- Not checked yetcites Shin et al., 2014 source
plaincorrelationaln = 7,824- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“Comparing to most GWAS findings, these associations showed much larger effect sizes than those for disease traits (Suhre et al., 2011)”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
What the sources say
- Not checked yetcites Suhre et al., 2011 source
plaincorrelationaln = 2,820- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“The advance in quantitative proteomics allows a genome-wide map of pQTL model organisms (Foss et al., 2007; Picotti et al., 2013).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
What the sources say
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The functional impact of these tissue-specific meQTLs may be important to understand the pathophysiology of diseases in target tissues.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“These reference epigenomes, combined with other genomics data, have provided functional and causal insights about several human disease (De Jager et al., 2014; Farh et al., 2015; Yao, Tak, Berman, & Farnham, 2014).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
What the sources say
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“Many meQTL studies prioritized on the local genetic associations with DNA methylation sites (i.e. cis-meQTLs), which typically had larger effects and required less number of pair-wise tests than the trans-meQTL analysis (Sun, 2014).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
What the sources say
- Not checked yetcites Sun, 2014 source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“In numerous examples, the disease-associated loci were hinted to function via the regulation of gene expression or the modification of epigenetic markers (Liu, Aryee, et al.; Shi et al., 2014).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
What the sources say
- Not checked yetcites Liu, Aryee, et al.
- Not checked yetcites Shi et al., 2014 source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Because the genetic variant is randomly assigned to the offspring during meiosis in a population, the genotype distribution should not be biased by confounding.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“Secondly, these QTLs can be used as the instrumental variables in Mendelian Randomization (MR) study of the omic markers for their potential causal roles (Relton & Davey Smith, 2010, 2012).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
What the sources say
- Not matchedcites Relton & Davey Smith, 2010, 2012
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Only the genetic variant in the causal pathway should be associated with disease outcome by carrying the association through the causal exposure.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Using precise MS measurement of peptides over a large number of samples, recent pQTL analyses revealed complex genetic influence on the levels of proteins in yeast (Picotti et al., 2013) and mouse (Holdt et al., 2013).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
What the sources say
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If the exposure truly alters the disease risk, the genetic variant should also be associated with the disease through the causal pathway.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“The study design and analytical issues have been recently discussed for epigenomic study (Relton & Davey Smith, 2010, 2012). but the same principles can be applied to other omic layers.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
What the sources say
- Not matchedcites Relton & Davey Smith, 2010, 2012
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Not matched
In the paper
“Huang, Vanderweele, and Lin (2014) developed a method that integrates SNP and gene expression data, treating gene expression as the mediator in the causal mechanism from SNPs to the disease outcome (Figure 2).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.1 Regression-based joint modeling
What the sources say
- Not checked yetcites Huang, Vanderweele, and Lin (2014)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“MR approach can be applied to study the causal omic risk factor for human diseases.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Metabolomics, epigenomics and other omics approaches can complement genomics research by identifying time-varying omic markers in pathways and networks associated with a particular environmental exposure and a disease state.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.2 Environmental influences on multi-omic markers
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Although we are not able to fully measure or model the exposome, improved omics technologies including metabolomics and epigenomics provide promising methods to partially investigate the human exposome (Miller & Jones, 2014; Rappaport et al., 2014).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.2 Environmental influences on multi-omic markers
What the sources say
- Not checked yetcites Miller & Jones, 2014
- Not checked yetcites Rappaport et al., 2014 source
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, the static genome has its limitation to capture the time-varying changes caused by environmental factors or physiological conditions.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.2 Environmental influences on multi-omic markers
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“When the SNPs are associated with the gene expression (i.e., eQTL SNPs; αG ≠ 0), the null hypothesis (3) is equivalent to the null hypothesis of DE = 0 and IE = 0 (i.e., no TE of the SNPs).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.1 Regression-based joint modeling
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The multi-omic studies have the potential to transform not only the research of genetic variants, but also the research of the environmental exposure and the biological responses to the environment underlying disease development.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.2 Environmental influences on multi-omic markers
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Later, Huang (2015) extended the work of Huang et al. (2014) to jointly analyze SNP, DNA methylation, and gene expression data with respective to a disease outcome, adding the layer of DNA methylation data to the existing framework.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.1 Regression-based joint modeling
What the sources say
- Not checked yetcites Huang (2015) source
- Not checked yetcites Huang et al. (2014)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Evidently, if there does not exist an effect of G on E, ΔG→E→Y is zero.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.1 Regression-based joint modeling
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“By contrast, the IE ΔG→E→Y of SNPs mediated through expression is affected by the G-M-E relationship.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.1 Regression-based joint modeling
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“Thus, the testing procedure in Huang (2015) can be applied in settings where methylation and/or expression data are not collected in the subjects of GWAS but their associations with SNPs (i.e., μM, μE and μME) can be consistently estimated from external meQTL and eQTL studies.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.1 Regression-based joint modeling
What the sources say
- Not checked yetcites Huang (2015) source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“With different omics data on different subjects, the only testable effect is the overall SNP effect on the disease outcome, not any of the path-specific effects.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.1 Regression-based joint modeling
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“To overcome this problem, Huang et al. (2014) proposed a variance component test.”
- Also stated in: § 4.1 Regression-based joint modeling · plain
“Therefore, the proposed variance component test is insensitive to the number of SNPs in G.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.1 Regression-based joint modeling
What the sources say
- Not checked yetcites Huang et al. (2014)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- The authors state this as an assumption: In Huang et al.'s variance component test, the components in βG are independent and follow an arbitrary distribution with mean 0 and variance τG, and the components in βGE are independent and follow an arbitrary distribution with mean 0 and variance GE.
- Also stated in: § 4.1 Regression-based joint modeling · plain
In the paper
“As the true disease model is unknown and can be different from (1), e.g., without the interaction term, Huang et al. (2014) further proposed an omnibus test that accommodates different possible disease models.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.1 Regression-based joint modeling
What the sources say
- Not checked yetcites Huang et al. (2014)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This is motivated by the fact that the GWAS and QTL studies are likely to be conducted in different subjects due to the availability of tissue samples and the tissue specificity of expression and DNA methylation.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.1 Regression-based joint modeling
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“While Huang et al. (2014) and Huang (2015) jointly analyze multi-omics data from the same subjects, Huang (2014) extended the methodologies to analyze the data from different subjects.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.1 Regression-based joint modeling
What the sources say
- Not checked yetcites Huang (2014)
- Not checked yetcites Huang et al. (2014)
- Not checked yetcites Huang (2015) source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As the number of SNPs in G may be large and some SNPs may be highly correlated with each other due to linkage disequilibrium (LD), the standard likelihood ratio test (LRT) or multivariate Wald test for the null hypothesis (3) would use a large number of degrees of freedom and would thus have limited power.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.1 Regression-based joint modeling
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“In the later work, Huang (2015) studied path-specific effects, as depicted in the causal diagram (Figure 3), by jointly modeling a set of SNPs within a gene, the DNA methylation and expression of the gene, and the disease outcome as a biological process.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.1 Regression-based joint modeling
What the sources say
- Not checked yetcites Huang (2015) source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“Indeed, Kenny and Judd (2014) noted that in the absence of a DE, testing the IE in a mediation analysis can be dramatically more powerful than the standard method testing SNP-disease associations directly.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.1 Regression-based joint modeling
What the sources say
- Not checked yetcites Kenny and Judd (2014) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If, for a significant number of SNPs, Uj = 1 and Vj = 1, then it is likely that Z = 1.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.2 Matching Patterns of eQTL and GWAS
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“He et al. (2013) implemented the above idea of genetic signature matching by a Bayesian framework.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.2 Matching Patterns of eQTL and GWAS
What the sources say
- Not checked yetcites He et al. (2013)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“Even in the presence of a DE so that model (8) mis-specifies the true disease risk, Zhao et al. (2014) showed, both analytically and numerically, that their method is still more powerful than the standard method when the magnitude of DE is lower than the magnitude of IE.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.1 Regression-based joint modeling
What the sources say
- Not checked yetcites Zhao et al. (2014) source
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Based on the two-stage model, one can test for SNP-disease association by testing H0: αG = 0, assuming that the SNP affects disease risk through affecting the gene expression levels.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.1 Regression-based joint modeling
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- The authors state this as an assumption: In Zhao et al.'s two-stage model, the SNP affects disease risk through affecting gene expression levels.
In the paper
“He et al. (2013) developed a method to detect disease-associated genes (i.e., genes whose expression level influences the disease risk) by matching the eQTL patterns of each gene with the patterns of disease-associated SNPs.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.2 Matching Patterns of eQTL and GWAS
What the sources say
- Not checked yetcites He et al. (2013)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The rationale is that, for a disease-associated gene, any genetic variation that perturbs its expression is also likely to influence the disease risk (Figure 4).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.2 Matching Patterns of eQTL and GWAS
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Because many eQTLs act in trans, this approach can identify important genes that are distal to any GWAS association signals and thus impossible to be detected with GWAS alone.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.2 Matching Patterns of eQTL and GWAS
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This method works best when there is no DE of the SNPs on the disease outcome, such that the SNPs act only through regulating gene expression.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.1 Regression-based joint modeling
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This method is especially useful when eQTL and GWAS studies were conducted on different samples.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.2 Matching Patterns of eQTL and GWAS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“Although Xiong et al. (2012) only focused on integrating the gene expression and SNP genotype data”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.3 Aggregating evidence of multi-omics data over gene set/pathway
What the sources say
- Not checked yetcites Xiong et al. (2012) source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Because this method does not directly test the relationships between genotypes, gene expression, and disease outcomes, but only requires p-values, the eQTL and GWAS data do not have to come from the same subjects.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.2 Matching Patterns of eQTL and GWAS
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“(If Bayesian inference has been performed in both the eQTL and GWAS analysis, it is straightforward to combine the resulting BFs to obtain the BF for the gene.)”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.2 Matching Patterns of eQTL and GWAS
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Under the alternative, P(TeQTL,j | Uj = 1) and P(TGWAS,j | Vj = 1) depend on the tests through which the test statistics are derived and the effect size of the SNP.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.2 Matching Patterns of eQTL and GWAS
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“Xiong et al. (2012) developed a statistical framework, called Gene Set Association Analysis (GSAA), that aggregates genetic and gene expression evidence in terms of “association scores” at the level of gene sets or pathways for genome-wide association analysis of gene sets or pathways.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.3 Aggregating evidence of multi-omics data over gene set/pathway
What the sources say
- Not checked yetcites Xiong et al. (2012) source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“the flexibility of this framework allows integration of other omic data such as DNA methylation, proteomics, and metabolomics data (Figure 5)”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.3 Aggregating evidence of multi-omics data over gene set/pathway
What the sources say
- Not checked yetcites Xiong et al. (2012) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus the BF of the gene being tested depends only on α, β, and SNP-level BFs.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.2 Matching Patterns of eQTL and GWAS
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This method is also generalizable to molecular traits other than gene expression, such as metabolites, non-coding RNAs, and epigenetic modifications.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.2 Matching Patterns of eQTL and GWAS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“When all SNPs are unlinked, the BF of the gene is the product of the BFs of all SNPs: B=∏j=1mBj=∏jP(peQTL,j,pGWAS,j|Z=1)P(peQTL,j,pGWAS,j|Z=0).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.2 Matching Patterns of eQTL and GWAS
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Probably not a substantial claim. 47
- It may state two separate claims as one. 54
- The quote only partly states this claim. 1
Every source the paper cites for this claim was checked and supports it.
In the paper
“This chemical conversion does not distinguish 5-methylcytosine (5mC) from 5-hydroxymethylcytosine (5hmC) (Jin, Kadam, & Pfeifer, 2010)”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.2 Epigenome-wide association studies
What the sources say
- Supportedcites Jin et al., 2010 source
The Discussion states that sodium bisulfite sequencing and related approaches cannot distinguish 5mC from 5hmC. The Results likewise report that both modified bases remain distinguishable from unmodified cytosine after bisulfite treatment, but not from each other. (LLM, conf=0.95)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
None of the sources cited for this claim have been checked yet.
In the paper
“RNA-Seq provides comprehensive information on mRNA abundance, alternative splicing, nucleotide variation, and structure alteration.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.1 Genome-wide association study (GWAS) and transcriptomics studies of human diseases
What the sources say
- Not checked yetcites Ozsolak & Milos, 2011
- Not checked yetcites Z. Wang et al., 2009 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Similarly, NGS technology has been used to call SNP genotypes and structural variations and provides the ultimate coverage and resolution of genetic variations including private mutations of individuals.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.1 Genome-wide association study (GWAS) and transcriptomics studies of human diseases
What the sources say
- Not checked yetcites Nielsen et al., 2011 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“thousands of genetic variants have been linked with hundreds of human traits and diseases (Welter et al., 2014).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.1 Genome-wide association study (GWAS) and transcriptomics studies of human diseases
What the sources say
- Not checked yetcites Welter et al., 2014 source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The integrative approach of multi-omic data may enhance the understanding of the molecular dynamics underlying the pathophysiology of diseases, and may lead to novel strategies for early detection, prevention and treatment of human diseases.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 1. Introduction
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Proteomics measures all proteins and peptides in biological samples, and offers highly complementary information to genomics. As many biological functions are transmitted through proteins, proteomics can yield new biology and insights into disease.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“For both GWAS and transcriptomic studies, the next generation sequencing (NGS) technologies provide the most complete genome-wide coverage (Metzker, 2010).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.1 Genome-wide association study (GWAS) and transcriptomics studies of human diseases
What the sources say
- Not checked yetcites Metzker, 2010 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The new multi-omic studies will provide a combined view of multiple functional layer at a system level, and pave the road to precision medicine”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Epigenetic modifications, through DNA methylation (DNAm) and other mechanisms, can regulate gene expression and exert a long-term impact on the development of chronic diseases.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.2 Epigenome-wide association studies
What the sources say
- Not checked yetcites Bird, 2007 source
- Not checked yetcites Foley et al.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“DNA, RNA, protein, and metabolite often have complementary roles to jointly perform a certain biological function. Such complementary effects and synergistic interactions between omic layers in the life-course can only be captured by integrative study of multiple molecular layers.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“GWAS have successfully identified the genetic susceptibility of human diseases, however, it cannot capture the intra-individual changes over time and how such changes relate to the disease risk”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Although each omics technology is capable of measuring one type of biomolecule accurately and comprehensively, by themselves, they are all limited by the functional roles of each type of molecule in a biological system.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“On the down side, it only represents ~2% of all DNAm sites of the human genome, and over-represents the genic regions (75% of sites) including promoter, gene body and 3’-UTR (Sandoval et al., 2011).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.2 Epigenome-wide association studies
What the sources say
- Not checked yetcites Sandoval et al., 2011 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Epigenetic modifications are shaped by environmental (e.g. smoking (Breitling, Yang, Korn, Burwinkel, & Brenner, 2011; Shenker et al.; Sun, 2014; Sun, Smith, et al., 2013), poor nutrition(Waterland & Jirtle, 2003), genetic factors (Sun, 2014; D. Zhang et al., 2010), and age – all well known risk factors human diseases”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.2 Epigenome-wide association studies
What the sources say
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The 450K chip offers accurate and reproducible performance (Bibikova et al., 2011; Sandoval et al., 2011) with known but adjustable bias between Infinium I and II assays (Dedeurwaerder et al., 2011).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.2 Epigenome-wide association studies
What the sources say
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“But these sequencing-based methods suffer from unique technical biases and variations in sample preparation, processing and experimental procedures, which may not be fully addressable in the statistical analyses or require intensive bioinformatic/biostatistical analyses (Michels et al., 2013).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.2 Epigenome-wide association studies
What the sources say
- Not checked yetcites Michels et al., 2013 source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“They provide better genomic coverage than 450K array, and can assess the genetic variants and allele-specific methylation beyond the preselected microarray probes.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.2 Epigenome-wide association studies
What the sources say
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Additionally, inflammatory markers have been associated with epigenetics at the gene level in peripheral leukocytes (Sun, Lazarus, et al., 2013; Uddin et al., 2011), which play a critical role in acute and chronic inflammation related to pathophysiology of many human diseases.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.2 Epigenome-wide association studies
What the sources say
- Not checked yetcites Sun, Lazarus, et al., 2013
- Not checked yetcites Uddin et al., 2011 source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These omics-specific procedures for obtaining high-quality data are essential for the success of the multi-omics study.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.2 Epigenome-wide association studies
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Proteomics can also measure where and when proteins localize in the cell or tissue, that is important to understand the disease process but cannot be captured by other genomic technologies.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.3 Proteomic studies of human biology and diseases
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although epigenetic markers can induce the transcriptional regulation, the epigenetic modification may not be sufficient to cause changes of gene expression levels. In other words, epigenetic modifications potentiate the gene expression changes conditional on other co-regulators.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.2 Epigenome-wide association studies
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Thus, proteomics is positioned to define the functional roles of proteins in normal and disease-related cellular processes, and to enable hypothesis-driven and discovery research of human diseases (Nilsson et al., 2010).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.3 Proteomic studies of human biology and diseases
What the sources say
- Not checked yetcites Nilsson et al., 2010 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Recently, the protein products of 17,294 genes were identified and mapped in the draft of human proteome using 30 human samples of tissues and cells (Kim et al., 2014).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.3 Proteomic studies of human biology and diseases
What the sources say
- Not checked yetcites Kim et al., 2014 source
plainn = 30- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The technological advance in mass spectrometry (MS) and protein separation now allows rapid and accurate detection of hundreds of human proteins and peptides from a small amount of body fluid or tissue (Kraemer et al., 2011).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.3 Proteomic studies of human biology and diseases
What the sources say
- Not checked yetcites Kraemer et al., 2011 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Individual proteins can span 10 orders of magnitude in abundance (e.g. serum albumin and interleukin 6) (Anderson & Anderson, 2002).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.3 Proteomic studies of human biology and diseases
What the sources say
- Not checked yetcites Anderson & Anderson, 2002
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Proper procedures in sample collection, sample preparation, MS experiments and data analysis are all critical to obtain high-quality data for hypothesis-driven or proteome-wide discovery research (Nilsson et al., 2010).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.3 Proteomic studies of human biology and diseases
What the sources say
- Not checked yetcites Nilsson et al., 2010 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Despite these encouraging findings from the first wave of EWAS of disease traits, numerous issues such as imperfect epityping technologies, limited types of specimens from human subjects, cell-type specificity, sample size and data analysis framework need to be further addressed and improved (Heijmans & Mill, 2012).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.2 Epigenome-wide association studies
What the sources say
- Not checked yetcites Heijmans & Mill, 2012
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“This draft proteome demonstrated the feasibility of building a complete human proteome encompassing over 200 cell types and all body fluids.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.3 Proteomic studies of human biology and diseases
What the sources say
- Not checked yetcites Kim et al., 2014 source
boosteddirectn = 30- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Although, highly abundant “housekeeping” proteins from 2,350 genes constitute approximately 75% of total protein mass, low-abundance proteins constitute the majority of the protein species in human (Kim et al., 2014).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.3 Proteomic studies of human biology and diseases
What the sources say
- Not checked yetcites Kim et al., 2014 source
plainn = 30- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although the use of blood as surrogate tissue is sometimes relevant (e.g. autoimmune diseases and inflammatory processes), the biological relevance of blood-based omic profiles may not be apparent for many human diseases.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.5 Common issues and limitations of omics approaches
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“A tissue sample always involves several cell types, each having a unique omic profile.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.5 Common issues and limitations of omics approaches
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Proteomics approach has been widely adopted in studies of human diseases including cancer (Frantzi et al., 2015; S. Pan, Brentnall, & Chen, 2015; Petricoin et al., 2002; Tsai et al., 2015), multiple sclerosis (Farias, Pradella, Schmitt, Santos, & Martins-de-Souza, 2014) and schizophrenia (Al Awam et al., 2015; Nascimento & Martins-de-Souza, 2015).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.3 Proteomic studies of human biology and diseases
What the sources say
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The tissue and cell type specificity leads to two important issues in multi-omics studies, selection of tissues and cell types, and heterogeneity of tissues.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.5 Common issues and limitations of omics approaches
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“An alternative approach is to conduct initial association study using a large sample of mixed cell types adjusted for the effect of heterogeneity. Once an averaged effect is identified, a follow up study of the sorted cells can focus on a specific omic maker to identify the most relevant cell types.”
- Also stated in: § 2.5 Common issues and limitations of omics approaches · plain
“The analysis in the second stage does not require expensive omic profiling or large amount of samples, but provides direct inference on the molecular mechanism of a disease.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 2. Overview of omics technologies and association studies > 2.5 Common issues and limitations of omics approaches
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one. The quote only partly states this claim.
- Also stated in: § 2.5 Common issues and limitations of omics approaches · plain
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Key genes such as DNA methyltransferases (DNMTs) directly control the DNA methylation profile.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“Most GWAS findings map to non-coding regions, but a large proportion map to ENCODE regulatory elements (Schaub, Boyle, Kundaje, Batzoglou, & Snyder, 2012), suggesting the important role of transcriptional regulation underlying human diseases.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
What the sources say
- Not checked yetcites Schaub, Boyle, Kundaje, Batzoglou, & Snyder, 2012
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Other genetic variants may also influence the patterns of DNA methylation by modifying the accessibility or binding affinity of enzymes.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Disease-associated genetic variants do not directly cause disease at the molecular level. They affect intermediate phenotypes that in turn induce molecular and physiological changes.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, identifying the intermediate phenotypes of gene expression, DNA methylation, protein and metabolite levels that directly influence by genetic variants has the potential to provide the functional information of disease-causing genes and pathways, and to unravel the genetic-controlled molecular systems underlying the changes of health and disease status.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Several systems genetics studies of metabolites in human plasma or serum have been reported (Gieger et al., 2008; Kettunen et al., 2012; Shin et al., 2014; Suhre et al., 2011; Yu et al., 2013).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
What the sources say
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The national institute of health Roadmap Epigenomics Consortium generated 111 human reference epigenomes for primary cells and tissues, the largest collection so far (Roadmap Epigenomics et al., 2015).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
What the sources say
- Not checked yetcites Roadmap Epigenomics et al., 2015 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Metabolites play critical roles in biological pathways, and are partially controlled by genetic regulations.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Another population study of 8,330 European individuals analyzed 216 serum metabolites using NMR (Kettunen et al., 2012). The GWAS of these metabolites identified 31 mQTLs including 11 novel loci without known association with any traits or diseases.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
What the sources say
- Not checked yetcites Kettunen et al., 2012 source
plaincorrelationaln = 8,330- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Early pQTL studies suffered from an inconsistent detection of proteins across samples and a limited dynamic range for low abundance proteins (Foss et al., 2007).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
What the sources say
- Not checked yetcites Foss et al., 2007 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The levels of a set of metabolites are strongly associated with genetic loci, and some of these loci overlapped with GWAS loci for disease traits.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Because of the large number of SNP-DNA methylation site pairs to test, the trans-meQTL analysis is more computational intensive and requires a more stringent multiple-testing correction than the cis-meQTL analysis.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The MR approach uses the genetic variant mimicking the biological effects of a modifiable exposure.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Significant GWAS loci are enriched for e QTLs (Nicolae et al., 2010) and meQTLs (X. Zhang et al., 2014).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
What the sources say
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Picotti et al. used a nearly complete map of yeast proteome and genetic variation data to identify strong genetic effects of protein abundance, and to demonstrate epistatic interactions affecting protein levels (Picotti et al., 2013).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
What the sources say
- Not checked yetcites Picotti et al., 2013 source
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The pQTL analysis of mouse plasma identified strong genetic determinants for approximately 40% of tested proteins, and suggested causal genetic variants affecting abundance of plasma proteins (Holdt et al., 2013).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
What the sources say
- Not checked yetcites Holdt et al., 2013 source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, these studies catalogued a large number of QTLs across multiple omic layers in multiple tissues and cell types, and provide a rich resource not only to understand the genetic regulation of intermediate phenotypes, but also to illustrate important molecular networks mediating the interaction between genetic variants and environment for human diseases.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“A GWAS of 42 proteins identified eight strong pQTLs with rather large effect sizes (0.19 to 0.69 standard deviations per allele). However, the panel of proteins only covered a small fraction of the proteome (Melzer et al., 2008).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
What the sources say
- Not checked yetcites Melzer et al., 2008 source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“MR was initially proposed as an epidemiologic method to obtain unbiased estimates of the putative casual effects without conducting a randomized trial (Gray & Wheatley, 1991; Katan, 1986).”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
What the sources say
- Not checked yetcites Gray & Wheatley, 1991
- Not checked yetcites Katan, 1986 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“First, these QTLs have been used to infer the functional link between genetic variants and disease traits in recent GWAS, and lead to follow-up studies uncovering the biological functions of disease-associated loci.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.1 Genetic determinants of omic markers - studies of quantitative trait loci (QTL)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“When the SNPs have no effect on the gene expression (i.e., not eQTL SNPs; αG = 0), then there is no IE of the SNPs on Y, so that the null hypothesis (3) is not equivalent to testing for no TE, but simply whether there exists a joint effect of the SNPs, the gene expression, and possibly their interactive effect on the disease risk.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.1 Regression-based joint modeling
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The regression-based approach jointly models multi-omics data, using the framework of mediation analysis. These data are typically collected on the same subjects.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.1 Regression-based joint modeling
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Another type of datasets is multi-omics data collected on different sets of individuals from different studies.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Both genetic and environmental factors contribute to the development of human diseases.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.2 Environmental influences on multi-omic markers
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The multi-omics approach thus holds the promises to further advance human disease research.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Different research questions can be answered for each type of multi-omic dataset using corresponding statistical approaches.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Existing studies in human and model organisms highlighted the complexity of genomic information flow and the interactive networks in biological processes and disease development.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“On the other hand, a comparable high-throughput approach to studying the environmental causes of human diseases remains unavailable, which leaves a large proportion of the phenotypic variance unexplained.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.2 Environmental influences on multi-omic markers
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The ideal datasets for such an integrative analysis are multi-omics data all collected on the same set of samples.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The non-genetic factors can cause important changes in proteins, nucleic acids, lipids and other biomolecules, which have direct roles in biochemical and cellular functions.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 3. Genetic and environmental determinants of epigenomic, transcriptomic, metabolomic and proteomic markers > 3.2 Environmental influences on multi-omic markers
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, this is often not possible because of the cost or because the control samples simply do not have the appropriate tissues to study.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Specifically, in GWAS, SNPs and the disease outcomes are collected, but not gene expression/methylation; in QTL studies, SNPs, gene expression and methylation are collected, but not the disease outcome.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.1 Regression-based joint modeling
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If there exists an effect of G on E, ΔG→E→Y corresponds to βE, βME, βGE, and βGME; it means that the test of the hypothesis (7) is equivalent to the test of the IE of SNPs mediated through gene expression.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.1 Regression-based joint modeling
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For example, the DE ΔG→Y corresponds to βG, βGM, βGE, and βGME, which is not influenced by the relationship among G, M, and E.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.1 Regression-based joint modeling
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Zhao et al. (2014) considered the same omic datasets that Huang et al. (2014) have dealt with, i.e., SNP, gene expression, and disease data collected on the same set of subjects. However, Zhao et al. (2014) focused on testing the IE of the SNPs on the disease outcome that is regulated by gene expression.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.1 Regression-based joint modeling
What the sources say
- Not checked yetcites Zhao et al. (2014) source
- Not checked yetcites Huang et al. (2014)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Note that the meQTL and eQTL studies should be conducted on the same subjects in order to calculate μME.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.1 Regression-based joint modeling
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“This work is analogous to the work by Huang et al. (2014) but focuses solely on increasing the power of SNP association testing, rather than on assigning causal interpretations to any of the parameters.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.1 Regression-based joint modeling
What the sources say
- Not checked yetcites Huang et al. (2014)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, the eQTLs of the gene, which constitute a unique “genetic signature” of this gene, should overlap significantly with the set of loci associated with the disease.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.2 Matching Patterns of eQTL and GWAS
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although Uj and Vj are not observed, they are related to peQTL,j and pGWAS,j: when peQTL,j (pGWAS,j) is small, it is likely that Uj (Vj) = 1.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.2 Matching Patterns of eQTL and GWAS
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“To apply their method in an agnostic, genome-wide manner, they proposed to consider one gene in E at a time; to reduce the multiple testing burden imposed by the huge number of pairwise tests they proposed to restrict to testing only those SNPs located cis to each gene.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.1 Regression-based joint modeling
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Model (9) is significantly different from model (2) in that the former does not consider the regulation of the SNP on the expression of an individual gene, but on one particular linear combination of them; it hence requires estimating fewer parameters.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.1 Regression-based joint modeling
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“When a particular set of genes or a pathway is of interest such that the number of genes in E does not exceed the number of subjects, Zhao et al. (2014) proposed to use the standard estimating equation theory for inference.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.1 Regression-based joint modeling
What the sources say
- Not checked yetcites Zhao et al. (2014) source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Based on a phenotype-based permutation procedure that preserves LD structure in SNP data and gene-gene correlation structure in gene expression data, the false discovery rate (FDR) or the family-wise error rate (FWER) can be calculated and the significant gene sets are declared controlling for FDR or FWER below a certain threshold.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.3 Aggregating evidence of multi-omics data over gene set/pathway
What the sources say
- Not checked yetcites Xiong et al. (2012) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It has been implemented in a software program called Sherlock.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.2 Matching Patterns of eQTL and GWAS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Under the null, P(TeQTL,j | Uj = 0) and P(TGWAS,j | Vj = 0) follow the standard normal distribution.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.2 Matching Patterns of eQTL and GWAS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“To assess the statistical significance of BF, a simulation approach was proposed to compute the p-value of the BF for a gene.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.2 Matching Patterns of eQTL and GWAS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“When Z = 1 and Uj = 1, Vj should always be 1, as a true eQTL of the gene is expected to be associated with the disease.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.2 Matching Patterns of eQTL and GWAS
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- The authors state this as an assumption: In He et al.'s method, when Z = 1 and Uj = 1, Vj should always be 1, because a true eQTL of the gene is expected to be associated with disease.
In the paper
“Third, the gene set is evaluated by a weighted Kolmogorov-Smirnov (K-S) statistic (i.e., gene set association score) to determine whether the genes belonging to this gene set are preferentially near the top of the ranked ordered list based on gene association scores.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.3 Aggregating evidence of multi-omics data over gene set/pathway
What the sources say
- Not checked yetcites Xiong et al. (2012) source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The gene expression data and the SNP genotype data are allowed to be collected on the same samples or different samples.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.3 Aggregating evidence of multi-omics data over gene set/pathway
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“When Z = 0, the gene is irrelevant to the disease and thus Uj and Vj are independent.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.2 Matching Patterns of eQTL and GWAS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“When Z = 1 and Uj =0, this SNP is not an eQTL and thus Uj and Vj are also independent.”
§ Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases > 4. Analytical approaches and methods for multi-omic association studies > 4.2 Matching Patterns of eQTL and GWAS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper relies on this without support.
The functional implications and mechanisms of GWAS-associated loci are largely unknown.
“although thousands of SNPs have been identified for complex diseases and traits, the functional implications and mechanisms of the associated loci are largely unknown.”
The paper relies on this without support.
Genomic variants alone cannot explain changes in disease risk across the lifespan.
“Additionally, the genomic variants alone are not able to explain the changing disease risk across the life span.”
The paper relies on this without support.
DNA methylome is the only epigenomic mechanism that is robustly measured in population samples.
“We focus on DNA methylome in this article, because it is the only epigenomic mechanism being robustly measured in population samples.”
The paper relies on this without support.
Most human diseases arise from both genetic and environmental factors and from their interplay.
“Most human diseases are thought to be due to both genetic and environmental factors, and the interplay between genes and environment.”
The authors state this as an assumption.
In Zhao et al.'s two-stage model, the SNP affects disease risk through affecting gene expression levels.
“assuming that the SNP affects disease risk through affecting the gene expression levels.”
The authors state this as an assumption.
In He et al.'s method, when Z = 1 and Uj = 1, Vj should always be 1, because a true eQTL of the gene is expected to be associated with disease.
“When Z = 1 and Uj = 1, Vj should always be 1, as a true eQTL of the gene is expected to be associated with the disease.”
The authors state this as an assumption.
In Huang et al.'s variance component test, the components in βG are independent and follow an arbitrary distribution with mean 0 and variance τG, and the components in βGE are independent and follow an arbitrary distribution with mean 0 and variance GE.
“They assumed that the components in the vector βG are independent and follow an arbitrary distribution with mean 0 and variance τG, and that the components in βGE are independent and follow an arbitrary distribution with mean 0 and variance GE.”
The paper relies on this without support.
Multi-omics study has been hampered mainly by lack of feasible technologies for large-scale population studies, availability of biospecimens, and high cost.
“Despite of its appealing scientific gain, the materialization of the multi-omics study has been hampered mainly due to the lack of feasible technologies for large-scale population studies, availability of biospecimens and the high cost attached to them.”
The paper relies on this without support.
Core technologies behind high-throughput assays such as sequencing and mass spectrometry have become more sensitive, accurate, and affordable in recent years.
“In recent years, the core technologies behind the high-throughput assays, such as sequencing and mass spectrometry, have become more and more sensitive, accurate, and affordable.”
The paper relies on this without support.
Interrogation of complementary omics beyond a single omic layer has been explored for several diseases and model systems to demonstrate the utility and feasibility of multi-omics research.
“Interrogation of complementary omics beyond a single omic layer has been explored for several diseases and model systems to demonstrate the utility and feasibility of multi-omics research.”
The paper relies on this without support.
Current multi-omics research of human diseases demands a large amount of resources to carry out a population study.
“Current multi-omics research of human diseases demands a large amount of resources to carry out a population study.”
The paper relies on this without support.
Single-omic scans such as GWAS and EWAS continue to provide insights into the mechanism and etiology of human diseases.
“The thorough scan of a single type of biologically function features (e.g., GWAS and EWAS) continues to provide insights into the mechanism and etiology of human diseases.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“For example, a regression-based method termed “mediation analysis” was developed to integrate SNP and gene expression data, treating the gene expression as the mediator in the causal mechanism from SNPs to disease [45, 46].”
From Multi-omics approaches to disease (2017) · cites this paper as [46]
In the citing paper
“Similar approaches have been applied to other omics layers [46, 47].”
From Multi-omics approaches to disease (2017) · cites this paper as [46]
Citations
Cites
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879
Estimating the Dimension of a Model
Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033
KEGG: Kyoto Encyclopedia of Genes and Genomes
Kanehisa, M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.27 · Cited by 37,077
KEGG: Kyoto Encyclopedia of Genes and Genomes
Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., & Kanehisa, M. (1999) · Nucleic Acids Research · doi:10.1093/nar/27.1.29 · Cited by 31,232
An integrated encyclopedia of DNA elements in the human genome
The ENCODE Project Consortium (2012) · Nature · doi:10.1038/nature11247 · Cited by 18,072
RNA-Seq: a revolutionary tool for transcriptomics
Wang, Z., Gerstein, M., & Snyder, M. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2484 · Cited by 13,174
Trapnell, C., Roberts, A., Goff, L. A., Pertea, G. M., Kim, D., Kelley, D. R., Pimentel, H., Salzberg, S. L., Rinn, J. L., & Pachter, L. S. (2012) · Nature Protocols · doi:10.1038/nprot.2012.016 · Cited by 12,145
Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling
Alizadeh, A. A., Eisen, M. B., Davis, R. E., Ma, C., Lossos, I. S., Rosenwald, A., Boldrick, J. C., Sabet, H., Tran, T., Yu, X., et al. (2000) · Nature · doi:10.1038/35000501 · Cited by 9,925
Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls
The Wellcome Trust Case Control Consortium, Management Committee, Burton, P. R., Clayton, D. G., Cardon, L. R., Craddock, N., Deloukas, P., Duncanson, A., Kwiatkowski, D. P., McCarthy, M. I., et al. (2007) · Nature · doi:10.1038/nature05911 · Cited by 9,442
An integrated map of genetic variation from 1,092 human genomes
The 1000 Genomes Project Consortium (2012) · Nature · doi:10.1038/nature11632 · Cited by 7,711
Cited by
Sorted by citations
Multi-omics approaches to disease
Hasin, Y., Seldin, M., & Lusis, A. (2017) · Genome Biology · doi:10.1186/s13059-017-1215-1 · Cited by 2,953
Majorbio Cloud: A one‐stop, comprehensive bioinformatic platform for multiomics analyses
Ren, Y., Guo, Y. L., Shi, C., Liu, L., Guo, Q., Han, C., Zhang, D., Zhang, L. J., Liu, B., Gao, H., et al. (2022) · iMeta · doi:10.1002/imt2.12 · Cited by 922
Systems biology and multi-omics integration: viewpoints from the metabolomics research community
Pinu, F. R., Beale, D. J., Paten, A. M., Kouremenos, K., Swarup, S., Schirra, H. J., & Wishart, D. (2019) · Metabolites · doi:10.3390/metabo9040076 · Cited by 723
Integration strategies of multi-omics data for machine learning analysis
Picard, M., Scott-Boyer, M.-P., Bodein, A., Périn, O., & Droit, A. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.06.030 · Cited by 696
Network Pharmacology Approach for Medicinal Plants: Review and Assessment
Noor, F., Qamar, M. T. U., Ashfaq, U. A., Albutti, A., Alwashmi, A. S. S., & Aljasir, M. A. (2022) · Pharmaceuticals · doi:10.3390/ph15050572 · Cited by 605
Metadata
- Authors
- Sun, Y. V., & Hu, Y.-J.
- Affiliations
- Emory University (US)
- Corresponding author
- Sun, Yan V.
- Venue
- Advances in Genetics
- Publisher
- Elsevier
- Published
- 2016
- Volume
- 93
- Pages
- 147-190
- ISSN
- 0065-2660 (ISSN-L)
- PMC
- PMC5742494
- OpenAlex
- W2276765849
- Cited by
- 459
- References
- 159
- Citations by year
- 2026: 28 · 2025: 51 · 2024: 79 · 2023: 48 · 2022: 70 · 2021: 63 · 2020: 43 · 2019: 39 · 2018: 24 · 2017: 11 · 2016: 3
- FWCI
- 31.82
- Type
- book chapter
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Advances in genetics (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- American Heart Association (13GRNT17060002)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Health, Environment, Cognitive Aging
- Keywords
- multi-omics integration, complex diseases, gene discovery, functional genomics, etiology, single nucleotide polymorphisms, genome-wide association study, life course, population genetics
- MeSH terms
- Data Interpretation, Statistical, Disease, Environmental Exposure, Genetic Markers, Humans, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Proteomics, Genome-Wide Association Study, Metabolomics, Epigenomics, Transcriptome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-05, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-05 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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