外文摘要：Large genotyping datasets, obtained from high-density single nucleotide polymorphism (SNP) arrays, developed for different livestock species, can be used to describe and differentiate breeds or populations. To identify the most discriminating genetic markers among thousands of genotyped SNPs, a few statistical approaches have been proposed. In this study, we applied the Boruta algorithm, a wrapper of the machine learning random forest algorithm, on a database of 23 European pig breeds (20 autochthonous and three cosmopolitan breeds) genotyped with a 70k SNP chip, to pre-select informative SNPs. To identify different sets of SNPs, these pre-selected markers were then ranked with random forest based on their mean decrease accuracy and mean decrease gene indexes. We evaluated the efficiency of these subsets for breed classification and the usefulness of this approach to detect candidate genes affecting breed-specific phenotypes and relevant production traits that might differ among breeds. The lowest overall classification error (2.3%) was reached with a subpanel including only 398 SNPs (ranked based on their mean decrease accuracy), with no classification error in seven breeds using up to 49 SNPs. Several SNPs of these selected subpanels were in genomic regions in which previous studies had identified signatures of selection or genes associated with morphological or production traits that distinguish the analysed breeds. Therefore, even if these approaches have not been originally designed to identify signatures of selection, the obtained results showed that they could potentially be useful for this purpose.
外文关键词：random forest;SNP;Sus scrofa;genome;population genomics;signatures of selection
作者：Candek-Potokar, Marjeta;Schiavo, Giuseppina;Bertolini, Francesca;Bovo, Samuele;Galimberti, Giuliano;Munoz, Maria;Bozzi, Riccardo;Ovilo, Cristina;Fontanesi, Luca
作者单位：Univ Bologna;INIA;Univ Firenze;Agr Inst Slovenia
期刊名称：ANIMAL GENETICS
期刊影响因子：0.0
出版年份：2024
出版刊次：55（2）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台