2017
DOI: 10.3168/jds.2016-12249
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Derivation and genome-wide association study of a principal component-based measure of heat tolerance in dairy cattle

Abstract: Heat stress represents a key factor that negatively affects the productive and reproductive performance of farm animals. In the present work, a new measure of tolerance to heat stress for dairy cattle was developed using principal component analysis. Data were from 590,174 test-day records for milk yield, fat and protein percentages, and somatic cell score of 39,261 Italian Holstein cows. Test-day records adjusted for main systematic factors were grouped into 11 temperature-humidity index (THI) classes. Daught… Show more

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Cited by 53 publications
(44 citation statements)
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“…These were present on BTA1, BTA3, BTA5, BTA9, BTA11, BTA12, BTA15, BTA19, BTA20, BTA21, BTA24 and BTA25. Some of these chromosomes correspond to those where significant associations were identified in the present study A GWAS performed in dairy cattle by Macciotta et al (2017) showed a SNP (ARS-BFGL-NGS-29678 on BTA26) associated to heat tolerance, and Dikmen et al (2013) identified a SNP also located on BTA26 that was associated with sweating rate in Holstein cows during heat stress. Another aspect to be taken into account in the GWAS for heat tolerance traits is the associated phenotype.…”
Section: Discussionsupporting
confidence: 61%
“…These were present on BTA1, BTA3, BTA5, BTA9, BTA11, BTA12, BTA15, BTA19, BTA20, BTA21, BTA24 and BTA25. Some of these chromosomes correspond to those where significant associations were identified in the present study A GWAS performed in dairy cattle by Macciotta et al (2017) showed a SNP (ARS-BFGL-NGS-29678 on BTA26) associated to heat tolerance, and Dikmen et al (2013) identified a SNP also located on BTA26 that was associated with sweating rate in Holstein cows during heat stress. Another aspect to be taken into account in the GWAS for heat tolerance traits is the associated phenotype.…”
Section: Discussionsupporting
confidence: 61%
“…In addition to this, even the method of genetic selection is changing, and the new genomic approach will lead faster improvements and the possibility to select productive and functional traits together. Recently, a genome-wide association analysis was carried out on 423 genotyped bulls and eight markers for productive traits were identified (Macciotta et al 2017). The gene Figure 6.…”
Section: Discussionmentioning
confidence: 99%
“…In the past decade, several studies analyzed G×E interactions for milk production traits using either pedigree-based relationship matrices (pRRM; Calus et al, 2006;Hammami et al, 2013;Carabaño et al, 2016) or genomic relationship matrices (gRRM; Haile-Mariam et al, 2015;Tiezzi et al, 2017). To quantify differences in gene expressions in various environments, genomic multiple-trait models (Haile-Mariam et al, 2015;Yao et al, 2017) and reaction norm or random regression models (RRM; Macciotta et al, 2017;Tiezzi et al, 2017) were applied. Yao et al (2017) and Tiezzi et al (2017) demonstrated the advantages of statistical models with interaction effects for the estimation of environment-specific genomic parameters.…”
Section: Introductionmentioning
confidence: 99%