Genome-wide associations and detection of candidate genes for direct and maternal genetic effects influencing growth traits in the Montana Tropical (R) Composite population
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The Montana Tropical (R) Composite beef cattle have been developed in Brazil to serve as a genetic resource to meet the consumers' needs for higher-quality meat while animals are raised in extensive production systems under tropical conditions. In order to optimize the selection process for economically important traits in this population, various genetic and genomic studies are still lacking. In this regard, the aim of this study was to assess genetic parameters and identify genomic regions and potential candidate genes associated with various growth traits, using the single-step Genomic Best Linear Unbiased Predictor (ssGBLUP) method. Approximately 400,000 cows, bulls and progeny had measurements for birth weight (BW), weaning weight (WW), yearling weight (YW) and post-weaning weight gain (WG). A total of 1394 animals were genotyped for 27,199 SNPs (after the quality control) to enable implementation of weighted single-step genome-wide association studies. The traits included in this study were shown to be moderately heritable (i.e. heritability estimates ranging from 0.16 +/- 0.01 to 0.33 +/- 0.04) and the genetic correlations ranged from 0.60 +/- 0.067 (between WW and WG) to 0.88 +/- 0.08 (between BW and WW). Single-trait weighted genome-wide association studies enabled the identification of 83 genomic regions for direct genetic effects (all traits) and 29 genomic regions associated with maternal genetic effects on BW and WW traits. Furthermore, biological processes and pathways associated with survival to adult age, calf behavior, fatty acid metabolism, muscle development, fertility, and immune system were identified. The findings of this study greatly contribute to a better understanding of the genetic architecture of growth traits in the Montana Tropical (R) Composite population. Furthermore, the genomic regions identified can be given more importance (weight) when implementing genomic selection for these traits, by using weighted ssGBLUP or Bayesian approaches.