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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.


Related in: MedlinePlus

Proportion of explained genomic variance by random gene groups for the trait Fat yield as a function of number of markers in the gene groups, showing that groups with DGAT1 genes consistently increase the expected amount of explained genomic variance. For groups that do not contain one of the DGAT1 genes, the situation is the same as for Mastitis 1.2. The dots corresponds to a random gene group, and the lines are the 50th and 95th percentile of these. The random gene groups are colour coded according to whether the likelihood ratio is larger than 95 % of the likelihood ratios of the same trait. The regression lines are coloured according to whether they describe gene groups containing DGAT1 genes; the grey, dashed line corresponds to the naïve expectation of the infinitesimal model, where all markers contribute with the same effect
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Fig4: Proportion of explained genomic variance by random gene groups for the trait Fat yield as a function of number of markers in the gene groups, showing that groups with DGAT1 genes consistently increase the expected amount of explained genomic variance. For groups that do not contain one of the DGAT1 genes, the situation is the same as for Mastitis 1.2. The dots corresponds to a random gene group, and the lines are the 50th and 95th percentile of these. The random gene groups are colour coded according to whether the likelihood ratio is larger than 95 % of the likelihood ratios of the same trait. The regression lines are coloured according to whether they describe gene groups containing DGAT1 genes; the grey, dashed line corresponds to the naïve expectation of the infinitesimal model, where all markers contribute with the same effect

Mentions: To illustrate how much of the genomic variance was explained by a randomly sampled group of genes, results for two trait are presented: a health trait (Mastitis 1.2 in Fig. 3) and a milk production trait (Fat yield in Fig. 4). Similar plots for all traits are in Additional file 2: Figures S2, S3, S4, S5, S6, S7, and S8.Fig. 3


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)

Proportion of explained genomic variance by random gene groups for the trait Fat yield as a function of number of markers in the gene groups, showing that groups with DGAT1 genes consistently increase the expected amount of explained genomic variance. For groups that do not contain one of the DGAT1 genes, the situation is the same as for Mastitis 1.2. The dots corresponds to a random gene group, and the lines are the 50th and 95th percentile of these. The random gene groups are colour coded according to whether the likelihood ratio is larger than 95 % of the likelihood ratios of the same trait. The regression lines are coloured according to whether they describe gene groups containing DGAT1 genes; the grey, dashed line corresponds to the naïve expectation of the infinitesimal model, where all markers contribute with the same effect
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig4: Proportion of explained genomic variance by random gene groups for the trait Fat yield as a function of number of markers in the gene groups, showing that groups with DGAT1 genes consistently increase the expected amount of explained genomic variance. For groups that do not contain one of the DGAT1 genes, the situation is the same as for Mastitis 1.2. The dots corresponds to a random gene group, and the lines are the 50th and 95th percentile of these. The random gene groups are colour coded according to whether the likelihood ratio is larger than 95 % of the likelihood ratios of the same trait. The regression lines are coloured according to whether they describe gene groups containing DGAT1 genes; the grey, dashed line corresponds to the naïve expectation of the infinitesimal model, where all markers contribute with the same effect
Mentions: To illustrate how much of the genomic variance was explained by a randomly sampled group of genes, results for two trait are presented: a health trait (Mastitis 1.2 in Fig. 3) and a milk production trait (Fat yield in Fig. 4). Similar plots for all traits are in Additional file 2: Figures S2, S3, S4, S5, S6, S7, and S8.Fig. 3

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.


Related in: MedlinePlus