Evidence map›Paper›PMID 24456127›Full record

ArticleBMC genomics2014

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

Lesley-Ann Raven, Benjamin G Cocks, Ben J Hayes

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Article in BMC genomics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
64citing papers in PubMed, 2 pooled it
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

64 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Lesley-Ann RavenBiosciences Research Division, Department of Primary Industries Victoria, 5 Ring Road, Bundoora 3086, Australia. lesleyraven@mail.com.
Benjamin G Cocks
Ben J Hayes

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGenome 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.

resultsBy 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.

conclusionWhere 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.

Indexed as

GenomeGenome-Wide Association StudyAnimalsATP-Binding Cassette TransportersBreedingCattleDiacylglycerol O-AcyltransferaseFemaleGenotypeLactationLinkage DisequilibriumMilkPhenotypePolymorphism, Single NucleotideQuantitative Trait LociATP-Binding Cassette TransportersDiacylglycerol O-Acyltransferase

Identifiers

PMID24456127
PMCPMC3905911

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.