scholarly journals Genome Wide Association Studies for Milk Production Traits in Dairy Cattle: A Review

2018 ◽  
Vol 10 (2) ◽  
pp. 339
Author(s):  
Alok Kumar Yadav ◽  
Anupama Mukherjee ◽  
Jitendra Singh
Keyword(s):  
Dairy Cattle ◽  
Milk Production ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Production Traits ◽  
Milk Production Traits ◽  
Genome Wide

scholarly journals Genome‐wide association studies for milk production traits in Valle del Belice sheep using repeated measures

2019 ◽  
Vol 50 (3) ◽  
pp. 311-314 ◽  
Author(s):  
A. M. Sutera ◽  
V. Riggio ◽  
S. Mastrangelo ◽  
R. Di Gerlando ◽  
M. T. Sardina ◽  
...  
Keyword(s):  
Milk Production ◽  
Repeated Measures ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Production Traits ◽  
Milk Production Traits ◽  
Genome Wide

scholarly journals Genome Wide Association Studies for Milk Production Traits in Chinese Holstein Population

PLoS ONE ◽  
2010 ◽  
Vol 5 (10) ◽  
pp. e13661 ◽  
Author(s):  
Li Jiang ◽  
Jianfeng Liu ◽  
Dongxiao Sun ◽  
Peipei Ma ◽  
Xiangdong Ding ◽  
...  
Keyword(s):  
Milk Production ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Production Traits ◽  
Milk Production Traits ◽  
Chinese Holstein ◽  
Genome Wide ◽  
Holstein Population

scholarly journals Confirmed effects of candidate variants for milk production, udder health, and udder morphology in dairy cattle

2020 ◽  
Vol 52 (1) ◽  
Author(s):  
Thierry Tribout ◽  
Pascal Croiseau ◽  
Rachel Lefebvre ◽  
Anne Barbat ◽  
Mekki Boussaha ◽  
...  
Keyword(s):  
Dairy Cattle ◽  
Candidate Genes ◽  
Milk Production ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Production Traits ◽  
Milk Production Traits ◽  
Udder Health ◽  
Gwas Approach

Abstract Background Over the last years, genome-wide association studies (GWAS) based on imputed whole-genome sequences (WGS) have been used to detect quantitative trait loci (QTL) and highlight candidate genes for important traits. However, in general this approach does not allow to validate the effects of candidate mutations or determine if they are truly causative for the trait(s) in question. To address these questions, we applied a two-step, within-breed GWAS approach on 15 traits (5 linked with milk production, 2 with udder health, and 8 with udder morphology) in Montbéliarde (MON), Normande (NOR), and Holstein (HOL) cattle. We detected the most-promising candidate variants (CV) using imputed WGS of 2515 MON, 2203 NOR, and 6321 HOL bulls, and validated their effects in three younger populations of 23,926 MON, 9400 NOR, and 51,977 HOL cows. Results Bull sequence-based GWAS detected 84 QTL: 13, 10, and 30 for milk production traits; 3, 0, and 2 for somatic cell score (SCS); and 8, 2 and 16 for udder morphology traits, in MON, NOR, and HOL respectively. Five genomic regions with effects on milk production traits were shared among the three breeds whereas six (2 for production and 4 for udder morphology and health traits) had effects in two breeds. In 80 of these QTL, 855 CV were highlighted based on the significance of their effects and functional annotation. The subsequent GWAS on MON, NOR, and HOL cows validated 8, 9, and 23 QTL for production traits; 0, 0, and 1 for SCS; and 4, 1, and 8 for udder morphology traits, respectively. In 47 of the 54 confirmed QTL, the CV identified in bulls had more significant effects than single nucleotide polymorphisms (SNPs) from the standard 50K chip. The best CV for each validated QTL was located in a gene that was functionally related to production (36 QTL) or udder (9 QTL) traits. Conclusions Using this two-step GWAS approach, we identified and validated 54 QTL that included CV mostly located within functional candidate genes and explained up to 6.3% (udder traits) and 37% (production traits) of the genetic variance of economically important dairy traits. These CV are now included in the chip used to evaluate French dairy cattle and can be integrated into routine genomic evaluation.

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