Paper Info
Title
GWAS on your notebook: fast semi-parallel linear and logistic regression for genome-wide association studies.
Abstract
Genome-wide association studies have become very popular in identifying genetic contributions to phenotypes. Millions of SNPs are being tested for their association with diseases and traits using linear or logistic regression models. This conceptually simple strategy encounters the following computational issues: a large number of tests and very large genotype files (many Gigabytes) which cannot be directly loaded into the software memory. One of the solutions applied on a grand scale is cluster computing involving large-scale resources. We show how to speed up the computations using matrix operations in pure R code.We improve speed: computation time from 6 hours is reduced to 10-15 minutes. Our approach can handle essentially an unlimited amount of covariates efficiently, using projections. Data files in GWAS are vast and reading them into computer memory becomes an important issue. However, much improvement can be made if the data is structured beforehand in a way allowing for easy access to blocks of SNPs. We propose several solutions based on the R packages ff and ncdf.We adapted the semi-parallel computations for logistic regression. We show that in a typical GWAS setting, where SNP effects are very small, we do not lose any precision and our computations are few hundreds times faster than standard procedures.We provide very fast algorithms for GWAS written in pure R code. We also show how to rearrange SNP data for fast access.
Year
DOI
Venue
2013
10.1186/1471-2105-14-166
BMC Bioinformatics
Keywords
Field
DocType
genome wide association study,algorithms,genotype,microarrays,linear models,bioinformatics,computational biology
Data mining,Linear model,Computer science,Genome-wide association study,Software,Genetic association,Bioinformatics,Genetics,Logistic regression,Matrix multiplication,Computer cluster,Speedup
Journal
Volume
Issue
ISSN
14
1
1471-2105
Citations 
PageRank 
References 
8
0.58
4
Authors
4
Name
Order
Citations
PageRank
Karolina Sikorska180.58
Emmanuel Lesaffre2296.86
Patrick J. F. Groenen38411.72
Paul H. C. Eilers417930.08