Open AccessGenome-wide identification of major genes and genomic prediction using high-density and text-mined gene-based SNP panels in Hanwoo (Korean cattle)
Author(s) -
Hyo Jun Lee,
Young Jin Chung,
Sungbong Jang,
Dongil Seo,
Hak Kyo Lee,
Duhak Yoon,
Dajeong Lim,
Seung Hwan Lee
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0241848
Subject(s) - hanwoo , single nucleotide polymorphism , intramuscular fat , snp , biology , genetics , longissimus dorsi , genome wide association study , gene , computational biology , genotype , biochemistry , food science , zoology
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.