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This Article Full Text (PDF) All Versions of this Article: jas.2008-1021v1 86/12/3358    most recent Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Long, N. Articles by Avendaño, S. Search for Related Content PubMed PubMed Citation Articles by Long, N. Articles by Avendaño, S.

Marker-assisted assessment of genotype by environment interaction: a case study of SNP-mortality association in broilers in two hygiene environments

N. Long^*, D. Gianola ^*,, G. J. M. Rosa , K. A. Weigel and S. Avendaño

^* Department of Animal Sciences, University of Wisconsin, Madison, WI 53706 , Department of Dairy Science, University of Wisconsin, Madison, WI 53706 Aviagen Ltd., Newbridge, Midlothian, EH28 8SZ, UK

nlong{at}wisc.edu

Abstract

Interplay between genetic and environmental factors, genotype by environment interactions (G x E), affect phenotypes of complex traits. A methodology for assessing G x E was investigated by detecting hygiene (low and high) environment-specific single nucleotide polymorphism (SNP) subsets associated with broiler chicken mortality, followed by an examination of consistency between SNP subsets selected from the 2 environments. The trait was mean progeny mortality rate in 253 sire families, after adjusting records for nuisance effects affecting mortality at the individual bird level. Over 5,000 whole genome SNPs were narrowed down via a machine learning (filter-wrapper) feature selection procedure applied to mortality rates in each of the environments. For both early and late mortality, it was found that the selected SNP subsets differed across hygiene environments, in terms of either across-environment predictive ability or extent of linkage disequilibrium (LD) between the subsets. Reduction in predictive ability due to G x E was assessed by the ratio of 2 predicted residual sum of squares (PRESS) statistics, one associated with SNPs selected from the same hygiene environment, and the other associated with the SNP subset from a different environment. Reduction was 30% and 20% for early and late mortality, respectively. An extremely low level of LD between SNP subsets selected under low and high hygiene also indicated G x E. Findings suggest that there may not be a universally optimal SNP subset for predicting mortality, and that interactions between genome and environmental factors need to be considered in association analysis of complex traits.

Key Words: chicken • genetic association • genotype by environment interaction • machine learning • mortality • SNP