scholarly journals Targeted Analysis Reveals an Important Role of JAK-STAT-SOCS Genes for Milk Production Traits in Australian Dairy Cattle

2015 ◽  
Vol 6 ◽  
Author(s):  
Sondur J. Arun ◽  
Peter C. Thomson ◽  
Paul A. Sheehy ◽  
Mehar S. Khatkar ◽  
Herman W. Raadsma ◽  
...  
Keyword(s):  
Dairy Cattle ◽  
Milk Production ◽  
Production Traits ◽  
Milk Production Traits ◽  
Targeted Analysis

scholarly journals Associations among milk production traits and glycosylated haemoglobin in dairy cattle; importance of lactose synthesis potential

2009 ◽  
Vol 34 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Homayon Reza Shahbazkia ◽  
Mahmoud Aminlari ◽  
Atoosa Tavasoli ◽  
Ahmad Reza Mohamadnia ◽  
Alfredo Cravador
Keyword(s):  
Dairy Cattle ◽  
Milk Production ◽  
Glycosylated Haemoglobin ◽  
Production Traits ◽  
Milk Production Traits ◽  
Lactose Synthesis

167 Genetic and genomic estimation for somatic cell score in relation with milk production traits of Russian Holstein dairy cattle

2017 ◽  
Vol 95 (suppl_4) ◽  
pp. 82-83 ◽  
Author(s):  
A. A. Sermyagin ◽  
E. A. Gladyr' ◽  
A. A. Kharzhau ◽  
K. V. Plemyashov ◽  
E. N. Tyurenkova ◽  
...  
Keyword(s):  
Dairy Cattle ◽  
Somatic Cell ◽  
Milk Production ◽  
Somatic Cell Score ◽  
Production Traits ◽  
Milk Production Traits

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.

scholarly journals Effects of the Osteopontin Gene Variants on Milk Production Traits in Dairy Cattle

2005 ◽  
Vol 88 (11) ◽  
pp. 4083-4086 ◽  
Author(s):  
S. Leonard ◽  
H. Khatib ◽  
V. Schutzkus ◽  
Y.M. Chang ◽  
C. Maltecca
Keyword(s):  
Dairy Cattle ◽  
Milk Production ◽  
Gene Variants ◽  
Production Traits ◽  
Milk Production Traits

Linear and threshold model genetic parameters for disease, fertility and milk production in dairy cattle

2000 ◽  
Vol 71 (3) ◽  
pp. 411-419 ◽  
Author(s):  
H. N. Kadarmideen ◽  
R. Thompson ◽  
G. Simm
Keyword(s):  
Dairy Cattle ◽  
Milk Production ◽  
Fixed Effects ◽  
Genetic Parameters ◽  
Threshold Model ◽  
Production Traits ◽  
Milk Production Traits ◽  
Model Framework ◽  
Data Set ◽  
Fertility Traits

AbstractThis study provides estimates of genetic parameters for various diseases, fertility and 305-day milk production traits in dairy cattle using data from a UK national milk recording scheme. The data set consisted of 63891 multiple lactation records on diseases (mastitis, lameness, milk fever, ketosis and tetany), fertility traits (calving interval, conception to first service, number of services for a conception, and number of days to first service), dystocia and 305-day milk, fat and protein yield. All traits were analysed by multi-trait repeatability linear animal models (LM). Binary diseases and fertility traits were further analysed by threshold sire models (TM). Both LM and TM analyses were based on the generalized linear mixed model framework. The LM included herd-year-season of calving (HYS), age at calving and parity as fixed effects and genetic, permanent environmental and residual effects as random. The TM analyses included the same effects as for LM, but HYS effects were treated as random to avoid convergence problems when HYS sub-classes had 0 or 100% incidence. Because HYS effects were treated as random, herd effects were fitted as fixed effects to account for effect of herds in the data. The LM estimates of heritability ranged from 0•389 to 0•399 for 305-day milk production traits, 0•010 to 0•029 for fertility traits and 0•004 to 0•038 for diseases. The LM estimates of repeatability ranged from 0•556 to 0•586 for 305-day milk production traits, 0•029 to 0•086 for fertility traits and 0•004 to 0•100 for diseases. The TM estimates of heritabilities and repeatabilities were greater than LM estimates for binary traits and were in the range 0•012 to 0•126 and 0•013 to 0•168, respectively. Genetic correlations between milk production traits and fertility and diseases were all unfavorable: they ranged from 0•07 to 0•37 for milk production and diseases, 0•31 to 0•54 for milk production and poor fertility and 0•06 to 0•41 for diseases and poor fertility. These results show that future selection programmes should include disease and fertility for genetic improvement of health and reproduction and for sustained economic growth in the dairy cattle industry.

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