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

作者：Shuang Xie, Karissa Isaacs, Gabrielle M. Becker, Brenda M. Murdoch · 发表于：Journal of Animal Science and Biotechnology/Journal of animal science and biotechnology · 年份：2023 · DOI：10.1186/s40104-023-00923-3 · 被引用次数：20 · 研究领域：Genetic and phenotypic traits in livestock、Genetic Associations and Epidemiology、Milk Quality and Mastitis in Dairy Cows

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 c...