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Wilson S, Malosetti M, Maliepaard C, Mulder HA, Visser RGF, van Eeuwijk F. Training Set Construction for Genomic Prediction in Auto-Tetraploids: An Example in Potato. FRONTIERS IN PLANT SCIENCE 2021; 12:771075. [PMID: 34899794 PMCID: PMC8651708 DOI: 10.3389/fpls.2021.771075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Accepted: 10/20/2021] [Indexed: 06/14/2023]
Abstract
Training set construction is an important prerequisite to Genomic Prediction (GP), and while this has been studied in diploids, polyploids have not received the same attention. Polyploidy is a common feature in many crop plants, like for example banana and blueberry, but also potato which is the third most important crop in the world in terms of food consumption, after rice and wheat. The aim of this study was to investigate the impact of different training set construction methods using a publicly available diversity panel of tetraploid potatoes. Four methods of training set construction were compared: simple random sampling, stratified random sampling, genetic distance sampling and sampling based on the coefficient of determination (CDmean). For stratified random sampling, population structure analyses were carried out in order to define sub-populations, but since sub-populations accounted for only 16.6% of genetic variation, there were negligible differences between stratified and simple random sampling. For genetic distance sampling, four genetic distance measures were compared and though they performed similarly, Euclidean distance was the most consistent. In the majority of cases the CDmean method was the best sampling method, and compared to simple random sampling gave improvements of 4-14% in cross-validation scenarios, and 2-8% in scenarios with an independent test set, while genetic distance sampling gave improvements of 5.5-10.5% and 0.4-4.5%. No interaction was found between sampling method and the statistical model for the traits analyzed.
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Affiliation(s)
- Stefan Wilson
- Biometris, Wageningen University & Research, Wageningen, Netherlands
| | - Marcos Malosetti
- Biometris, Wageningen University & Research, Wageningen, Netherlands
| | - Chris Maliepaard
- Plant Breeding, Wageningen University & Research, Wageningen, Netherlands
| | - Han A. Mulder
- Wageningen University & Research, Animal Breeding and Genomics, Wageningen, Netherlands
| | | | - Fred van Eeuwijk
- Biometris, Wageningen University & Research, Wageningen, Netherlands
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Tyler AL, El Kassaby B, Kolishovski G, Emerson J, Wells AE, Mahoney JM, Carter GW. Effects of kinship correction on inflation of genetic interaction statistics in commonly used mouse populations. G3 (BETHESDA, MD.) 2021; 11:jkab131. [PMID: 33892506 PMCID: PMC8496251 DOI: 10.1093/g3journal/jkab131] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 03/31/2021] [Indexed: 12/04/2022]
Abstract
It is well understood that variation in relatedness among individuals, or kinship, can lead to false genetic associations. Multiple methods have been developed to adjust for kinship while maintaining power to detect true associations. However, relatively unstudied are the effects of kinship on genetic interaction test statistics. Here, we performed a survey of kinship effects on studies of six commonly used mouse populations. We measured inflation of main effect test statistics, genetic interaction test statistics, and interaction test statistics reparametrized by the Combined Analysis of Pleiotropy and Epistasis (CAPE). We also performed linear mixed model (LMM) kinship corrections using two types of kinship matrix: an overall kinship matrix calculated from the full set of genotyped markers, and a reduced kinship matrix, which left out markers on the chromosome(s) being tested. We found that test statistic inflation varied across populations and was driven largely by linkage disequilibrium. In contrast, there was no observable inflation in the genetic interaction test statistics. CAPE statistics were inflated at a level in between that of the main effects and the interaction effects. The overall kinship matrix overcorrected the inflation of main effect statistics relative to the reduced kinship matrix. The two types of kinship matrices had similar effects on the interaction statistics and CAPE statistics, although the overall kinship matrix trended toward a more severe correction. In conclusion, we recommend using an LMM kinship correction for both main effects and genetic interactions and further recommend that the kinship matrix be calculated from a reduced set of markers in which the chromosomes being tested are omitted from the calculation. This is particularly important in populations with substantial population structure, such as recombinant inbred lines in which genomic replicates are used.
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Affiliation(s)
- Anna L Tyler
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
| | | | | | - Jake Emerson
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
| | - Ann E Wells
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
| | - J Matthew Mahoney
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
- Department of Neurological Sciences, University of Vermont, Burlington, VT 05405, USA
- Department of Computer Science, University of Vermont, Burlington, VT 05405, USA
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Sannemann W, Lisker A, Maurer A, Léon J, Kazman E, Cöster H, Holzapfel J, Kempf H, Korzun V, Ebmeyer E, Pillen K. Adaptive selection of founder segments and epistatic control of plant height in the MAGIC winter wheat population WM-800. BMC Genomics 2018; 19:559. [PMID: 30064354 PMCID: PMC6069784 DOI: 10.1186/s12864-018-4915-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Accepted: 07/02/2018] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND Multi-parent advanced generation intercross (MAGIC) populations are a newly established tool to dissect quantitative traits. We developed the high resolution MAGIC wheat population WM-800, consisting of 910 F4:6 lines derived from intercrossing eight recently released European winter wheat cultivars. RESULTS Genotyping WM-800 with 7849 SNPs revealed a low mean genetic similarity of 59.7% between MAGIC lines. WM-800 harbours distinct genomic regions exposed to segregation distortion. These are mainly located on chromosomes 2 to 6 of the wheat B genome where founder specific DNA segments were positively or negatively selected. This suggests adaptive selection of individual founder alleles during population development. The application of a genome-wide association study identified 14 quantitative trait loci (QTL) controlling plant height in WM-800, including the known semi-dwarf genes Rht-B1 and Rht-D1 and a potentially novel QTL on chromosome 5A. Additionally, epistatic effects controlled plant height. For example, two loci on chromosomes 2B and 7B gave rise to an additive epistatic effect of 13.7 cm. CONCLUSION The present study demonstrates that plant height in the MAGIC-WHEAT population WM-800 is mainly determined by large-effect QTL and di-genic epistatic interactions. As a proof of concept, our study confirms that WM-800 is a valuable tool to dissect the genetic architecture of important agronomic traits.
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Affiliation(s)
- Wiebke Sannemann
- Chair of Plant Breeding, Martin Luther University Halle-Wittenberg, Betty-Heimann Straße 3, 06120 Halle, Germany
| | - Antonia Lisker
- Chair of Plant Breeding, Martin Luther University Halle-Wittenberg, Betty-Heimann Straße 3, 06120 Halle, Germany
| | - Andreas Maurer
- Chair of Plant Breeding, Martin Luther University Halle-Wittenberg, Betty-Heimann Straße 3, 06120 Halle, Germany
| | - Jens Léon
- Institute of Crop Science and Resource Conservation, Crop Genetics and Biotechnology Unit, University of Bonn, Katzenburgweg 5, Bonn, Germany
| | - Ebrahim Kazman
- Syngenta Seeds GmbH, Kroppenstedter Straße 4, 39387 Oschersleben (Bode), Hadmersleben, Germany
| | - Hilmar Cöster
- RAGT 2n, Steinesche 5A, 38855 - Silstedt, Wernigerode, Germany
| | - Josef Holzapfel
- Secobra Saatzucht GmbH, Feldkirchen 3, 85368 Moosburg an der Isar, Germany
| | - Hubert Kempf
- Secobra Saatzucht GmbH, Feldkirchen 3, 85368 Moosburg an der Isar, Germany
| | - Viktor Korzun
- KWS SAAT SE, Grimsehlstraße 31, 37555 Einbeck, Germany
| | - Erhard Ebmeyer
- KWS LOCHOW GMBH, Ferdinand-Lochow-Straße 5, 29303 Bergen/Wohlde, Germany
| | - Klaus Pillen
- Chair of Plant Breeding, Martin Luther University Halle-Wittenberg, Betty-Heimann Straße 3, 06120 Halle, Germany
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Stich B, Van Inghelandt D. Prospects and Potential Uses of Genomic Prediction of Key Performance Traits in Tetraploid Potato. FRONTIERS IN PLANT SCIENCE 2018; 9:159. [PMID: 29563919 PMCID: PMC5845909 DOI: 10.3389/fpls.2018.00159] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Accepted: 01/29/2018] [Indexed: 05/20/2023]
Abstract
Genomic prediction is a routine tool in breeding programs of most major animal and plant species. However, its usefulness for potato breeding has not yet been evaluated in detail. The objectives of this study were to (i) examine the prospects of genomic prediction of key performance traits in a diversity panel of tetraploid potato modeling additive, dominance, and epistatic effects, (ii) investigate the effects of size and make up of training set, number of test environments and molecular markers on prediction accuracy, and (iii) assess the effect of including markers from candidate genes on the prediction accuracy. With genomic best linear unbiased prediction (GBLUP), BayesA, BayesCπ, and Bayesian LASSO, four different prediction methods were used for genomic prediction of relative area under disease progress curve after a Phytophthora infestans infection, plant maturity, maturity corrected resistance, tuber starch content, tuber starch yield (TSY), and tuber yield (TY) of 184 tetraploid potato clones or subsets thereof genotyped with the SolCAP 8.3k SNP array. The cross-validated prediction accuracies with GBLUP and the three Bayesian approaches for the six evaluated traits ranged from about 0.5 to about 0.8. For traits with a high expected genetic complexity, such as TSY and TY, we observed an 8% higher prediction accuracy using a model with additive and dominance effects compared with a model with additive effects only. Our results suggest that for oligogenic traits in general and when diagnostic markers are available in particular, the use of Bayesian methods for genomic prediction is highly recommended and that the diagnostic markers should be modeled as fixed effects. The evaluation of the relative performance of genomic prediction vs. phenotypic selection indicated that the former is superior, assuming cycle lengths and selection intensities that are possible to realize in commercial potato breeding programs.
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Affiliation(s)
- Benjamin Stich
- Institute for Quantitative Genetics and Genomics of Plants, Heinrich Heine University, Düsseldorf, Germany
- Cluster of Excellence on Plant Sciences, From Complex Traits towards Synthetic Modules, Düsseldorf, Germany
| | - Delphine Van Inghelandt
- Institute for Quantitative Genetics and Genomics of Plants, Heinrich Heine University, Düsseldorf, Germany
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He S, Reif JC, Korzun V, Bothe R, Ebmeyer E, Jiang Y. Genome-wide mapping and prediction suggests presence of local epistasis in a vast elite winter wheat populations adapted to Central Europe. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2017; 130:635-647. [PMID: 27995275 DOI: 10.1007/s00122-016-2840-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2016] [Accepted: 12/01/2016] [Indexed: 05/05/2023]
Abstract
Genome-wide association mapping as well as marker- and haplotype-based genome-wide selection unraveled a complex genetic architecture of grain yield with absence of large effect QTL and presence of local epistatic effects. The genetic architecture of grain yield determines to a large extent the optimum design of genomic-assisted wheat breeding programs. The main goal of our study was to examine the potential and limitations to dissect the genetic architecture of grain yield in wheat using a large experimental data set. Our study was based on phenotypic information and genomic data of 13,901 SNPs of a diverse set of 3816 elite wheat lines adapted to Central Europe. We applied genome-wide association mapping based on experimental and simulated data sets and performed marker- and haplotype-based genomic prediction. Computer simulations revealed for our mapping population a high power to detect QTL, even if they individually explained only 2.5% of the genetic variation. Despite this, we found no stable marker-trait associations when validating in independent subsets. A two-dimensional scan for marker-marker interactions indicated presence of local epistasis which was further supported by improved prediction abilities when shifting from marker- to haplotype-based genome-wide prediction approaches. We observed that marker effects estimated using genome-wide prediction approaches strongly varied across years albeit resulting in high prediction abilities. Thus, our results suggested that the prediction accuracy of genomic selection in wheat is mainly driven by relatedness rather than by exploiting knowledge of the genetic architecture.
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Affiliation(s)
- Sang He
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Gatersleben, Germany
| | - Jochen C Reif
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Gatersleben, Germany.
| | | | | | | | - Yong Jiang
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Gatersleben, Germany
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Rosyara UR, De Jong WS, Douches DS, Endelman JB. Software for Genome-Wide Association Studies in Autopolyploids and Its Application to Potato. THE PLANT GENOME 2016; 9. [PMID: 27898814 DOI: 10.3835/plantgenome2015.08.0073] [Citation(s) in RCA: 97] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Genome-wide association studies (GWAS) are widely used in diploid species to study complex traits in diversity and breeding populations, but GWAS software tailored to autopolyploids is lacking. The objectives of this research were to (i) develop an R package for autopolyploids based on the + mixed model, (ii) validate the software with simulated data, and (iii) analyze a diversity panel of tetraploid potatoes. A unique feature of the R package, called GWASpoly, is its ability to model different types of polyploid gene action, including additive, simplex dominant, and duplex dominant. Using a simulated tetraploid population, we confirmed our hypothesis that statistical power is higher when the assumed gene action in the GWAS model matches the gene action at unobserved quantitative trait loci (QTL). Thirteen traits were analyzed in the Solanaceae Coordinated Agricultural Project (SolCAP) potato diversity panel and, consistent with previous studies, significant QTL for tuber shape and eye depth co-localized on chromosome 10. For the other traits, only marginally significant QTL were detected, most likely due to insufficient statistical power: for simulated traits with a heritability () of 0.3, the median genome-wide power was only 0.01. Our results indicate that both marker density and population size were limiting factors for GWAS with the SolCAP panel.
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