RESUMO
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 concordant SNPs and Indels from Freebayes and GATK after our new method were significantly improved 12%-32% compared with raw variants and advantageously found low frequency variants of individual sheep involved several traits including nipples number (GPC5), scrapie pathology (PAPSS2), seasonal reproduction and litter size (GRM1), coat color (RAB27A), and lentivirus susceptibility (TMEM154). CONCLUSION: The new method used the computational strategy to reduce the number of false positives, and simultaneously improve the identification of genetic variants. This strategy did not incur any extra cost by using any additional samples or sequencing data information and advantageously identified rare variants which can be important for practical applications of animal breeding.
RESUMO
Adoption of electronic identification ear tags (EID) and DNA testing by commercial range sheep producers in the Western United States has been low, despite the availability of these technologies for over a decade. Jointly, these technologies offer an approach to provide individual animal performance data to improve flock health, genetic and reproductive management. This project involved a collaboration with five California sheep producers representing a broad geographic range, varying levels of pre-project EID adoption, and diverse operational practices. Tissue samples were collected from, and ear EIDs were placed in, a total of 2,936 rams and their potential lambs. We partnered with a commercial packing company, Superior Farms, to genotype the animals. Superior Farms used a targeted genotyping panel to assign parentage, and link individual animal identification (ID) to camera-graded carcass measurements. This enabled the collection of individual progeny carcass data and provided insight into sire performance, providing for the within-flock identification of prolific sires that were producing lambs with significantly more saleable meat as compared to their flock mates. Overall, almost 91% of lambs were successfully matched to their sire, and prolificacy ranging from 0-135 lambs per ram. There was as much as an $80 difference in the average edible product from camera-graded carcasses derived from lamb groups sired by different rams. A partial budget analysis modeling investment in an EID system coupled with an autodrafter and scale to collect individual weights and improve labor efficiency during processing, and a sheep flip chute to improve worker safety during foot trimmings, yielded a greater than 7:1 return on investment over a five-year time frame. Ideally, the data collection enabled by EIDs and DNA testing would feed into data-driven genetic evaluation programs to enable selection for more productive and profitable animals, and allow the US sheep industry to accelerate the rate of genetic improvement.
