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Lindholm-Perry A.K.,U.S. Department of Agriculture | Rohrer G.A.,U.S. Department of Agriculture | Kuehn L.A.,U.S. Department of Agriculture | Keele J.W.,U.S. Department of Agriculture | And 4 more authors.
BMC Genetics | Year: 2010

Background: A back curvature defect similar to kyphosis in humans has been observed in swine herds. The defect ranges from mild to severe curvature of the thoracic vertebrate in split carcasses and has an estimated heritability of 0.3. The objective of this study was to identify genomic regions that affect this trait.Results: Single nucleotide polymorphism (SNP) associations performed with 198 SNPs and microsatellite markers in a Duroc-Landrace-Yorkshire resource population (U.S. Meat Animal Research Center, USMARC resource population) of swine provided regions of association with this trait on 15 chromosomes. Positional candidate genes, especially those involved in human skeletal development pathways, were selected for SNP identification. SNPs in 16 candidate genes were genotyped in an F2 population (n = 371) and the USMARC resource herd (n = 1,257) with kyphosis scores. SNPs in KCNN2 on SSC2, RYR1 and PLOD1 on SSC6 and MYST4 on SSC14 were significantly associated with kyphosis in the resource population of swine (P ≤ 0.05). SNPs in CER1 and CDH7 on SSC1, PSMA5 on SSC4, HOXC6 and HOXC8 on SSC5, ADAMTS18 on SSC6 and SOX9 on SSC12 were significantly associated with the kyphosis trait in the F2 population of swine (P ≤ 0.05).Conclusions: These data suggest that this kyphosis trait may be affected by several loci and that these may differ by population. Carcass value could be improved by effectively removing this undesirable trait from pig populations. © 2010 Lindholm-Perry et al; licensee BioMed Central Ltd. Source

Zumbach B.,University of Georgia | Misztal I.,University of Georgia | Chen C.Y.,University of Georgia | Tsuruta S.,University of Georgia | And 4 more authors.
Journal of Animal Breeding and Genetics | Year: 2010

This study examined the utility of serial weights from FIRE (Feed Intake Recording Equipment, Osborne Industries, Inc., Osborne, KS, USA) stations for an analysis of daily gain. Data included 884 132 body weight records from 3888 purebred Duroc pigs. Pigs entered the feeder station at age 77-149 days and left at age 95-184 days. A substantial number of records were abnormal, showing body weight close to 0 or up to twice the average weight. Plots of body weights for some animals indicated two parallel growth curves. Initial editing used a robust regression, which was a two-step procedure. In the first step, a quadratic growth curve was estimated assuming small or 0 weights for points far away from the curve; the process is iterative. In the second step, weights more than 1.5 SD from the estimated growth curve were treated as outliers. The retained body weight records (607 597) were averaged to create average daily weight (170 443) and then used to calculate daily gains (152 636). Additional editing steps included retaining only animals with ≥50 body weight records and SD of the daily gain ≤2 kg, followed by removing records outside 3 SD from the mean for given age, across all the animals - the resulting data set included 69 068 records of daily gain from 1921 animals. Daily gain based on daily, weekly and bi-weekly intervals was analysed using repeatability models. Heritability estimates were 0.04, 6 and 9%, respectively. The last two estimates correspond to heritability of 28% for a 12 week interval. For daily gain averaged weekly, the estimate of heritability obtained with a random regression model varied from 0.07 to 0.10. After extensive editing, body weight records from automatic feeding stations are useful for genetic analyses of daily gain from weekly or bi-weekly but not daily intervals. © 2009 Blackwell Verlag GmbH. Source

Badke Y.M.,Michigan State University | Bates R.O.,Michigan State University | Ernst C.W.,Michigan State University | Fix J.,Smithfield Premium Genetics Group | Steibel J.P.,Michigan State University
G3: Genes, Genomes, Genetics | Year: 2014

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 deregressed 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 (0.6520.68). 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. ©2014 Badke et al. Source

Engblom L.,Iowa State University | Calderon Diaz J.A.,Iowa State University | Nikkila M.,Iowa State University | Gray K.,Smithfield Premium Genetics Group | And 5 more authors.
Journal of Animal Breeding and Genetics | Year: 2016

Sow longevity is a key component for efficient and profitable pig farming; however, approximately 50% of sows are removed annually from a breeding herd. There is no consensus in the scientific literature regarding a definition for sow longevity; however, it has been suggested that it can be measured using several methods such as stayability and economic indicators such as lifetime piglets produced. Sow longevity can be improved by genetic selection; however, it is rarely included in genetic evaluations. One reason is elongated time intervals required to collect complete lifetime data. The effect of genetic parameter estimation software in handling incomplete data (censoring) and possible early indicator traits were evaluated analysing a 30% censored data set (12 725 pedigreed Landrace × Large White sows that included approximately 30% censored data) with DMU6, THRGIBBS1F90 and GIBBS2CEN. Heritability estimates were low for all the traits evaluated. The results show that the binary stayability traits benefited from being analysed with a threshold model compared to analysing with a linear model. Sires were ranked very similarly regardless if the program handled censoring when all available data were included. Accumulated born alive and stayability were good indicators for lifetime born alive traits. Number of piglets born alive within each parity could be used as an early indicator trait for sow longevity. © 2016 Blackwell Verlag GmbH. Source

Chen C.Y.,University of Georgia | Misztal I.,University of Georgia | Tsuruta S.,University of Georgia | Zumbach B.,University of Georgia | And 3 more authors.
Journal of Animal Breeding and Genetics | Year: 2010

Genetic parameters for daily feed intake (DFI, g/day) and daily gain (DG, g/day) were estimated using records of 1916 Duroc boars from electronic feeder stations. Management was limited and resulted in varied ranges of age and weight on test. Boars were housed in 102 pens, each equipped with one feeder, and allowed ad libitum feeding. Weekly averages of DFI and DG were used due to large variation in daily records. Six traits were defined as DFI and DG during 85-106 (period 1), 107-128 (period 2) and 129-150 days of age (period 3). A six-trait model included age as a linear and a quadratic covariate for DFI and a linear covariate for DG with a fixed effect of year-week-pen and random effects of litter, additive genetic animal and permanent environmental animal. Variance components were estimated by a Bayesian approach using Gibbs sampling algorithm. Estimates of heritability for respective periods were 18%, 12% and 10% for DFI and 21%, 11% and 10% for DG. Genetic correlations between DFI and DG in the same period were 0.70, 0.73 and 0.32 for the respective periods. DFI and DG obtained from automatic feeders can be analysed to reveal variation across testing periods by using weekly averages when many monthly averages are incomplete. © 2009 Blackwell Verlag GmbH. Source

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