[Show abstract][Hide abstract] ABSTRACT: The identification of genetic markers associated with complex traits that are expensive to record such as feed intake or feed efficiency would allow these traits to be included in selection programs. To identify large-effect QTL, we performed a series of genome-wide association studies and functional analyses using 50 K and 770 K SNP genotypes scored in 5,133 animals from 4 independent beef cattle populations (Cycle VII, Angus, Hereford and Simmental x Angus) with phenotypes for average daily gain, dry matter intake, metabolic mid-test body weight and residual feed intake.
A total of 5, 6, 11 and 10 significant QTL (defined as 1-Mb genome windows with Bonferroni-corrected P-value <0.05) were identified for average daily gain, dry matter intake, metabolic mid-test body weight and residual feed intake, respectively. The identified QTL were population-specific and had little overlap across the 4 populations. The pleiotropic or closely linked QTL on BTA 7 at 23 Mb identified in the Angus population harbours a promising candidate gene ACSL6 (acyl-CoA synthetase long-chain family member 6), and was the largest effect QTL associated with dry matter intake and mid-test body weight explaining 10.39% and 14.25% of the additive genetic variance, respectively. Pleiotropic or closely linked QTL associated with average daily gain and mid-test body weight were detected on BTA 6 at 38 Mb and BTA 7 at 93 Mb confirming previous reports. No QTL for residual feed intake explained more than 2.5% of the additive genetic variance in any population. Marker-based estimates of heritability ranged from 0.21 to 0.49 for residual feed intake across the 4 populations.
This GWAS study, which is the largest performed for feed efficiency and its component traits in beef cattle to date, identified several large-effect QTL that cumulatively explained a significant percentage of additive genetic variance within each population. Differences in the QTL identified among the different populations may be due to differences in power to detect QTL, environmental variation, or differences in the genetic architecture of trait variation among breeds. These results enhance our understanding of the biology of growth, feed intake and utilisation in beef cattle.
[Show abstract][Hide abstract] ABSTRACT: Abstract Text: Length of post-partum anoestrus affects pregnancy rates in cattle. Our objective was to predict pregnancy outcome using a selected panel of markers. To select informative markers several experiments were combined in a meta-analysis: 1) genome-wide association studies for post-partum anoestrous interval using two breeds (Brahman and Tropical Composite), 2) hypothalamic gene expression analysis comparing cycling Brahman cows to cows in post-partum anoestrus, 3) identification of transcription factors linked to selected genes, and 4) whole-genome and RNA-sequencing data. A panel of 140 SNP emerged out of this meta-analysis. Its predictive performance was compared to sets of randomly selected SNP. The 140 SNP panel accounted for 29% of additive variation in rebreeding in an independent, multi-breed cow herd, and accounted for more variation than random sets. Selected marker panels based on functional genomics present an alternative to genomic selection procedures in beef cattle.
10th World Congress on Genetics Applied to Livestock Production; 08/2014
[Show abstract][Hide abstract] ABSTRACT: Abstract Text: The efficient utilization of feedstuffs is an economically important trait in beef production. The rumen is important to the digestive process of steers interacting with feed, microbial populations, and volatile fatty acids indicating it may play a critical role in feed efficiency. To gain an understanding of the molecules and pathways involved in gain, intake and utilization and identify candidate genes associated with steer feed efficiency, RNA-Seq was performed on rumen papillae from steers with extreme feed efficiency phenotypes (n=16). The study population was divided into four Cartesian quadrants for intake × gain and steers (n=4) from each quadrant were sampled. Three statistical analyses were performed to identify differentially expressed genes among feed efficiency phenotype. Two analyses were performed on total gene expression, the Negative Binomial and the Kruskall-Wallis. A separate analysis was performed by Cofactor Genomics on exon cluster expression. The Negative Binomial analysis identified 27 genes differentially expressed among feed efficiency phenotypes based on false discovery rate (FDR < 0.05). The Kruskall-Wallis analysis identified 19 differentially expressed genes based on P-value (P < 0.05). Cofactor Genomics identified 187 differentially expressed genes based on P-value (P < 0.05) and fold change (FC >2). All genes identified by the Negative Binomial and Kruskall-Wallis analyses were tested for validation using real-time PCR and a subset of genes (n=23) identified by Cofactor Genomics were tested for validation. Several genes (ACAT1, CYP1A2, KLK10, KLK12, MIF, PDEE1A, and MYL1) were identified by at least one analysis in this study and are supported by other studies. Five genes were identified by more than one analysis in this study (KLK7, KLK10, KLK12, ARHGAP27, and RGS5). Cell death and survival, immunological disease, and metabolic disease were the top gene networks identified in association with gain, intake, and efficiency respectively. Genes expressed in rumen papillae of beef steers may play a role in the feed efficiency of the animal. USDA is an equal opportunity provider and employer.
Keywords: RNA-Seq, Beef Cattle, Rumen Papillae
[Show abstract][Hide abstract] ABSTRACT: Abstract Text:
To develop a resource to identify polymorphisms present in common beef cattle breeds, and relate those polymorphisms to phenotypic differences, low-coverage genomic sequence was obtained on 186 purebred bulls from 15 predominant breeds in the United States, and 84 crossbred sons of these bulls. These bulls were influential in the USMARC Germplasm evaluation population (GPE), enabling sequence-derived genotypes to be imputed throughout the population of individuals genotyped with the BovineHD (HD; n=1027) and BovineSNP50 (n=8697) platforms. Variants detected from these sequences were classified according to predicted effect on gene function, with 4,699 predicted to cause a loss of gene function (LOF); 66,484 non-synonymous (NS) SNP causing an amino acid change in the protein produced by a gene, and 59,092 which may have a role in gene regulation (REG), occurring in annotated non-coding RNA or regions immediately surrounding a gene. Imputed genotypes of 685 purebred genotyped grandsires in the GPE population (18 to 74 bulls per breed) were used to represent each breed to assess diversity and determine breed effects on carcass merit. Relative genetic distances between breeds were consistent regardless of the set of genotypes considered. Brahman was furthest from any other breed, and Hereford the most distant from any other taurine breed. Similar distances were obtained using HD and ND SNP. The mean between-breed distance estimated with REG variants was about 10% higher than HD or NS, and distances using LOF variants were about 30% lower. Heritability estimates from GBLUP considering records of 5990 genotyped carcasses and treating breeds as genetic groups, ranged from 0.49 for ribeye area to 0.59 for marbling score when using only HD genotypes. Similar estimates were obtained in independent analyses using NS and REG, but the LOF estimates were lower, between 0.29 and 0.32. In four-component analyses with different genomic relationship matrices for HD, NS, REG and LOF, 68% to 76% of additive variance was attributed to HD, and 2% or less to LOF. Contributions of both NS and REG were between 10% and 16% for carcass weight, marbling and fat thickness. For ribeye area, the REG component was 31% of additive variance, and NS was nil. The GBLUP breed solutions were consistent with breed differences estimated in previous analyses. USDA is an equal opportunity employer and provider.
Keywords: beef cattle, genomic prediction, functional polymorphisms
[Show abstract][Hide abstract] 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 genomewide association study (GWAS) approach based on the >700,000 SNP marker assay, using a procedure based on genotyping multianimal 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 U.S. Meat Animal Research Center, 2 large commercial ranch populations, and a number of smaller populations (<100 head) across the United States. We detected 2 SNP with significant genomewide association (P ≤ 1.49 × 10(-7)), on BTA21 and BTA29, 3 SNP with suggestive associations (P ≤ 2.91 × 10(-6)) on BTA5, and 1 SNP with suggestive association each on BTA1 and BTA25. In addition to our novel findings, we confirmed previously published associations for SNP on BTA-X and all autosomes except 3 (BTA21, BTA22, and BTA28) 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 in cattle.
Journal of Animal Science 05/2014; 92(5):1945-1957. · 2.09 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The length of the post-partum anoestrous interval affects reproductive efficiency in many tropical beef cattle herds. In this study, results from genome-wide association studies (Experiment 1: GWAS) and gene expression (Experiment 2: microarray) were combined in a systems approach to reveal genetic markers, genes and pathways underlying the physiology of post-partum anoestrus in tropically adapted cattle. The microarray study measured the expression of 13,964 genes in the hypothalamus of Brahman cows. A total of 366 genes were differentially expressed (DE) in the post-partum period, when acyclic cows were compared to cows that had resumed ovarian cycles. Associated markers (P<0.05) from a high density GWAS pointed to 2,829 genes that were associated with post-partum anoestrous interval (PPAI) in two populations of beef cattle: Brahman and Tropical Composite. Together the experiments provided evidence for 63 genes that are likely to influence the resumption of ovulation post-partum in tropically adapted beef cattle. Functional annotation analysis revealed that some of the 63 genes have known roles in hormonal activity, energy balance and neuronal synapse plasticity. Polymorphisms within candidate genes identified by this systems approach could have biological significance in post-partum anoestrus and help select Zebu (Bos indicus) influenced cattle with genetic potential for shorter post-partum anoestrus.
[Show abstract][Hide abstract] 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
[Show abstract][Hide abstract] ABSTRACT: Puberty is a complex physiological event by which animals mature into an adult capable of sexual reproduction. In order to enhance our understanding of the genes and regulatory pathways and networks involved in puberty, we characterized the transcriptome of five reproductive tissues (i.e. hypothalamus, pituitary gland, ovary, uterus, and endometrium) as well as tissues known to be relevant to growth and metabolism needed to achieve puberty (i.e., longissimus dorsi muscle, adipose, and liver). These tissues were collected from pre- and post-pubertal Brangus heifers (3/8 Brahman; Bos indicus x 5/8 Angus; Bos taurus) derived from a population of cattle used to identify quantitative trait loci associated with fertility traits (i.e., age of first observed corpus luteum (ACL), first service conception (FSC), and heifer pregnancy (HPG)). In order to exploit the power of complementary omics analyses, pre- and post-puberty co-expression gene networks were constructed by combining the results from genome-wide association studies (GWAS), RNA-Seq, and bovine transcription factors. Eight tissues among pre-pubertal and post-pubertal Brangus heifers revealed 1,515 differentially expressed and 943 tissue-specific genes within the 17,832 genes confirmed by RNA-Seq analysis. The hypothalamus experienced the most notable up-regulation of genes via puberty (i.e., 204 out of 275 genes). Combining the results of GWAS and RNA-Seq, we identified 25 loci containing a single nucleotide polymorphism (SNP) associated with ACL, FSC, and (or) HPG. Seventeen of these SNP were within a gene and 13 of the genes were expressed in uterus or endometrium. Multi-tissue omics analyses revealed 2,450 co-expressed genes relative to puberty. The pre-pubertal network had 372,861 connections whereas the post-pubertal network had 328,357 connections. A sub-network from this process revealed key transcriptional regulators (i.e., PITX2, FOXA1, DACH2, PROP1, SIX6, etc.). Results from these multi-tissue omics analyses improve understanding of the number of genes and their complex interactions for puberty in cattle.
PLoS ONE 01/2014; 9(7):e102551. · 3.53 Impact Factor
[Show abstract][Hide abstract] 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
[Show abstract][Hide abstract] 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.
[Show abstract][Hide abstract] 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
[Show abstract][Hide abstract] 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
[Show abstract][Hide abstract] 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
[Show abstract][Hide abstract] 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
[Show abstract][Hide abstract] 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
[Show abstract][Hide abstract] 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
[Show abstract][Hide abstract] 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
[Show abstract][Hide abstract] 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.
[Show abstract][Hide abstract] 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
[Show abstract][Hide abstract] 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.