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Weber SE, Roscher-Ehrig L, Kox T, Abbadi A, Stahl A, Snowdon RJ. Genomic prediction in Brassica napus: evaluating the benefit of imputed whole-genome sequencing data. Genome 2024; 67:210-222. [PMID: 38708850 DOI: 10.1139/gen-2023-0126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2024]
Abstract
Advances in sequencing technology allow whole plant genomes to be sequenced with high quality. Combining genotypic and phenotypic data in genomic prediction helps breeders to select crossing partners in partially phenotyped populations. In plant breeding programs, the cost of sequencing entire breeding populations still exceeds available genotyping budgets. Hence, the method for genotyping is still mainly single nucleotide polymorphism (SNP) arrays; however, arrays are unable to assess the entire genome- and population-wide diversity. A compromise involves genotyping the entire population using an SNP array and a subset of the population with whole-genome sequencing. Both datasets can then be used to impute markers from whole-genome sequencing onto the entire population. Here, we evaluate whether imputation of whole-genome sequencing data enhances genomic predictions, using data from a nested association mapping population of rapeseed (Brassica napus). Employing two cross-validation schemes that mimic scenarios for the prediction of close and distant relatives, we show that imputed marker data do not significantly improve prediction accuracy, likely due to redundancy in relationship estimates and imputation errors. In simulation studies, only small improvements were observed, further corroborating the findings. We conclude that SNP arrays are already equipped with the information that is added by imputation through relationship and linkage disequilibrium.
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Affiliation(s)
- Sven E Weber
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Giessen, Germany
| | - Lennard Roscher-Ehrig
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Giessen, Germany
| | | | | | - Andreas Stahl
- Julius Kuehn Institute (JKI), Federal Research Centre for Cultivated Plants, Institute for Resistance Research and Stress Tolerance, Quedlinburg, Germany
| | - Rod J Snowdon
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Giessen, Germany
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Li W, Li W, Song Z, Gao Z, Xie K, Wang Y, Wang B, Hu J, Zhang Q, Ning C, Wang D, Fan X. Marker Density and Models to Improve the Accuracy of Genomic Selection for Growth and Slaughter Traits in Meat Rabbits. Genes (Basel) 2024; 15:454. [PMID: 38674388 PMCID: PMC11050255 DOI: 10.3390/genes15040454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 03/25/2024] [Accepted: 03/27/2024] [Indexed: 04/28/2024] Open
Abstract
The selection and breeding of good meat rabbit breeds are fundamental to their industrial development, and genomic selection (GS) can employ genomic information to make up for the shortcomings of traditional phenotype-based breeding methods. For the practical implementation of GS in meat rabbit breeding, it is necessary to assess different marker densities and GS models. Here, we obtained low-coverage whole-genome sequencing (lcWGS) data from 1515 meat rabbits (including parent herd and half-sibling offspring). The specific objectives were (1) to derive a baseline for heritability estimates and genomic predictions based on randomly selected marker densities and (2) to assess the accuracy of genomic predictions for single- and multiple-trait linear mixed models. We found that a marker density of 50 K can be used as a baseline for heritability estimation and genomic prediction. For GS, the multi-trait genomic best linear unbiased prediction (GBLUP) model results in more accurate predictions for virtually all traits compared to the single-trait model, with improvements greater than 15% for all of them, which may be attributed to the use of information on genetically related traits. In addition, we discovered a positive correlation between the performance of the multi-trait GBLUP and the genetic correlation between the traits. We anticipate that this approach will provide solutions for GS, as well as optimize breeding programs, in meat rabbits.
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Affiliation(s)
- Wenjie Li
- Department of Animal Genetics and Breeding, Shandong Agricultural University, Taian 271000, China; (W.L.); (W.L.); (Z.S.); (K.X.); (B.W.); (J.H.); (Q.Z.); (C.N.)
- Department of Animal Genetics and Breeding, University of Anhui Agricultural, Hefei 230031, China
| | - Wenqiang Li
- Department of Animal Genetics and Breeding, Shandong Agricultural University, Taian 271000, China; (W.L.); (W.L.); (Z.S.); (K.X.); (B.W.); (J.H.); (Q.Z.); (C.N.)
| | - Zichen Song
- Department of Animal Genetics and Breeding, Shandong Agricultural University, Taian 271000, China; (W.L.); (W.L.); (Z.S.); (K.X.); (B.W.); (J.H.); (Q.Z.); (C.N.)
| | - Zihao Gao
- Department of Animal Genetics and Breeding, Shandong Agricultural University, Taian 271000, China; (W.L.); (W.L.); (Z.S.); (K.X.); (B.W.); (J.H.); (Q.Z.); (C.N.)
| | - Kerui Xie
- Department of Animal Genetics and Breeding, Shandong Agricultural University, Taian 271000, China; (W.L.); (W.L.); (Z.S.); (K.X.); (B.W.); (J.H.); (Q.Z.); (C.N.)
| | - Yubing Wang
- Department of Animal Genetics and Breeding, Shandong Agricultural University, Taian 271000, China; (W.L.); (W.L.); (Z.S.); (K.X.); (B.W.); (J.H.); (Q.Z.); (C.N.)
| | - Bo Wang
- Department of Animal Genetics and Breeding, Shandong Agricultural University, Taian 271000, China; (W.L.); (W.L.); (Z.S.); (K.X.); (B.W.); (J.H.); (Q.Z.); (C.N.)
| | - Jiaqing Hu
- Department of Animal Genetics and Breeding, Shandong Agricultural University, Taian 271000, China; (W.L.); (W.L.); (Z.S.); (K.X.); (B.W.); (J.H.); (Q.Z.); (C.N.)
| | - Qin Zhang
- Department of Animal Genetics and Breeding, Shandong Agricultural University, Taian 271000, China; (W.L.); (W.L.); (Z.S.); (K.X.); (B.W.); (J.H.); (Q.Z.); (C.N.)
| | - Chao Ning
- Department of Animal Genetics and Breeding, Shandong Agricultural University, Taian 271000, China; (W.L.); (W.L.); (Z.S.); (K.X.); (B.W.); (J.H.); (Q.Z.); (C.N.)
| | - Dan Wang
- Key Laboratory of Efficient Utilization of Non-Grain Feed Resources (Co-Construction by Ministry and Province), College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Ministry of Agriculture and Rural Affairs, Taian 271000, China
| | - Xinzhong Fan
- Department of Animal Genetics and Breeding, Shandong Agricultural University, Taian 271000, China; (W.L.); (W.L.); (Z.S.); (K.X.); (B.W.); (J.H.); (Q.Z.); (C.N.)
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Sánchez-Roncancio C, García B, Gallardo-Hidalgo J, Yáñez JM. GWAS on Imputed Whole-Genome Sequence Variants Reveal Genes Associated with Resistance to Piscirickettsia salmonis in Rainbow Trout ( Oncorhynchus mykiss). Genes (Basel) 2022; 14:114. [PMID: 36672855 PMCID: PMC9859203 DOI: 10.3390/genes14010114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/27/2022] [Accepted: 12/28/2022] [Indexed: 12/31/2022] Open
Abstract
Genome-wide association studies (GWAS) allow the identification of associations between genetic variants and important phenotypes in domestic animals, including disease-resistance traits. Whole Genome Sequencing (WGS) data can help increase the resolution and statistical power of association mapping. Here, we conduced GWAS to asses he facultative intracellular bacterium Piscirickettsia salmonis, which affects farmed rainbow trout, Oncorhynchus mykiss, in Chile using imputed genotypes at the sequence level and searched for candidate genes located in genomic regions associated with the trait. A total of 2130 rainbow trout were intraperitoneally challenged with P. salmonis under controlled conditions and genotyped using a 57K single nucleotide polymorphism (SNP) panel. Genotype imputation was performed in all the genotyped animals using WGS data from 102 individuals. A total of 488,979 imputed WGS variants were available in the 2130 individuals after quality control. GWAS revealed genome-wide significant quantitative trait loci (QTL) in Omy02, Omy03, Omy25, Omy26 and Omy27 for time to death and in Omy26 for binary survival. Twenty-four (24) candidate genes associated with P. salmonis resistance were identified, which were mainly related to phagocytosis, innate immune response, inflammation, oxidative response, lipid metabolism and apoptotic process. Our results provide further knowledge on the genetic variants and genes associated with resistance to intracellular bacterial infection in rainbow trout.
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Affiliation(s)
- Charles Sánchez-Roncancio
- Doctorado en Acuicultura, Programa Cooperativo: Universidad de Chile. Universidad Católica del Norte. Pontificia Universidad Católica de Valparaíso, Chile
- Center for Research and Innovation in Aquaculture (CRIA), Universidad de Chile, Santiago 8820808, Chile
| | - Baltasar García
- Center for Research and Innovation in Aquaculture (CRIA), Universidad de Chile, Santiago 8820808, Chile
- Facultad de Ciencias Veterinarias y Pecuarias, Universidad de Chile, La Pintana, Santiago 8820808, Chile
| | - Jousepth Gallardo-Hidalgo
- Center for Research and Innovation in Aquaculture (CRIA), Universidad de Chile, Santiago 8820808, Chile
- Facultad de Ciencias Veterinarias y Pecuarias, Universidad de Chile, La Pintana, Santiago 8820808, Chile
| | - José M. Yáñez
- Center for Research and Innovation in Aquaculture (CRIA), Universidad de Chile, Santiago 8820808, Chile
- Facultad de Ciencias Veterinarias y Pecuarias, Universidad de Chile, La Pintana, Santiago 8820808, Chile
- Núcleo Milenio de Salmonidos Invasores Australes (INVASAL), Concepcion 4030000, Chile
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Ning C, Xie K, Huang J, Di Y, Wang Y, Yang A, Hu J, Zhang Q, Wang D, Fan X. Marker density and statistical model designs to increase accuracy of genomic selection for wool traits in Angora rabbits. Front Genet 2022; 13:968712. [PMID: 36118881 PMCID: PMC9478554 DOI: 10.3389/fgene.2022.968712] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 08/17/2022] [Indexed: 11/13/2022] Open
Abstract
The Angora rabbit, a well-known breed for fiber production, has been undergoing traditional breeding programs relying mainly on phenotypes. Genomic selection (GS) uses genomic information and promises to accelerate genetic gain. Practically, to implement GS in Angora rabbit breeding, it is necessary to evaluate different marker densities and GS models to develop suitable strategies for an optimized breeding pipeline. Considering a lack in microarray, low-coverage sequencing combined with genotype imputation was used to boost the number of SNPs across the rabbit genome. Here, in a population of 629 Angora rabbits, a total of 18,577,154 high-quality SNPs were imputed (imputation accuracy above 98%) based on low-coverage sequencing of 3.84X genomic coverage, and wool traits and body weight were measured at 70, 140 and 210 days of age. From the original markers, 0.5K, 1K, 3K, 5K, 10K, 50K, 100K, 500K, 1M and 2M were randomly selected and evaluated, resulting in 50K markers as the baseline for the heritability estimation and genomic prediction. Comparing to the GS performance of single-trait models, the prediction accuracy of nearly all traits could be improved by multi-trait models, which might because multiple-trait models used information from genetically correlated traits. Furthermore, we observed high significant negative correlation between the increased prediction accuracy from single-trait to multiple-trait models and estimated heritability. The results indicated that low-heritability traits could borrow more information from correlated traits and hence achieve higher prediction accuracy. The research first reported heritability estimation in rabbits by using genome-wide markers, and provided 50K as an optimal marker density for further microarray design, genetic evaluation and genomic selection in Angora rabbits. We expect that the work could provide strategies for GS in early selection, and optimize breeding programs in rabbits.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Dan Wang
- *Correspondence: Dan Wang, ; Xinzhong Fan,
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Bermann M, Cesarani A, Misztal I, Lourenco D. Past, present, and future developments in single-step genomic models. ITALIAN JOURNAL OF ANIMAL SCIENCE 2022. [DOI: 10.1080/1828051x.2022.2053366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Matias Bermann
- Department of Animal and Dairy Science, University of Georgia, Athens, GA, USA
| | - Alberto Cesarani
- Department of Animal and Dairy Science, University of Georgia, Athens, GA, USA
- Dipartimento di Agraria, Università degli Studi di Sassari, Sassari, Italy
| | - Ignacy Misztal
- Dipartimento di Agraria, Università degli Studi di Sassari, Sassari, Italy
| | - Daniela Lourenco
- Department of Animal and Dairy Science, University of Georgia, Athens, GA, USA
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Liao Y, Wang Z, Glória LS, Zhang K, Zhang C, Yang R, Luo X, Jia X, Lai SJ, Chen SY. Genome-Wide Association Studies for Growth Curves in Meat Rabbits Through the Single-Step Nonlinear Mixed Model. Front Genet 2021; 12:750939. [PMID: 34691158 PMCID: PMC8531506 DOI: 10.3389/fgene.2021.750939] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Accepted: 09/15/2021] [Indexed: 11/13/2022] Open
Abstract
Growth is a complex trait with moderate to high heritability in livestock and must be described by the longitudinal data measured over multiple time points. Therefore, the used phenotype in genome-wide association studies (GWAS) of growth traits could be either the measures at the preselected time point or the fitted parameters of whole growth trajectory. A promising alternative approach was recently proposed that combined the fitting of growth curves and estimation of single-nucleotide polymorphism (SNP) effects into single-step nonlinear mixed model (NMM). In this study, we collected the body weights at 35, 42, 49, 56, 63, 70, and 84 days of age for 401 animals in a crossbred population of meat rabbits and compared five fitting models of growth curves (Logistic, Gompertz, Brody, Von Bertalanffy, and Richards). The logistic model was preferably selected and subjected to GWAS using the approach of single-step NMM, which was based on 87,704 genome-wide SNPs. A total of 45 significant SNPs distributed on five chromosomes were found to simultaneously affect the two growth parameters of mature weight (A) and maturity rate (K). However, no SNP was found to be independently associated with either A or K. Seven positional genes, including KCNIP4, GBA3, PPARGC1A, LDB2, SHISA3, GNA13, and FGF10, were suggested to be candidates affecting growth performances in meat rabbits. To the best of our knowledge, this is the first report of GWAS based on single-step NMM for longitudinal traits in rabbits, which also revealed the genetic architecture of growth traits that are helpful in implementing genome selection.
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Affiliation(s)
- Yonglan Liao
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Zhicheng Wang
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Leonardo S Glória
- Laboratory of Animal Science, State University of Northern of Rio de Janeiro, Campos dos Goytacazes, Brazil
| | - Kai Zhang
- Sichuan Academy of Grassland Sciences, Chengdu, China
| | - Cuixia Zhang
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu, China
| | - Rui Yang
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu, China
| | - Xinmao Luo
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Xianbo Jia
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Song-Jia Lai
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Shi-Yi Chen
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
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Genomic Prediction in Local Breeds: The Rendena Cattle as a Case Study. Animals (Basel) 2021; 11:ani11061815. [PMID: 34207091 PMCID: PMC8234894 DOI: 10.3390/ani11061815] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 06/16/2021] [Accepted: 06/16/2021] [Indexed: 01/26/2023] Open
Abstract
Simple Summary Although genomic selection is being used in many livestock species, it has not yet been considered in local breeds due to the lower population size and the potential less effective impact on the genetic evaluation of these breeds. The current research aims to investigate how genomic data can impact the accuracy of genetic predictions for beef traits in Rendena, a small local cattle breed of the North-East of Italy selected for a dual purpose. Classical animal models using only phenotypic information were compared with two models that integrated genomic data with pedigree information. The genomic models presented better accuracy in estimated breeding values of the animals than the ‘classical’ animal model, especially the ‘simpler’ one assuming homogeneous variances of single nucleotide polymorphisms. Our results show that the inclusion of genomic information can be successfully applied to breeding selection scenarios even in small local cattle breeds such as Rendena. Abstract The maintenance of local cattle breeds is key to selecting for efficient food production, landscape protection, and conservation of biodiversity and local cultural heritage. Rendena is an indigenous cattle breed from the alpine North-East of Italy, selected for dual purpose, but with lesser emphasis given to beef traits. In this situation, increasing accuracy for beef traits could prevent detrimental effects due to the antagonism with milk production. Our study assessed the impact of genomic information on estimated breeding values (EBVs) in Rendena performance-tested bulls. Traits considered were average daily gain, in vivo EUROP score, and in vivo estimate of dressing percentage. The final dataset contained 1691 individuals with phenotypes and 8372 animals in pedigree, 1743 of which were genotyped. Using the cross-validation method, three models were compared: (i) Pedigree-BLUP (PBLUP); (ii) single-step GBLUP (ssGBLUP), and (iii) weighted single-step GBLUP (WssGBLUP). Models including genomic information presented higher accuracy, especially WssGBLUP. However, the model with the best overall properties was the ssGBLUP, showing higher accuracy than PBLUP and optimal values of bias and dispersion parameters. Our study demonstrated that integrating phenotypes for beef traits with genomic data can be helpful to estimate EBVs, even in a small local breed.
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