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Knapp SJ, Cole GS, Pincot DDA, Dilla-Ermita CJ, Bjornson M, Famula RA, Gordon TR, Harshman JM, Henry PM, Feldmann MJ. Transgressive segregation, hopeful monsters, and phenotypic selection drove rapid genetic gains and breakthroughs in predictive breeding for quantitative resistance to Macrophomina in strawberry. HORTICULTURE RESEARCH 2024; 11:uhad289. [PMID: 38487295 PMCID: PMC10939388 DOI: 10.1093/hr/uhad289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 12/17/2023] [Indexed: 03/17/2024]
Abstract
Two decades have passed since the strawberry (Fragaria x ananassa) disease caused by Macrophomina phaseolina, a necrotrophic soilborne fungal pathogen, began surfacing in California, Florida, and elsewhere. This disease has since become one of the most common causes of plant death and yield losses in strawberry. The Macrophomina problem emerged and expanded in the wake of the global phase-out of soil fumigation with methyl bromide and appears to have been aggravated by an increase in climate change-associated abiotic stresses. Here we show that sources of resistance to this pathogen are rare in gene banks and that the favorable alleles they carry are phenotypically unobvious. The latter were exposed by transgressive segregation and selection in populations phenotyped for resistance to Macrophomina under heat and drought stress. The genetic gains were immediate and dramatic. The frequency of highly resistant individuals increased from 1% in selection cycle 0 to 74% in selection cycle 2. Using GWAS and survival analysis, we found that phenotypic selection had increased the frequencies of favorable alleles among 10 loci associated with resistance and that favorable alleles had to be accumulated among four or more of these loci for an individual to acquire resistance. An unexpectedly straightforward solution to the Macrophomina disease resistance breeding problem emerged from our studies, which showed that highly resistant cultivars can be developed by genomic selection per se or marker-assisted stacking of favorable alleles among a comparatively small number of large-effect loci.
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Affiliation(s)
- Steven J Knapp
- Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
| | - Glenn S Cole
- Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
| | - Dominique D A Pincot
- Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
| | - Christine Jade Dilla-Ermita
- Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
- Crop Improvement and Protection Research, USDA-ARS, 1636 E. Alisal Street, CA 93905, USA
| | - Marta Bjornson
- Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
| | - Randi A Famula
- Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
| | - Thomas R Gordon
- Department of Plant Pathology, University of California, One Shields Avenue, Davis, CA 95616, USA
| | - Julia M Harshman
- Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
| | - Peter M Henry
- Crop Improvement and Protection Research, USDA-ARS, 1636 E. Alisal Street, CA 93905, USA
| | - Mitchell J Feldmann
- Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
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Seck F, Covarrubias-Pazaran G, Gueye T, Bartholomé J. Realized Genetic Gain in Rice: Achievements from Breeding Programs. RICE (NEW YORK, N.Y.) 2023; 16:61. [PMID: 38099942 PMCID: PMC10724102 DOI: 10.1186/s12284-023-00677-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 12/10/2023] [Indexed: 12/18/2023]
Abstract
Genetic improvement is crucial for ensuring food security globally. Indeed, plant breeding has contributed significantly to increasing the productivity of major crops, including rice, over the last century. Evaluating the efficiency of breeding strategies necessitates a quantification of this progress. One approach involves assessing the genetic gain achieved through breeding programs based on quantitative traits. This study aims to provide a theoretical understanding of genetic gain, summarize the major results of genetic gain studies in rice breeding, and suggest ways of improving breeding program strategies and future studies on genetic gain. To achieve this, we present the concept of genetic gain and the essential aspects of its estimation. We also provide an extensive literature review of genetic gain studies in rice (Oryza sativa L.) breeding programs to understand the advances made to date. We reviewed 29 studies conducted between 1999 and 2023, covering different regions, traits, periods, and estimation methods. The genetic gain for grain yield, in particular, showed significant variation, ranging from 1.5 to 167.6 kg/ha/year, with a mean value of 36.3 kg/ha/year. This translated into a rate of genetic gain for grain yield ranging from 0.1% to over 3.0%. The impact of multi-trait selection on grain yield was clarified by studies that reported genetic gains for other traits, such as plant height, days to flowering, and grain quality. These findings reveal that while breeding programs have achieved significant gains, further improvements are necessary to meet the growing demand for rice. We also highlight the limitations of these studies, which hinder accurate estimations of genetic gain. In conclusion, we offer suggestions for improving the estimation of genetic gain based on quantitative genetic principles and computer simulations to optimize rice breeding strategies.
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Affiliation(s)
- Fallou Seck
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO Box7777, Metro Manila, Philippines
- University Iba Der Thiam of Thiès, GrandStanding, Thiès, Senegal
| | - Giovanny Covarrubias-Pazaran
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO Box7777, Metro Manila, Philippines
| | - Tala Gueye
- University Iba Der Thiam of Thiès, GrandStanding, Thiès, Senegal
| | - Jérôme Bartholomé
- CIRAD, UMR AGAP, Cali, Colombia.
- AGAP, Univ Montpellier, CIRAD, INRA, Montpellier SupAgro, Montpellier, France.
- Alliance Bioversity-CIAT, Cali, Colombia.
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3
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Evaluation of Bagging approach versus GBLUP and Bayesian LASSO in genomic prediction. J Genet 2022. [DOI: 10.1007/s12041-022-01358-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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4
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Zhang W, Boyle K, Brule-Babel A, Fedak G, Gao P, Djama ZR, Polley B, Cuthbert R, Randhawa H, Graf R, Jiang F, Eudes F, Fobert PR. Evaluation of Genomic Prediction for Fusarium Head Blight Resistance with a Multi-Parental Population. BIOLOGY 2021; 10:biology10080756. [PMID: 34439988 PMCID: PMC8389552 DOI: 10.3390/biology10080756] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 08/01/2021] [Accepted: 08/02/2021] [Indexed: 12/12/2022]
Abstract
Simple Summary Genomic selection is a promising approach to select superior wheat lines with better resistance to Fusarium head blight. The accuracy of genomic selection is determined by many factors. In this study, we found a training population with large size, genomic selection models incorporating biological information, and multi-environment modelling led to considerably better predictabilities. A training population designed by the coefficient of determination (CDmean) could increase accuracy of prediction. Relatedness between training population (TP) and testing population is the key for accuracies of genomic selection across populations. Abstract Fusarium head blight (FHB) resistance is quantitatively inherited, controlled by multiple minor effect genes, and highly affected by the interaction of genotype and environment. This makes genomic selection (GS) that uses genome-wide molecular marker data to predict the genetic breeding value as a promising approach to select superior lines with better resistance. However, various factors can affect accuracies of GS and better understanding how these factors affect GS accuracies could ensure the success of applying GS to improve FHB resistance in wheat. In this study, we performed a comprehensive evaluation of factors that affect GS accuracies with a multi-parental population designed for FHB resistance. We found larger sample sizes could get better accuracies. Training population designed by CDmean based optimization algorithms significantly increased accuracies than random sampling approach, while mean of predictor error variance (PEVmean) had the poorest performance. Different genomic selection models performed similarly for accuracies. Including prior known large effect quantitative trait loci (QTL) as fixed effect into the GS model considerably improved the predictability. Multi-traits models had almost no effects, while the multi-environment model outperformed the single environment model for prediction across different environments. By comparing within and across family prediction, better accuracies were obtained with the training population more closely related to the testing population. However, achieving good accuracies for GS prediction across populations is still a challenging issue for GS application.
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Affiliation(s)
- Wentao Zhang
- Aquatic and Crop Resources Development, National Research Council of Canada, Saskatoon, SK S7N 0W9, Canada; (K.B.); (P.G.); (B.P.)
- Correspondence: (W.Z.); (P.R.F.)
| | - Kerry Boyle
- Aquatic and Crop Resources Development, National Research Council of Canada, Saskatoon, SK S7N 0W9, Canada; (K.B.); (P.G.); (B.P.)
| | - Anita Brule-Babel
- Department of Plant Science, Agriculture Building, University of Manitoba, Winnipeg, MB R3T 2N2, Canada;
| | - George Fedak
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada; (G.F.); (Z.R.D.)
| | - Peng Gao
- Aquatic and Crop Resources Development, National Research Council of Canada, Saskatoon, SK S7N 0W9, Canada; (K.B.); (P.G.); (B.P.)
| | - Zeinab Robleh Djama
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada; (G.F.); (Z.R.D.)
| | - Brittany Polley
- Aquatic and Crop Resources Development, National Research Council of Canada, Saskatoon, SK S7N 0W9, Canada; (K.B.); (P.G.); (B.P.)
| | - Richard Cuthbert
- Swift Current Research and Development Centre, Agriculture and Agri-Food Canada, Swift Current, SK S9H 3X2, Canada;
| | - Harpinder Randhawa
- Lethbridge Research and Development Centre, Agriculture and Agri-Food Canada, Lethbridge, AB T1J 4B1, Canada; (H.R.); (R.G.); (F.J.); (F.E.)
| | - Robert Graf
- Lethbridge Research and Development Centre, Agriculture and Agri-Food Canada, Lethbridge, AB T1J 4B1, Canada; (H.R.); (R.G.); (F.J.); (F.E.)
| | - Fengying Jiang
- Lethbridge Research and Development Centre, Agriculture and Agri-Food Canada, Lethbridge, AB T1J 4B1, Canada; (H.R.); (R.G.); (F.J.); (F.E.)
| | - Francois Eudes
- Lethbridge Research and Development Centre, Agriculture and Agri-Food Canada, Lethbridge, AB T1J 4B1, Canada; (H.R.); (R.G.); (F.J.); (F.E.)
| | - Pierre R. Fobert
- Aquatic and Crop Resources Development, National Research Council of Canada, Ottawa, ON K1A 0R6, Canada
- Correspondence: (W.Z.); (P.R.F.)
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5
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Comparison of long-term effects of genomic selection index and genomic selection using different Bayesian methods. Livest Sci 2020. [DOI: 10.1016/j.livsci.2020.104207] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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6
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Abolhassani Targhi MV, Asgari Jafarabadi G, Aminafshar M, Emam Jomeh Kashan N. The effect of genotype imputation and some important factors on the accuracy of genomic prediction and its persistency over time. GENE REPORTS 2019. [DOI: 10.1016/j.genrep.2019.100425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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7
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Abolhassani Targhi MV, Asgari Jafarabadi G, Aminafshar M, Emam Jomeh Kashan N. Comparison of non-parametric methods in genomic evaluation of discrete traits. GENE REPORTS 2019. [DOI: 10.1016/j.genrep.2019.100379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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8
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Muleta KT, Pressoir G, Morris GP. Optimizing Genomic Selection for a Sorghum Breeding Program in Haiti: A Simulation Study. G3 (BETHESDA, MD.) 2019; 9:391-401. [PMID: 30530641 PMCID: PMC6385988 DOI: 10.1534/g3.118.200932] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Accepted: 12/02/2018] [Indexed: 12/13/2022]
Abstract
Young breeding programs in developing countries, like the Chibas sorghum breeding program in Haiti, face the challenge of increasing genetic gain with limited resources. Implementing genomic selection (GS) could increase genetic gain, but optimization of GS is needed to account for these programs' unique challenges and advantages. Here, we used simulations to identify conditions under which genomic-assisted recurrent selection (GARS) would be more effective than phenotypic recurrent selection (PRS) in small new breeding programs. We compared genetic gain, cost per unit gain, genetic variance, and prediction accuracy of GARS (two or three cycles per year) vs. PRS (one cycle per year) assuming various breeding population sizes and trait genetic architectures. For oligogenic architecture, the maximum relative genetic gain advantage of GARS over PRS was 12-88%, which was observed only during the first few cycles. For the polygenic architecture, GARS provided maximum relative genetic gain advantage of 26-165%, and was always superior to PRS. Average prediction accuracy declines substantially after several cycles of selection, suggesting the prediction models should be updated regularly. Updating prediction models every year increased the genetic gain by up to 33-39% compared to no-update scenarios. For small populations and oligogenic traits, cost per unit gain was lower in PRS than GARS. However, with larger populations and polygenic traits cost per unit gain was up to 67% lower in GARS than PRS. Collectively, the simulations suggest that GARS could increase the genetic gain in small young breeding programs by accelerating the breeding cycles and enabling evaluation of larger populations.
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Affiliation(s)
- Kebede T Muleta
- Department of Agronomy, Kansas State University, Manhattan, Kansas
| | - Gael Pressoir
- Chibas and Faculty of Agriculture and Environmental Sciences, Quisqueya University, Port-au-Prince, Haiti
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Howard JT, Rathje TA, Bruns CE, Wilson-Wells DF, Kachman SD, Spangler ML. The impact of truncating data on the predictive ability for single-step genomic best linear unbiased prediction. J Anim Breed Genet 2018; 135:251-262. [PMID: 29882604 DOI: 10.1111/jbg.12334] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Revised: 04/08/2018] [Accepted: 04/25/2018] [Indexed: 11/29/2022]
Abstract
Simulated and swine industry data sets were utilized to assess the impact of removing older data on the predictive ability of selection candidate estimated breeding values (EBV) when using single-step genomic best linear unbiased prediction (ssGBLUP). Simulated data included thirty replicates designed to mimic the structure of swine data sets. For the simulated data, varying amounts of data were truncated based on the number of ancestral generations back from the selection candidates. The swine data sets consisted of phenotypic and genotypic records for three traits across two breeds on animals born from 2003 to 2017. Phenotypes and genotypes were iteratively removed 1 year at a time based on the year an animal was born. For the swine data sets, correlations between corrected phenotypes (Cp) and EBV were used to evaluate the predictive ability on young animals born in 2016-2017. In the simulated data set, keeping data two generations back or greater resulted in no statistical difference (p-value > 0.05) in the reduction in the true breeding value at generation 15 compared to utilizing all available data. Across swine data sets, removing phenotypes from animals born prior to 2011 resulted in a negligible or a slight numerical increase in the correlation between Cp and EBV. Truncating data is a method to alleviate computational issues without negatively impacting the predictive ability of selection candidate EBV.
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Affiliation(s)
- Jeremy T Howard
- Department of Animal Science, University of Nebraska-Lincoln, Lincoln, Nebraska
| | | | | | | | - Stephen D Kachman
- Department of Statistics, University of Nebraska-Lincoln, Lincoln, Nebraska
| | - Matthew L Spangler
- Department of Animal Science, University of Nebraska-Lincoln, Lincoln, Nebraska
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10
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Yin T, König S. Heritabilities and genetic correlations in the same traits across different strata of herds created according to continuous genomic, genetic, and phenotypic descriptors. J Dairy Sci 2018; 101:2171-2186. [DOI: 10.3168/jds.2017-13575] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Accepted: 10/25/2017] [Indexed: 11/19/2022]
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11
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Which Individuals To Choose To Update the Reference Population? Minimizing the Loss of Genetic Diversity in Animal Genomic Selection Programs. G3-GENES GENOMES GENETICS 2018; 8:113-121. [PMID: 29133511 PMCID: PMC5765340 DOI: 10.1534/g3.117.1117] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Genomic selection (GS) is commonly used in livestock and increasingly in plant breeding. Relying on phenotypes and genotypes of a reference population, GS allows performance prediction for young individuals having only genotypes. This is expected to achieve fast high genetic gain but with a potential loss of genetic diversity. Existing methods to conserve genetic diversity depend mostly on the choice of the breeding individuals. In this study, we propose a modification of the reference population composition to mitigate diversity loss. Since the high cost of phenotyping is the limiting factor for GS, our findings are of major economic interest. This study aims to answer the following questions: how would decisions on the reference population affect the breeding population, and how to best select individuals to update the reference population and balance maximizing genetic gain and minimizing loss of genetic diversity? We investigated three updating strategies for the reference population: random, truncation, and optimal contribution (OC) strategies. OC maximizes genetic merit for a fixed loss of genetic diversity. A French Montbéliarde dairy cattle population with 50K SNP chip genotypes and simulations over 10 generations were used to compare these different strategies using milk production as the trait of interest. Candidates were selected to update the reference population. Prediction bias and both genetic merit and diversity were measured. Changes in the reference population composition slightly affected the breeding population. Optimal contribution strategy appeared to be an acceptable compromise to maintain both genetic gain and diversity in the reference and the breeding populations.
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12
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Kyriakidou M, Tai HH, Anglin NL, Ellis D, Strömvik MV. Current Strategies of Polyploid Plant Genome Sequence Assembly. FRONTIERS IN PLANT SCIENCE 2018; 9:1660. [PMID: 30519250 PMCID: PMC6258962 DOI: 10.3389/fpls.2018.01660] [Citation(s) in RCA: 101] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Accepted: 10/25/2018] [Indexed: 05/14/2023]
Abstract
Polyploidy or duplication of an entire genome occurs in the majority of angiosperms. The understanding of polyploid genomes is important for the improvement of those crops, which humans rely on for sustenance and basic nutrition. As climate change continues to pose a potential threat to agricultural production, there will increasingly be a demand for plant cultivars that can resist biotic and abiotic stresses and also provide needed and improved nutrition. In the past decade, Next Generation Sequencing (NGS) has fundamentally changed the genomics landscape by providing tools for the exploration of polyploid genomes. Here, we review the challenges of the assembly of polyploid plant genomes, and also present recent advances in genomic resources and functional tools in molecular genetics and breeding. As genomes of diploid and less heterozygous progenitor species are increasingly available, we discuss the lack of complexity of these currently available reference genomes as they relate to polyploid crops. Finally, we review recent approaches of haplotyping by phasing and the impact of third generation technologies on polyploid plant genome assembly.
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Affiliation(s)
- Maria Kyriakidou
- Department of Plant Science, McGill University, Montreal, QC, Canada
| | - Helen H. Tai
- Fredericton Research and Development Centre, Agriculture and Agri-Food Canada, Fredericton, NB, Canada
| | | | | | - Martina V. Strömvik
- Department of Plant Science, McGill University, Montreal, QC, Canada
- *Correspondence: Martina V. Strömvik
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Elsen JM. An analytical framework to derive the expected precision of genomic selection. Genet Sel Evol 2017; 49:95. [PMID: 29281960 PMCID: PMC5745666 DOI: 10.1186/s12711-017-0366-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Accepted: 12/01/2017] [Indexed: 11/16/2022] Open
Abstract
Background Formulae to predict the precision or accuracy of genomic estimated breeding values (GEBV) are important when modelling selection schemes. Simple versions of such formulae have been proposed in the past, based on a number of simplifying hypotheses, including absence of linkage disequilibrium and linkage between loci, a population made up of unrelated individuals, and that all genetic variability of the trait is explained by the genotyped loci. These formulae were based on approximations that were not always clear. The objective of this paper is to offer a unique framework to derive equations that predict the precision of GEBV from the size of the reference population and the heritability of and number of QTL controlling the quantitative trait. Results The exact formulation of the precision of GEBV involves the expectation of the inverse of a linear function of the genomic matrix, which cannot be calculated from simple algebra but can be approximated using a Taylor polynomial expansion. First order approximations performed better than the initial prediction equations published in the literature. Second order approximations produced almost perfect estimates of precision when compared to results obtained when simulating situations that agreed with the assumptions that were required to derive the precision equations. Using this proposed framework, we present several generalizations, including multi-trait genomic evaluation. Conclusions Although further improvements are needed to account for the complexity of practical situations, the equations proposed here can be used to derive the precision of GEBV when comparing breeding schemes a priori. Electronic supplementary material The online version of this article (10.1186/s12711-017-0366-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jean-Michel Elsen
- GenPhySE (Génétique Physiologie et Systèmes d'Elevage), Université de Toulouse, INRA, ENVT, 31326, Castanet-Tolosan, France.
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de Rezende Neves HH, Carvalheiro R, de Queiroz SA. Trait-specific long-term consequences of genomic selection in beef cattle. Genetica 2017; 146:85-99. [PMID: 29119314 DOI: 10.1007/s10709-017-9999-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2016] [Accepted: 10/31/2017] [Indexed: 11/30/2022]
Abstract
Simulation studies allow addressing consequences of selection schemes, helping to identify effective strategies to enable genetic gain and maintain genetic diversity. The aim of this study was to evaluate the long-term impact of genomic selection (GS) in genetic progress and genetic diversity of beef cattle. Forward-in-time simulation generated a population with pattern of linkage disequilibrium close to that previously reported for real beef cattle populations. Different scenarios of GS and traditional pedigree-based BLUP (PBLUP) selection were simulated for 15 generations, mimicking selection for female reproduction and meat quality. For GS scenarios, an alternative selection criterion was simulated (wGBLUP), intended to enhance long-term gains by attributing more weight to favorable alleles with low frequency. GS allowed genetic progress up to 40% greater than PBLUP, for female reproduction and meat quality. The alternative criterion wGBLUP did not increase long-term response, although allowed reducing inbreeding rates and loss of favorable alleles. The results suggest that GS outperforms PBLUP when the selected trait is under less polygenic background and that attributing more weight to low-frequency favorable alleles can reduce inbreeding rates and loss of favorable alleles in GS.
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Affiliation(s)
- Haroldo Henrique de Rezende Neves
- Departamento de Zootecnia, School of Agricultural and Veterinarian Sciences (FCAV), São Paulo State University (UNESP), Via de Acesso Prof. Paulo Donato Castellane s/n, Jaboticabal, SP, 14884-900, Brazil.,GenSys Consultores Associados S/S Ltda., Rua Guilherme Alves, 170. Cj 304, Porto Alegre, RS, 90680-000, Brazil
| | - Roberto Carvalheiro
- Departamento de Zootecnia, School of Agricultural and Veterinarian Sciences (FCAV), São Paulo State University (UNESP), Via de Acesso Prof. Paulo Donato Castellane s/n, Jaboticabal, SP, 14884-900, Brazil
| | - Sandra Aidar de Queiroz
- Departamento de Zootecnia, School of Agricultural and Veterinarian Sciences (FCAV), São Paulo State University (UNESP), Via de Acesso Prof. Paulo Donato Castellane s/n, Jaboticabal, SP, 14884-900, Brazil.
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Persistency of Prediction Accuracy and Genetic Gain in Synthetic Populations Under Recurrent Genomic Selection. G3-GENES GENOMES GENETICS 2017; 7:801-811. [PMID: 28064189 PMCID: PMC5345710 DOI: 10.1534/g3.116.036582] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Recurrent selection (RS) has been used in plant breeding to successively improve synthetic and other multiparental populations. Synthetics are generated from a limited number of parents [Formula: see text] but little is known about how [Formula: see text] affects genomic selection (GS) in RS, especially the persistency of prediction accuracy ([Formula: see text]) and genetic gain. Synthetics were simulated by intermating [Formula: see text]= 2-32 parent lines from an ancestral population with short- or long-range linkage disequilibrium ([Formula: see text]) and subjected to multiple cycles of GS. We determined [Formula: see text] and genetic gain across 30 cycles for different training set (TS) sizes, marker densities, and generations of recombination before model training. Contributions to [Formula: see text] and genetic gain from pedigree relationships, as well as from cosegregation and [Formula: see text] between QTL and markers, were analyzed via four scenarios differing in (i) the relatedness between TS and selection candidates and (ii) whether selection was based on markers or pedigree records. Persistency of [Formula: see text] was high for small [Formula: see text] where predominantly cosegregation contributed to [Formula: see text], but also for large [Formula: see text] where [Formula: see text] replaced cosegregation as the dominant information source. Together with increasing genetic variance, this compensation resulted in relatively constant long- and short-term genetic gain for increasing [Formula: see text] > 4, given long-range LDA in the ancestral population. Although our scenarios suggest that information from pedigree relationships contributed to [Formula: see text] for only very few generations in GS, we expect a longer contribution than in pedigree BLUP, because capturing Mendelian sampling by markers reduces selective pressure on pedigree relationships. Larger TS size ([Formula: see text]) and higher marker density improved persistency of [Formula: see text] and hence genetic gain, but additional recombinations could not increase genetic gain.
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Kariuki CM, Brascamp EW, Komen H, Kahi AK, van Arendonk JAM. Economic evaluation of progeny-testing and genomic selection schemes for small-sized nucleus dairy cattle breeding programs in developing countries. J Dairy Sci 2017; 100:2258-2268. [PMID: 28109609 DOI: 10.3168/jds.2016-11816] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2016] [Accepted: 11/18/2016] [Indexed: 11/19/2022]
Abstract
In developing countries minimal and erratic performance and pedigree recording impede implementation of large-sized breeding programs. Small-sized nucleus programs offer an alternative but rely on their economic performance for their viability. We investigated the economic performance of 2 alternative small-sized dairy nucleus programs [i.e., progeny testing (PT) and genomic selection (GS)] over a 20-yr investment period. The nucleus was made up of 453 male and 360 female animals distributed in 8 non-overlapping age classes. Each year 10 active sires and 100 elite dams were selected. Populations of commercial recorded cows (CRC) of sizes 12,592 and 25,184 were used to produce test daughters in PT or to create a reference population in GS, respectively. Economic performance was defined as gross margins, calculated as discounted revenues minus discounted costs following a single generation of selection. Revenues were calculated as cumulative discounted expressions (CDE, kg) × 0.32 (€/kg of milk) × 100,000 (size commercial population). Genetic superiorities, deterministically simulated using pseudo-BLUP index and CDE, were determined using gene flow. Costs were for one generation of selection. Results show that GS schemes had higher cumulated genetic gain in the commercial cow population and higher gross margins compared with PT schemes. Gross margins were between 3.2- and 5.2-fold higher for GS, depending on size of the CRC population. The increase in gross margin was mostly due to a decreased generation interval and lower running costs in GS schemes. In PT schemes many bulls are culled before selection. We therefore also compared 2 schemes in which semen was stored instead of keeping live bulls. As expected, semen storage resulted in an increase in gross margins in PT schemes, but gross margins remained lower than those of GS schemes. We conclude that implementation of small-sized GS breeding schemes can be economically viable for developing countries.
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Affiliation(s)
- C M Kariuki
- Department of Animal Sciences, Chuka University, PO Box 109-60400, Chuka, Kenya; Animal Breeding and Genomics Centre, Wageningen University, PO Box 338, 6700 AH Wageningen, the Netherlands.
| | - E W Brascamp
- Animal Breeding and Genomics Centre, Wageningen University, PO Box 338, 6700 AH Wageningen, the Netherlands
| | - H Komen
- Animal Breeding and Genomics Centre, Wageningen University, PO Box 338, 6700 AH Wageningen, the Netherlands
| | - A K Kahi
- Animal Breeding and Genomics Group, Department of Animal Sciences, Egerton University, PO Box 536-20115, Egerton, Kenya
| | - J A M van Arendonk
- Animal Breeding and Genomics Centre, Wageningen University, PO Box 338, 6700 AH Wageningen, the Netherlands
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17
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Bartholomé J, Van Heerwaarden J, Isik F, Boury C, Vidal M, Plomion C, Bouffier L. Performance of genomic prediction within and across generations in maritime pine. BMC Genomics 2016; 17:604. [PMID: 27515254 DOI: 10.1186/s12864-12016-12879-12868] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2016] [Accepted: 07/05/2016] [Indexed: 05/28/2023] Open
Abstract
BACKGROUND Genomic selection (GS) is a promising approach for decreasing breeding cycle length in forest trees. Assessment of progeny performance and of the prediction accuracy of GS models over generations is therefore a key issue. RESULTS A reference population of maritime pine (Pinus pinaster) with an estimated effective inbreeding population size (status number) of 25 was first selected with simulated data. This reference population (n = 818) covered three generations (G0, G1 and G2) and was genotyped with 4436 single-nucleotide polymorphism (SNP) markers. We evaluated the effects on prediction accuracy of both the relatedness between the calibration and validation sets and validation on the basis of progeny performance. Pedigree-based (best linear unbiased prediction, ABLUP) and marker-based (genomic BLUP and Bayesian LASSO) models were used to predict breeding values for three different traits: circumference, height and stem straightness. On average, the ABLUP model outperformed genomic prediction models, with a maximum difference in prediction accuracies of 0.12, depending on the trait and the validation method. A mean difference in prediction accuracy of 0.17 was found between validation methods differing in terms of relatedness. Including the progenitors in the calibration set reduced this difference in prediction accuracy to 0.03. When only genotypes from the G0 and G1 generations were used in the calibration set and genotypes from G2 were used in the validation set (progeny validation), prediction accuracies ranged from 0.70 to 0.85. CONCLUSIONS This study suggests that the training of prediction models on parental populations can predict the genetic merit of the progeny with high accuracy: an encouraging result for the implementation of GS in the maritime pine breeding program.
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Affiliation(s)
| | - Joost Van Heerwaarden
- Biometris, Wageningen University and Research Centre, NL-6700 AC, Wageningen, The Netherlands
| | - Fikret Isik
- Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC, USA
| | | | - Marjorie Vidal
- BIOGECO, INRA, Univ. Bordeaux, 33610, Cestas, France
- FCBA, Biotechnology and Advanced Silviculture Department, Genetics & Biotechnology Team, 33610, Cestas, France
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18
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Bartholomé J, Van Heerwaarden J, Isik F, Boury C, Vidal M, Plomion C, Bouffier L. Performance of genomic prediction within and across generations in maritime pine. BMC Genomics 2016; 17:604. [PMID: 27515254 PMCID: PMC4981999 DOI: 10.1186/s12864-016-2879-8] [Citation(s) in RCA: 65] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2016] [Accepted: 07/05/2016] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Genomic selection (GS) is a promising approach for decreasing breeding cycle length in forest trees. Assessment of progeny performance and of the prediction accuracy of GS models over generations is therefore a key issue. RESULTS A reference population of maritime pine (Pinus pinaster) with an estimated effective inbreeding population size (status number) of 25 was first selected with simulated data. This reference population (n = 818) covered three generations (G0, G1 and G2) and was genotyped with 4436 single-nucleotide polymorphism (SNP) markers. We evaluated the effects on prediction accuracy of both the relatedness between the calibration and validation sets and validation on the basis of progeny performance. Pedigree-based (best linear unbiased prediction, ABLUP) and marker-based (genomic BLUP and Bayesian LASSO) models were used to predict breeding values for three different traits: circumference, height and stem straightness. On average, the ABLUP model outperformed genomic prediction models, with a maximum difference in prediction accuracies of 0.12, depending on the trait and the validation method. A mean difference in prediction accuracy of 0.17 was found between validation methods differing in terms of relatedness. Including the progenitors in the calibration set reduced this difference in prediction accuracy to 0.03. When only genotypes from the G0 and G1 generations were used in the calibration set and genotypes from G2 were used in the validation set (progeny validation), prediction accuracies ranged from 0.70 to 0.85. CONCLUSIONS This study suggests that the training of prediction models on parental populations can predict the genetic merit of the progeny with high accuracy: an encouraging result for the implementation of GS in the maritime pine breeding program.
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Affiliation(s)
| | - Joost Van Heerwaarden
- Biometris, Wageningen University and Research Centre, NL-6700 AC Wageningen, The Netherlands
| | - Fikret Isik
- Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC USA
| | | | - Marjorie Vidal
- BIOGECO, INRA, Univ. Bordeaux, 33610 Cestas, France
- FCBA, Biotechnology and Advanced Silviculture Department, Genetics & Biotechnology Team, 33610 Cestas, France
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19
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Xavier A, Muir WM, Rainey KM. Assessing Predictive Properties of Genome-Wide Selection in Soybeans. G3 (BETHESDA, MD.) 2016; 6:2611-6. [PMID: 27317786 PMCID: PMC4978914 DOI: 10.1534/g3.116.032268] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2016] [Accepted: 06/16/2016] [Indexed: 11/30/2022]
Abstract
Many economically important traits in plant breeding have low heritability or are difficult to measure. For these traits, genomic selection has attractive features and may boost genetic gains. Our goal was to evaluate alternative scenarios to implement genomic selection for yield components in soybean (Glycine max L. merr). We used a nested association panel with cross validation to evaluate the impacts of training population size, genotyping density, and prediction model on the accuracy of genomic prediction. Our results indicate that training population size was the factor most relevant to improvement in genome-wide prediction, with greatest improvement observed in training sets up to 2000 individuals. We discuss assumptions that influence the choice of the prediction model. Although alternative models had minor impacts on prediction accuracy, the most robust prediction model was the combination of reproducing kernel Hilbert space regression and BayesB. Higher genotyping density marginally improved accuracy. Our study finds that breeding programs seeking efficient genomic selection in soybeans would best allocate resources by investing in a representative training set.
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Affiliation(s)
- Alencar Xavier
- Department of Agronomy, Purdue University, West Lafayette, Indiana 47907
| | - William M Muir
- Department of Animal Science, Purdue University, West Lafayette, Indiana 47907
| | - Katy Martin Rainey
- Department of Agronomy, Purdue University, West Lafayette, Indiana 47907
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20
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van den Berg S, Calus MPL, Meuwissen THE, Wientjes YCJ. Across population genomic prediction scenarios in which Bayesian variable selection outperforms GBLUP. BMC Genet 2015; 16:146. [PMID: 26698836 PMCID: PMC4690391 DOI: 10.1186/s12863-015-0305-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2015] [Accepted: 12/10/2015] [Indexed: 12/21/2022] Open
Abstract
Background The use of information across populations is an attractive approach to increase the accuracy of genomic prediction for numerically small populations. However, accuracies of across population genomic prediction, in which reference and selection individuals are from different populations, are currently disappointing. It has been shown for within population genomic prediction that Bayesian variable selection models outperform GBLUP models when the number of QTL underlying the trait is low. Therefore, our objective was to identify across population genomic prediction scenarios in which Bayesian variable selection models outperform GBLUP in terms of prediction accuracy. In this study, high density genotype information of 1033 Holstein Friesian, 105 Groningen White Headed, and 147 Meuse-Rhine-Yssel cows were used. Phenotypes were simulated using two changing variables: (1) the number of QTL underlying the trait (3000, 300, 30, 3), and (2) the correlation between allele substitution effects of QTL across populations, i.e. the genetic correlation of the simulated trait between the populations (1.0, 0.8, 0.4). Results The accuracy obtained by the Bayesian variable selection model was depending on the number of QTL underlying the trait, with a higher accuracy when the number of QTL was lower. This trend was more pronounced for across population genomic prediction than for within population genomic prediction. It was shown that Bayesian variable selection models have an advantage over GBLUP when the number of QTL underlying the simulated trait was small. This advantage disappeared when the number of QTL underlying the simulated trait was large. The point where the accuracy of Bayesian variable selection and GBLUP became similar was approximately the point where the number of QTL was equal to the number of independent chromosome segments (Me) across the populations. Conclusion Bayesian variable selection models outperform GBLUP when the number of QTL underlying the trait is smaller than Me. Across populations, Me is considerably larger than within populations. So, it is more likely to find a number of QTL underlying a trait smaller than Me across populations than within population. Therefore Bayesian variable selection models can help to improve the accuracy of across population genomic prediction.
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Affiliation(s)
- S van den Berg
- Animal Breeding and Genomics Centre, Wageningen University, 6700, AH, Wageningen, The Netherlands.
| | - M P L Calus
- Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, 6700, AH, Wageningen, The Netherlands.
| | - T H E Meuwissen
- Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, P. O. Box 5003, 1432, Ås, Norway.
| | - Y C J Wientjes
- Animal Breeding and Genomics Centre, Wageningen University, 6700, AH, Wageningen, The Netherlands. .,Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, 6700, AH, Wageningen, The Netherlands.
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21
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Schopp P, Riedelsheimer C, Utz HF, Schön CC, Melchinger AE. Forecasting the accuracy of genomic prediction with different selection targets in the training and prediction set as well as truncation selection. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2015; 128:2189-2201. [PMID: 26231985 DOI: 10.1007/s00122-015-2577-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2015] [Accepted: 06/26/2015] [Indexed: 06/04/2023]
Abstract
Deterministic formulas accurately forecast the decline in predictive ability of genomic prediction with changing testers, target environments or traits and truncation selection. Genomic prediction of testcross performance (TP) was found to be a promising selection tool in hybrid breeding as long as the same tester and environments are used in the training and prediction set. In practice, however, selection targets often change in terms of testers, target environments or traits leading to a reduced predictive ability. Hence, it would be desirable to estimate for given training data the expected decline in the predictive ability of genomic prediction under such settings by deterministic formulas that require only quantitative genetic parameters available from the breeding program. Here, we derived formulas for forecasting the predictive ability under different selection targets in the training and prediction set and applied these to predict the TP of lines based on line per se or testcross evaluations. On the basis of two experiments with maize, we validated our approach in four scenarios characterized by different selection targets. Forecasted and empirically observed predictive abilities obtained by cross-validation generally agreed well, with deviations between -0.06 and 0.01 only. Applying the prediction model to a different tester and/or year reduced the predictive ability by not more than 18%. Accounting additionally for truncation selection in our formulas indicated a substantial reduction in predictive ability in the prediction set, amounting, e.g., to 53% for a selected fraction α = 10%. In conclusion, our deterministic formulas enable forecasting the predictive abilities of new selection targets with sufficient precision and could be used to calculate parameters required for optimizing the allocation of resources in multi-stage genomic selection.
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Affiliation(s)
- Pascal Schopp
- Department of Applied Genetics, Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Fruwirthstr. 21, 70593, Stuttgart, Germany
| | - Christian Riedelsheimer
- Department of Applied Genetics, Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Fruwirthstr. 21, 70593, Stuttgart, Germany
| | - H Friedrich Utz
- Department of Applied Genetics, Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Fruwirthstr. 21, 70593, Stuttgart, Germany
| | - Chris-Carolin Schön
- Plant Breeding, Center of Life and Food Sciences Weihenstephan, Technische Universität München, Liesel-Beckmann-Straße 2, 85354, Freising, Germany
| | - Albrecht E Melchinger
- Department of Applied Genetics, Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Fruwirthstr. 21, 70593, Stuttgart, Germany.
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22
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Gamal El-Dien O, Ratcliffe B, Klápště J, Chen C, Porth I, El-Kassaby YA. Prediction accuracies for growth and wood attributes of interior spruce in space using genotyping-by-sequencing. BMC Genomics 2015; 16:370. [PMID: 25956247 PMCID: PMC4424896 DOI: 10.1186/s12864-015-1597-y] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2015] [Accepted: 04/28/2015] [Indexed: 02/02/2024] Open
Abstract
Background Genomic selection (GS) in forestry can substantially reduce the length of breeding cycle and increase gain per unit time through early selection and greater selection intensity, particularly for traits of low heritability and late expression. Affordable next-generation sequencing technologies made it possible to genotype large numbers of trees at a reasonable cost. Results Genotyping-by-sequencing was used to genotype 1,126 Interior spruce trees representing 25 open-pollinated families planted over three sites in British Columbia, Canada. Four imputation algorithms were compared (mean value (MI), singular value decomposition (SVD), expectation maximization (EM), and a newly derived, family-based k-nearest neighbor (kNN-Fam)). Trees were phenotyped for several yield and wood attributes. Single- and multi-site GS prediction models were developed using the Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) and the Generalized Ridge Regression (GRR) to test different assumption about trait architecture. Finally, using PCA, multi-trait GS prediction models were developed. The EM and kNN-Fam imputation methods were superior for 30 and 60% missing data, respectively. The RR-BLUP GS prediction model produced better accuracies than the GRR indicating that the genetic architecture for these traits is complex. GS prediction accuracies for multi-site were high and better than those of single-sites while multi-site predictability produced the lowest accuracies reflecting type-b genetic correlations and deemed unreliable. The incorporation of genomic information in quantitative genetics analyses produced more realistic heritability estimates as half-sib pedigree tended to inflate the additive genetic variance and subsequently both heritability and gain estimates. Principle component scores as representatives of multi-trait GS prediction models produced surprising results where negatively correlated traits could be concurrently selected for using PCA2 and PCA3. Conclusions The application of GS to open-pollinated family testing, the simplest form of tree improvement evaluation methods, was proven to be effective. Prediction accuracies obtained for all traits greatly support the integration of GS in tree breeding. While the within-site GS prediction accuracies were high, the results clearly indicate that single-site GS models ability to predict other sites are unreliable supporting the utilization of multi-site approach. Principle component scores provided an opportunity for the concurrent selection of traits with different phenotypic optima. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1597-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Omnia Gamal El-Dien
- Department of Forest and Conservation Sciences, Faculty of Forestry, The University of British Columbia, 2424 Main Mall, Vancouver, British Columbia, V6T 1Z4, Canada.
| | - Blaise Ratcliffe
- Department of Forest and Conservation Sciences, Faculty of Forestry, The University of British Columbia, 2424 Main Mall, Vancouver, British Columbia, V6T 1Z4, Canada.
| | - Jaroslav Klápště
- Department of Forest and Conservation Sciences, Faculty of Forestry, The University of British Columbia, 2424 Main Mall, Vancouver, British Columbia, V6T 1Z4, Canada. .,Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Kamycka 129, 165 21, Prague 6, Czech Republic.
| | - Charles Chen
- Department of Biochemistry and Molecular Biology, Oklahoma State University, Stillwater, OK, 74078-3035, USA.
| | - Ilga Porth
- Department of Forest and Conservation Sciences, Faculty of Forestry, The University of British Columbia, 2424 Main Mall, Vancouver, British Columbia, V6T 1Z4, Canada.
| | - Yousry A El-Kassaby
- Department of Forest and Conservation Sciences, Faculty of Forestry, The University of British Columbia, 2424 Main Mall, Vancouver, British Columbia, V6T 1Z4, Canada.
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Rodríguez-Ramilo ST, García-Cortés LA, de Cara MÁR. Artificial selection with traditional or genomic relationships: consequences in coancestry and genetic diversity. Front Genet 2015; 6:127. [PMID: 25904933 PMCID: PMC4388001 DOI: 10.3389/fgene.2015.00127] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2014] [Accepted: 03/17/2015] [Indexed: 11/13/2022] Open
Abstract
Estimated breeding values (EBVs) are traditionally obtained from pedigree information. However, EBVs from high-density genotypes can have higher accuracy than EBVs from pedigree information. At the same time, it has been shown that EBVs from genomic data lead to lower increases in inbreeding compared with traditional selection based on genealogies. Here we evaluate the performance with BLUP selection based on genealogical coancestry with three different genome-based coancestry estimates: (1) an estimate based on shared segments of homozygosity, (2) an approach based on SNP-by-SNP count corrected by allelic frequencies, and (3) the identity by state methodology. We evaluate the effect of different population sizes, different number of genomic markers, and several heritability values for a quantitative trait. The performance of the different measures of coancestry in BLUP is evaluated in the true breeding values after truncation selection and also in terms of coancestry and diversity maintained. Accordingly, cross-performances were also carried out, that is, how prediction based on genealogical records impacts the three other measures of coancestry and inbreeding, and viceversa. Our results show that the genetic gains are very similar for all four coancestries, but the genomic-based methods are superior to using genealogical coancestries in terms of maintaining diversity measured as observed heterozygosity. Furthermore, the measure of coancestry based on shared segments of the genome seems to provide slightly better results on some scenarios, and the increase in inbreeding and loss in diversity is only slightly larger than the other genomic selection methods in those scenarios. Our results shed light on genomic selection vs. traditional genealogical-based BLUP and make the case to manage the population variability using genomic information to preserve the future success of selection programmes.
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Affiliation(s)
- Silvia Teresa Rodríguez-Ramilo
- Departamento de Mejora Genetica Animal, Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria Madrid, Spain
| | - Luis Alberto García-Cortés
- Departamento de Mejora Genetica Animal, Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria Madrid, Spain
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Liu H, Meuwissen THE, Sørensen AC, Berg P. Upweighting rare favourable alleles increases long-term genetic gain in genomic selection programs. Genet Sel Evol 2015; 47:19. [PMID: 25886296 PMCID: PMC4367977 DOI: 10.1186/s12711-015-0101-0] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2014] [Accepted: 01/29/2015] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND The short-term impact of using different genomic prediction (GP) models in genomic selection has been intensively studied, but their long-term impact is poorly understood. Furthermore, long-term genetic gain of genomic selection is expected to improve by using Jannink's weighting (JW) method, in which rare favourable marker alleles are upweighted in the selection criterion. In this paper, we extend the JW method by including an additional parameter to decrease the emphasis on rare favourable alleles over the time horizon, with the purpose of further improving the long-term genetic gain. We call this new method dynamic weighting (DW). The paper explores the long-term impact of different GP models with or without weighting methods. METHODS Different selection criteria were tested by simulating a population of 500 animals with truncation selection of five males and 50 females. Selection criteria included unweighted and weighted genomic estimated breeding values using the JW or DW methods, for which ridge regression (RR) and Bayesian lasso (BL) were used to estimate marker effects. The impacts of these selection criteria were compared under three genetic architectures, i.e. varying numbers of QTL for the trait and for two time horizons of 15 (TH15) or 40 (TH40) generations. RESULTS For unweighted GP, BL resulted in up to 21.4% higher long-term genetic gain and 23.5% lower rate of inbreeding under TH40 than RR. For weighted GP, DW resulted in 1.3 to 5.5% higher long-term gain compared to unweighted GP. JW, however, showed a 6.8% lower long-term genetic gain relative to unweighted GP when BL was used to estimate the marker effects. Under TH40, both DW and JW obtained significantly higher genetic gain than unweighted GP. With DW, the long-term genetic gain was increased by up to 30.8% relative to unweighted GP, and also increased by 8% relative to JW, although at the expense of a lower short-term gain. CONCLUSIONS Irrespective of the number of QTL simulated, BL is superior to RR in maintaining genetic variance and therefore results in higher long-term genetic gain. Moreover, DW is a promising method with which high long-term genetic gain can be expected within a fixed time frame.
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Affiliation(s)
- Huiming Liu
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, P. O. Box 50, 8830, Tjele, Denmark.
| | - Theo H E Meuwissen
- Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, P. O. Box 5003, 1432, Ås, Norway.
| | - Anders C Sørensen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, P. O. Box 50, 8830, Tjele, Denmark.
| | - Peer Berg
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, P. O. Box 50, 8830, Tjele, Denmark.
- Nordic Genetic Resource Center, P. O. Box 115, 1431, Ås, Norway.
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25
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Muir WM, Cheng HW, Croney C. Methods to address poultry robustness and welfare issues through breeding and associated ethical considerations. Front Genet 2014; 5:407. [PMID: 25505483 PMCID: PMC4244538 DOI: 10.3389/fgene.2014.00407] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2014] [Accepted: 11/03/2014] [Indexed: 11/13/2022] Open
Abstract
As consumers and society in general become more aware of ethical and moral dilemmas associated with intensive rearing systems, pressure is put on the animal and poultry industries to adopt alternative forms of housing. This presents challenges especially regarding managing competitive social interactions between animals. However, selective breeding programs are rapidly advancing, enhanced by both genomics and new quantitative genetic theory that offer potential solutions by improving adaptation of the bird to existing and proposed production environments. The outcomes of adaptation could lead to improvement of animal welfare by increasing fitness of the animal for the given environments, which might lead to increased contentment and decreased distress of birds in those systems. Genomic selection, based on dense genetic markers, will allow for more rapid improvement of traits that are expensive or difficult to measure, or have a low heritability, such as pecking, cannibalism, robustness, mortality, leg score, bone strength, disease resistance, and thus has the potential to address many poultry welfare concerns. Recently selection programs to include social effects, known as associative or indirect genetic effects (IGEs), have received much attention. Group, kin, multi-level, and multi-trait selection including IGEs have all been shown to be highly effective in reducing mortality while increasing productivity of poultry layers and reduce or eliminate the need for beak trimming. Multi-level selection was shown to increases robustness as indicated by the greater ability of birds to cope with stressors. Kin selection has been shown to be easy to implement and improve both productivity and animal well-being. Management practices and rearing conditions employed for domestic animal production will continue to change based on ethical and scientific results. However, the animal breeding tools necessary to provide an animal that is best adapted to these changing conditions are readily available and should be used, which will ultimately lead to the best possible outcomes for all impacted.
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Affiliation(s)
- William M. Muir
- Department of Animal Sciences, Purdue UniversityWest Lafayette, IN, USA
| | - Heng-Wei Cheng
- Livestock Behavior Research Unit, United States Department of Agriculture – Agricultural Research ServiceWest Lafayette, IN, USA
| | - Candace Croney
- Department of Comparative Pathobiology and Department of Animal Sciences, Purdue UniversityWest Lafayette, IN, USA
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Brard S, Ricard A. Is the use of formulae a reliable way to predict the accuracy of genomic selection? J Anim Breed Genet 2014; 132:207-17. [PMID: 25377121 DOI: 10.1111/jbg.12123] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2014] [Accepted: 09/16/2014] [Indexed: 11/28/2022]
Abstract
We studied four formulae used to predict the accuracy of genomic selection prior to genotyping. The objectives of our study were to investigate the impact of the parameters of each formula on the values of accuracy calculated using these formulae, and to check whether the accuracies reported in the literature are in agreement with the formulae. First, we computed the marginal distribution of accuracy (by integration) for each parameter of all four formulae: heritability h(2) , reference population size T, number of markers M and number of effective segments in the genome Me . Then, we collected 145 accuracies and corresponding parameters reported in 13 publications on genomic selection (mainly in dairy cattle), and performed analysis of variance to test the differences between observed and predicted accuracy with effects of formulae and parameters. The variation of accuracy for different values of each parameter indicated that two parameters, T and Me, had a significant impact and that considerable differences existed between the formulae (mean accuracies differed by up to 0.20 point). The results of our meta-analysis showed a big formula effect on the accuracies predicted using each formula, and also a significant effect of the value obtained for Me calculated from Ne (effective population size). Each formula can therefore be demonstrated to be optimal depending on the assumption used for Me . In conclusion, no rules can be applied to predict the reliability of genomic selection using these formulae.
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Affiliation(s)
- S Brard
- INRA, GenPhySE (Génétique, Physiologie et Systèmes d'Elevage), Castanet-Tolosan, France; Université de Toulouse, INP, ENSAT, GenPhySE (Génétique, Physiologie et Systèmes d'Elevage), Castanet-Tolosan, France; Université de Toulouse, INP, ENVT, GenPhySE (Génétique, Physiologie et Systèmes d'Elevage), Toulouse, France
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Henryon M, Berg P, Sørensen A. Animal-breeding schemes using genomic information need breeding plans designed to maximise long-term genetic gains. Livest Sci 2014. [DOI: 10.1016/j.livsci.2014.06.016] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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Systematic differences in the response of genetic variation to pedigree and genome-based selection methods. Heredity (Edinb) 2014; 113:503-13. [PMID: 25074573 DOI: 10.1038/hdy.2014.55] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2013] [Revised: 03/24/2014] [Accepted: 04/22/2014] [Indexed: 12/18/2022] Open
Abstract
Genomic selection (GS) is a DNA-based method of selecting for quantitative traits in animal and plant breeding, and offers a potentially superior alternative to traditional breeding methods that rely on pedigree and phenotype information. Using a 60 K SNP chip with markers spaced throughout the entire chicken genome, we compared the impact of GS and traditional BLUP (best linear unbiased prediction) selection methods applied side-by-side in three different lines of egg-laying chickens. Differences were demonstrated between methods, both at the level and genomic distribution of allele frequency changes. In all three lines, the average allele frequency changes were larger with GS, 0.056 0.064 and 0.066, compared with BLUP, 0.044, 0.045 and 0.036 for lines B1, B2 and W1, respectively. With BLUP, 35 selected regions (empirical P < 0.05) were identified across the three lines. With GS, 70 selected regions were identified. Empirical thresholds for local allele frequency changes were determined from gene dropping, and differed considerably between GS (0.167-0.198) and BLUP (0.105-0.126). Between lines, the genomic regions with large changes in allele frequencies showed limited overlap. Our results show that GS applies selection pressure much more locally than BLUP, resulting in larger allele frequency changes. With these results, novel insights into the nature of selection on quantitative traits have been gained and important questions regarding the long-term impact of GS are raised. The rapid changes to a part of the genetic architecture, while another part may not be selected, at least in the short term, require careful consideration, especially when selection occurs before phenotypes are observed.
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Yamazaki T, Togashi K, Iwama S, Matsumoto S, Moribe K, Nakanishi T, Hagiya K, Hayasaka K. Effects of a breeding scheme combined by genomic pre-selection and progeny testing on annual genetic gain in a dairy cattle population. Anim Sci J 2014; 85:639-49. [PMID: 24612342 DOI: 10.1111/asj.12186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2013] [Accepted: 11/29/2013] [Indexed: 11/30/2022]
Abstract
The effectiveness of the incorporation of genomic pre-selection into dairy cattle progeny testing (GS-PT) was compared with that of progeny testing (PT) where the fraction of dam to breed bull (DB) selected was 0.01. When the fraction of sires to breed bulls (SB) selected without being progeny tested to produce young bulls (YB) in the next generation was 0.2, the annual genetic gain from GS-PT was 13% to 43% greater when h(2) = 0.3 and 16% to 53% greater when h(2) = 0.1 compared with that from PT. Given h(2) = 0.3, a selection accuracy of 0.8 for both YB and DB, and selected fractions of 0.117 for YB and 0.04 for DB, GS-PT produced 40% to 43% greater annual genetic gain than PT. Given h(2) = 0.1, a selection accuracy of 0.6 for both YB and DB, and selected fractions of 0.117 for YB and 0.04 for DB, annual genetic gain from GS-PT was 48% to 53% greater than that from PT. When h(2) = 0.3, progeny testing capacity had little effect on annual genetic gain from GS-PT. However, when h(2) = 0.1, annual genetic gain from GS-PT increased with increasing progeny testing capacity.
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Guo Z, Tucker DM, Basten CJ, Gandhi H, Ersoz E, Guo B, Xu Z, Wang D, Gay G. The impact of population structure on genomic prediction in stratified populations. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2014; 127:749-62. [PMID: 24452438 DOI: 10.1007/s00122-013-2255-x] [Citation(s) in RCA: 82] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2013] [Accepted: 12/14/2013] [Indexed: 05/18/2023]
Abstract
Impacts of population structure on the evaluation of genomic heritability and prediction were investigated and quantified using high-density markers in diverse panels in rice and maize. Population structure is an important factor affecting estimation of genomic heritability and assessment of genomic prediction in stratified populations. In this study, our first objective was to assess effects of population structure on estimations of genomic heritability using the diversity panels in rice and maize. Results indicate population structure explained 33 and 7.5% of genomic heritability for rice and maize, respectively, depending on traits, with the remaining heritability explained by within-subpopulation variation. Estimates of within-subpopulation heritability were higher than that derived from quantitative trait loci identified in genome-wide association studies, suggesting 65% improvement in genetic gains. The second objective was to evaluate effects of population structure on genomic prediction using cross-validation experiments. When population structure exists in both training and validation sets, correcting for population structure led to a significant decrease in accuracy with genomic prediction. In contrast, when prediction was limited to a specific subpopulation, population structure showed little effect on accuracy and within-subpopulation genetic variance dominated predictions. Finally, effects of genomic heritability on genomic prediction were investigated. Accuracies with genomic prediction increased with genomic heritability in both training and validation sets, with the former showing a slightly greater impact. In summary, our results suggest that the population structure contribution to genomic prediction varies based on prediction strategies, and is also affected by the genetic architectures of traits and populations. In practical breeding, these conclusions may be helpful to better understand and utilize the different genetic resources in genomic prediction.
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Affiliation(s)
- Zhigang Guo
- Syngenta Biotechnology, Inc., 3054 E Cornwallis Rd., Durham, NC, 27709, USA,
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Nishio M, Satoh M. Impacts of genotyping strategies on long-term genetic response in genomic selection. Anim Sci J 2014; 85:511-6. [PMID: 24506177 DOI: 10.1111/asj.12184] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2013] [Accepted: 11/28/2013] [Indexed: 11/30/2022]
Abstract
The present study investigated the effects of the choices of animals of reference populations on long-term responses to genomic selection. Simulated populations comprised 300 individuals and 10 generations of selection practiced for a trait with heritability of 0.1, 0.3 or 0.5. Thirty individuals were randomly selected in the first five generations and selected by estimated breeding values from best linear unbiased prediction (BLUP) and genomic BLUP in the subsequent five generations. The reference populations comprise all animals for all generations (scenario 1), all animals for 6-10 generations (scenario 2) and 2-6 generations (scenario 3), and half of the animals for all generations (scenario 4). For all heritability levels, the genetic gains in generation 10 were similar in scenarios 1 and 2. Among scenarios 2 to 4, the highest genetic gains were obtained in scenario 2, with heritabilities of 0.1 and 0.3 as well as scenario 4 with heritability of 0.5. The inbreeding coefficients in scenarios 1, 2 and 4 were lower than those in BLUP, especially within cases with low heritability. These results indicate an appropriate choice of reference population can improve genetic gain and restrict inbreeding even when the reference population size is limited.
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Affiliation(s)
- Motohide Nishio
- NARO Institute of Livestock and Grassland Science, Tsukuba, Japan
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Ren YY, Overmyer KA, Qi NR, Treutelaar MK, Heckenkamp L, Kalahar M, Koch LG, Britton SL, Burant CF, Li JZ. Genetic analysis of a rat model of aerobic capacity and metabolic fitness. PLoS One 2013; 8:e77588. [PMID: 24147032 PMCID: PMC3795692 DOI: 10.1371/journal.pone.0077588] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2013] [Accepted: 09/10/2013] [Indexed: 11/20/2022] Open
Abstract
Aerobic capacity is a strong predictor of all-cause mortality and can influence many complex traits. To explore the biological basis underlying this connection, we developed via artificial selection two rat lines that diverge for intrinsic (i.e. inborn) aerobic capacity and differ in risk for complex disease traits. Here we conduct the first in-depth pedigree and molecular genetic analysis of these lines, the high capacity runners (HCR) and low capacity runners (LCR). Our results show that both HCR and LCR lines maintain considerable narrow-sense heritability (h2) for the running capacity phenotype over 28 generations (h2 = 0.47 ± 0.02 and 0.43 ± 0.02, respectively). To minimize inbreeding, the lines were maintained by rotational mating. Pedigree records predict that the inbreeding coefficient increases at a rate of <1% per generation, ~37-38% slower than expected for random mating. Genome-wide 10K SNP genotype data for generations 5, 14, and 26 demonstrate substantial genomic evolution: between-line differentiation increased progressively, while within-line diversity deceased. Genome-wide average heterozygosity decreased at a rate of <1% per generation, consistent with pedigree-based predictions and confirming the effectiveness of rotational breeding. Linkage disequilibrium index r2 decreases to 0.3 at ~3 Mb, suggesting that the resolution for mapping quantitative trait loci (QTL) can be as high as 2-3 cM. To establish a test population for QTL mapping, we conducted an HCR-LCR intercross. Running capacity of the F1 population (n=176) was intermediate of the HCR and LCR parentals (28 pairs); and the F2 population (n=645) showed a wider range of phenotypic distribution. Importantly, heritability in the F0-F2 pedigree remained high (h2~0.6). These results suggest that the HCR-LCR lines can serve as a valuable system for studying genomic evolution, and a powerful resource for mapping QTL for a host of characters relevant to human health.
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Affiliation(s)
- Yu-yu Ren
- Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Katherine A. Overmyer
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Nathan R. Qi
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Mary K. Treutelaar
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Lori Heckenkamp
- Department of Anesthesiology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Molly Kalahar
- Department of Anesthesiology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Lauren G. Koch
- Department of Anesthesiology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Steven L. Britton
- Department of Anesthesiology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Charles F. Burant
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Jun Z. Li
- Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, United States of America
- * E-mail:
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Abstract
Genomic best linear unbiased prediction (BLUP) is a statistical method that uses relationships between individuals calculated from single-nucleotide polymorphisms (SNPs) to capture relationships at quantitative trait loci (QTL). We show that genomic BLUP exploits not only linkage disequilibrium (LD) and additive-genetic relationships, but also cosegregation to capture relationships at QTL. Simulations were used to study the contributions of those types of information to accuracy of genomic estimated breeding values (GEBVs), their persistence over generations without retraining, and their effect on the correlation of GEBVs within families. We show that accuracy of GEBVs based on additive-genetic relationships can decline with increasing training data size and speculate that modeling polygenic effects via pedigree relationships jointly with genomic breeding values using Bayesian methods may prevent that decline. Cosegregation information from half sibs contributes little to accuracy of GEBVs in current dairy cattle breeding schemes but from full sibs it contributes considerably to accuracy within family in corn breeding. Cosegregation information also declines with increasing training data size, and its persistence over generations is lower than that of LD, suggesting the need to model LD and cosegregation explicitly. The correlation between GEBVs within families depends largely on additive-genetic relationship information, which is determined by the effective number of SNPs and training data size. As genomic BLUP cannot capture short-range LD information well, we recommend Bayesian methods with t-distributed priors.
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Cole J, Null D. Visualization of the transmission of direct genomic values for paternal and maternal chromosomes for 15 traits in US Brown Swiss, Holstein, and Jersey cattle. J Dairy Sci 2013; 96:2713-2726. [DOI: 10.3168/jds.2012-6008] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2012] [Accepted: 12/19/2012] [Indexed: 11/19/2022]
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de Los Campos G, Hickey JM, Pong-Wong R, Daetwyler HD, Calus MPL. Whole-genome regression and prediction methods applied to plant and animal breeding. Genetics 2013; 193:327-45. [PMID: 22745228 PMCID: PMC3567727 DOI: 10.1534/genetics.112.143313] [Citation(s) in RCA: 484] [Impact Index Per Article: 44.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2012] [Accepted: 06/11/2012] [Indexed: 11/18/2022] Open
Abstract
Genomic-enabled prediction is becoming increasingly important in animal and plant breeding and is also receiving attention in human genetics. Deriving accurate predictions of complex traits requires implementing whole-genome regression (WGR) models where phenotypes are regressed on thousands of markers concurrently. Methods exist that allow implementing these large-p with small-n regressions, and genome-enabled selection (GS) is being implemented in several plant and animal breeding programs. The list of available methods is long, and the relationships between them have not been fully addressed. In this article we provide an overview of available methods for implementing parametric WGR models, discuss selected topics that emerge in applications, and present a general discussion of lessons learned from simulation and empirical data analysis in the last decade.
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Affiliation(s)
- Gustavo de Los Campos
- Department of Biostatistics, School of Public Health, University of Alabama, Birmingham, AL 35294, USA.
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