Rare-Variant Association Analysis: Study Designs and Statistical Tests
Open accessAbstract
Despite the extensive discovery of trait- and disease-associated common variants, much of the genetic contribution to complex traits remains unexplained. Rare variants can explain additional disease risk or trait variability. An increasing number of studies are underway to identify trait- and disease-associated rare variants. In this review, we provide an overview of statistical issues in rare-variant association studies with a focus on study designs and statistical tests. We present the design and analysis pipeline of rare-variant studies and review cost-effective sequencing designs and genotyping platforms. We compare various gene- or region-based association tests, including burden tests, variance-component tests, and combined omnibus tests, in terms of their assumptions and performance. Also discussed are the related topics of meta-analysis, population-stratification adjustment, genotype imputation, follow-up studies, and heritability due to rare variants. We provide guidelines for analysis and discuss some of the challenges inherent in these studies and future research directions.
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The most natural grouping for rare variants is within a gene, based on predicted functional consequence or evidence for deleteriousness.
“The most natural grouping for rare variants is within a gene, based on predicted functional consequence or evidence for deleteriousness 13,14 .”
From Exome sequencing identifies rare coding variants in 10 genes which confer substantial risk for schizophrenia (2020) · cites this paper as [14]
URV analysis has had limited success in individual gene discovery for schizophrenia due to power limitations, with only SETD1A robustly associated.
“However, the analysis of URVs has had limited success in delivering individual gene discovery in schizophrenia because of power limitations, with only a single gene, SETD1A, identified as robustly associated16,21.”
From Rare coding variants in ten genes confer substantial risk for schizophrenia (2022) · cites this paper as [16]
The most natural grouping for rare variants is within a gene based on predicted functional consequence or deleteriousness.
“The most natural grouping for rare variants is within a gene, based on predicted functional consequence or evidence for deleteriousness16,17.”
From Rare coding variants in ten genes confer substantial risk for schizophrenia (2022) · cites this paper as [16]
Natural selection tends to keep large-effect risk variants at lower frequencies, especially in disorders with reduced fecundity such as schizophrenia.
“Theory predicts that the forces of natural selection will tend to keep large effect risk variants at much lower frequencies in the population, especially in disorders such as schizophrenia that are associated with reduced fecundity16.”
From Rare coding variants in ten genes confer substantial risk for schizophrenia (2022) · cites this paper as [16]
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“The most natural grouping for rare variants is within a gene, based on predicted functional consequence or evidence for deleteriousness 13,14 .”
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- Authors
- Lee, S., Abecasis, G. R., Boehnke, M., & Lin, X.
- Affiliations
- University of Michigan (US)
- Harvard University (US)
- Corresponding author
- Lin, Xihong
- Venue
- The American Journal of Human Genetics
- Publisher
- Elsevier BV
- Published
- 2014-07
- Volume
- 95
- Issue
- 1
- Pages
- 5-23
- ISSN
- 0002-9297 (ISSN-L)
- PMC
- PMC4085641
- OpenAlex
- W2063655230
- Cited by
- 1,042
- References
- 152
- Citations by year
- 2026: 46 · 2025: 80 · 2024: 82 · 2023: 84 · 2022: 74 · 2021: 103 · 2020: 95 · 2019: 75 · 2018: 109 · 2017: 103 · 2016: 105 · 2015: 67 · 2014: 17 · 2013: 1
- FWCI
- 12.89
- 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 Heart, Lung, and Blood Institute (R00HL113164, R01HL117626)
- National Human Genome Research Institute (HG 007022, R56HG000376, HG006513, R01HG000376, U01HG006513, R01HG007022, HG000376)
- National Cancer Institute (R37CA076404, P01CA134294, R35CA197449)
- National Institute of Diabetes and Digestive and Kidney Diseases (P30DK020572)
- National Institute of Environmental Health Sciences (P42ES016454, P30ES000002)
- 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, research design, burden test, genotyping platforms, meta-analysis, population stratification, genotype imputation, follow-up study, heritability, complex traits, disease risk
- MeSH terms
- Genotype, Humans, Genome-Wide Association Study
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