Cited by 474Bioinformatics and Genomic NetworksGenetic Associations and EpidemiologyHealth, Environment, Cognitive Aging

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Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases

Open access

Sun, Yan V., Hu, Yi-Juan·2016·Advances in Genetics

DOI 10.1016/bs.adgen.2015.11.004PMC PMC5742494

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Abstract

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.

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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
- Not checked yetcites Civelek & Lusis, 2014 source
- Not checked yetcites Ritchie et al., 2015 source
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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
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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
- Not checked yetcites Foley et al., 2009 source
- Not checked yetcites Jablonka, 2004 source
- Not checked yetcites Relton & Davey Smith, 2010 source
Load-bearingStrength:hedged· not yet calibratedCausal:conditional· not yet calibrated
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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
Load-bearingStrength:hedged· not yet calibratedCausal:correlational· not yet calibratedn = 1,814
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
None of the sources cited for this claim have been checked yet.
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
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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
- Not checked yetcites Demerath et al., 2015 source
- Not checked yetcites Dick et al., 2014 source
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Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
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
Strength:hedged· not yet calibratedCausal:correlational· not yet calibratedn = 1,814
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
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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
- Not checked yetcites Demerath et al., 2015 source
- Not checked yetcites Dick et al., 2014 source
- Not checked yetcites Hidalgo et al., 2014 source
Strength:plain· not yet calibratedCausal:correlational· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes

Show all 78 claims

It may state two separate claims as one. 54
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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
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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
Strength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
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
Strength:plain· not yet calibratedCausal:correlational· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it.
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
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
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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.
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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
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Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
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
- Not checked yetcites Breitling et al., 2011 source
- Not checked yetcites Waterland & Jirtle, 2003
- Not checked yetcites Sun, 2014 source
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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
- Not checked yetcites Bibikova et al., 2011 source
- Not checked yetcites Sandoval et al., 2011 source
- Not checked yetcites Dedeurwaerder et al., 2011 source
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
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
Strength:hedged· not yet calibratedCausal:conditional· not yet calibrated
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It may state two separate claims as one.
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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
- Not checked yetcites Serre et al., 2010 source
- Not checked yetcites Meissner et al., 2008 source
- Not checked yetcites Y. Li et al., 2010
Strength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
It may state two separate claims as one.
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
- Not checked yetcites Jin et al., 2010 source
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
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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
Strength:plain· not yet calibratedCausal:correlational· not yet calibrated
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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
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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
Strength:plain· not yet calibratedn = 30
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
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
Strength:plain· not yet calibratedCausal:direct· not yet calibrated
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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
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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
Strength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
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
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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
Strength:boosted· not yet calibratedCausal:direct· not yet calibratedn = 30
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
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
Strength:plain· not yet calibratedn = 30
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
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
“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
- Not checked yetcites Frantzi et al., 2015 source
- Not checked yetcites Farias et al., 2014 source
- Not checked yetcites Al Awam et al., 2015 source
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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
“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
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It may state two separate claims as one. The quote only partly states this claim.
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
Load-bearingStrength:hedged· not yet calibratedCausal:correlational· not yet calibrated
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It may state two separate claims as one.
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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
- Not checked yetcites Gieger et al., 2008 source
- Not checked yetcites Kettunen et al., 2012 source
- Not checked yetcites Shin et al., 2014 source
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
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
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it.
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
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedn = 8,330
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
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
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
It may state two separate claims as one.
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
- Not checked yetcites Nicolae et al., 2010 source
- Not checked yetcites X. Zhang et al., 2014 source
Load-bearingStrength:plain· not yet calibratedCausal:correlational· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
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
Strength:boosted· not yet calibratedCausal:correlational· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
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
Strength:hedged· not yet calibratedCausal:conditional· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
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
Strength:plain· not yet calibratedCausal:correlational· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
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
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
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
“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)
Strength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
It may state two separate claims as one.
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)
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
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
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
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
Load-bearingStrength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
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 = 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
Load-bearingStrength:plain· not yet calibratedCausal:correlational· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
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
Load-bearingStrength:plain· not yet calibratedCausal:correlational· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
Probably not a substantial claim.

85 of 85

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.”

12 of 12

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?

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Background
Transposable elements: targets for early nutritional effects on epigenetic gene regulation
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High density DNA methylation array with single CpG site resolution
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An atlas of genetic influences on human blood metabolites
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BackgroundMethodInfluential
Alzheimer's disease: early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2 and other loci
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Background
Predicting Network Activity from High Throughput Metabolomics
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Mapping complex disease traits with global gene expression
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DNA methylation patterns associate with genetic and gene expression variation in HapMap cell lines
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Abundant quantitative trait loci exist for DNA methylation and gene expression in human brain
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Linking disease associations with regulatory information in the human genomePreprint
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Authors: Sun, Y. V., & Hu, Y.-J.
Affiliations: Emory University (US)
Corresponding author: Sun, Yan V.
ORCID: Sun, Yan V.
Hu, Yi-Juan
Venue: Advances in Genetics
Publisher: Elsevier
Published: 2016
Volume: 93
Pages: 147-190
ISSN: 0065-2660 (ISSN-L)
DOI: 10.1016/bs.adgen.2015.11.004
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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

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Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases

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