Multibreed genome wide association can improve precision of mapping causative variants underlying milk production in dairy cattle

Raven, Lesley-Ann, Cocks, Benjamin G. and Hayes, Ben J. (2014) Multibreed genome wide association can improve precision of mapping causative variants underlying milk production in dairy cattle. BMC Genomics, 15 . doi:10.1186/1471-2164-15-62


Author Raven, Lesley-Ann
Cocks, Benjamin G.
Hayes, Ben J.
Title Multibreed genome wide association can improve precision of mapping causative variants underlying milk production in dairy cattle
Journal name BMC Genomics   Check publisher's open access policy
ISSN 1471-2164
Publication date 2014-01-24
Year available 2014
Sub-type Article (original research)
DOI 10.1186/1471-2164-15-62
Open Access Status DOI
Volume 15
Total pages 14
Place of publication London, United Kingdom
Publisher BioMed Central
Language eng
Formatted abstract
Background: Genome wide association studies (GWAS) in most cattle breeds result in large genomic intervals of significant associations making it difficult to identify causal mutations. This is due to the extensive, low-level linkage disequilibrium within a cattle breed. As there is less linkage disequilibrium across breeds, multibreed GWAS may improve precision of causal variant mapping. Here we test this hypothesis in a Holstein and Jersey cattle data set with 17,925 individuals with records for production and functional traits and 632,003 SNP markers.

Results: By using a cross validation strategy within the Holstein and Jersey data sets, we were able to identify and confirm a large number of QTL. As expected, the precision of mapping these QTL within the breeds was limited. In the multibreed analysis, we found that many loci were not segregating in both breeds. This was partly an artefact of power of the experiments, with the number of QTL shared between the breeds generally increasing with trait heritability. False discovery rates suggest that the multibreed analysis was less powerful than between breed analyses, in terms of how much genetic variance was explained by the detected QTL. However, the multibreed analysis could more accurately pinpoint the location of the well-described mutations affecting milk production such as DGAT1. Further, the significant SNP in the multibreed analysis were significantly enriched in genes regions, to a considerably greater extent than was observed in the single breed analyses. In addition, we have refined QTL on BTA5 and BTA19 to very small intervals and identified a small number of potential candidate genes in these, as well as in a number of other regions.

Conclusion: Where QTL are segregating across breed, multibreed GWAS can refine these to reasonably small genomic intervals. However, such QTL appear to represent only a fraction of the genetic variation. Our results suggest a significant proportion of QTL affecting milk production segregate within rather than across breeds, at least for Holstein and Jersey cattle.
Keyword Dairy cattle
Genomic selection
Multibreed analysis
Single nucleotide polymorphism
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: Queensland Alliance for Agriculture and Food Innovation
 
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