Literature DB >> 23038745

Bayesian genome-wide association analysis of growth and yearling ultrasound measures of carcass traits in Brangus heifers.

S O Peters1, K Kizilkaya, D J Garrick, R L Fernando, J M Reecy, R L Weaber, G A Silver, M G Thomas.   

Abstract

Data from developing Brangus heifers (3/8 Brahman-Bos indicus × 5/8 Angus-Bos taurus; n ≈ 802 from 67 sires) registered with International Brangus Breeders Association were analyzed to detect QTL associated with growth traits and ultrasound measures of carcass traits. Genotypes were from BovineSNP50 (Infinium BeadChip, Illumina, San Diego, CA; 53,692 SNP). Phenotypes included BW collected at birth and ∼205 and 365 d of age, and yearling ultrasound assessment of LM area, percent intramuscular fat, and depth of rib fat. Simultaneous association of SNP windows with phenotype were undertaken with Bayes C analyses, using GenSel software. The SNP windows were ≈ 5 SNP in length. Analyses fitted a mixture model that treated SNP effects as random, with an assumed fraction pi = 0.999 having no effect on phenotype. Bootstrap analyses were used to obtain significance values for the SNP windows with the greatest contribution to observed variation. The SNP windows with P < 0.01 were considered as QTL associated with a trait in which case their location was queried from dbSNP and the presence of a previously reported QTL in that location was checked in CattleQTLdb. For 9 traits, QTL were mapped to 139 regions on 25 chromosomes. Forty-one of these QTL were already described in CattleQTLdb, so 98 are new additions. The SNP windows on chromosomes 1, 3, and 6 were associated with multiple traits (i.e., 205- and 365- d BW, and ADG from birth to 205 and 365 d of age). Several chromosomes harbored regions associated with multiple traits; however, the SNP that comprised the window often varied among traits (i.e., chromosomes 1, 3, 4, 5, 6, 7, 9, 10, 11, 13, 14, 15, 16, 20, 21, 22, 24, 28, and 29). Results from whole genome association of SNP with growth and ultrasound carcass traits in developing Brangus heifers confirmed several published QTL and detected several new QTL.

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Year:  2012        PMID: 23038745     DOI: 10.2527/jas.2012-4507

Source DB:  PubMed          Journal:  J Anim Sci        ISSN: 0021-8812            Impact factor:   3.159


  34 in total

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Authors:  G A Oliveira Júnior; B C Perez; J B Cole; M H A Santana; J Silveira; G Mazzoni; R V Ventura; M L Santana Júnior; H N Kadarmideen; D J Garrick; J B S Ferraz
Journal:  J Anim Sci       Date:  2017-11       Impact factor: 3.159

2.  Genome-wide association study of Stayability and Heifer Pregnancy in Red Angus cattle.

Authors:  S E Speidel; B A Buckley; R J Boldt; R M Enns; J Lee; M L Spangler; M G Thomas
Journal:  J Anim Sci       Date:  2018-04-03       Impact factor: 3.159

3.  Whole-Genome Resequencing Analysis of Hanwoo and Yanbian Cattle to Identify Genome-Wide SNPs and Signatures of Selection.

Authors:  Jung-Woo Choi; Bong-Hwan Choi; Seung-Hwan Lee; Seung-Soo Lee; Hyeong-Cheol Kim; Dayeong Yu; Won-Hyong Chung; Kyung-Tai Lee; Han-Ha Chai; Yong-Min Cho; Dajeong Lim
Journal:  Mol Cells       Date:  2015-05-15       Impact factor: 5.034

4.  Genome-wide association study for backfat thickness in Canchim beef cattle using Random Forest approach.

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Journal:  BMC Genet       Date:  2013-06-05       Impact factor: 2.797

5.  Genotyping-by-sequencing (GBS): a novel, efficient and cost-effective genotyping method for cattle using next-generation sequencing.

Authors:  Marcos De Donato; Sunday O Peters; Sharon E Mitchell; Tanveer Hussain; Ikhide G Imumorin
Journal:  PLoS One       Date:  2013-05-17       Impact factor: 3.240

6.  Estimates of marker effects for measures of milk flow in the Italian brown Swiss dairy cattle population.

Authors:  Kent A Gray; Christian Maltecca; Alessandro Bagnato; Marlies Dolezal; Attilio Rossoni; Antonia B Samore; Joseph P Cassady
Journal:  BMC Vet Res       Date:  2012-10-23       Impact factor: 2.741

7.  Genome-wide association study of reproductive traits in Nellore heifers using Bayesian inference.

Authors:  Raphael B Costa; Gregório M F Camargo; Iara D P S Diaz; Natalia Irano; Marina M Dias; Roberto Carvalheiro; Arione A Boligon; Fernando Baldi; Henrique N Oliveira; Humberto Tonhati; Lucia G Albuquerque
Journal:  Genet Sel Evol       Date:  2015-08-19       Impact factor: 4.297

8.  QTL fine mapping with Bayes C(π): a simulation study.

Authors:  Irene van den Berg; Sébastien Fritz; Didier Boichard
Journal:  Genet Sel Evol       Date:  2013-06-19       Impact factor: 4.297

9.  SNPchiMp: a database to disentangle the SNPchip jungle in bovine livestock.

Authors:  Ezequiel Luis Nicolazzi; Matteo Picciolini; Francesco Strozzi; Robert David Schnabel; Cindy Lawley; Ali Pirani; Fiona Brew; Alessandra Stella
Journal:  BMC Genomics       Date:  2014-02-11       Impact factor: 3.969

10.  Genome-wide association analysis and genomic prediction of Mycobacterium avium subspecies paratuberculosis infection in US Jersey cattle.

Authors:  Yalda Zare; George E Shook; Michael T Collins; Brian W Kirkpatrick
Journal:  PLoS One       Date:  2014-02-11       Impact factor: 3.240

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