Literature DB >> 24906026

Within- and across-breed imputation of high-density genotypes in dairy and beef cattle from medium- and low-density genotypes.

D P Berry1, M C McClure, M P Mullen.   

Abstract

The objective of this study was to evaluate, using three different genotype density panels, the accuracy of imputation from lower- to higher-density genotypes in dairy and beef cattle. High-density genotypes consisting of 777,962 single-nucleotide polymorphisms (SNP) were available on 3122 animals comprised of 269, 196, 710, 234, 719, 730 and 264 Angus, Belgian Blue, Charolais, Hereford, Holstein-Friesian, Limousin and Simmental bulls, respectively. Three different genotype densities were generated: low density (LD; 6501 autosomal SNPs), medium density (50K; 47,770 autosomal SNPs) and high density (HD; 735,151 autosomal SNPs). Imputation from lower- to higher-density genotype platforms was undertaken within and across breeds exploiting population-wide linkage disequilibrium. The mean allele concordance rate per breed from LD to HD when undertaken using a single breed or multiple breed reference population varied from 0.956 to 0.974 and from 0.947 to 0.967, respectively. The mean allele concordance rate per breed from 50K to HD when undertaken using a single breed or multiple breed reference population varied from 0.987 to 0.994 and from 0.987 to 0.993, respectively. The accuracy of imputation was generally greater when the reference population was solely comprised of the breed to be imputed compared to when the reference population comprised of multiple breeds, although the impact was less when imputing from 50K to HD compared to imputing from LD.
© 2013 Blackwell Verlag GmbH.

Entities:  

Keywords:  Beagle; Illumina; genomic selection; genotype; impute

Mesh:

Year:  2013        PMID: 24906026     DOI: 10.1111/jbg.12067

Source DB:  PubMed          Journal:  J Anim Breed Genet        ISSN: 0931-2668            Impact factor:   2.380


  13 in total

1.  High imputation accuracy from informative low-to-medium density single nucleotide polymorphism genotypes is achievable in sheep1.

Authors:  Aine C O'Brien; Michelle M Judge; Sean Fair; Donagh P Berry
Journal:  J Anim Sci       Date:  2019-04-03       Impact factor: 3.159

2.  Assessing accuracy of genotype imputation in the Afrikaner and Brahman cattle breeds of South Africa.

Authors:  S Mdyogolo; M D MacNeil; F W C Neser; M M Scholtz; M L Makgahlela
Journal:  Trop Anim Health Prod       Date:  2022-02-08       Impact factor: 1.559

3.  How imputation can mitigate SNP ascertainment Bias.

Authors:  Johannes Geibel; Christian Reimer; Torsten Pook; Steffen Weigend; Annett Weigend; Henner Simianer
Journal:  BMC Genomics       Date:  2021-05-12       Impact factor: 3.969

4.  Accuracy of genome-wide imputation in Braford and Hereford beef cattle.

Authors:  Mario L Piccoli; José Braccini; Fernando F Cardoso; Medhi Sargolzaei; Steven G Larmer; Flávio S Schenkel
Journal:  BMC Genet       Date:  2014-12-29       Impact factor: 2.797

5.  A genome-wide association study for genetic susceptibility to Mycobacterium bovis infection in dairy cattle identifies a susceptibility QTL on chromosome 23.

Authors:  Ian W Richardson; Donagh P Berry; Heather L Wiencko; Isabella M Higgins; Simon J More; Jennifer McClure; David J Lynn; Daniel G Bradley
Journal:  Genet Sel Evol       Date:  2016-03-09       Impact factor: 4.297

6.  Novel methods for genotype imputation to whole-genome sequence and a simple linear model to predict imputation accuracy.

Authors:  Steven G Larmer; Mehdi Sargolzaei; Luiz F Brito; Ricardo V Ventura; Flávio S Schenkel
Journal:  BMC Genet       Date:  2017-12-27       Impact factor: 2.797

7.  Linkage Disequilibrium-Based Inference of Genome Homology and Chromosomal Rearrangements Between Species.

Authors:  Daniel Jordan de Abreu Santos; Gregório Miguel Ferreira de Camargo; Diercles Francisco Cardoso; Marcos Eli Buzanskas; Rusbel Raul Aspilcueta-Borquis; Naudin Alejandro Hurtado-Lugo; Francisco Ribeiro de Araújo Neto; Lúcia Galvão de Albuquerque; Li Ma; Humberto Tonhati
Journal:  G3 (Bethesda)       Date:  2020-07-07       Impact factor: 3.154

8.  Comparing SNP panels and statistical methods for estimating genomic breed composition of individual animals in ten cattle breeds.

Authors:  Jun He; Yage Guo; Jiaqi Xu; Hao Li; Anna Fuller; Richard G Tait; Xiao-Lin Wu; Stewart Bauck
Journal:  BMC Genet       Date:  2018-08-09       Impact factor: 2.797

9.  Strategies for imputation to whole genome sequence using a single or multi-breed reference population in cattle.

Authors:  Rasmus Froberg Brøndum; Bernt Guldbrandtsen; Goutam Sahana; Mogens Sandø Lund; Guosheng Su
Journal:  BMC Genomics       Date:  2014-08-27       Impact factor: 3.969

10.  Challenges and opportunities in genetic improvement of local livestock breeds.

Authors:  Filippo Biscarini; Ezequiel L Nicolazzi; Alessandra Stella; Paul J Boettcher; Gustavo Gandini
Journal:  Front Genet       Date:  2015-02-25       Impact factor: 4.599

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