Quantifying feed efficiency of dairy cattle for genome-wide association analysis

2015 ◽  
Author(s):  
Tingyang Xu ◽  
Jiangwen Sun ◽  
Erin E Connor ◽  
Jinbo Bi
Keyword(s):  
Dairy Cattle ◽  
Association Analysis ◽  
Feed Efficiency ◽  
Genome Wide Association ◽  
Genome Wide Association Analysis ◽  
Genome Wide

scholarly journals Genome-wide association analysis for β-hydroxybutyrate concentration in Milk in Holstein dairy cattle

BMC Genetics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
S. Nayeri ◽  
F. Schenkel ◽  
A. Fleming ◽  
V. Kroezen ◽  
M. Sargolzaei ◽  
...  
Keyword(s):  
Dairy Cattle ◽  
Association Analysis ◽  
Genome Wide Association ◽  
Genome Wide Association Analysis ◽  
Genome Wide

scholarly journals Genome‐wide association analysis for feed efficiency in Angus cattle

2011 ◽  
Vol 43 (4) ◽  
pp. 367-374 ◽  
Author(s):  
M. M. Rolf ◽  
J. F. Taylor ◽  
R. D. Schnabel ◽  
S. D. McKay ◽  
M. C. McClure ◽  
...  
Keyword(s):  
Association Analysis ◽  
Feed Efficiency ◽  
Genome Wide Association ◽  
Genome Wide Association Analysis ◽  
Angus Cattle ◽  
Genome Wide

scholarly journals Using Genome-Wide Association Analysis to Characterize Environmental Sensitivity of Milk Traits in Dairy Cattle

2013 ◽  
Vol 3 (7) ◽  
pp. 1085-1093 ◽  
Author(s):  
Melanie Streit ◽  
Robin Wellmann ◽  
Friedrich Reinhardt ◽  
Georg Thaller ◽  
Hans-Peter Piepho ◽  
...  
Keyword(s):  
Dairy Cattle ◽  
Association Analysis ◽  
Genome Wide Association ◽  
Environmental Sensitivity ◽  
Milk Traits ◽  
Genome Wide Association Analysis ◽  
Genome Wide

scholarly journals 10 Can single-step genome-wide association analysis (ssGWAA) account for population structure?

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 9-10
Author(s):  
Enrico Mancin
Keyword(s):  
Population Structure ◽  
Beef Cattle ◽  
Dairy Cattle ◽  
Association Analysis ◽  
Single Step ◽  
Genome Wide Association ◽  
Relationship Matrix ◽  
Cattle Population ◽  
Genome Wide Association Analysis ◽  
Genome Wide

Abstract Several methods are available for genome-wide association analysis, including the classical GWAA (cGWAA) based on fixed, single-SNP regression; efficient mixed-model association expedited (EMMAX) that fits single-SNP regressions together with a relationship matrix to account for population structure; and single-step GWAA (ssGWAA) where all data, including non-genotyped animals, are used. The objectives of this study were to: 1) investigate the ability of ssGWAA to account for population structure and correctly identify quantitative trait nucleotides (QTN); and 2) compare ssGWAA with cGWAA and EMMAX. Three simulated datasets were used, which mimic fish, beef cattle, and dairy cattle populations. The fish population was composed of 2,040 fish, out of which 1,040 were genotyped and had phenotypes for a trait with heritability of 0.25. The beef cattle population had 6,010 animals in the pedigree, but only 1,500 with phenotypes (h2 = 0.35) and genotypes. Lastly, the dairy cattle population had 40,800 pedigreed animals, of which 20,000 females had phenotypes (h2 = 0.32) and 2,400 males were genotyped. All phenotypes, pedigree, and genotypes were used in ssGWAA, whereas only genotypes and phenotypes were used in cGWAA and EMMAX for the fish and beef cattle analyses. For the dairy cattle analysis using the last two methods, deregressed proofs had to be used instead of phenotypes. The ability to correctly identify QTN and the number of statistically significant SNP (P < 0.05/number of SNP) was assessed among methods. In all populations, cGWAA was able to identify some of the strongest QTN but showed a large number of false positives. EMMAX and ssGWAA did not show false associations and correctly identified the top QTN, with more signals observed in ssGWAA. The ssGWAA accounts for population structure and is a proper association method, especially for livestock populations where sparse genotyping is a reality and phenotypes may not be recorded in genotyped animals.

scholarly journals Bivariate Genome-Wide Association Analysis of the Growth and Intake Components of Feed Efficiency

PLoS ONE ◽  
2013 ◽  
Vol 8 (10) ◽  
pp. e78530 ◽  
Author(s):  
Nick V. L. Serão ◽  
Dianelys González-Peña ◽  
Jonathan E. Beever ◽  
Germán A. Bollero ◽  
Bruce R. Southey ◽  
...  
Keyword(s):  
Association Analysis ◽  
Feed Efficiency ◽  
Genome Wide Association ◽  
Genome Wide Association Analysis ◽  
Genome Wide

A Caution in Association of ABO Blood Group with COVID-19

2020 ◽  
Keyword(s):  
Gene Cluster ◽  
Clinical Outcomes ◽  
Blood Group ◽  
Association Analysis ◽  
Blood Type ◽  
Genome Wide Association ◽  
Chromosome 3 ◽  
Abo Blood Group ◽  
Genome Wide Association Analysis ◽  
Genome Wide

A comment on Zhao J, Yang Y, Huang H, Li D, Gu D, Lu X, et al. Association of ABO blood group and Covid19 susceptability. medRxiv [PREPRINT]. 2020; https://doi.org/10.1101/2020.03.11.20031096. Zeng X, Fan H, Lu D, Huang F, Meng X, Li Z, et al. Association between ABO blood group and clinical outcomes of Covid19. medRxiv[PREPRINT].2020; https://doi.org/10.1101/2020.04.15.20063107. Zietz M, Tatonetti N. Testing the association between blood type and COVID-19 infection, intubation, and death medRxiv [PREPRINT]. 2020; https://doi.org/10.1101/2020.04.08.20058073. Ellinghaus D, Degenhardt F, Bujanda L, al. e. The ABO blood group and a chromosome 3 gene cluster associate with SRAS-CoV2 respitarory failure in an Italy-Spain genome-wide association analysis. medRxiv. 2020; https://doi.org/10.1101/2020.05.31.20114991.

Genome-wide Association Analysis of Kernel Row Number in Maize

2014 ◽  
Vol 40 (1) ◽  
pp. 1 ◽  
Author(s):  
Huan-Xin ZHANG ◽  
Jian-Feng WENG ◽  
Xiao-Cong ZHANG ◽  
Chang-Lin LIU ◽  
Hong-Jun YONG ◽  
...  
Keyword(s):  
Association Analysis ◽  
Genome Wide Association ◽  
Genome Wide Association Analysis ◽  
Genome Wide ◽  
Kernel Row Number
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