Abstract
The availability of an increasing collection of sequencing data provides the opportunity to study genetic variation with an unprecedented level of detail. There is much interest in uncovering the role of rare variants and their contribution to disease. However, detecting associations of rare variants with small minor allele frequencies (MAF) and modest effects remains a challenge for rare variant association methods. Due to this low signal-to-noise ratio, most methods are underpowered to detect associations even when conducting rare variant association tests at the gene level. We present a new method for detecting rare variant associations. The algorithm consists of two steps. In the first step, a genetic algorithm searches for a promising genomic region containing a collection of genes with causal rare variants. In the second step, a genetic algorithm aims at removing false positives from the located genomic region. We tested the proposed method with a collection of datasets obtained from real exome data. The proposed method possesses sufficient power for detecting associations of rare variants with complex phenotypes. This method can be used for studying the contribution of rare variants with complex disease, particularly in cases where single-variant or gene-based tests are underpowered.
Highlights
R ECENT advances in sequencing technologies have revolutionized human genetics research
We have developed and tested an algorithm for conducting rare-variant association studies that test a collection of genes within a genomic region for detecting association of rare variants with a phenotype
The datasets used in this investigation were collected using the SEQPower package [25]. This software enables the generation of datasets using different models that are useful to evaluate the performance of Rare-Variant Association Studies (RVAS) methods
Summary
R ECENT advances in sequencing technologies have revolutionized human genetics research. The availability of an increasing collection of human genomic sequences enables the study of genome variation to an unprecedented level of detail These studies hold the promise to transform our understanding of genomic variation and its contribution to human disease. Preliminary studies of human variation on large genome samples have discovered a high abundance of rare genetic variants in the human genome [18]. Recent studies of both exome sequencing and a combination of whole-genome sequencing and imputation have started to identify a collection of rare genetic variants associated with different human complex diseases and traits [1], [20], [32]. More recent GWAS arrays have been designed to capture rare variation, but they are still unable to identify novel variants
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