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.
Highlights
Rare variants have increasingly become a focus in studies of complex traits
Method comparison Wang et al [57] compared existing family-based methods for binary traits including the rare variant transmission disequilibrium test (RV-TDT) [55], the generalized estimating equations–based–kernel association (GEE-KM) test [83], an extended CMC test for pedigree data known as PedCMC [84], a gene-level kernel and burden association tests for pedigree data (PedGene) [80], and the family-based rare variant association test (FARVAT) [85]
Through simulation based on the 6 genes with the largest effects on both simulated SBP and DBP, they found that the FARVAT method based on optimal weights was more powerful than the PedCMC, GEE-KM, or any of the RV-TDT tests
Summary
There are many reasons for this increasing interest Accessibility, both in cost and technology, of next-generation sequencing has led to the discovery of a plethora of rare variants. Nelson et al [1] estimated that 95 % of variants were rare with a minor allele frequency (MAF) of less than 0.5 %. This is in stark contrast to previous research suggesting that nearly one-third of variants have a frequency below 5 % [2]. Evolutionary theory suggests that deleterious variants are selected against and should be rare [3]. Despite the effects of this purifying selection, the 1000 Genomes project estimates that individuals carry 76 to 190 rare nonsynonymous variants predicted to be deleterious [6]
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