Regularized Rare Variant Enrichment Analysis for Case‐Control Exome Sequencing Data

Abstract

Rare variants have recently garnered an immense amount of attention in genetic association analysis. However, unlike methods traditionally used for single marker analysis in GWAS, rare variant analysis often requires some method of aggregation, since single marker approaches are poorly powered for typical sequencing study sample sizes. Advancements in sequencing technologies have rendered next-generation sequencing platforms a realistic alternative to traditional genotyping arrays. Exome sequencing in particular not only provides base-level resolution of genetic coding regions, but also a natural paradigm for aggregation via genes and exons. Here we propose the use of penalized regression in combination with variant aggregation measures to identify rare variant enrichment in exome sequencing data. In contrast to marginal gene-level testing, we simultaneously evaluate the effects of rare variants in multiple genes, focusing on gene-based LASSO and exon-based sparse group LASSO models. By using gene membership as a grouping variable, the sparse group LASSO can be used as a gene-centric analysis of rare variants while also providing a penalized approach toward identifying specific regions of interest. We apply extensive simulations to evaluate the performance of these approaches with respect to specificity and sensitivity, comparing these results to multiple competing marginal testing methods. Finally, we discuss our findings and outline future research.

Fine‐Mapping Additive and Dominant SNP Effects Using Group‐LASSO and Fractional Resample Model Averaging

“…[], Ayers et al. [], Ayers and Cordell [], or Larson and Schaid []. In this case, a given procedure would be applied in full to multiple independent fractional resamples, with each such resample defined through a reweighting of the likelihood component (as in Equation ); the results of each application would then be aggregated in the manner described for Equation .…”

“…Our association model focuses on grouped effects for single SNPs but it could be extended to consider different types of grouped effects, such as differential effects of local haplotype combinations, structural variants, or combinations of rare variants within a gene or LD block. For the last of these, our FReMA framework could also be used to provide model-averaged inference for existing single-solution rare variant selection procedures such as those of Zhou et al [2010], Ayers et al [2011], Ayers and Cordell [2013], or Larson and Schaid [2014]. In this case, a given procedure would be applied in full to multiple independent fractional resamples, with each such resample defined through a reweighting of the likelihood component (as in Equation (2)); the results of each application would then be aggregated in the manner described for Equation (7).…”

Recent advances in systemic lupus erythematosus genetics in an Asian population

“…However, a much larger number of samples are essential in NGS studies to overcome the limited power of discovering rare variants. Moreover, several new bioinformatics tools, such as a gene‐based approach and a pathway‐based analysis for rare variants or integrated genetic data of common and rare variants, should be applied in order to discover novel variants that are implicated in SLE pathogenesis …”

Assessing the Power of Exome Chips

“…With respect to the increasing use of exome genotyping chips, we aimed to investigate the sample size requirements for association studies using these chips. The power for different statistical approaches for analysing low-frequent and rare variants has been investigated and compared to each other by others [ 30 , 33 , 37 , 40 , 41 ]. The corresponding simulations were performed for varying properties of a single unit (i.e.…”