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Estimating Additive and Non-Additive Genetic Variances and Predicting Genetic Merits Using Genome-Wide Dense Single Nucleotide Polymorphism Markers

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  • Guosheng Su
  • Ole F Christensen
  • Tage Ostersen
  • Mark Henryon
  • Mogens S Lund

Abstract

Non-additive genetic variation is usually ignored when genome-wide markers are used to study the genetic architecture and genomic prediction of complex traits in human, wild life, model organisms or farm animals. However, non-additive genetic effects may have an important contribution to total genetic variation of complex traits. This study presented a genomic BLUP model including additive and non-additive genetic effects, in which additive and non-additive genetic relation matrices were constructed from information of genome-wide dense single nucleotide polymorphism (SNP) markers. In addition, this study for the first time proposed a method to construct dominance relationship matrix using SNP markers and demonstrated it in detail. The proposed model was implemented to investigate the amounts of additive genetic, dominance and epistatic variations, and assessed the accuracy and unbiasedness of genomic predictions for daily gain in pigs. In the analysis of daily gain, four linear models were used: 1) a simple additive genetic model (MA), 2) a model including both additive and additive by additive epistatic genetic effects (MAE), 3) a model including both additive and dominance genetic effects (MAD), and 4) a full model including all three genetic components (MAED). Estimates of narrow-sense heritability were 0.397, 0.373, 0.379 and 0.357 for models MA, MAE, MAD and MAED, respectively. Estimated dominance variance and additive by additive epistatic variance accounted for 5.6% and 9.5% of the total phenotypic variance, respectively. Based on model MAED, the estimate of broad-sense heritability was 0.506. Reliabilities of genomic predicted breeding values for the animals without performance records were 28.5%, 28.8%, 29.2% and 29.5% for models MA, MAE, MAD and MAED, respectively. In addition, models including non-additive genetic effects improved unbiasedness of genomic predictions.

Suggested Citation

  • Guosheng Su & Ole F Christensen & Tage Ostersen & Mark Henryon & Mogens S Lund, 2012. "Estimating Additive and Non-Additive Genetic Variances and Predicting Genetic Merits Using Genome-Wide Dense Single Nucleotide Polymorphism Markers," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-7, September.
    • Handle: RePEc:plo:pone00:0045293
    DOI: 10.1371/journal.pone.0045293
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    Cited by:

    1. Chung-Feng Kao & Jia-Rou Liu & Hung Hung & Po-Hsiu Kuo, 2015. "A Robust GWSS Method to Simultaneously Detect Rare and Common Variants for Complex Disease," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-14, April.
    2. Morteza Mahdavi & Gholam Reza Dashab & Mehdi Vafaye Valleh & Mohammad Rokouei & Mehdi Sargolzaei, 2018. "Genomic evaluation and variance component estimation of additive and dominance effects using single nucleotide polymorphism markers in heterogeneous stock mice," Czech Journal of Animal Science, Czech Academy of Agricultural Sciences, vol. 63(12), pages 492-506.
    3. Martini, Johannes W.R. & Toledo, Fernando H. & Crossa, José, 2020. "On the approximation of interaction effect models by Hadamard powers of the additive genomic relationship," Theoretical Population Biology, Elsevier, vol. 132(C), pages 16-23.
    4. Starr, Alexandra & Riemann, Rainer, 2022. "Common genetic and environmental effects on cognitive ability, conscientiousness, self-perceived abilities, and school performance," Intelligence, Elsevier, vol. 93(C).
    5. Bryan Irvine Lopez & Vanessa Viterbo & Choul Won Song & Kang Seok Seo, 2019. "Estimation of genetic parameters and accuracy of genomic prediction for production traits in Duroc pigs," Czech Journal of Animal Science, Czech Academy of Agricultural Sciences, vol. 64(4), pages 160-165.
    6. Tianfei Liu & Chenglong Luo & Jie Wang & Jie Ma & Dingming Shu & Mogens Sandø Lund & Guosheng Su & Hao Qu, 2017. "Assessment of the genomic prediction accuracy for feed efficiency traits in meat-type chickens," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-11, March.

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