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Sheep show characteristics of phenotypic diversity and adaptation to diverse climatic regions. Previous studies indicated associations between copy number variations (CNVs) and climate-driven adaptive evolution in humans and other domestic animals. Here, we constructed a genomic landscape of CNVs (n = 39,145) in 47 old autochthonous populations genotyped at a set of high-density (600 K) SNPs to detect environment-driven signatures of CNVs using a multivariate regression model. We found 136 deletions and 52 duplications that were significantly (Padj. < 0.05) associated with climatic variables. These climate-mediated selective CNVs are involved in functional candidate genes for heat stress and cold climate adaptation (e.g., B3GNTL1, UBE2L3, and TRAF2), coat and wool-related traits (e.g., TMEM9, STRA6, RASGRP2, and PLA2G3), repairing damaged DNA (e.g., HTT), GTPase activity (e.g., COPG), fast metabolism (e.g., LMF2 and LPIN3), fertility and reproduction (e.g., SLC19A1 and CCDC155), growth-related traits (e.g., ADRM1 and IGFALS), and immune response (e.g., BEGAIN and RNF121) in sheep. In particular, we identified significant (Padj. < 0.05) associations between probes in deleted/duplicated CNVs and solar radiation. Enrichment analysis of the gene sets among all the CNVs revealed significant (Padj. < 0.05) enriched gene ontology terms and pathways related to functions such as nucleotide, protein complex, and GTPase activity. Additionally, we observed overlapping between the CNVs and 140 known sheep QTLs. Our findings imply that CNVs can serve as genomic markers for the selection of sheep adapted to specific climatic conditions. [ABSTRACT FROM AUTHOR]

Genomic assessment of local sheep breeds is relevant to the reconstruction of post-domestication migrations and to filling in gaps in the developmental history and contemporary phylogeographic-differentiation patterns in Eurasia. In this study, we aimed to reveal possible genetic relationships between local sheep breeds in Russia and the Persian Highlands (Iran) based on high-density SNP genotypes. All samples (n = 395) from 11 Iranian and 10 Russian sheep breeds were genotyped by using the Ovine Infinium HD BeadChip (Illumina, San Diego, CA, USA). Principal Component Analysis, maximum-likelihood assessment, and Neighbor-Net graph analysis demonstrated a clear differentiation between Russian sheep breeds of European ancestry from the Iranian local cluster and Russian breeds of Asian origin. Admixture analysis revealed a shared ancestral background, which was detected in several Iranian-local and Russian-local fat-tailed coarse-wool sheep breeds. Our findings contribute to a better understanding of the pattern of historic admixture, which is present in the genomes of many Eurasian sheep breeds. [ABSTRACT FROM AUTHOR]

The Quantitative Trait Loci (QTL) mapping problem aims to identify regions in the genome that are linked to phenotypic features of the developed organism that vary in degree. It is a principle step in determining targets for further genetic analysis and is key in decoding the role of specific genes that control quantitative traits within species. Applications include identifying genetic causes of disease, optimization of cross-breeding for desired traits and understanding trait diversity in populations. In this paper a new multi-objective evolutionary algorithm (MOEA) method is introduced and is shown to increase the accuracy of QTL mapping identification for both independent and epistatic loci interactions. The MOEA method optimizes over the space of possible partial least squares (PLS) regression QTL models and considers the conflicting objectives of model simplicity versus model accuracy. By optimizing for minimal model complexity, MOEA has the advantage of solving the over-fitting problem of conventional PLS models. The effectiveness of the method is confirmed by comparing the new method with Bayesian Interval Mapping approaches over a series of test cases where the optimal solutions are known. This approach can be applied to many problems that arise in analysis of genomic data sets where the number of features far exceeds the number of observations and where features can be highly correlated. [ABSTRACT FROM AUTHOR]