Functional Partitioning of Genomic Variance and Genome-Wide Association Study for Carcass Traits in Korean Hanwoo Cattle Using Imputed Sequence Level SNP Data

Quantitative traits are usually controlled by numerous genomic variants with small individual effects, and variances associated with those traits are explained in a continuous manner. However, the relative contributions of genomic regions to observed genetic variations have not been well explored using sequence level single nucleotide polymorphism (SNP) information. Here, imputed sequence level SNP data (11,278,153 SNPs) of 2109 Hanwoo steers (Korean native cattle) were partitioned according to functional annotation, chromosome, and minor allele frequency (MAF). Genomic relationship matrices (GRMs) were constructed for each classified region and fitted in the model both separately and together for carcass weight (CWT), eye muscle area (EMA), backfat thickness (BFT), and marbling score (MS) traits. A genome-wide association study (GWAS) was performed to identify significantly associated variants in genic and exon regions using a linear mixed model, and the genetic contribution of each exonic SNP was determined using a Bayesian mixture model. Considering all SNPs together, the heritability estimates for CWT, EMA, BFT, and MS were 0.57 ± 0.05, 0.46 ± 0.05, 0.45 ± 0.05, and 0.49 ± 0.05, respectively, which reflected substantial genomic contributions. Joint analysis revealed that the variance explained by each chromosome was proportional to its physical length with weak linear relationships for all traits. Moreover, genomic variances explained by functional category and MAF class differed greatly among the traits studied in joint analysis. For example, exon regions had larger contributions for BFT (0.13 ± 0.08) and MS (0.22 ± 0.08), whereas intron and intergenic regions explained most of the total genomic variances for CWT and EMA (0.22 ± 0.09–0.32 ± 0.11). Considering different functional classes of exon regions and the per SNP contribution revealed the largest proportion of genetic variance was attributable to synonymous variants. GWAS detected 206 and 27 SNPs in genic and exon regions, respectively, on BTA4, BTA6, and BTA14 that were significantly associated with CWT and EMA. These SNPs were harbored by 31 candidate genes, among which TOX, FAM184B, PPARGC1A, PRKDC, LCORL, and COL1A2 were noteworthy. BayesR analysis found that most SNPs (>93%) had very small effects and the 4.02–6.92% that had larger effects (10-4 × σA2, 10-3 × σA2, and 10-2 × σA2) explained most of the total genetic variance, confirming polygenic components of the traits studied

Comparison of accuracy of breeding value for cow from three methods in Hanwoo (Korean cattle) population

In Korea, Korea Proven Bulls (KPN) program has been well-developed. Breeding and evaluation of cows are also an essential factor to increase earnings and genetic gain. This study aimed to evaluate the accuracy of cow breeding value by using three methods (pedigree index [PI], pedigree-based best linear unbiased prediction [PBLUP], and genomic-BLUP [GBLUP]). The reference population (n = 16,971) was used to estimate breeding values for 481 females as a test population. The accuracy of GBLUP was 0.63, 0.66, 0.62 and 0.63 for carcass weight (CWT), eye muscle area (EMA), back-fat thickness (BFT), and marbling score (MS), respectively. As for the PBLUP method, accuracy of prediction was 0.43 for CWT, 0.45 for EMA, 0.43 for MS, and 0.44 for BFT. Accuracy of PI method was the lowest (0.28 to 0.29 for carcass traits). The increase by approximate 20% in accuracy of GBLUP method than other methods could be because genomic information may explain Mendelian sampling error that pedigree information cannot detect. Bias can cause reducing accuracy of estimated breeding value (EBV) for selected animals. Regression coefficient between true breeding value (TBV) and GBLUP EBV, PBLUP EBV, and PI EBV were 0.78, 0.625, and 0.35, respectively for CWT. This showed that genomic EBV (GEBV) is less biased than PBLUP and PI EBV in this study. In addition, number of effective chromosome segments (Me) statistic that indicates the independent loci is one of the important factors affecting the accuracy of BLUP. The correlation between Me and the accuracy of GBLUP is related to the genetic relationship between reference and test population. The correlations between Me and accuracy were −0.74 in CWT, −0.75 in EMA, −0.73 in MS, and −0.75 in BF, which were strongly negative. These results proved that the estimation of genetic ability using genomic data is the most effective, and the smaller the Me, the higher the accuracy of EBV

Identification of Missense Variants Affecting Carcass Traits for Hanwoo Precision Breeding

This study aimed to identify causal variants associated with important carcass traits such as weight and meat quality in Hanwoo cattle. We analyzed missense mutations extracted from imputed sequence data (ARS-UCD1.2) and performed an exon-specific association test on the carcass traits of 16,970 commercial Hanwoo. We found 33, 2, 1, and 3 significant SNPs associated with carcass weight (CW), backfat thickness (BFT), eye muscle area (EMA), and marbling score (MS), respectively. In CW and EMA, the most significant missense SNP was identified at 19,524,263 on BTA14 and involved the PRKDC. A missense SNP in the ZFAND2B, located at 107,160,304 on BTA2 was identified as being involved in BFT. For MS, missense SNP in the ACVR2B gene, located at 11,849,704 in BTA22 was identified as the most significant marker. The contribution of the most significant missense SNPs to genetic variance was confirmed to be 8.47%, 2.08%, 1.73%, and 1.19% in CW, BFT, EMA, and MS, respectively. We generated favorable and unfavorable haplotype combinations based on the significant SNPs for CW. Significant differences in GEBV (Genomic Estimated Breeding Values) were observed between groups with each favorable and unfavorable haplotype combination. In particular, the missense SNPs in PRKDC, MRPL9, and ANKFN1 appear to significantly affect the protein’s function and structure, making them strong candidates as causal mutations. These missense SNPs have the potential to serve as valuable markers for improving carcass traits in Hanwoo commercial farms

Estimation of Genetic Parameters and Optimum Breeding Programme Design in Korean Flatfish Breeding Population

Olive flounder (Paralichthys olivaceus) is a vital aquaculture species in East Asia. However, few studies that estimate the genetic parameters of this species have been conducted. We estimated the genetic parameters of growth traits and designed an optimum breeding programme for this species. Heritability, genetic and phenotypic correlations, and breeding values were estimated for growth traits: body weight (BW), total length (TL), and condition factor (CF). A linear mixed animal model using the restricted maximum likelihood (REML) algorithm was applied to the statistical analysis of 9 traits (BW, TL, and CF at 11, 18, and 22 months of age) for a total of 54,159 animals from 7 generations. Increases of 13%, 8%, and 6.5% in BW, TL, and CF at the harvest stage were observed, respectively, after 7 generations of selection. The heritabilities of all growth traits were moderate, ranging from 0.35 to 0.46. The phenotypic and genetic correlations between BW and TL were high and positive in all three stages (0.91 and 0.92, 0.91 and 0.93, and 0.88 and 0.91). The estimated breeding values of BW and TL increased over the generations; however, the estimated breeding value of CF fluctuated. The optimum progeny number within full-sib families for an accuracy of 0.632 is suggested to be between 10 and 25. Findings indicated that a considerable response to selection and single-trait selection based on BW would be effective in olive flounder

Estimation of Genetic Parameters and Optimum Breeding Programme Design in Korean Flatfish Breeding Population

Olive flounder (Paralichthys olivaceus) is a vital aquaculture species in East Asia. However, few studies that estimate the genetic parameters of this species have been conducted. We estimated the genetic parameters of growth traits and designed an optimum breeding programme for this species. Heritability, genetic and phenotypic correlations, and breeding values were estimated for growth traits: body weight (BW), total length (TL), and condition factor (CF). A linear mixed animal model using the restricted maximum likelihood (REML) algorithm was applied to the statistical analysis of 9 traits (BW, TL, and CF at 11, 18, and 22 months of age) for a total of 54,159 animals from 7 generations. Increases of 13%, 8%, and 6.5% in BW, TL, and CF at the harvest stage were observed, respectively, after 7 generations of selection. The heritabilities of all growth traits were moderate, ranging from 0.35 to 0.46. The phenotypic and genetic correlations between BW and TL were high and positive in all three stages (0.91 and 0.92, 0.91 and 0.93, and 0.88 and 0.91). The estimated breeding values of BW and TL increased over the generations; however, the estimated breeding value of CF fluctuated. The optimum progeny number within full-sib families for an accuracy of 0.632 is suggested to be between 10 and 25. Findings indicated that a considerable response to selection and single-trait selection based on BW would be effective in olive flounder

Table_1_Functional Partitioning of Genomic Variance and Genome-Wide Association Study for Carcass Traits in Korean Hanwoo Cattle Using Imputed Sequence Level SNP Data.DOCX

<p>Quantitative traits are usually controlled by numerous genomic variants with small individual effects, and variances associated with those traits are explained in a continuous manner. However, the relative contributions of genomic regions to observed genetic variations have not been well explored using sequence level single nucleotide polymorphism (SNP) information. Here, imputed sequence level SNP data (11,278,153 SNPs) of 2109 Hanwoo steers (Korean native cattle) were partitioned according to functional annotation, chromosome, and minor allele frequency (MAF). Genomic relationship matrices (GRMs) were constructed for each classified region and fitted in the model both separately and together for carcass weight (CWT), eye muscle area (EMA), backfat thickness (BFT), and marbling score (MS) traits. A genome-wide association study (GWAS) was performed to identify significantly associated variants in genic and exon regions using a linear mixed model, and the genetic contribution of each exonic SNP was determined using a Bayesian mixture model. Considering all SNPs together, the heritability estimates for CWT, EMA, BFT, and MS were 0.57 ± 0.05, 0.46 ± 0.05, 0.45 ± 0.05, and 0.49 ± 0.05, respectively, which reflected substantial genomic contributions. Joint analysis revealed that the variance explained by each chromosome was proportional to its physical length with weak linear relationships for all traits. Moreover, genomic variances explained by functional category and MAF class differed greatly among the traits studied in joint analysis. For example, exon regions had larger contributions for BFT (0.13 ± 0.08) and MS (0.22 ± 0.08), whereas intron and intergenic regions explained most of the total genomic variances for CWT and EMA (0.22 ± 0.09–0.32 ± 0.11). Considering different functional classes of exon regions and the per SNP contribution revealed the largest proportion of genetic variance was attributable to synonymous variants. GWAS detected 206 and 27 SNPs in genic and exon regions, respectively, on BTA4, BTA6, and BTA14 that were significantly associated with CWT and EMA. These SNPs were harbored by 31 candidate genes, among which TOX, FAM184B, PPARGC1A, PRKDC, LCORL, and COL1A2 were noteworthy. BayesR analysis found that most SNPs (>93%) had very small effects and the 4.02–6.92% that had larger effects (10^-4 × σA2, 10^-3 × σA2, and 10^-2 × σA2) explained most of the total genetic variance, confirming polygenic components of the traits studied.</p