A conditional multi-trait sequence GWAS discovers pleiotropic candidate genes and variants for sheep wool, skin wrinkle and breech cover traits

A Study of the Resistance of Hu Sheep Lambs to Escherichia coli F17 Based on Whole Genome Sequencing

This study aims to analyze the whole genome sequencing of E. coli F17 in antagonistic and susceptible Hu sheep lambs. The objective is to investigate the critical mutation loci in sheep and understand the genetic mechanism of sheep resistance to E. coli F17 at the genome level. Antagonist and susceptible venous blood samples were collected from Hu sheep lambs for whole genome sequencing and whole genome association analysis. A total of 466 genes with significant SNPs (p < 1.0 × 10-3) were found. GO and KEGG enrichment analysis and protein interaction network analysis were performed on these genes, and preliminary investigations showed that SNPs on CTNNB1, CDH8, APOD, HCLS1, Tet2, MTSS1 and YAP1 genes may be associated with the antagonism and susceptibility of Hu sheep lambs to E. coli F17. There are still some shortcomings that have not been explored via in vivo and in vitro functional experiments of the candidate genes, which will be our next research work. This study provides genetic loci and candidate genes for resistance of Hu sheep lambs to E. coli F17 infection, and provides a genetic basis for breeding disease-resistant sheep.

Genome-Wide Association Study of Fiber Diameter in Alpacas

The aim of this study was the identification of candidate genomic regions associated with fiber diameter in alpacas. DNA samples were collected from 1011 female Huacaya alpacas from two geographical Andean regions in Peru (Pasco and Puno), and three alpaca farms within each region. The samples were genotyped using an Affymetrix Custom Alpaca genotyping array containing 76,508 SNPs. After the quality controls, 960 samples and 51,742 SNPs were retained. Three association study methodologies were performed. The GWAS based on a linear model allowed us to identify 11 and 35 SNPs (-log10(p-values) > 4) using information on all alpacas and alpacas with extreme values of fiber diameter, respectively. The haplotype and marker analysis method allowed us to identify nine haplotypes with standardized haplotype heritability higher than six standard deviations. The selection signatures based on cross-population extended haplotype homozygosity (XP-EHH) allowed us to identify 180 SNPs with XP-EHH values greater than |3|. Four candidate regions with adjacent SNPs identified via two association methods of analysis are located on VPA6, VPA9, VPA29 and one chromosomally unassigned scaffold. This study represents the first analysis of alpaca whole genome association with fiber diameter, using a recently assembled alpaca SNP microarray.

A computational framework for improving genetic variants identification from 5,061 sheep sequencing data

BACKGROUND

Pan-genomics is a recently emerging strategy that can be utilized to provide a more comprehensive characterization of genetic variation. Joint calling is routinely used to combine identified variants across multiple related samples. However, the improvement of variants identification using the mutual support information from multiple samples remains quite limited for population-scale genotyping.

RESULTS

In this study, we developed a computational framework for joint calling genetic variants from 5,061 sheep by incorporating the sequencing error and optimizing mutual support information from multiple samples' data. The variants were accurately identified from multiple samples by using four steps: (1) Probabilities of variants from two widely used algorithms, GATK and Freebayes, were calculated by Poisson model incorporating base sequencing error potential; (2) The variants with high mapping quality or consistently identified from at least two samples by GATK and Freebayes were used to construct the raw high-confidence identification (rHID) variants database; (3) The high confidence variants identified in single sample were ordered by probability value and controlled by false discovery rate (FDR) using rHID database; (4) To avoid the elimination of potentially true variants from rHID database, the variants that failed FDR were reexamined to rescued potential true variants and ensured high accurate identification variants. The results indicated that the percent of concordant SNPs and Indels from Freebayes and GATK after our new method were significantly improved 12%-32% compared with raw variants and advantageously found low frequency variants of individual sheep involved several traits including nipples number (GPC5), scrapie pathology (PAPSS2), seasonal reproduction and litter size (GRM1), coat color (RAB27A), and lentivirus susceptibility (TMEM154).

CONCLUSION

The new method used the computational strategy to reduce the number of false positives, and simultaneously improve the identification of genetic variants. This strategy did not incur any extra cost by using any additional samples or sequencing data information and advantageously identified rare variants which can be important for practical applications of animal breeding.

A comprehensive analysis of the genetic diversity and environmental adaptability in worldwide Merino and Merino-derived sheep breeds

BACKGROUND

To enhance and extend the knowledge about the global historical and phylogenetic relationships between Merino and Merino-derived breeds, 19 populations were genotyped with the OvineSNP50 BeadChip specifically for this study, while an additional 23 populations from the publicly available genotypes were retrieved. Three complementary statistical tests, Rsb (extended haplotype homozygosity between-populations), XP-EHH (cross-population extended haplotype homozygosity), and runs of homozygosity (ROH) islands were applied to identify genomic variants with potential impact on the adaptability of Merino genetic type in two contrasting climate zones.

RESULTS

The results indicate that a large part of the Merino's genetic relatedness and admixture patterns are explained by their genetic background and/or geographic origin, followed by local admixture. Multi-dimensional scaling, Neighbor-Net, Admixture, and TREEMIX analyses consistently provided evidence of the role of Australian, Rambouillet and German strains in the extensive gene introgression into the other Merino and Merino-derived breeds. The close relationship between Iberian Merinos and other South-western European breeds is consistent with the Iberian origin of the Merino genetic type, with traces from previous contributions of other Mediterranean stocks. Using Rsb and XP-EHH approaches, signatures of selection were detected spanning four genomic regions located on Ovis aries chromosomes (OAR) 1, 6 and 16, whereas two genomic regions on OAR6, that partially overlapped with the previous ones, were highlighted by ROH islands. Overall, the three approaches identified 106 candidate genes putatively under selection. Among them, genes related to immune response were identified via the gene interaction network. In addition, several candidate genes were found, such as LEKR1, LCORL, GHR, RBPJ, BMPR1B, PPARGC1A, and PRKAA1, related to morphological, growth and reproductive traits, adaptive thermogenesis, and hypoxia responses.

CONCLUSIONS

To the best of our knowledge, this is the first comprehensive dataset that includes most of the Merino and Merino-derived sheep breeds raised in different regions of the world. The results provide an in-depth picture of the genetic makeup of the current Merino and Merino-derived breeds, highlighting the possible selection pressures associated with the combined effect of anthropic and environmental factors. The study underlines the importance of Merino genetic types as invaluable resources of possible adaptive diversity in the context of the occurring climate changes.

Genetic association of wool quality characteristics in United States Rambouillet sheep

Introduction: Fine wool production is an important source of revenue, accounting for up to 13% of total revenue in extensively managed wool sheep production systems of the United States. The Rambouillet are a predominant breed that excels in wool quality characteristics. Understanding the genetic basis of wool quality characteristics would aid in the development of genomic breeding strategies to facilitate genetic improvement. Methods: Wool characteristics and DNA were collected for rams enrolled in the North Dakota State University and University of Wyoming annual central performance ram tests over a three-year period (2019-2021, N = 313). The relationships of wool quality characteristics including grease fleece weight adjusted 365 days (wt. 365 adj.), clean fleece wt. 365 adj., staple length 365 adj., average fiber diameter, face wool cover, amount of skin wrinkles and belly wool were evaluated through genome-wide association studies (GWAS), Pearson correlation and ANOVA. Results: The GWAS identified four genome-wide significant genetic markers (p-value <1.19e-06) and five chromosome-wide significant markers (p-value <1.13e-05) on chromosomes 1, 2, 4, 15, and 19. Significant markers were associated with genes notable for relevant wool biological functions, including the gene ABCC8 which codes for SUR1, an ATP-sensitive potassium channel known to affect hair growth and 60S ribosomal protein L17-like, previously found to be expressed during follicle formation. The strongest Pearson correlation coefficients were identified between clean fleece wt. 365 adj. and grease fleece wt. 365 adj. (r = 0.83) and between clean fleece wt. 365 adj. and staple length 365 adj. (r = 0.53). Additionally, clean fleece wt. 365 adj. was correlated with final body weight (r = 0.35) and scrotal circumference (r = 0.16). Staple length 365 adj. (p-value = 5e-04), average fiber diameter (p-value = .0053) and clean fleece wt. 365 adj. (p-value = .014) were significantly associated with belly wool score. Discussion: The results of this study provide important insight into the relationships between wool quality characteristics and report specific markers that Rambouillet sheep producers may use to help inform selection and breeding decisions for improved wool quality.

Genomic Regions Associated with Wool, Growth and Reproduction Traits in Uruguayan Merino Sheep

The aim of this study was to identify genomic regions and genes associated with the fiber diameter (FD), clean fleece weight (CFW), live weight (LW), body condition score (BCS), pregnancy rate (PR) and lambing potential (LP) of Uruguayan Merino sheep. Phenotypic records of approximately 2000 mixed-age ewes were obtained from a Merino nucleus flock. Genome-wide association studies were performed utilizing single-step Bayesian analysis. For wool traits, a total of 35 genomic windows surpassed the significance threshold (PVE ≥ 0.25%). The proportion of the total additive genetic variance explained by those windows was 4.85 and 9.06% for FD and CFW, respectively. There were 42 windows significantly associated with LWM, which collectively explained 43.2% of the additive genetic variance. For BCS, 22 relevant windows accounted for more than 40% of the additive genetic variance, whereas for the reproduction traits, 53 genomic windows (24 and 29 for PR and LP, respectively) reached the suggestive threshold of 0.25% of the PVE. Within the top 10 windows for each trait, we identified several genes showing potential associations with the wool (e.g., IGF-1, TGFB2R, PRKCA), live weight (e.g., CAST, LAP3, MED28, HERC6), body condition score (e.g., CDH10, TMC2, SIRPA, CPXM1) or reproduction traits (e.g., ADCY1, LEPR, GHR, LPAR2) of the mixed-age ewes.

Genomic prediction with whole-genome sequence data in intensely selected pig lines

Background

Early simulations indicated that whole-genome sequence data (WGS) could improve the accuracy of genomic predictions within and across breeds. However, empirical results have been ambiguous so far. Large datasets that capture most of the genomic diversity in a population must be assembled so that allele substitution effects are estimated with high accuracy. The objectives of this study were to use a large pig dataset from seven intensely selected lines to assess the benefits of using WGS for genomic prediction compared to using commercial marker arrays and to identify scenarios in which WGS provides the largest advantage.

Methods

We sequenced 6931 individuals from seven commercial pig lines with different numerical sizes. Genotypes of 32.8 million variants were imputed for 396,100 individuals (17,224 to 104,661 per line). We used BayesR to perform genomic prediction for eight complex traits. Genomic predictions were performed using either data from a standard marker array or variants preselected from WGS based on association tests.

Results

The accuracies of genomic predictions based on preselected WGS variants were not robust across traits and lines and the improvements in prediction accuracy that we achieved so far with WGS compared to standard marker arrays were generally small. The most favourable results for WGS were obtained when the largest training sets were available and standard marker arrays were augmented with preselected variants with statistically significant associations to the trait. With this method and training sets of around 80k individuals, the accuracy of within-line genomic predictions was on average improved by 0.025. With multi-line training sets, improvements of 0.04 compared to marker arrays could be expected.

Conclusions

Our results showed that WGS has limited potential to improve the accuracy of genomic predictions compared to marker arrays in intensely selected pig lines. Thus, although we expect that larger improvements in accuracy from the use of WGS are possible with a combination of larger training sets and optimised pipelines for generating and analysing such datasets, the use of WGS in the current implementations of genomic prediction should be carefully evaluated against the cost of large-scale WGS data on a case-by-case basis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-022-00756-0.