ArticleBMC genetics2016
Progress in methods for rare variant association.
Article in BMC genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Gene-based genome-wide association studies and meta-analyses of conotruncal heart defects.PloS one · 2019Pooled it
- An in silico protocol for predicting genetic biomarkers in rare diseases: a case study in sporadic amyotrophic lateral sclerosis.Frontiers in genetics · 2026Article
- Marginal interaction test for detecting interactions between genetic marker sets and environment in genome-wide studies.G3 (Bethesda, Md.) · 2025Article
- Emerging roles of rare and low-frequency genetic variants in type 1 diabetes mellitus.Journal of medical genetics · 2021Review
- Gene-based analyses of the maternal genome implicate maternal effect genes as risk factors for conotruncal heart defects.PloS one · 2020Article
- MULTILAYER KNOCKOFF FILTER: CONTROLLED VARIABLE SELECTION AT MULTIPLE RESOLUTIONS.The annals of applied statistics · 2019Article
- Benefits and Challenges of Rare Genetic Variation in Alzheimer's Disease.Current genetic medicine reports · 2019Article
- Four Genome-Wide Association Studies Identify New Extreme Longevity Variants.The journals of gerontology. Series A, Biological sciences and medical sciences · 2017Article
- A Zoom-Focus algorithm (ZFA) to locate the optimal testing region for rare variant association tests.Bioinformatics (Oxford, England) · 2017Article
- To aggregate or not, that is the question. A commentary on single-nucleotide variant proportion in genes: a new concept to explore major depression based on DNA sequencing data.Journal of human genetics · 2017Article
- Associations Between Common and Rare Exonic Genetic Variants and Serum Levels of 20 Cardiovascular-Related Proteins: The Tromsø Study.Circulation. Cardiovascular genetics · 2016Article
- Filtering genetic variants and placing informative priors based on putative biological function.BMC genetics · 2016Article
- Omics-squared: human genomic, transcriptomic and phenotypic data for genetic analysis workshop 19.BMC proceedings · 2016Article
- Genetic Analysis Workshop 19: methods and strategies for analyzing human sequence and gene expression data in extended families and unrelated individuals.BMC proceedings · 2016Article
Corrections and comments
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2 authors.
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Abstract
Empirical studies and evolutionary theory support a role for rare variants in the etiology of complex traits. Given this motivation and increasing affordability of whole-exome and whole-genome sequencing, methods for rare variant association have been an active area of research for the past decade. Here, we provide a survey of the current literature and developments from the Genetics Analysis Workshop 19 (GAW19) Collapsing Rare Variants working group. In particular, we present the generalized linear regression framework and associated score statistic for the 2 major types of methods: burden and variance components methods. We further show that by simply modifying weights within these frameworks we arrive at many of the popular existing methods, for example, the cohort allelic sums test and sequence kernel association test. Meta-analysis techniques are also described. Next, we describe the 6 contributions from the GAW19 Collapsing Rare Variants working group. These included development of new methods, such as a retrospective likelihood for family data, a method using genomic structure to compare cases and controls, a haplotype-based meta-analysis, and a permutation-based method for combining different statistical tests. In addition, one contribution compared a mega-analysis of family-based and population-based data to meta-analysis. Finally, the power of existing family-based methods for binary traits was compared. We conclude with suggestions for open research questions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.