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Statistical analysis strategies for association studies involving rare variants
Open accessAbstract
The limitations of genome-wide association (GWA) studies that focus on the phenotypic influence of common genetic variants have motivated human geneticists to consider the contribution of rare variants to phenotypic expression. The increasing availability of high-throughput sequencing technologies has enabled studies of rare variants but these methods will not be sufficient for their success as appropriate analytical methods are also needed. We consider data analysis approaches to testing associations between a phenotype and collections of rare variants in a defined genomic region or set of regions. Ultimately, although a wide variety of analytical approaches exist, more work is needed to refine them and determine their properties and power in different contexts.
Overview
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TL;DR
This review surveys statistical strategies for testing whether collections of rare variants in genomic regions associate with phenotypes, emphasizing collapsing approaches and alternatives. It concludes that many methods exist but their relative power and robustness remain insufficiently characterized.
Key findings
- Rare variants, conventionally defined as variants with frequencies below 1% but sometimes considered below 0.1% or 0.01%, are expected to contribute to complex traits alongside common variants, yet single-variant association tests have low power even in large samples.
- Collapsing rare variants into sets and testing collective frequency differences can substantially improve power; extensions include frequency weighting, functional annotation, regression-based modeling, and sequence-similarity or diversity methods.
- Regression-based collapsed-variant models can accommodate covariates, common variants, interactions, and quantitative traits, but correlated predictors and many variables raise overfitting and collinearity concerns; regularized methods such as ridge regression and LASSO are proposed as remedies.
- Power comparisons are limited: most compare a new method with single-locus tests, simulate mainly under extreme allelic heterogeneity, and often ignore other scenarios or linkage disequilibrium. In one cited empirical comparison, 11 methods plus 9 high-dimensional regression procedures applied to FAAH and MGLL data from 142 morbidly obese and 147 control subjects did not consistently agree on the most associated regions or likely causal variants.
Methods
- Narrative review of statistical analysis approaches for rare-variant association studies, not a primary empirical study.
- Organizes methods into three broad categories: group summary statistics such as CAST, CMC, and weighted-sum tests; sequence similarity or diversity approaches; and multiple regression or data-mining methods, while also discussing collapsing by function or proximity, moving windows, and multiple-testing issues.
- Draws on published examples and power studies, including cited sequencing data from FAAH and MGLL in 142 morbidly obese and 147 control subjects and a comparison of 11 rare-variant methods plus 9 high-dimensional regression procedures.
Limitations
- The paper states that more work is needed to refine methods and determine their properties and power across contexts; power studies are not exhaustive, often use extreme allelic heterogeneity simulations, and rarely consider other effect scenarios, linkage disequilibrium, or hierarchical functional collapsing.
- The paper notes that not enough time has elapsed to compare all methods in a large study, some methods are designed only for specific situations, and assumptions of population-genetics simulation models are unclear.
- Evident from the text: as a review, it does not provide new primary data or a systematic quantitative meta-analysis; its conclusions about method performance rely on cited studies and conceptual discussion.
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Claims the paper makes
58 Not checked yet · 25 Own interpretation · 7 No basis given
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In the paper
“The effect of collapsing variants and testing their collective frequency differences on power can be substantial, as depicted in Figure 1B.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Capturing the Effects of Rare Variants > Single locus tests vs. ‘collapsing’ sets of rare variants
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In the paper
“It is now widely believed that many genetic and epigenetic factors are likely to contribute to common complex diseases, including multiple rare SNVs (defined by convention as those that have frequencies < 1%), copy number variations (CNVs), and other forms of structural variation. 3–12”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Introduction
What the sources say
- Not checked yetcites [3] Pinto, D. et al. Functional impact of global rare copy number variation in autism spectrum disorders. Nature 466, 368–372 (2010). source
- Not checked yetcites [4] Frazer, K. A., Murray, S. S., Schork, N. J. & Topol, E. J. Human genetic variation and its contribution to complex traits. Nature Rev. Genet. 10, 241–251 (2009). source
- Not checked yetcites [5] Tycko, B. Mapping allele-specific DNA methylation: a new tool for maximizing information from GWAS. Am. J. Hum. Genet. 86, 109–112 (2010). source
- Not checked yetcites [6] Kong, A. et al. Parental origin of sequence variants associated with complex diseases. Nature 462, 868–874 (2009). source
- Not checked yetcites [7] Eichler, E. E. et al. Completing the map of human genetic variation. Nature 447, 161–165 (2007). source
- Not checked yetcites [8] Hunter, D. J. Gene–environment interactions in human diseases. Nature Rev. Genet. 6, 287–298 (2005). source
- Not checked yetcites [9] Cordell, H. J. Detecting gene–gene interactions that underlie human diseases. Nature Rev. Genet. 10, 392–404 (2009). source
- Not checked yetcites [10] Bodmer, W. & Bonilla, C. Common and rare variants in multifactorial susceptibility to common diseases. Nature Genet. 40, 695–701 (2008). source
- Not checked yetcites [11] Schork, N. J., Murray, S. S., Frazer, K. A. & Topol, E. J. Common vs. rare allele hypotheses for complex diseases. Curr. Opin. Genet. Dev. 19, 212–219 (2009). source
- Not checked yetcites [12] Cirulli, E. T. et al. Common genetic variation and performance on standardized cognitive tests. Eur. J. Hum. Genet. 18, 815–820 (2010). source
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In the paper
“Fifth, importantly, sequencing studies focusing on specific genes have shown that collections of rare variants can indeed associate with particular phenotypes (Table 1).”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Introduction
What the sources say
- Not checked yetcites Table 1
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In the paper
“In order to overcome power issues associated with testing rare variants individually, one could ‘collapse’ sets of rare variants into a single group and test their collective frequency differences between cases and controls.28, 30”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Capturing the Effects of Rare Variants > Single locus tests vs. ‘collapsing’ sets of rare variants
What the sources say
- Not checked yetcites [28] Li, B. & Leal, S. M. Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am. J. Hum. Genet. 83, 311–321 (2008). One of the first papers to comprehensively evaluate statistical methods for testing collapsed sets of rare variants to a trait. The paper discussed both distance-based and regression approaches. source
- Not checked yetcites [30] Morgenthaler, S. & Thilly, W. G. A strategy to discover genes that carry multi-allelic or mono-allelic risk for common diseases: a cohort allelic sums test (CAST). Mutat. Res. 615, 28–56 (2007). This paper introduced the notion of collapsing sets of variants into a single group whose collective frequency could be contrasted between groups. source
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In the paper
“Thus, leveraging phase information in an association study of rare variants may be crucial, but obtaining phase from sequence data alone is not trivial.24, 91–93”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Introduction > Phasing
What the sources say
- Not checked yetcites [24] Roach, J. C. et al. Analysis of genetic inheritance in a family quartet by whole-genome sequencing. Science 328, 636–639 (2010). source
- Not checked yetcites [91] Andres, A. M. et al. Understanding the accuracy of statistical haplotype inference with sequence data of known phase. Genet. Epidemiol. 31, 659–671 (2007). source
- Not checked yetcites [92] Kim, J. H., Waterman, M. S. & Li, L. M. Accuracy assessment of diploid consensus sequences. IEEE/ACM Trans. Comput. Biol. Bioinform. 4, 88–97 (2007). source
- Not checked yetcites [93] Levy, S. et al. The diploid genome sequence of an individual human. PLoS Biol. 5, e254 (2007). source
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In the paper
“Second, the discovery of rare independent somatic mutations within and across genes contributing to tumorigenesis may parallel the functional effects of inherited variants contributing to congenital disease.11, 16, 17”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Introduction
What the sources say
- Not checked yetcites [11] Schork, N. J., Murray, S. S., Frazer, K. A. & Topol, E. J. Common vs. rare allele hypotheses for complex diseases. Curr. Opin. Genet. Dev. 19, 212–219 (2009). source
- Not checked yetcites [16] Wood, L. D. et al. The genomic landscapes of human breast and colorectal cancers. Science 318, 1108–1113 (2007). This study suggests that many different mutations in key genes are likely to drive tumorigenesis so that, although patients might have unique mutations, these mutations are likely to be in genes that harbour mutations across many patients. This rare variant heterogeneity may also contribute to the inherited basis of many common chronic diseases. source
- Not checked yetcites [17] Lahiry, P., Torkamani, A., Schork, N. J. & Hegele, R. A. Kinase mutations in human disease: interpreting genotype-phenotype relationships. Nature Rev. Genet. 11, 60–74 (2010). source
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In the paper
“Fourth, the identification of multiple functional variants within the same gene and the association of these variants with both in vitro and clinical phenotypes indicates that multiple rare variants could influence general clinical phenotypic expression20.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Introduction
What the sources say
- Not checked yetcites [20] Schork, N. J., Wessel, J. & Malo, N. DNA sequence-based phenotypic association analysis. Adv. Genet. 60, 195–217 (2008). source
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In the paper
“It has been shown that differential genotyping error rate can have substantial impact on common-variant based GWA studies.89”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Introduction > Sequencing and Genotyping errors
What the sources say
- Not checked yetcites [89] Moskvina, V., Craddock, N., Holmans, P., Owen, M. J. & O'Donovan, M. C. Effects of differential genotyping error rate on the type I error probability of case-control studies. Hum. Hered. 61, 55–64 (2006). source
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In the paper
“Pre-specified Bonferroni-like corrections on association p-values are not likely to be appropriate given possible correlations between defined groups of rare variants and/or overlapping windows to be tested.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Introduction > Accommodating Multiple Comparisons
What the sources say
- Not checked yetcites [99] Efron, B. Correlation and large-sclae simultaneous significance testing J. Am. Stat. Asso. 102, 92–103 (2007). source
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In the paper
“Simulation studies and permutation testing that consider the entire set of tests performed (e.g., all windows and groups of variants across all genomic regions considered) to a get a global false positive rate are the most appropriate given their flexibility and sound theoretical bases, but will likely be very computationally intensive.75”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Introduction > Accommodating Multiple Comparisons
What the sources say
- Not checked yetcites [75] Hoggart, C. J., Whittaker, J. C., De Iorio, M. & Balding, D. J. Simultaneous analysis of all SNPs in genome-wide and re-sequencing association studies. PLoS Genet. 4, e1000130 (2008). Refs 74 and 75 introduced regularized regression techniques for accommodating a large number of predictors in a genetic association study and to separate causally associated from non-causally associated variants. source
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In the paper
“This strategy is highly problematic given, for example, the poor power that such statistical tests have to detect small rare variant frequency differences between diagnostic or phenotypic groups (figures 1A and 1B).14, 28, 29”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Capturing the Effects of Rare Variants > Single locus tests vs. ‘collapsing’ sets of rare variants
What the sources say
- Not checked yetcites [14] Gorlov, I. P., Gorlova, O. Y., Sunyaev, S. R., Spitz, M. R. & Amos, C. I. Shifting paradigm of association studies: value of rare single-nucleotide polymorphisms. Am. J. Hum. Genet. 82, 100–112 (2008). source
- Not checked yetcites [28] Li, B. & Leal, S. M. Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am. J. Hum. Genet. 83, 311–321 (2008). One of the first papers to comprehensively evaluate statistical methods for testing collapsed sets of rare variants to a trait. The paper discussed both distance-based and regression approaches. source
- Not checked yetcites [29] Altshuler, D., Daly, M. J. & Lander, E. S. Genetic mapping in human disease. Science 322, 881–888 (2008). source
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In the paper
“In addition, if one included other factors in a regression model - such as covariate effects, the effects of previously identified common variants, or other collapsed sets of rare variants - then the power to detect the association involving rare variants could increase substantially (Figure 1C).”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Capturing the Effects of Rare Variants > Quantitative Traits and Conditional Analysis
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In the paper
“The practice of identifying and quantifying allele frequencies in a group of individuals and comparing them with historical or publicly available ‘control’ sets in studies involving rare variants is highly problematic due to the potential for stratification and sampling variation effects.96”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Introduction > The Use of In Silico Controls
What the sources say
- Not checked yetcites [96] Li, B. & Leal, S. M. Discovery of rare variants via sequencing: implications for the design of complex trait association studies. PLoS Genet. 5, e1000481 (2009). source
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In the paper
“Third, the identification of multiple rare variants within the same gene contributing to largely monogenic disorders such as Cystic Fibrosis and BRCA1 and BRCA2-associated breast cancer18, 19 suggests that rare variants might also influence common complex traits and diseases.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Introduction
What the sources say
- Not checked yetcites [18] Bobadilla, J. L., Macek, M. Jr, Fine, J. P. & Farrell, P. M. Cystic fibrosis: a worldwide analysis of CFTR mutations — correlation with incidence data and application to screening. Hum. Mutat. 19, 575–606 (2002). source
- Not checked yetcites [19] Easton, D. F. et al. A systematic genetic assessment of 1,433 sequence variants of unknown clinical significance in the BRCA1 and BRCA2 breast cancer-predisposition genes. Am. J. Hum. Genet. 81, 873–883 (2007). source
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In the paper
“In addition, just as interaction effects involving either genetic or environmental factors must be considered in standard GWA studies9, they are also likely to be important in association studies involving rare variants.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Capturing the Effects of Rare Variants > The nature of the effect of rare variants
What the sources say
- Not checked yetcites [9] Cordell, H. J. Detecting gene–gene interactions that underlie human diseases. Nature Rev. Genet. 10, 392–404 (2009). source
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In the paper
“Regression-based collapsed variant and conditional tests can greatly enhance association studies involving rare variants.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Capturing the Effects of Rare Variants > Quantitative Traits and Conditional Analysis
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In the paper
“Irrespective of how one might define ‘rare variant’ (which, although we have adopted the convention <1% frequency, might range from <0.1% to <0.01% depending on the context13) it is essential to recognize that such variants likely contribute to phenotypic expression in conjunction with, or over-and-above, common variants.”
- Also stated in: § The nature of the effect of rare variants · hedged
“As noted, rare variants are likely to influence a trait along with common variants.4, 14”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Introduction
What the sources say
- Not checked yetcites [4] Frazer, K. A., Murray, S. S., Schork, N. J. & Topol, E. J. Human genetic variation and its contribution to complex traits. Nature Rev. Genet. 10, 241–251 (2009). source
- Not checked yetcites [14] Gorlov, I. P., Gorlova, O. Y., Sunyaev, S. R., Spitz, M. R. & Amos, C. I. Shifting paradigm of association studies: value of rare single-nucleotide polymorphisms. Am. J. Hum. Genet. 82, 100–112 (2008). source
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- Also stated in: § The nature of the effect of rare variants · hedged
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 potential for false positive associations due to population stratification is large in studies involving rare variants since specific rare variants are more likely to be unique to a particular geoethnic group.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Introduction > Stratification
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In the paper
“Although highly problematic in situations involving de novo or even moderately rare variants (<1%), imputation methods involving rare variants have begun to receive attention and could be extremely useful in future association studies.98”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Introduction > Imputation
What the sources say
- Not checked yetcites [97] Li, Y., Willer, C., Sanna, S. & Abecasis, G. Genotype imputation. Annu. Rev. Genomics Hum. Genet. 10, 387–406 (2009). source
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In the paper
“There are many reasons to believe that multiple rare variants, both within the same gene and across different genes, collectively influence the expression and prevalence of traits and diseases in the population at large.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Introduction
What the sources say
- Not checked yetcites [14] Gorlov, I. P., Gorlova, O. Y., Sunyaev, S. R., Spitz, M. R. & Amos, C. I. Shifting paradigm of association studies: value of rare single-nucleotide polymorphisms. Am. J. Hum. Genet. 82, 100–112 (2008). source
- Not checked yetcites [15] Pritchard, J. K. Are rare variants responsible for susceptibility to complex diseases? Am. J. Hum. Genet. 69, 124–137 (2001). source
- Not checked yetcites [20] Schork, N. J., Wessel, J. & Malo, N. DNA sequence-based phenotypic association analysis. Adv. Genet. 60, 195–217 (2008). source
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In the paper
“First, it has been argued that population phenomena, such as the recent expansion of the human population, are likely to have resulted in a large number of segregating, functionally-relevant, rare variants that mediate phenotypic variation.14, 15”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Introduction
What the sources say
- Not checked yetcites [14] Gorlov, I. P., Gorlova, O. Y., Sunyaev, S. R., Spitz, M. R. & Amos, C. I. Shifting paradigm of association studies: value of rare single-nucleotide polymorphisms. Am. J. Hum. Genet. 82, 100–112 (2008). source
- Not checked yetcites [15] Pritchard, J. K. Are rare variants responsible for susceptibility to complex diseases? Am. J. Hum. Genet. 69, 124–137 (2001). source
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In the paper
“Association analyses involving rare variants are not as straightforward as analyses involving common variations since the power to detect an association between a single rare variant is low in even very large samples (Figure 1).14, 28, 29”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Introduction
What the sources say
- Not checked yetcites [14] Gorlov, I. P., Gorlova, O. Y., Sunyaev, S. R., Spitz, M. R. & Amos, C. I. Shifting paradigm of association studies: value of rare single-nucleotide polymorphisms. Am. J. Hum. Genet. 82, 100–112 (2008). source
- Not checked yetcites [28] Li, B. & Leal, S. M. Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am. J. Hum. Genet. 83, 311–321 (2008). One of the first papers to comprehensively evaluate statistical methods for testing collapsed sets of rare variants to a trait. The paper discussed both distance-based and regression approaches. source
- Not checked yetcites [29] Altshuler, D., Daly, M. J. & Lander, E. S. Genetic mapping in human disease. Science 322, 881–888 (2008). source
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In the paper
“Second, if one collapses variants by counting individuals possessing rare variants, then if either the frequency of those variants is large enough or if there are many of them, the percentage of individuals possessing any one of them could reach 100%.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Capturing the Effects of Rare Variants > Defining Collapsing Sets of Rare Variants via Function or Proximity
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In the paper
“Newer regression techniques that make use of regularization and shrinkage parameters to control for collinearity and overfitting can be used to overcome these problems.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Multiple regression and data mining methods
What the sources say
- Not checked yetcites [74] Malo, N., Libiger, O. & Schork, N. J. Accommodating linkage disequilibrium in genetic-association analyses via ridge regression. Am. J. Hum. Genet. 82, 375–385 (2008). source
- Not checked yetcites [75] Hoggart, C. J., Whittaker, J. C., De Iorio, M. & Balding, D. J. Simultaneous analysis of all SNPs in genome-wide and re-sequencing association studies. PLoS Genet. 4, e1000130 (2008). Refs 74 and 75 introduced regularized regression techniques for accommodating a large number of predictors in a genetic association study and to separate causally associated from non-causally associated variants. source
- Not checked yetcites [76] Zhou, H., Sehl, M. E., Sinsheimer, J. S. & Lange, K. Association screening of common and rare genetic variants by penalized regression. Bioinformatics 6 Aug 2010 (doi:10.1093/bioinformatics/btq448). source
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In the paper
“For within-species studies assessing the ancestral relationships between populations based on DNA sequence, it has been shown that the choice of a distance measure can impact the interpretation of the results50, 128.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models
What the sources say
- Not checked yetcites [50] Jost, L. GST and its relatives do not measure differentiation. Mol. Ecol. 17, 4015–4026 (2008). source
- Not checked yetcites [128] Libiger, O., Nievergelt, C. M. & Schork, N. J. Comparison of genetic distance measures using human SNP genotype data. Hum. Biol. 81, 389–406 (2009). source
plainconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“Such statistics would be highly appropriate in situations, such as the EAH situation, in which a group of individuals (e.g., cases) are hypothesized to simply possess more unique variants or more unique combinations of variants than another group of individuals (e.g., controls) in a defined genomic region.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Approaches based on similarities among individual sequences
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In the paper
“In addition, there will likely be many potential predictor variables to choose from if many individual common and rare variants, as well as collapsed sets of variants, are considered.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Multiple regression and data mining methods
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.
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
“In addition, if enough rare variants are studied, each individual in a sample of cases and controls may have their own unique haplotypes, making summary statistic approaches impossible.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Methods based on summary statistics
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
“Since a genomic region may harbor common, moderately common, and rare variants, some of which may influence phenotypic expression, the choice of a q value for association studies based on diversity indices may be problematic.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models
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
“Alternatively, statistics that exploit pairwise sequence similarity can be used65 as alternatives to classical summary statistic measures of sequence diversity differences between groups.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Approaches based on similarities among individual sequences
What the sources say
- Not checked yetcites [65] Anderson, M. J. Distance-based tests for homogeneity of multivariate dispersions. Biometrics 62, 245–253 (2006). 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.
None of the sources cited for this claim have been checked yet.
In the paper
“Such methods provide a flexible framework for assessing the contribution of collections of rare variants to a phenotype.28, 33”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Multiple regression and data mining methods
What the sources say
- Not checked yetcites [28] Li, B. & Leal, S. M. Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am. J. Hum. Genet. 83, 311–321 (2008). One of the first papers to comprehensively evaluate statistical methods for testing collapsed sets of rare variants to a trait. The paper discussed both distance-based and regression approaches. source
- Not checked yetcites [33] Morris, A. P. & Zeggini, E. An evaluation of statistical approaches to rare variant analysis in genetic association studies. Genet. Epidemiol. 34, 188–193 (2010). 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
“Other summary statistic methods essentially ignore direction of effect and hence may be problematic in settings in which rare variants are not necessarily more frequent in disease or certain a priori defined phenotypic states.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Methods based on summary statistics
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
“However, many of these forms of variation are thought to exert or manifest their effects throughout the genome and not necessarily as a group of variants in a singular region of the genome.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Capturing the Effects of Rare Variants > Defining Collapsing Sets of Rare Variants via Function or Proximity
What the sources say
- Not checked yetcites [3] Pinto, D. et al. Functional impact of global rare copy number variation in autism spectrum disorders. Nature 466, 368–372 (2010). source
- Not checked yetcites [37] Sebat, J., Levy, D. & McCarthy, S. E. Rare structural variants in schizophrenia: one disorder, multiple mutations; one mutation, multiple disorders. Trends Genet. 25, 528–535 (2009). source
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
“Recently, Morris and Zeggini33 assessed the power of simple regression methods for testing collapsed sets of rare variants for association with a quantitative trait and found that such approaches are indeed intuitive, flexible and powerful.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Multiple regression and data mining methods
What the sources say
- Not checked yetcites [33] Morris, A. P. & Zeggini, E. An evaluation of statistical approaches to rare variant analysis in genetic association studies. Genet. Epidemiol. 34, 188–193 (2010). 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
“In addition, a limitation of these methods is that a specific DNA similarity or distance measure or metric must be chosen and this can be problematic (Box 3).59”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Approaches based on similarities among individual sequences
What the sources say
- Not checked yetcites [59] Ickstadt, K., Selinski, S. & Muller, T. D. in SFB 475 Komplexitatsreduktion in Multivariaten Datenstrukturen (Univ. Dortmund, Germany, 2005).
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
“Price et al.35 showed that their method is more powerful than approaches that consider fixed weights.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Methods based on summary statistics
What the sources say
- Not checked yetcites [35] Price, A. L. et al. Pooled association tests for rare variants in exon-resequencing studies. Am. J. Hum. Genet. 86, 832–838 (2010). This paper describes a method for explicitly incorporating information about the likely functional effect of specific rare variants into the formulation of an association statistic. However, the proposed method only considers coding variations. 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
“Many recent papers have described flexible strategies for testing genetic associations that leverage individual sequence similarity information,20, 52–57 and it has been shown that such strategies can be as powerful, if not more so, than some traditional tests of association in many settings involving common variations.58”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Approaches based on similarities among individual sequences
What the sources say
- Not checked yetcites [58] Lin, W. Y. & Schaid, D. J. Power comparisons between similarity-based multilocus association methods, logistic regression, and score tests for haplotypes. Genet. Epidemiol. 33, 183–197 (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.
In the paper
“However, their direct application via simple extensions of the methods described by Morris and Zeggini33 to the analysis of multiple individual rare variants or collapsed sets of variants may be problematic.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Multiple regression and data mining methods
What the sources say
- Not checked yetcites [33] Morris, A. P. & Zeggini, E. An evaluation of statistical approaches to rare variant analysis in genetic association studies. Genet. Epidemiol. 34, 188–193 (2010). source
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.
- Rests on an assumption: In traditional linear regression, severe multicollinearity leads to numerical and interpretive problems.
- Rests on an assumption: When a standard linear regression has many predictors, or more predictors than subjects, it is prone to numerical instability and overfitting.
None of the sources cited for this claim have been checked yet.
In the paper
“However, not all approaches to linkage analysis are very powerful, and this is especially true for non-parametric approaches involving small families69, 70, although transmission/disequilibrium tests may have merit in the analysis of rare variants.71”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Approaches based on similarities among individual sequences
What the sources say
- Not checked yetcites [69] Kruglyak, L., Daly, M. J., Reeve-Daly, M. P. & Lander, E. S. Parametric and nonparametric linkage analysis: a unified multipoint approach. Am. J. Hum. Genet. 58, 1347–1363 (1996).
- Not checked yetcites [70] Risch, N. & Merikangas, K. The future of genetic studies of complex human diseases. Science 273, 1516–1517 (1996). 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.
None of the sources cited for this claim have been checked yet.
In the paper
“In fact, two recent papers23, 36 suggest that leveraging functional annotations and computational methods for predicting the consequences of specific rare variants can be used to great advantage in the identification of disease-predisposing variants, at least for rare monogenic conditions.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Capturing the Effects of Rare Variants > Defining Collapsing Sets of Rare Variants via Function or Proximity
What the sources say
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
“In addition, if a large enough genomic region is considered, then one could merely ‘collapse’ all variants unique to each case and then unique to each control, resulting in a set of variants that completely and perfectly discriminate cases from controls.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Approaches based on similarities among individual sequences
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.
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 problem of choosing a DNA sequence similarity measure based purely on nucleotide content matching or genealogical or cladistic distance is rooted in the fact that, ultimately, functional nucleotide content (i.e., what nucleotides and nucleotide combinations an individual possesses that impact function) determines gene activity, rather than the phylogenetic origins of those nucleotides.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Approaches based on similarities among individual sequences
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: unclear — It is unclear why the paper says it holds.
In the paper
“Madsen and Browning showed that their proposed statistic is more powerful than either the CAST or CMC methods in a number of settings, but more work in this area is needed to clarify the advantages, if any, of each.34”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Methods based on summary statistics
What the sources say
- Not checked yetcites [34] Madsen, B. E. & Browning, S. R. A groupwise association test for rare mutations using a weighted sum statistic. PLoS Genet. 5, e1000384 (2009). 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.
In the paper
“As a result, such methods are fairly robust to allelic heterogeneity.68”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Approaches based on similarities among individual sequences
What the sources say
- Not checked yetcites [68] Ott, J. Analysis of Human Genetic Linkage (Johns Hopkins Univ. Press, Baltimore, 1991).
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
“Measures of nucleotide similarity for the comparison of DNA sequences between pairs of individuals within a species are also problematic for this reason.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models
What the sources say
- Not checked yetcites [50] Jost, L. GST and its relatives do not measure differentiation. Mol. Ecol. 17, 4015–4026 (2008). 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
“They found that the use of a tally may be more powerful.33”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Multiple regression and data mining methods
What the sources say
- Not checked yetcites [33] Morris, A. P. & Zeggini, E. An evaluation of statistical approaches to rare variant analysis in genetic association studies. Genet. Epidemiol. 34, 188–193 (2010). 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
“Although intuitively appealing, such methods are problematic in that the determination of an optimal subset of variants based on group differences can be computationally-intensive.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Approaches based on similarities among individual sequences
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.
None of the sources cited for this claim have been checked yet.
In the paper
“The use of different q values in the construction of Δ measures for the comparison of the genetic diversities of two (or more) populations will have the same effect50, 130, 131: small q values will impact differences in rare variants and large q values emphasize differences in common variants132.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models
What the sources say
- Not checked yetcites [50] Jost, L. GST and its relatives do not measure differentiation. Mol. Ecol. 17, 4015–4026 (2008). source
- Not checked yetcites [130] Keylock, C. J. Simpson diversity and the Shannon–Wiener index as special cases of a generalized entropy. Oikos 109, 203–207 (2005). source
- Not checked yetcites [131] Lande, R. Statistics and partitioning of species diversity, and similarity among multiple communities. Oikos 76, 5–13 (1996). 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.
None of the sources cited for this claim have been checked yet.
In the paper
“Exploiting sequence similarity or diversity in genetic association studies can be problematic due to the fact that the choice of a similarity or diversity measure can impact the interpretation of the results.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models
What the sources say
- Not checked yetcites [59] Ickstadt, K., Selinski, S. & Muller, T. D. in SFB 475 Komplexitatsreduktion in Multivariaten Datenstrukturen (Univ. Dortmund, Germany, 2005).
- Not checked yetcites [126] Everitt, B. S. Cluster Analysis (John Wiley and Sons, New York, 2009).
- Not checked yetcites [127] Wong, K. M., Suchard, M. A. & Huelsenbeck, J. P. Alignment uncertainty and genomic analysis. Science 319, 473–476 (2008). source
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.
None of the sources cited for this claim have been checked yet.
In the paper
“Multiple regression models have been applied in many standard GWA studies in an effort to identify the most likely causal variants in a particular genomic region harboring many associated variants72, 73.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Multiple regression and data mining methods
What the sources say
- Not checked yetcites [72] Haiman, C. A. et al. Multiple regions within 8q24 independently affect risk for prostate cancer. Nature Genet. 39, 638–644 (2007). source
- Not checked yetcites [73] Clarke, R. et al. Genetic variants associated with Lp(a) lipoprotein level and coronary disease. N. Engl. J. Med. 361, 2518–2528 (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.
In the paper
“Tibshirani82 compared the relative merits of standard stepwise regression, ridge regression, and the LASSO in different non-genetic contexts and concluded that each method seems is best suited for different specific settings, depending on the number and effect sizes of the predictors.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Multiple regression and data mining methods
What the sources say
- Not checked yetcites [82] Tibshirani, R. Regression shrinkage and selection via the Lasso. J. R. Stat. Soc. Series B Stat. Methodol. 58, 267–288 (1996). 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.
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, power studies comparing newer methods with more simplistic methods for rare variant analysis have been pursued (Table 3).”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Power Studies
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
“Most studies assessing associations between rare variants and a phenotype have relied on rather simple collapsing strategies (Table 1).”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Power 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.
None of the sources cited for this claim have been checked yet.
In the paper
“However, methods which can account for subtle synergistic effects of many loci within a defined region and/or different forms of variation that might contribute to gene function, such as those rooted in sequence or functional similarity56, 57, 87, 88 are also likely to be appropriate.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Conclusions and future directions
What the sources say
- Not checked yetcites [56] Clayton, D., Chapman, J. & Cooper, J. Use of unphased multilocus genotype data in indirect association studies. Genet. Epidemiol. 27, 415–428 (2004). source
- Not checked yetcites [57] Tzeng, J. Y., Zhang, D., Chang, S. M., Thomas, D. C. & Davidian, M. Gene–trait similarity regression for multimarker-based association analysis. Biometrics 65, 822–832 (2009). source
- Not checked yetcites [87] Wessel, J., Schork, A. J., Tiwari, H. K. & Schork, N. J. Powerful designs for genetic association studies that consider twins and sibling pairs with discordant genotypes. Genet. Epidemiol. 31, 789–796 (2007). source
- Not checked yetcites [88] Nievergelt, C. M., Libiger, O. & Schork, N. J. Generalized analysis of molecular variance. PLoS Genet. 3, e51 (2007). source
hedged- 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.
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 obvious issues with the current assessments of the power and other properties of rare variant analysis methods concern the simple fact that not enough time has elapsed since their introduction for someone to compare them all in a large study.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Power Studies
plaindirect- 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.
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 problem of assigning causality to rare variants in a set may be more pronounced than it is in assigning causality to a single common variant.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Conclusions and future directions
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.
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 is problematic in the context of genetic association analyses since one will not necessarily know a priori how many common, rare, or collapsed sets of variants might influence a phenotype, nor what kind of effects those variants have.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Multiple regression and data mining methods
hedged- 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.
- Rests on an assumption: The number of common, rare, or collapsed variant sets that affect a phenotype, and the nature of their effects, cannot be known in advance.
None of the sources cited for this claim have been checked yet.
In the paper
“Logic regression, which is similar in ways to the method proposed by Han and Pan,40 was initially proposed for analyzing sequence data and does not assume that variants have been collapsed a priori.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Multiple regression and data mining methods
What the sources say
- Not checked yetcites [67] Kooperberg, C., Ruczinski, I., LeBlanc, M. L. & Hsu, L. Sequence analysis using logic regression. Genet. Epidemiol. 21 (Suppl. 1), S626–S631 (2001). One of the first papers to consider statistical methods for identifying optimal sets of predictors of a phenotype from sequence data based purely on the strength of statistical association. This paper proposed a novel regression method for this task. source
- Not checked yetcites [40] Han, F. & Pan, W. A data-adaptive sum test for disease association with multiple common or rare variants. Hum. Hered. 70, 42–54 (2010). 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.
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 number of rare variant association analysis methods have been proposed that build off the notion of collapsing variants into groups based on either functional annotations of the genomic regions they reside in or on their location in a defined genomic region or ‘window.’”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Conclusions and future directions
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
“In addition, some methods are clearly nuanced and are unlikely to work in situations other than those for which they were designed.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Power Studies
hedged- 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
“In this context merely finding that a set of rare variants appears to be collectively associated with a phenotype in no way suggests that all those variants are indeed functional or causally related to the phenotype.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Conclusions and future directions
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. 16
- Probably not a substantial claim. 15
None of the sources cited for this claim have been checked yet.
In the paper
“Rare variant effects can manifest as compound heterozygosity,90 the ‘unmasking’ of deleterious variants via deletions on a homologous chromosome12, and other haplotype context-dependent phenomena.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Introduction > Phasing
What the sources say
- Not checked yetcites [90] Zschocke, J. Dominant versus recessive: molecular mechanisms in metabolic disease. J. Inherit. Metab. Dis. 31, 599–618 (2008). source
- Not checked yetcites [12] Cirulli, E. T. et al. Common genetic variation and performance on standardized cognitive tests. Eur. J. Hum. Genet. 18, 815–820 (2010). 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
“Controlling for false positive findings due to multiple testing is necessary.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Introduction > Accommodating Multiple Comparisons
plain- 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: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
In the paper
“This strategy should increase the fraction of variation explained by the variants as a whole and hence increase the power to detect their collective, rather than individual, effects.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Capturing the Effects of Rare Variants > Quantitative Traits and Conditional Analysis
What the sources say
- Not checked yetcites [33] Morris, A. P. & Zeggini, E. An evaluation of statistical approaches to rare variant analysis in genetic association studies. Genet. Epidemiol. 34, 188–193 (2010). source
plaindirectn = 1,000- 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
“Not all analysis methods proposed for rare variant studies, however, can accommodate additional factors in their formulations and hence leverage conditioning effects. In addition, not all models can accommodate quantitative trait analysis unless the phenotype is broken into quantiles and stratified analysis is pursued (Table 2).”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Capturing the Effects of Rare Variants > Quantitative Traits and Conditional Analysis
What the sources say
- Not checked yetcites Table 2
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
“Although conceptually easier to accommodate in statistical analysis models, there is no reason to believe that the EAH setting is the rule rather than the exception with respect to rare variant influences on phenotypic expression.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Capturing the Effects of Rare Variants > The nature of the effect of rare variants
plain- 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: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. 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
“Although there are a number of ways to leverage functional annotations to guide the collapsing of rare variants in association studies, their use will only be as good as the science behind those annotations.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Capturing the Effects of Rare Variants > Defining Collapsing Sets of Rare Variants via Function or Proximity
plaincorrelational- 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: fails — The paper may give no 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. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Despite the success of genome wide association (GWA) studies in identifying common single nucleotide variants (SNVs) that contribute to complex diseases1, the vast majority of genetic variants contributing to disease susceptibility are yet to be discovered.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Introduction
What the sources say
- Not checked yetcites [1] Manolio, T. A., Brooks, L. D. & Collins, F. S. A HapMap harvest of insights into the genetics of common disease. J. Clin. Invest. 118, 1590–1605 (2008). source
- Not checked yetcites [2] Manolio, T. A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009). This paper describes the motivation for considering alternative approaches to discovering the genes that influence common complex diseases. It essentially argues that current GWA study paradigms focusing on common variants have failed to identify the majority of genetic variants that influence particular phenotypes. 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.
It may state two separate claims as one.
In the paper
“In addition, flaws with measures such as Fst and Gst have been pointed out that may not allow them to reliably capture diversity, differences in diversity, or population differentiation in general in some of the most trivial settings, given their focus on heterozygosity.50”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Methods based on summary statistics
What the sources say
- Not checked yetcites [50] Jost, L. GST and its relatives do not measure differentiation. Mol. Ecol. 17, 4015–4026 (2008). source
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.
None of the sources cited for this claim have been checked yet.
In the paper
“Two such techniques, ridge regression74 and the LASSO75,76 have been considered in genetic association analysis contexts, and other methods have also been proposed as well.77–80, 81”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Multiple regression and data mining methods
What the sources say
- Not checked yetcites [74] Malo, N., Libiger, O. & Schork, N. J. Accommodating linkage disequilibrium in genetic-association analyses via ridge regression. Am. J. Hum. Genet. 82, 375–385 (2008). source
- Not checked yetcites [75] Hoggart, C. J., Whittaker, J. C., De Iorio, M. & Balding, D. J. Simultaneous analysis of all SNPs in genome-wide and re-sequencing association studies. PLoS Genet. 4, e1000130 (2008). Refs 74 and 75 introduced regularized regression techniques for accommodating a large number of predictors in a genetic association study and to separate causally associated from non-causally associated variants. source
- Not checked yetcites [76] Zhou, H., Sehl, M. E., Sinsheimer, J. S. & Lange, K. Association screening of common and rare genetic variants by penalized regression. Bioinformatics 6 Aug 2010 (doi:10.1093/bioinformatics/btq448). source
- Not checked yetcites [77] Clark, T. G., De Iorio, M., Griffiths, R. C. & Farrall, M. Finding associations in dense genetic maps: a genetic algorithm approach. Hum. Hered. 60, 97–108 (2005). source
- Not checked yetcites [78] Guo, W. & Lin, S. Generalized linear modeling with regularization for detecting common disease rare haplotype association. Genet. Epidemiol. 33, 308–316 (2009). source
- Not checked yetcites [79] Luan, Y. H. & Li, H. Z. Group additive regression models for genomic data analysis. Biostatistics 9, 100–113 (2008). source
- Not checked yetcites [80] Kwee, L. C., Liu, D. W., Lin, X. H., Ghosh, D. & Epstein, M. P. A powerful and flexible multilocus association test for quantitative traits. Am. J. Hum. Genet. 82, 386–397 (2008). source
- Not checked yetcites [81] Capanu, M. & Begg, C. B. Hierarchical modeling for estimating relative risks of rare genetic variants: properties of the pseudo-likelihood method. Biometrics 5 Aug 2010 (doi:10.1111/j.1541-0420.2010.01469.x). 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.
In the paper
“In addition, linkage analysis approaches not only come with the often difficult and expensive need to sample family members, but many phenotypes may not exhibit familial aggregation, undermining the motivation to consider family-based studies10.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Approaches based on similarities among individual sequences
What the sources say
- Not checked yetcites [10] Bodmer, W. & Bonilla, C. Common and rare variants in multifactorial susceptibility to common diseases. Nature Genet. 40, 695–701 (2008). 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.
It may state two separate claims as one.
In the paper
“although transmission/disequilibrium tests may have merit in the analysis of rare variants.71”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Approaches based on similarities among individual sequences
What the sources say
- Not checked yetcites [71] Oexle, K. A remark on rare variants. J. Hum. Genet. 55, 219–226 (2010). source
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.
In the paper
“However, they did not consider conditioning effects (Figure 1C) or problems associated with analyses involving many correlated predictor variables.33”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Multiple regression and data mining methods
What the sources say
- Not checked yetcites [33] Morris, A. P. & Zeggini, E. An evaluation of statistical approaches to rare variant analysis in genetic association studies. Genet. Epidemiol. 34, 188–193 (2010). 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.
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, collapsed sets of variants might be correlated due to LD with an additional common variant included in the model or due to the manner in which different subsets of variants are collapsed based on functional annotations”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Multiple regression and data mining methods
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.
None of the sources cited for this claim have been checked yet.
In the paper
“Thus, the use of q values less than 1.0 produces a measure that emphasizes rare variants and the use of λ values greater than 1.0 produces a measure that emphasizes common variants.50, 129”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models
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.
In the paper
“The CMC statistic has desirable properties in that it appropriately controls type I error rates even when non-functional variants are included in the set of variants to be tested, and has better power than the standard CAST method.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Methods based on summary statistics
What the sources say
- Not checked yetcites [28] Li, B. & Leal, S. M. Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am. J. Hum. Genet. 83, 311–321 (2008). One of the first papers to comprehensively evaluate statistical methods for testing collapsed sets of rare variants to a trait. The paper discussed both distance-based and regression approaches. 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.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“These methods are more or less agnostic to allele frequencies, but can provide insight into differences between groups over many rare variants. However, their utility and power have not been assessed in association analysis settings.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Methods based on summary statistics
What the sources say
- Not checked yetcites [47] Hartl, D. L. & Clark, A. G. Principles of Population Genetics (Sinauer Associates, Sunderland, Massachusetts, 2007).
- Not checked yetcites [48] Holsinger, K. E. & Weir, B. S. Genetics in geographically structured populations: defining, estimating and interpreting FST . Nature Rev. Genet. 10, 639–650 (2009). source
- Not checked yetcites [49] Nei, M. Molecular Evolutionary Genetics (Columbia Univ. Press, New York, 1987). 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
“The method as first proposed does not easily accommodate covariates, cannot be used with quantitative phenotypes, and does not consider weighting of the variants using, for example, variant frequency or functional annotations.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Methods based on summary statistics
What the sources say
- Not checked yetcites [30] Morgenthaler, S. & Thilly, W. G. A strategy to discover genes that carry multi-allelic or mono-allelic risk for common diseases: a cohort allelic sums test (CAST). Mutat. Res. 615, 28–56 (2007). This paper introduced the notion of collapsing sets of variants into a single group whose collective frequency could be contrasted between groups. 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
“They applied 11 of the methods described in this review plus 9 high-dimensional regression procedures, and showed that the methods do not consistently agree on the most strongly associated regions of the genes or the most likely causal variants.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Power Studies
What the sources say
- Not checked yetcites [86] Bansal, V., Libiger, O., Torkamani, A. & Schork, N. J. An application and empirical comparison of statistical analysis methods for associating rare variants to a complex phenotype. Pacific Symposium on Biocomputing Proceedings (in the press).
plainn = 147- 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, almost all such studies consider comparisons between a proposed novel method and simple single locus analyses, which is an obvious comparison at some level, but does not reflect the sophistication and utility of the proposed method.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Power 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.
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
“Such an approach might mitigate a range of concerns, for example about accommodating confounding variables and the functional assessment of variants.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Conclusions and future directions
hedgeddirect- 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.
In the paper
“In addition, almost all of the studies considered simulations under some version of the EAH model of rare variant effects and do not consider other scenarios (Figure 2) or the influence of LD structure among multiple common and rare variants (of the type that might create ‘synthetic associations’85).”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Power Studies
What the sources say
- Not checked yetcites [85] Dickson, S. P., Wang, K., Krantz, I., Hakonarson, H. & Goldstein, D. B. Rare variants create synthetic genome-wide associations. PLoS Biol. 8, e1000294 (2010). 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.
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 advantages of more sophisticated data analysis methods are therefore unclear from a practical and implementation standpoint.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Power Studies
plain- 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
“Thus, for example, methods which can accommodate covariates, previously identified genetic factors, allelic heterogeneity, and different sets of collapsed variants simultaneously, such as regression-based methods, are clearly advantageous.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Conclusions and future directions
boosted- 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
“Finally, although many such published power studies simulate data assuming a population genetics model for the propagation of rare variants, the appropriateness of the assumptions of these models is unclear.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Power Studies
hedged- 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.
None of the sources cited for this claim have been checked yet.
In the paper
“In this light, Bansal et al.86 recently considered the analysis of sequencing data obtained on two genes, FAAH and MGLL, thought be associated with morbid obesity among 142 morbidly obese and 147 control subjects discussed in a previous study66.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Power Studies
What the sources say
- Not checked yetcites [86] Bansal, V., Libiger, O., Torkamani, A. & Schork, N. J. An application and empirical comparison of statistical analysis methods for associating rare variants to a complex phenotype. Pacific Symposium on Biocomputing Proceedings (in the press).
- Not checked yetcites [66] Bhatia, G. et al. A covering method for detecting genetic associations between rare variants and common phenotypes. PLoS Genet. (in the press).
plainn = 142- 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
“Their results emphasize the need for simulation and theoretical studies of different methodologies.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Power Studies
plain- 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.
In the paper
“Logic regression67 may be a particularly attractive regression-based approach, at least in theory, for the analysis of rare variants.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Multiple regression and data mining methods
What the sources say
- Not checked yetcites [67] Kooperberg, C., Ruczinski, I., LeBlanc, M. L. & Hsu, L. Sequence analysis using logic regression. Genet. Epidemiol. 21 (Suppl. 1), S626–S631 (2001). One of the first papers to consider statistical methods for identifying optimal sets of predictors of a phenotype from sequence data based purely on the strength of statistical association. This paper proposed a novel regression method for this task. source
hedged- 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
“For example, some methods do not take into account the possible direction of a rare variant effect, such as the methods described by Li and Leal28 and Madsen and Browning34 whereas other methods are designed to handle these situations40.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Power Studies
What the sources say
- Not checked yetcites [28] Li, B. & Leal, S. M. Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am. J. Hum. Genet. 83, 311–321 (2008). One of the first papers to comprehensively evaluate statistical methods for testing collapsed sets of rare variants to a trait. The paper discussed both distance-based and regression approaches. source
- Not checked yetcites [34] Madsen, B. E. & Browning, S. R. A groupwise association test for rare mutations using a weighted sum statistic. PLoS Genet. 5, e1000384 (2009). source
- Not checked yetcites [40] Han, F. & Pan, W. A data-adaptive sum test for disease association with multiple common or rare variants. Hum. Hered. 70, 42–54 (2010). 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.
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
“In addition, studies so far have not considered tests within a hierarchical collapsing framework that leverages functional annotations of genomic regions to separate truly causal variants from collections of rare variants that merely contain causal variants.”
§ Statistical Analysis Strategies for Association Studies Involving Rare Variants > Specific analysis models > Power 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 authors state this as an assumption.
Some formulations of collapsed tests assume that each subject is likely to have only a single rare variant.
“First, some formulations of collapsed tests assume that each subject is likely to have only a single rare variant.”
The authors state this as an assumption.
Similarity-based approaches assume some form of interaction among variants or at least a simple shaping of gene function by the balance of variations an individual possesses.
“Thus, such approaches assume some form of interaction among variants or at least a simple shaping of gene function by the balance of variations an individual possesses.”
The paper relies on this without support.
Current sequencing protocols have inherent error rates.
“Given that current sequencing protocols have inherent error rates, more research is needed to understand how false positive variant calls and nucleotide misassignments in sequence-based association studies of rare variants will impact inferences.”
The paper relies on this without support.
In traditional linear regression, severe multicollinearity leads to numerical and interpretive problems.
“Furthermore, strong multicollinearity is known to cause numerical and interpretation issues in traditional linear regression analysis.”
The paper relies on this without support.
When a standard linear regression has many predictors, or more predictors than subjects, it is prone to numerical instability and overfitting.
“Having many independent variables, or more independent variables than subjects, creates enormous potential for numerical instabilities and overfitting in standard linear regression models.”
The paper relies on this without support.
The number of common, rare, or collapsed variant sets that affect a phenotype, and the nature of their effects, cannot be known in advance.
“This is problematic in the context of genetic association analyses since one will not necessarily know a priori how many common, rare, or collapsed sets of variants might influence a phenotype, nor what kind of effects those variants have.”
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
“These frequencies are of interest because they have a major influence on the power of association test [8].”
From Integrated Model of De Novo and Inherited Genetic Variants Yields Greater Power to Identify Risk Genes (2013) · cites this paper as [8]
Citations
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Regression shrinkage and selection via the lasso
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Research works printed but not linked: 2
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Cited by
Sorted by citations
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Exome sequencing as a tool for Mendelian disease gene discovery
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Rare and common variants: twenty arguments
Gibson, G. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3118 · Cited by 1,278
Statistical power and significance testing in large-scale genetic studies
Sham, P. C., & Purcell, S. M. (2014) · Nature Reviews Genetics · doi:10.1038/nrg3706 · Cited by 573
Genetic architecture: the shape of the genetic contribution to human traits and disease
Timpson, N. J., Greenwood, C. M. T., Soranzo, N., Lawson, D. J., & Richards, J. B. (2018) · Nature Reviews Genetics · doi:10.1038/nrg.2017.101 · Cited by 434
Metadata
- Authors
- Bansal, V., Libiger, O., Torkamani, A., & Schork, N. J.
- Affiliations
- Scripps Research Institute (US)
- Corresponding author
- Schork, Nicholas J.
- Venue
- Nature Reviews Genetics
- Published
- 2010-10-13
- Volume
- 11
- Issue
- 11
- Pages
- 773-785
- ISSN
- 1471-0056 (ISSN-L)
- 1471-0064
- PMC
- PMC3743540
- OpenAlex
- W2007733142
- Semantic Scholar
- a487c664b21fd199054b7821f651ff63c8c1195b
- Cited by
- 455
- References
- 156
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 8 · 2023: 7 · 2022: 8 · 2021: 14 · 2020: 13 · 2019: 21 · 2018: 21 · 2017: 22 · 2016: 31 · 2015: 39 · 2014: 60 · 2013: 52 · 2012: 94
- FWCI
- 6.85
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Reviews Genetics (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (5u01da024417-02, n01mh22005-16-0-1)
- National Institute on Aging (R01AG030474, U19AG023122)
- National Heart, Lung, and Blood Institute (R01HL089655)
- National Institute of Mental Health (N01MH022005, R01MH078151, R01MH080134, P50MH081755)
- National Institute on Drug Abuse (U01-DA024417, U01 DA024417-01)
- National Cancer Institute (U54CA143906)
- National Center for Research Resources (UL1RR025774)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- rare variants, association studies, statistical power, next-generation sequencing, common diseases, genome-wide association study, genetic epidemiology
- MeSH terms
- Data Interpretation, Statistical, Gene Frequency, Humans, Phenotype, Case-Control Studies, Polymorphism, Single Nucleotide, Genome-Wide Association Study, DNA Copy Number Variations
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 2026-10-06, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-06 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
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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