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Title:   Author  item
                                            Rohrer, Garyitem
                                            Rempel, Leaitem
                                            Miles, Jeremyitem
                                            Keele, Johnitem
                                            Wiedmann, Ralphitem
                                            Vallet, Jeff
  Submitted to: Animal Genetics
                                    Publication Type: Peer Reviewed Journal
                                    Publication Acceptance Date: 12/17/2013
                                    Publication  Date: 6/1/2014
                                    Publication URL: http://handle.nal.usda.gov/10113/59655Citation: Rohrer, G.A., Rempel, L.A., Miles, J.R., Keele, J.W., Wiedmann, R.T., Vallet, J.L. 2014. Identifying genetic loci controlling neonatal passive transfer of immunity using a hybrid genotyping strategy. Animal Genetics. 45(3):340-349.Interpretive Summary: Consumption of colostrum is critical to the survival of the newborn piglet. If genetic markers able to predict a piglet’s ability to acquire colostrum were available, producers could then apply marker-assisted selection to improve this trait and thus reduce piglet deaths. Therefore, we scanned the genome for regions that contain genes affecting a piglet’s ability to acquire colostrum. Our approach pooled DNA samples from piglets with high values for colostrum consumption (immunocrit value) and compared the allele frequencies with frequencies estimated in DNA pools of animals with low immunocrit values. The pooled DNA samples were assayed with the Illumina Porcine SNP60 BeadChip where over 60,000 different SNP markers were evaluated. Thirty-seven SNP markers selected from the results of the pooled DNA analysis were successfully evaluated in 1,512 individually genotyped pigs. The association analyses found nine significant SNP markers located in 8 different regions of the pig genome. For two locations, the significant markers were located near a gene previously known to regulate appetite. These results indicate it is possible to develop markers that can improve piglet immunocrit values and also implicate appetite regulation as one of the critical biological functions controlling a piglet’s ability to acquire colostrum.
                                Technical Abstract: Colostrum intake is critical to a piglet’s survival and can be measured by precipitating out the gamma-immunoglobulins from serum with ammonium sulfate (immunocrit). Genetic analysis of immunocrits on 5,312 piglets indicated that the heritabilities (se) for direct and maternal effects were 0.13(0.06) and 0.53(0.08), respectively. To identify quantitative trait loci (QTL) for direct genetic effects, piglets with the highest and lowest immunocrits from 470 litters were selected. Six sets of DNA pools were created based on sire of the litter. These 12 DNA pools were applied to Illumina Porcine SNP60 BeadChips. Normalized X and Y values were analyzed. Three different SNP selection methods were used: deviation of the mean from high versus low pools, the deviation adjusted for variance based on binomial theory, and ANOVA. The 25 highest ranking SNP were selected from each evaluation for further study along with 12 regions selected based on a 5-SNP window approach. Selected SNP were individually genotyped in the 988 piglets included in pools as well as 524 piglets that had intermediate immunocrits. Association analyses were conducted fitting an animal model using the estimated genetic parameters. Nineteen SNP were nominally associated (P < 0.01) with immunocrit values, of which 9 remained significant (P < 0.05) after Bonferroni correction, located in 16 genomic regions on 13 chromosomes. In conclusion, the pooling strategy reduced the cost to scan the genome by more than 80% and identified genomic regions associated with a piglet’s ability to acquire gamma-immunoglobulin from colostrum. Each method to rank SNP from the pooled analyses contributed unique validated markers suggesting that multiple analyses will reveal more QTL than a single analysis.


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