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Partitioning of genomic variance reveals biological pathways associated with udder health and milk production traits in dairy cattle.

Edwards SM, Thomsen B, Madsen P, Sørensen P - Genet. Sel. Evol. (2015)

Bottom Line: Several 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.

View Article: PubMed Central - PubMed

Affiliation: 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. Stefan.Hoj-Edwards@roslin.ed.ac.uk.

ABSTRACT

Background: We 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?

Results: We 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.

Conclusions: Several 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.

No MeSH data available.


Chromosomal location of genes associated with the KEGG pathway Immune System. The pathway consists of several sub-pathways and genes that can be associated to none, one or several pathways. Since the chromosomal location of the genes is known, it is possible to link a pathway to a set of markers
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Fig1: Chromosomal location of genes associated with the KEGG pathway Immune System. The pathway consists of several sub-pathways and genes that can be associated to none, one or several pathways. Since the chromosomal location of the genes is known, it is possible to link a pathway to a set of markers

Mentions: To enhance the analysis and biological interpretation, instead of partitioning according to the physical structuring of genes (i.e. chromosomes), we applied external information, evidence that was obtained from other experiments or from other organisms through homology, to the partitioning concept; here we used the Kyoto Encyclopedia of Genes and Genomes (KEGG) [12, 13]. Thus, we were able to answer questions such as ‘How much of the observed phenotypic variance is accounted for by markers linked to a specific biological pathway?’ Although it is straightforward to determine a variance component for a group of markers or a pathway of interest, it is not so easy to determine whether it represents a significant amount of genomic variance. The linear mixed model approach allows us to use a likelihood ratio test (LRT) to compare different pathway-based partitionings of the genomic variance. However, genes and markers that can be associated to biological pathways are scattered throughout the genome (Fig. 1) and this may influence the distribution of the likelihood ratios. Furthermore, under the infinitesimal model, we expect that an infinite number of markers contribute to the observed genomic variance, and therefore we must also determine whether the variance contributed by the markers of interest is larger than that of the same number of randomly sampled markers.Fig. 1


Partitioning of genomic variance reveals biological pathways associated with udder health and milk production traits in dairy cattle.

Edwards SM, Thomsen B, Madsen P, Sørensen P - Genet. Sel. Evol. (2015)

Chromosomal location of genes associated with the KEGG pathway Immune System. The pathway consists of several sub-pathways and genes that can be associated to none, one or several pathways. Since the chromosomal location of the genes is known, it is possible to link a pathway to a set of markers
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4499908&req=5

Fig1: Chromosomal location of genes associated with the KEGG pathway Immune System. The pathway consists of several sub-pathways and genes that can be associated to none, one or several pathways. Since the chromosomal location of the genes is known, it is possible to link a pathway to a set of markers
Mentions: To enhance the analysis and biological interpretation, instead of partitioning according to the physical structuring of genes (i.e. chromosomes), we applied external information, evidence that was obtained from other experiments or from other organisms through homology, to the partitioning concept; here we used the Kyoto Encyclopedia of Genes and Genomes (KEGG) [12, 13]. Thus, we were able to answer questions such as ‘How much of the observed phenotypic variance is accounted for by markers linked to a specific biological pathway?’ Although it is straightforward to determine a variance component for a group of markers or a pathway of interest, it is not so easy to determine whether it represents a significant amount of genomic variance. The linear mixed model approach allows us to use a likelihood ratio test (LRT) to compare different pathway-based partitionings of the genomic variance. However, genes and markers that can be associated to biological pathways are scattered throughout the genome (Fig. 1) and this may influence the distribution of the likelihood ratios. Furthermore, under the infinitesimal model, we expect that an infinite number of markers contribute to the observed genomic variance, and therefore we must also determine whether the variance contributed by the markers of interest is larger than that of the same number of randomly sampled markers.Fig. 1

Bottom Line: Several 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.

View Article: PubMed Central - PubMed

Affiliation: 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. Stefan.Hoj-Edwards@roslin.ed.ac.uk.

ABSTRACT

Background: We 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?

Results: We 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.

Conclusions: Several 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.

No MeSH data available.