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Whole-Genome Sequence Data Suggest Environmental Adaptation of Ethiopian Sheep Populations.

ABSTRACT: Great progress has been made over recent years in the identification of selection signatures in the genomes of livestock species. This work has primarily been carried out in commercial breeds for which the dominant selection pressures are associated with artificial selection. As agriculture and food security are likely to be strongly affected by climate change, a better understanding of environment-imposed selection on agricultural species is warranted. Ethiopia is an ideal setting to investigate environmental adaptation in livestock due to its wide variation in geo-climatic characteristics and the extensive genetic and phenotypic variation of its livestock. Here, we identified over three million single nucleotide variants across 12 Ethiopian sheep populations and applied landscape genomics approaches to investigate the association between these variants and environmental variables. Our results suggest that environmental adaptation for precipitation-related variables is stronger than that related to altitude or temperature, consistent with large-scale meta-analyses of selection pressure across species. The set of genes showing association with environmental variables was enriched for genes highly expressed in human blood and nerve tissues. There was also evidence of enrichment for genes associated with high-altitude adaptation although no strong association was identified with hypoxia-inducible-factor (HIF) genes. One of the strongest altitude-related signals was for a collagen gene, consistent with previous studies of high-altitude adaptation. Several altitude-associated genes also showed evidence of adaptation with temperature, suggesting a relationship between responses to these environmental factors. These results provide a foundation to investigate further the effects of climatic variables on small ruminant populations.

SUBMITTER: Wiener P

PROVIDER: S-EPMC7955157 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Whole-genome sequence (WGS) data could contain information on genetic variants at or in high linkage disequilibrium with causative mutations that underlie the genetic variation of polygenic traits. Thus far, genomic prediction accuracy has shown limited increase when using such information in dairy cattle studies, in which one or few breeds with limited diversity predominate. The objective of our study was to evaluate the accuracy of genomic prediction in a multi-breed Australian sheep population of relatively less related target individuals, when using information on imputed WGS genotypes. METHODS:Between 9626 and 26,657 animals with phenotypes were available for nine economically important sheep production traits and all had WGS imputed genotypes. About 30% of the data were used to discover predictive single nucleotide polymorphism (SNPs) based on a genome-wide association study (GWAS) and the remaining data were used for training and validation of genomic prediction. Prediction accuracy using selected variants from imputed sequence data was compared to that using a standard array of 50k SNP genotypes, thereby comparing genomic best linear prediction (GBLUP) and Bayesian methods (BayesR/BayesRC). Accuracy of genomic prediction was evaluated in two independent populations that were each lowly related to the training set, one being purebred Merino and the other crossbred Border Leicester x Merino sheep. RESULTS:A substantial improvement in prediction accuracy was observed when selected sequence variants were fitted alongside 50k genotypes as a separate variance component in GBLUP (2GBLUP) or in Bayesian analysis as a separate category of SNPs (BayesRC). From an average accuracy of 0.27 in both validation sets for the 50k array, the average absolute increase in accuracy across traits with 2GBLUP was 0.083 and 0.073 for purebred and crossbred animals, respectively, whereas with BayesRC it was 0.102 and 0.087. The average gain in accuracy was smaller when selected sequence variants were treated in the same category as 50k SNPs. Very little improvement over 50k prediction was observed when using all WGS variants. CONCLUSIONS:Accuracy of genomic prediction in diverse sheep populations increased substantially by using variants selected from whole-genome sequence data based on an independent multi-breed GWAS, when compared to genomic prediction using standard 50K genotypes.

Project description:BackgroundThe use of whole-genome sequence (WGS) data for genomic prediction and association studies is highly desirable because the causal mutations should be present in the data. The sequencing of 935 sheep from a range of breeds provides the opportunity to impute sheep genotyped with single nucleotide polymorphism (SNP) arrays to WGS. This study evaluated the accuracy of imputation from SNP genotypes to WGS using this reference population of 935 sequenced sheep.ResultsThe accuracy of imputation from the Ovine Infinium® HD BeadChip SNP (~ 500 k) to WGS was assessed for three target breeds: Merino, Poll Dorset and F1 Border Leicester × Merino. Imputation accuracy was highest for the Poll Dorset breed, although there were more Merino individuals in the sequenced reference population than Poll Dorset individuals. In addition, empirical imputation accuracies were higher (by up to 1.7%) when using larger multi-breed reference populations compared to using a smaller single-breed reference population. The mean accuracy of imputation across target breeds using the Minimac3 or the FImpute software was 0.94. The empirical imputation accuracy varied considerably across the genome; six chromosomes carried regions of one or more Mb with a mean imputation accuracy of < 0.7. Imputation accuracy in five variant annotation classes ranged from 0.87 (missense) up to 0.94 (intronic variants), where lower accuracy corresponded to higher proportions of rare alleles. The imputation quality statistic reported from Minimac3 (R2) had a clear positive relationship with the empirical imputation accuracy. Therefore, by first discarding imputed variants with an R2 below 0.4, the mean empirical accuracy across target breeds increased to 0.97. Although accuracy of genomic prediction was less affected by filtering on R2 in a multi-breed population of sheep with imputed WGS, the genomic heritability clearly tended to be lower when using variants with an R2 ≤ 0.4.ConclusionsThe mean imputation accuracy was high for all target breeds and was increased by combining smaller breed sets into a multi-breed reference. We found that the Minimac3 software imputation quality statistic (R2) was a useful indicator of empirical imputation accuracy, enabling removal of very poorly imputed variants before downstream analyses.

Project description:Copy number variation (CNV), an important source of genomic structural variation, can disturb genetic structure, dosage, regulation and expression, and is associated with phenotypic diversity and adaptation to local environments in mammals. In the present study, 24 resequencing datasets were used to characterize CNVs in three ecotypic populations of Tibetan sheep and assess CNVs related to domestication and adaptation in Qinghai-Tibetan Plateau. A total of 87,832 CNV events accounting for 0.3% of the sheep genome were detected. After merging the overlapping CNVs, 2777 CNV regions (CNVRs) were obtained, among which 1098 CNVRs were shared by the three populations. The average length of these CNVRs was more than 3 kb, and duplication events were more frequent than deletions. Functional analysis showed that the shared CNVRs were significantly enriched in 56 GO terms and 18 KEGG pathways that were mainly concerned with ABC transporters, olfactory transduction and oxygen transport. Moreover, 188 CNVRs overlapped with 97 quantitative trait loci (QTLs), such as growth and carcass QTLs, immunoglobulin QTLs, milk yield QTLs and fecal egg counts QTLs. PCDH15, APP and GRID2 overlapped with body weight QTLs. Furthermore, Vst analysis showed that RUNX1, LOC101104348, LOC105604082 and PAG11 were highly divergent between Highland-type Tibetan Sheep (HTS) and Valley-type Tibetan sheep (VTS), and RUNX1 and LOC101111988 were significantly differentiated between VTS and Oura-type Tibetan sheep (OTS). The duplication of RUNX1 may facilitate the hypoxia adaptation of OTS and HTS in Qinghai-Tibetan Plateau, which deserves further research in detail. In conclusion, for the first time, we represented the genome-wide distribution characteristics of CNVs in Tibetan sheep by resequencing, and provided a valuable genetic variation resource, which will facilitate the elucidation of the genetic basis underlying the distinct phenotypic traits and local adaptation of Tibetan sheep.

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Whole-Genome Sequence Data Suggest Environmental Adaptation of Ethiopian Sheep Populations.

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