R. O. Bates

Estimation of linkage disequilibrium in four US pig breeds

BackgroundThe success of marker assisted selection depends on the amount of linkage disequilibrium (LD) across the genome. To implement marker assisted selection in the swine breeding industry, information about extent and degree of LD is essential. The objective of this study is to estimate LD in four US breeds of pigs (Duroc, Hampshire, Landrace, and Yorkshire) and subsequently calculate persistence of phase among them using a 60 k SNP panel. In addition, we report LD when using only a fraction of the available markers, to estimate persistence of LD over distance.ResultsAverage r2 between adjacent SNP across all chromosomes was 0.36 for Landrace, 0.39 for Yorkshire, 0.44 for Hampshire and 0.46 for Duroc. For markers 1 Mb apart, r2 ranged from 0.15 for Landrace to 0.20 for Hampshire. Reducing the marker panel to 10% of its original density, average r2 ranged between 0.20 for Landrace to 0.25 for Duroc. We also estimated persistence of phase as a measure of prediction reliability of markers in one breed by those in another and found that markers less than 10 kb apart could be predicted with a maximal accuracy of 0.92 for Landrace with Yorkshire.ConclusionsOur estimates of LD, although in good agreement with previous reports, are more comprehensive and based on a larger panel of markers. Our estimates also confirmed earlier findings reporting higher LD in pigs than in American Holstein cattle, especially at increasing marker distances (> 1 Mb). High average LD (r2 > 0.4) between adjacent SNP found in this study is an important precursor for the implementation of marker assisted selection within a livestock species.Results of this study are relevant to the US purebred pig industry and critical for the design of programs of whole genome marker assisted evaluation and selection. In addition, results indicate that a more cost efficient implementation of marker assisted selection using low density panels with genotype imputation, would be feasible for these breeds.

Sow performance when housed either in groups with electronic sow feeders or stalls

Lactation and reproductive performance of sows housed in groups with electronic sow feeding in gestation (ESF-G) and lactation (ESF-L) were compared to those housed individually in stalls in gestation (SG) and lactation (SL) in a commercial production system. No interaction between gestation and lactation housing was observed. Sows housed in ESF-G had a higher farrow rate than those housed in SG (94.3 vs. 89.4%; respectively). In addition, sows housed in ESF-G had subsequently higher litter birth weight (17.7 vs. 16.7 kg, respectively) and higher litter wean weight (57.1 vs. 56.2 kg, respectively) than those housed in SG. Gestation housing system did not influence the number born alive or weaned. Number weaned had a parity by lactation housing interaction. Parity 1, 3 and 5 sows housed in ESF-L weaned fewer piglets than sows housed in SL. In addition, SL sows had litter wean weights 2.9 kg heavier than sows in ESF-L. Gestating sows housed in groups with electronic sow feeding had either similar or improved performance compared to sows gestated in stalls. However, lactating sows had poorer litter weaning performance when housed in groups with electronic sow feeding compared to those housed individually in stalls.

Developmental factors that influence sow longevity1

The length of adult sow life is now recognized as both an economic and a welfare concern. However, there are no consistent definitions to measure sow longevity. This study assessed 6 different descriptions of longevity and determined their relationship with developmental performance factors. Longevity definitions included stayability (probability of a sow producing 40 pigs or probability of her reaching 4 parities), lifespan (number of parities a female has accumulated before culling), lifetime prolificacy (number of pigs born alive during the productive lifetime of a female), herd life (time from first farrowing to culling), and pigs produced per day of life. Data consisted of 14,262 records of Yorkshire females from both nucleus and multiplication herds across 21 farms from 4 seedstock systems. Within a subset of the data, information was available on the litter birth record of the female and her growth and composition data. Therefore, data were subdivided into 2 data sets, consisting of 1) data A, data from the farrowing records of a female, and 2) data B, data A and information from the litter birth record of a female and the growth and backfat data from a female. A Cox proportional hazards model was used to determine the relationship of developmental factors and first farrowing record with longevity. Those factors that were significantly (P < 0.0001) associated with longevity, regardless of definition, were age at first farrowing, litter size at first farrowing and last farrowing, number of stillborn in the first litter, adjusted 21-d litter weight of the first litter, herd type, backfat, and growth. Within a contemporary group, fatter, slower growing gilts had a decreased risk of being culled. Additionally, sows that had more pigs born alive, fewer stillborn pigs, and heavier litters at 21 d of lactation in their first litter had a decreased risk of being culled. Furthermore, sows from nucleus herds experienced a greater risk of being culled. Many factors affected longevity, regardless of definition. Pork producers can implement management protocols that can extend the productive life of breeding females, resulting in improved profitability and animal welfare.

Pork quality variation is not explained by glycolytic enzyme capacity

Our objective was to determine if increased glycolytic enzyme capacity accommodates rapid glycolysis, which leads to inferior pork color and water-holding capacity. Progeny from HAL-1843 free Duroc (n=16) or Pietrain (n=16) sires were harvested over a 2-week period. Coupled enzyme assays were used to quantify total capacity of pyruvate kinase (PK) and phosphofructokinase (PFK) in the sarcoplasmic fractions and crude homogenates of longissimus muscle (LM), respectively. Capacity of PK was not correlated with LM pH (20, 45, 180 min or 24 h), purge, drip loss, or CIE L* (P > 0.2). However, PFK capacity was inversely related to fluid loss (P<0.05). This finding was unexpected, but may result from PFK becoming partially denatured and inactivated by 20 min postmortem in samples that undergo a rapid pH decline. These data indicate that lighter pork color and reduced water-holding capacity are not associated with an increase in the capacity of enzymes that catalyze regulated steps of glycolysis.

Genotype imputation accuracy in a F2 pig population using high density and low density SNP panels

BackgroundF2 resource populations have been used extensively to map QTL segregating between pig breeds. A limitation associated with the use of these resource populations for fine mapping of QTL is the reduced number of founding individuals and recombinations of founding haplotypes occurring in the population. These limitations, however, become advantageous when attempting to impute unobserved genotypes using within family segregation information. A trade-off would be to re-type F2 populations using high density SNP panels for founding individuals and low density panels (tagSNP) in F2 individuals followed by imputation. Subsequently a combined meta-analysis of several populations would provide adequate power and resolution for QTL mapping, and could be achieved at relatively low cost. Such a strategy allows the wealth of phenotypic information that has previously been obtained on experimental resource populations to be further mined for QTL identification. In this study we used experimental and simulated high density genotypes (HD-60K) from an F2 cross to estimate imputation accuracy under several genotyping scenarios.ResultsSelection of tagSNP using physical distance or linkage disequilibrium information produced similar imputation accuracies. In particular, tagSNP sets averaging 1 SNP every 2.1 Mb (1,200 SNP genome-wide) yielded imputation accuracies (IA) close to 0.97. If instead of using custom panels, the commercially available 9K chip is used in the F2, IA reaches 0.99. In order to attain such high imputation accuracy the F0 and F1 generations should be genotyped at high density. Alternatively, when only the F0 is genotyped at HD, while F1 and F2 are genotyped with a 9K panel, IA drops to 0.90.ConclusionsCombining 60K and 9K panels with imputation in F2 populations is an appealing strategy to re-genotype existing populations at a fraction of the cost.

Genome-wide linkage analysis of global gene expression in loin muscle tissue identifies candidate genes in pigs.

Background Nearly 6,000 QTL have been reported for 588 different traits in pigs, more than in any other livestock species. However, this effort has translated into only a few confirmed causative variants. A powerful strategy for revealing candidate genes involves expression QTL (eQTL) mapping, where the mRNA abundance of a set of transcripts is used as the response variable for a QTL scan. Methodology/Principal Findings We utilized a whole genome expression microarray and an F2 pig resource population to conduct a global eQTL analysis in loin muscle tissue, and compared results to previously inferred phenotypic QTL (pQTL) from the same experimental cross. We found 62 unique eQTL (FDR <10%) and identified 3 gene networks enriched with genes subject to genetic control involved in lipid metabolism, DNA replication, and cell cycle regulation. We observed strong evidence of local regulation (40 out of 59 eQTL with known genomic position) and compared these eQTL to pQTL to help identify potential candidate genes. Among the interesting associations, we found aldo-keto reductase 7A2 (AKR7A2) and thioredoxin domain containing 12 (TXNDC12) eQTL that are part of a network associated with lipid metabolism and in turn overlap with pQTL regions for marbling, % intramuscular fat (% fat) and loin muscle area on Sus scrofa (SSC) chromosome 6. Additionally, we report 13 genomic regions with overlapping eQTL and pQTL involving 14 local eQTL. Conclusions/Significance Results of this analysis provide novel candidate genes for important complex pig phenotypes.

Methods of tagSNP selection and other variables affecting imputation accuracy in swine

BackgroundGenotype imputation is a cost efficient alternative to use of high density genotypes for implementing genomic selection. The objective of this study was to investigate variables affecting imputation accuracy from low density tagSNP (average distance between tagSNP from 100kb to 1Mb) sets in swine, selected using LD information, physical location, or accuracy for genotype imputation. We compared results of imputation accuracy based on several sets of low density tagSNP of varying densities and selected using three different methods. In addition, we assessed the effect of varying size and composition of the reference panel of haplotypes used for imputation.ResultsTagSNP density of at least 1 tagSNP per 340kb (∼7000 tagSNP) selected using pairwise LD information was necessary to achieve average imputation accuracy higher than 0.95. A commercial low density (9K) tagSNP set for swine was developed concurrent to this study and an average accuracy of imputation of 0.951 based on these tagSNP was estimated. Construction of a haplotype reference panel was most efficient when these haplotypes were obtained from randomly sampled individuals. Increasing the size of the original reference haplotype panel (128 haplotypes sampled from 32 sire/dam/offspring trios phased in a previous study) led to an overall increase in imputation accuracy (IA = 0.97 with 512 haplotypes), but was especially useful in increasing imputation accuracy of SNP with MAF below 0.1 and for SNP located in the chromosomal extremes (within 5% of chromosome end).ConclusionThe new commercially available 9K tagSNP set can be used to obtain imputed genotypes with high accuracy, even when imputation is based on a comparably small panel of reference haplotypes (128 haplotypes). Average imputation accuracy can be further increased by adding haplotypes to the reference panel. In addition, our results show that randomly sampling individuals to genotype for the construction of a reference haplotype panel is more cost efficient than specifically sampling older animals or trios with no observed loss in imputation accuracy. We expect that the use of imputed genotypes in swine breeding will yield highly accurate predictions of GEBV, based on the observed accuracy and reported results in dairy cattle, where genomic evaluation of some individuals is based on genotypes imputed with the same accuracy as our Yorkshire population.

Identification of Carcass and Meat Quality QTL in an F2 Duroc × Pietrain Pig Resource Population Using Different Least-Squares Analysis Models

A three-generation resource population was constructed by crossing pigs from the Duroc and Pietrain breeds. In this study, 954 F2 animals were used to identify quantitative trait loci (QTL) affecting carcass and meat quality traits. Based on results of the first scan analyzed with a line-cross (LC) model using 124 microsatellite markers and 510 F2 animals, 9 chromosomes were selected for genotyping of additional markers. Twenty additional markers were genotyped for 954 F2 animals and 20 markers used in the first scan were genotyped for 444 additional F2 animals. Three different Mendelian models using least-squares for QTL analysis were applied for the second scan: a LC model, a half-sib (HS) model, and a combined LC and HS model. Significance thresholds were determined by false discovery rate (FDR). In total, 50 QTL using the LC model, 38 QTL using the HS model, and 3 additional QTL using the combined LC and HS model were identified (q < 0.05). The LC and HS models revealed strong evidence for QTL regions on SSC6 for carcass traits (e.g., 10th-rib backfat; q < 0.0001) and on SSC15 for meat quality traits (e.g., tenderness, color, pH; q < 0.01), respectively. QTL for pH (SSC3), dressing percent (SSC7), marbling score and moisture percent (SSC12), CIE a* (SSC16), and carcass length and spareribs weight (SSC18) were also significant (q < 0.01). Additional marker and animal genotypes increased the statistical power for QTL detection, and applying different analysis models allowed confirmation of QTL and detection of new QTL.

Accuracy of Estimation of Genomic Breeding Values in Pigs Using Low-Density Genotypes and Imputation

Genomic selection has the potential to increase genetic progress. Genotype imputation of high-density single-nucleotide polymorphism (SNP) genotypes can improve the cost efficiency of genomic breeding value (GEBV) prediction for pig breeding. Consequently, the objectives of this work were to: (1) estimate accuracy of genomic evaluation and GEBV for three traits in a Yorkshire population and (2) quantify the loss of accuracy of genomic evaluation and GEBV when genotypes were imputed under two scenarios: a high-cost, high-accuracy scenario in which only selection candidates were imputed from a low-density platform and a low-cost, low-accuracy scenario in which all animals were imputed using a small reference panel of haplotypes. Phenotypes and genotypes obtained with the PorcineSNP60 BeadChip were available for 983 Yorkshire boars. Genotypes of selection candidates were masked and imputed using tagSNP in the GeneSeek Genomic Profiler (10K). Imputation was performed with BEAGLE using 128 or 1800 haplotypes as reference panels. GEBV were obtained through an animal-centric ridge regression model using de-regressed breeding values as response variables. Accuracy of genomic evaluation was estimated as the correlation between estimated breeding values and GEBV in a 10-fold cross validation design. Accuracy of genomic evaluation using observed genotypes was high for all traits (0.65−0.68). Using genotypes imputed from a large reference panel (accuracy: R2 = 0.95) for genomic evaluation did not significantly decrease accuracy, whereas a scenario with genotypes imputed from a small reference panel (R2 = 0.88) did show a significant decrease in accuracy. Genomic evaluation based on imputed genotypes in selection candidates can be implemented at a fraction of the cost of a genomic evaluation using observed genotypes and still yield virtually the same accuracy. On the other side, using a very small reference panel of haplotypes to impute training animals and candidates for selection results in lower accuracy of genomic evaluation.

Lifetime productivity in gilts previously selected for decreased age at puberty

A study was conducted to evaluate the effect of decreasing age of puberty on lifetime productivity in sows. Two lines of gilts from the Nebraska Gene Pool population were used in this study: a line that had been selected for decreased age at puberty (AP) and a line in which selection had been random (RS). The study was conducted in two parts. In part one, 75 gilts were mated at second oestrus and the productivity measured over five parities. A second experiment utilizing 68 gilts was conducted to provide further data for comparing litter size at parity 1, and also to compare ovulation rates in the two lines at second oestrus. Results showed that litter size was similar in both lines across parities. After five parities the percentage of sows farrowing relative to parity 1 was 58-8% for the AP line but only 39·4% for the RS line (P = 0·17). Litter birth weight, litter size and weight at 21 days, number weaned, and lactation food consumption were similar for both lines. Lactation weight loss was not significantly different between the two lines (60·9 (s.e. 5·9) v. 527 (s.e. 5·0) kg, for RS and AP gilts, respectively) but was consistent with the slightly longer weaning to remating intervals in the RS line (7·8 (s.e. 0·7) v. 6·6 (s.e. 0·7) days, P = 0·22). Ovulation rate at second oestrus did not differ between the two lines (14·1 (s.e. 0·9) v. 14·3 (s.e. 0·5), for RS and AP gilts, respectively). The regression of mean accumulative productivity on time was in favour of the AP line (P = 0·05). These results suggest that reproductive performance is not impaired in gilts which have been selected to reach puberty at earlier ages, and productivity at a specific age may be enhanced.