Partitioning of genomic variance reveals biological pathways associated with udder health and milk production traits in dairy cattle |
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| Authors: | Stefan M Edwards Bo Thomsen Per Madsen Peter S?rensen |
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| Institution: | .Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Blichers Allé 20, P.O. Box 50, Tjele, DK-8830 Denmark ;.Department of Molecular Biology and Genetics, Aarhus University, Blichers Allé 20, P.O. Box 50, Tjele, DK-8830 Denmark |
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| Abstract: | BackgroundWe have used a linear mixed model (LMM) approach to examine the joint contribution of genetic markers associated with a biological pathway. However, with these markers being scattered throughout the genome, we are faced with the challenge of modelling the contribution from several, sometimes even all, chromosomes at once. Due to linkage disequilibrium (LD), all markers may be assumed to account for some genomic variance; but the question is whether random sets of markers account for the same genomic variance as markers associated with a biological pathway?ResultsWe applied the LMM approach to identify biological pathways associated with udder health and milk production traits in dairy cattle. A random gene sampling procedure was applied to assess the biological pathways in a dataset that has an inherently complex genetic correlation pattern due to the population structure of dairy cattle, and to linkage disequilibrium within the bovine genome and within the genes associated to the biological pathway.ConclusionsSeveral biological pathways that were significantly associated with health and production traits were identified in dairy cattle; i.e. the markers linked to these pathways explained more of the genomic variance and provided a better model fit than 95 % of the randomly sampled gene groups. Our results show that immune related pathways are associated with production traits, and that pathways that include a causal marker for production traits are identified with our procedure.We are confident that the LMM approach provides a general framework to exploit and integrate prior biological information and could potentially lead to improved understanding of the genetic architecture of complex traits and diseases.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-015-0132-6) contains supplementary material, which is available to authorized users. |
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