Submitted to: Journal of Animal Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: March 29, 2012
Publication Date: October 1, 2012
Repository URL: http://handle.nal.usda.gov/10113/57881
Citation: Schneider, J.F., Rempel, L.A., Snelling, W.M., Wiedmann, R.T., Nonneman, D.J., Rohrer, G.A. 2012. Genome-wide association study of swine farrowing traits. Part II: Bayesian analysis of marker data. Journal of Animal Science. 90(10):3360-3367. Interpretive Summary: Improvement in the efficiency of swine production has become more important as the costs have risen in recent years. The purpose of this study was to use the Illumina PorcineSNP60 BeadChip to study the relationship between 64,000 gene markers and swine farrowing traits known to have an impact on reproductive efficiency. A total of 124 statistically significant areas of gene expression (groups of 5 gene markers) were found to be associated with 1 or more farrowing traits; 14 with number born alive, 1 with number born dead, 11 with total number born, 33 with total litter weight, and 65 with average piglet birth weight. These groups of gene markers, when combined with information on the actual genes found in the same regions, should provide useful information that could be used in various types of genomic selection applications in commercial pig populations. Selection may be further enhanced in the future by identification of additional gene markers in the areas of statistical significance found by this study.
Technical Abstract: Reproductive efficiency has a great impact on the economic success of pork production. Number born alive (NBA) and average piglet birth weight (ABW) contribute greatly to reproductive efficiency. To better understand the underlying genetics of birth traits, a genome wide association study (GWAS) was undertaken. DNA samples were collected and tested using the Illumina PorcineSNP60 BeadChip from 1,152 parity 1 gilts. Traits included total number born (TNB), NBA, number born dead (NBD), number still born (NSB), number of mummies (MUM), total litter birth weight (LBW) and ABW. SNP totaling 41,151 were tested using a Bayesian approach. SNP were then assigned to groups of 5 consecutive SNP by chromosome-position order beginning with the first 5 SNP on chromosome 1 and ending with the last 5 SNP on the X chromosome and analyzed again using a Bayesian approach. From that analysis, 5-SNP groups were selected having no overlap with another 5-SNP group, and no overlap across chromosomes. These selected 5-SNP non-overlapping groups were defined as QTL. Of the available 8,814 QTL, 124 were found to be statistically significant (P < 0.01). Multiple testing was considered using the probability of false positives. Eleven QTL were found for TNB, 3 on SSC 1, 3 on SSC 4, 1 on SSC 13, 1 on SSC 14, 2 on SSC 15 and 1 on SSC 17. Statistical testing for NBA identified 14 QTL, 4 on SSC 1, 1 on SSC 4, 1 on SSC 6, 1 on SSC 10, 1 on SSC 13, 3 on SSC 15 and 3 on SSC 17. A single NBD QTL was found on SSC 11. No QTL were identified for NSB or MUM. Thirty three QTL were found for LBW, 3 on SSC 1, 1 on SSC 2, 1 on SSC 3, 5 on SSC 4, 2 on SSC 5, 5 on SSC6, 3 on SSC 7, 2 on SSC 9, 1 on SSC 10, 2 on SSC 14, 6 on SSC 15 and 2 on SSC 17. A total of 65 QTL were found for ABW, 9 on SSC 1, 3 on SSC 2, 9 on SSC 5, 5 on SSC 6, 1 on SSC 7, 2 on SSC 8, 2 on SSC 9, 3 on SSC 10, 1 on SSC 11, 3 on SSC 12, 2 on SSC 13, 8 on SSC 14, 8 on SSC 15, 1 on SSC 17 and 8 on SSC 18. Several candidate genes have been identified that overlap QTL locations among TNB, NBA, NBD, and ABW. 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.