Methods for Detecting Associations with Rare Variants for Common Diseases: Application to Analysis of Sequence Data
Open accessAbstract
Although whole-genome association studies using tagSNPs are a powerful approach for detecting common variants, they are underpowered for detecting associations with rare variants. Recent studies have demonstrated that common diseases can be due to functional variants with a wide spectrum of allele frequencies, ranging from rare to common. An effective way to identify rare variants is through direct sequencing. The development of cost-effective sequencing technologies enables association studies to use sequence data from candidate genes and, in the future, from the entire genome. Although methods used for analysis of common variants are applicable to sequence data, their performance might not be optimal. In this study, it is shown that the collapsing method, which involves collapsing genotypes across variants and applying a univariate test, is powerful for analyzing rare variants, whereas multivariate analysis is robust against inclusion of noncausal variants. Both methods are superior to analyzing each variant individually with univariate tests. In order to unify the advantages of both collapsing and multiple-marker tests, we developed the Combined Multivariate and Collapsing (CMC) method and demonstrated that the CMC method is both powerful and robust. The CMC method can be applied to either candidate-gene or whole-genome sequence data.
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The power to detect an association between a single rare variant is low even in very large samples.
“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”
From Statistical analysis strategies for association studies involving rare variants (2010) · cites this paper as [28]
Collapsing sets of rare variants into a single group and testing their collective frequency differences can overcome the power issues associated with testing rare variants individually.
“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”
From Statistical analysis strategies for association studies involving rare variants (2010) · cites this paper as [28]
Testing rare variants individually with standard contingency table and regression methods is highly problematic because such tests have poor power to detect small rare variant frequency differences between groups.
“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”
From Statistical analysis strategies for association studies involving rare variants (2010) · cites this paper as [28]
The CMC statistic appropriately controls type I error rates even when non-functional variants are included in the set of variants to be tested, and it has better power than the standard CAST method.
“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.”
From Statistical analysis strategies for association studies involving rare variants (2010) · cites this paper as [28]
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 [28]
Certain approaches, including those of Li and Leal and Madsen and Browning, ignore the direction of a rare variant's effect, while others are built to account for it.
“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.”
From Statistical analysis strategies for association studies involving rare variants (2010) · cites this paper as [28]
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- Authors
- Li, B., & Leal, S. M.
- Affiliations
- Baylor College of Medicine (US)
- Corresponding author
- Leal, Suzanne M.
- Venue
- The American Journal of Human Genetics
- Publisher
- Elsevier BV
- Published
- 2008-09
- Volume
- 83
- Issue
- 3
- Pages
- 311-321
- ISSN
- 0002-9297 (ISSN-L)
- PMC
- PMC2842185
- OpenAlex
- W2058148991
- Cited by
- 1,527
- References
- 42
- Citations by year
- 2026: 21 · 2025: 41 · 2024: 60 · 2023: 52 · 2022: 62 · 2021: 64 · 2020: 72 · 2019: 86 · 2018: 91 · 2017: 95 · 2016: 126 · 2015: 124 · 2014: 156 · 2013: 138 · 2012: 116
- FWCI
- 42.36
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- The American Journal of Human Genetics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- National Institute on Deafness and Other Communication Disorders (R01-DC03594, R01DC003594)
- National Institute of Neurological Disorders and Stroke (R01NS049130)
- 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, common diseases, collapsing methods, multivariate analysis, whole genome sequencing, candidate genes, tag SNPs, statistical power
- MeSH terms
- Genetic Markers, Haplotypes, Humans, Models, Genetic, Genetic Variation, Linkage Disequilibrium, Genome, Human, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide
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