Abstract
BackgroundGenome-wide association studies have made substantial progress in identifying common variants associated with human diseases. Despite such success, a large portion of heritability remains unexplained. Evolutionary theory and empirical studies suggest that rare mutations could play an important role in human diseases, which motivates comprehensive investigation of rare variants in sequencing studies. To explore the association of rare variants with human diseases, many statistical approaches have been developed with different ways of modeling genetic structure (ie, linkage disequilibrium). Nevertheless, the appropriate strategy to model genetic structure of sequencing data and its effect on association analysis have not been well studied.MethodsWe investigate 3 statistical approaches that use 3 different strategies to model the genetic structure of sequencing data. We proceed by comparing a burden test that assumes independence among sequencing variants, a burden test that considers pairwise linkage disequilibrium (LD), and a functional analysis of variance (FANOVA) test that models genetic data through fitting continuous curves on individuals’ genotypes.ResultsThrough simulations, we find that FANOVA attains better or comparable performance to the 2 burden tests. Overall, the burden test that considers pairwise LD has comparable performance to the burden test that assumes independence between sequencing variants. However, for 1 gene, where the disease-associated variant is located in an LD block, we find that considering pairwise LD could improve the test’s performance.ConclusionsThe structure of sequencing variants is complex in nature and its patterns vary across the whole genome. In certain cases (eg, a disease-susceptibility variant is in an LD block), ignoring the genetic structure in the association analysis could result in suboptimal performance. Through this study, we show that a functional-based method is promising for modeling the underlying genetic structure of sequencing data, which could lead to better performance.
Highlights
Genome-wide association studies have made substantial progress in identifying common variants associated with human diseases
The emerging sequencing data facilitates the study of massive amounts of single nucleotide variants (SNVs), including both rare and common variants, for their potential role in complex human diseases
Various statistical methods have been proposed to group SNVs with or without considering the underlying genetic structure
Summary
Genome-wide association studies have made substantial progress in identifying common variants associated with human diseases. Despite such success, a large portion of heritability remains unexplained. To explore the association of rare variants with human diseases, many statistical approaches have been developed with different ways of modeling genetic structure (ie, linkage disequilibrium). The emerging sequencing data facilitates the study of massive amounts of single nucleotide variants (SNVs), including both rare and common variants, for their potential role in complex human diseases. These studies hold great promise for identification of new disease-susceptibility variants, the extremely large number of SNVs brings. As an initial step to investigate this issue, we chose 3 tests with different ways of modeling LD between SNVs: (a) a weighted burden test assuming independence among SNVs (BT) [3]; (b) a weighted burden test considering pairwise LD (BTCOV) [4]; and (c) a functional analysis of variance (FANOVA) [5] test that considers LD among nearby loci and models the genotype profile of an individual as a continuous function
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