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Antonelo DS, Dos Santos-Donado PR, Ferreira CR, Colnago LA, Ocampos FMM, Ribeiro GH, Ventura RV, Gerrard DE, Delgado EF, Contreras-Castillo CJ, Balieiro JCC. Exploratory lipidome and metabolome profiling contributes to understanding differences in high and normal ultimate pH beef. Meat Sci 2022; 194:108978. [PMID: 36116280 DOI: 10.1016/j.meatsci.2022.108978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 09/05/2022] [Accepted: 09/06/2022] [Indexed: 10/14/2022]
Abstract
The aim of this work was to compare the lipidome and metabolome profiling in the Longissimus thoracis muscle early and late postmortem from high and normal ultimate pH (pHu) beef. Lipid profiling discriminated between high and normal pHu beef based on fatty acid metabolism and mitochondrial beta-oxidation of long chain saturated fatty acids at 30 min postmortem, and phospholipid biosynthesis at 44 h postmortem. Metabolite profiling also discriminated between high and normal pHu beef, mainly through glutathione, purine, arginine and proline, and glycine, serine and threonine metabolisms at 30 min postmortem, and glycolysis, TCA cycle, glutathione, tyrosine, and pyruvate metabolisms at 44 h postmortem. Lipid and metabolite profiles showed reduced glycolysis and increased use of alternative energy metabolic processes that were central to differentiating high and normal pHu beef. Phospholipid biosynthesis modification suggested high pHu beef experienced greater oxidative stress.
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Affiliation(s)
- Daniel S Antonelo
- College of Veterinary Medicine and Animal Science, University of Sao Paulo, Pirassununga, SP 13635-900, Brazil.
| | | | - Christina R Ferreira
- Metabolite Profiling Facility, Bindley Bioscience Center, Purdue University, West Lafayette, IN 47907, USA
| | - Luiz A Colnago
- EMBRAPA Instrumentation, Sao Carlos, SP 13560-970, Brazil
| | | | | | - Ricardo V Ventura
- College of Veterinary Medicine and Animal Science, University of Sao Paulo, Pirassununga, SP 13635-900, Brazil
| | - David E Gerrard
- Department of Animal and Poultry Sciences, Virginia Tech, Blacksburg, VA 24061, USA
| | - Eduardo F Delgado
- Department of Animal Science, University of Sao Paulo, Piracicaba, SP 13418-900, Brazil
| | | | - Julio C C Balieiro
- College of Veterinary Medicine and Animal Science, University of Sao Paulo, Pirassununga, SP 13635-900, Brazil
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Lee K, Munro J, Ventura RV, Schenkel FS, Voort GV, Cánovas4 A. PSX-A-8 Updating Pre-Existing Genetic Evaluations System to Evaluate High-Throughput Data in Purebred and Crossbred Beef Cattle. J Anim Sci 2022. [DOI: 10.1093/jas/skac247.513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
High-throughput technologies are available to aid producers in efficiently and sustainably raising many animals, including automated sensory technologies for phenotype measurement and genomic data. However, uptake of genetic selection by the beef cattle sector has been limited by segmentation of the industry. Therefore, easy to use genetic evaluations systems (GES) that can convert ‘big data’ into real-time and comprehensive results are needed, so that beef cattle producers can utilize the technologies that are becoming available to make accurate breeding decisions. The purpose of this study is to update a pre-existing purebred and crossbred beef cattle GES to become both flexible and efficient in its ability to evaluate high-throughput phenotypic data, and to assess the feasibility of including genotypes in a single-step genetic evaluation procedure (ssGBLUP). Firstly, computational operations required for the calculation of breeding values will be quantified and evaluated. A purebred and crossbred reference population will be assembled using data from Canadian beef breed associations purebred (n = 186,928) and commercial animals (n = 14,406). Multiple breeds will be considered (Angus, Charolais, Hereford, and Simmental), and multiple phenotypes analyzed (birth weight, weaning weight, yearling weight, and calving ease). Key component matrix operations with and without the use of Python Libraries (NumPy) will be evaluated. Computational performance of the different strategies will be compared, including CPU time, and memory allocation. Subsequently, a simulated ssGBLUP will be conducted using a population which mimics that observed in the preliminary analysis. Computational costs associated with the implementation of ssGBLUP will be compared to the existing GES. The results of this study will be directly applied to provide beef producers with the tools to improve the genetics of their own herds. This will facilitate the uptake of technology by the industry, thus increasing the economic value and sustainability of beef production.
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Affiliation(s)
| | | | | | - Flavio S Schenkel
- University of Guelph, Department of Animal Biosciences, Centre for Genetic Improvement of Livestock
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Selli A, Ventura RV, Fonseca PAS, Buzanskas ME, Andrietta LT, Balieiro JCC, Brito LF. Detection and Visualization of Heterozygosity-Rich Regions and Runs of Homozygosity in Worldwide Sheep Populations. Animals (Basel) 2021; 11:2696. [PMID: 34573664 PMCID: PMC8472390 DOI: 10.3390/ani11092696] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 09/11/2021] [Accepted: 09/13/2021] [Indexed: 12/25/2022] Open
Abstract
In this study, we chose 17 worldwide sheep populations of eight breeds, which were intensively selected for different purposes (meat, milk, or wool), or locally-adapted breeds, in order to identify and characterize factors impacting the detection of runs of homozygosity (ROH) and heterozygosity-rich regions (HRRs) in sheep. We also applied a business intelligence (BI) tool to integrate and visualize outputs from complementary analyses. We observed a prevalence of short ROH, and a clear distinction between the ROH profiles across populations. The visualizations showed a fragmentation of medium and long ROH segments. Furthermore, we tested different scenarios for the detection of HRR and evaluated the impact of the detection parameters used. Our findings suggest that HRRs are small and frequent in the sheep genome; however, further studies with higher density SNP chips and different detection methods are suggested for future research. We also defined ROH and HRR islands and identified common regions across the populations, where genes related to a variety of traits were reported, such as body size, muscle development, and brain functions. These results indicate that such regions are associated with many traits, and thus were under selective pressure in sheep breeds raised for different purposes. Interestingly, many candidate genes detected within the HRR islands were associated with brain integrity. We also observed a strong association of high linkage disequilibrium pattern with ROH compared with HRR, despite the fact that many regions in linkage disequilibrium were not located in ROH regions.
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Affiliation(s)
- Alana Selli
- Department of Nutrition and Animal Production, School of Veterinary Medicine and Animal Science (FMVZ), University of São Paulo (USP), Pirassununga 13635-900, São Paulo, Brazil; (L.T.A.); (J.C.C.B.)
| | - Ricardo V. Ventura
- Department of Nutrition and Animal Production, School of Veterinary Medicine and Animal Science (FMVZ), University of São Paulo (USP), Pirassununga 13635-900, São Paulo, Brazil; (L.T.A.); (J.C.C.B.)
| | - Pablo A. S. Fonseca
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada;
| | - Marcos E. Buzanskas
- Department of Animal Science, Federal University of Paraíba, João Pessoa 58051-900, Paraiba, Brazil;
| | - Lucas T. Andrietta
- Department of Nutrition and Animal Production, School of Veterinary Medicine and Animal Science (FMVZ), University of São Paulo (USP), Pirassununga 13635-900, São Paulo, Brazil; (L.T.A.); (J.C.C.B.)
| | - Júlio C. C. Balieiro
- Department of Nutrition and Animal Production, School of Veterinary Medicine and Animal Science (FMVZ), University of São Paulo (USP), Pirassununga 13635-900, São Paulo, Brazil; (L.T.A.); (J.C.C.B.)
| | - Luiz F. Brito
- Department of Animal Sciences, Purdue University, West Lafayette, IN 47907, USA;
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Fernandes Júnior GA, Carvalheiro R, de Oliveira HN, Sargolzaei M, Costilla R, Ventura RV, Fonseca LFS, Neves HHR, Hayes BJ, de Albuquerque LG. Imputation accuracy to whole-genome sequence in Nellore cattle. Genet Sel Evol 2021; 53:27. [PMID: 33711929 PMCID: PMC7953568 DOI: 10.1186/s12711-021-00622-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Accepted: 03/05/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND A cost-effective strategy to explore the complete DNA sequence in animals for genetic evaluation purposes is to sequence key ancestors of a population, followed by imputation mechanisms to infer marker genotypes that were not originally reported in a target population of animals genotyped with single nucleotide polymorphism (SNP) panels. The feasibility of this process relies on the accuracy of the genotype imputation in that population, particularly for potential causal mutations which may be at low frequency and either within genes or regulatory regions. The objective of the present study was to investigate the imputation accuracy to the sequence level in a Nellore beef cattle population, including that for variants in annotation classes which are more likely to be functional. METHODS Information of 151 key sequenced Nellore sires were used to assess the imputation accuracy from bovine HD BeadChip SNP (~ 777 k) to whole-genome sequence. The choice of the sires aimed at optimizing the imputation accuracy of a genotypic database, comprised of about 10,000 genotyped Nellore animals. Genotype imputation was performed using two computational approaches: FImpute3 and Minimac4 (after using Eagle for phasing). The accuracy of the imputation was evaluated using a fivefold cross-validation scheme and measured by the squared correlation between observed and imputed genotypes, calculated by individual and by SNP. SNPs were classified into a range of annotations, and the accuracy of imputation within each annotation classification was also evaluated. RESULTS High average imputation accuracies per animal were achieved using both FImpute3 (0.94) and Minimac4 (0.95). On average, common variants (minor allele frequency (MAF) > 0.03) were more accurately imputed by Minimac4 and low-frequency variants (MAF ≤ 0.03) were more accurately imputed by FImpute3. The inherent Minimac4 Rsq imputation quality statistic appears to be a good indicator of the empirical Minimac4 imputation accuracy. Both software provided high average SNP-wise imputation accuracy for all classes of biological annotations. CONCLUSIONS Our results indicate that imputation to whole-genome sequence is feasible in Nellore beef cattle since high imputation accuracies per individual are expected. SNP-wise imputation accuracy is software-dependent, especially for rare variants. The accuracy of imputation appears to be relatively independent of annotation classification.
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Affiliation(s)
| | - Roberto Carvalheiro
- School of Agricultural and Veterinarian Sciences, UNESP, Jaboticabal, SP, 14884-900, Brazil.,National Council for Scientific and Technological Development, CNPq, Brasília, DF, 71605-001, Brazil
| | - Henrique N de Oliveira
- School of Agricultural and Veterinarian Sciences, UNESP, Jaboticabal, SP, 14884-900, Brazil.,National Council for Scientific and Technological Development, CNPq, Brasília, DF, 71605-001, Brazil
| | - Mehdi Sargolzaei
- Ontario Veterinary College, UG, Guelph, Canada.,Select Sires Inc., Plain City, OH, USA
| | - Roy Costilla
- Queensland Alliance for Agriculture and Food Innovation, UQ, Brisbane, QLD, 4072, Australia
| | - Ricardo V Ventura
- School of Veterinary Medicine and Animal Science, USP, Pirassununga, SP, 13635-900, Brazil
| | - Larissa F S Fonseca
- School of Agricultural and Veterinarian Sciences, UNESP, Jaboticabal, SP, 14884-900, Brazil
| | | | - Ben J Hayes
- Queensland Alliance for Agriculture and Food Innovation, UQ, Brisbane, QLD, 4072, Australia
| | - Lucia G de Albuquerque
- School of Agricultural and Veterinarian Sciences, UNESP, Jaboticabal, SP, 14884-900, Brazil. .,National Council for Scientific and Technological Development, CNPq, Brasília, DF, 71605-001, Brazil.
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Ventura RV, Lopes RZ, Andrietta LT, Bussiman F, Balieiro J, carvalheiro R, Silva F, Brito L, Alves A. 402 Audio information retrieval for describing gait patterns in Brazilian horses. J Anim Sci 2020. [DOI: 10.1093/jas/skaa278.048] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
The Brazilian gaited horse industry is growing steadily, even after a recession period that affected different economic sectors in the whole country. Recent numbers suggested an increase on the exports, which reveals the relevance of this horse market segment. Horses are classified according to the gait criteria, which divide the horses in two groups associated with the animal movements: lateral (Marcha Picada) or diagonal (Marcha_Batida). These two gait groups usually show remarkable differences related to speed and number of steps per fixed unit of time, among other factors. Audio retrieval refers to the process of information extraction obtained from audio signals. This new data analysis area, in comparison to traditional methods to evaluate and classify gait types (as, for example, human subjective evaluation and video monitoring), provides a potential method to collect phenotypes in a reduced cost manner. Audio files (n = 80) were obtained after extracting audio features from freely available YouTube videos. Videos were manually labeled according to the two gait groups (Marcha Picada or Marcha Batida) and thirty animals were used after a quality control filter step. This study aimed to investigate different metrics associated with audio signal processing, in order to first cluster animals according to the gait type and subsequently include additional traits that could be useful to improve accuracy during the identification of genetically superior animals. Twenty-eight metrics, based on frequency or physical audio aspects, were carried out individually or in groups of relative importance to perform Principal Component Analysis (PCA), as well as to describe the two gait types. The PCA results indicated that over 87% of the animals were correctly clustered. Challenges regarding environmental interferences and noises must be further investigated. These first findings suggest that audio information retrieval could potentially be implemented in animal breeding programs, aiming to improve horse gait.
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Alves AAC, Espigolan R, Bresolin T, Costa RM, Fernandes Júnior GA, Ventura RV, Carvalheiro R, Albuquerque LG. Genome-enabled prediction of reproductive traits in Nellore cattle using parametric models and machine learning methods. Anim Genet 2020; 52:32-46. [PMID: 33191532 DOI: 10.1111/age.13021] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/13/2020] [Indexed: 12/31/2022]
Abstract
This study aimed to assess the predictive ability of different machine learning (ML) methods for genomic prediction of reproductive traits in Nellore cattle. The studied traits were age at first calving (AFC), scrotal circumference (SC), early pregnancy (EP) and stayability (STAY). The numbers of genotyped animals and SNP markers available were 2342 and 321 419 (AFC), 4671 and 309 486 (SC), 2681 and 319 619 (STAY) and 3356 and 319 108 (EP). Predictive ability of support vector regression (SVR), Bayesian regularized artificial neural network (BRANN) and random forest (RF) were compared with results obtained using parametric models (genomic best linear unbiased predictor, GBLUP, and Bayesian least absolute shrinkage and selection operator, BLASSO). A 5-fold cross-validation strategy was performed and the average prediction accuracy (ACC) and mean squared errors (MSE) were computed. The ACC was defined as the linear correlation between predicted and observed breeding values for categorical traits (EP and STAY) and as the correlation between predicted and observed adjusted phenotypes divided by the square root of the estimated heritability for continuous traits (AFC and SC). The average ACC varied from low to moderate depending on the trait and model under consideration, ranging between 0.56 and 0.63 (AFC), 0.27 and 0.36 (SC), 0.57 and 0.67 (EP), and 0.52 and 0.62 (STAY). SVR provided slightly better accuracies than the parametric models for all traits, increasing the prediction accuracy for AFC to around 6.3 and 4.8% compared with GBLUP and BLASSO respectively. Likewise, there was an increase of 8.3% for SC, 4.5% for EP and 4.8% for STAY, comparing SVR with both GBLUP and BLASSO. In contrast, the RF and BRANN did not present competitive predictive ability compared with the parametric models. The results indicate that SVR is a suitable method for genome-enabled prediction of reproductive traits in Nellore cattle. Further, the optimal kernel bandwidth parameter in the SVR model was trait-dependent, thus, a fine-tuning for this hyper-parameter in the training phase is crucial.
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Affiliation(s)
- A A C Alves
- Department of Animal Science, School of Agricultural and Veterinary Sciences, Sao Paulo State University (UNESP), Jaboticabal, 14884-900, Brazil
| | - R Espigolan
- Department of Animal Science, School of Agricultural and Veterinary Sciences, Sao Paulo State University (UNESP), Jaboticabal, 14884-900, Brazil
| | - T Bresolin
- Department of Animal Science, School of Agricultural and Veterinary Sciences, Sao Paulo State University (UNESP), Jaboticabal, 14884-900, Brazil
| | - R M Costa
- Department of Exact Sciences, School of Agricultural and Veterinary Sciences, Sao Paulo State University (UNESP), Jaboticabal, 4884-900, Brazil
| | - G A Fernandes Júnior
- Department of Animal Science, School of Agricultural and Veterinary Sciences, Sao Paulo State University (UNESP), Jaboticabal, 14884-900, Brazil
| | - R V Ventura
- Department of Animal Nutrition and Production, School of Veterinary Medicine and Animal Science, University of Sao Paulo (USP), Pirassununga, 13635-900, Brazil
| | - R Carvalheiro
- Department of Animal Science, School of Agricultural and Veterinary Sciences, Sao Paulo State University (UNESP), Jaboticabal, 14884-900, Brazil.,National Council of Technological and Scientific Development (CNPq), Brasília, 71605-001, Brazil
| | - L G Albuquerque
- Department of Animal Science, School of Agricultural and Veterinary Sciences, Sao Paulo State University (UNESP), Jaboticabal, 14884-900, Brazil.,National Council of Technological and Scientific Development (CNPq), Brasília, 71605-001, Brazil
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Ventura RV, Brito LF, Oliveira GA, Daetwyler HD, Schenkel FS, Sargolzaei M, Vandervoort G, Fonseca e Silva F, Miller SP, Carvalho ME, Santana MHA, Mattos EC, Fonseca P, Eler JP, Ferraz JBS. A comprehensive comparison of high-density SNP panels and an alternative ultra-high-density panel for genomic analyses in Nellore cattle. Anim Prod Sci 2020. [DOI: 10.1071/an18305] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
There is evidence that some genotyping platforms might not work very well for Zebu cattle when compared with Taurine breeds. In addition, the availability of panels with low to moderate number of overlapping markers is a limitation for combining datasets for genomic evaluations, especially when animals are genotyped using different SNP panels. In the present study, we compared the performance of medium- and high-density (HD) commercially available panels and investigated the feasibility of developing an ultra-HD panel (SP) containing markers from an Illumina (HD_I) and an Affymetrix (HD_A) panels. The SP panel contained 1123442 SNPs. After performing SNP pruning on the basis of linkage disequilibrium, HD_A, HD_I and SP contained 429624, 365225 and 658770 markers distributed across the whole genome. The overall mean proportion of markers pruned out per chromosome for HD_A, HD_I and SP was 15.17%, 43.18%, 38.63% respectively. The HD_I panel presented the highest mean number of runs-of-homozygosity segments per animal (45.48%, an increment of 5.11% compared with SP) and longer segments, on average (3057.95 kb per segment), than did both HD_A and SP. HD_I also showed the highest mean number of SNPs per run-of-homozygosity segment. Consequently, the majority of animals presented the highest genomic inbreeding levels when genotyped using HD_I. The visual examination of marker distribution along the genome illustrated uncovered regions among the different panels. Haplotype-block comparison among panels and the average haplotype size constructed on the basis of HD_A were smaller than those from HD_I. The average number of SNPs per haplotype was different between HD_A and HD_I. Both HD_A and HD_I panels achieved high imputation accuracies when used as the lower-density panels for imputing to SP. However, imputation accuracy from HD_A to SP was greater than was imputation from HD_I to SP. Imputation from one HD panel to the other is also feasible. Low- and medium-density panels, composed of markers that are subsets of both HD_A and HD_I panels, should be developed to achieve better imputation accuracies to both HD levels. Therefore, the genomic analyses performed in the present study showed significant differences among the SNP panels used.
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Oliveira Júnior GA, Santos DJA, Cesar ASM, Boison SA, Ventura RV, Perez BC, Garcia JF, Ferraz JBS, Garrick DJ. Fine mapping of genomic regions associated with female fertility in Nellore beef cattle based on sequence variants from segregating sires. J Anim Sci Biotechnol 2019; 10:97. [PMID: 31890201 PMCID: PMC6913038 DOI: 10.1186/s40104-019-0403-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Accepted: 11/11/2019] [Indexed: 12/26/2022] Open
Abstract
Background Impaired fertility in cattle limits the efficiency of livestock production systems. Unraveling the genetic architecture of fertility traits would facilitate their improvement by selection. In this study, we characterized SNP chip haplotypes at QTL blocks then used whole-genome sequencing to fine map genomic regions associated with reproduction in a population of Nellore (Bos indicus) heifers. Methods The dataset comprised of 1337 heifers genotyped using a GeneSeek® Genomic Profiler panel (74677 SNPs), representing the daughters from 78 sires. After performing marker quality control, 64800 SNPs were retained. Haplotypes carried by each sire at six previously identified QTL on BTAs 5, 14 and 18 for heifer pregnancy and BTAs 8, 11 and 22 for antral follicle count were constructed using findhap software. The significance of the contrasts between the effects of every two paternally-inherited haplotype alleles were used to identify sires that were heterozygous at each QTL. Whole-genome sequencing data localized to the haplotypes from six sires and 20 other ancestors were used to identify sequence variants that were concordant with the haplotype contrasts. Enrichment analyses were applied to these variants using KEGG and MeSH libraries. Results A total of six (BTA 5), six (BTA 14) and five (BTA 18) sires were heterozygous for heifer pregnancy QTL whereas six (BTA 8), fourteen (BTA 11), and five (BTA 22) sires were heterozygous for number of antral follicles’ QTL. Due to inadequate representation of many haplotype alleles in the sequenced animals, fine mapping analysis could only be reliably performed for the QTL on BTA 5 and 14, which had 641 and 3733 concordant candidate sequence variants, respectively. The KEGG “Circadian rhythm” and “Neurotrophin signaling pathway” were significantly associated with the genes in the QTL on BTA 5 whereas 32 MeSH terms were associated with the QTL on BTA 14. Among the concordant sequence variants, 0.2% and 0.3% were classified as missense variants for BTAs 5 and 14, respectively, highlighting the genes MTERF2, RTMB, ENSBTAG00000037306 (miRNA), ENSBTAG00000040351, PRKDC, and RGS20. The potential causal mutations found in the present study were associated with biological processes such as oocyte maturation, embryo development, placenta development and response to reproductive hormones. Conclusions The identification of heterozygous sires by positionally phasing SNP chip data and contrasting haplotype effects for previously detected QTL can be used for fine mapping to identify potential causal mutations and candidate genes. Genomic variants on genes MTERF2, RTBC, miRNA ENSBTAG00000037306, ENSBTAG00000040351, PRKDC, and RGS20, which are known to have influence on reproductive biological processes, were detected.
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Affiliation(s)
- Gerson A Oliveira Júnior
- 1Department of Veterinary Medicine, University of São Paulo (USP), Faculty of Animal Science and Food Engineer, Pirassununga, SP Brazil.,2Department of Animal Bioscience, Center for Genetic Improvement of Livestock, University of Guelph, Guelph, ON Canada
| | - Daniel J A Santos
- 3Department of Animal and Avian Sciences, University of Maryland, College Park, Maryland, USA
| | - Aline S M Cesar
- 4Department of Animal Science, University of São Paulo (USP), Piracicaba, SP Brazil
| | - Solomon A Boison
- 5Department of Sustainable Agricultural Systems, University of Natural Resources and Life Sciences, Vienna, Austria
| | - Ricardo V Ventura
- 2Department of Animal Bioscience, Center for Genetic Improvement of Livestock, University of Guelph, Guelph, ON Canada.,6Department of Animal Nutrition and Production, School of Veterinary Medicine and Animal Science, University of São Paulo (USP), Pirassununga, Brazil
| | - Bruno C Perez
- 1Department of Veterinary Medicine, University of São Paulo (USP), Faculty of Animal Science and Food Engineer, Pirassununga, SP Brazil
| | - José F Garcia
- 7Department of Support, Production and Animal Health, School of Veterinary Medicine, São Paulo State University (Unesp), Araçatuba, SP Brazil
| | - José Bento S Ferraz
- 1Department of Veterinary Medicine, University of São Paulo (USP), Faculty of Animal Science and Food Engineer, Pirassununga, SP Brazil
| | - Dorian J Garrick
- 8School of Agriculture, Massey University, Ruakura Ag Centre, Hamilton, New Zealand
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Perez BC, Balieiro JCC, Carvalheiro R, Tirelo F, Oliveira Junior GA, Dementshuk JM, Eler JP, Ferraz JBS, Ventura RV. Accounting for population structure in selective cow genotyping strategies. J Anim Breed Genet 2018; 136:23-39. [DOI: 10.1111/jbg.12369] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Revised: 11/09/2018] [Accepted: 11/12/2018] [Indexed: 11/26/2022]
Affiliation(s)
- Bruno C. Perez
- Faculdade de Zootecnia e Engenharia de Alimentos; Universidade de São Paulo; Pirassununga Brasil
| | - Julio C. C. Balieiro
- Faculdade de Medicina Veterinária e Zootecnia; Universidade de São Paulo; Pirassununga Brasil
| | - Roberto Carvalheiro
- Departamento de Zootecnia; Universidade Estadual Paulista Julio de Mesquita Filho; Jaboticabal Brasil
| | | | - Gerson A. Oliveira Junior
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences; University of Guelph; Guelph ON Canada
| | - Juliana M. Dementshuk
- Departamento de Zootecnia; Universidade Federal do Rio Grande do Sul; Porto Alegre Brasil
| | - Joanir P. Eler
- Grupo de Melhoramento Animal e Biotecnologia, Departmento de Ciências Veterinárias, Faculdade de Zootecnia e Engenharia de Alimentos; Universidade de São Paulo (GMAB-FZEA/USP); Pirassununga Brasil
| | - José B. S. Ferraz
- Grupo de Melhoramento Animal e Biotecnologia, Departmento de Ciências Veterinárias, Faculdade de Zootecnia e Engenharia de Alimentos; Universidade de São Paulo (GMAB-FZEA/USP); Pirassununga Brasil
| | - Ricardo V. Ventura
- Faculdade de Medicina Veterinária e Zootecnia; Universidade de São Paulo; Pirassununga Brasil
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Morota G, Ventura RV, Silva FF, Koyama M, Fernando SC. BIG DATA ANALYTICS AND PRECISION ANIMAL AGRICULTURE SYMPOSIUM: Machine learning and data mining advance predictive big data analysis in precision animal agriculture. J Anim Sci 2018; 96:1540-1550. [PMID: 29385611 PMCID: PMC6140937 DOI: 10.1093/jas/sky014] [Citation(s) in RCA: 95] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Precision animal agriculture is poised to rise to prominence in the livestock enterprise in the domains of management, production, welfare, sustainability, health surveillance, and environmental footprint. Considerable progress has been made in the use of tools to routinely monitor and collect information from animals and farms in a less laborious manner than before. These efforts have enabled the animal sciences to embark on information technology-driven discoveries to improve animal agriculture. However, the growing amount and complexity of data generated by fully automated, high-throughput data recording or phenotyping platforms, including digital images, sensor and sound data, unmanned systems, and information obtained from real-time noninvasive computer vision, pose challenges to the successful implementation of precision animal agriculture. The emerging fields of machine learning and data mining are expected to be instrumental in helping meet the daunting challenges facing global agriculture. Yet, their impact and potential in "big data" analysis have not been adequately appreciated in the animal science community, where this recognition has remained only fragmentary. To address such knowledge gaps, this article outlines a framework for machine learning and data mining and offers a glimpse into how they can be applied to solve pressing problems in animal sciences.
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Affiliation(s)
- Gota Morota
- Department of Animal Science, University of Nebraska, Lincoln, NE
| | - Ricardo V Ventura
- Beef Improvement Opportunities, Elora, Ontario, Canada
- Department of Animal Nutrition and Production, School of Veterinary Medicine and Animal Science, University of São Paulo, Pirassununga, São Paulo, Brazil
| | - Fabyano F Silva
- Department of Animal Science, Universidade Federal de Viçosa, Viçosa, Brazil
| | - Masanori Koyama
- Department of Mathematical Sciences, Ritsumeikan University, Shiga, Japan
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11
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Bussiman FO, Perez BC, Ventura RV, Peixoto MGCD, Curi RA, Balieiro JCC. Pedigree analysis and inbreeding effects over morphological traits in Campolina horse population. Animal 2018; 12:2246-2255. [PMID: 29467044 DOI: 10.1017/s175173111800023x] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
Genetic improvement, without control of inbreeding, can go to loss of genetic variability, reducing the potential for genetic gains in the domestic populations. The aim of this study was to analyze the population structure and the inbreeding depression in Campolina horses. Phenotype information from 43 465 individuals was analyzed, data provided by the Campolina Breeders Association. A pedigree file containing 107 951 horses was used to connected the phenotyped individuals. The inbreeding coefficient was performed by use of the diagonal of the relationship matrix and the genealogical parameters were computed using proper softwares. The effective population size was estimated based on the rate of inbreeding and census information, and the stratification of the population was verified by the average relationship coefficient between animals born in different regions of Brazil. The effects of inbreeding on morphological traits were made by the use of inbreeding coefficient as a covariate in the model of random regression. The inbreeding coefficient increased from 1990 on, impacting effective population size and, consequently, shrinking genetic variability. The paternal inbreeding was greater than maternal, which may be attributed to the preference for inbred animals in reproduction. The average genetic relationship coefficient of animals born in different states was lower than individuals born within the same state. The increase in the inbreeding coefficient was negatively associated with all studied traits, showing the importance to avoid genetic losses in the long term. Although results do not indicate a severe narrowing of the population until the present date, the average relationship coefficient shows signs of increase, which could cause a drastic reduction in genetic variability if inbred mating is not successfully controlled in the Campolina horse population.
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Affiliation(s)
- F O Bussiman
- 1Department of Animal Science, College of Animal Science and Food Engineering,University of São Paulo (FZEA/USP),Av. Duque de Caxias Norte,225, Campus Fernando Costa,Pirassununga, São Paulo, 13.635-900,Brazil
| | - B C Perez
- 1Department of Animal Science, College of Animal Science and Food Engineering,University of São Paulo (FZEA/USP),Av. Duque de Caxias Norte,225, Campus Fernando Costa,Pirassununga, São Paulo, 13.635-900,Brazil
| | - R V Ventura
- 1Department of Animal Science, College of Animal Science and Food Engineering,University of São Paulo (FZEA/USP),Av. Duque de Caxias Norte,225, Campus Fernando Costa,Pirassununga, São Paulo, 13.635-900,Brazil
| | - M G C D Peixoto
- 4National Centre of Research on Dairy Cattle, Brazilian Agricultural Research Corporation (CNPGL/EMBRAPA), Rua Eugênio do Nascimento,610, Dom Bosco, Juiz de Fora, Minas Gerais, 36.038-330,Brazil
| | - R A Curi
- 5Department of Animal Improvement and Nutrition,College of Veterinary Medicine and Animal Science, São Paulo State University (FMVZ/UNESP),Rua José Barbosa de Barros, 1780,Fazenda Experimental Lageado,18.618-307, Botucatu, São Paulo,Brazil
| | - J C C Balieiro
- 6Department of Animal Nutrition and Production,College of Veterinary Medicine and Animal Science, University of São Paulo (FMVZ/USP),Av. Duque de Caxias Norte, 225,Campus Pirassununga,Pirassununga, São Paulo, 13.635-900,Brazil
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12
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Larmer SG, Sargolzaei M, Brito LF, Ventura RV, Schenkel FS. Novel methods for genotype imputation to whole-genome sequence and a simple linear model to predict imputation accuracy. BMC Genet 2017; 18:120. [PMID: 29281958 PMCID: PMC5746022 DOI: 10.1186/s12863-017-0588-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2017] [Accepted: 12/15/2017] [Indexed: 11/10/2022] Open
Abstract
Background Accurate imputation plays a major role in genomic studies of livestock industries, where the number of genotyped or sequenced animals is limited by costs. This study explored methods to create an ideal reference population for imputation to Next Generation Sequencing data in cattle. Methods Methods for clustering of animals for imputation were explored, using 1000 Bull Genomes Project sequence data on 1146 animals from a variety of beef and dairy breeds. Imputation from 50 K to 777 K was first carried out to choose an ideal clustering method, using ADMIXTURE or PLINK clustering algorithms with either genotypes or reconstructed haplotypes. Results Due to efficiency, accuracy and ease of use, clustering with PLINK using haplotypes as quasi-genotypes was chosen as the most advantageous grouping method. It was found that using a clustered population slightly decreased computing time, while maintaining accuracy across the population. Although overall accuracy remained the same, a slight increase in accuracy was observed for groups of animals in some breeds (primarily purebred beef cattle from breeds with fewer sequenced animals) and for other groups, primarily crossbreed animals, a slight decrease in accuracy was observed. However, it was noted that some animals in each breed were poorly imputed across all methods. When imputed sequences were included in the reference population to aid imputation of poorly imputed animals, a small increase in overall accuracy was observed for nearly every individual in the population. Two models were created to predict imputation accuracy, a complete model using all information available including Euclidean distances from genotypes and haplotypes, pedigree information, and clustering groups and a simple model using only breed and an Euclidean distance matrix as predictors. Both models were successful in predicting imputation accuracy, with correlations between predicted and true imputation accuracy as measured by concordance rate of 0.87 and 0.83, respectively. Conclusions A clustering methodology can be very useful to subgroup cattle for efficient genotype imputation. In addition, accuracy of genotype imputation from medium to high-density Single Nucleotide Polymorphisms (SNP) chip panels to whole-genome sequence can be predicted well using a simple linear model defined in this study.
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Affiliation(s)
- Steven G Larmer
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, 50 Stone Road East, Guelph, ON, N1G 2W1, Canada. .,The Semex Alliance, 5653 Highway 6 North, Guelph, ON, N1H 6J2, Canada.
| | - Mehdi Sargolzaei
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, 50 Stone Road East, Guelph, ON, N1G 2W1, Canada.,The Semex Alliance, 5653 Highway 6 North, Guelph, ON, N1H 6J2, Canada
| | - Luiz F Brito
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, 50 Stone Road East, Guelph, ON, N1G 2W1, Canada
| | - Ricardo V Ventura
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, 50 Stone Road East, Guelph, ON, N1G 2W1, Canada.,Bringing Intelligence Opportunities, 294 Mill St. East, Elora, ON, N0B 1S0, Canada
| | - Flávio S Schenkel
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, 50 Stone Road East, Guelph, ON, N1G 2W1, Canada
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13
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Fonseca PAS, Leal TP, Santos FC, Gouveia MH, Id-Lahoucine S, Rosse IC, Ventura RV, Bruneli FAT, Machado MA, Peixoto MGCD, Tarazona-Santos E, Carvalho MRS. Reducing cryptic relatedness in genomic data sets via a central node exclusion algorithm. Mol Ecol Resour 2017; 18:435-447. [PMID: 29271609 DOI: 10.1111/1755-0998.12746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2017] [Revised: 12/04/2017] [Accepted: 12/14/2017] [Indexed: 11/30/2022]
Abstract
Cryptic relatedness is a confounding factor in genetic diversity and genetic association studies. Development of strategies to reduce cryptic relatedness in a sample is a crucial step for downstream genetic analyses. This study uses a node selection algorithm, based on network degrees of centrality, to evaluate its applicability and impact on evaluation of genetic diversity and population stratification. 1,036 Guzerá (Bos indicus) females were genotyped using Illumina Bovine SNP50 v2 BeadChip. Four strategies were compared. The first and second strategies consist on a iterative exclusion of most related individuals based on PLINK kinship coefficient (φij) and VanRaden's φij, respectively. The third and fourth strategies were based on a node selection algorithm. The fourth strategy, Network G matrix, preserved the larger number of individuals with a better diversity and representation from the initial sample. Determining the most probable number of populations was directly affected by the kinship metric. Network G matrix was the better strategy for reducing relatedness due to producing a larger sample, with more distant individuals, a more similar distribution when compared with the full data set in the MDS plots and keeping a better representation of the population structure. Resampling strategies using VanRaden's φij as a relationship metric was better to infer the relationships among individuals. Moreover, the resampling strategies directly impact the genomic inflation values in genomewide association studies. The use of the node selection algorithm also implies better selection of the most central individuals to be removed, providing a more representative sample.
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Affiliation(s)
- Pablo A S Fonseca
- Departamento de Biologia Geral, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Thiago P Leal
- Departamento de Biologia Geral, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Fernanda C Santos
- Departamento de Biologia Geral, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Mateus H Gouveia
- Departamento de Biologia Geral, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Samir Id-Lahoucine
- Center for Genetic Improvement of Livestock, University of Guelph, Guelph, ON, Canada
| | - Izinara C Rosse
- Departamento de Biologia Geral, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Ricardo V Ventura
- Center for Genetic Improvement of Livestock, University of Guelph, Guelph, ON, Canada.,Beef Improvement Opportunities, Guelph, ON, Canada
| | | | | | | | - Eduardo Tarazona-Santos
- Departamento de Biologia Geral, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Maria Raquel S Carvalho
- Departamento de Biologia Geral, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
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14
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Oliveira Júnior GA, Chud TCS, Ventura RV, Garrick DJ, Cole JB, Munari DP, Ferraz JBS, Mullart E, DeNise S, Smith S, da Silva MVGB. Genotype imputation in a tropical crossbred dairy cattle population. J Dairy Sci 2017; 100:9623-9634. [PMID: 28987572 DOI: 10.3168/jds.2017-12732] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2017] [Accepted: 08/16/2017] [Indexed: 11/19/2022]
Abstract
The objective of this study was to investigate different strategies for genotype imputation in a population of crossbred Girolando (Gyr × Holstein) dairy cattle. The data set consisted of 478 Girolando, 583 Gyr, and 1,198 Holstein sires genotyped at high density with the Illumina BovineHD (Illumina, San Diego, CA) panel, which includes ∼777K markers. The accuracy of imputation from low (20K) and medium densities (50K and 70K) to the HD panel density and from low to 50K density were investigated. Seven scenarios using different reference populations (RPop) considering Girolando, Gyr, and Holstein breeds separately or combinations of animals of these breeds were tested for imputing genotypes of 166 randomly chosen Girolando animals. The population genotype imputation were performed using FImpute. Imputation accuracy was measured as the correlation between observed and imputed genotypes (CORR) and also as the proportion of genotypes that were imputed correctly (CR). This is the first paper on imputation accuracy in a Girolando population. The sample-specific imputation accuracies ranged from 0.38 to 0.97 (CORR) and from 0.49 to 0.96 (CR) imputing from low and medium densities to HD, and 0.41 to 0.95 (CORR) and from 0.50 to 0.94 (CR) for imputation from 20K to 50K. The CORRanim exceeded 0.96 (for 50K and 70K panels) when only Girolando animals were included in RPop (S1). We found smaller CORRanim when Gyr (S2) was used instead of Holstein (S3) as RPop. The same behavior was observed between S4 (Gyr + Girolando) and S5 (Holstein + Girolando) because the target animals were more related to the Holstein population than to the Gyr population. The highest imputation accuracies were observed for scenarios including Girolando animals in the reference population, whereas using only Gyr animals resulted in low imputation accuracies, suggesting that the haplotypes segregating in the Girolando population had a greater effect on accuracy than the purebred haplotypes. All chromosomes had similar imputation accuracies (CORRsnp) within each scenario. Crossbred animals (Girolando) must be included in the reference population to provide the best imputation accuracies.
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Affiliation(s)
- Gerson A Oliveira Júnior
- Departamento de Medicina Veterinária, Universidade de São Paulo (USP), Faculdade de Zootecnia e Engenharia de Alimentos, Pirassununga, SP, 13635-900, Brazil
| | - Tatiane C S Chud
- Departamento de Ciências Exatas, Universidade Estadual Paulista (Unesp), Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal, SP, 14884-900, Brazil
| | - Ricardo V Ventura
- Beef Improvement Opportunities, Guelph, ON N1K1E5, Canada; Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, ON N1G2W1, Canada
| | - Dorian J Garrick
- Department of Animal Science, Iowa State University, Ames 50011-3150
| | - John B Cole
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD, 20705-2350
| | - Danísio P Munari
- Departamento de Ciências Exatas, Universidade Estadual Paulista (Unesp), Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal, SP, 14884-900, Brazil
| | - José B S Ferraz
- Departamento de Medicina Veterinária, Universidade de São Paulo (USP), Faculdade de Zootecnia e Engenharia de Alimentos, Pirassununga, SP, 13635-900, Brazil
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15
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Brito LF, Kijas JW, Ventura RV, Sargolzaei M, Porto-Neto LR, Cánovas A, Feng Z, Jafarikia M, Schenkel FS. Genetic diversity and signatures of selection in various goat breeds revealed by genome-wide SNP markers. BMC Genomics 2017; 18:229. [PMID: 28288562 PMCID: PMC5348779 DOI: 10.1186/s12864-017-3610-0] [Citation(s) in RCA: 87] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2016] [Accepted: 03/07/2017] [Indexed: 01/08/2023] Open
Abstract
Background The detection of signatures of selection has the potential to elucidate the identities of genes and mutations associated with phenotypic traits important for livestock species. It is also very relevant to investigate the levels of genetic diversity of a population, as genetic diversity represents the raw material essential for breeding and has practical implications for implementation of genomic selection. A total of 1151 animals from nine goat populations selected for different breeding goals and genotyped with the Illumina Goat 50K single nucleotide polymorphisms (SNP) Beadchip were included in this investigation. Results The proportion of polymorphic SNPs ranged from 0.902 (Nubian) to 0.995 (Rangeland). The overall mean HO and HE was 0.374 ± 0.021 and 0.369 ± 0.023, respectively. The average pairwise genetic distance (D) ranged from 0.263 (Toggenburg) to 0.323 (Rangeland). The overall average for the inbreeding measures FEH, FVR, FLEUT, FROH and FPED was 0.129, −0.012, −0.010, 0.038 and 0.030, respectively. Several regions located on 19 chromosomes were potentially under selection in at least one of the goat breeds. The genomic population tree constructed using all SNPs differentiated breeds based on selection purpose, while genomic population tree built using only SNPs in the most significant region showed a great differentiation between LaMancha and the other breeds. We hypothesized that this region is related to ear morphogenesis. Furthermore, we identified genes potentially related to reproduction traits, adult body mass, efficiency of food conversion, abdominal fat deposition, conformation traits, liver fat metabolism, milk fatty acids, somatic cells score, milk protein, thermo-tolerance and ear morphogenesis. Conclusions In general, moderate to high levels of genetic variability were observed for all the breeds and a characterization of runs of homozygosity gave insights into the breeds’ development history. The information reported here will be useful for the implementation of genomic selection and other genomic studies in goats. We also identified various genome regions under positive selection using smoothed FST and hapFLK statistics and suggested genes, which are potentially under selection. These results can now provide a foundation to formulate biological hypotheses related to selection processes in goats. Electronic supplementary material The online version of this article (doi:10.1186/s12864-017-3610-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Luiz F Brito
- Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, Ontario, Canada.
| | - James W Kijas
- CSIRO Agriculture & Food, Brisbane, Queensland, Australia
| | - Ricardo V Ventura
- Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, Ontario, Canada.,Beef Improvement Opportunities, Guelph, Ontario, Canada
| | - Mehdi Sargolzaei
- Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, Ontario, Canada.,The Semex Alliance, Guelph, Ontario, Canada
| | | | - Angela Cánovas
- Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, Ontario, Canada
| | - Zeny Feng
- Department of Mathematics and Statistics, University of Guelph, Guelph, Ontario, Canada
| | - Mohsen Jafarikia
- Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, Ontario, Canada.,Canadian Centre for Swine Improvement Inc., Ottawa, Ontario, Canada
| | - Flávio S Schenkel
- Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, Ontario, Canada
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16
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Freua MC, Santana MHA, Ventura RV, Ferraz JBS. Parameters of a dynamic mechanistic model of cattle growth retain enough biological interpretation for genotype-to-phenotype mapping. Genet Mol Res 2016; 15:gmr-15-04-gmr.15048931. [PMID: 27966739 DOI: 10.4238/gmr15048931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
This study aimed to investigate the predictability of a phenotype when using a dynamic model of cattle growth. Genotypic and phenotypic information on Nellore (Bos indicus) cattle were used in a genome-wide association analysis designed to contrast the biological interpretation of core parameters [conversion efficiency of metabolizable energy to net energy for gain (kg) and adjusted final shrunk body weight (AFSBW)] to their associated genomic regions and nearby quantitative trait loci (QTLs). Single nucleotide polymorphisms (SNPs) were used to develop prediction equations for kg and AFSBW, which enter the model for simulative prediction purposes. QTLs and genes, one related to mature body weight and another to growth efficiency, are consistent with the model equations. Significantly associated SNPs were used to compute parameters, which yielded reasonable model outcomes when compared with regular parameter computations. Our results provide evidence of the biological validity of using such parameters as component traits of higher phenotypes and the possibility of using genomic data for genotype-to-parameter mapping.
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Affiliation(s)
- M C Freua
- Departmento de Medicina Veterinária, Faculdade de Zootecnia e Engenharia de Alimentos, São Paulo, SP, Brasil
| | - M H A Santana
- Departmento de Medicina Veterinária, Faculdade de Zootecnia e Engenharia de Alimentos, São Paulo, SP, Brasil
| | - R V Ventura
- Departmento de Medicina Veterinária, Faculdade de Zootecnia e Engenharia de Alimentos, São Paulo, SP, Brasil.,Centre for Genetic Improvement for Livestock, University of Guelph, Guelph, Ontario, Canada
| | - J B S Ferraz
- Departmento de Medicina Veterinária, Faculdade de Zootecnia e Engenharia de Alimentos, São Paulo, SP, Brasil
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17
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Santana MHA, Freua MC, Do DN, Ventura RV, Kadarmideen HN, Ferraz JBS. Systems genetics and genome-wide association approaches for analysis of feed intake, feed efficiency, and performance in beef cattle. Genet Mol Res 2016; 15:gmr-15-gmr15048930. [PMID: 27813603 DOI: 10.4238/gmr15048930] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Feed intake, feed efficiency, and weight gain are important economic traits of beef cattle in feedlots. In the present study, we investigated the physiological processes underlying such traits from the point of view of systems genetics. Firstly, using data from 1334 Nellore (Bos indicus) cattle and 943,577 single nucleotide polymorphisms (SNPs), a genome-wide association analysis was performed for dry matter intake, average daily weight gain, feed conversion ratio, and residual feed intake with a Bayesian Lasso procedure. Genes within 50-kb SNPs, most relevant for explaining the genomic variance, were annotated and the biological processes underlying the traits were inferred from Database for Annotation, Visualization and Integrated Discovery (DAVID) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Our results indicated several putative genomic regions associated with the target phenotypes and showed that almost all genomic variances were in the SNPs located in the intergenic and intronic regions. We further identified five main metabolic pathways related to ion transport, body composition, and feed intake control, which influenced the four phenotypes simultaneously. The systems genetics approach used in this study revealed novel pathways related to feed efficiency traits in beef cattle.
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Affiliation(s)
- M H A Santana
- Departamento de Medicina Veterinária, Faculdade de Zootecnia e Engenharia de Alimentos, Universidade do Estado de São Paulo, Pirassununga, SP, Brasil
| | - M C Freua
- Departamento de Medicina Veterinária, Faculdade de Zootecnia e Engenharia de Alimentos, Universidade do Estado de São Paulo, Pirassununga, SP, Brasil
| | - D N Do
- Department of Animal Science, McGill University, Sainte-Anne-de-Bellevue, Quebec, Canada
| | - R V Ventura
- Departamento de Medicina Veterinária, Faculdade de Zootecnia e Engenharia de Alimentos, Universidade do Estado de São Paulo, Pirassununga, SP, Brasil.,Centre for Genetic Improvement for Livestock, University of Guelph, Guelph, Ontario, Canada
| | - H N Kadarmideen
- Section for Animal Welfare and Disease Control, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg, Denmark
| | - J B S Ferraz
- Departamento de Medicina Veterinária, Faculdade de Zootecnia e Engenharia de Alimentos, Universidade do Estado de São Paulo, Pirassununga, SP, Brasil
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18
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Ventura RV, Miller SP, Dodds KG, Auvray B, Lee M, Bixley M, Clarke SM, McEwan JC. Assessing accuracy of imputation using different SNP panel densities in a multi-breed sheep population. Genet Sel Evol 2016; 48:71. [PMID: 27663120 PMCID: PMC5035503 DOI: 10.1186/s12711-016-0244-7] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2015] [Accepted: 08/31/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Genotype imputation is a key element of the implementation of genomic selection within the New Zealand sheep industry, but many factors can influence imputation accuracy. Our objective was to provide practical directions on the implementation of imputation strategies in a multi-breed sheep population genotyped with three single nucleotide polymorphism (SNP) panels: 5K, 50K and HD (600K SNPs). RESULTS Imputation from 5K to HD was slightly better (0.6 %) than imputation from 5K to 50K. Two-step imputation from 5K to 50K and then from 50K to HD outperformed direct imputation from 5K to HD. A slight loss in imputation accuracy was observed when a large fixed reference population was used compared to a smaller within-breed reference (including all 50K genotypes on animals from different breeds excluding those in the validation set i.e. to be imputed), but only for a few animals across all imputation scenarios from 5K to 50K. However, a major gain in imputation accuracy for a large proportion of animals (purebred and crossbred), justified the use of a fixed and large reference dataset for all situations. This study also investigated the loss in imputation accuracy specifically for SNPs located at the ends of each chromosome, and showed that only chromosome 26 had an overall imputation (5K to 50K) accuracy for 100 SNPs at each end higher than 60 % (r2). Most of the chromosomes displayed reduced imputation accuracy at least at one of their ends. Prediction of imputation accuracy based on the relatedness of low-density genotypes to those of the reference dataset, before imputation (without running an imputation software) was also investigated. FIMPUTE V2.2 outperformed BEAGLE 3.3.2 across all imputation scenarios. CONCLUSIONS Imputation accuracy in sheep breeds can be improved by following a set of recommendations on SNP panels, software, strategies of imputation (one- or two-step imputation), and choice of the animals to be genotyped using both high- and low-density SNP panels. We present a method that predicts imputation accuracy for individual animals at the low-density level, before running imputation, which can be used to restrict genomic prediction only to the animals that can be imputed with sufficient accuracy.
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Affiliation(s)
- Ricardo V Ventura
- Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, ON, N1G2W1, Canada.,Beef Improvement Opportunities, Guelph, ON, N1K1E5, Canada
| | - Stephen P Miller
- Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, ON, N1G2W1, Canada. .,Invermay Agricultural Centre, AgResearch Limited, Mosgiel, 9053, New Zealand.
| | - Ken G Dodds
- Invermay Agricultural Centre, AgResearch Limited, Mosgiel, 9053, New Zealand
| | - Benoit Auvray
- Department of Mathematics and Statistics, University of Otago, Dunedin, 9016, New Zealand
| | - Michael Lee
- Department of Mathematics and Statistics, University of Otago, Dunedin, 9016, New Zealand
| | - Matthew Bixley
- Invermay Agricultural Centre, AgResearch Limited, Mosgiel, 9053, New Zealand
| | - Shannon M Clarke
- Invermay Agricultural Centre, AgResearch Limited, Mosgiel, 9053, New Zealand
| | - John C McEwan
- Invermay Agricultural Centre, AgResearch Limited, Mosgiel, 9053, New Zealand
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19
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Chud TCS, Ventura RV, Schenkel FS, Carvalheiro R, Buzanskas ME, Rosa JO, Mudadu MDA, da Silva MVGB, Mokry FB, Marcondes CR, Regitano LCA, Munari DP. Strategies for genotype imputation in composite beef cattle. BMC Genet 2015; 16:99. [PMID: 26250698 PMCID: PMC4527250 DOI: 10.1186/s12863-015-0251-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2015] [Accepted: 07/09/2015] [Indexed: 11/23/2022] Open
Abstract
Background Genotype imputation has been used to increase genomic information, allow more animals in genome-wide analyses, and reduce genotyping costs. In Brazilian beef cattle production, many animals are resulting from crossbreeding and such an event may alter linkage disequilibrium patterns. Thus, the challenge is to obtain accurately imputed genotypes in crossbred animals. The objective of this study was to evaluate the best fitting and most accurate imputation strategy on the MA genetic group (the progeny of a Charolais sire mated with crossbred Canchim X Zebu cows) and Canchim cattle. The data set contained 400 animals (born between 1999 and 2005) genotyped with the Illumina BovineHD panel. Imputation accuracy of genotypes from the Illumina-Bovine3K (3K), Illumina-BovineLD (6K), GeneSeek-Genomic-Profiler (GGP) BeefLD (GGP9K), GGP-IndicusLD (GGP20Ki), Illumina-BovineSNP50 (50K), GGP-IndicusHD (GGP75Ki), and GGP-BeefHD (GGP80K) to Illumina-BovineHD (HD) SNP panels were investigated. Seven scenarios for reference and target populations were tested; the animals were grouped according with birth year (S1), genetic groups (S2 and S3), genetic groups and birth year (S4 and S5), gender (S6), and gender and birth year (S7). Analyses were performed using FImpute and BEAGLE software and computation run-time was recorded. Genotype imputation accuracy was measured by concordance rate (CR) and allelic R square (R2). Results The highest imputation accuracy scenario consisted of a reference population with males and females and a target population with young females. Among the SNP panels in the tested scenarios, from the 50K, GGP75Ki and GGP80K were the most adequate to impute to HD in Canchim cattle. FImpute reduced computation run-time to impute genotypes from 20 to 100 times when compared to BEAGLE. Conclusion The genotyping panels possessing at least 50 thousands markers are suitable for genotype imputation to HD with acceptable accuracy. The FImpute algorithm demonstrated a higher efficiency of imputed markers, especially in lower density panels. These considerations may assist to increase genotypic information, reduce genotyping costs, and aid in genomic selection evaluations in crossbred animals. Electronic supplementary material The online version of this article (doi:10.1186/s12863-015-0251-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Tatiane C S Chud
- Departamento de Ciências Exatas, UNESP - Univ Estadual Paulista "Júlio de Mesquita Filho", Jaboticabal, SP, Brazil.
| | - Ricardo V Ventura
- Beef Improvement Opportunities, Guelph, ON, Canada. .,University of Guelph, Guelph, ON, Canada.
| | | | - Roberto Carvalheiro
- Departamento de Zootecnia, UNESP - Univ Estadual Paulista "Júlio de Mesquita Filho", Jaboticabal, SP, Brazil.
| | - Marcos E Buzanskas
- Departamento de Ciências Exatas, UNESP - Univ Estadual Paulista "Júlio de Mesquita Filho", Jaboticabal, SP, Brazil.
| | - Jaqueline O Rosa
- Departamento de Ciências Exatas, UNESP - Univ Estadual Paulista "Júlio de Mesquita Filho", Jaboticabal, SP, Brazil.
| | | | | | - Fabiana B Mokry
- Department of Genetics and Evolution, Federal University of São Carlos, São Carlos, SP, Brazil.
| | - Cintia R Marcondes
- Embrapa Southeast Livestock - Brazilian Corporation of Agricultural Research, São Carlos, SP, Brazil.
| | - Luciana C A Regitano
- Embrapa Southeast Livestock - Brazilian Corporation of Agricultural Research, São Carlos, SP, Brazil.
| | - Danísio P Munari
- Departamento de Ciências Exatas, UNESP - Univ Estadual Paulista "Júlio de Mesquita Filho", Jaboticabal, SP, Brazil.
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Brito LF, Jafarikia M, Grossi DA, Kijas JW, Porto-Neto LR, Ventura RV, Salgorzaei M, Schenkel FS. Characterization of linkage disequilibrium, consistency of gametic phase and admixture in Australian and Canadian goats. BMC Genet 2015; 16:67. [PMID: 26108536 PMCID: PMC4479065 DOI: 10.1186/s12863-015-0220-1] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2015] [Accepted: 05/19/2015] [Indexed: 11/11/2022] Open
Abstract
Background Basic understanding of linkage disequilibrium (LD) and population structure, as well as the consistency of gametic phase across breeds is crucial for genome-wide association studies and successful implementation of genomic selection. However, it is still limited in goats. Therefore, the objectives of this research were: (i) to estimate genome-wide levels of LD in goat breeds using data generated with the Illumina Goat SNP50 BeadChip; (ii) to study the consistency of gametic phase across breeds in order to evaluate the possible use of a multi-breed training population for genomic selection and (iii) develop insights concerning the population history of goat breeds. Results Average r2 between adjacent SNP pairs ranged from 0.28 to 0.11 for Boer and Rangeland populations. At the average distance between adjacent SNPs in the current 50 k SNP panel (~0.06 Mb), the breeds LaMancha, Nubian, Toggenburg and Boer exceeded or approached the level of linkage disequilibrium that is useful (r2 > 0.2) for genomic predictions. In all breeds LD decayed rapidly with increasing inter-marker distance. The estimated correlations for all the breed pairs, except Canadian and Australian Boer populations, were lower than 0.70 for all marker distances greater than 0.02 Mb. These results are not high enough to encourage the pooling of breeds in a single training population for genomic selection. The admixture analysis shows that some breeds have distinct genotypes based on SNP50 genotypes, such as the Boer, Cashmere and Nubian populations. The other groups share higher genome proportions with each other, indicating higher admixture and a more diverse genetic composition. Conclusions This work presents results of a diverse collection of breeds, which are of great interest for the implementation of genomic selection in goats. The LD results indicate that, with a large enough training population, genomic selection could potentially be implemented within breed with the current 50 k panel, but some breeds might benefit from a denser panel. For multi-breed genomic evaluation, a denser SNP panel also seems to be required. Electronic supplementary material The online version of this article (doi:10.1186/s12863-015-0220-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Luiz F Brito
- Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, ON, Canada.
| | - Mohsen Jafarikia
- Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, ON, Canada. .,Canadian Centre for Swine Improvement Inc, Ottawa, ON, Canada.
| | - Daniela A Grossi
- Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, ON, Canada.
| | - James W Kijas
- CSIRO Agriculture Flagship, Brisbane, QLD, Australia.
| | | | - Ricardo V Ventura
- Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, ON, Canada. .,Beef Improvement Opportunities, Guelph, ON, Canada.
| | - Mehdi Salgorzaei
- Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, ON, Canada. .,The Semex Alliance, Guelph, ON, Canada.
| | - Flavio S Schenkel
- Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, ON, Canada.
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21
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Santana MHA, Ventura RV, Utsunomiya YT, Neves HHR, Alexandre PA, Oliveira Junior GA, Gomes RC, Bonin MN, Coutinho LL, Garcia JF, Silva SL, Fukumasu H, Leme PR, Ferraz JBS. A genomewide association mapping study using ultrasound-scanned information identifies potential genomic regions and candidate genes affecting carcass traits in Nellore cattle. J Anim Breed Genet 2015; 132:420-7. [PMID: 26016521 DOI: 10.1111/jbg.12167] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2014] [Accepted: 02/11/2015] [Indexed: 01/02/2023]
Abstract
The aim of this study was to identify candidate genes and genomic regions associated with ultrasound-derived measurements of the rib-eye area (REA), backfat thickness (BFT) and rumpfat thickness (RFT) in Nellore cattle. Data from 640 Nellore steers and young bulls with genotypes for 290 863 single nucleotide polymorphisms (SNPs) were used for genomewide association mapping. Significant SNP associations were explored to find possible candidate genes related to physiological processes. Several of the significant markers detected were mapped onto functional candidate genes including ARFGAP3, CLSTN2 and DPYD for REA; OSBPL3 and SUDS3 for BFT; and RARRES1 and VEPH1 for RFT. The physiological pathway related to lipid metabolism (CLSTN2, OSBPL3, RARRES1 and VEPH1) was identified. The significant markers within previously reported QTLs reinforce the importance of the genomic regions, and the other loci offer candidate genes that have not been related to carcass traits in previous investigations.
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Affiliation(s)
- M H A Santana
- Faculdade de Zootecnia e Engenharia de Alimentos - USP, Pirassununga, Brazil
| | - R V Ventura
- Faculdade de Zootecnia e Engenharia de Alimentos - USP, Pirassununga, Brazil.,Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, ON, Canada.,Beef Improvement Opportunties (BIO), Guelph, ON, Canada
| | - Y T Utsunomiya
- Faculdade de Ciências Agrárias e Veterinárias, UNESP, Jaboticabal, Brazil
| | - H H R Neves
- Faculdade de Ciências Agrárias e Veterinárias, UNESP, Jaboticabal, Brazil.,GenSys Consultores Associados S/C Ltda, Porto Alegre, Brazil
| | - P A Alexandre
- Faculdade de Zootecnia e Engenharia de Alimentos - USP, Pirassununga, Brazil
| | - G A Oliveira Junior
- Faculdade de Zootecnia e Engenharia de Alimentos - USP, Pirassununga, Brazil
| | - R C Gomes
- Empresa Brasileira de Pesquisa Agropecuária, CNPGC/EMBRAPA, Campo Grande, Brazil
| | - M N Bonin
- Empresa Brasileira de Pesquisa Agropecuária, CNPGC/EMBRAPA, Campo Grande, Brazil
| | - L L Coutinho
- Escola Superior de Agricultura Luiz de Queiroz, USP, Piracicaba, Brazil
| | - J F Garcia
- Faculdade de Ciências Agrárias e Veterinárias, UNESP, Jaboticabal, Brazil
| | - S L Silva
- Faculdade de Zootecnia e Engenharia de Alimentos - USP, Pirassununga, Brazil
| | - H Fukumasu
- Faculdade de Zootecnia e Engenharia de Alimentos - USP, Pirassununga, Brazil
| | - P R Leme
- Faculdade de Zootecnia e Engenharia de Alimentos - USP, Pirassununga, Brazil
| | - J B S Ferraz
- Faculdade de Zootecnia e Engenharia de Alimentos - USP, Pirassununga, Brazil
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22
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Buzanskas ME, Grossi DA, Ventura RV, Schenkel FS, Sargolzaei M, Meirelles SLC, Mokry FB, Higa RH, Mudadu MA, da Silva MVGB, Niciura SCM, Júnior RAAT, Alencar MM, Regitano LCA, Munari DP. Genome-wide association for growth traits in Canchim beef cattle. PLoS One 2014; 9:e94802. [PMID: 24733441 PMCID: PMC3986245 DOI: 10.1371/journal.pone.0094802] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2013] [Accepted: 03/20/2014] [Indexed: 12/01/2022] Open
Abstract
Studies are being conducted on the applicability of genomic data to improve the accuracy of the selection process in livestock, and genome-wide association studies (GWAS) provide valuable information to enhance the understanding on the genetics of complex traits. The aim of this study was to identify genomic regions and genes that play roles in birth weight (BW), weaning weight adjusted for 210 days of age (WW), and long-yearling weight adjusted for 420 days of age (LYW) in Canchim cattle. GWAS were performed by means of the Generalized Quasi-Likelihood Score (GQLS) method using genotypes from the BovineHD BeadChip and estimated breeding values for BW, WW, and LYW. Data consisted of 285 animals from the Canchim breed and 114 from the MA genetic group (derived from crossings between Charolais sires and ½ Canchim + ½ Zebu dams). After applying a false discovery rate correction at a 10% significance level, a total of 4, 12, and 10 SNPs were significantly associated with BW, WW, and LYW, respectively. These SNPs were surveyed to their corresponding genes or to surrounding genes within a distance of 250 kb. The genes DPP6 (dipeptidyl-peptidase 6) and CLEC3B (C-type lectin domain family 3 member B) were highlighted, considering its functions on the development of the brain and skeletal system, respectively. The GQLS method identified regions on chromosome associated with birth weight, weaning weight, and long-yearling weight in Canchim and MA animals. New candidate regions for body weight traits were detected and some of them have interesting biological functions, of which most have not been previously reported. The observation of QTL reports for body weight traits, covering areas surrounding the genes (SNPs) herein identified provides more evidence for these associations. Future studies targeting these areas could provide further knowledge to uncover the genetic architecture underlying growth traits in Canchim cattle.
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Affiliation(s)
- Marcos E. Buzanskas
- Departamento de Ciências Exatas, UNESP - Univ Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal, São Paulo, Brazil
| | - Daniela A. Grossi
- Department of Animal and Poultry Science, University of Guelph, Centre for Genetic Improvement of Livestock (CGIL), Guelph, Ontario, Canada
| | - Ricardo V. Ventura
- Department of Animal and Poultry Science, University of Guelph, Centre for Genetic Improvement of Livestock (CGIL), Guelph, Ontario, Canada
- Beef Improvement Opportunities (BIO), Guelph, Ontario, Canada
| | - Flávio S. Schenkel
- Department of Animal and Poultry Science, University of Guelph, Centre for Genetic Improvement of Livestock (CGIL), Guelph, Ontario, Canada
| | - Mehdi Sargolzaei
- Department of Animal and Poultry Science, University of Guelph, Centre for Genetic Improvement of Livestock (CGIL), Guelph, Ontario, Canada
- The Semex Alliance, Guelph, Ontario, Canada
| | - Sarah L. C. Meirelles
- Department of Animal Science, Federal University of Lavras (UFLA), Lavras, Minas Gerais, Brazil
| | - Fabiana B. Mokry
- Department of Genetics and Evolution, Federal University of São Carlos (UFSCar), São Carlos, São Paulo, Brazil
| | - Roberto H. Higa
- Embrapa Agricultural Informatics, Campinas, São Paulo, Brazil
| | | | | | | | | | | | | | - Danísio P. Munari
- Departamento de Ciências Exatas, UNESP - Univ Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal, São Paulo, Brazil
- * E-mail:
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23
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Ventura RV, Lu D, Schenkel FS, Wang Z, Li C, Miller SP. Impact of reference population on accuracy of imputation from 6K to 50K single nucleotide polymorphism chips in purebred and crossbreed beef cattle. J Anim Sci 2014; 92:1433-44. [PMID: 24663187 DOI: 10.2527/jas.2013-6638] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Genotyping with lower density but lower cost panels enables more animals to be genotyped for genomic selection. Imputation enables the determination of missing SNP genotypes in animals genotyped with a low-density panel by using information from a reference population genotyped with a higher density panel, which should increase accuracy of genomic EBV. In this study, population imputation, using linkage disequilibrium among markers, was implemented using the software BEAGLE, FIMPUTE 2.2, and IMPUTE2 in a multibreed, crossbred taurine beef cattle population genotyped with the Illumina SNP50. Different combinations of reference populations and imputed animals were defined based on breed composition. Number of animals (n = 250 to 4,932) and the presence of closer relatives in the reference population (only for Angus animals) were investigated. The overall average imputation accuracy for purebred animals ranged from 94.20 to 97.93% using FIMPUTE, from 95.35 to 98.31% using IMPUTE2, and from 90.02 to 96.38% when BEAGLE software was used. Imputation accuracy of crossbred animals ranged from 54.15 to 97.53% (FIMPUTE), from 57.04 to 97.46% (IMPUTE2), and from 54.35 to 95.64% (BEAGLE). Higher imputation accuracies were obtained when closer relatives along with the breed composition of imputed animals was well represented in the reference population. Within breed imputation from 6K to 50K did not improve when an additional purebred population was added to the reference population. FIMPUTE reduced the run time by 13 to 52 times compared to BEAGLE and 51 to 108 times compared to IMPUTE2.
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Affiliation(s)
- R V Ventura
- Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, Ontario, N1G2W1 Canada
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