An evaluation of statistical approaches to rare variant analysis in genetic association studies
Open accessAbstract
Genome‐wide association (GWA) studies have proved to be extremely successful in identifying novel common polymorphisms contributing effects to the genetic component underlying complex traits. Nevertheless, one source of, as yet, undiscovered genetic determinants of complex traits are those mediated through the effects of rare variants. With the increasing availability of large‐scale re‐sequencing data for rare variant discovery, we have developed a novel statistical method for the detection of complex trait associations with these loci, based on searching for accumulations of minor alleles within the same functional unit. We have undertaken simulations to evaluate strategies for the identification of rare variant associations in population‐based genetic studies when data are available from re‐sequencing discovery efforts or from commercially available GWA chips. Our results demonstrate that methods based on accumulations of rare variants discovered through re‐sequencing offer substantially greater power than conventional analysis of GWA data, and thus provide an exciting opportunity for future discovery of genetic determinants of complex traits. Genet. Epidemiol. 34: 188–193, 2010. © 2009 Wiley‐Liss, Inc.
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Combining rare variants into a single predictor variable should increase the fraction of variation explained by the variants as a whole and hence increase power to detect their collective effects.
“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.”
From Statistical analysis strategies for association studies involving rare variants (2010) · cites this paper as [33]
Morris and Zeggini found that simple regression methods for testing collapsed sets of rare variants for association with a quantitative trait are intuitive, flexible and powerful.
“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.”
From Statistical analysis strategies for association studies involving rare variants (2010) · cites this paper as [33]
Morris and Zeggini did not consider conditioning effects or problems associated with analyses involving many correlated predictor variables.
“However, they did not consider conditioning effects (Figure 1C) or problems associated with analyses involving many correlated predictor variables.33”
From Statistical analysis strategies for association studies involving rare variants (2010) · cites this paper as [33]
Regression models provide a flexible framework for assessing the contribution of collections of rare variants to a phenotype.
“Such methods provide a flexible framework for assessing the contribution of collections of rare variants to a phenotype.28, 33”
From Statistical analysis strategies for association studies involving rare variants (2010) · cites this paper as [33]
Applying multiple regression by simply extending Morris and Zeggini's approach to many individual rare variants or collapsed variant sets could be problematic.
“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.”
From Statistical analysis strategies for association studies involving rare variants (2010) · cites this paper as [33]
Morris and Zeggini found that using a tally of the number of rare variants possessed by an individual across a large region as a predictor may be more powerful than using a simple indicator of possession of any rare variant.
“They found that the use of a tally may be more powerful.33”
From Statistical analysis strategies for association studies involving rare variants (2010) · cites this paper as [33]
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ARIEL and AMELIA: Testing for an Accumulation of Rare Variants Using Next-Generation Sequencing Data
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- Authors
- Morris, A. P., & Zeggini, E.
- Affiliations
- Centre for Human Genetics (GB)
- Corresponding author
- Morris, Andrew P.
- Venue
- Genetic Epidemiology
- Publisher
- Wiley
- Published
- 2009-10-06
- Volume
- 34
- Issue
- 2
- Pages
- 188-193
- ISSN
- 0741-0395 (ISSN-L)
- 1098-2272
- PMC
- PMC2962811
- OpenAlex
- W2153899794
- Cited by
- 481
- References
- 22
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 8 · 2023: 14 · 2022: 11 · 2021: 14 · 2020: 13 · 2019: 30 · 2018: 20 · 2017: 32 · 2016: 42 · 2015: 47 · 2014: 53 · 2013: 48 · 2012: 59
- FWCI
- 27.30
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Genetic Epidemiology (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Wellcome Trust (081682, 064890, WT081682/Z/06/Z, WT088885/Z/09/Z)
- 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, genetic association studies, complex traits, statistical power, resequencing, functional units
- MeSH terms
- Chromosome Mapping, Computer Simulation, Genetics, Population, Genotype, Humans, Models, Genetic, Phenotype, Genetic Variation, Models, Statistical, Genome, Human, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Genome-Wide Association Study
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