Genomic prediction using multi-allelic haplotype models improved the prediction accuracy for all seven human phenotypes, the normality transformed high density lipoproteins, low density lipoproteins, total cholesterol, triglycerides, weight, and the original height and body mass index without normality transformation. Eight SNP sets with 40,941-380,705 SNPs were evaluated. The increase in prediction accuracy due to haplotypes was 1.86-8.12%. Haplotypes using fixed chromosome distances had the best prediction accuracy for four phenotypes, fixed number of SNPs for two phenotypes, and gene-based haplotypes for high density lipoproteins and height (tied for best). Haplotypes of coding genes were more accurate than haplotypes of all autosome genes that included both coding and noncoding genes for triglycerides and weight, and nearly the same as haplotypes of all autosome genes for the other phenotypes. Haplotypes of noncoding genes (mostly lncRNAs) only improved the prediction accuracy over the SNP models for high density lipoproteins, total cholesterol, and height. ChIP-seq haplotypes had better prediction accuracy than gene-based haplotypes for total cholesterol, body mass index and low density lipoproteins. The accuracy of ChIP-seq haplotypes was most striking for low density lipoproteins, where all four haplotype models with ChIP-seq haplotypes had similarly high prediction accuracy over the best prediction model with gene-based haplotypes. Haplotype epistasis was shown to be the reason for the increased accuracy due to haplotypes. Low density lipoproteins had the largest haplotype epistasis heritability that explained 14.70% of the phenotypic variance and was 31.27% of the SNP additive heritability, and the largest increase in prediction accuracy relative to the best SNP model (8.12%). Relative to the SNP additive heritability of the same regions, noncoding genes had the highest haplotype epistasis heritability, followed by coding genes and ChIP-seq for the seven phenotypes. SNP and haplotype heritability profiles showed that the integration of SNP and haplotype additive values compensated the weakness of haplotypes in estimating SNP heritabilities for four phenotypes, whereas models with haplotype additive values fully accounted for SNP additive values for three phenotypes. These results showed that haplotype analysis can be a method to utilize functional and structural genomic information to improve the accuracy of genomic prediction.
Background Genomic selection using single nucleotide polymorphism (SNP) markers has been widely used for genetic improvement of livestock, but most current methods of genomic selection are based on SNP models. In this study, we investigated the prediction accuracies of haplotype models based on fixed chromosome distances and gene boundaries compared to those of SNP models for genomic prediction of phenotypic values. We also examined the reasons for the successes and failures of haplotype genomic prediction. Methods We analyzed a swine population of 3195 Duroc boars with records on eight traits: body judging score (BJS), teat number (TN), age (AGW), loin muscle area (LMA), loin muscle depth (LMD) and back fat thickness (BF) at 100 kg live weight, and average daily gain (ADG) and feed conversion rate (FCR) from 30 to100 kg live weight. Ten-fold validation was used to evaluate the prediction accuracy of each SNP model and each multi-allelic haplotype model based on 488,124 autosomal SNPs from low-coverage sequencing. Haplotype blocks were defined using fixed chromosome distances or gene boundaries. Results Compared to the best SNP model, the accuracy of predicting phenotypic values using a haplotype model was greater by 7.4% for BJS, 7.1% for AGW, 6.6% for ADG, 4.9% for FCR, 2.7% for LMA, 1.9% for LMD, 1.4% for BF, and 0.3% for TN. The use of gene-based haplotype blocks resulted in the best prediction accuracy for LMA, LMD, and TN. Compared to estimates of SNP additive heritability, estimates of haplotype epistasis heritability were strongly correlated with the increase in prediction accuracy by haplotype models. The increase in prediction accuracy was largest for BJS, AGW, ADG, and FCR, which also had the largest estimates of haplotype epistasis heritability, 24.4% for BJS, 14.3% for AGW, 14.5% for ADG, and 17.7% for FCR. SNP and haplotype heritability profiles across the genome identified several genes with large genetic contributions to phenotypes: NUDT3 for LMA, LMD and BF, VRTN for TN, COL5A2 for BJS, BSND for ADG, and CARTPT for FCR. Conclusions Haplotype prediction models improved the accuracy for genomic prediction of phenotypes in Duroc pigs. For some traits, the best prediction accuracy was obtained with haplotypes defined using gene regions, which provides evidence that functional genomic information can improve the accuracy of haplotype genomic prediction for certain traits.
A genome-wide association study (GWAS) of age at first calving (AFC) using 813,114 first lactation Holstein cows and 75,524 SNPs identified 2063 additive effects and 29 dominance effects with p-values < 10−8. Three chromosomes had highly significant additive effects in the regions of 7.86–8.12 Mb of Chr15, 27.07–27.48 Mb and 31.25–32.11 Mb of Chr19, and 26.92–32.60 Mb of Chr23. Two of the genes in those regions were reproductive hormone genes with known biological functions that should be relevant to AFC, the sex hormone binding globulin (SHBG) gene, and the progesterone receptor (PGR) gene. The most significant dominance effects were near or in EIF4B and AAAS of Chr05 and AFF1 and KLHL8 of Chr06. All dominance effects were positive overdominance effects where the heterozygous genotype had an advantage, and the homozygous recessive genotype of each SNP had a very negative dominance value. Results from this study provided new evidence and understanding about the genetic variants and genome regions affecting AFC in U.S. Holstein cows.
Heavy chain only antibodies (HCAbs) represent a rare type of antibody that is devoid of light chains and the CH1 domain that have been reported in cartilaginous fish and camelids. By analyzing transcript data and genome sequences, we conducted a comprehensive analysis of Bactrian camel immunoglobulin heavy chain genes. Based on the transcript data, one μ gene, five γ genes, one α gene and one ε gene were found. Additionally, the variable region of HCAbs (VHH) and the conventional antibodies (VH) sequences associated with the γ3, γ1a/b and μ genes were amplified. Based on these genome sequences, seven DH, six JH, μ, γ2a, γ2c, α, and ε genes and a portion of a γ3 gene were observed. Different Kozak sequences within different VH families were found in our analysis, and the variability index differed between the VHH3 and VH3 families. Phylogenetic analysis of the constant regions of the camelid immunoglobulin genes indicates that these genes appeared before the evolutionary divergence of Bactrian camels and dromedaries.
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