Genome-wide identification of major genes and genomic prediction using high-density and text-mined gene-based SNP panels in Hanwoo (Korean cattle)

PLoS One. 2020 Dec 2;15(12):e0241848. doi: 10.1371/journal.pone.0241848. eCollection 2020.

Abstract

It was hypothesized that single-nucleotide polymorphisms (SNPs) extracted from text-mined genes could be more tightly related to causal variant for each trait and that differentially weighting of this SNP panel in the GBLUP model could improve the performance of genomic prediction in cattle. Fitting two GRMs constructed by text-mined SNPs and SNPs except text-mined SNPs from 777k SNPs set (exp_777K) as different random effects showed better accuracy than fitting one GRM (Im_777K) for six traits (e.g. backfat thickness: + 0.002, eye muscle area: + 0.014, Warner-Bratzler Shear Force of semimembranosus and longissimus dorsi: + 0.024 and + 0.068, intramuscular fat content of semimembranosus and longissimus dorsi: + 0.008 and + 0.018). These results can suggest that attempts to incorporate text mining into genomic predictions seem valuable, and further study using text mining can be expected to present the significant results.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Breeding
  • Cattle
  • Data Mining
  • Genome / genetics*
  • Genome-Wide Association Study*
  • Genomics
  • Genotype
  • Hamstring Muscles / growth & development
  • Hamstring Muscles / metabolism
  • Humans
  • Models, Genetic
  • Pedigree
  • Phenotype
  • Polymorphism, Single Nucleotide / genetics
  • Quantitative Trait Loci / genetics*
  • Republic of Korea

Grants and funding

This study was funded by awards from the Molecular Breeding program (Grant no. PJ0131692020) of the Next Generation BIOGREEN21 project of the National Institute of Animal Science, RDA, Republic of Korea. Hyo Jun Lee was also partly supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2020-0-01441, Artificial Intelligence Convergence Research Center (Chungnam National University)).