Genome-wide association analyses for carcass quality in crossbred beef cattle

AbstractUnderstanding genotype-by-environment interactions (G × E) is crucial to understand environmental adaptation in mammals and improve the sustainability of agricultural production. Here, we present an extensive study investigating the interaction of genome-wide SNP markers with a vast assortment of environmental variables and searching for SNPs controlling phenotypic variance (vQTL) using a large beef cattle dataset. We showed that G × E contribute 10.1%, 3.8%, and 2.8% of the phenotypic variance of birth weight, weaning weight, and yearling weight, respectively. G × E genome-wide association analysis (GWAA) detected a large number of G × E loci affecting growth traits, which the traditional GWAA did not detect, showing that functional loci may have non-additive genetic effects regardless of differences in genotypic means. Further, variance-heterogeneity GWAA detected loci enriched with G × E effects without requiring prior knowledge of the interacting environmental factors. Functional annotation and pathway analysis of G × E genes revealed biological mechanisms by which cattle respond to changes in their environment, such as neurotransmitter activity, hypoxia-induced processes, keratinization, hormone, thermogenic and immune pathways. We unraveled the relevance and complexity of the genetic basis of G × E underlying growth traits, providing new insights into how different environmental conditions interact with specific genes influencing adaptation and productivity in beef cattle and potentially across mammals.

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Genome-Wide Association Analysis of Growth Curve Parameters in Chinese Simmental Beef Cattle

Animals ◽

10.3390/ani11010192 ◽

2021 ◽

Vol 11 (1) ◽

pp. 192

Author(s):

Xinghai Duan ◽

Bingxing An ◽

Lili Du ◽

Tianpeng Chang ◽

Mang Liang ◽

...

Keyword(s):

Beef Cattle ◽

Candidate Genes ◽

Growth Curve ◽

Growth And Development ◽

Genome Wide Association Study ◽

Genome Wide Association ◽

Coefficient Of Determination ◽

Nucleotide Polymorphisms ◽

Genome Wide ◽

A Genome

The objective of the present study was to perform a genome-wide association study (GWAS) for growth curve parameters using nonlinear models that fit original weight–age records. In this study, data from 808 Chinese Simmental beef cattle that were weighed at 0, 6, 12, and 18 months of age were used to fit the growth curve. The Gompertz model showed the highest coefficient of determination (R2 = 0.954). The parameters’ mature body weight (A), time-scale parameter (b), and maturity rate (K) were treated as phenotypes for single-trait GWAS and multi-trait GWAS. In total, 9, 49, and 7 significant SNPs associated with A, b, and K were identified by single-trait GWAS; 22 significant single nucleotide polymorphisms (SNPs) were identified by multi-trait GWAS. Among them, we observed several candidate genes, including PLIN3, KCNS3, TMCO1, PRKAG3, ANGPTL2, IGF-1, SHISA9, and STK3, which were previously reported to associate with growth and development. Further research for these candidate genes may be useful for exploring the full genetic architecture underlying growth and development traits in livestock.

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27. Genome wide association study for compensatory growth in Irish beef cattle

Animal - science proceedings ◽

10.1016/j.anscip.2021.03.028 ◽

2021 ◽

Vol 12 (1) ◽

pp. 17

Author(s):

Yvonne Mullins ◽

Alan Kelly ◽

David Kenny ◽

Sinead Waters

Keyword(s):

Beef Cattle ◽

Association Study ◽

Compensatory Growth ◽

Genome Wide Association Study ◽

Genome Wide Association ◽

Genome Wide

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Genome-wide association study of growth and body composition traits in Brangus beef cattle

Livestock Science ◽

10.1016/j.livsci.2015.11.011 ◽

2016 ◽

Vol 183 ◽

pp. 4-11 ◽

Cited By ~ 14

Author(s):

Ziqing Weng ◽

Hailin Su ◽

Mahdi Saatchi ◽

Jungjae Lee ◽

Milton G. Thomas ◽

...

Keyword(s):

Body Composition ◽

Beef Cattle ◽

Association Study ◽

Genome Wide Association Study ◽

Genome Wide Association ◽

Genome Wide

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Genome-wide association for heifer reproduction and calf performance traits in beef cattle

Genome ◽

10.1139/gen-2015-0031 ◽

2015 ◽

Vol 58 (12) ◽

pp. 549-557 ◽

Cited By ~ 5

Author(s):

Everestus C. Akanno ◽

Graham Plastow ◽

Carolyn Fitzsimmons ◽

Stephen P. Miller ◽

Vern Baron ◽

...

Keyword(s):

Beef Cattle ◽

Genome Wide Association Study ◽

Average Daily Gain ◽

Snp Markers ◽

Genome Wide Association ◽

Research Centre ◽

Genome Wide ◽

A Genome ◽

Starting Point ◽

And Performance

The aim of this study was to identify SNP markers that associate with variation in beef heifer reproduction and performance of their calves. A genome-wide association study was performed by means of the generalized quasi-likelihood score (GQLS) method using heifer genotypes from the BovineSNP50 BeadChip and estimated breeding values for pre-breeding body weight (PBW), pregnancy rate (PR), calving difficulty (CD), age at first calving (AFC), calf birth weight (BWT), calf weaning weight (WWT), and calf pre-weaning average daily gain (ADG). Data consisted of 785 replacement heifers from three Canadian research herds, namely Brandon Research Centre, Brandon, Manitoba, University of Alberta Roy Berg Kinsella Ranch, Kinsella, Alberta, and Lacombe Research Centre, Lacombe, Alberta. After applying a false discovery rate correction at a 5% significance level, a total of 4, 3, 3, 9, 6, 2, and 1 SNPs were significantly associated with PBW, PR, CD, AFC, BWT, WWT, and ADG, respectively. These SNPs were located on chromosomes 1, 5–7, 9, 13–16, 19–21, 24, 25, and 27–29. Chromosomes 1, 5, and 24 had SNPs with pleiotropic effects. New significant SNPs that impact functional traits were detected, many of which have not been previously reported. The results of this study support quantitative genetic studies related to the inheritance of these traits, and provides new knowledge regarding beef cattle quantitative trait loci effects. The identification of these SNPs provides a starting point to identify genes affecting heifer reproduction traits and performance of their calves (BWT, WWT, and ADG). They also contribute to a better understanding of the biology underlying these traits and will be potentially useful in marker- and genome-assisted selection and management.

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