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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.


Overview of all pathways significant for both empirical cut-offs of LR and  (LR95 and ), which shows that some pathways are consistently significant for multiple traits. Pathways are colour coded by group, points are sized by proportion of explained genomic variance ()
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Fig5: Overview of all pathways significant for both empirical cut-offs of LR and (LR95 and ), which shows that some pathways are consistently significant for multiple traits. Pathways are colour coded by group, points are sized by proportion of explained genomic variance ()

Mentions: Figure 5 summarises the pathways that have a significant LR after adjusting for multiple testing using FDR. The values for , adjusted p-value, and LR statistics for these pathways are in Table 3. After correcting for multiple testing, there were no pathways associated with ‘Mastitis 1.2’ or ‘Somatic Cell Count’. A summary of all tested pathways and traits is in Table S2 (see Additional file 3: Table S2).Fig. 5


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)

Overview of all pathways significant for both empirical cut-offs of LR and  (LR95 and ), which shows that some pathways are consistently significant for multiple traits. Pathways are colour coded by group, points are sized by proportion of explained genomic variance ()
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig5: Overview of all pathways significant for both empirical cut-offs of LR and (LR95 and ), which shows that some pathways are consistently significant for multiple traits. Pathways are colour coded by group, points are sized by proportion of explained genomic variance ()
Mentions: Figure 5 summarises the pathways that have a significant LR after adjusting for multiple testing using FDR. The values for , adjusted p-value, and LR statistics for these pathways are in Table 3. After correcting for multiple testing, there were no pathways associated with ‘Mastitis 1.2’ or ‘Somatic Cell Count’. A summary of all tested pathways and traits is in Table S2 (see Additional file 3: Table S2).Fig. 5

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.