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ARS Home » Plains Area » Clay Center, Nebraska » U.S. Meat Animal Research Center » Genetics and Animal Breeding » Research » Publications at this Location » Publication #360379

Research Project: Identifying Genomic Solutions to Improve Efficiency of Swine Production

Location: Genetics and Animal Breeding

Title: Evaluation of genotype quality parameters for SowPro90, a new genotyping array for swine

item WIJESENA, HIRUNI - University Of Nebraska
item Rohrer, Gary
item Nonneman, Danny - Dan
item Keel, Brittney
item PETERSEN, JESSICA - University Of Nebraska
item KACHMAN, STEVE - University Of Nebraska
item CIOBANU, DANIEL - University Of Nebraska

Submitted to: Journal of Animal Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/24/2019
Publication Date: 8/1/2019
Citation: Wijesena, H.R., Rohrer, G.A., Nonneman, D.J., Keel, B.N., Petersen, J.L., Kachman, S.D., Ciobanu, D.C. 2019. Evaluation of genotype quality parameters for SowPro90, a novel genotyping platform for swine. Journal of Animal Science. 97(8):3262-3273.

Interpretive Summary: A new single nucleotide polymorphism (SNP) genotyping platform has been created for pigs named SowPro90. This platform contains most of the important SNP from the widely used Porcine SNP60 BeadArray as well as a large number of SNP in important regions for reproduction and SNP believed to affect gene expression. Distribution of genotype quality across different platforms tends to differ, likely due to different chemistries and allelic detection approaches used for genotyping. Therefore, a comparison between the different platforms was conducted. Results indicate it is not ideal to use the same threshold parameters for quality evaluations across different genotyping platforms. The approach used in this study utilizing the concordance between genotypes generated by two different platforms allowed us to identify specific quality thresholds to retain the maximum amount of information and the highest quality. This strategy will be helpful in identifying high quality genotypes across different platforms when integrating data from various genotyping sources for genomic evaluations. The performance of SowPro90 indicated it will be a very useful tool for swine breeders to use for selection of female replacement animals.

Technical Abstract: Understanding early predictors of sow fertility the potential to improve genomic predictions. A custom SNP array (SowPro90 produced by Affymetrix) was developed to include genetic variants overlapping quantitative trait loci for age at puberty, one of the earliest indicators of sow fertility, as well as variants related to innante and adaptive immunity. The polymorphisms included in the custom genotyping array were identified using multiple genomic approaches including deep genomic and transcriptomic sequencing and genome-wide associations. Animals from research and commercial populations (n = 2,586) were genotyped for 103,476 SNP included in SowPro90. To assess the quality of data generated, genotype concordance was evaluated between the SowPro90 and Porcine SNP60 BeadArray using a subset of common SNP (n = 44,708) and animals (n = 277). The mean genotype concordance rate per SNP was 98.4%. Differences in distribution of data quality were observed between the platforms indicating the need for platform specific thresholds for quality parameters. The optimal thresholds for SowPro90 (>=97% SNP and >=93% sample call rate) were obtained by analyzing the data quality distribution and genotype concordance per SNP across platforms. At =97% SNP call rate, there were 42,151 SNPs (94.3%) retained with a mean genotype concordance of 98.6% across platforms. Similarly, =94% SNPs and =85% sample call rates were established as thresholds for Porcine SNP60 BeadArray. At =94% SNPs call rate, there were 41,043 SNPs (91.8%) retained with a mean genotype concordance of 98.6% across platforms. Final evaluation of SowPro90 array content (n = 103,476) at =97% SNPs and =93% sample call rates allowed retention of 89,040 SNPs (86%) for downstream analysis. The findings and strategy for quality control could be helpful in identifying consistent, high-quality genotypes for genomic evaluations, especially when integrating genotype data from different platforms.