Multiple-trait QTL mapping and genomic prediction for wool traits in sheep

Genetics of Wool and Cashmere Fibre: Progress, Challenges, and Future Research

Wool (sheep) and cashmere (goat) fibres have unique biological, physical, and chemical properties and these fibres are becoming more important as the demand for natural products increases. However, these complex protein fibres are at times compromised by natural variability in their properties, and this can impact their use and value. Genetic improvement via selection and breeding can partly overcome this problem, enabling the farming of sheep and goats that produce more desirable fibre. This review explores the challenges in improving wool and cashmere fibre characteristics using genetics, with a focus on improving our understanding of the key protein components of fibres, wool keratins and keratin-associated proteins (KAPs). Despite progress in our knowledge of these proteins, gaining a better understanding of them and how they affect these fibres remains an ongoing challenge. This is not straight-forward, given the large number of similar yet unique genes that produce the proteins and the gaps that remain in their identification and characterisation. More research is required to clarify gene and protein sequence variability and the location and patterns of gene expression, which in turn limits our understanding of fibre growth and variation. Several aspects that currently hinder our progress in this quest include the incomplete identification of all the genes and weaknesses in the approaches used to characterise them, including newer omics technologies. We describe future research directions and challenges, including the need for ongoing gene identification, variation characterisation, and gene expression analysis and association studies to enable further improvement to these valuable natural fibres.

Caprine and Ovine Genomic Selection-Progress and Application

The advancement of sequencing technology and molecular breeding methods has provided technical support and assurance for accurate breeding. Genomic Selection (GS) utilizes genomic information to improve livestock breeding, and it is more accurate and more efficient than traditional selection methods. GS has been widely applied in domestic animal breeding, especially in cattle. However, there are still limited studies on the application and research of GS in sheep and goats. This paper outlines the principles, analysis methods, and influential factors of GS and elaborates on the research progress, challenges, and prospects of applying GS in sheep and goat breeding. Through the review of these aspects, this paper is expected to provide valuable references for the implementation of GS in the field of sheep and goat breeding.

Genomic Characterization of Quality Wool Traits in Spanish Merino Sheep

The native Spanish Merino breed was the founder of all the other Merino and Merino-derived breeds worldwide. Despite the fact that this breed was created and improved to produce the highest quality fine wool, the global wool market crisis led to the wholescale crossing of most of the herds with breeds for meat purposes. Nevertheless, there are still some purebred animals with a high potential for producing quality wool. The objective of this study was to characterize the current wool quality of the breed and identify genes associated with these parameters. To achieve this, over 12,800 records from the most representative animals of the breed (registered in the herd book) were analyzed using the Australian OFDA 2000 system, for parameters such as fiber diameter (FD), standard deviation (SD), coefficient of variation (CV), fibers over 15 microns (>15%), staple length (SL), and comfort factor (CRV). Additionally, animals with the most extreme FD values were whole-genome sequenced using NGS. Genome-wide association studies (GWAS) determined the association of 74 variants with the different traits studied, which were located in 70 different genes. Of these genes, EDN2, COL18A1, and LRP1B, associated with fibers over 15%, and FGF12 and ADAM17, associated with SL, play a key role in hair follicle growth and development. Our study reveals the great potential for recovering this breed for fine wool production, and identifies five candidate genes whose understanding may aid in that selection process.

Integration of selective sweeps across the sheep genome: understanding the relationship between production and adaptation traits

BACKGROUND

Livestock populations are under constant selective pressure for higher productivity levels for different selective purposes. This pressure results in the selection of animals with unique adaptive and production traits. The study of genomic regions associated with these unique characteristics has the potential to improve biological knowledge regarding the adaptive process and how it is connected to production levels and resilience, which is the ability of an animal to adapt to stress or an imbalance in homeostasis. Sheep is a species that has been subjected to several natural and artificial selective pressures during its history, resulting in a highly specialized species for production and adaptation to challenging environments. Here, the data from multiple studies that aim at mapping selective sweeps across the sheep genome associated with production and adaptation traits were integrated to identify confirmed selective sweeps (CSS).

RESULTS

In total, 37 studies were used to identify 518 CSS across the sheep genome, which were classified as production (147 prodCSS) and adaptation (219 adapCSS) CSS based on the frequency of each type of associated study. The genes within the CSS were associated with relevant biological processes for adaptation and production. For example, for adapCSS, the associated genes were related to the control of seasonality, circadian rhythm, and thermoregulation. On the other hand, genes associated with prodCSS were related to the control of feeding behaviour, reproduction, and cellular differentiation. In addition, genes harbouring both prodCSS and adapCSS showed an interesting association with lipid metabolism, suggesting a potential role of this process in the regulation of pleiotropic effects between these classes of traits.

CONCLUSIONS

The findings of this study contribute to a deeper understanding of the genetic link between productivity and adaptability in sheep breeds. This information may provide insights into the genetic mechanisms that underlie undesirable genetic correlations between these two groups of traits and pave the way for a better understanding of resilience as a positive ability to respond to environmental stressors, where the negative effects on production level are minimized.

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.

Genetics of the phenotypic evolution in sheep: a molecular look at diversity-driving genes

BACKGROUND

After domestication, the evolution of phenotypically-varied sheep breeds has generated rich biodiversity. This wide phenotypic variation arises as a result of hidden genomic changes that range from a single nucleotide to several thousands of nucleotides. Thus, it is of interest and significance to reveal and understand the genomic changes underlying the phenotypic variation of sheep breeds in order to drive selection towards economically important traits.

REVIEW

Various traits contribute to the emergence of variation in sheep phenotypic characteristics, including coat color, horns, tail, wool, ears, udder, vertebrae, among others. The genes that determine most of these phenotypic traits have been investigated, which has generated knowledge regarding the genetic determinism of several agriculturally-relevant traits in sheep. In this review, we discuss the genomic knowledge that has emerged in the past few decades regarding the phenotypic traits in sheep, and our ultimate aim is to encourage its practical application in sheep breeding. In addition, in order to expand the current understanding of the sheep genome, we shed light on research gaps that require further investigation.

CONCLUSIONS

Although significant research efforts have been conducted in the past few decades, several aspects of the sheep genome remain unexplored. For the full utilization of the current knowledge of the sheep genome, a wide practical application is still required in order to boost sheep productive performance and contribute to the generation of improved sheep breeds. The accumulated knowledge on the sheep genome will help advance and strengthen sheep breeding programs to face future challenges in the sector, such as climate change, global human population growth, and the increasing demand for products of animal origin.

A Study of Combined Genotype Effects of SHCBP1 on Wool Quality Traits in Chinese Merino

SHCBP1 (Shc SH2-domain binding protein 1) is a member of the Src and collagen homolog (Shc) protein family and is closely associated with multiple signaling pathways that play important roles during hair follicle induction, morphogenesis, and cycling. The purpose of this study was to investigate SHCBP1 gene expression, polymorphisms, and the association between SHCBP1 and wool quality traits in Chinese Merino sheep. The SHCBP1 gene was shown, by qPCR, to be ubiquitously expressed in sheep tissues and differentially expressed in the adult skin of Chinese Merino and Suffolk sheep. Four SNPs (termed SHCBP1SNPs 1-4) were identified by Sanger sequencing and were located in exon 2, intron 9, intron 12, and exon 13 of the sheep SHCBP1 gene, respectively. SHCBP1SNPs 3 and 4 (rs411176240 and rs160910635) were significantly associated with wool crimp (P < 0.05). The combined polymorphism (SHCBP1SNP3-SHCBP1SNP4) was significantly associated with wool crimp (P < 0.05). Bioinformatics analysis showed that the SNPs associated with wool crimp (SHCBP1SNPs 3 and 4) might affect the pre-mRNA splicing by creating binding sites for serine-arginine-rich proteins and that SHCBP1SNP4 might alter the SHCBP1 mRNA and protein secondary structure. Our results suggest that SHCBP1 influences wool crimp and SHCBP1SNPs 3 and 4 might be useful markers for marker-assisted selection and sheep breeding.

Application of Bayesian genomic prediction methods to genome-wide association analyses

Background

Bayesian genomic prediction methods were developed to simultaneously fit all genotyped markers to a set of available phenotypes for prediction of breeding values for quantitative traits, allowing for differences in the genetic architecture (distribution of marker effects) of traits. These methods also provide a flexible and reliable framework for genome-wide association (GWA) studies. The objective here was to review developments in Bayesian hierarchical and variable selection models for GWA analyses.

Results

By fitting all genotyped markers simultaneously, Bayesian GWA methods implicitly account for population structure and the multiple-testing problem of classical single-marker GWA. Implemented using Markov chain Monte Carlo methods, Bayesian GWA methods allow for control of error rates using probabilities obtained from posterior distributions. Power of GWA studies using Bayesian methods can be enhanced by using informative priors based on previous association studies, gene expression analyses, or functional annotation information. Applied to multiple traits, Bayesian GWA analyses can give insight into pleiotropic effects by multi-trait, structural equation, or graphical models. Bayesian methods can also be used to combine genomic, transcriptomic, proteomic, and other -omics data to infer causal genotype to phenotype relationships and to suggest external interventions that can improve performance.

Conclusions

Bayesian hierarchical and variable selection methods provide a unified and powerful framework for genomic prediction, GWA, integration of prior information, and integration of information from other -omics platforms to identify causal mutations for complex quantitative traits.

A guidance of model selection for genomic prediction based on linear mixed models for complex traits

Brain imaging outcomes are important for Alzheimer's disease (AD) detection, and their prediction based on both genetic and demographic risk factors can facilitate the ongoing prevention and treatment of AD. Existing studies have identified numerous significantly AD-associated SNPs. However, how to make the best use of them for prediction analyses remains unknown. In this research, we first explored the relationship between genetic architecture and prediction accuracy of linear mixed models via visualizing the Manhattan plots generated based on the data obtained from the Wellcome Trust Case Control Consortium, and then constructed prediction models for eleven AD-related brain imaging outcomes using data from United Kingdom Biobank and Alzheimer's Disease Neuroimaging Initiative studies. We found that the simple Manhattan plots can be informative for the selection of prediction models. For traits that do not exhibit any significant signals from the Manhattan plots, the simple genomic best linear unbiased prediction (gBLUP) model is recommended due to its robust and accurate prediction performance as well as its computational efficiency. For diseases and traits that show spiked signals on the Manhattan plots, the latent Dirichlet process regression is preferred, as it can flexibly accommodate both the oligogenic and omnigenic models. For the prediction of AD-related traits, the Manhattan plots suggest their polygenic nature, and gBLUP has achieved robust performance for all these traits. We found that for these AD-related traits, genetic factors themselves only explain a very small proportion of the heritability, and the well-known AD risk factors can substantially improve the prediction model.

Detecting Single Nucleotide Polymorphisms in MEF2B and UCP3 and Elucidating Their Association with Sheep Growth Traits

Herein we detected single nucleotide polymorphisms in MEF2B and UCP3 by DNA sequencing and the KASPar technology and analyzed their association with sheep growth traits. Two synonymous mutations, g.1826 C > T and g.10266 G > C, were detected, respectively, and they were found to be significantly associated with sheep growth traits (p < 0.05). In case of MEF2B g.1826 C > T, the average body weight and chest and cannon circumference of sheep with the CC genotype were significantly higher than those of sheep with the CT and TT genotypes (p < 0.05). Moreover, in case of UCP3 g.10266 G > C, the average body weight and chest and cannon circumference of sheep with the GG genotype were significantly higher than those of sheep with the GC and CC genotypes (p < 0.05). Moreover, the average body weight of sheep with the CC/GG genotype was higher compared with those of other genotype combinations. We also assessed MEF2B and UCP3 expression in different sheep tissues, confirming their expression in all examined tissues. To summarize, we believe that the polymorphisms identified in MEF2B and UCP3 can serve as molecular markers for sheep growth traits.

Control of sheep flystrike: what's been tried in the past and where to from here

Flystrike remains a serious financial and animal welfare issue for the sheep industry in Australia despite many years of research into control methods. The present paper provides an extensive review of past research on flystrike, and highlights areas that hold promise for providing long-term control options. We describe areas where the application of modern scientific advances may provide increased impetus to some novel, as well as some previously explored, control methods. We provide recommendations for research activities: insecticide resistance management, novel delivery methods for therapeutics, improved breeding indices for flystrike-related traits, mechanism of nematode-induced scouring in mature animals. We also identify areas where advances can be made in flystrike control through the greater adoption of well-recognised existing management approaches: optimal insecticide-use patterns, increased use of flystrike-related Australian Sheep Breeding Values, and management practices to prevent scouring in young sheep. We indicate that breeding efforts should be primarily focussed on the adoption and improvement of currently available breeding tools and towards the future integration of genomic selection methods. We describe factors that will impact on the ongoing availability of insecticides for flystrike control and on the feasibility of vaccination. We also describe areas where the blowfly genome may be useful in providing impetus to some flystrike control strategies, such as area-wide approaches that seek to directly suppress or eradicate sheep blowfly populations. However, we also highlight the fact that commercial and feasibility considerations will act to temper the potential for the genome to act as the basis for providing some control options.

Evaluation of Bayesian alphabet and GBLUP based on different marker density for genomic prediction in Alpine Merino sheep

The marker density, the heritability level of trait and the statistical models adopted are critical to the accuracy of genomic prediction (GP) or selection (GS). If the potential of GP is to be fully utilized to optimize the effect of breeding and selection, in addition to incorporating the above factors into simulated data for analysis, it is essential to incorporate these factors into real data for understanding their impact on GP accuracy, more clearly and intuitively. Herein, we studied the GP of six wool traits of sheep by two different models, including Bayesian Alphabet (BayesA, BayesB, BayesCπ, and Bayesian LASSO) and genomic best linear unbiased prediction (GBLUP). We adopted fivefold cross-validation to perform the accuracy evaluation based on the genotyping data of Alpine Merino sheep (n = 821). The main aim was to study the influence and interaction of different models and marker densities on GP accuracy. The GP accuracy of the six traits was found to be between 0.28 and 0.60, as demonstrated by the cross-validation results. We showed that the accuracy of GP could be improved by increasing the marker density, which is closely related to the model adopted and the heritability level of the trait. Moreover, based on two different marker densities, it was derived that the prediction effect of GBLUP model for traits with low heritability was better; while with the increase of heritability level, the advantage of Bayesian Alphabet would be more obvious, therefore, different models of GP are appropriate in different traits. These findings indicated the significance of applying appropriate models for GP which would assist in further exploring the optimization of GP.

Whole-genome re-sequencing association study on yearling wool traits in Chinese fine-wool sheep

To investigate single nucleotide polymorphism (SNP) loci associated with yearling wool traits of fine-wool sheep for optimizing marker-assisted selection and dissection of the genetic architecture of wool traits, we conducted a genome-wide association study (GWAS) based on the fixed and random model circulating probability unification (FarmCPU) for yearling staple length (YSL), yearling mean fiber diameter (YFD), yearling greasy fleece weight (YGFW), and yearling clean fleece rate (YCFR) by using the whole-genome re-sequenced data (totaling 577 sheep) from the following four fine-wool sheep breeds in China: Alpine Merino sheep (AMS), Chinese Merino sheep (CMS), Qinghai fine-wool sheep (QHS), and Aohan fine-wool sheep (AHS). A total of 16 SNPs were detected above the genome-wise significant threshold (P = 5.45E-09), and 79 SNPs were located above the suggestive significance threshold (P = 5.00E-07) from the GWAS results. For YFD and YGFW traits, 7 and 9 SNPs reached the genome-wise significance thresholds, whereas 10 and 12 SNPs reached the suggestive significance threshold, respectively. For YSL and YCFR traits, none of the SNPs reached the genome-wise significance thresholds, whereas 57 SNPs exceeded the suggestive significance threshold. We recorded 14 genes located at the region of ±50-kb near the genome-wise significant SNPs and 59 genes located at the region of ±50-kb near the suggestive significant SNPs. Meanwhile, we used the Average Information Restricted Maximum likelihood algorithm (AI-REML) in the “HIBLUP” package to estimate the heritability and variance components of the four desired yearling wool traits. The estimated heritability values (h²) of YSL, YFD, YGFW, and YCFR were 0.6208, 0.7460, 0.6758, and 0.5559, respectively. We noted that the genetic parameters in this study can be used for fine-wool sheep breeding. The newly detected significant SNPs and the newly identified candidate genes in this study would enhance our understanding of yearling wool formation, and significant SNPs can be applied to genome selection in fine-wool sheep breeding.

Robust regression based genome-wide multi-trait QTL analysis

In genome-wide quantitative trait locus (QTL) mapping studies, multiple quantitative traits are often measured along with the marker genotypes. Multi-trait QTL (MtQTL) analysis, which includes multiple quantitative traits together in a single model, is an efficient technique to increase the power of QTL identification. The two most widely used classical approaches for MtQTL mapping are Gaussian Mixture Model-based MtQTL (GMM-MtQTL) and Linear Regression Model-based MtQTL (LRM-MtQTL) analyses. There are two types of LRM-MtQTL approach known as least squares-based LRM-MtQTL (LS-LRM-MtQTL) and maximum likelihood-based LRM-MtQTL (ML-LRM-MtQTL). These three classical approaches are equivalent alternatives for QTL detection, but ML-LRM-MtQTL is computationally faster than GMM-MtQTL and LS-LRM-MtQTL. However, one major limitation common to all the above classical approaches is that they are very sensitive to outliers, which leads to misleading results. Therefore, in this study, we developed an LRM-based robust MtQTL approach, called LRM-RobMtQTL, for the backcross population based on the robust estimation of regression parameters by maximizing the β-likelihood function induced from the β-divergence with multivariate normal distribution. When β = 0, the proposed LRM-RobMtQTL method reduces to the classical ML-LRM-MtQTL approach. Simulation studies showed that both ML-LRM-MtQTL and LRM-RobMtQTL methods identified the same QTL positions in the absence of outliers. However, in the presence of outliers, only the proposed method was able to identify all the true QTL positions. Real data analysis results revealed that in the presence of outliers only our LRM-RobMtQTL approach can identify all the QTL positions as those identified in the absence of outliers by both methods. We conclude that our proposed LRM-RobMtQTL analysis approach outperforms the classical MtQTL analysis methods.

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-021-00651-0.

Integration of a single-step genome-wide association study with a multi-tissue transcriptome analysis provides novel insights into the genetic basis of wool and weight traits in sheep

BACKGROUND

Genetic improvement of wool and growth traits is a major goal in the sheep industry, but their underlying genetic architecture remains elusive. To improve our understanding of these mechanisms, we conducted a weighted single-step genome-wide association study (WssGWAS) and then integrated the results with large-scale transcriptome data for five wool traits and one growth trait in Merino sheep: mean fibre diameter (MFD), coefficient of variation of the fibre diameter (CVFD), crimp number (CN), mean staple length (MSL), greasy fleece weight (GFW), and live weight (LW).

RESULTS

Our dataset comprised 7135 individuals with phenotype data, among which 1217 had high-density (HD) genotype data (n = 372,534). The genotypes of 707 of these animals were imputed from the Illumina Ovine single nucleotide polymorphism (SNP) 54 BeadChip to the HD Array. The heritability of these traits ranged from 0.05 (CVFD) to 0.36 (MFD), and between-trait genetic correlations ranged from - 0.44 (CN vs. LW) to 0.77 (GFW vs. LW). By integrating the GWAS signals with RNA-seq data from 500 samples (representing 87 tissue types from 16 animals), we detected tissues that were relevant to each of the six traits, e.g. liver, muscle and the gastrointestinal (GI) tract were the most relevant tissues for LW, and leukocytes and macrophages were the most relevant cells for CN. For the six traits, 54 quantitative trait loci (QTL) were identified covering 81 candidate genes on 21 ovine autosomes. Multiple candidate genes showed strong tissue-specific expression, e.g. BNC1 (associated with MFD) and CHRNB1 (LW) were specifically expressed in skin and muscle, respectively. By conducting phenome-wide association studies (PheWAS) in humans, we found that orthologues of several of these candidate genes were significantly (FDR < 0.05) associated with similar traits in humans, e.g. BNC1 was significantly associated with MFD in sheep and with hair colour in humans, and CHRNB1 was significantly associated with LW in sheep and with body mass index in humans.

CONCLUSIONS

Our findings provide novel insights into the biological and genetic mechanisms underlying wool and growth traits, and thus will contribute to the genetic improvement and gene mapping of complex traits in sheep.

Genome-wide association studies detects candidate genes for wool traits by re-sequencing in Chinese fine-wool sheep

BACKGROUND

The quality and yield of wool determine the economic value of the fine-wool sheep. Therefore, discovering markers or genes relevant to wool traits is the cornerstone for the breeding of fine-wool sheep. In this study, we used the Illumina HiSeq X Ten platform to re-sequence 460 sheep belonging to four different fine-wool sheep breeds, namely, Alpine Merino sheep (AMS), Chinese Merino sheep (CMS), Aohan fine-wool sheep (AHS) and Qinghai fine-wool sheep (QHS). Eight wool traits, including fiber diameter (FD), fiber diameter coefficient of variance (FDCV), fiber diameter standard deviation (FDSD), staple length (SL), greasy fleece weight (GFW), clean wool rate (CWR), staple strength (SS) and staple elongation (SE) were examined. A genome-wide association study (GWAS) was performed to detect the candidate genes for the eight wool traits.

RESULTS

A total of 8.222 Tb of raw data was generated, with an average of approximately 8.59X sequencing depth. After quality control, 12,561,225 SNPs were available for analysis. And a total of 57 genome-wide significant SNPs and 30 candidate genes were detected for the desired wool traits. Among them, 7 SNPs and 6 genes are related to wool fineness indicators (FD, FDCV and FDSD), 10 SNPs and 7 genes are related to staple length, 13 SNPs and 7 genes are related to wool production indicators (GFW and CWR), 27 SNPs and 10 genes associated with staple elongation. Among these candidate genes, UBE2E3 and RHPN2 associated with fiber diameter, were found to play an important role in keratinocyte differentiation and cell proliferation. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment results, revealed that multitude significant pathways are related to keratin and cell proliferation and differentiation, such as positive regulation of canonical Wnt signaling pathway (GO:0090263).

CONCLUSION

This is the first GWAS on the wool traits by using re-sequencing data in Chinese fine-wool sheep. The newly detected significant SNPs in this study can be used in genome-selective breeding for the fine-wool sheep. And the new candidate genes would provide a good theoretical basis for the fine-wool sheep breeding.

FSH promotes the proliferation of sheep granulosa cells by inhibiting the expression of TSP1

Thrombospondin (TSP1) plays an important role as an antiangiogenic factor in the reproductive system of female mammals. However, its expression and function in sheep are still unclear. In the present research, the Altay sheep (a native Chinese breed) was used to analyze the expression of TSP1 in the ovary and its potential function in granulosa cells. TSP1 was widely expressed in most tissues, as shown by qPCR. In the ovary, TSP1 mRNA expression decreased during follicular to luteal growth. The TSP1 protein was expressed in a wide variety of follicles of different diameters and localized to the cytoplasm and nucleus of granulosa cells. In in vitro studies, follicle-stimulating hormone (FSH) significantly inhibited the expression of TSP1 in sheep granulosa cells. Functionally, FSH- and TSP1-specific siRNAs can promote the proliferation of sheep granulosa cells. In contrast, TSP1 mimetic peptide, ABT510, offsets the proliferation of sheep granulosa cells. Different signaling pathway inhibitors all promoted FSH-inhibited TSP1 expression, but each inhibitor had different effects on TSP1. Among them, the PI3K and ERK pathway inhibitors significantly promoted TSP1 expression and inhibited the proliferation of sheep granulosa cells.

Detecting novel Indel variants within the GHR gene and their associations with growth traits in Luxi Blackhead sheep

The growth hormone is important in the regulation of metabolism and energy homeostasis and acts through a growth hormone receptor (GHR). In this work, genetic variations within the ovine GHR gene were identified and tested for associations with body morphometric traits in Chinese Luxi Blackhead (LXBH) sheep. Novel deletion loci in the LXBH GHR gene included P2-del-23 bp and P8-del-23 bp indel variants. The polymorphic information content (PIC) was 0.329 in P2-del-23 bp and 0.257 in P8-del-23 bp. Moreover, both indel polymorphisms were not at Hardy-Weinberg equilibrium (p < 0.05) in the LXBH population. Statistical analyses revealed that the P2-del-23 bp and P8-del-23 bp indels were significantly associated (p < 0.05) with several growth traits in rams and ewes, including body weight, body height, chest depth, chest width, chest circumference, cannon circumference, paunch girth and hip width. Among the tested sheep, the body traits of those with genotype DD were superior to those with II and ID genotypes, suggesting that the 'D' allele was responsible for the positive effects on growth traits. Thus, these results indicate that the P2-del-23 bp and P8-del-23 bp indel sites and the DD genotype can be useful in marker-assisted selection in sheep.

The Effect of Integrating Genomic Information into Genetic Evaluations of Chinese Merino Sheep

Simple Summary

Genetic improvement of wool production and quality traits in fine-wool sheep is an appealing option for enhancing the market value of wool products. We estimated genetic parameters and the accuracies of estimated breeding values for various wool production and quality traits in fine-wool sheep using pedigree-based best linear unbiased prediction (PBLUP) and single-step genomic best linear unbiased prediction (ssGBLUP) strategies. ssGBLUP performed slightly better than PBLUP for the studied traits. Therefore, the single-step genetic evaluation method could be successfully implemented in genomic evaluations of fine-wool sheep and the prediction of future breeding values in young Merino sheep as part of an early preselection strategy in the near future.

Abstract

Genomic evaluations are a method for improving the accuracy of breeding value estimation. This study aimed to compare estimates of genetic parameters and the accuracy of breeding values for wool traits in Merino sheep between pedigree-based best linear unbiased prediction (PBLUP) and single-step genomic best linear unbiased prediction (ssGBLUP) using Bayesian inference. Data were collected from 28,391 yearlings of Chinese Merino sheep (classified in 1992–2018) at the Xinjiang Gonaisi Fine Wool Sheep-Breeding Farm, China. Subjectively-assessed wool traits, namely, spinning count (SC), crimp definition (CRIM), oil (OIL), and body size (BS), and objectively-measured traits, namely, fleece length (FL), greasy fleece weight (GFW), mean fiber diameter (MFD), crimp number (CN), and body weight pre-shearing (BWPS), were analyzed. The estimates of heritability for wool traits were low to moderate. The largest h² values were observed for FL (0.277) and MFD (0.290) with ssGBLUP. The heritabilities estimated for wool traits with ssGBLUP were slightly higher than those obtained with PBLUP. The accuracies of breeding values were low to moderate, ranging from 0.362 to 0.573 for the whole population and from 0.318 to 0.676 for the genotyped subpopulation. The correlation between the estimated breeding values (EBVs) and genomic EBVs (GEBVs) ranged from 0.717 to 0.862 for the whole population, and the relative increase in accuracy when comparing EBVs with GEBVs ranged from 0.372% to 7.486% for these traits. However, in the genotyped population, the rank correlation between the estimates obtained with PBLUP and ssGBLUP was reduced to 0.525 to 0.769, with increases in average accuracy of 3.016% to 11.736% for the GEBVs in relation to the EBVs. Thus, genomic information could allow us to more accurately estimate the relationships between animals and improve estimates of heritability and the accuracy of breeding values by ssGBLUP.

A Combined Multi-Cohort Approach Reveals Novel and Known Genome-Wide Selection Signatures for Wool Traits in Merino and Merino-Derived Sheep Breeds

Merino sheep represents a valuable genetic resource worldwide. In this study, we investigated selection signatures in Merino (and Merino-derived) sheep breeds using genome-wide SNP data and two different approaches: a classical FST-outlier method and an approach based on the analysis of local ancestry in admixed populations. In order to capture the most reliable signals, we adopted a combined, multi-cohort approach. In particular, scenarios involving four Merino breeds (Spanish Merino, Australian Merino, Chinese Merino, and Sopravissana) were tested via the local ancestry approach, while nine pair-wise breed comparisons contrasting the above breeds, as well as the Gentile di Puglia breed, with non-Merino breeds from the same geographic area were tested via the FST-outlier method. Signals observed using both methods were compared with genome-wide patterns of distribution of runs of homozygosity (ROH) islands. Novel and known selection signatures were detected. The most reliable signals were observed on OAR 3 (MSRB3 and LEMD3), OAR10 (FRY and RXFP2), OAR 13 (RALY), OAR17 (FAM101A), and OAR18 (NFKBIA, SEC23A, and PAX9). All the above overlapped with known QTLs for wool traits, and evidences from the literature of their involvement in skin/hair/wool biology, as well as gene network analysis, further corroborated these results. The signal on OAR10 also contains well known evidence for association with horn morphology and polledness. More elusive biological evidences of association with the Merino phenotype were observed for a number of other genes, notably LOC101120019 and TMEM132B (OAR17), LOC105609948 (OAR3), LOC101110773 (OAR10), and EIF2S2 (OAR17). Taken together, the above results further contribute to decipher the genetic basis underlying the Merino phenotype.

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 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$- log_{10} (p\,value)$$\end{document}-log10(pvalue) 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 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$- log_{10} (p\,value)$$\end{document}-log10(pvalue) 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.

Combining multi-OMICs information to identify key-regulator genes for pleiotropic effect on fertility and production traits in beef cattle

The identification of biological processes related to the regulation of complex traits is a difficult task. Commonly, complex traits are regulated through a multitude of genes contributing each to a small part of the total genetic variance. Additionally, some loci can simultaneously regulate several complex traits, a phenomenon defined as pleiotropy. The lack of understanding on the biological processes responsible for the regulation of these traits results in the decrease of selection efficiency and the selection of undesirable hitchhiking effects. The identification of pleiotropic key-regulator genes can assist in developing important tools for investigating biological processes underlying complex traits. A multi-breed and multi-OMICs approach was applied to study the pleiotropic effects of key-regulator genes using three independent beef cattle populations evaluated for fertility traits. A pleiotropic map for 32 traits related to growth, feed efficiency, carcass and meat quality, and reproduction was used to identify genes shared among the different populations and breeds in pleiotropic regions. Furthermore, data-mining analyses were performed using the Cattle QTL database (CattleQTLdb) to identify the QTL category annotated in the regions around the genes shared among breeds. This approach allowed the identification of a main gene network (composed of 38 genes) shared among breeds. This gene network was significantly associated with thyroid activity, among other biological processes, and displayed a high regulatory potential. In addition, it was possible to identify genes with pleiotropic effects related to crucial biological processes that regulate economically relevant traits associated with fertility, production and health, such as MYC, PPARG, GSK3B, TG and IYD genes. These genes will be further investigated to better understand the biological processes involved in the expression of complex traits and assist in the identification of functional variants associated with undesirable phenotypes, such as decreased fertility, poor feed efficiency and negative energetic balance.

Genome-wide association study and prediction of genomic breeding values for fatty-acid composition in Korean Hanwoo cattle using a high-density single-nucleotide polymorphism array

Genomic selection using high-density single-nucleotide polymorphism (SNP) markers is used in dairy and beef cattle breeds to accurately estimate genomic breeding values and accelerate genetic improvement by enabling selection of animals with high genetic merit. This genome-wide association study (GWAS) aimed to identify genetic variants associated with beef fatty-acid composition (FAC) traits and to evaluate the accuracy of genomic predictions (GPs) for those traits using genomic best linear unbiased prediction (GBLUP), pedigree BLUP (PBLUP), and BayesR models. Samples of the longissimus dorsi muscle of 965 thirty-month-old Hanwoo steers (progeny of 73 proven bulls) were used to investigate 14 FAC traits. Animals were genotyped or imputed using two bovine SNP platforms (50K and 777K), and after quality control, 38,715 (50K) and 633,448 (777K) SNPs were subjected to GWAS and GP study using a cross-validation scheme. SNP-based heritability estimates were moderate to high (0.25 to 0.47) for all studied traits, with some exceptions for polyunsaturated fatty acids. Association analysis revealed that 19 SNPs in BTA19 (98.7 kb) were significantly associated (P < 7.89 × 10-8) with C14:0 and C18:1n-9; these SNPs were in the fatty-acid synthase (FASN) and coiled-coil domain-containing 57 (CCDC57) genes. BayesR analysis revealed that 0.41 to 0.78% of the total SNPs (n = 2,571 to 4,904) explained almost all of the genetic variance; the majority of the SNPs (>99%) had negligible effects, suggesting that the FAC traits were polygenic. Genome partitioning analysis indicated mostly nonlinear and weak correlations between the variance explained by each chromosome and its length, which also reflected the considerable contributions of relatively few genes. The prediction accuracy of breeding values for FAC traits varied from low to high (0.25 to 0.57); the estimates using the GBLUP and BayesR methods were superior to those obtained by the PBLUP method. The BayesR method performed similarly to GBLUP for most of the studied traits but substantially better for those traits that were controlled by SNPs with large effects; this was supported by the GWAS results. In addition, the predictive abilities of the 50K and 777K SNP arrays were almost similar; thus, both are suitable for GP in Hanwoo cattle. In conclusion, this study provides important insight into the genetic architecture and predictive ability of FAC traits in Hanwoo cattle. Our findings could be used in selection and breeding programs to promote production of meat with enhanced nutritional value.

Linkage disequilibrium in Brazilian Santa Inês breed, Ovis aries

For genomic selection to be successful, there must be sufficient linkage disequilibrium between the markers and the causal mutations. The objectives of this study were to evaluate the extent of LD in ovine using the Santa Inês breed and to infer the minimum number of markers required to reach reasonable prediction accuracy. In total, 38,168 SNPs and 395 samples were used. The mean LD between adjacent marker pairs measured by r² and |D′| were 0.166 and 0.617, respectively. LD values between adjacent marker pairs ranged from 0.135 to 0.194 and from 0.568 to 0.650 for r² for |D′| across all chromosomes. The average r² between all pairwise SNPs on each chromosome was 0.018. SNPs separated by between 0.10 to 0.20 Mb had an estimated average r² equal to 0.1033. The identified haplotype blocks consisted of 2 to 21 markers. Moreover, estimates of average coefficients of inbreeding and effective population size were 0.04 and 96, respectively. LD estimated in this study was lower than that reported in other species and was characterized by short haplotype blocks. Our results suggest that the use of a higher density SNP panel is recommended for the implementation of genomic selection in the Santa Inês breed.

A comparison of transcriptomic patterns measured in the skin of Chinese fine and coarse wool sheep breeds

We characterised wool traits, and skin gene expression profiles of fine wool Super Merino (SM) and coarse wool Small Tail Han (STH) sheep. SM sheep had a significantly higher total density of wool follicles, heavier fleeces, finer fibre diameter, and increased crimp frequency, staple length and wool grease (lanolin) production. We found 435 genes were expressed at significantly different levels in the skin of the two breeds (127 genes more highly in SM and 308 genes more highly in STH sheep). Classification of the genes more highly expressed in SM sheep revealed numerous lipid metabolic genes as well as genes encoding keratins, keratin-associated proteins, and wool follicle stem cell markers. In contrast, mammalian epidermal development complex genes and other genes associated with skin cornification and muscle function were more highly expressed in STH sheep. Genes identified in this study may be further evaluated for inclusion in breeding programs, or as targets for therapeutic or genetic interventions, aimed at altering wool quality or yield. Expression of the lipid metabolic genes in the skin of sheep may be used as a novel trait with the potential to alter the content or properties of lanolin or the fleece.