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Searching for missing heritability: Designing rare variant association studies
Open accessAbstract
Significance: Discovering the genetic basis of common diseases, such as diabetes, heart disease, and schizophrenia, is a key goal in biomedicine. Genomic studies have revealed thousands of common genetic variants underlying disease, but these variants explain only a portion of the heritability. Rare variants are also likely to play an important role, but few examples are known thus far, and initial discovery efforts with small sample sizes have had only limited success. In this paper, we describe an analytical framework for the design of rare variant association studies of disease. It provides guidance with respect to sample size, as well as the roles of selection, disruptive and missense alleles, gene-specific allele frequency thresholds, isolated populations, gene sets, and coding vs. noncoding regions.
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“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 associated 13,18 .”
From Exome sequencing identifies rare coding variants in 10 genes which confer substantial risk for schizophrenia (2020) · cites this paper as [13]
In the citing paper
“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 [13]
In the citing paper
“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 fecundity 13 .”
From Exome sequencing identifies rare coding variants in 10 genes which confer substantial risk for schizophrenia (2020) · cites this paper as [13]
In the citing paper
“Because individuals affected with schizophrenia have fewer offspring, purifying selection is expected to prevent high-risk alleles from reaching even modest allele frequencies6.”
From Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia (2016) · cites this paper as [6]
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Metadata
- Authors
- Zuk, O., Schaffner, S. F., Samocha, K., Do, R., Hechter, E., Kathiresan, S., Daly, M. J., Neale, B. M., Sunyaev, S. R., & Lander, E. S.
- Affiliations
- Broad Institute (US)
- ORCID
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2014-01-17
- Volume
- 111
- Issue
- 4
- Pages
- E455-64
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC3910587
- OpenAlex
- W1986818660
- Cited by
- 635
- References
- 65
- Citations by year
- 2026: 11 · 2025: 18 · 2024: 36 · 2023: 40 · 2022: 35 · 2021: 64 · 2020: 41 · 2019: 59 · 2018: 52 · 2017: 71 · 2016: 86 · 2015: 70 · 2014: 48
- FWCI
- 59.70
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Funders
- National Human Genome Research Institute (U54HG003067)
- 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
- missing heritability, research design, sample size, isolated populations, gene set analysis, de novo mutations, selection coefficients, replication
- MeSH terms
- Gene Frequency, Humans, Mutation, Genetic Variation, Genetic Predisposition to Disease, Genome-Wide Association Study
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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.
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