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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Animal Genomics and Improvement Laboratory » Research » Publications at this Location » Publication #292899

Title: Analysis of population structure and genetic history of cattle breeds based on high-density SNP data

item XU, LINGYANG - University Of Maryland
item Huson, Heather
item HOU, YALI - Chinese Academy Of Sciences
item Bickhart, Derek
item SONG, JIUZHOU - University Of Maryland
item Sonstegard, Tad
item Van Tassell, Curtis - Curt
item Liu, Ge - George

Submitted to: BARC Poster Day
Publication Type: Abstract Only
Publication Acceptance Date: 3/27/2013
Publication Date: 4/18/2013
Citation: Xu, L., Huson, H.J., Hou, Y., Bickhart, D.M., Song, J., Sonstegard, T.S., Van Tassell, C.P., Liu, G. 2013. Analysis of population structure and genetic history of cattle breeds based on high-density SNP data. BARC Poster Day. p.55 Abstract 48.

Interpretive Summary:

Technical Abstract: Advances in single nucleotide polymorphism (SNP) genotyping microarrays have facilitated a new understanding of population structure and evolutionary history for several species. Most existing studies in livestock were based on low density SNP arrays. The first wave of low density SNP studies on cattle have provided outlines of population structure at a coarse-scale resolution. This includes basic patterns of migration after domestication where cattle have spread into different geographic environments. Subsequent artificial selection has also contributed to the differentiation of cattle breeds into broad phenotypes.Recently, two high-density SNP genotyping arrays became available for bovine genomic analyses, including the Illumina BovineHD BeadChip Array and the Affymetrix Axiom Genome-Wide BOS 1 Array. We report a new population genetics study using the Bovine HapMap samples based on Illumina BovineHD arrays. After studying genetic diversity in the different breeds, we performed a detailed analysis of population structure at both the individual and population levels. We validated and extended the previous results about spatial patterns of variations, evolution history, and genome-wide admixture within and among populations. We further investigated the population structure and spatial patterns within European cattle breeds. Data from this high-density array will offer valuable information about genetic structure, population history and evolution, and also facilitate cattle genetic evaluations and genome-wide association studies (GWAS) for quantitative trait loci mapping. We draw the lessons from this study to predict the directions that will be most fruitful to pursue for the emerging next generation sequencing era. We expect sampling more breeds, detecting rare SNP variant and emerging next generation sequencing data will provide additional insights into the worldwide and local cattle genetic diversity and population structure.