Sinnwell, Jason P.

ABSTRACTSearching for rare genetic variants associated with complex diseases can be facilitated by enriching for diseased carriers of rare variants by sampling cases from pedigrees enriched for disease, possibly with related or unrelated controls. This strategy, however, complicates analyses because of shared genetic ancestry, as well as linkage disequilibrium among genetic markers. To overcome these problems, we developed broad classes of “burden” statistics and kernel statistics, extending commonly used methods for unrelated case‐control data to allow for known pedigree relationships, for autosomes and the X chromosome. Furthermore, by replacing pedigree‐based genetic correlation matrices with estimates of genetic relationships based on large‐scale genomic data, our methods can be used to account for population‐structured data. By simulations, we show that the type I error rates of our developed methods are near the asymptotic nominal levels, allowing rapid computation of P‐values. Our simulations also show that a linear weighted kernel statistic is generally more powerful than a weighted “burden” statistic. Because the proposed statistics are rapid to compute, they can be readily used for large‐scale screening of the association of genomic sequence data with disease status.

AbstractGene‐set analyses have been widely used in gene expression studies, and some of the developed methods have been extended to genome wide association studies (GWAS). Yet, complications due to linkage disequilibrium (LD) among single nucleotide polymorphisms (SNPs), and variable numbers of SNPs per gene and genes per gene‐set, have plagued current approaches, often leading to ad hoc “fixes.” To overcome some of the current limitations, we developed a general approach to scan GWAS SNP data for both gene‐level and gene‐set analyses, building on score statistics for generalized linear models, and taking advantage of the directed acyclic graph structure of the gene ontology when creating gene‐sets. However, other types of gene‐set structures can be used, such as the popular Kyoto Encyclopedia of Genes and Genomes (KEGG). Our approach combines SNPs into genes, and genes into gene‐sets, but assures that positive and negative effects of genes on a trait do not cancel. To control for multiple testing of many gene‐sets, we use an efficient computational strategy that accounts for LD and provides accurate step‐down adjustedP‐values for each gene‐set. Application of our methods to two different GWAS provide guidance on the potential strengths and weaknesses of our proposed gene‐set analyses.