Submitted to: Journal of Animal Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/17/2014
Publication Date: 9/1/2014
Citation: Schneider, J.F., Nonneman, D.J., Wiedmann, R.T., Vallet, J.L., Rohrer, G.A. 2014. Genomewide association and identification of candidate genes for ovulation rate in swine. Journal of Animal Science. 92(9):3792-3803.
Interpretive Summary: Reproductive efficiency has a great impact on the economic success of pork production and ovulation rate is a key early component of reproduction. In order to better understand the underlying genetics of ovulation rate, a study was undertaken to identify genetic markers associated with ovulation rate. Samples of DNA were collected from 1,180 females with ovulation rate measurements. DNA samples were processed and statistically evaluated. One hundred three genetic markers were found to be statistically significant and were located on all chromosomes except the Y (male) chromosome. The fifteen most significant gene markers explained nearly half of the known genetic variation. Five compelling genes were also located in the same regions where the markers were found. These genetic markers, when combined with information on genes found in the same regions, should provide information that could be used for marker assisted selection, marker assisted management, or genomic selection applications in large science-based commercial pig populations. Future work is required to enhance methods for marker assisted selection to be beneficial to many pork production systems.
Technical Abstract: Reproductive efficiency has a great impact on the economic success of pork production. Ovulation rate (OR) is an early component of reproduction efficiency and contributes to the number of pigs born in a litter. To better understand the underlying genetics of ovulation rate, a genome-wide association study (GWAS) was undertaken. Samples of DNA were collected and tested using a 60,000 SNP marker panel from 1,180 females with OR measurements ranging from never farrowed (P0) to measurements taken after parity 2 (P2). A total of 41,848 SNP were tested using the Bayes C option of GENSEL, a freely available software for genomic analyses. After the Bayes C analysis, SNP were assigned to sliding windows of five consecutive SNP by chromosome-position order beginning with the first five SNP on SSC1 and ending with the last five SNP on SSCX. The five-SNP windows were defined as putative QTL and analyzed using the Predict option of GENSEL. From the Predict analysis, putative QTL were selected having no overlap with other five-SNP groups, no overlap across chromosomes, and the highest genetic variation. These putative QTL were submitted to statistical testing utilizing the bootstrap option of GENSEL. Of the available 8,294 putative QTL, 103 were found to be statistically significant (P < 0.01). Ten QTL were found on SSC1, sixteen on SSC2, four on SSC3, one on SSC4, four on SSC5, four on SSC6, eleven on SSC7, ten on SSC8, two on SSC9, one on SSC10, three on SSC11, one on SSC12, six on SSC13, three on SSC14, eight on SSC15, six on SSC16, eight on SSC17, three on SSC18, and two on SSCX. Fifteen QTL were found to be statistically significant at the P < 0.001 level. These 15 QTL explained 47.91% of the total QTL variance. The most compelling candidate genes these regions include ESR1, FSHB, GDF9, activin receptor ACVR1B and inhibin beta A. These QTL when combined with information on genes found in the same regions should provide useful information that could be used for marker assisted selection, marker assisted management, or genomic selection applications in commercial pig populations.