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Genome‐wide association study and genomic evaluation of feed efficiency traits in Japanese Black cattle using single‐step genomic best linear unbiased prediction method
Author(s) -
Takeda Masayuki,
Uemoto Yoshinobu,
Inoue Keiichi,
Ogino Atushi,
Nozaki Takayoshi,
Kurogi Kazuhito,
Yasumori Takanori,
Satoh Masahiro
Publication year - 2019
Publication title -
animal science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.606
H-Index - 38
eISSN - 1740-0929
pISSN - 1344-3941
DOI - 10.1111/asj.13316
Subject(s) - best linear unbiased prediction , genomic selection , biology , computational biology , genome wide association study , genetics , genome , computer science , selection (genetic algorithm) , gene , genotype , artificial intelligence , single nucleotide polymorphism
The objectives of this study were to better understand the genetic architecture and the possibility of genomic evaluation for feed efficiency traits by (i) performing genome‐wide association studies (GWAS), and (ii) assessing the accuracy of genomic evaluation for feed efficiency traits, using single‐step genomic best linear unbiased prediction (ssGBLUP)‐based methods. The analyses were performed in residual feed intake (RFI), residual body weight gain (RG), and residual intake and body weight gain (RIG) during three different fattening periods. The phenotypes from 4,578 Japanese Black steers, which were progenies of 362 progeny‐tested bulls and the genotypes from the bulls were used in this study. The results of GWAS showed that a total of 16, 8, and 12 gene ontology terms were related to RFI, RG, and RIG, respectively, and the candidate genes identified in RFI and RG were involved in olfactory transduction and the phosphatidylinositol signaling system, respectively. The realized reliabilities of genomic estimated breeding values were low to moderate in the feed efficiency traits. In conclusion, ssGBLUP‐based method can lead to understand some biological functions related to feed efficiency traits, even with small population with genotypes, however, an alternative strategy will be needed to enhance the reliability of genomic evaluation.