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Niehoff T, Pook T, Gholami M, Beissinger T. Imputation of low-density marker chip data in plant breeding: Evaluation of methods based on sugar beet. THE PLANT GENOME 2022; 15:e20257. [PMID: 36258672 DOI: 10.1002/tpg2.20257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 08/02/2022] [Indexed: 06/16/2023]
Abstract
Low-density genotyping followed by imputation reduces genotyping costs while still providing high-density marker information. An increased marker density has the potential to improve the outcome of all applications that are based on genomic data. This study investigates techniques for 1k to 20k genomic marker imputation for plant breeding programs with sugar beet (Beta vulgaris L. ssp. vulgaris) as an example crop, where these are realistic marker numbers for modern breeding applications. The generally accepted 'gold standard' for imputation, Beagle 5.1, was compared with the recently developed software AlphaPlantImpute2 which is designed specifically for plant breeding. For Beagle 5.1 and AlphaPlantImpute2, the imputation strategy as well as the imputation parameters were optimized in this study. We found that the imputation accuracy of Beagle could be tremendously improved (0.22 to 0.67) by tuning parameters, mainly by lowering the values for the parameter for the effective population size and increasing the number of iterations performed. Separating the phasing and imputation steps also improved accuracies when optimized parameters were used (0.67 to 0.82). We also found that the imputation accuracy of Beagle decreased when more low-density lines were included for imputation. AlphaPlantImpute2 produced very high accuracies without optimization (0.89) and was generally less responsive to optimization. Overall, AlphaPlantImpute2 performed relatively better for imputation whereas Beagle was better for phasing. Combining both tools yielded the highest accuracies.
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Affiliation(s)
- Tobias Niehoff
- Animal Breeding and Genomics, Wageningen Univ. & Research, Postbox 338, 6700AH, Wageningen, The Netherlands
- Dep. of Crop Sciences, Division of Plant Breeding Methodology, Univ. of Göttingen, Göttingen, 37075, Germany
| | - Torsten Pook
- Animal Breeding and Genomics, Wageningen Univ. & Research, Postbox 338, 6700AH, Wageningen, The Netherlands
- Dep. of Animal Sciences, Animal Breeding and Genetics Group, Univ. of Göttingen, Göttingen, 37075, Germany
- Center for Integrated Breeding Research, Univ. of Göttingen, Göttingen, 37075, Germany
| | - Mahmood Gholami
- RD-SBCE-BTA, KWS SAAT SE & Co. KGaA, Grimsehlstr. 31, Einbeck, 37574, Germany
| | - Timothy Beissinger
- Dep. of Crop Sciences, Division of Plant Breeding Methodology, Univ. of Göttingen, Göttingen, 37075, Germany
- Center for Integrated Breeding Research, Univ. of Göttingen, Göttingen, 37075, Germany
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Genotyping, the Usefulness of Imputation to Increase SNP Density, and Imputation Methods and Tools. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2467:113-138. [PMID: 35451774 DOI: 10.1007/978-1-0716-2205-6_4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Imputation has become a standard practice in modern genetic research to increase genome coverage and improve accuracy of genomic selection and genome-wide association study as a large number of samples can be genotyped at lower density (and lower cost) and, imputed up to denser marker panels or to sequence level, using information from a limited reference population. Most genotype imputation algorithms use information from relatives and population linkage disequilibrium. A number of software for imputation have been developed originally for human genetics and, more recently, for animal and plant genetics considering pedigree information and very sparse SNP arrays or genotyping-by-sequencing data. In comparison to human populations, the population structures in farmed species and their limited effective sizes allow to accurately impute high-density genotypes or sequences from very low-density SNP panels and a limited set of reference individuals. Whatever the imputation method, the imputation accuracy, measured by the correct imputation rate or the correlation between true and imputed genotypes, increased with the increasing relatedness of the individual to be imputed with its denser genotyped ancestors and as its own genotype density increased. Increasing the imputation accuracy pushes up the genomic selection accuracy whatever the genomic evaluation method. Given the marker densities, the most important factors affecting imputation accuracy are clearly the size of the reference population and the relationship between individuals in the reference and target populations.
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Pégard M, Rogier O, Bérard A, Faivre-Rampant P, Paslier MCL, Bastien C, Jorge V, Sánchez L. Sequence imputation from low density single nucleotide polymorphism panel in a black poplar breeding population. BMC Genomics 2019; 20:302. [PMID: 30999856 PMCID: PMC6471894 DOI: 10.1186/s12864-019-5660-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Accepted: 03/29/2019] [Indexed: 12/30/2022] Open
Abstract
Background Genomic selection accuracy increases with the use of high SNP (single nucleotide polymorphism) coverage. However, such gains in coverage come at high costs, preventing their prompt operational implementation by breeders. Low density panels imputed to higher densities offer a cheaper alternative during the first stages of genomic resources development. Our study is the first to explore the imputation in a tree species: black poplar. About 1000 pure-breed Populus nigra trees from a breeding population were selected and genotyped with a 12K custom Infinium Bead-Chip. Forty-three of those individuals corresponding to nodal trees in the pedigree were fully sequenced (reference), while the remaining majority (target) was imputed from 8K to 1.4 million SNPs using FImpute. Each SNP and individual was evaluated for imputation errors by leave-one-out cross validation in the training sample of 43 sequenced trees. Some summary statistics such as Hardy-Weinberg Equilibrium exact test p-value, quality of sequencing, depth of sequencing per site and per individual, minor allele frequency, marker density ratio or SNP information redundancy were calculated. Principal component and Boruta analyses were used on all these parameters to rank the factors affecting the quality of imputation. Additionally, we characterize the impact of the relatedness between reference population and target population. Results During the imputation process, we used 7540 SNPs from the chip to impute 1,438,827 SNPs from sequences. At the individual level, imputation accuracy was high with a proportion of SNPs correctly imputed between 0.84 and 0.99. The variation in accuracies was mostly due to differences in relatedness between individuals. At a SNP level, the imputation quality depended on genotyped SNP density and on the original minor allele frequency. The imputation did not appear to result in an increase of linkage disequilibrium. The genotype densification not only brought a better distribution of markers all along the genome, but also we did not detect any substantial bias in annotation categories. Conclusions This study shows that it is possible to impute low-density marker panels to whole genome sequence with good accuracy under certain conditions that could be common to many breeding populations. Electronic supplementary material The online version of this article (10.1186/s12864-019-5660-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Marie Pégard
- BioForA, INRA, ONF, 45075, Orléans, France, 2163 Avenue de la Pomme de Pin CS 40001 ARDON, Orléans Cedex 2, 45075, France
| | - Odile Rogier
- BioForA, INRA, ONF, 45075, Orléans, France, 2163 Avenue de la Pomme de Pin CS 40001 ARDON, Orléans Cedex 2, 45075, France
| | - Aurélie Bérard
- Etude du Polymorphisme des Génomes Végétaux (EPGV), INRA, Université Paris-Saclay, 91000, 2 rue Gaston Crémieux, Evry, 9100, France
| | - Patricia Faivre-Rampant
- Etude du Polymorphisme des Génomes Végétaux (EPGV), INRA, Université Paris-Saclay, 91000, 2 rue Gaston Crémieux, Evry, 9100, France
| | - Marie-Christine Le Paslier
- Etude du Polymorphisme des Génomes Végétaux (EPGV), INRA, Université Paris-Saclay, 91000, 2 rue Gaston Crémieux, Evry, 9100, France
| | - Catherine Bastien
- BioForA, INRA, ONF, 45075, Orléans, France, 2163 Avenue de la Pomme de Pin CS 40001 ARDON, Orléans Cedex 2, 45075, France
| | - Véronique Jorge
- BioForA, INRA, ONF, 45075, Orléans, France, 2163 Avenue de la Pomme de Pin CS 40001 ARDON, Orléans Cedex 2, 45075, France
| | - Leopoldo Sánchez
- BioForA, INRA, ONF, 45075, Orléans, France, 2163 Avenue de la Pomme de Pin CS 40001 ARDON, Orléans Cedex 2, 45075, France.
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Raoul J, Swan AA, Elsen JM. Using a very low-density SNP panel for genomic selection in a breeding program for sheep. Genet Sel Evol 2017; 49:76. [PMID: 29065868 PMCID: PMC5655911 DOI: 10.1186/s12711-017-0351-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2017] [Accepted: 10/17/2017] [Indexed: 01/11/2023] Open
Abstract
Background Building an efficient reference population for genomic selection is an issue when the recorded population is small and phenotypes are poorly informed, which is often the case in sheep breeding programs. Using stochastic simulation, we evaluated a genomic design based on a reference population with medium-density genotypes [around 45 K single nucleotide polymorphisms (SNPs)] of dams that were imputed from very low-density genotypes (≤ 1000 SNPs). Methods A population under selection for a maternal trait was simulated using real genotypes. Genetic gains realized from classical selection and genomic selection designs were compared. Genomic selection scenarios that differed in reference population structure (whether or not dams were included in the reference) and genotype quality (medium-density or imputed to medium-density from very low-density) were evaluated. Results The genomic design increased genetic gain by 26% when the reference population was based on sire medium-density genotypes and by 54% when the reference population included both sire and dam medium-density genotypes. When medium-density genotypes of male candidates and dams were replaced by imputed genotypes from very low-density SNP genotypes (1000 SNPs), the increase in gain was 22% for the sire reference population and 42% for the sire and dam reference population. The rate of increase in inbreeding was lower (from − 20 to − 34%) for the genomic design than for the classical design regardless of the genomic scenario. Conclusions We show that very low-density genotypes of male candidates and dams combined with an imputation process result in a substantial increase in genetic gain for small sheep breeding programs.
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Affiliation(s)
- Jérôme Raoul
- Institut de l'Elevage, Castanet-Tolosan, France. .,GenPhySE, INRA, Castanet-Tolosan, France.
| | - Andrew A Swan
- Animal Genetics and Breeding Unit, University of New England, Armidale, Australia
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