Polydactyl inheritance in the pig.

Two pigs were identified having "extra feet" (preaxial polydactyly) within a purebred population of Yorkshire pigs. Polydactyly is an inherited disorder in many species that may be controlled by either recessive or dominant genes. Experimental matings were conducted using pigs that had produced affected offspring with the result of 12 polydactyl offspring out of 95 piglets. One polydactyl-producing boar was also mated to 4 Duroc sows and 8 distantly related Yorkshire sows to produce 129 unaffected offspring. Together, these results suggest a recessive mode of inheritance, possibly with incomplete penetrance. Candidate genes, LMBR1, EN2, HOXA10-13, GLI3, WNT2, WNT16, and SHH, were identified based on association with similar phenotypes in other species. Homologues for these genes are all found on SSC18. Sequencing and linkage studies showed no evidence for association with HOXA10-13, WNT2, and WNT16. Results for the regions including GLI3, LMBR1, and SHH, however, were inconclusive. A whole genome scan was conducted on DNA samples from 10 affected pigs and 12 close relatives using the Illumina PorcineSNP60 BeadChip and compared with 69 more distantly related animals in the same population. No evidence was found for a major gene causing polydactyly. However, a 25-Mb stretch of homozygosity on SSC8 was identified as fairly unique to the family segregating for this trait. Therefore, this chromosome segment may play a role in development of polydactyly in concert with other genes.

Mining ESTs to determine the usefulness of SNPs across shrimp species.

Expressed sequence tag (EST) libraries from members of the Penaeidae family and brine shrimp (Artemia franciscana) are currently the primary source of sequence data for shrimp species. Penaeid shrimp are the most commonly farmed worldwide, but selection methods for improving shrimp are limited. A better understanding of shrimp genomics is needed for farmers to use genetic markers to select the best breeding animals. The ESTs from Litopenaeus vannamei have been previously mined for single nucleotide polymorphisms (SNPs). This present study took publicly available ESTs from nine shrimp species, excluding L. vannamei, clustered them with CAP3, predicted SNPs within them using SNPidentifier, and then analyzed whether the SNPs were intra- or interspecies. Major goals of the project were to predict SNPs that may distinguish shrimp species, locate SNPs that may segregate in multiple species, and determine the genetic similarities between L. vannamei and the other shrimp species based on their EST sequences. Overall, 4,597 SNPs were predicted from 4,600 contigs with 703 of them being interspecies SNPs, 735 of them possibly predicting species' differences, and 18 of them appearing to segregate in multiple species. While sequences appear relatively well conserved, SNPs do not appear to be well conserved across shrimp species.

Whole Genome Association Studies of Residual Feed Intake and Related Traits in the Pig.

BACKGROUND: Residual feed intake (RFI), a measure of feed efficiency, is the difference between observed feed intake and the expected feed requirement predicted from growth and maintenance. Pigs with low RFI have reduced feed costs without compromising their growth. Identification of genes or genetic markers associated with RFI will be useful for marker-assisted selection at an early age of animals with improved feed efficiency. METHODOLOGY/PRINCIPAL FINDINGS: Whole genome association studies (WGAS) for RFI, average daily feed intake (ADFI), average daily gain (ADG), back fat (BF) and loin muscle area (LMA) were performed on 1,400 pigs from the divergently selected ISU-RFI lines, using the Illumina PorcineSNP60 BeadChip. Various statistical methods were applied to find SNPs and genomic regions associated with the traits, including a Bayesian approach using GenSel software, and frequentist approaches such as allele frequency differences between lines, single SNP and haplotype analyses using PLINK software. Single SNP and haplotype analyses showed no significant associations (except for LMA) after genomic control and FDR. Bayesian analyses found at least 2 associations for each trait at a false positive probability of 0.5. At generation 8, the RFI selection lines mainly differed in allele frequencies for SNPs near (<0.05 Mb) genes that regulate insulin release and leptin functions. The Bayesian approach identified associations of genomic regions containing insulin release genes (e.g., GLP1R, CDKAL, SGMS1) with RFI and ADFI, of regions with energy homeostasis (e.g., MC4R, PGM1, GPR81) and muscle growth related genes (e.g., TGFB1) with ADG, and of fat metabolism genes (e.g., ACOXL, AEBP1) with BF. Specifically, a very highly significantly associated QTL for LMA on SSC7 with skeletal myogenesis genes (e.g., KLHL31) was identified for subsequent fine mapping. CONCLUSIONS/SIGNIFICANCE: Important genomic regions associated with RFI related traits were identified for future validation studies prior to their incorporation in marker-assisted selection programs.