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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
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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
“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
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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
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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
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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
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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
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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
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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.
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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.
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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.
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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.
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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
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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.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.
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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.
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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.
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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.
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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.
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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]
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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
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