Genomic prediction for parasite resistance in sheep using whole-genome sequence data
Genomic prediction for parasite resistance in sheep using whole-genome sequence data The objective of the study is to compare QTL mapping precision and the accuracy of genomic prediction for parasite resistance in sheep using pre-selected variants identified from the high-density SNP panel (600k) and the imputed whole-genome sequence (WGS) data and to evaluate the prediction accuracy when using selected SNPs. The results of this paper show that the use of WGS variants located within or close to QTL regions can improve the prediction accuracy of parasite resistance compared to using variants selected from the high-density SNP panel.
Strategies and cost-benefit of selecting for a polled sheep nucleus by using DNA testing
The present study assessed the effectiveness and cost-benefit of several genotyping strategies for breeding poll Merino sheep in a closed nucleus with different initial allele frequencies and assuming a single-gene responsible for the horn or poll phenotype. We assumed that selection was based on phenotypes or genotypes for a single gene conferring polledness via a complete-dominance model. Under such a model, a complete fixation of the 'polled allele' (P) requires genotyping of the ewe-selection candidates. Testing a higher proportion of female candidates resulted in a faster fixation of the P-allele. Fixation ranged from 1 year of selection with a high starting P-allele frequency of 0.9, to 7 years for low starting P-allele frequencies of 0.3. When premiums of AU$50 or AU$100 were paid for rams with a PP genotype, breeding for PP genotypes was not profitable when the starting P-allele frequency was below 0.7. If the starting allele frequency was above 0.7, net profitability was positive over 10 years when premiums of AU$200 were paid for known PP-genotype rams. While fixing the P-allele, genetic gain for production traits was slowed down in the first 5 years of selection by up to 23% and 3% for initial P allele-frequencies of 0.3 and 0.9 respectively. Lost genetic gain due to fixing the P-allele, which can never be recovered in a closed nucleus, incurred 200-800% higher costs than the DNA testing costs. Rates of genetic gain recovered to pre-P-allele selection level rates of genetic gain once the P-allele was fixed. Testing a maximum of 25% ewe-selection candidates was the least expensive strategy across all starting allele frequencies and premiums. To avoid large losses of genetic gain in a closed nucleus with low P-allele starting frequencies, opening the nucleus should be considered to increase starting P-allele frequencies and also to potentially increase rates of genetic gain to offset the economic loss caused by P-selection.
Increase of Power and Efficiency to Fine-Map Genetic Defects Using Genotype Probabilities Through Segregation Analyses
This simulation study shows a method which makes more efficient use of pedigree and genomic information to increase the chance to detect genetic disorders. We make use of Geneprob, a program which uses segregation analysis to calculate the genotype probabilities of pedigreed animals. The results show that our method, for a trait with a recessive inheritance pattern, is better in the detection of the region of the causative mutation compared to a method which used allele frequencies of cases and controls only. This method can be used across all pedigreed species.
Genome-Wide Association Study of Meat Quality Traits in Hanwoo Beef Cattle Using Imputed Whole-Genome Sequence Data
The discovery of single nucleotide polymorphisms (SNP) and the subsequent genotyping of large numbers of animals have enabled large-scale analyses to begin to understand the biological processes that underpin variation in animal populations. In beef cattle, genome-wide association studies using genotype arrays have revealed many quantitative trait loci (QTL) for various production traits such as growth, efficiency and meat quality. Most studies regarding meat quality have focused on marbling, which is a key trait associated with meat eating quality. However, other important traits like meat color, texture and fat color have not commonly been studied. Developments in genome sequencing technologies provide new opportunities to identify regions associated with these traits more precisely. The objective of this study was to estimate variance components and identify significant variants underpinning variation in meat quality traits using imputed whole genome sequence data. Phenotypic and genomic data from 2,110 Hanwoo cattle were used. The estimated heritabilities for the studied traits were 0.01, 0.16, 0.31, and 0.49 for fat color, meat color, meat texture and marbling score, respectively. Marbling score and meat texture were highly correlated. The genome-wide association study revealed 107 significant SNPs located on 14 selected chromosomes (one QTL region per selected chromosome). Four QTL regions were identified on BTA2, 12, 16, and 24 for marbling score and two QTL regions were found for meat texture trait on BTA12 and 29. Similarly, three QTL regions were identified for meat color on BTA2, 14 and 24 and five QTL regions for fat color on BTA7, 10, 12, 16, and 21. Candidate genes were identified for all traits, and their potential influence on the given trait was discussed. The significant SNP will be an important inclusion into commercial genotyping arrays to select new breeding animals more accurately.
Estimation of genetic parameters for BW and body measurements in Brahman cattle
Body weight and body measurements are commonly used to represent growth and measured at several growth stages in beef cattle. Those economically important traits should be genetically improved. To achieve breeding programs, genetic parameters are prerequisite, as they are needed for designing and predicting outcomes of breeding programs, as well as estimating of breeding values. (Co)variance components were estimated for BW and body measurements on Brahman cattle born between 1990 and 2016 from 17 research herds across Thailand. The traits measured were BW, heart girth (GR), hip height (HH) and body length (BL) and were measured at birth, 200 days, 400 days and 600 days of age. The number of records varied between traits from 18 890 for birth BW to 876 for GR at 600 days. Estimation of variance components was performed using restricted maximum likelihood using univariate and multivariate animal models. Pre-weaning traits were influenced by genetic and/or permanent environmental effects of the dam, except for BL. Heritability estimates from birth to 600 days of age ranged from 0.28±0.01 to 0.50±0.06 for BW, 0.27±0.01 to 0.43±0.09 for GR, 0.28±0.01 to 0.58±0.08 for HH and 0.34±0.01 to 0.51±0.08 for BL using univariate analysis. Heritability estimates for the traits studied increased with age. A similar trend was observed for the phenotypic and genetic correlations between subsequent BW and body measurements. A positive correlation was observed between different traits measured at a similar age, ranging from 0.22±0.01 to 0.72±0.01 for the phenotypic correlation and 0.25±0.04 to 0.97±0.11 for the genetic correlation. Also, a positive correlation was observed for similar traits across different age classes ranging from 0.07±0.03 to 0.76±0.02 for the phenotypic correlation and 0.24±0.11 to 0.92±0.05 for the genetic correlation. Therefore, all correlations between body measurements at the same age and across age classes were positive. The results show the potential improvement of growth traits in Brahman cattle, and those traits can be improved simultaneously under the same breeding program.
Genome-wide association and gene expression studies to decipher the genetics of residual feed intake in Angus cattle
The aim of this study was to identify quantitative trait loci (QTL) associated with residual feed intake (RFI) and genes whose expression varied significantly with phenotypic differences in RFI. We used data from 2,190 Angus steers with phenotypic records for RFI, all with imputed high density array (770k) genotypes. We used RNA-seq in a multi-tissue experiment from 126 Angus steers divergently selected for RFI for approximately three generations, to analyze the expression of genes significantly associated (GSA) with RFI, with special attention to the genes close by significant QTLs.
Genomic evaluation based on selected variants from imputed whole-genome sequence data in Australian sheep populations
This study investigates improvement in accuracy of genomic prediction for growth and eating quality traits in Australian sheep populations based on selected variants from imputed whole genome sequence (WGS) data combined with a 50k-SNP array. Selection of SNP variants was based on single trait multi-breed genome wide association studies (GWAS) on WGS data in an independent data subset. Genomic prediction was based on genomic best linear unbiased prediction (GBLUP) using training sets of between 6,353 and 11,067 multi-breed purebred and crossbred animals. Four different genotype sets were compared: 50k SNP genotypes, WGS variants, selected sequence variants from GWAS and selected sequence variants combined with 50k genotypes. The latter set was modeled as either one or as two subsets with different variance components. Results showed a substantial improvement in prediction accuracy when selected sequence variants from GWAS were added to the standard 50k-SNP array. Absolute value of increase in accuracy across different traits was on average 6.2% and 4.1% for purebred and crossbred Merino sheep, respectively, when selected sequence variants and 50k genotypes were fitted as two variance components simultaneously. The improvement in prediction accuracy across different traits was on average 4.4% and 3.8% for purebred and crossbred Merino sheep, respectively, when selected sequence variants combined with 50k SNP arrays were fitted as one variance component.
Accuracy of Genomic Prediction for Milk Production Traits in Philippine Dairy Buffaloes
The objective of this study was to compare the accuracies of genomic prediction for milk yield, fat yield, and protein yield from Philippine dairy buffaloes using genomic best linear unbiased prediction (GBLUP) and single-step GBLUP (ssGBLUP) with the accuracies based on pedigree BLUP (pBLUP). To also assess the bias of the prediction, the regression coefficient (slope) of the adjusted phenotypes on the predicted breeding values (BVs) was also calculated. Two data sets were analyzed. The GENO data consisting of all female buffaloes that have both phenotypes and genotypes (n = 904 with 1,773,305-days lactation records) were analyzed using pBLUP and GBLUP. The ALL data, consisting of the GENO data plus females with phenotypes but not genotyped (n = 1,975 with 3,821,305-days lactation records), were analyzed using pBLUP and ssGBLUP. Animals were genotyped with the Affymetrix 90k buffalo genotyping array. After quality control, 60,827 single-nucleotide polymorphisms were used for downward analysis. A pedigree file containing 2,642 animals was used for pBLUP and ssGBLUP. Accuracy of prediction was calculated as the correlation between the predicted BVs of the test set and adjusted phenotypes, which were corrected for fixed effects, divided by the square root of the heritability of the trait, corrected for the number of lactations used in the test set. To assess the bias of the prediction, the regression coefficient (slope) of the adjusted phenotypes on the predicted BVs was also calculated. Results showed that genomic methods (GBLUP and ssGBLUP) provide more accurate predictions compared to pBLUP. Average GBLUP and ssGBLUP accuracies were 0.24 and 0.29, respectively, whereas average pBLUP accuracies (for GENO and ALL data) were 0.21 and 0.22, respectively. Slopes of the two genomic methods were also closer to one, indicating lesser bias, compared to pBLUP. Average GBLUP and ssGBLUP slopes were 0.89 and 0.84, respectively, whereas the average pBLUP (for GENO and ALL data) slopes were 0.80 and 0.54, respectively.
A conditional multi-trait sequence GWAS discovers pleiotropic candidate genes and variants for sheep wool, skin wrinkle and breech cover traits
Background:
Imputation to whole-genome sequence is now possible in large sheep populations. It is therefore of interest to use this data in genome-wide association studies (GWAS) to investigate putative causal variants and genes that underpin economically important traits. Merino wool is globally sought after for luxury fabrics, but some key wool quality attributes are unfavourably correlated with the characteristic skin wrinkle of Merinos. In turn, skin wrinkle is strongly linked to susceptibility to "fly strike" (Cutaneous myiasis), which is a major welfare issue. Here, we use whole-genome sequence data in a multi-trait GWAS to identify pleiotropic putative causal variants and genes associated with changes in key wool traits and skin wrinkle.
Results:
A stepwise conditional multi-trait GWAS (CM-GWAS) identified putative causal variants and related genes from 178 independent quantitative trait loci (QTL) of 16 wool and skin wrinkle traits, measured on up to 7218 Merino sheep with 31 million imputed whole-genome sequence (WGS) genotypes. Novel candidate gene findings included the MAT1A gene that encodes an enzyme involved in the sulphur metabolism pathway critical to production of wool proteins, and the ESRP1 gene. We also discovered a significant wrinkle variant upstream of the HAS2 gene, which in dogs is associated with the exaggerated skin folds in the Shar-Pei breed.
Conclusions:
The wool and skin wrinkle traits studied here appear to be highly polygenic with many putative candidate variants showing considerable pleiotropy. Our CM-GWAS identified many highly plausible candidate genes for wool traits as well as breech wrinkle and breech area wool cover.
Using imputed whole-genome sequence data to improve the accuracy of genomic prediction for parasite resistance in Australian sheep
Background: This study aimed at (1) comparing the accuracies of genomic prediction for parasite resistance in sheep based on whole-genome sequence (WGS) data to those based on 50k and high-density (HD) single nucleotide polymorphism (SNP) panels; (2) investigating whether the use of variants within quantitative trait loci (QTL) regions that were selected from regional heritability mapping (RHM) in an independent dataset improved the accuracy more than variants selected from genome-wide association studies (GWAS); and (3) comparing the prediction accuracies between variants selected from WGS data to variants selected from the HD SNP panel.
Results: The accuracy of genomic prediction improved marginally from 0.16 ± 0.02 and 0.18 ± 0.01 when using all the variants from 50k and HD genotypes, respectively, to 0.19 ± 0.01 when using all the variants from WGS data. Fitting a GRM from the selected variants alongside a GRM from the 50k SNP genotypes improved the prediction accuracy substantially compared to fitting the 50k SNP genotypes alone. The gain in prediction accuracy was slightly more pronounced when variants were selected from WGS data compared to when variants were selected from the HD panel. When sequence variants that passed the GWAS -log10(p value) threshold of 3 across the entire genome were selected, the prediction accuracy improved by 5% (up to 0.21 ± 0.01), whereas when selection was limited to sequence variants that passed the same GWAS −log10(p value) threshold of 3 in regions identified by RHM, the accuracy improved by 9% (up to 0.25 ± 0.01).
Conclusions: Our results show that through careful selection of sequence variants from the QTL regions, the accuracy of genomic prediction for parasite resistance in sheep can be improved. These findings have important implications for genomic prediction in sheep.