Submitted to: Journal of Animal Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: January 16, 2007
Publication Date: May 1, 2007
Citation: Kuehn, L.A., Rohrer, G.A., Nonneman, D.J., Thallman, R.M., Leymaster, K.A. 2007. Detection of single nucleotide polymorphisms associated with ultrasonic backfat depth in a segregating Meishan x White Composite population. Journal of Animal Science. 85:1111-1119. Interpretive Summary: Backfat depth as a measure of carcass leanness and growth efficiency is an economically relevant trait in swine production. Genetic markers provide a tool for producers in both the seedstock and commercial sectors. Therefore, the objective of this study was to detect genetic markers for backfat depth. Candidate markers were selected from chromosomal regions that were previously shown to affect backfat depth. Each candidate marker was evaluated to determine whether it had an effect on backfat depth. Markers associated with backfat depth were identified on chromosomes 3, 7, and X. These results confirm that development of genetic markers for selection on backfat depth is possible. After validation in other populations, these markers can be used in commercial production of swine breeding stock.
Technical Abstract: Multiple genomic scans have identified QTL for backfat deposition across the porcine genome. The objective of this study was to detect SNP and genomic regions associated with ultrasonic backfat. A total of 75 SNP across 5 different chromosomes (SSC 1, 3, 7, 8, and 10) were selected based on their proximity to backfat QTL or to QTL for other traits of interest in the experimental population. Gilts were also genotyped for a SNP thought to influence backfat in the thyroxine-binding globulin gene (TBG) on SSCX. Genotypic data were collected on 298 gilts, divided between the F8 and F10 generations of the USMARC Meishan resource population (¼ Meishan composition). Backfat depths were recorded from three locations along the back at approximately 210 and 235 d of age in the F8 and F10 generations, respectively. Ultrasound measures were averaged for association analyses. Regressors for additive, dominant, and imprinting effects of each SNP were calculated using genotypic probabilities computed by allelic peeling algorithms in GenoProb. The association model included fixed effects of scan date and TBG genotype, covariates of weight and SNP regressors, and random additive polygenic effects to account for genetic similarities between animals not explained by known genotypes. Variance components for polygenic effects and error were estimated using MTDFREML. Initially, each SNP was fitted (once with and once without imprinting effects) separately due to potential multicollinearity between regressions of closely-linked markers. All significant SNP across chromosomes were included in a common model and individually removed in successive iterations based on significance to form a final model. Across all analyses, TBG was significant with an additive effect of approximately 1.2 to 1.6 mm of backfat. Three SNP on SSC3 remained in the final model even though few studies have identified QTL for backfat on this chromosome. Two of these SNP exhibited irregular imprinting effects and may not have been detected in other genome scans. One significant SNP on SSC7 remained in the final, backward-selected model; the estimated effect of this marker was similar in magnitude and direction to previously identified QTL. This SNP can potentially be used to introgress the leaner Meishan allele into commercial swine populations.