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      How old are quantitative trait loci and how widely do they segregate?

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          Abstract

          The mutations that cause genetic variation in quantitative traits could be old and segregate across many breeds or they could be young and segregate only within one breed. This has implications for our understanding of the evolution of quantitative traits and for genomic prediction to improve livestock. We investigated the age of quantitative trait loci (QTL) for milk production traits identified as segregating in Holstein dairy cattle. We use a multitrait method and found that six of 11 QTL also segregate in Jerseys. Variants identified as Holstein-only QTL were fixed or rare [minor allele frequency (MAF) < 0.05] in Jersey. The age of the QTL mutations appears to vary from perhaps 2000 to 50,000 generations old. The older QTL tend to have high derived allele frequencies and often segregate across both breeds. Holstein-only QTL were often embedded within longer haplotypes, supporting the conclusion that they are typically younger mutations that have occurred more recently than QTL that segregate in both breeds. A reference population for genomic prediction using both Holsteins and Jersey cattle incorrectly predicted a QTL in Jersey cattle when the QTL only segregates in Holsteins. Overcoming this error should help to make genomic prediction more accurate in smaller breeds.

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          Author and article information

          Journal
          J. Anim. Breed. Genet.
          Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie
          1439-0388
          0931-2668
          Apr 2015
          : 132
          : 2
          Affiliations
          [1 ] Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, Vic., Australia.
          Article
          10.1111/jbg.12152
          25823838
          7f903d83-36e6-4acb-80d3-a51dcaecf6de
          © 2015 Blackwell Verlag GmbH.
          History

          Dairy cattle,QTL mapping,genetic architecture,genomic prediction,genomic sequence variants

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