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Jang S, Tsuruta S, Leite NG, Misztal I, Lourenco D. Dimensionality of genomic information and its impact on genome-wide associations and variant selection for genomic prediction: a simulation study. Genet Sel Evol 2023; 55:49. [PMID: 37460964 DOI: 10.1186/s12711-023-00823-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 07/03/2023] [Indexed: 07/20/2023] Open
Abstract
BACKGROUND Identifying true positive variants in genome-wide associations (GWA) depends on several factors, including the number of genotyped individuals. The limited dimensionality of genomic information may give insights into the optimal number of individuals to be used in GWA. This study investigated different discovery set sizes based on the number of largest eigenvalues explaining a certain proportion of variance in the genomic relationship matrix (G). In addition, we investigated the impact on the prediction accuracy by adding variants, which were selected based on different set sizes, to the regular single nucleotide polymorphism (SNP) chips used for genomic prediction. METHODS We simulated sequence data that included 500k SNPs with 200 or 2000 quantitative trait nucleotides (QTN). A regular 50k panel included one in every ten simulated SNPs. Effective population size (Ne) was set to 20 or 200. GWA were performed using a number of genotyped animals equivalent to the number of largest eigenvalues of G (EIG) explaining 50, 60, 70, 80, 90, 95, 98, and 99% of the variance. In addition, the largest discovery set consisted of 30k genotyped animals. Limited or extensive phenotypic information was mimicked by changing the trait heritability. Significant and large-effect size SNPs were added to the 50k panel and used for single-step genomic best linear unbiased prediction (ssGBLUP). RESULTS Using a number of genotyped animals corresponding to at least EIG98 allowed the identification of QTN with the largest effect sizes when Ne was large. Populations with smaller Ne required more than EIG98. Furthermore, including genotyped animals with a higher reliability (i.e., a higher trait heritability) improved the identification of the most informative QTN. Prediction accuracy was highest when the significant or the large-effect SNPs representing twice the number of simulated QTN were added to the 50k panel. CONCLUSIONS Accurately identifying causative variants from sequence data depends on the effective population size and, therefore, on the dimensionality of genomic information. This dimensionality can help identify the most suitable sample size for GWA and could be considered for variant selection, especially when resources are restricted. Even when variants are accurately identified, their inclusion in prediction models has limited benefits.
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Affiliation(s)
- Sungbong Jang
- Department of Animal and Dairy Science, University of Georgia, Athens, GA, 30602, USA.
| | - Shogo Tsuruta
- Department of Animal and Dairy Science, University of Georgia, Athens, GA, 30602, USA
| | - Natalia Galoro Leite
- Department of Animal and Dairy Science, University of Georgia, Athens, GA, 30602, USA
| | - Ignacy Misztal
- Department of Animal and Dairy Science, University of Georgia, Athens, GA, 30602, USA
| | - Daniela Lourenco
- Department of Animal and Dairy Science, University of Georgia, Athens, GA, 30602, USA
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Walakira A, Ocira J, Duroux D, Fouladi R, Moškon M, Rozman D, Van Steen K. Detecting gene-gene interactions from GWAS using diffusion kernel principal components. BMC Bioinformatics 2022; 23:57. [PMID: 35105309 PMCID: PMC8805268 DOI: 10.1186/s12859-022-04580-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 01/18/2022] [Indexed: 11/10/2022] Open
Abstract
Genes and gene products do not function in isolation but as components of complex networks of macromolecules through physical or biochemical interactions. Dependencies of gene mutations on genetic background (i.e., epistasis) are believed to play a role in understanding molecular underpinnings of complex diseases such as inflammatory bowel disease (IBD). However, the process of identifying such interactions is complex due to for instance the curse of high dimensionality, dependencies in the data and non-linearity. Here, we propose a novel approach for robust and computationally efficient epistasis detection. We do so by first reducing dimensionality, per gene via diffusion kernel principal components (kpc). Subsequently, kpc gene summaries are used for downstream analysis including the construction of a gene-based epistasis network. We show that our approach is not only able to recover known IBD associated genes but also additional genes of interest linked to this difficult gastrointestinal disease.
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Affiliation(s)
- Andrew Walakira
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Junior Ocira
- BIO3 - Laboratory for Systems Genetics, GIGA-R Medical Genomics, University of Liège, Liège, Belgium
| | - Diane Duroux
- BIO3 - Laboratory for Systems Genetics, GIGA-R Medical Genomics, University of Liège, Liège, Belgium
| | - Ramouna Fouladi
- BIO3 - Laboratory for Systems Genetics, GIGA-R Medical Genomics, University of Liège, Liège, Belgium
| | - Miha Moškon
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Damjana Rozman
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Kristel Van Steen
- BIO3 - Laboratory for Systems Genetics, GIGA-R Medical Genomics, University of Liège, Liège, Belgium
- BIO3 - Laboratory for Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, Belgium
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Asgari Z, Ehsani A, Masoudi AA, Vaez Torshizi R. Bayes factors revealed selection signature for time to market body weight in chicken: a genome-wide association study using BayesCpi methodology. ITALIAN JOURNAL OF ANIMAL SCIENCE 2021. [DOI: 10.1080/1828051x.2021.1965920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Zeinab Asgari
- Department of Animal Science, Tarbiat Modares University, Tehran, Iran
| | - Alireza Ehsani
- Department of Animal Science, Tarbiat Modares University, Tehran, Iran
| | - Ali Akbar Masoudi
- Department of Animal Science, Tarbiat Modares University, Tehran, Iran
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Alves AAC, da Costa RM, Bresolin T, Fernandes Júnior GA, Espigolan R, Ribeiro AMF, Carvalheiro R, de Albuquerque LG. Genome-wide prediction for complex traits under the presence of dominance effects in simulated populations using GBLUP and machine learning methods. J Anim Sci 2020; 98:5849339. [PMID: 32474602 DOI: 10.1093/jas/skaa179] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Accepted: 05/22/2020] [Indexed: 01/05/2023] Open
Abstract
The aim of this study was to compare the predictive performance of the Genomic Best Linear Unbiased Predictor (GBLUP) and machine learning methods (Random Forest, RF; Support Vector Machine, SVM; Artificial Neural Network, ANN) in simulated populations presenting different levels of dominance effects. Simulated genome comprised 50k SNP and 300 QTL, both biallelic and randomly distributed across 29 autosomes. A total of six traits were simulated considering different values for the narrow and broad-sense heritability. In the purely additive scenario with low heritability (h2 = 0.10), the predictive ability obtained using GBLUP was slightly higher than the other methods whereas ANN provided the highest accuracies for scenarios with moderate heritability (h2 = 0.30). The accuracies of dominance deviations predictions varied from 0.180 to 0.350 in GBLUP extended for dominance effects (GBLUP-D), from 0.06 to 0.185 in RF and they were null using the ANN and SVM methods. Although RF has presented higher accuracies for total genetic effect predictions, the mean-squared error values in such a model were worse than those observed for GBLUP-D in scenarios with large additive and dominance variances. When applied to prescreen important regions, the RF approach detected QTL with high additive and/or dominance effects. Among machine learning methods, only the RF was capable to cover implicitly dominance effects without increasing the number of covariates in the model, resulting in higher accuracies for the total genetic and phenotypic values as the dominance ratio increases. Nevertheless, whether the interest is to infer directly on dominance effects, GBLUP-D could be a more suitable method.
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Affiliation(s)
- Anderson Antonio Carvalho Alves
- Department of Animal Science, Faculty of Agricultural and Veterinary Sciences, Sao Paulo State University (UNESP), Jaboticabal, SP, Brazil
| | - Rebeka Magalhães da Costa
- Department of Animal Science, Faculty of Agricultural and Veterinary Sciences, Sao Paulo State University (UNESP), Jaboticabal, SP, Brazil
| | - Tiago Bresolin
- Department of Animal Sciences, University of Wisconsin, Madison, WI
| | - Gerardo Alves Fernandes Júnior
- Department of Animal Science, Faculty of Agricultural and Veterinary Sciences, Sao Paulo State University (UNESP), Jaboticabal, SP, Brazil
| | - Rafael Espigolan
- Department of Animal Science, Faculty of Animal Science and Food Engineering, University of Sao Paulo, Pirassununga, SP, Brazil
| | | | - Roberto Carvalheiro
- Department of Animal Science, Faculty of Agricultural and Veterinary Sciences, Sao Paulo State University (UNESP), Jaboticabal, SP, Brazil.,National Council of Technological and Scientific Development (CNPq), Brasilia, Brazil
| | - Lucia Galvão de Albuquerque
- Department of Animal Science, Faculty of Agricultural and Veterinary Sciences, Sao Paulo State University (UNESP), Jaboticabal, SP, Brazil.,National Council of Technological and Scientific Development (CNPq), Brasilia, Brazil
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Garcia BF, Melo TPD, Neves HHDR, Carvalheiro R. Comparison of GWA statistical methods for traits under different genetic structures: A simulation study. Livest Sci 2020. [DOI: 10.1016/j.livsci.2020.104213] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Ren D, An L, Li B, Qiao L, Liu W. Efficient weighting methods for genomic best linear-unbiased prediction (BLUP) adapted to the genetic architectures of quantitative traits. Heredity (Edinb) 2020; 126:320-334. [PMID: 32980863 DOI: 10.1038/s41437-020-00372-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 09/12/2020] [Accepted: 09/13/2020] [Indexed: 11/09/2022] Open
Abstract
Genomic best linear-unbiased prediction (GBLUP) assumes equal variance for all marker effects, which is suitable for traits that conform to the infinitesimal model. For traits controlled by major genes, Bayesian methods with shrinkage priors or genome-wide association study (GWAS) methods can be used to identify causal variants effectively. The information from Bayesian/GWAS methods can be used to construct the weighted genomic relationship matrix (G). However, it remains unclear which methods perform best for traits varying in genetic architecture. Therefore, we developed several methods to optimize the performance of weighted GBLUP and compare them with other available methods using simulated and real data sets. First, two types of methods (marker effects with local shrinkage or normal prior) were used to obtain test statistics and estimates for each marker effect. Second, three weighted G matrices were constructed based on the marker information from the first step: (1) the genomic-feature-weighted G, (2) the estimated marker-variance-weighted G, and (3) the absolute value of the estimated marker-effect-weighted G. Following the above process, six different weighted GBLUP methods (local shrinkage/normal-prior GF/EV/AEWGBLUP) were proposed for genomic prediction. Analyses with both simulated and real data demonstrated that these options offer flexibility for optimizing the weighted GBLUP for traits with a broad spectrum of genetic architectures. The advantage of weighting methods over GBLUP in terms of accuracy was trait dependant, ranging from 14.8% to marginal for simulated traits and from 44% to marginal for real traits. Local-shrinkage prior EVWGBLUP is superior for traits mainly controlled by loci of a large effect. Normal-prior AEWGBLUP performs well for traits mainly controlled by loci of moderate effect. For traits controlled by some loci with large effects (explain 25-50% genetic variance) and a range of loci with small effects, GFWGBLUP has advantages. In conclusion, the optimal weighted GBLUP method for genomic selection should take both the genetic architecture and number of QTLs of traits into consideration carefully.
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Affiliation(s)
- Duanyang Ren
- College of Animal Science and Veterinary Medicine, Shanxi Agricultural University, Taigu, China
| | - Lixia An
- College of Information, Shanxi Agricultural University, Taigu, China
| | - Baojun Li
- College of Animal Science and Veterinary Medicine, Shanxi Agricultural University, Taigu, China
| | - Liying Qiao
- College of Animal Science and Veterinary Medicine, Shanxi Agricultural University, Taigu, China
| | - Wenzhong Liu
- College of Animal Science and Veterinary Medicine, Shanxi Agricultural University, Taigu, China.
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Takeda M, Uemoto Y, Satoh M. Effect of genotyped bulls with different numbers of phenotyped progenies on quantitative trait loci detection and genomic evaluation in a simulated cattle population. Anim Sci J 2020; 91:e13432. [PMID: 32779330 PMCID: PMC7507195 DOI: 10.1111/asj.13432] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 06/18/2020] [Accepted: 07/01/2020] [Indexed: 02/02/2023]
Abstract
The objective of this study was to assess the effect of genotyped bulls with different numbers of phenotyped progenies on quantitative trait loci (QTL) detection and genomic evaluation using a simulated cattle population. Twelve generations (G1–G12) were simulated from the base generation (G0). The recent population had different effective population sizes, heritability, and number of QTL. G0–G4 were used for pedigree information. A total of 300 genotyped bulls from G5–G10 were randomly selected. Their progenies were generated in G6–G11 with different numbers of progeny per bull. Scenarios were considered according to the number of progenies and whether the genotypes were possessed by the bulls or the progenies. A genome‐wide association study and genomic evaluation were performed with a single‐step genomic best linear unbiased prediction method to calculate the power of QTL detection and the genomic estimated breeding value (GEBV). We found that genotyped bulls could be available for QTL detection depending on conditions. Additionally, using a reference population, including genotyped bulls, which had more progeny phenotypes, enabled a more accurate prediction of GEBV. However, it is desirable to have more than 4,500 individuals consisting of both genotypes and phenotypes for practical genomic evaluation.
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Affiliation(s)
- Masayuki Takeda
- National Livestock Breeding Center, Nishigo, Japan.,Graduate School of Agricultural Science, Tohoku University, Sendai, Japan
| | - Yoshinobu Uemoto
- Graduate School of Agricultural Science, Tohoku University, Sendai, Japan
| | - Masahiro Satoh
- Graduate School of Agricultural Science, Tohoku University, Sendai, Japan
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Gaikpa DS, Miedaner T. Genomics-assisted breeding for ear rot resistances and reduced mycotoxin contamination in maize: methods, advances and prospects. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2019; 132:2721-2739. [PMID: 31440772 DOI: 10.1007/s00122-019-03412-2] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Accepted: 08/13/2019] [Indexed: 05/26/2023]
Abstract
Genetic mapping, genomic profiling and bioinformatic approaches were used to identify putative resistance genes for ear rots and low mycotoxin contamination in maize. Genomic selection seems to have good perspectives. Maize is globally an indispensable crop for humans and livestock. About 30% of yield is lost by fungal diseases with Gibberella, Fusarium and Aspergillus ear rots (ERs) having a high economic impact in most maize-growing regions of the world. They reduce not only yield, but also contaminate grains with mycotoxins like deoxynivalenol, zearalenone, fumonisins and aflatoxins, respectively. These mycotoxins pose serious health problems to humans and animals. A number of studies have been conducted to dissect the genetic architecture of resistance to these three major ear rots over the past decade. The review concentrates on studies carried out to locate quantitative trait loci (QTL) and candidate genes (CG) on the maize genome as well as the application of genomic selection in maize for resistance against Fusarium graminearum, Fusarium verticillioides and Aspergillus flavus. QTL studies by linkage or genome-wide association mapping, omic technologies (genomics, proteomics, transcriptomics and metabolomics) and bioinformatics are the methods used in the current studies to propose resistance genes against ear rot pathogens. Though a number of QTL and CG are reported, only a few specific genes were found to directly confer ER resistance in maize. A combination of two or more gene identification methods would provide a more powerful and reliable tool. Genomic selection seems to be promising for ER resistance breeding, but there are only a limited number of studies in this area. A strategy that can accurately validate and predict genotypes with major effect QTL and CG for selection will be worthwhile for practical breeding against ERs and mycotoxin contamination in maize.
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Affiliation(s)
- David Sewordor Gaikpa
- State Plant Breeding Institute, University of Hohenheim, Fruwirthstr. 21, 70599, Stuttgart, Germany
| | - Thomas Miedaner
- State Plant Breeding Institute, University of Hohenheim, Fruwirthstr. 21, 70599, Stuttgart, Germany.
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Ballesta P, Maldonado C, Pérez-Rodríguez P, Mora F. SNP and Haplotype-Based Genomic Selection of Quantitative Traits in Eucalyptus globulus. PLANTS 2019; 8:plants8090331. [PMID: 31492041 PMCID: PMC6783840 DOI: 10.3390/plants8090331] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 09/02/2019] [Accepted: 09/03/2019] [Indexed: 01/02/2023]
Abstract
Eucalyptus globulus (Labill.) is one of the most important cultivated eucalypts in temperate and subtropical regions and has been successfully subjected to intensive breeding. In this study, Bayesian genomic models that include the effects of haplotype and single nucleotide polymorphisms (SNP) were assessed to predict quantitative traits related to wood quality and tree growth in a 6-year-old breeding population. To this end, the following markers were considered: (a) ~14 K SNP markers (SNP), (b) ~3 K haplotypes (HAP), and (c) haplotypes and SNPs that were not assigned to a haplotype (HAP-SNP). Predictive ability values (PA) were dependent on the genomic prediction models and markers. On average, Bayesian ridge regression (BRR) and Bayes C had the highest PA for the majority of traits. Notably, genomic models that included the haplotype effect (either HAP or HAP-SNP) significantly increased the PA of low-heritability traits. For instance, BRR based on HAP had the highest PA (0.58) for stem straightness. Consistently, the heritability estimates from genomic models were higher than the pedigree-based estimates for these traits. The results provide additional perspectives for the implementation of genomic selection in Eucalyptus breeding programs, which could be especially beneficial for improving traits with low heritability.
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Affiliation(s)
- Paulina Ballesta
- Institute of Biological Sciences, University of Talca, 2 Norte 685, Talca 3460000, Chile.
| | - Carlos Maldonado
- Institute of Biological Sciences, University of Talca, 2 Norte 685, Talca 3460000, Chile.
| | - Paulino Pérez-Rodríguez
- Colegio de Postgraduados, Statistics and Computer Sciences, Montecillos, Edo. de México 56230, Mexico.
| | - Freddy Mora
- Institute of Biological Sciences, University of Talca, 2 Norte 685, Talca 3460000, Chile.
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Carreño LOD, da Conceição Pessoa M, Espigolan R, Takada L, Bresolin T, Cavani L, Baldi F, Carvalheiro R, de Albuquerque LG, da Fonseca R. Genome Association Study for Visual Scores in Nellore Cattle Measured at Weaning. BMC Genomics 2019; 20:150. [PMID: 30786866 PMCID: PMC6381746 DOI: 10.1186/s12864-019-5520-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2017] [Accepted: 02/07/2019] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Genome-wide association studies (GWAS) are utilized in cattle to identify regions or genetic variants associated with phenotypes of interest, and thus, to identify design strategies that allow for the increase of the frequency of favorable alleles. Visual scores are important traits of cattle production in Brazil because they are utilized as selection criteria, helping to choose more harmonious animals. Despite its importance, there are still no studies on the genome association for these traits. This study aimed to identify genome regions associated with the traits of conformation, precocity and muscling, based on a visual score measured at weaning. RESULTS Bayesian approaches with BayesC and Bayesian LASSO were utilized with 2873 phenotypes of Nellore cattle for a GWAS. The animals were genotyped with Illumina BovineHD BeadChip, and a total of 309,865 SNPs were utilized after quality control. In the analyses, phenotype and deregressed breeding values were utilized as dependent variables; a threshold model was utilized for the former and a linear model for the latter. The association criterion was the percentage of genetic variance explained by SNPs found in 1 Mb-long windows. The Bayesian approach BayesC was better adjusted to the data because it could explain a larger phenotypic variance for both dependent variables. CONCLUSIONS There were no large effects for the visual scores, indicating that they have a polygenic nature; however, regions in chromosomes 1, 3, 5, 7, 14, 15, 16, 19, 20 and 23 were identified and explained a large part of the genetic variance.
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Affiliation(s)
- Luis Orlando Duitama Carreño
- Animal Science Department, School of Agricultural and Veterinary Sciences, São Paulo State University (Unesp), Jaboticabal, São Paulo Brazil
| | - Matilde da Conceição Pessoa
- Animal Science Department, School of Agricultural and Veterinary Sciences, São Paulo State University (Unesp), Jaboticabal, São Paulo Brazil
| | - Rafael Espigolan
- Animal Science Department, School of Agricultural and Veterinary Sciences, São Paulo State University (Unesp), Jaboticabal, São Paulo Brazil
| | - Luciana Takada
- Animal Science Department, School of Agricultural and Veterinary Sciences, São Paulo State University (Unesp), Jaboticabal, São Paulo Brazil
| | - Tiago Bresolin
- Animal Science Department, School of Agricultural and Veterinary Sciences, São Paulo State University (Unesp), Jaboticabal, São Paulo Brazil
| | - Ligia Cavani
- Animal Science Department, School of Agricultural and Veterinary Sciences, São Paulo State University (Unesp), Jaboticabal, São Paulo Brazil
| | - Fernando Baldi
- Animal Science Department, School of Agricultural and Veterinary Sciences, São Paulo State University (Unesp), Jaboticabal, São Paulo Brazil
| | - Roberto Carvalheiro
- Animal Science Department, School of Agricultural and Veterinary Sciences, São Paulo State University (Unesp), Jaboticabal, São Paulo Brazil
| | - Lucia Galvão de Albuquerque
- Animal Science Department, School of Agricultural and Veterinary Sciences, São Paulo State University (Unesp), Jaboticabal, São Paulo Brazil
| | - Ricardo da Fonseca
- Animal Science Department, São Paulo State University (Unesp), Dracena, São Paulo, Brazil
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van den Berg I, Meuwissen THE, MacLeod IM, Goddard ME. Predicting the effect of reference population on the accuracy of within, across, and multibreed genomic prediction. J Dairy Sci 2019; 102:3155-3174. [PMID: 30738664 DOI: 10.3168/jds.2018-15231] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Accepted: 12/08/2018] [Indexed: 01/24/2023]
Abstract
Genomic prediction is widely used to select candidates for breeding. Size and composition of the reference population are important factors influencing prediction accuracy. In Holstein dairy cattle, large reference populations are used, but this is difficult to achieve in numerically small breeds and for traits that are not routinely recorded. The prediction accuracy is usually estimated using cross-validation, requiring the full data set. It would be useful to have a method to predict the benefit of multibreed reference populations that does not require the availability of the full data set. Our objective was to study the effect of the size and breed composition of the reference population on the accuracy of genomic prediction using genomic BLUP and Bayes R. We also examined the effect of trait heritability and validation breed on prediction accuracy. Using these empirical results, we investigated the use of a formula to predict the effect of the size and composition of the reference population on the accuracy of genomic prediction. Phenotypes were simulated in a data set containing real genotypes of imputed sequence variants for 22,752 dairy bulls and cows, including Holstein, Jersey, Red Holstein, and Australian Red cattle. Different reference populations were constructed, varying in size and composition, to study within-breed, multibreed, and across-breed prediction. Phenotypes were simulated varying in heritability, number of chromosomes, and number of quantitative trait loci. Genomic prediction was carried out using genomic BLUP and Bayes R. We used either the genomic relationship matrix (GRM) to estimate the number of independent chromosomal segments and subsequently to predict accuracy, or the accuracies obtained from single-breed reference populations to predict the accuracies of larger or multibreed reference populations. Using the GRM overestimated the accuracy; this overestimation was likely due to close relationships among some of the reference animals. Consequently, the GRM could not be used to predict the accuracy of genomic prediction reliably. However, a method using the prediction accuracies obtained by cross-validation using a small, single-breed reference population predicted the accuracy using a multibreed reference population well and slightly overestimated the accuracy for a larger reference population of the same breed, but gave a reasonably close estimate of the accuracy for a multibreed reference population. This method could be useful for making decisions regarding the size and composition of the reference population.
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Affiliation(s)
- I van den Berg
- Faculty of Veterinary & Agricultural Science, University of Melbourne, 3010 Parkville, Victoria, Australia; Agriculture Victoria, AgriBio, Centre for AgriBioscience, 3083 Bundoora, Victoria, Australia.
| | - T H E Meuwissen
- Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, 1432 Ås, Norway
| | - I M MacLeod
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, 3083 Bundoora, Victoria, Australia
| | - M E Goddard
- Faculty of Veterinary & Agricultural Science, University of Melbourne, 3010 Parkville, Victoria, Australia; Agriculture Victoria, AgriBio, Centre for AgriBioscience, 3083 Bundoora, Victoria, Australia
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Neves HHR, Vargas G, Brito LF, Schenkel FS, Albuquerque LG, Carvalheiro R. Genetic and genomic analyses of testicular hypoplasia in Nellore cattle. PLoS One 2019; 14:e0211159. [PMID: 30677076 PMCID: PMC6345487 DOI: 10.1371/journal.pone.0211159] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Accepted: 01/08/2019] [Indexed: 12/11/2022] Open
Abstract
Reproductive performance is a key indicator of the long-term sustainability of any livestock production system. Testicular hypoplasia (TH) is a morphological and functional reproductive disorder that affects bulls around the world and consequently causes major economic losses due to reduced fertility rates. Despite the improvements in management practices to enhance performance of affected animals, the use of hypoplastic animals for reproduction might contribute to expand the prevalence of this disorder. The aim of this study was to identify genomic regions that are associated with TH in Nellore cattle by performing a genome-wide association study (GWAS) and functional analyses. Phenotypic and pedigree data from 47,563 animals and genotypes (500,689 Single Nucleotide Polymorphism, SNPs) from 265 sires were used in this study. TH was evaluated as a binary trait measured at 18 months of age. The estimated breeding values (EBVs) were calculated by fitting a single-trait threshold animal model using a Bayesian approach. The SNP effects were estimated using the Bayes C method and de-regressed EBVs for TH as the response variable (pseudo-phenotype). The top-15 ranking windows (5-adjacent SNPs) that explained the highest proportion of variance were identified for further functional and biological network analyses. The posterior mean (95% highest posterior density) of the heritability for TH was 0.16 (0.08; 0.23). The most important genomic windows were located on BTA1, BTA3, BTA4, BTA5, BTA9, BTA22, BTA23, and BTA25. These windows explained together 22.69% of the total additive genetic variance for TH. Strong candidate genes associated with metabolism and synthesis of steroids, cell survival, spermatogenesis process and sperm motility were identified, which might play an important role in the expression of TH. Our findings contribute to a better biological understanding of TH and future characterization of causal variants might enable improved genomic prediction of this trait in beef cattle.
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Affiliation(s)
| | - Giovana Vargas
- Department of Animal Sciences, School of Agricultural and Veterinary Sciences, São Paulo State University (UNESP), Jaboticabal, São Paulo, Brazil
| | - Luiz F. Brito
- Department of Animal Sciences, Purdue University, West Lafayette, Indiana, United States of America
| | - Flavio S. Schenkel
- Centre for Genetic Improvement of Livestock (CGIL), Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada
| | - Lucia G. Albuquerque
- Department of Animal Sciences, School of Agricultural and Veterinary Sciences, São Paulo State University (UNESP), Jaboticabal, São Paulo, Brazil
- National Council for Science and Technological Development (Cnpq), Brasília, Distrito Federal, Brazil
| | - Roberto Carvalheiro
- GenSys Associated Consultants, Porto Alegre, Rio Grande do Sul, Brazil
- National Council for Science and Technological Development (Cnpq), Brasília, Distrito Federal, Brazil
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Braz CU, Taylor JF, Decker JE, Bresolin T, Espigolan R, Garcia DA, Gordo DGM, Magalhães AFB, de Albuquerque LG, de Oliveira HN. Polymorphism analysis in genes associated with meat tenderness in Nelore cattle. Meta Gene 2018. [DOI: 10.1016/j.mgene.2018.08.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022] Open
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Pszczola M, Strabel T, Mucha S, Sell-Kubiak E. Genome-wide association identifies methane production level relation to genetic control of digestive tract development in dairy cows. Sci Rep 2018; 8:15164. [PMID: 30310168 PMCID: PMC6181922 DOI: 10.1038/s41598-018-33327-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2018] [Accepted: 09/24/2018] [Indexed: 11/08/2022] Open
Abstract
The global temperatures are increasing. This increase is partly due to methane (CH4) production from ruminants, including dairy cattle. Recent studies on dairy cattle have revealed the existence of a heritable variation in CH4 production that enables mitigation strategies based on selective breeding. We have exploited the available heritable variation to study the genetic architecture of CH4 production and detected genomic regions affecting CH4 production. Although the detected regions explained only a small proportion of the heritable variance, we showed that potential QTL regions affecting CH4 production were located within QTLs related to feed efficiency, milk-related traits, body size and health status. Five candidate genes were found: CYP51A1 on BTA 4, PPP1R16B on BTA 13, and NTHL1, TSC2, and PKD1 on BTA 25. These candidate genes were involved in a number of metabolic processes that are possibly related to CH4 production. One of the most promising candidate genes (PKD1) was related to the development of the digestive tract. The results indicate that CH4 production is a highly polygenic trait.
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Affiliation(s)
- M Pszczola
- Department of Genetics and Animal Breeding, Poznan University of Life Sciences, Wolynska 33, Poznan, Poland.
| | - T Strabel
- Department of Genetics and Animal Breeding, Poznan University of Life Sciences, Wolynska 33, Poznan, Poland.
| | - S Mucha
- Department of Genetics and Animal Breeding, Poznan University of Life Sciences, Wolynska 33, Poznan, Poland
| | - E Sell-Kubiak
- Department of Genetics and Animal Breeding, Poznan University of Life Sciences, Wolynska 33, Poznan, Poland
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Metodiev S, Thekkoot D, Young J, Onteru S, Rothschild M, Dekkers J. A whole-genome association study for litter size and litter weight traits in pigs. Livest Sci 2018. [DOI: 10.1016/j.livsci.2018.03.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Mohanta TK, Bashir T, Hashem A, Abd Allah EF. Systems biology approach in plant abiotic stresses. PLANT PHYSIOLOGY AND BIOCHEMISTRY : PPB 2017; 121:58-73. [PMID: 29096174 DOI: 10.1016/j.plaphy.2017.10.019] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2017] [Revised: 09/28/2017] [Accepted: 10/20/2017] [Indexed: 05/05/2023]
Abstract
Plant abiotic stresses are the major constraint on plant growth and development, causing enormous crop losses across the world. Plants have unique features to defend themselves against these challenging adverse stress conditions. They modulate their phenotypes upon changes in physiological, biochemical, molecular and genetic information, thus making them tolerant against abiotic stresses. It is of paramount importance to determine the stress-tolerant traits of a diverse range of genotypes of plant species and integrate those traits for crop improvement. Stress-tolerant traits can be identified by conducting genome-wide analysis of stress-tolerant genotypes through the highly advanced structural and functional genomics approach. Specifically, whole-genome sequencing, development of molecular markers, genome-wide association studies and comparative analysis of interaction networks between tolerant and susceptible crop varieties grown under stress conditions can greatly facilitate discovery of novel agronomic traits that protect plants against abiotic stresses.
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Affiliation(s)
- Tapan Kumar Mohanta
- Department of Biotechnology, Yeungnam University, Gyeongsan, 38541, Republic of Korea.
| | - Tufail Bashir
- Department of Biotechnology, Yeungnam University, Gyeongsan, 38541, Republic of Korea
| | - Abeer Hashem
- Botany and Microbiology Department, College of Science, King Saud University, P.O. Box 2460, Riyadh, 11451, Saudi Arabia
| | - Elsayed Fathi Abd Allah
- Plant Production Department, College of Food and Agricultural Science, King Saud University, P.O. Box 24160, Riyadh, 11451, Saudi Arabia
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Mehrban H, Lee DH, Moradi MH, IlCho C, Naserkheil M, Ibáñez-Escriche N. Predictive performance of genomic selection methods for carcass traits in Hanwoo beef cattle: impacts of the genetic architecture. Genet Sel Evol 2017; 49:1. [PMID: 28093066 PMCID: PMC5240470 DOI: 10.1186/s12711-016-0283-0] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2015] [Accepted: 12/22/2016] [Indexed: 12/15/2022] Open
Abstract
Background Hanwoo beef is known for its marbled fat, tenderness, juiciness and characteristic flavor, as well as for its low cholesterol and high omega 3 fatty acid contents. As yet, there has been no comprehensive investigation to estimate genomic selection accuracy for carcass traits in Hanwoo cattle using dense markers. This study aimed at evaluating the accuracy of alternative statistical methods that differed in assumptions about the underlying genetic model for various carcass traits: backfat thickness (BT), carcass weight (CW), eye muscle area (EMA), and marbling score (MS). Methods Accuracies of direct genomic breeding values (DGV) for carcass traits were estimated by applying fivefold cross-validation to a dataset including 1183 animals and approximately 34,000 single nucleotide polymorphisms (SNPs). Results Accuracies of BayesC, Bayesian LASSO (BayesL) and genomic best linear unbiased prediction (GBLUP) methods were similar for BT, EMA and MS. However, for CW, DGV accuracy was 7% higher with BayesC than with BayesL and GBLUP. The increased accuracy of BayesC, compared to GBLUP and BayesL, was maintained for CW, regardless of the training sample size, but not for BT, EMA, and MS. Genome-wide association studies detected consistent large effects for SNPs on chromosomes 6 and 14 for CW. Conclusions The predictive performance of the models depended on the trait analyzed. For CW, the results showed a clear superiority of BayesC compared to GBLUP and BayesL. These findings indicate the importance of using a proper variable selection method for genomic selection of traits and also suggest that the genetic architecture that underlies CW differs from that of the other carcass traits analyzed. Thus, our study provides significant new insights into the carcass traits of Hanwoo cattle. Electronic supplementary material The online version of this article (doi:10.1186/s12711-016-0283-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Hossein Mehrban
- Department of Animal Science, Shahrekord University, P.O. Box 115, Shahrekord, 88186-34141, Iran
| | - Deuk Hwan Lee
- Department of Animal Life and Environment Science, Hankyong National University, Jungang-ro 327, Anseong-si, Gyeonggi-do, 456-749, Korea.
| | - Mohammad Hossein Moradi
- Department of Animal Science, Faculty of Agriculture and Natural Resources, Arak University, Arāk, 38156-8-8349, Iran
| | - Chung IlCho
- Hanwoo Improvement Center, National Agricultural Cooperative Federation, Haeun-ro 691, Unsan-myeon, Seosan-si, Chungnam-do, 356-831, Korea
| | - Masoumeh Naserkheil
- Department of Animal Science, University College of Agriculture and Natural Resources, University of Tehran, P.O. Box 4111, Karaj, 31587-11167, Iran
| | - Noelia Ibáñez-Escriche
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, University of Edinburgh, Roslin, UK
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18
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Melo TP, Takada L, Baldi F, Oliveira HN, Dias MM, Neves HHR, Schenkel FS, Albuquerque LG, Carvalheiro R. Assessing the value of phenotypic information from non-genotyped animals for QTL mapping of complex traits in real and simulated populations. BMC Genet 2016; 17:89. [PMID: 27328759 PMCID: PMC4915095 DOI: 10.1186/s12863-016-0394-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2015] [Accepted: 06/09/2016] [Indexed: 01/08/2023] Open
Abstract
Background QTL mapping through genome-wide association studies (GWAS) is challenging, especially in the case of low heritability complex traits and when few animals possess genotypic and phenotypic information. When most of the phenotypic information is from non-genotyped animals, GWAS can be performed using the weighted single-step GBLUP (WssGBLUP) method, which permits to combine all available information, even that of non-genotyped animals. However, it is not clear to what extent phenotypic information from non-genotyped animals increases the power of QTL detection, and whether factors such as the extent of linkage disequilibrium (LD) in the population and weighting SNPs in WssGBLUP affect the importance of using information from non-genotyped animals in GWAS. These questions were investigated in this study using real and simulated data. Results Analysis of real data showed that the use of phenotypes of non-genotyped animals affected SNP effect estimates and, consequently, QTL mapping. Despite some coincidence, the most important genomic regions identified by the analyses, either using or ignoring phenotypes of non-genotyped animals, were not the same. The simulation results indicated that the inclusion of all available phenotypic information, even that of non-genotyped animals, tends to improve QTL detection for low heritability complex traits. For populations with low levels of LD, this trend of improvement was less pronounced. Stronger shrinkage on SNPs explaining lower variance was not necessarily associated with better QTL mapping. Conclusions The use of phenotypic information from non-genotyped animals in GWAS may improve the ability to detect QTL for low heritability complex traits, especially in populations in which the level of LD is high.
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Affiliation(s)
- Thaise P Melo
- UNESP, Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal, 14884-900, São Paulo, Brazil
| | - Luciana Takada
- UNESP, Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal, 14884-900, São Paulo, Brazil
| | - Fernando Baldi
- UNESP, Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal, 14884-900, São Paulo, Brazil
| | - Henrique N Oliveira
- UNESP, Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal, 14884-900, São Paulo, Brazil
| | - Marina M Dias
- UNESP, Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal, 14884-900, São Paulo, Brazil
| | - Haroldo H R Neves
- UNESP, Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal, 14884-900, São Paulo, Brazil.,GenSys Consultores Associados S/C Ltda, Porto Alegre, 90680-000, Brazil
| | - Flavio S Schenkel
- Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, N1G2W1, ON, Canada
| | - Lucia G Albuquerque
- UNESP, Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal, 14884-900, São Paulo, Brazil
| | - Roberto Carvalheiro
- UNESP, Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal, 14884-900, São Paulo, Brazil.
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Boichard D, Ducrocq V, Croiseau P, Fritz S. Genomic selection in domestic animals: Principles, applications and perspectives. C R Biol 2016; 339:274-7. [PMID: 27185591 DOI: 10.1016/j.crvi.2016.04.007] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2016] [Accepted: 04/14/2016] [Indexed: 10/21/2022]
Abstract
The principles of genomic selection are described, with the main factors affecting its efficiency and the assumptions underlying the different models proposed. The reasons of its fast adoption in dairy cattle are explained and the conditions of its application to other species are discussed. Perspectives of development include: selection for new traits and new breeding objectives; adoption of more robust approaches based on information on causal variants; predictions of genotype×environment interactions.
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Affiliation(s)
- Didier Boichard
- GABI, INRA, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France.
| | - Vincent Ducrocq
- GABI, INRA, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France
| | - Pascal Croiseau
- GABI, INRA, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France
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Sell-Kubiak E, Duijvesteijn N, Lopes MS, Janss LLG, Knol EF, Bijma P, Mulder HA. Genome-wide association study reveals novel loci for litter size and its variability in a Large White pig population. BMC Genomics 2015; 16:1049. [PMID: 26652161 PMCID: PMC4674943 DOI: 10.1186/s12864-015-2273-y] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2015] [Accepted: 12/03/2015] [Indexed: 01/11/2023] Open
Abstract
Background In many traits, not only individual trait levels are under genetic control, but also the variation around that level. In other words, genotypes do not only differ in mean, but also in (residual) variation around the genotypic mean. New statistical methods facilitate gaining knowledge on the genetic architecture of complex traits such as phenotypic variability. Here we study litter size (total number born) and its variation in a Large White pig population using a Double Hierarchical Generalized Linear model, and perform a genome-wide association study using a Bayesian method. Results In total, 10 significant single nucleotide polymorphisms (SNPs) were detected for total number born (TNB) and 9 SNPs for variability of TNB (varTNB). Those SNPs explained 0.83 % of genetic variance in TNB and 1.44 % in varTNB. The most significant SNP for TNB was detected on Sus scrofa chromosome (SSC) 11. A possible candidate gene for TNB is ENOX1, which is involved in cell growth and survival. On SSC7, two possible candidate genes for varTNB are located. The first gene is coding a swine heat shock protein 90 (HSPCB = Hsp90), which is a well-studied gene stabilizing morphological traits in Drosophila and Arabidopsis. The second gene is VEGFA, which is activated in angiogenesis and vasculogenesis in the fetus. Furthermore, the genetic correlation between additive genetic effects on TNB and on its variation was 0.49. This indicates that the current selection to increase TNB will also increase the varTNB. Conclusions To the best of our knowledge, this is the first study reporting SNPs associated with variation of a trait in pigs. Detected genomic regions associated with varTNB can be used in genomic selection to decrease varTNB, which is highly desirable to avoid very small or very large litters in pigs. However, the percentage of variance explained by those regions was small. The SNPs detected in this study can be used as indication for regions in the Sus scrofa genome involved in maintaining low variability of litter size, but further studies are needed to identify the causative loci.
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Affiliation(s)
- E Sell-Kubiak
- Animal Breeding and Genomics Center, Wageningen University, P.O. Box 338, 6700, Wageningen, AH, The Netherlands.
| | - N Duijvesteijn
- Topigs Norsvin Research Center B.V, P.O. Box 43, 6640, Beuningen, AA, The Netherlands.
| | - M S Lopes
- Topigs Norsvin Research Center B.V, P.O. Box 43, 6640, Beuningen, AA, The Netherlands.
| | - L L G Janss
- Department of Molecular Biology and Genetics, Aarhus University, P.O. Box 50, 8830, Tjele, Denmark.
| | - E F Knol
- Topigs Norsvin Research Center B.V, P.O. Box 43, 6640, Beuningen, AA, The Netherlands.
| | - P Bijma
- Animal Breeding and Genomics Center, Wageningen University, P.O. Box 338, 6700, Wageningen, AH, The Netherlands.
| | - H A Mulder
- Animal Breeding and Genomics Center, Wageningen University, P.O. Box 338, 6700, Wageningen, AH, The Netherlands.
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Freitas MS, Freitas LS, Weber T, Yamaki M, Cantão ME, Peixoto JO, Ledur MC. Comparison of iterated single-step and Bayesian regressions on genomic evaluations for age at 100 kg in swine. J Anim Sci 2015; 93:4675-83. [PMID: 26523560 DOI: 10.2527/jas.2014-8842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The effects of modified single-step genomic best linear unbiased prediction (ssGBLUP) iterations on GEBV and SNP were investigated using 85,388 age at 100 kg phenotypes from the BRF SA breeding program Landrace pure line animals, off-tested between 2002 and 2013. Pedigree data comprised animals born between 1999 and 2013. A total of 1,068 animals were assigned to the training population, in which all of them had genotypes, original and corrected age at 100 kg phenotypes, and weighted deregressed proof records. A total of 100 genotyped animals, with high accuracy age at 100 kg estimated breeding values, were assigned to the validation population. After applying the quality control workflow, a set of 41,042 SNP was used for the analysis. Standard and modified ssGBLUP, BayesCπ, and Bayesian Lasso were compared, and their predictive abilities were accessed by approximate true and GEBV correlations. Modified ssGBLUP iteration effects on SNP estimates and GEBV were relevant, in which assigned differential weights and shrinkage caused important losses on ssGBLUP predictive ability for age at 100 kg GEBV. Even though ssGBLUP accuracy can be equal or better than the compared Bayesian methods, additional gains can be obtained by correctly identifying the number of iterations required for best ssGBLUP performance.
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22
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Doran AG, Berry DP, Creevey CJ. Whole genome association study identifies regions of the bovine genome and biological pathways involved in carcass trait performance in Holstein-Friesian cattle. BMC Genomics 2014; 15:837. [PMID: 25273628 PMCID: PMC4192274 DOI: 10.1186/1471-2164-15-837] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2013] [Accepted: 09/25/2014] [Indexed: 12/25/2022] Open
Abstract
Background Four traits related to carcass performance have been identified as economically important in beef production: carcass weight, carcass fat, carcass conformation of progeny and cull cow carcass weight. Although Holstein-Friesian cattle are primarily utilized for milk production, they are also an important source of meat for beef production and export. Because of this, there is great interest in understanding the underlying genomic structure influencing these traits. Several genome-wide association studies have identified regions of the bovine genome associated with growth or carcass traits, however, little is known about the mechanisms or underlying biological pathways involved. This study aims to detect regions of the bovine genome associated with carcass performance traits (employing a panel of 54,001 SNPs) using measures of genetic merit (as predicted transmitting abilities) for 5,705 Irish Holstein-Friesian animals. Candidate genes and biological pathways were then identified for each trait under investigation. Results Following adjustment for false discovery (q-value < 0.05), 479 quantitative trait loci (QTL) were associated with at least one of the four carcass traits using a single SNP regression approach. Using a Bayesian approach, 46 QTL were associated (posterior probability > 0.5) with at least one of the four traits. In total, 557 unique bovine genes, which mapped to 426 human orthologs, were within 500kbs of QTL found associated with a trait using the Bayesian approach. Using this information, 24 significantly over-represented pathways were identified across all traits. The most significantly over-represented biological pathway was the peroxisome proliferator-activated receptor (PPAR) signaling pathway. Conclusions A large number of genomic regions putatively associated with bovine carcass traits were detected using two different statistical approaches. Notably, several significant associations were detected in close proximity to genes with a known role in animal growth such as glucagon and leptin. Several biological pathways, including PPAR signaling, were shown to be involved in various aspects of bovine carcass performance. These core genes and biological processes may form the foundation for further investigation to identify causative mutations involved in each trait. Results reported here support previous findings suggesting conservation of key biological processes involved in growth and metabolism. Electronic supplementary material The online version of this article (doi:10.1186/1471-2164-15-837) contains supplementary material, which is available to authorized users.
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Affiliation(s)
| | | | - Christopher J Creevey
- Teagasc Animal and Bioscience Research Department, Animal & Grassland Research and Innovation Centre, Teagasc, Grange, Dunsany, Co, Meath, Ireland.
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Duijvesteijn N, Veltmaat JM, Knol EF, Harlizius B. High-resolution association mapping of number of teats in pigs reveals regions controlling vertebral development. BMC Genomics 2014; 15:542. [PMID: 24981054 PMCID: PMC4092218 DOI: 10.1186/1471-2164-15-542] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2013] [Accepted: 06/25/2014] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Selection pressure on the number of teats has been applied to be able to provide enough teats for the increase in litter size in pigs. Although many QTL were reported, they cover large chromosomal regions and the functional mutations and their underlying biological mechanisms have not yet been identified. To gain a better insight in the genetic architecture of the trait number of teats, we performed a genome-wide association study by genotyping 936 Large White pigs using the Illumina PorcineSNP60 Beadchip. The analysis is based on deregressed breeding values to account for the dense family structure and a Bayesian approach for estimation of the SNP effects. RESULTS The genome-wide association study resulted in 212 significant SNPs. In total, 39 QTL regions were defined including 170 SNPs on 13 Sus scrofa chromosomes (SSC) of which 5 regions on SSC7, 9, 10, 12 and 14 were highly significant. All significantly associated regions together explain 9.5% of the genetic variance where a QTL on SSC7 explains the most genetic variance (2.5%). For the five highly significant QTL regions, a search for candidate genes was performed. The most convincing candidate genes were VRTN and Prox2 on SSC7, MPP7, ARMC4, and MKX on SSC10, and vertebrae δ-EF1 on SSC12. All three QTL contain candidate genes which are known to be associated with vertebral development. In the new QTL regions on SSC9 and SSC14, no obvious candidate genes were identified. CONCLUSIONS Five major QTL were found at high resolution on SSC7, 9, 10, 12, and 14 of which the QTL on SSC9 and SSC14 are the first ones to be reported on these chromosomes. The significant SNPs found in this study could be used in selection to increase number of teats in pigs, so that the increasing number of live-born piglets can be nursed by the sow. This study points to common genetic mechanisms regulating number of vertebrae and number of teats.
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Affiliation(s)
- Naomi Duijvesteijn
- />TOPIGS Research Center IPG, PO Box 43, 6640AA Beuningen, The Netherlands
| | - Jacqueline M Veltmaat
- />Institute of Molecular and Cell Biology, A*STAR (Agency for Science, Technology and Research), 61, Biopolis Drive, Singapore, Singapore 138673
| | - Egbert F Knol
- />TOPIGS Research Center IPG, PO Box 43, 6640AA Beuningen, The Netherlands
| | - Barbara Harlizius
- />TOPIGS Research Center IPG, PO Box 43, 6640AA Beuningen, The Netherlands
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van den Berg I, Fritz S, Rodriguez S, Rocha D, Boussaha M, Lund MS, Boichard D. Concordance analysis for QTL detection in dairy cattle: a case study of leg morphology. Genet Sel Evol 2014; 46:31. [PMID: 24884971 PMCID: PMC4046048 DOI: 10.1186/1297-9686-46-31] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2013] [Accepted: 04/29/2014] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND The present availability of sequence data gives new opportunities to narrow down from QTL (quantitative trait locus) regions to causative mutations. Our objective was to decrease the number of candidate causative mutations in a QTL region. For this, a concordance analysis was applied for a leg conformation trait in dairy cattle. Several QTL were detected for which the QTL status (homozygous or heterozygous for the QTL) was inferred for each individual. Subsequently, the inferred QTL status was used in a concordance analysis to reduce the number of candidate mutations. METHODS Twenty QTL for rear leg set side view were mapped using Bayes C. Marker effects estimated during QTL mapping were used to infer the QTL status for each individual. Subsequently, polymorphisms present in the QTL regions were extracted from the whole-genome sequences of 71 Holstein bulls. Only polymorphisms for which the status was concordant with the QTL status were kept as candidate causative mutations. RESULTS QTL status could be inferred for 15 of the 20 QTL. The number of concordant polymorphisms differed between QTL and depended on the number of QTL statuses that could be inferred and the linkage disequilibrium in the QTL region. For some QTL, the concordance analysis was efficient and narrowed down to a limited number of candidate mutations located in one or two genes, while for other QTL a large number of genes contained concordant polymorphisms. CONCLUSIONS For regions for which the concordance analysis could be performed, we were able to reduce the number of candidate mutations. For part of the QTL, the concordant analyses narrowed QTL regions down to a limited number of genes, of which some are known for their role in limb or skeletal development in humans and mice. Mutations in these genes are good candidates for QTN (quantitative trait nucleotides) influencing rear leg set side view.
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Affiliation(s)
- Irene van den Berg
- INRA, UMR1313 Génétique Animale et Biologie Intégrative, 78350 Jouy-en-Josas, France.
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