Larson, Nicholas B.

ABSTRACTRare 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 least absolute shrinkage and selection operator (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.

ABSTRACTGene‐gene interactions are increasingly being addressed as a potentially important contributor to the variability of complex traits. Consequently, attentions have moved beyond single locus analysis of association to more complex genetic models. Although several single‐marker approaches toward interaction analysis have been developed, such methods suffer from very high testing dimensionality and do not take advantage of existing information, notably the definition of genes as functional units. Here, we propose a comprehensive family of gene‐level score tests for identifying genetic elements of disease risk, in particular pairwise gene‐gene interactions. Using kernel machine methods, we devise score‐based variance component tests under a generalized linear mixed model framework. We conducted simulations based upon coalescent genetic models to evaluate the performance of our approach under a variety of disease models. These simulations indicate that our methods are generally higher powered than alternative gene‐level approaches and at worst competitive with exhaustive SNP‐level (where SNP is single‐nucleotide polymorphism) analyses. Furthermore, we observe that simulated epistatic effects resulted in significant marginal testing results for the involved genes regardless of whether or not true main effects were present. We detail the benefits of our methods and discuss potential genome‐wide analysis strategies for gene‐gene interaction analysis in a case‐control study design.

Abstract Summary: We have developed a novel Bayesian method, PurBayes, to estimate tumor purity and detect intratumor heterogeneity based on next-generation sequencing data of paired tumor-normal tissue samples, which uses finite mixture modeling methods. We demonstrate our approach using simulated data and discuss its performance under varying conditions. Availability: PurBayes is implemented as an R package, and source code is available for download through CRAN at http://cran.r-project.org/package=PurBayes. Contact: larson.nicholas@mayo.edu Supplementary information: Supplementary data are available online at Bioinformatics online.