Evaluation of SNP genotyping in alpacas using the bovine HD genotyping beadchip

Alpacas are one of four South American Camelid species living in the highlands of the Andes. Production of alpaca fiber contributes to the economy of the region and the livelihood of many rural families. Fiber quantity and quality are important and in need of a modern breeding program based on genomic selection to accelerate genetic gain. To achieve this is necessary to discover enough molecular markers, single nucleotide polymorphisms (SNPs) in particular, to provide genome coverage and facilitate genome wide association studies to fiber production characteristics. The aim of this study was to discover alpaca SNPs by genotyping forty alpaca DNA samples using the BovineHD Genotyping Beadchip. Data analysis was performed with GenomeStudio (Illumina) software. Because different filters and thresholds are reported in the literature we investigated the effects of no-call threshold (≥0.05, ≥0.15, and ≥0.25) and call frequency (≥0.9 and =1.0) in identifying positive SNPs. Average GC Scores, calculated as the average of the 10% and 50% GenCall scores for each SNP (≥0.70) and the GenTrain score ≥ 0.25 parameters were applied to all comparisons. SNPs with minor allele frequency (MAF) ≥ 0.05 or ≥ 0.01 were retained. Since detection of SNPs is based on the stable binding of oligonucleotide probes to the target DNA immediately adjacent to the variant nucleotide, all positive SNP flanking sequences showing perfect alignments between the bovine and alpaca genomes for the first 21 or 26 nucleotides flanking the variant nucleotide at either side were selected. Only SNPs localized in one scaffold were assumed unique. Unique SNPs identified in both reference genomes were kept and mapped on the Vicugna_pacos 2.0.2 genome. The effects of the no-call threshold ≥ 0.25, call frequency = 1 and average GC ≥ 0.7 were meaningful and identified 6756 SNPs of which 400 were unique and polymorphic (MAF ≥ 0.01). Assignment to alpaca chromosomes was possible for 292 SNPs. Likewise, 209 SNPs were localized in 202 alpaca gene loci and 29 of these share the same loci with the dromedary. Interestingly, 69 of 400 alpaca SNPs have 100% similarity with dromedary

Using Classification Trees to Detect Induced Sow Lameness with a Transient Model

Feet and legs issues are some of the main causes for sow removal in the US swine industry. More timely lameness detection among breeding herd females will allow better treatment decisions and outcomes. Producers will be able to treat lame females before the problem becomes too severe and cull females while they still have salvage value. The objective of this study was to compare the predictive abilities and accuracies of weight distribution and gait measures relative to each other and to a visual lameness detection method when detecting induced lameness among multiparous sows. Developing an objective lameness diagnosis algorithm will benefit animals, producers and scientists in timely and effective identification of lame individuals as well as aid producers in their efforts to decrease herd lameness by selecting animals that are less prone to become lame. In the early stages of lameness, weight distribution and gait are impacted. Lameness was chemically induced for a short time period in 24 multiparous sows and their weight distribution and walking gait were measured in the days following lameness induction. A linear mixed model was used to determine differences between measurements collected from day to day. Using a classification tree analysis, it was determined that the mean weight being placed on each leg was the most predictive measurement when determining whether the leg was sound or lame. The classification tree’s predictive ability decreased as the number of days post-lameness induction increased. The weight distribution measurements had a greater predictive ability compared with the gait measurements. The error rates associated with the weight distribution trees were 29.2% and 31.3% at 6 days post-lameness induction for front and rear injected feet, respectively. For the gait classification trees, the error rates were 60.9% and 29.8% at 6 days post-lameness induction for front and rear injected feet, respectively. More timely lameness detection can improve sow lifetime productivity as well as animal welfare

Genetic associations for gilt growth, compositional, and structural soundness traits with sow longevity and lifetime reproductive performance

The objective of this study was to estimate genetic associations for gilt growth, compositional, and structural soundness with sow longevity and lifetime reproduction. Performance and pedigree information from 1,447 commercial females from 2 genetic lines were included in the data analyzed. Growth was expressed as days to 113.5 kg BW (DAYS) and compositional traits included loin muscle area (LMA), 10th rib backfat (BF10), and last rib backfat (LRF). Structural soundness traits included body structure traits [length (BL), depth (BD), width (BWD), rib shape (BRS), top line (BTL), and hip structure (BHS)], leg structure traits [front legs: legs turned (FLT), buck knees (FBK), pastern posture (FPP), foot size (FFS), and uneven toes (FUT); rear legs: legs turned (RLT), leg posture (RLP), pastern posture (RPP), foot size (RFS), and uneven toes (RUT)], and overall leg action (OLA). Lifetime (LT) and removal parity (RP) were considered as longevity traits whereas lifetime reproductive traits included lifetime total number born (LNB), lifetime number born alive (LBA), number born alive per lifetime day (LBA/LT), and percentage productive days from total herd days (PD%). Genetic parameters were estimated with linear animal models using the average information REML algorithm. Second, to account for censored longevity and lifetime reproduction records, genetic parameters were estimated using Markov Chain Monte Carlo and Gibbs sampling methods. Similar estimates were obtained across the analysis methods. Heritability estimates for growth and compositional traits ranged from 0.50 to 0.70 and for structural soundness traits from 0.07 to 0.31. Longevity and lifetime reproductive trait heritability estimates ranged from 0.14 to 0.17 when REML was used. Unfavorable genetic correlations were obtained for DAYS with LT, RP, LNB, LBA, and PD% and for LRF with PD%. However, LMA was favorably associated with LT, RP, and LNB. Moderate to high correlations were obtained for BL and BRS with all longevity and lifetime reproductive traits. Correlations of BWD with LT and RP were moderate. Associations for leg soundness traits with longevity and lifetime reproductive traits were mainly low and nonsignificant (P ≥ 0.10). However, RLP was moderately correlated with LBA/LT and PD%. Current results indicate that selection for fewer DAYS has an antagonistic effect on lifetime performance. Furthermore, great BL, flat BRS, narrow BWD, and upright RLP seem detrimental to sow longevity and lifetime reproduction

Gene Discovery and Functional Genomics in the Pig

Advances in gene mapping and genomics in farm animals have been considerable over the past decade. Medium resolution linkage and physical maps have been reported, and specific chromosomal regions and genes associated with traits of biological and economic interest have been identified. We have reached an exciting stage in gene identification, mapping and quantitative trait locus discovery in pigs, as new molecular information is accumulating rapidly. Significant progress has been made by identifying candidate gene associations and low-resolution regions containing quantitative trail loci (QTL). However, we are still disadvantaged by the lack of tools available to efficiently use much of this new information. For example, current pig maps are neither of high enough resolution nor sufficiently informative at the comparative level for positional candidate gene cloning within QTL regions. As well, studying biological mechanisms underlying economically important traits such as reproduction is limited by the lack of molecular resources. This is especially important, as reproduction is very difficult to genetically improve by classical breeding methods due to the relatively low heritability and high expense in data collection. Thus, an improved understanding of porcine reproductive biology is of crucial economic importance, yet reproductive processes are poorly characterized at the molecular level. Recently, new methodologies have been brought to bear on a better understanding of pig molecular biology for accelerating genetic improvement in pigs. Several groups are developing molecular information in the pig, and the total Genbank sequence entries for porcine expressed genes have recently topped 100,000. Our Midwest EST Consortium has produced cDNA libraries containing the majority of genes expressed in major female reproductive tissues, and we have deposited nearly 15,000 gene sequences into public databases. These sequences represent over 8,900 different genes, based on sequence comparison among these data. Furthermore, we have developed computer software to automatically extract sequence similarity of these pig genes with their human counterparts, as well as the mapping information of these human homologues. Within our data set, we have identified nearly 1,500 pig genes with strong similarity to mapped human genes, and we are in the process of mapping 700 of these genes to improve the human-pig comparative map. This work and the complementary work of others can now be used to more rapidly understand and identify the genes controlling reproduction, so that genetic improvement of reproduction phenotypes can accelerate

Association and Haplotype Analyses of Positional Candidate Genes in Five Genomic Regions Linked to Scrotal Hernia in Commercial Pig Lines

Scrotal hernia in pigs is a complex trait likely affected by genetic and environmental factors. A large-scale association analysis of positional and functional candidate genes was conducted in four previously identified genomic regions linked to hernia susceptibility on Sus scrofa chromosomes 2 and 12, as well as the fifth region around 67 cM on chromosome 2, respectively. In total, 151 out of 416 SNPs discovered were genotyped successfully. Using a family-based analysis we found that four regions surrounding ELF5, KIF18A, COL23A1 on chromosome 2, and NPTX1 on chromosome 12, respectively, may contain the genetic variants important for the development of the scrotal hernia in pigs. These findings were replicated in another case-control dataset. The SNPs around the ELF5 region were in high linkage disequilibrium with each other, and a haplotype containing SNPs from ELF5 and CAT was highly significantly associated with hernia development. Extensive resequencing work focused on the KIF18A gene did not detect any further SNPs with extensive association signals. These genes may be involved in the estrogen receptor signaling pathway (KIF18A and NPTX1), the epithelial-mesenchymal transition (ELF5) and the collagen metabolism pathway (COL23A1), which are associated with the important molecular characteristics of hernia pathophysiology. Further investigation on the molecular mechanisms of these genes may provide more molecular clues on hernia development in pigs