W M Snelling

North Carolina State University, Raleigh, NC, United States

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Publications (63)188.81 Total impact

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    ABSTRACT: Reproductive efficiency is of economic importance in commercial beef cattle production, as failure to achieve pregnancy reduces the number of calves marketed per cow exposed. Identification of genetic markers with predictive merit for reproductive success would facilitate early selection of sires with daughters having improved reproductive rate without increasing generation intervals. To identify regions of the genome harboring variation affecting reproductive success, we applied a genome wide association study (GWAS) approach based on the >700,000 SNP marker assay, using a procedure based on genotyping multi-animal pools of DNA to increase the number of animals that could be genotyped with available resources. Cows from several populations were classified according to reproductive efficiency, and DNA was pooled within population and phenotype prior to genotyping. Populations evaluated included a research population at the US Meat Animal Research Center (USMARC), 2 large commercial ranch populations, and a number of smaller populations (<100 head) across the US. We detected 2 SNP with significant genome-wide association (P ≤1.49×10(-7)), on BTA21 and 29, 3 SNP with suggestive associations (P ≤ 2.91×10(-6)) on BTA5, and 1 SNP with suggestive association each on BTA1 and 25. In addition to our novel findings, we confirmed previously published associations for SNP on BTA X and all autosomes except 3 (BTA21, 22 and 28) encompassing substantial breed diversity including Bos indicus and Bos taurus breeds. The study identified regions of the genome associated with reproductive efficiency, which are being targeted for further analysis to develop robust marker systems, and demonstrated that DNA pooling can be used to substantially reduce the cost of GWAS studies in cattle.
    Journal of Animal Science 03/2014; · 2.09 Impact Factor
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    ABSTRACT: Animal breeding and reproductive physiology have been closely related throughout the history of animal production science. Artificial insemination provides the best method of increasing the influence of sires with superior genetics to improve production traits. Multiple ovulation embryo transfer (MOET) provides some ability to increase the genetic influence of the maternal line as well. The addition of genetic technologies to this paradigm allows for improved methods of selecting sires and dams carrying the best genes for production and yield of edible products and resistance to diseases and parasites. However, decreasing the number of influential parents within a population also increases the risk of propagating a recessive gene that could negatively impact the species (Reprod Domest Anim 44:792-796, 2009; BMC Genomics 11:337, 2010). Furthermore, antagonistic genotypic relationships between production traits and fertility (Anim Prod Sci 49:399-412, 2009; Anim Genet 43:442-446, 2012) suggest that care must be taken to ensure that increasing the frequency of genes with a positive influence on production does not negatively impact the fertility of the replacement females entering the herd.
    Advances in experimental medicine and biology 01/2014; 752:77-96. · 1.83 Impact Factor
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    ABSTRACT: Cattle are reared in diverse environments and collecting phenotypic body temperature (BT) measurements to characterize BT variation across diverse environments is difficult and expensive. To better understand the genetic basis of BT regulation, a genome-wide association study was conducted utilizing crossbred steers and heifers totaling 239 animals of unknown pedigree and breed fraction. During predicted extreme heat and cold stress events, hourly tympanic and vaginal BT devices were placed in steers and heifers, respectively. Individuals were genotyped with the BovineSNP50K_v2 assay and data analyzed using Bayesian models for area under the curve (AUC), a measure of BT over time, using hourly BT observations summed across 5-days (AUC summer 5-day (AUCS5D) and AUC winter 5-day (AUCW5D)). Posterior heritability estimates were moderate to high and were estimated to be 0.68 and 0.21 for AUCS5D and AUCW5D, respectively. Moderately positive correlations between direct genomic values for AUCS5D and AUCW5D (0.40) were found, although a small percentage of the top 5 % 1-Mb windows were in common. Different sets of genes were associated with BT during winter and summer, thus simultaneous selection for animals tolerant to both heat and cold appears possible.
    International Journal of Biometeorology 12/2013; · 2.59 Impact Factor
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    ABSTRACT: Although the efficacy of genomic predictors based on within-breed training looks promising, it is necessary to develop and evaluate across-breed predictors for the technology to be fully applied in the beef industry. The efficacies of genomic predictors trained in one breed and utilized to predict genetic merit in differing breeds based on simulation studies have been reported, as have the efficacies of predictors trained using data from multiple breeds to predict the genetic merit of purebreds. However, comparable studies using beef cattle field data have not been reported. Molecular breeding values for weaning and yearling weight were derived and evaluated using a database containing BovineSNP50 genotypes for 7294 animals from 13 breeds in the training set and 2277 animals from seven breeds (Angus, Red Angus, Hereford, Charolais, Gelbvieh, Limousin, and Simmental) in the evaluation set. Six single-breed and four across-breed genomic predictors were trained using pooled data from purebred animals. Molecular breeding values were evaluated using field data, including genotypes for 2227 animals and phenotypic records of animals born in 2008 or later. Accuracies of molecular breeding values were estimated based on the genetic correlation between the molecular breeding value and trait phenotype. With one exception, the estimated genetic correlations of within-breed molecular breeding values with trait phenotype were greater than 0.28 when evaluated in the breed used for training. Most estimated genetic correlations for the across-breed trained molecular breeding values were moderate (> 0.30). When molecular breeding values were evaluated in breeds that were not in the training set, estimated genetic correlations clustered around zero. Even for closely related breeds, within- or across-breed trained molecular breeding values have limited prediction accuracy for breeds that were not in the training set. For breeds in the training set, across- and within-breed trained molecular breeding values had similar accuracies. The benefit of adding data from other breeds to a within-breed training population is the ability to produce molecular breeding values that are more robust across breeds and these can be utilized until enough training data has been accumulated to allow for a within-breed training set.
    Genetics Selection Evolution 08/2013; 45(1):30. · 2.86 Impact Factor
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    ABSTRACT: Pork quality has a large impact on consumer preference and perception of eating quality. A genome-wide association was performed for pork quality traits (intramuscular fat (IMF), slice shear force (SSF), color attributes, purge, cooking loss and pH) from 531 to 1,237 records on barrows and gilts of a Landrace-Duroc-Yorkshire population using the Illumina PorcineSNP60 Beadchip. Associations were detected using MTDFREML for all traits. IMF had the greatest number of SNP associations, followed by pH, purge, cooking loss, shear force and color. Two regions contained associations for multiple traits; one on SSC1 at 255 Mb near calcineurin subunit B (PPP3R2) was associated with slice shear force, moisture loss and pH, and one on SSC6 from 28 to 29.5 Mb for purge and IMF containing the candidate genes glucose-6-phosphate isomerase (GPI) and KCTD15. Some of the other compelling candidate genes in regions associated with meat quality include CEBPA, SNAI1 and FAM132A for IMF, CAPN1 for slice shear force, GLUL for pH, and PRKAG3 and ITGB1 with cooking loss.
    Journal of Animal Science 08/2013; · 2.09 Impact Factor
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    ABSTRACT: Many traits affecting profitability and sustainability of meat, milk, and fiber production are polygenic, with no single gene having an overwhelming influence on observed variation. No knowledge of the specific genes controlling these traits has been needed to make substantial improvement through selection. Significant gains have been made through phenotypic selection, enhanced by pedigree relationships and continually-improving statistical methodology. Genomic selection, recently enabled by assays for dense SNP located throughout the genome, promises to increase selection accuracy and accelerate genetic improvement by emphasizing the SNP most strongly correlated to phenotype, although the genes and sequence variants affecting phenotype remain largely unknown. These genomic predictions theoretically rely on linkage disequilibrium (LD) between genotyped SNP and unknown functional variants, but familial linkage may increase effectiveness when predicting individuals related to those in the training data. Genomic selection with functional SNP genotypes should be less reliant on LD patterns shared by training and target populations, possibly allowing robust prediction across unrelated populations. While the specific variants causing polygenic variation may never be known with certainty, a number of tools and resources can be employed to identify those most likely to affect phenotype. Associations of dense SNP genotypes with phenotype provide a one-dimensional approach for identifying genes affecting specific traits; in contrast, associations with multiple traits allow defining networks of genes interacting to affect correlated traits. Such networks are especially compelling when corroborated by existing functional annotation and established molecular pathways. The SNP occurring within network genes, obtained from public databases or derived from genome and transcriptome sequences, may be classified according to expected effects on gene products. As illustrated by functionally informed genomic predictions being more accurate than naive whole-genome predictions of beef tenderness, coupling evidence from livestock genotypes, phenotypes, gene expression and genomic variants with existing knowledge of gene functions and interactions may provide greater insight into the genes and genomic mechanisms affecting polygenic traits, and facilitate functional genomic selection for economically important traits.
    Journal of Animal Science 10/2012; · 2.09 Impact Factor
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    ABSTRACT: Several organizations have developed prediction models for molecular breeding values (MBV) for quantitative growth and carcass traits in beef cattle using BovineSNP50 genotypes and phenotypic or EBV data. MBV for Angus cattle have been developed by IGENITY, Pfizer Animal Genetics, and a collaboration between researchers from Iowa State University (ISU) and the University of Missouri-Columbia (UMC). The U.S. Meat Animal Research Center (USMARC; Clay Center, NE) has also developed MBV for 16 cattle breeds using two multi-breed populations, the GermPlasm Evaluation program (GPE) and the 2,000 Bull Project (2K(ALL)), and two single breed subpopulations of the 2,000 Bull Project, Angus (2K(AN)) and Hereford (2K(HH)). In this study, these MBV were assessed relative to commercial ranch EBV estimated from the progeny phenotypes of Angus bulls naturally mated in multi-sire breeding pastures to commercial cows: 121 for USMARC MBV, 99 for ISU/UMC MBV, and 29 for IGENITY and Pfizer MBV (selected based on number of progeny carcass records). Five traits were analyzed: weaning weight (WW), hot carcass weight (CW), marbling score (MS), rib-eye muscle area (RE), and, for IGENITY and Pfizer only, feedlot average daily gain (ADG). The average accuracies of MBV across traits were: IGENITY 0.38±0.05, Pfizer 0.61±0.12, ISU/UMC 0.46±0.12, GPE 0.16±0.04, 2K(ALL) 0.26±0.05, 2K(AN) 0.24±0.04, and 2K(HH) 0.02±0.12. Angus-based MBV (IGENITY, Pfizer, ISU/UMC, and 2K(AN)) explained larger proportions of genetic variance in this population than GPE, 2K(ALL), or 2K(HH) MBV for the same traits. In this data set, IGENITY, Pfizer, and ISU/UMC MBV were predictive of realized performance of progeny, and incorporation of that information into national genetic evaluations would be expected to improve EPD accuracy, particularly for young animals.
    Journal of Animal Science 08/2012; 90:4191-4202. · 2.09 Impact Factor
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    ABSTRACT: The objective of this study was to investigate alternative methods of designing and utilizing reduced single nucleotide polymorphism (SNP) panels for imputing SNP genotypes. Two purebred Hereford populations, an experimental population known as Line 1 Hereford (L1, N=240) and registered Hereford with American Hereford Association (AHA, N=311), were utilized. Using different reference samples of 62 to 311 animals with 39,497 SNPs on 29 autosomes, and study samples of 57 or 62 animals for which genotypes were available for ∼2,600 SNPs (reduced panels), imputations were performed to predict the other ∼36,900 loci which had been masked. An imputation package including LinkPHASE and DAGPHASE (Druet and Georges, 2010) was used for imputation. Four reduced panels differing in minor allele frequency (MAF) and marker spacing were evaluated. Reduced panels included every fifteenth SNP across the genome (SNP_space); commercial Illumina Bovine3K Beadchip (SNP_3K); SNPs with the highest MAF (SNP_MAF); and SNPs with high MAF which were also evenly spaced across the genome (SNP_MS). Imputation accuracy was defined as the correlation of imputed genotypes and real genotypes. Reference samples were either from L1 or AHA. Among animals with genotypes, genetic relationships were estimated based on molecular marker genotypes or pedigree. Reduced panel design, number of animals in the reference sample, reference origin and the genetic relationship between animals in the reference and study samples all affected imputation accuracy (P < 0.001). Across genotyping schemes, imputed genotypes from SNP_MS had the greatest accuracy. A 0.1 increase in average pedigree relationship or average molecular relationship between reference and study samples increased imputation accuracy 10 to 20%. Using reference samples from the L1 population resulted in lower imputation accuracy than using reference samples from the admixed population AHA (P < 0.001). Increasing the number of animals in the reference panel by one hundred individuals increased imputation accuracy by 8% when pedigree relationship was used as a covariate and 6% when molecular relationship was used as a covariate. It was concluded that imputation accuracy would be increased through optimization of reduced panel design and genotyping strategy.
    Journal of Animal Science 08/2012; · 2.09 Impact Factor
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    ABSTRACT: Genomic selection involves the assessment of genetic merit through prediction equations which allocate genetic variation with dense marker genotypes. It has the potential to provide accurate breeding values for selection candidates at an early age, and facilitate selection for expensive or difficult to measure traits. Accurate across-breed prediction would allow genomic selection to be applied on a larger scale in the beef industry, but the limited availability of large populations for the development of prediction equations has delayed researchers from providing genomic predictions that are accurate across multiple beef breeds. In this study, the accuracy of genomic predictions for six growth and carcass traits were derived and evaluated using two multi-breed beef cattle populations, 3,358 crossbred cattle of the U.S. Meat Animal Research Center (USMARC) Germplasm Evaluation Program (GPE) and 1,834 high accuracy bull sires of the 2,000 Bull Project representing influential breeds in the U.S. beef cattle industry (2000_BULL). 2000_BULL EPD were deregressed, scaled, and weighted to adjust for between and within breed heterogeneous variance before use in training and validation. Molecular breeding values (MBV) trained in each multi-breed population and in the Angus and Hereford purebred sires of 2000_BULL were derived using the GenSel BayesCπ function (Fernando and Garrick, 2009) and cross-validated. Less than 10% of large effect loci were shared between prediction equations trained on USMARC_GPE relative to 2000_BULL, though locus effects were moderately to highly correlated for most traits and the traits themselves were highly correlated between populations. Prediction of MBV accuracy was low and variable between populations. For growth traits, MBV accounted for up to 18% of genetic variation in a pooled, multi-breed analysis and up to 28% in single breeds, and for carcass traits MBV explained up to 8% of genetic variation in a pooled, multi-breed analysis and up to 42% in single breeds. Prediction equations trained in multi-breed populations were more accurate for Angus and Hereford subpopulations, as those were the breeds most highly represented in the training populations. Accuracies were lower for prediction equations trained in a single breed due to the smaller number of records derived from a single breed in the training populations.
    Journal of Animal Science 07/2012; · 2.09 Impact Factor
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    ABSTRACT: Measures of heifer fertility are economically relevant traits for beef production systems and knowledge of candidate genes could be incorporated into future genomic selection strategies. Ten traits related to growth and fertility were measured in 890 Brangus heifers (3/8 Brahman × 5/8 Angus, from 67 sires). These traits were: BW and hip height adjusted to 205 and 365 d of age, postweaning ADG, yearling assessment of carcass traits (i.e., back fat thickness, intramuscular fat, and LM area), as well as heifer pregnancy and first service conception (FSC). These fertility traits were collected from controlled breeding seasons initiated with estrous synchronization and AI targeting heifers to calve by 24 mo of age. The BovineSNP50 BeadChip was used to ascertain 53,692 SNP genotypes for ∼802 heifers. Associations of genotypes and phenotypes were performed and SNP effects were estimated for each trait. Minimally associated SNP (P < 0.05) and their effects across the 10 traits formed the basis for an association weight matrix and its derived gene network related to FSC (57.3% success and heritability = 0.06 ± 0.05). These analyses yielded 1,555 important SNP, which inferred genes linked by 113,873 correlations within a network. Specifically, 1,386 SNP were nodes and the 5,132 strongest correlations (|r| ≥ 0.90) were edges. The network was filtered with genes queried from a transcriptome resource created from deep sequencing of RNA (i.e., RNA-Seq) from the hypothalamus of a prepubertal and a postpubertal Brangus heifer. The remaining hypothalamic-influenced network contained 978 genes connected by 2,560 edges or predicted gene interactions. This hypothalamic gene network was enriched with genes involved in axon guidance, which is a pathway known to influence pulsatile release of LHRH. There were 5 transcription factors with 21 or more connections: ZMAT3, STAT6, RFX4, PLAGL1, and NR6A1 for FSC. The SNP that identified these genes were intragenic and were on chromosomes 1, 5, 9, and 11. Chromosome 5 harbored both STAT6 and RFX4. The large number of interactions and genes observed with network analyses of multiple sources of genomic data (i.e., GWAS and RNA-Seq) support the concept of FSC being a polygenic trait.
    Journal of Animal Science 06/2012; 90(9):2894-906. · 2.09 Impact Factor
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    ABSTRACT: Reproductive efficiency has a great impact on the economic success of pork (sus scrofa) production. Number born alive (NBA) and average piglet birth weight (ABW) contribute greatly to reproductive efficiency. To better understand the underlying genetics of birth traits, a genome-wide association study (GWAS) was undertaken. Samples of DNA were collected and tested using the Illumina PorcineSNP60 BeadChip from 1,152 first parity gilts. Traits included total number born (TNB), NBA, number born dead (NBD), number stillborn (NSB), number of mummies (MUM), total litter birth weight (LBW), and ABW. A total of 41,151 SNP were tested using a Bayesian approach. Beginning with the first 5 SNP on SSC1 and ending with the last 5 SNP on the SSCX, SNP were assigned to groups of 5 consecutive SNP by chromosome-position order and analyzed again using a Bayesian approach. From that analysis, 5-SNP groups were selected having no overlap with another 5-SNP groups and no overlap across chromosomes. These selected 5-SNP non-overlapping groups were defined as QTL. Of the available 8,814 QTL, 124 were found to be statistically significant (P < 0.01). Multiple testing was considered using the probability of false positives. Eleven QTL were found for TNB, 3 on SSC1, 3 on SSC4, 1 on SSC13, 1 on SSC14, 2 on SSC15, and 1 on SSC17. Statistical testing for NBA identified 14 QTL, 4 on SSC1, 1 on SSC4, 1 on SSC6, 1 on SSC10, 1on SSC13, 3 on SSC15, and 3 on SSC17. A single NBD QTL was found on SSC11. No QTL were identified for NSB or MUM. Thirty-three QTL were found for LBW, 3 on SSC1, 1 on SSC2, 1 on SSC3, 5 on SSC4, 2 on SSC5, 5 on SSC6, 3 on SSC7, 2 on SSC9, 1 on SSC10, 2 on SSC14, 6 on SSC15, and 2 on SSC17. A total of 65 QTL were found for ABW, 9 on SSC1, 3 on SSC2, 9 on SSC5, 5 on SSC6, 1 on SSC7, 2 on SSC8, 2 on SSC9, 3 on SSC10, 1 on SSC11, 3 on SSC12, 2 on SSC13, 8 on SSC14, 8 on SSC15, 1 on SSC17, and 8 on SSC18. Several candidate genes have been identified that overlap QTL locations among TNB, NBA, NBD, and ABW. These QTL when combined with information on genes found in the same regions should provide useful information that could be used for marker assisted selection, marker assisted management, or genomic selection applications in commercial pig populations.
    Journal of Animal Science 05/2012; 90(10):3360-7. · 2.09 Impact Factor
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    ABSTRACT: Feed cost for beef cattle is the largest expense incurred by cattle producers. The development of genetic markers to enhance selection of more efficient animals that require less feed while still achieving acceptable levels of production has the potential to substantially reduce production costs. A genome-wide marker association approach based on the Illumina BovineSNP50 BeadChip™ was used to identify genomic regions affecting average daily feed intake (ADFI), average daily gain (ADG) and residual feed intake traits in a population of 1159 crossbred steers. This approach identified a region on BTA14 from 22.02 to 23.92 Mb containing several single-nucleotide polymorphisms (SNPs) that have significant association with at least one of the traits. Two genes in this region, lysophospholipase 1 (LYPLA1) and transmembrane protein 68 (TMEM68), appeared to be logical positional and functional candidate genes. LYPLA1 deacylates ghrelin, a hormone involved in the regulation of appetite in the rat stomach, while TMEM68 is expressed in bovine rumen, abomasum, intestine and adipose tissue in cattle, and likely affects lipid biosynthetic processes. SNPs lying in or near these two genes were identified by sequencing a subset of animals with extreme phenotypes. A total of 55 SNPs were genotyped and tested for association with the same population of steers. After correction for multiple testing, five markers within 22.79-22.84 Mb, located downstream of TMEM68, and between TMEM68 and the neighbouring gene XKR4, were significant for both ADFI and ADG. Genetic markers predictive of feed intake and weight gain phenotypes in this population of cattle may be useful for the identification and selection of animals that consume less feed, although further evaluation of these markers for effects on other production traits and validation in additional populations will be required.
    Animal Genetics 04/2012; 43(2):216-9. · 2.58 Impact Factor
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    ABSTRACT: Reproductive efficiency is of economic importance in commercial beef cattle production, since failure to achieve pregnancy reduces the number of calves marketed. Identification of genetic markers with predictive merit for reproductive success would facilitate early selection of females and avoid inefficiencies associated with sub-fertile cows. To identify regions of the genome harboring variation affecting reproductive success, we applied a genome-wide association approach based on the >700,000 SNP marker assay. To include the largest number of individuals possible under the available budget, cows from several populations were assigned to extremes for reproductive efficiency, and DNA was pooled within population and phenotype before genotyping. Surprisingly, pools prepared from DNA of low reproductive cattle returned fluorescence intensity data intermediate between fertile females and males for SNP mapped to the Y chromosome (i.e., male sex chromosome). The presence of Y-associated material in low reproductive heifers or cows was confirmed by Y-directed PCR, which revealed that 21 to 29% of females in the low reproductive category were positive by a Y chromosome PCR test normally used to sex embryos. The presence of the Y chromosome anomaly was further confirmed with application of additional Y-specific PCR amplicons, indicating the likelihood of the presence of some portion of male sex chromosome in female cattle in various beef cattle herds across the U.S. Discovery of this Y anomaly in low reproductive females may make an important contribution to management of reproductive failures in beef cattle operations.
    Journal of Animal Science 03/2012; 90(7):2142-51. · 2.09 Impact Factor
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    ABSTRACT: With the high cost of feed for animal production, genetic selection for animals that metabolize feed more efficiently could result in substantial cost savings for cattle producers. The purpose of this study was to identify DNA markers predictive for differences among cattle for traits associated with feed efficiency. Crossbred steers were fed a high-corn diet for 140 days and average daily feed intake (ADFI), average daily gain (ADG), and residual feed intake (RFI) phenotypes were obtained. A region on chromosome 14 was previously associated with RFI in this population of animals. To develop markers with the highest utility for predicting an animal's genetic potential for RFI, we genotyped additional markers within this chromosomal region. These polymorphisms were genotyped on the same animals (n = 1066) and tested for association with ADFI, ADG and RFI. Six markers within this region were associated with RFI (P ≤ 0.05). After conservative correction for multiple testing, one marker at 25.09 Mb remained significant (P = 0.02) and is responsible for 3.6% of the RFI phenotypic variation in this population of animals. Several of these markers were also significant for ADG, although none were significant after correction. Marker alleles with positive effects on ADG corresponded to lower RFI, suggesting an effect increasing growth without increasing feed intake. All markers were also assessed for their effects on meat quality and carcass traits. All of the markers associated with RFI were associated with adjusted fat thickness (AFT, P ≤ 0.009) and three were also associated with hot carcass weight (HCW, P ≤ 0.003). Marker alleles associated with lower RFI were also associated with reduced AFT, and if they were associated for HCW, the effect was an increase in weight. These markers may be useful as prediction tools for animals that utilize feed more efficiently; however, validation with additional populations of cattle is required.
    Animal Genetics 02/2012; 43(5):599-603. · 2.58 Impact Factor
  • Animal Genetics 02/2012; 43(1):112. · 2.58 Impact Factor
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    ABSTRACT: Hereford is a major beef breed in the USA, and a sub-population, known as Line 1 (L1), was established in 1934 using two paternal half-sib bulls and 50 unrelated females. L1 has since been maintained as a closed population and selected for growth to 1 year of age. Objectives were to characterize the molecular genetic architecture of L1 (n = 240) by comparing a cross-section of L1 with the general US. Hereford population (AHA, n = 311), estimating effects of imposed selection within L1 based on allele frequencies at 50 K SNP loci, and examining loci-specific effects of heterozygosity on the selection criterion. Animals were genotyped using the Illumina BovineSNP50 Beadchip, and SNP were mapped to UMD3.0 assembly of the bovine genome sequence. Average linkage disequilibrium (LD), measured by square of Pearson correlation, of adjacent SNP was 0.36 and 0.16 in L1 and AHA, respectively. Difference in LD between L1 and AHA decreased as SNP spacing increased. Persistence of phase between L1 and AHA decreased from 0.45 to 0.14 as SNP spacing increased from 50 to 5,000 kb. Extended haplotype homozygosity was greater in L1 than in AHA for 95.6% of the SNP. Knowledge of selection applied to L1 facilitated a novel approach to QTL discovery. Minor allele frequency was (FDR < 0.01) affected by cumulative selection differential at 191 out of 25,901 SNP. With the FDR relaxed to 0.05, 13 regions on BTA2, 5, 6, 9, 11, 14, 15, 18, 23, and 26 are co-located with previously identified QTL for growth. After adjustment of postweaning gain phenotypes for fixed effects and direct additive genetic effects, regression of residuals on genome-wide heterozygosity was -235.3 ± 91.6 kg. However, no SNP-specific loci where heterozygotes were significantly superior to the average of homozygotes were revealed (FDR ≥ 0.17). In conclusion, genome-wide SNP genotypes clarified effects of selection and inbreeding within L1 and differences in genomic architecture between the population segment L1 and the AHA population.
    Frontiers in Genetics 01/2012; 3:285.
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    ABSTRACT: In a previously reported genome-wide association study based on a high-density bovine SNP genotyping array, 8 SNP were nominally associated (P ≤ 0.003) with average daily gain (ADG) and 3 of these were also associated (P ≤ 0.002) with average daily feed intake (ADFI) in a population of crossbred beef cattle. The SNP were clustered in a 570 kb region around 38 Mb on the draft sequence of bovine chromosome 6 (BTA6), an interval containing several positional and functional candidate genes including the bovine LAP3, NCAPG, and LCORL genes. The goal of the present study was to develop and examine additional markers in this region to optimize the ability to distinguish favorable alleles, with potential to identify functional variation. Animals from the original study were genotyped for 47 SNP within or near the gene boundaries of the three candidate genes. Sixteen markers in the NCAPG-LCORL locus displayed significant association with both ADFI and ADG even after stringent correction for multiple testing (P ≤ 005). These markers were evaluated for their effects on meat and carcass traits. The alleles associated with higher ADFI and ADG were also associated with higher hot carcass weight (HCW) and ribeye area (REA), and lower adjusted fat thickness (AFT). A reduced set of markers was genotyped on a separate, crossbred population including genetic contributions from 14 beef cattle breeds. Two of the markers located within the LCORL gene locus remained significant for ADG (P ≤ 0.04). Several markers within the NCAPG-LCORL locus were significantly associated with feed intake and body weight gain phenotypes. These markers were also associated with HCW, REA and AFT suggesting that they are involved with lean growth and reduced fat deposition. Additionally, the two markers significant for ADG in the validation population of animals may be more robust for the prediction of ADG and possibly the correlated trait ADFI, across multiple breeds and populations of cattle.
    BMC Genetics 12/2011; 12:103. · 2.81 Impact Factor
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    ABSTRACT: The promise of genomic selection is accurate prediction of the genetic potential of animals from their genotypes. Simple DNA tests might replace low-accuracy predictions for expensive or lowly heritable measures of puberty and fertility based on performance and pedigree. Knowing with some certainty which DNA variants (e.g., SNP) affect puberty and fertility is the best way to fulfill the promise. Several SNP from the BovineSNP50 assay have tentatively been associated with reproductive traits including age at puberty, antral follicle count, and pregnancy observed on different sets of heifers. However, sample sizes are too small and SNP density is too sparse to definitively determine genomic regions harboring causal variants affecting reproductive success. Additionally, associations between individual SNP and similar phenotypes are inconsistent across data sets, and genomic predictions do not appear to be globally applicable to cattle of different breeds. Discrepancies may be a result of different QTL segregating in the sampled populations, differences in linkage disequilibrium (LD) patterns such that the same SNP are not correlated with the same QTL, and spurious correlations with phenotype. Several approaches can be used independently or in combination to improve detection of genomic factors affecting heifer puberty and fertility. Larger samples and denser SNP will increase power to detect real associations with SNP having more consistent LD with underlying QTL. Meta-analysis combining results from different studies can also be used to effectively increase sample size. High-density genotyping with heifers pooled by pregnancy status or early and late puberty can be a cost-effective means to sample large numbers. Networks of genes, implicated by associations with multiple traits correlated with puberty and fertility, could provide insight into the complex nature of these traits, especially if corroborated by functional annotation, established gene interaction pathways, and transcript expression. Example analyses are provided to demonstrate how integrating information about gene function and regulation with statistical associations from whole-genome SNP genotyping assays might enhance knowledge of genomic mechanisms affecting puberty and fertility, enabling reliable DNA tests to guide heifer selection decisions.
    Journal of Animal Science 10/2011; 90(4):1152-65. · 2.09 Impact Factor
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    ABSTRACT: Growth, feed intake, and temperament indicator data, collected over 5 yr on a total of 1,141 to 1,183 mixed-breed steers, were used to estimate genetic and phenotypic parameters. All steers had a portion of Hereford, Angus, or both as well as varying percentages of Simmental, Charolais, Limousin, Gelbvieh, Red Angus, and MARC III composite. Because the steers were slaughtered on various dates each year and the animals thus varied in days on feed, BW and feed data were adjusted to a 140-d feeding period basis. Adjustment of measures of feed efficiency [G:F or residual feed intake (RFI), intake adjusted for metabolic body size, and BW gain] for body fatness recorded at slaughter had little effect on the results of analyses. Average daily gain was less heritable (0.26) than was midtest BW (MBW; 0.35). Measures of feed intake had greater estimates of heritability, with 140-d DMI at 0.40 and RFI at 0.52; the heritability estimate for G:F was 0.27. Flight speed (FS), as an indicator of temperament, had an estimated heritability of 0.34 and a repeatability of 0.63. As expected, a strong genetic (0.86) correlation was estimated between ADG and MBW; genetic correlations were less strong between DMI and ADG or MBW (0.56 and 0.71). Residual feed intake and DMI had a genetic correlation of 0.66. Indexes for phenotypic RFI and genotypically restricted RFI (no correlation with BW gain) were compared with simple economic indexes incorporating feed intake and growth to elucidate expected selection responses under different criteria. In general, few breed differences were detected across the various measurements. Heterosis contributed to greater DMI, RFI, and MBW, but it did not significantly affect ADG, G:F, or FS. Balancing output (growth) with input costs (feed) is needed in practicing selection, and FS would not be recommended as an indicator trait for selection to change feed efficiency. An index including BW gain and RFI produced the best economic outcome.
    Journal of Animal Science 05/2011; 89(11):3452-9. · 2.09 Impact Factor
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    ABSTRACT: The effects of individual SNP and the variation explained by sets of SNP associated with DMI, metabolic midtest BW, BW gain, and feed efficiency, expressed as phenotypic and genetic residual feed intake, were estimated from BW and the individual feed intake of 1,159 steers on dry lot offered a 3.0 Mcal/kg ration for at least 119 d before slaughter. Parents of these F(1) × F(1) (F(1)(2)) steers were AI-sired F(1) progeny of Angus, Charolais, Gelbvieh, Hereford, Limousin, Red Angus, and Simmental bulls mated to US Meat Animal Research Center Angus, Hereford, and MARC III composite females. Steers were genotyped with the BovineSNP50 BeadChip assay (Illumina Inc., San Diego, CA). Effects of 44,163 SNP having minor allele frequencies >0.05 in the F(1)(2) generation were estimated with a mixed model that included genotype, breed composition, heterosis, age of dam, and slaughter date contemporary groups as fixed effects, and a random additive genetic effect with recorded pedigree relationships among animals. Variance in this population attributable to sets of SNP was estimated with models that partitioned the additive genetic effect into a polygenic component attributable to pedigree relationships and a genotypic component attributable to genotypic relationships. The sets of SNP evaluated were the full set of 44,163 SNP and subsets containing 6 to 40,000 SNP selected according to association with phenotype. Ninety SNP were strongly associated (P < 0.0001) with at least 1 efficiency or component trait; these 90 accounted for 28 to 46% of the total additive genetic variance of each trait. Trait-specific sets containing 96 SNP having the strongest associations with each trait explained 50 to 87% of additive variance for that trait. Expected accuracy of steer breeding values predicted with pedigree and genotypic relationships exceeded the accuracy of their sires predicted without genotypic information, although gains in accuracy were not sufficient to encourage that performance testing be replaced by genotyping and genomic evaluations.
    Journal of Animal Science 02/2011; 89(6):1731-41. · 2.09 Impact Factor

Publication Stats

928 Citations
188.81 Total Impact Points


  • 2012
    • North Carolina State University
      • Department of Animal Science
      Raleigh, NC, United States
  • 1998–2012
    • United States Department of Agriculture
      • Agricultural Research Service (ARS)
      Washington, D. C., DC, United States
  • 2007
    • Agricultural Research Service
      Kerrville, Texas, United States
  • 2002–2004
    • University of Alberta
      • Department of Agricultural, Food, and Nutritional Science
      Edmonton, Alberta, Canada
  • 1994–1995
    • Colorado State University
      • Department of Animal Sciences
      Fort Collins, CO, United States