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Multi-Trait GWAS and New Candidate Genes Annotation for Growth Curve Parameters in Brahman Cattle

Author

Listed:
  • Aline Camporez Crispim
  • Matthew John Kelly
  • Simone Eliza Facioni Guimarães
  • Fabyano Fonseca e Silva
  • Marina Rufino Salinas Fortes
  • Raphael Rocha Wenceslau
  • Stephen Moore

Abstract

Understanding the genetic architecture of beef cattle growth cannot be limited simply to the genome-wide association study (GWAS) for body weight at any specific ages, but should be extended to a more general purpose by considering the whole growth trajectory over time using a growth curve approach. For such an approach, the parameters that are used to describe growth curves were treated as phenotypes under a GWAS model. Data from 1,255 Brahman cattle that were weighed at birth, 6, 12, 15, 18, and 24 months of age were analyzed. Parameter estimates, such as mature weight (A) and maturity rate (K) from nonlinear models are utilized as substitutes for the original body weights for the GWAS analysis. We chose the best nonlinear model to describe the weight-age data, and the estimated parameters were used as phenotypes in a multi-trait GWAS. Our aims were to identify and characterize associated SNP markers to indicate SNP-derived candidate genes and annotate their function as related to growth processes in beef cattle. The Brody model presented the best goodness of fit, and the heritability values for the parameter estimates for mature weight (A) and maturity rate (K) were 0.23 and 0.32, respectively, proving that these traits can be a feasible alternative when the objective is to change the shape of growth curves within genetic improvement programs. The genetic correlation between A and K was -0.84, indicating that animals with lower mature body weights reached that weight at younger ages. One hundred and sixty seven (167) and two hundred and sixty two (262) significant SNPs were associated with A and K, respectively. The annotated genes closest to the most significant SNPs for A had direct biological functions related to muscle development (RAB28), myogenic induction (BTG1), fetal growth (IL2), and body weights (APEX2); K genes were functionally associated with body weight, body height, average daily gain (TMEM18), and skeletal muscle development (SMN1). Candidate genes emerging from this GWAS may inform the search for causative mutations that could underpin genomic breeding for improved growth rates.

Suggested Citation

  • Aline Camporez Crispim & Matthew John Kelly & Simone Eliza Facioni Guimarães & Fabyano Fonseca e Silva & Marina Rufino Salinas Fortes & Raphael Rocha Wenceslau & Stephen Moore, 2015. "Multi-Trait GWAS and New Candidate Genes Annotation for Growth Curve Parameters in Brahman Cattle," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-19, October.
  • Handle: RePEc:plo:pone00:0139906
    DOI: 10.1371/journal.pone.0139906
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    References listed on IDEAS

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    1. Tessel E Galesloot & Kristel van Steen & Lambertus A L M Kiemeney & Luc L Janss & Sita H Vermeulen, 2014. "A Comparison of Multivariate Genome-Wide Association Methods," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-8, April.
    2. Sunduimijid Bolormaa & Jennie E Pryce & Antonio Reverter & Yuandan Zhang & William Barendse & Kathryn Kemper & Bruce Tier & Keith Savin & Ben J Hayes & Michael E Goddard, 2014. "A Multi-Trait, Meta-analysis for Detecting Pleiotropic Polymorphisms for Stature, Fatness and Reproduction in Beef Cattle," PLOS Genetics, Public Library of Science, vol. 10(3), pages 1-23, March.
    3. Nick V L Serão & Dianelys González-Peña & Jonathan E Beever & Germán A Bollero & Bruce R Southey & Daniel B Faulkner & Sandra L Rodriguez-Zas, 2013. "Bivariate Genome-Wide Association Analysis of the Growth and Intake Components of Feed Efficiency," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-11, October.
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    1. Claudia Cristina Paro de Paz & Guilherme Costa Venturini & Enio Contini & Ricardo Lopes Dias da Costa & Luara Paula Lameirinha & Celia Raquel Quirino, 2018. "Nonlinear models of Brazilian sheep in adjustment of growth curves," Czech Journal of Animal Science, Czech Academy of Agricultural Sciences, vol. 63(8), pages 331-338.

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