scholarly journals Assessment of Rumen Microbiota from a Large Dairy Cattle Cohort Reveals the Pan and Core Bacteriomes Contributing to Varied Phenotypes

2018 ◽  
Vol 84 (19) ◽  
Author(s):  
Mingyuan Xue ◽  
Huizeng Sun ◽  
Xuehui Wu ◽  
Le Luo Guan ◽  
Jianxin Liu

ABSTRACTCurrently, knowledge on the extent to which rumen microbiota differ in a large population of cattle fed the same diet and whether such differences are associated with animal performance is limited. This study was conducted to characterize the rumen microbiota of a large cohort of lactating Holstein dairy cows (n= 334) that were fed the same diet and raised under the same environment, aiming to uncover linkages between core and pan rumen microbiomes and host phenotypes. Amplicon sequencing of the partial 16S rRNA gene identified 391 bacterial genera in the pan bacteriome and 33 genera in the core bacteriome. Interanimal variation existed in the pan and core bacteriomes, with the effect of lactation stage being more prominent than that of parity (the number of pregnancies, ranging from 2 to 7) and sire. Spearman's correlation network analysis revealed significant correlations among bacteria, rumen short-chain fatty acids, and lactation performance, with the core and noncore genera accounting for 53.9 and 46.2% of the network, respectively. These results suggest that the pan rumen bacteriome together with the core bacteriome potentially contributes to variations in milk production traits. Our findings provide an understanding of the potential functions of noncore rumen microbes, suggesting the possibility of enhancing bacterial fermentation using strategies to manipulate the core and noncore bacteriomes for improved cattle performance.IMPORTANCEThis study revealed the rumen bacteriome from a large dairy cattle cohort (n= 334) raised under the same management and showed the linkages among the rumen core and pan bacteriomes, rumen short-chain fatty acids, and milk production phenotypes. The findings from this study suggest that the pan rumen bacteriome, together with the core bacteriome, potentially contributes to variations in host milk production traits. Fundamental knowledge on the rumen core and pan microbiomes and their roles in contributing to lactation performance provides novel insights into future strategies for manipulating rumen microbiota to enhance milk production in dairy cattle.

2009 ◽  
Vol 34 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Homayon Reza Shahbazkia ◽  
Mahmoud Aminlari ◽  
Atoosa Tavasoli ◽  
Ahmad Reza Mohamadnia ◽  
Alfredo Cravador

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 ◽  
...  

2020 ◽  
Vol 52 (1) ◽  
Author(s):  
Thierry Tribout ◽  
Pascal Croiseau ◽  
Rachel Lefebvre ◽  
Anne Barbat ◽  
Mekki Boussaha ◽  
...  

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.


2005 ◽  
Vol 88 (11) ◽  
pp. 4083-4086 ◽  
Author(s):  
S. Leonard ◽  
H. Khatib ◽  
V. Schutzkus ◽  
Y.M. Chang ◽  
C. Maltecca

2000 ◽  
Vol 71 (3) ◽  
pp. 411-419 ◽  
Author(s):  
H. N. Kadarmideen ◽  
R. Thompson ◽  
G. Simm

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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