Wei Pan1. 1. Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455-0378, USA. weip@biostat.umn.edu
Abstract
MOTIVATION: We consider the problem of multiple locus linkage analysis for expression traits of genes in a pathway or a network. To capitalize on co-expression of functionally related genes, we propose a penalized regression method that maps multiple expression quantitative trait loci (eQTLs) for all related genes simultaneously while accounting for their shared functions as specified a priori by a gene pathway or network. RESULTS: An analysis of a mouse dataset and simulation studies clearly demonstrate the advantage of the proposed method over a standard approach that ignores biological knowledge of gene networks.
MOTIVATION: We consider the problem of multiple locus linkage analysis for expression traits of genes in a pathway or a network. To capitalize on co-expression of functionally related genes, we propose a penalized regression method that maps multiple expression quantitative trait loci (eQTLs) for all related genes simultaneously while accounting for their shared functions as specified a priori by a gene pathway or network. RESULTS: An analysis of a mouse dataset and simulation studies clearly demonstrate the advantage of the proposed method over a standard approach that ignores biological knowledge of gene networks.
Authors: Eric E Schadt; Stephanie A Monks; Thomas A Drake; Aldons J Lusis; Nam Che; Veronica Colinayo; Thomas G Ruff; Stephen B Milligan; John R Lamb; Guy Cavet; Peter S Linsley; Mao Mao; Roland B Stoughton; Stephen H Friend Journal: Nature Date: 2003-03-20 Impact factor: 49.962
Authors: Daniel J Kliebenstein; Marilyn A L West; Hans van Leeuwen; Olivier Loudet; R W Doerge; Dina A St Clair Journal: BMC Bioinformatics Date: 2006-06-16 Impact factor: 3.169