High performance solutions for big-data GWAS

Abstract

In order to associate complex traits with genetic polymorphisms, genome-wide association studies process huge datasets involving tens of thousands of individuals genotyped for millions of polymorphisms. When handling these datasets, which exceed the main memory of contemporary computers, one faces two distinct challenges: 1) Millions of polymorphisms and thousands of phenotypes come at the cost of hundreds of gigabytes of data, which can only be kept in secondary storage; 2) the relatedness of the test population is represented by a relationship matrix, which, for large populations, can only fit in the combined main memory of a distributed architecture. In this paper, by using distributed resources such as Cloud or clusters, we address both challenges: The genotype and phenotype data is streamed from secondary storage using a double buffering technique, while the relationship matrix is kept across the main memory of a distributed memory system. With the help of these solutions, we develop separate algorithms for studies involving only one or a multitude of traits. We show that these algorithms sustain high-performance and allow the analysis of enormous datasets.

Bayesian large-scale multiple regression with summary statistics from genome-wide association studies

“…Despite these benefits, few GWAS are analyzed with multiple-SNP methods, presumably, at least in part, because existing methods require individual-level data that can be difficult to obtain. In addition, most multiple-SNP methods are computationally challenging for large studies [e.g., Loh et al (2015), Peise, Fabregat-Traver and Bientinesi (2015)]. Our methods help with both these issues, allowing inference to be performed with summary-level data, and reducing computation by exploiting matrix bandedness [Wen and Stephens (2010)].…”

Bayesian large-scale multiple regression with summary statistics from genome-wide association studies

“…In addition, most multiple-SNP methods are computationally challenging for large studies [e.g. Peise, Fabregat-Traver and Bientinesi (2015); Loh et al (2015)]. Our methods help with both these issues, allowing inference to be performed with summary-level data, and reducing computation by exploiting matrix bandedness (Wen and Stephens, 2010).…”

Studying the effects of haplotype partitioning methods on the RA-associated genomic results from the North American Rheumatoid Arthritis Consortium (NARAC) dataset

“…GWAS results represent a domain of big data with millions of SNPs tested against many phenotypes. These results have become a burden for bioinformaticians in terms of processing time and real-time visualization [10], [11].…”