Literature DB >> 27995275

Genome-wide mapping and prediction suggests presence of local epistasis in a vast elite winter wheat populations adapted to Central Europe.

Sang He1, Jochen C Reif2, Viktor Korzun3, Reiner Bothe3, Erhard Ebmeyer3, Yong Jiang1.   

Abstract

KEY MESSAGE: 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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Year:  2016        PMID: 27995275     DOI: 10.1007/s00122-016-2840-x

Source DB:  PubMed          Journal:  Theor Appl Genet        ISSN: 0040-5752            Impact factor:   5.699


  57 in total

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Authors:  Dion Bennett; Matthew Reynolds; Daniel Mullan; Ali Izanloo; Haydn Kuchel; Peter Langridge; Thorsten Schnurbusch
Journal:  Theor Appl Genet       Date:  2012-07-08       Impact factor: 5.699

2.  Efficient methods to compute genomic predictions.

Authors:  P M VanRaden
Journal:  J Dairy Sci       Date:  2008-11       Impact factor: 4.034

3.  Relatedness severely impacts accuracy of marker-assisted selection for disease resistance in hybrid wheat.

Authors:  M Gowda; Y Zhao; T Würschum; C F H Longin; T Miedaner; E Ebmeyer; R Schachschneider; E Kazman; J Schacht; J-P Martinant; M F Mette; J C Reif
Journal:  Heredity (Edinb)       Date:  2013-12-18       Impact factor: 3.821

Review 4.  The genetics of quantitative traits: challenges and prospects.

Authors:  Trudy F C Mackay; Eric A Stone; Julien F Ayroles
Journal:  Nat Rev Genet       Date:  2009-08       Impact factor: 53.242

5.  Influence of gene interaction on complex trait variation with multilocus models.

Authors:  Asko Mäki-Tanila; William G Hill
Journal:  Genetics       Date:  2014-07-01       Impact factor: 4.562

6.  Quantitative trait loci for grain yield and adaptation of durum wheat (Triticum durum Desf.) across a wide range of water availability.

Authors:  Marco Maccaferri; Maria Corinna Sanguineti; Simona Corneti; José Luis Araus Ortega; Moncef Ben Salem; Jordi Bort; Enzo DeAmbrogio; Luis Fernando Garcia del Moral; Andrea Demontis; Ahmed El-Ahmed; Fouad Maalouf; Hassan Machlab; Vanessa Martos; Marc Moragues; Jihan Motawaj; Miloudi Nachit; Nasserlehaq Nserallah; Hassan Ouabbou; Conxita Royo; Amor Slama; Roberto Tuberosa
Journal:  Genetics       Date:  2008-01       Impact factor: 4.562

7.  Detecting epistatic effects in association studies at a genomic level based on an ensemble approach.

Authors:  Jing Li; Benjamin Horstman; Yixuan Chen
Journal:  Bioinformatics       Date:  2011-07-01       Impact factor: 6.937

8.  Prospects and limits of marker imputation in quantitative genetic studies in European elite wheat (Triticum aestivum L.).

Authors:  Sang He; Yusheng Zhao; M Florian Mette; Reiner Bothe; Erhard Ebmeyer; Timothy F Sharbel; Jochen C Reif; Yong Jiang
Journal:  BMC Genomics       Date:  2015-03-11       Impact factor: 3.969

9.  Quantitative trait locus mapping methods for diversity outbred mice.

Authors:  Daniel M Gatti; Karen L Svenson; Andrey Shabalin; Long-Yang Wu; William Valdar; Petr Simecek; Neal Goodwin; Riyan Cheng; Daniel Pomp; Abraham Palmer; Elissa J Chesler; Karl W Broman; Gary A Churchill
Journal:  G3 (Bethesda)       Date:  2014-09-18       Impact factor: 3.154

10.  Locally epistatic genomic relationship matrices for genomic association and prediction.

Authors:  Deniz Akdemir; Jean-Luc Jannink
Journal:  Genetics       Date:  2015-01-22       Impact factor: 4.562

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  18 in total

1.  Multi-Trait Genomic Prediction Models Enhance the Predictive Ability of Grain Trace Elements in Rice.

Authors:  Blaise Pascal Muvunyi; Wenli Zou; Junhui Zhan; Sang He; Guoyou Ye
Journal:  Front Genet       Date:  2022-06-22       Impact factor: 4.772

2.  NeuralLasso: Neural Networks Meet Lasso in Genomic Prediction.

Authors:  Boby Mathew; Andreas Hauptmann; Jens Léon; Mikko J Sillanpää
Journal:  Front Plant Sci       Date:  2022-04-29       Impact factor: 6.627

3.  Extension of a haplotype-based genomic prediction model to manage multi-environment wheat data using environmental covariates.

Authors:  Sang He; Rebecca Thistlethwaite; Kerrie Forrest; Fan Shi; Matthew J Hayden; Richard Trethowan; Hans D Daetwyler
Journal:  Theor Appl Genet       Date:  2019-08-21       Impact factor: 5.699

Review 4.  Reciprocal recurrent genomic selection: an attractive tool to leverage hybrid wheat breeding.

Authors:  Maximilian Rembe; Yusheng Zhao; Yong Jiang; Jochen C Reif
Journal:  Theor Appl Genet       Date:  2018-11-28       Impact factor: 5.699

5.  Utilization of a Wheat55K SNP array-derived high-density genetic map for high-resolution mapping of quantitative trait loci for important kernel-related traits in common wheat.

Authors:  Tianheng Ren; Tao Fan; Shulin Chen; Chunsheng Li; Yongyan Chen; Xia Ou; Qing Jiang; Zhenglong Ren; Feiquan Tan; Peigao Luo; Chen Chen; Zhi Li
Journal:  Theor Appl Genet       Date:  2021-01-03       Impact factor: 5.699

6.  Prospects and Potential Uses of Genomic Prediction of Key Performance Traits in Tetraploid Potato.

Authors:  Benjamin Stich; Delphine Van Inghelandt
Journal:  Front Plant Sci       Date:  2018-03-07       Impact factor: 5.753

7.  Unlocking big data doubled the accuracy in predicting the grain yield in hybrid wheat.

Authors:  Yusheng Zhao; Patrick Thorwarth; Yong Jiang; Norman Philipp; Albert W Schulthess; Mario Gils; Philipp H G Boeven; C Friedrich H Longin; Johannes Schacht; Erhard Ebmeyer; Viktor Korzun; Vilson Mirdita; Jost Dörnte; Ulrike Avenhaus; Ralf Horbach; Hilmar Cöster; Josef Holzapfel; Ludwig Ramgraber; Simon Kühnle; Pierrick Varenne; Anne Starke; Friederike Schürmann; Sebastian Beier; Uwe Scholz; Fang Liu; Renate H Schmidt; Jochen C Reif
Journal:  Sci Adv       Date:  2021-06-11       Impact factor: 14.136

8.  Hybrid Performance of an Immortalized F2 Rapeseed Population Is Driven by Additive, Dominance, and Epistatic Effects.

Authors:  Peifa Liu; Yusheng Zhao; Guozheng Liu; Meng Wang; Dandan Hu; Jun Hu; Jinling Meng; Jochen C Reif; Jun Zou
Journal:  Front Plant Sci       Date:  2017-05-18       Impact factor: 5.753

9.  Haplotype-Based Genome-Wide Prediction Models Exploit Local Epistatic Interactions Among Markers.

Authors:  Yong Jiang; Renate H Schmidt; Jochen C Reif
Journal:  G3 (Bethesda)       Date:  2018-05-04       Impact factor: 3.154

10.  Increased genomic prediction accuracy in wheat breeding using a large Australian panel.

Authors:  Adam Norman; Julian Taylor; Emi Tanaka; Paul Telfer; James Edwards; Jean-Pierre Martinant; Haydn Kuchel
Journal:  Theor Appl Genet       Date:  2017-09-08       Impact factor: 5.699

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