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Sadeqi MB, Ballvora A, Léon J. Local and Bayesian Survival FDR Estimations to Identify Reliable Associations in Whole Genome of Bread Wheat. Int J Mol Sci 2023; 24:14011. [PMID: 37762314 PMCID: PMC10531084 DOI: 10.3390/ijms241814011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 09/02/2023] [Accepted: 09/07/2023] [Indexed: 09/29/2023] Open
Abstract
Estimating the FDR significance threshold in genome-wide association studies remains a major challenge in distinguishing true positive hypotheses from false positive and negative errors. Several comparative methods for multiple testing comparison have been developed to determine the significance threshold; however, these methods may be overly conservative and lead to an increase in false negative results. The local FDR approach is suitable for testing many associations simultaneously based on the empirical Bayes perspective. In the local FDR, the maximum likelihood estimator is sensitive to bias when the GWAS model contains two or more explanatory variables as genetic parameters simultaneously. The main criticism of local FDR is that it focuses only locally on the effects of single nucleotide polymorphism (SNP) in tails of distribution, whereas the signal associations are distributed across the whole genome. The advantage of the Bayesian perspective is that knowledge of prior distribution comes from other genetic parameters included in the GWAS model, such as linkage disequilibrium (LD) analysis, minor allele frequency (MAF) and call rate of significant associations. We also proposed Bayesian survival FDR to solve the multi-collinearity and large-scale problems, respectively, in grain yield (GY) vector in bread wheat with large-scale SNP information. The objective of this study was to obtain a short list of SNPs that are reliably associated with GY under low and high levels of nitrogen (N) in the population. The five top significant SNPs were compared with different Bayesian models. Based on the time to events in the Bayesian survival analysis, the differentiation between minor and major alleles within the association panel can be identified.
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Affiliation(s)
| | - Agim Ballvora
- INRES-Plant Breeding, Rheinische Friedrich-Wilhelms-Universität Bonn, 53113 Bonn, Germany; (M.B.S.); (J.L.)
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2
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Afsharyan NP, Sannemann W, Ballvora A, Léon J. Identifying developmental QTL alleles with favorable effect on grain yield components under late-terminal drought in spring barley MAGIC population. PLANT DIRECT 2023; 7:e516. [PMID: 37538189 PMCID: PMC10394678 DOI: 10.1002/pld3.516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 05/27/2023] [Accepted: 06/28/2023] [Indexed: 08/05/2023]
Abstract
Barley is the fourth most cultivated cereal worldwide, and drought is a major cause of its yield loss by negatively affecting its development. Hence, better understanding developmental mechanisms that control complex polygenic yield-related traits under drought is essential to uncover favorable yield regulators. This study evaluated seven above-ground yield-related traits under well-watered (WW) and late-terminal drought (TD) treatment using 534 spring barley multiparent advanced generation intercross double haploid (DH) lines. The analysis of quantitative trait loci (QTL) for WW, TD, marker by treatment interaction, and drought stress tolerance identified 69, 64, 25, and 25 loci, respectively, for seven traits from which 15 loci were common for at least three traits and 17 were shared by TD and drought stress tolerance. Evaluation of allelic effects for a QTL revealed varying effect of parental alleles. Results showed prominent QTL located on major flowering time gene Ppd-H1 with favorable effects for grain weight under TD when flowering time was not significantly affected, suggesting that this gene might be linked with increasing grain weight by ways other than timing of flowering under late-terminal drought stress. Furthermore, a desirable novel QTL allele was identified on chromosome 5H for grain number under TD nearby sucrose transporter gene HvSUT2. The findings indicated that spring barley multiparent advanced generation intercross population can provide insights to improve yield under complex condition of drought.
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Affiliation(s)
- Nazanin P. Afsharyan
- Institute for Crop Science and Resource Conservation, Chair of Plant BreedingUniversity of BonnBonnGermany
- Department of Plant BreedingJustus Liebig University GiessenGiessenGermany
| | - Wiebke Sannemann
- Institute for Crop Science and Resource Conservation, Chair of Plant BreedingUniversity of BonnBonnGermany
- KWS Saat SE & Co. KGaAEinbeckGermany
| | - Agim Ballvora
- Institute for Crop Science and Resource Conservation, Chair of Plant BreedingUniversity of BonnBonnGermany
| | - Jens Léon
- Institute for Crop Science and Resource Conservation, Chair of Plant BreedingUniversity of BonnBonnGermany
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3
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Wittern LM, Barrero JM, Bovill WD, Verbyla KL, Hughes T, Swain SM, Steed G, Webb AAR, Gardner K, Greenland A, Jacobs J, Frohberg C, Schmidt RC, Cavanagh C, Rohde A, Davey MW, Hannah MA. Overexpression of the WAPO-A1 gene increases the number of spikelets per spike in bread wheat. Sci Rep 2022; 12:14229. [PMID: 35987959 PMCID: PMC9392761 DOI: 10.1038/s41598-022-18614-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 08/16/2022] [Indexed: 11/09/2022] Open
Abstract
AbstractTwo homoeologous QTLs for number of spikelets per spike (SPS) were mapped on chromosomes 7AL and 7BL using two wheat MAGIC populations. Sets of lines contrasting for the QTL on 7AL were developed which allowed for the validation and fine mapping of the 7AL QTL and for the identification of a previously described candidate gene, WHEAT ORTHOLOG OF APO1 (WAPO1). Using transgenic overexpression in both a low and a high SPS line, we provide a functional validation for the role of this gene in determining SPS also in hexaploid wheat. We show that the expression levels of this gene positively correlate with SPS in multiple MAGIC founder lines under field conditions as well as in transgenic lines grown in the greenhouse. This work highlights the potential use of WAPO1 in hexaploid wheat for further yield increases. The impact of WAPO1 and SPS on yield depends on other genetic and environmental factors, hence, will require a finely balanced expression level to avoid the development of detrimental pleiotropic phenotypes.
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Zuanetti DA, Milan LA. Bayesian Modeling for Epistasis Analysis Using Data-Driven Reversible Jump. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1495-1506. [PMID: 33301408 DOI: 10.1109/tcbb.2020.3043857] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
We propose a procedure for modeling a phenotype using QTLs which estimate the additive and dominance effects of genotypes and epistasis. The estimation of the model is implemented through a Bayesian approach which uses the data-driven reversible jump (DDRJ) for multiple QTL mapping and model selection. We compare the DDRJ's performance with the usual reversible jump (RJ), QTLBim, multiple interval mapping (MIM) and LASSO using real and simulated data sets. The DDRJ outperforms the available methods to estimate the number of QTLs in epistatic models and it identifies their locations in the genome, without increasing the number of false-positive QTLs in the considered data. Since QTL mapping is a regression model involving complex non-observable variables and their interactions, the model selection procedure proposed here is also useful in other areas of research. The application for identifying main and epistatic relevant QTLs to systolic blood pressure after salt intervention is our main motivation.
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Odell SG, Hudson AI, Praud S, Dubreuil P, Tixier MH, Ross-Ibarra J, Runcie DE. Modeling allelic diversity of multiparent mapping populations affects detection of quantitative trait loci. G3 (BETHESDA, MD.) 2022; 12:6509518. [PMID: 35100382 PMCID: PMC8895984 DOI: 10.1093/g3journal/jkac011] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 10/27/2021] [Indexed: 12/02/2022]
Abstract
The search for quantitative trait loci that explain complex traits such as yield and drought tolerance has been ongoing in all crops. Methods such as biparental quantitative trait loci mapping and genome-wide association studies each have their own advantages and limitations. Multiparent advanced generation intercross populations contain more recombination events and genetic diversity than biparental mapping populations and are better able to estimate effect sizes of rare alleles than association mapping populations. Here, we discuss the results of using a multiparent advanced generation intercross population of doubled haploid maize lines created from 16 diverse founders to perform quantitative trait loci mapping. We compare 3 models that assume bi-allelic, founder, and ancestral haplotype allelic states for quantitative trait loci. The 3 methods have differing power to detect quantitative trait loci for a variety of agronomic traits. Although the founder approach finds the most quantitative trait loci, all methods are able to find unique quantitative trait loci, suggesting that each model has advantages for traits with different genetic architectures. A closer look at a well-characterized flowering time quantitative trait loci, qDTA8, which contains vgt1, highlights the strengths and weaknesses of each method and suggests a potential epistatic interaction. Overall, our results reinforce the importance of considering different approaches to analyzing genotypic datasets, and shows the limitations of binary SNP data for identifying multiallelic quantitative trait loci.
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Affiliation(s)
- Sarah G Odell
- Department of Plant Sciences, University of California, Davis, CA 95616, USA.,Department of Evolution and Ecology, University of California, Davis, CA 95616, USA
| | - Asher I Hudson
- Department of Evolution and Ecology, University of California, Davis, CA 95616, USA.,Center for Population Biology, University of California, Davis, CA 95616, USA
| | - Sébastien Praud
- Limagrain, Centre de Recherche de Chappes, Chappes 63720, France
| | - Pierre Dubreuil
- Limagrain, Centre de Recherche de Chappes, Chappes 63720, France
| | | | - Jeffrey Ross-Ibarra
- Department of Evolution and Ecology, University of California, Davis, CA 95616, USA.,Center for Population Biology, University of California, Davis, CA 95616, USA.,Genome Center, University of California, Davis, CA 95616, USA
| | - Daniel E Runcie
- Department of Plant Sciences, University of California, Davis, CA 95616, USA
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Roth M, Beugnot A, Mary-Huard T, Moreau L, Charcosset A, Fiévet JB. Improving genomic predictions with inbreeding and nonadditive effects in two admixed maize hybrid populations in single and multienvironment contexts. Genetics 2022; 220:6527635. [PMID: 35150258 PMCID: PMC8982028 DOI: 10.1093/genetics/iyac018] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Accepted: 01/28/2022] [Indexed: 11/12/2022] Open
Abstract
Genetic admixture, resulting from the recombination between structural groups, is frequently encountered in breeding populations. In hybrid breeding, crossing admixed lines can generate substantial nonadditive genetic variance and contrasted levels of inbreeding which can impact trait variation. This study aimed at testing recent methodological developments for the modeling of inbreeding and nonadditive effects in order to increase prediction accuracy in admixed populations. Using two maize (Zea mays L.) populations of hybrids admixed between dent and flint heterotic groups, we compared a suite of five genomic prediction models incorporating (or not) parameters accounting for inbreeding and nonadditive effects with the natural and orthogonal interaction approach in single and multienvironment contexts. In both populations, variance decompositions showed the strong impact of inbreeding on plant yield, height, and flowering time which was supported by the superiority of prediction models incorporating this effect (+0.038 in predictive ability for mean yield). In most cases dominance variance was reduced when inbreeding was accounted for. The model including additivity, dominance, epistasis, and inbreeding effects appeared to be the most robust for prediction across traits and populations (+0.054 in predictive ability for mean yield). In a multienvironment context, we found that the inclusion of nonadditive and inbreeding effects was advantageous when predicting hybrids not yet observed in any environment. Overall, comparing variance decompositions was helpful to guide model selection for genomic prediction. Finally, we recommend the use of models including inbreeding and nonadditive parameters following the natural and orthogonal interaction approach to increase prediction accuracy in admixed populations.
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Affiliation(s)
- Morgane Roth
- Plant Breeding Research Division, Agroscope, Wädenswil, 8820 Zurich, Switzerland,Corresponding author: INRAE GAFL, 67 Allée des Chênes 84140 Montfavet, France.
| | - Aurélien Beugnot
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91190 Gif-sur-Yvette, France
| | - Tristan Mary-Huard
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91190 Gif-sur-Yvette, France,Université Paris-Saclay, INRAE, AgroParisTech, UMR MIA-Paris Paris, 75005 Paris, France
| | - Laurence Moreau
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91190 Gif-sur-Yvette, France
| | - Alain Charcosset
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91190 Gif-sur-Yvette, France
| | - Julie B Fiévet
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91190 Gif-sur-Yvette, France
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Genomic interrogation of a MAGIC population highlights genetic factors controlling fiber quality traits in cotton. Commun Biol 2022; 5:60. [PMID: 35039628 PMCID: PMC8764025 DOI: 10.1038/s42003-022-03022-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 12/21/2021] [Indexed: 02/05/2023] Open
Abstract
Cotton (Gossypium hirsutum L.) fiber is the most important resource of natural and renewable fiber for the textile industry. However, the understanding of genetic components and their genome-wide interactions controlling fiber quality remains fragmentary. Here, we sequenced a multiple-parent advanced-generation inter-cross (MAGIC) population, consisting of 550 individuals created by inter-crossing 11 founders, and established a mosaic genome map through tracing the origin of haplotypes that share identity-by-descent (IBD). We performed two complementary GWAS methods—SNP-based GWAS (sGWAS) and IBD-based haplotype GWAS (hGWAS). A total of 25 sQTLs and 14 hQTLs related to cotton fiber quality were identified, of which 26 were novel QTLs. Two major QTLs detected by both GWAS methods were responsible for fiber strength and length. The gene Ghir_D11G020400 (GhZF14) encoding the MATE efflux family protein was identified as a novel candidate gene for fiber length. Beyond the additive QTLs, we detected prevalent epistatic interactions that contributed to the genetics of fiber quality, pinpointing another layer for trait variance. This study provides new targets for future molecular design breeding of superior fiber quality. Wang and colleagues use a complementary GWAS approach to identify genetic loci associated with cotton fiber quality. Using a multiparent advanced-generation inter-cross population, 26 new QTLs related to cotton fiber quality were found.
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Hautsalo J, Novakazi F, Jalli M, Göransson M, Manninen O, Isolahti M, Reitan L, Bergersen S, Krusell L, Damsgård Robertsen C, Orabi J, Due Jensen J, Jahoor A, Bengtsson T. Pyramiding of scald resistance genes in four spring barley MAGIC populations. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:3829-3843. [PMID: 34350474 PMCID: PMC8580920 DOI: 10.1007/s00122-021-03930-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 07/27/2021] [Indexed: 06/13/2023]
Abstract
Genome-Wide Association Studies (GWAS) of four Multi-parent Advanced Generation Inter-Cross (MAGIC) populations identified nine regions on chromosomes 1H, 3H, 4H, 5H, 6H and 7H associated with resistance against barley scald disease. Three of these regions are putatively novel resistance Quantitative Trait Loci (QTL). Barley scald is caused by Rhynchosporium commune, one of the most important barley leaf diseases that are prevalent in most barley-growing regions. Up to 40% yield losses can occur in susceptible barley cultivars. Four MAGIC populations were generated in a Nordic Public-Private Pre-breeding of spring barley project (PPP Barley) to introduce resistance to several important diseases. Here, these MAGIC populations consisting of six to eight founders each were tested for scald resistance in field trials in Finland and Iceland. Eight different model covariate combinations were compared for GWAS studies, and the models that deviated the least from the expected p-values were selected. For all QTL, candidate genes were identified that are predicted to be involved in pathogen defence. The MAGIC progenies contained new haplotypes of significant SNP-markers with high resistance levels. The lines with successfully pyramided resistance against scald and mildew and the significant markers are now distributed among Nordic plant breeders and will benefit development of disease-resistant cultivars.
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Affiliation(s)
- Juho Hautsalo
- Natural Resources Institute Finland (Luke), Survontie 9, 40500, Jyväskylä, Finland
| | - Fluturë Novakazi
- Department of Plant Breeding, Swedish University of Agricultural Sciences, P.O. Box 190, 234 22, Lomma, Sweden
| | - Marja Jalli
- Natural Resources Institute Finland (Luke), Tietotie 4, 31600, Jokioinen, Finland
| | - Magnus Göransson
- Faculty of Land and Animal Resources, The Agricultural University of Iceland, Hvanneyri, 311, Borgarnes, Iceland
| | - Outi Manninen
- Boreal Plant Breeding Ltd., Myllytie 10, 31600, Jokioinen, Norway
| | - Mika Isolahti
- Boreal Plant Breeding Ltd., Myllytie 10, 31600, Jokioinen, Norway
| | - Lars Reitan
- Graminor Ltd. Hommelstadvegen 60, 2322, Ridabu, Norway
| | | | - Lene Krusell
- Sejet Plant Breeding, Nørremarksvej 67, 8700, Horsens, Norway
| | | | - Jihad Orabi
- Nordic Seed A/S, Kornmarken 1, 8464, Galten, Denmark
| | | | - Ahmed Jahoor
- Department of Plant Breeding, Swedish University of Agricultural Sciences, P.O. Box 190, 234 22, Lomma, Sweden
- Nordic Seed A/S, Kornmarken 1, 8464, Galten, Denmark
| | - Therése Bengtsson
- Department of Plant Breeding, Swedish University of Agricultural Sciences, P.O. Box 190, 234 22, Lomma, Sweden.
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MIDESP: Mutual Information-Based Detection of Epistatic SNP Pairs for Qualitative and Quantitative Phenotypes. BIOLOGY 2021; 10:biology10090921. [PMID: 34571798 PMCID: PMC8469369 DOI: 10.3390/biology10090921] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 09/09/2021] [Accepted: 09/13/2021] [Indexed: 11/17/2022]
Abstract
Simple Summary The interactions between SNPs, which are known as epistasis, can strongly influence the phenotype. Their detection is still a challenge, which is made even more difficult through the existence of background associations that can hide correct epistatic interactions. To address the limitations of existing methods, we present in this study our novel method MIDESP for the detection of epistatic SNP pairs. It is the first mutual information-based method that can be applied to both qualitative and quantitative phenotypes and which explicitly accounts for background associations in the dataset. Abstract The interactions between SNPs result in a complex interplay with the phenotype, known as epistasis. The knowledge of epistasis is a crucial part of understanding genetic causes of complex traits. However, due to the enormous number of SNP pairs and their complex relationship to the phenotype, identification still remains a challenging problem. Many approaches for the detection of epistasis have been developed using mutual information (MI) as an association measure. However, these methods have mainly been restricted to case–control phenotypes and are therefore of limited applicability for quantitative traits. To overcome this limitation of MI-based methods, here, we present an MI-based novel algorithm, MIDESP, to detect epistasis between SNPs for qualitative as well as quantitative phenotypes. Moreover, by incorporating a dataset-dependent correction technique, we deal with the effect of background associations in a genotypic dataset to separate correct epistatic interaction signals from those of false positive interactions resulting from the effect of single SNP×phenotype associations. To demonstrate the effectiveness of MIDESP, we apply it on two real datasets with qualitative and quantitative phenotypes, respectively. Our results suggest that by eliminating the background associations, MIDESP can identify important genes, which play essential roles for bovine tuberculosis or the egg weight of chickens.
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Mathew B, Léon J, Dadshani S, Pillen K, Sillanpää MJ, Naz AA. Importance of correcting genomic relationships in single-locus QTL mapping model with an advanced backcross population. G3 GENES|GENOMES|GENETICS 2021; 11:6211194. [PMID: 33822941 PMCID: PMC8495747 DOI: 10.1093/g3journal/jkab105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 03/18/2021] [Indexed: 11/29/2022]
Abstract
Advanced backcross (AB) populations have been widely used to identify and utilize beneficial alleles in various crops such as rice, tomato, wheat, and barley. For the development of an AB population, a controlled crossing scheme is used and this controlled crossing along with the selection (both natural and artificial) of agronomically adapted alleles during the development of AB population may lead to unbalanced allele frequencies in the population. However, it is commonly believed that interval mapping of traits in experimental crosses such as AB populations is immune to the deviations from the expected frequencies under Mendelian segregation. Using two AB populations and simulated data sets as examples, we describe the severity of the problem caused by unbalanced allele frequencies in quantitative trait loci mapping and demonstrate how it can be corrected using the linear mixed model having a polygenic effect with the covariance structure (genomic relationship matrix) calculated from molecular markers.
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Affiliation(s)
- Boby Mathew
- Institute of Crop Science and Resource Conservation, Department of Plant Breeding, University of Bonn, 53115 Bonn, Germany
| | - Jens Léon
- Institute of Crop Science and Resource Conservation, Department of Plant Breeding, University of Bonn, 53115 Bonn, Germany
| | - Said Dadshani
- Institute of Crop Science and Resource Conservation, Department of Plant Breeding, University of Bonn, 53115 Bonn, Germany
| | - Klaus Pillen
- Department of Plant Breeding, Institute of Agricultural and Nutritional Sciences, Martin-Luther University Halle-Wittenberg, 06120 Halle (Saale), Germany
| | | | - Ali Ahmad Naz
- Institute of Crop Science and Resource Conservation, Department of Plant Breeding, University of Bonn, 53115 Bonn, Germany
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Puglisi D, Delbono S, Visioni A, Ozkan H, Kara İ, Casas AM, Igartua E, Valè G, Piero ARL, Cattivelli L, Tondelli A, Fricano A. Genomic Prediction of Grain Yield in a Barley MAGIC Population Modeling Genotype per Environment Interaction. FRONTIERS IN PLANT SCIENCE 2021; 12:664148. [PMID: 34108982 PMCID: PMC8183822 DOI: 10.3389/fpls.2021.664148] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 04/26/2021] [Indexed: 06/12/2023]
Abstract
Multi-parent Advanced Generation Inter-crosses (MAGIC) lines have mosaic genomes that are generated shuffling the genetic material of the founder parents following pre-defined crossing schemes. In cereal crops, these experimental populations have been extensively used to investigate the genetic bases of several traits and dissect the genetic bases of epistasis. In plants, genomic prediction models are usually fitted using either diverse panels of mostly unrelated accessions or individuals of biparental families and several empirical analyses have been conducted to evaluate the predictive ability of models fitted to these populations using different traits. In this paper, we constructed, genotyped and evaluated a barley MAGIC population of 352 individuals developed with a diverse set of eight founder parents showing contrasting phenotypes for grain yield. We combined phenotypic and genotypic information of this MAGIC population to fit several genomic prediction models which were cross-validated to conduct empirical analyses aimed at examining the predictive ability of these models varying the sizes of training populations. Moreover, several methods to optimize the composition of the training population were also applied to this MAGIC population and cross-validated to estimate the resulting predictive ability. Finally, extensive phenotypic data generated in field trials organized across an ample range of water regimes and climatic conditions in the Mediterranean were used to fit and cross-validate multi-environment genomic prediction models including G×E interaction, using both genomic best linear unbiased prediction and reproducing kernel Hilbert space along with a non-linear Gaussian Kernel. Overall, our empirical analyses showed that genomic prediction models trained with a limited number of MAGIC lines can be used to predict grain yield with values of predictive ability that vary from 0.25 to 0.60 and that beyond QTL mapping and analysis of epistatic effects, MAGIC population might be used to successfully fit genomic prediction models. We concluded that for grain yield, the single-environment genomic prediction models examined in this study are equivalent in terms of predictive ability while, in general, multi-environment models that explicitly split marker effects in main and environmental-specific effects outperform simpler multi-environment models.
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Affiliation(s)
- Damiano Puglisi
- Dipartimento di Agricoltura, Alimentazione e Ambiente (Di3A), Università di Catania, Catania, Italy
| | - Stefano Delbono
- Council for Agricultural Research and Economics–Research Centre for Genomics and Bioinformatics, Fiorenzuola d’Arda, Italy
| | - Andrea Visioni
- Biodiversity and Crop Improvement Program, International Center for Agricultural Research in the Dry Areas, Avenue Hafiane Cherkaoui, Rabat, Morocco
| | - Hakan Ozkan
- Department of Field Crops, Faculty of Agriculture, University of Cukurova, Adana, Turkey
| | - İbrahim Kara
- Bahri Dagdas International Agricultural Research Institute, Konya, Turkey
| | - Ana M. Casas
- Aula Dei Experimental Station (EEAD-CSIC), Spanish Research Council, Zaragoza, Spain
| | - Ernesto Igartua
- Aula Dei Experimental Station (EEAD-CSIC), Spanish Research Council, Zaragoza, Spain
| | - Giampiero Valè
- DiSIT, Dipartimento di Scienze e Innovazione Tecnologica, Università del Piemonte Orientale, Vercelli, Italy
| | - Angela Roberta Lo Piero
- Dipartimento di Agricoltura, Alimentazione e Ambiente (Di3A), Università di Catania, Catania, Italy
| | - Luigi Cattivelli
- Council for Agricultural Research and Economics–Research Centre for Genomics and Bioinformatics, Fiorenzuola d’Arda, Italy
| | - Alessandro Tondelli
- Council for Agricultural Research and Economics–Research Centre for Genomics and Bioinformatics, Fiorenzuola d’Arda, Italy
| | - Agostino Fricano
- Council for Agricultural Research and Economics–Research Centre for Genomics and Bioinformatics, Fiorenzuola d’Arda, Italy
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Kontio JAJ, Pyhäjärvi T, Sillanpää MJ. Model guided trait-specific co-expression network estimation as a new perspective for identifying molecular interactions and pathways. PLoS Comput Biol 2021; 17:e1008960. [PMID: 33939702 PMCID: PMC8118548 DOI: 10.1371/journal.pcbi.1008960] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 05/13/2021] [Accepted: 04/13/2021] [Indexed: 11/19/2022] Open
Abstract
A wide variety of 1) parametric regression models and 2) co-expression networks have been developed for finding gene-by-gene interactions underlying complex traits from expression data. While both methodological schemes have their own well-known benefits, little is known about their synergistic potential. Our study introduces their methodological fusion that cross-exploits the strengths of individual approaches via a built-in information-sharing mechanism. This fusion is theoretically based on certain trait-conditioned dependency patterns between two genes depending on their role in the underlying parametric model. Resulting trait-specific co-expression network estimation method 1) serves to enhance the interpretation of biological networks in a parametric sense, and 2) exploits the underlying parametric model itself in the estimation process. To also account for the substantial amount of intrinsic noise and collinearities, often entailed by expression data, a tailored co-expression measure is introduced along with this framework to alleviate related computational problems. A remarkable advance over the reference methods in simulated scenarios substantiate the method's high-efficiency. As proof-of-concept, this synergistic approach is successfully applied in survival analysis, with acute myeloid leukemia data, further highlighting the framework's versatility and broad practical relevance.
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Affiliation(s)
- Juho A. J. Kontio
- Research Unit of Mathematical Sciences, University of Oulu, Oulu, Finland
| | - Tanja Pyhäjärvi
- Department of Ecology and Genetics, University of Oulu, Oulu, Finland
- Department of Forest Sciences, University of Helsinki, Helsinki, Finland
| | - Mikko J. Sillanpää
- Research Unit of Mathematical Sciences, University of Oulu, Oulu, Finland
- * E-mail:
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13
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Tyler AL, Emerson J, El Kassaby B, Wells AE, Philip VM, Carter GW. The Combined Analysis of Pleiotropy and Epistasis (CAPE). Methods Mol Biol 2021; 2212:55-67. [PMID: 33733350 DOI: 10.1007/978-1-0716-0947-7_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Epistasis, or gene-gene interaction, contributes substantially to trait variation in organisms ranging from yeast to humans, and modeling epistasis directly is critical to understanding the genotype-phenotype map. However, inference of genetic interactions is challenging compared to inference of individual allele effects due to low statistical power. Furthermore, genetic interactions can appear inconsistent across different quantitative traits, presenting a challenge for the interpretation of detected interactions. Here we present a method called the Combined Analysis of Pleiotropy and Epistasis (CAPE) that combines information across multiple quantitative traits to infer directed epistatic interactions. By combining information across multiple traits, CAPE not only increases power to detect genetic interactions but also interprets these interactions across traits to identify a single interaction that is consistent across all observed data. This method generates informative, interpretable interaction networks that explain how variants interact with each other to influence groups of related traits. This method could potentially be used to link genetic variants to gene expression, physiological endophenotypes, and higher-level disease traits.
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14
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Scott MF, Ladejobi O, Amer S, Bentley AR, Biernaskie J, Boden SA, Clark M, Dell'Acqua M, Dixon LE, Filippi CV, Fradgley N, Gardner KA, Mackay IJ, O'Sullivan D, Percival-Alwyn L, Roorkiwal M, Singh RK, Thudi M, Varshney RK, Venturini L, Whan A, Cockram J, Mott R. Multi-parent populations in crops: a toolbox integrating genomics and genetic mapping with breeding. Heredity (Edinb) 2020; 125:396-416. [PMID: 32616877 PMCID: PMC7784848 DOI: 10.1038/s41437-020-0336-6] [Citation(s) in RCA: 76] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 06/16/2020] [Accepted: 06/16/2020] [Indexed: 11/21/2022] Open
Abstract
Crop populations derived from experimental crosses enable the genetic dissection of complex traits and support modern plant breeding. Among these, multi-parent populations now play a central role. By mixing and recombining the genomes of multiple founders, multi-parent populations combine many commonly sought beneficial properties of genetic mapping populations. For example, they have high power and resolution for mapping quantitative trait loci, high genetic diversity and minimal population structure. Many multi-parent populations have been constructed in crop species, and their inbred germplasm and associated phenotypic and genotypic data serve as enduring resources. Their utility has grown from being a tool for mapping quantitative trait loci to a means of providing germplasm for breeding programmes. Genomics approaches, including de novo genome assemblies and gene annotations for the population founders, have allowed the imputation of rich sequence information into the descendent population, expanding the breadth of research and breeding applications of multi-parent populations. Here, we report recent successes from crop multi-parent populations in crops. We also propose an ideal genotypic, phenotypic and germplasm 'package' that multi-parent populations should feature to optimise their use as powerful community resources for crop research, development and breeding.
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Affiliation(s)
| | | | - Samer Amer
- University of Reading, Reading, RG6 6AH, UK
- Faculty of Agriculture, Alexandria University, Alexandria, 23714, Egypt
| | - Alison R Bentley
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Jay Biernaskie
- Department of Plant Sciences, University of Oxford, South Parks Road, Oxford, OX1 3RB, UK
| | - Scott A Boden
- School of Agriculture, Food and Wine, University of Adelaide, Glen Osmond, SA, 5064, Australia
| | | | | | - Laura E Dixon
- Faculty of Biological Sciences, University of Leeds, Leeds, LS2 9JT, UK
| | - Carla V Filippi
- Instituto de Agrobiotecnología y Biología Molecular (IABIMO), INTA-CONICET, Nicolas Repetto y Los Reseros s/n, 1686, Hurlingham, Buenos Aires, Argentina
| | - Nick Fradgley
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Keith A Gardner
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Ian J Mackay
- SRUC, West Mains Road, Kings Buildings, Edinburgh, EH9 3JG, UK
| | | | | | - Manish Roorkiwal
- Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Rakesh Kumar Singh
- International Center for Biosaline Agriculture, Academic City, Dubai, United Arab Emirates
| | - Mahendar Thudi
- Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Rajeev Kumar Varshney
- Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | | | - Alex Whan
- CSIRO, GPO Box 1700, Canberra, ACT, 2601, Australia
| | - James Cockram
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Richard Mott
- UCL Genetics Institute, Gower Street, London, WC1E 6BT, UK
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15
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Shi J, Wang J, Zhang L. Genetic Mapping with Background Control for Quantitative Trait Locus (QTL) in 8-Parental Pure-Line Populations. J Hered 2020; 110:880-891. [PMID: 31419284 PMCID: PMC6916664 DOI: 10.1093/jhered/esz050] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Accepted: 08/12/2019] [Indexed: 12/17/2022] Open
Abstract
Multiparental advanced generation intercross (MAGIC) populations provide abundant genetic variation for use in plant genetics and breeding. In this study, we developed a method for quantitative trait locus (QTL) detection in pure-line populations derived from 8-way crosses, based on the principles of inclusive composite interval mapping (ICIM). We considered 8 parents carrying different alleles with different effects. To estimate the 8 genotypic effects, 1-locus genetic model was first built. Then, an orthogonal linear model of phenotypes against marker variables was established to explain genetic effects of the locus. The linear model was estimated by stepwise regression and finally used for phenotype adjustment and background genetic variation control in QTL mapping. Simulation studies using 3 genetic models demonstrated that the proposed method had higher detection power, lower false discovery rate (FDR), and unbiased estimation of QTL locations compared with other methods. Marginal bias was observed in the estimation of QTL effects. An 8-parental recombinant inbred line (RIL) population previously reported in cowpea and analyzed by interval mapping (IM) was reanalyzed by ICIM and genome-wide association mapping implemented in software FarmCPU. The results indicated that ICIM identified more QTLs explaining more phenotypic variation than did IM; ICIM provided more information on the detected QTL than did FarmCPU; and most QTLs identified by IM and FarmCPU were also detected by ICIM.
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Affiliation(s)
- Jinhui Shi
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Jiankang Wang
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Luyan Zhang
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
- Address correspondence to L. Zhang at the address above, or e-mail:
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16
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Kontio JAJ, Rinta-Aho MJ, Sillanpää MJ. Estimating Linear and Nonlinear Gene Coexpression Networks by Semiparametric Neighborhood Selection. Genetics 2020; 215:597-607. [PMID: 32414870 PMCID: PMC7337083 DOI: 10.1534/genetics.120.303186] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Accepted: 05/11/2020] [Indexed: 11/18/2022] Open
Abstract
Whereas nonlinear relationships between genes are acknowledged, there exist only a few methods for estimating nonlinear gene coexpression networks or gene regulatory networks (GCNs/GRNs) with common deficiencies. These methods often consider only pairwise associations between genes, and are, therefore, poorly capable of identifying higher-order regulatory patterns when multiple genes should be considered simultaneously. Another critical issue in current nonlinear GCN/GRN estimation approaches is that they consider linear and nonlinear dependencies at the same time in confounded form nonparametrically. This severely undermines the possibilities for nonlinear associations to be found, since the power of detecting nonlinear dependencies is lower compared to linear dependencies, and the sparsity-inducing procedures might favor linear relationships over nonlinear ones only due to small sample sizes. In this paper, we propose a method to estimate undirected nonlinear GCNs independently from the linear associations between genes based on a novel semiparametric neighborhood selection procedure capable of identifying complex nonlinear associations between genes. Simulation studies using the common DREAM3 and DREAM9 datasets show that the proposed method compares superiorly to the current nonlinear GCN/GRN estimation methods.
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Affiliation(s)
- Juho A J Kontio
- Research Unit of Mathematical Sciences, Biocenter Oulu, University of Oulu, 90014, Finland
| | - Marko J Rinta-Aho
- Research Unit of Mathematical Sciences, Biocenter Oulu, University of Oulu, 90014, Finland
| | - Mikko J Sillanpää
- Research Unit of Mathematical Sciences, Biocenter Oulu, University of Oulu, 90014, Finland
- Infotech Oulu, University of Oulu, 90014, Finland
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17
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Afsharyan NP, Sannemann W, Léon J, Ballvora A. Effect of epistasis and environment on flowering time in barley reveals a novel flowering-delaying QTL allele. JOURNAL OF EXPERIMENTAL BOTANY 2020; 71:893-906. [PMID: 31781747 PMCID: PMC6977191 DOI: 10.1093/jxb/erz477] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2019] [Accepted: 10/07/2019] [Indexed: 05/25/2023]
Abstract
Flowering time is a complex trait and has a key role in crop yield and adaptation to environmental stressors such as heat and drought. This study aimed to better understand the interconnected dynamics of epistasis and environment and look for novel regulators. We investigated 534 spring barley MAGIC DH lines for flowering time at various environments. Analysis of quantitative trait loci (QTLs), epistatic interactions, QTL × environment (Q×E) interactions, and epistasis × environment (E×E) interactions were performed with single SNP and haplotype approaches. In total, 18 QTLs and 2420 epistatic interactions were detected, including intervals harboring major genes such as Ppd-H1, Vrn-H1, Vrn-H3, and denso/sdw1. Epistatic interactions found in field and semi-controlled conditions were distinctive. Q×E and E×E interactions revealed that temperature influenced flowering time by triggering different interactions between known and newly detected regulators. A novel flowering-delaying QTL allele was identified on chromosome 1H (named 'HvHeading') and was shown to be engaged in epistatic and environment interactions. Results suggest that investigating epistasis, environment, and their interactions, rather than only single QTLs, is an effective approach for detecting novel regulators. We assume that barley can adapt flowering time to the environment via alternative routes within the pathway.
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Affiliation(s)
- Nazanin P Afsharyan
- Institute for Crop Science and Resource Conservation, Chair of Plant Breeding, University of Bonn, Bonn, Germany
| | - Wiebke Sannemann
- Chair of Plant Breeding, Martin Luther University Halle-Wittenberg, Halle, Germany
| | - Jens Léon
- Institute for Crop Science and Resource Conservation, Chair of Plant Breeding, University of Bonn, Bonn, Germany
| | - Agim Ballvora
- Institute for Crop Science and Resource Conservation, Chair of Plant Breeding, University of Bonn, Bonn, Germany
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18
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Christov NK. The role of epistasis and its interaction with environment in fine-tuning heading time in barley. JOURNAL OF EXPERIMENTAL BOTANY 2020; 71:743-746. [PMID: 31971242 PMCID: PMC6977021 DOI: 10.1093/jxb/erz503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Accepted: 11/07/2019] [Indexed: 06/10/2023]
Abstract
This article comments on: Afsharyan NP, Sannemann W, Léon J, Ballvora A. 2020. Effect of epistasis and environment on flowering time of barley reveals novel flowering-delaying QTL allele. Journal of Experimental Botany 71, 893–906.
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Affiliation(s)
- Nikolai K Christov
- Functional Genetics Department, AgroBioInstitute, Agricultural Academy, Sofia, Bulgaria
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19
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Kontio JAJ, Sillanpää MJ. Scalable Nonparametric Prescreening Method for Searching Higher-Order Genetic Interactions Underlying Quantitative Traits. Genetics 2019; 213:1209-1224. [PMID: 31585953 PMCID: PMC6893368 DOI: 10.1534/genetics.119.302658] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Accepted: 09/27/2019] [Indexed: 02/07/2023] Open
Abstract
Gaussian process (GP)-based automatic relevance determination (ARD) is known to be an efficient technique for identifying determinants of gene-by-gene interactions important to trait variation. However, the estimation of GP models is feasible only for low-dimensional datasets (∼200 variables), which severely limits application of the GP-based ARD method for high-throughput sequencing data. In this paper, we provide a nonparametric prescreening method that preserves virtually all the major benefits of the GP-based ARD method and extends its scalability to the typical high-dimensional datasets used in practice. In several simulated test scenarios, the proposed method compared favorably with existing nonparametric dimension reduction/prescreening methods suitable for higher-order interaction searches. As a real-data example, the proposed method was applied to a high-throughput dataset downloaded from the cancer genome atlas (TCGA) with measured expression levels of 16,976 genes (after preprocessing) from patients diagnosed with acute myeloid leukemia.
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Affiliation(s)
- Juho A J Kontio
- Research Unit of Mathematical Sciences, Biocenter Oulu, University of Oulu, 90014, Finland and
| | - Mikko J Sillanpää
- Research Unit of Mathematical Sciences, Biocenter Oulu, University of Oulu, 90014, Finland and
- Infotech Oulu, University of Oulu, 90014, Finland
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20
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He T, Hill CB, Angessa TT, Zhang XQ, Chen K, Moody D, Telfer P, Westcott S, Li C. Gene-set association and epistatic analyses reveal complex gene interaction networks affecting flowering time in a worldwide barley collection. JOURNAL OF EXPERIMENTAL BOTANY 2019; 70:5603-5616. [PMID: 31504706 PMCID: PMC6812734 DOI: 10.1093/jxb/erz332] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Accepted: 08/13/2019] [Indexed: 05/10/2023]
Abstract
Single-marker genome-wide association studies (GWAS) have successfully detected associations between single nucleotide polymorphisms (SNPs) and agronomic traits such as flowering time and grain yield in barley. However, the analysis of individual SNPs can only account for a small proportion of genetic variation, and can only provide limited knowledge on gene network interactions. Gene-based GWAS approaches provide enormous opportunity both to combine genetic information and to examine interactions among genetic variants. Here, we revisited a previously published phenotypic and genotypic data set of 895 barley varieties grown in two years at four different field locations in Australia. We employed statistical models to examine gene-phenotype associations, as well as two-way epistasis analyses to increase the capability to find novel genes that have significant roles in controlling flowering time in barley. Genetic associations were tested between flowering time and corresponding genotypes of 174 putative flowering time-related genes. Gene-phenotype association analysis detected 113 genes associated with flowering time in barley, demonstrating the unprecedented power of gene-based analysis. Subsequent two-way epistasis analysis revealed 19 pairs of gene×gene interactions involved in controlling flowering time. Our study demonstrates that gene-based association approaches can provide higher capacity for future crop improvement to increase crop performance and adaptation to different environments.
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Affiliation(s)
- Tianhua He
- Western Barley Genetics Alliance, Western Australian State Agricultural Biotechnology Centre, College of Science, Health, Engineering and Education, Murdoch University, Murdoch, WA, Australia
| | - Camilla Beate Hill
- Western Barley Genetics Alliance, Western Australian State Agricultural Biotechnology Centre, College of Science, Health, Engineering and Education, Murdoch University, Murdoch, WA, Australia
| | - Tefera Tolera Angessa
- Western Barley Genetics Alliance, Western Australian State Agricultural Biotechnology Centre, College of Science, Health, Engineering and Education, Murdoch University, Murdoch, WA, Australia
| | - Xiao-Qi Zhang
- Western Barley Genetics Alliance, Western Australian State Agricultural Biotechnology Centre, College of Science, Health, Engineering and Education, Murdoch University, Murdoch, WA, Australia
| | - Kefei Chen
- SAGI-WEST, Faculty of Science and Engineering, Curtin University, Bentley, WA, Australia
| | | | - Paul Telfer
- Australian Grain Technologies Pty Ltd (AGT), SA, Australia
| | - Sharon Westcott
- Agriculture and Food, Department of Primary Industries and Regional Development, South Perth, WA, Australia
| | - Chengdao Li
- Western Barley Genetics Alliance, Western Australian State Agricultural Biotechnology Centre, College of Science, Health, Engineering and Education, Murdoch University, Murdoch, WA, Australia
- Agriculture and Food, Department of Primary Industries and Regional Development, South Perth, WA, Australia
- Hubei Collaborative Innovation Centre for Grain Industry, Yangtze University, Hubei Jingzhou, China
- Correspondence:
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21
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Olatoye MO, Hu Z, Aikpokpodion PO. Epistasis Detection and Modeling for Genomic Selection in Cowpea ( Vigna unguiculata L. Walp.). Front Genet 2019; 10:677. [PMID: 31417604 PMCID: PMC6682672 DOI: 10.3389/fgene.2019.00677] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Accepted: 06/27/2019] [Indexed: 12/24/2022] Open
Abstract
Genetic architecture reflects the pattern of effects and interaction of genes underlying phenotypic variation. Most mapping and breeding approaches generally consider the additive part of variation but offer limited knowledge on the benefits of epistasis which explains in part the variation observed in traits. In this study, the cowpea multiparent advanced generation inter-cross (MAGIC) population was used to characterize the epistatic genetic architecture of flowering time, maturity, and seed size. In addition, consideration for epistatic genetic architecture in genomic-enabled breeding (GEB) was investigated using parametric, semi-parametric, and non-parametric genomic selection (GS) models. Our results showed that large and moderate effect-sized two-way epistatic interactions underlie the traits examined. Flowering time QTL colocalized with cowpea putative orthologs of Arabidopsis thaliana and Glycine max genes like PHYTOCLOCK1 (PCL1 [Vigun11g157600]) and PHYTOCHROME A (PHY A [Vigun01g205500]). Flowering time adaptation to long and short photoperiod was found to be controlled by distinct and common main and epistatic loci. Parametric and semi-parametric GS models outperformed non-parametric GS model, while using known quantitative trait nucleotide(s) (QTNs) as fixed effects improved prediction accuracy when traits were controlled by large effect loci. In general, our study demonstrated that prior understanding of the genetic architecture of a trait can help make informed decisions in GEB.
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Affiliation(s)
- Marcus O. Olatoye
- Department of Crop Sciences, University of Illinois, Urbana-Champaign, IL, United States
| | - Zhenbin Hu
- Department of Agronomy, Kansas State University, Manhattan, KS, United States
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22
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Chen AH, Ge W, Metcalf W, Jakobsson E, Mainzer LS, Lipka AE. An assessment of true and false positive detection rates of stepwise epistatic model selection as a function of sample size and number of markers. Heredity (Edinb) 2019; 122:660-671. [PMID: 30443009 PMCID: PMC6462028 DOI: 10.1038/s41437-018-0162-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Revised: 10/19/2018] [Accepted: 10/28/2018] [Indexed: 12/21/2022] Open
Abstract
Association studies have been successful at identifying genomic regions associated with important traits, but routinely employ models that only consider the additive contribution of an individual marker. Because quantitative trait variability typically arises from multiple additive and non-additive sources, utilization of statistical approaches that include main and two-way interaction marker effects of several loci in one model could lead to unprecedented characterization of these sources. Here we examine the ability of one such approach, called the Stepwise Procedure for constructing an Additive and Epistatic Multi-Locus model (SPAEML), to detect additive and epistatic signals simulated using maize and human marker data. Our results revealed that SPAEML was capable of detecting quantitative trait nucleotides (QTNs) at sample sizes as low as n = 300 and consistently specifying signals as additive and epistatic for larger sizes. Sample size and minor allele frequency had a major influence on SPAEML's ability to distinguish between additive and epistatic signals, while the number of markers tested did not. We conclude that SPAEML is a useful approach for providing further elucidation of the additive and epistatic sources contributing to trait variability when applied to a small subset of genome-wide markers located within specific genomic regions identified using a priori analyses.
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Affiliation(s)
- Angela H Chen
- Department of Statistics, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Weihao Ge
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
- Center for Biophysics and Computational Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - William Metcalf
- Department of Computer Sciences, Rose-Hulman Institute of Technology, Terre Haute, IN, 47803, USA
| | - Eric Jakobsson
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
- Center for Biophysics and Computational Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
- Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Liudmila Sergeevna Mainzer
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
| | - Alexander E Lipka
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
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23
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Liu HJ, Yan J. Crop genome-wide association study: a harvest of biological relevance. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2019; 97:8-18. [PMID: 30368955 DOI: 10.1111/tpj.14139] [Citation(s) in RCA: 112] [Impact Index Per Article: 22.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Revised: 10/13/2018] [Accepted: 10/22/2018] [Indexed: 05/20/2023]
Abstract
With the advent of rapid genotyping and next-generation sequencing technologies, genome-wide association study (GWAS) has become a routine strategy for decoding genotype-phenotype associations in many species. More than 1000 such studies over the last decade have revealed substantial genotype-phenotype associations in crops and provided unparalleled opportunities to probe functional genomics. Beyond the many 'hits' obtained, this review summarizes recent efforts to increase our understanding of the genetic architecture of complex traits by focusing on non-main effects including epistasis, pleiotropy, and phenotypic plasticity. We also discuss how these achievements and the remaining gaps in our knowledge will guide future studies. Synthetic association is highlighted as leading to false causality, which is prevalent but largely underestimated. Furthermore, validation evidence is appealing for future GWAS, especially in the context of emerging genome-editing technologies.
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Affiliation(s)
- Hai-Jun Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
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24
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You FM, Xiao J, Li P, Yao Z, Jia G, He L, Kumar S, Soto-Cerda B, Duguid SD, Booker HM, Rashid KY, Cloutier S. Genome-Wide Association Study and Selection Signatures Detect Genomic Regions Associated with Seed Yield and Oil Quality in Flax. Int J Mol Sci 2018; 19:ijms19082303. [PMID: 30082613 PMCID: PMC6121305 DOI: 10.3390/ijms19082303] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Revised: 08/02/2018] [Accepted: 08/03/2018] [Indexed: 12/31/2022] Open
Abstract
A genome-wide association study (GWAS) was performed on a set of 260 lines which belong to three different bi-parental flax mapping populations. These lines were sequenced to an averaged genome coverage of 19× using the Illumina Hi-Seq platform. Phenotypic data for 11 seed yield and oil quality traits were collected in eight year/location environments. A total of 17,288 single nucleotide polymorphisms were identified, which explained more than 80% of the phenotypic variation for days to maturity (DTM), iodine value (IOD), palmitic (PAL), stearic, linoleic (LIO) and linolenic (LIN) acid contents. Twenty-three unique genomic regions associated with 33 quantitative trait loci (QTL) for the studied traits were detected, thereby validating four genomic regions previously identified. The 33 QTL explained 48–73% of the phenotypic variation for oil content, IOD, PAL, LIO and LIN but only 8–14% for plant height, DTM and seed yield. A genome-wide selective sweep scan for selection signatures detected 114 genomic regions that accounted for 7.82% of the flax pseudomolecule and overlapped with the 11 GWAS-detected genomic regions associated with 18 QTL for 11 traits. The results demonstrate the utility of GWAS combined with selection signatures for dissection of the genetic structure of traits and for pinpointing genomic regions for breeding improvement.
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Affiliation(s)
- Frank M You
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada.
- Morden Research and Development Centre, Agriculture and Agri-Food Canada, Morden, MB R6M 1Y5, Canada.
| | - Jin Xiao
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada.
- Department of Agronomy, Nanjing Agricultural University, Nanjing 210095, China.
| | - Pingchuan Li
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada.
| | - Zhen Yao
- Morden Research and Development Centre, Agriculture and Agri-Food Canada, Morden, MB R6M 1Y5, Canada.
| | - Gaofeng Jia
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada.
- Crop Development Centre, University of Saskatchewan, Saskatoon, SK S7N 5A8, Canada.
| | - Liqiang He
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada.
| | - Santosh Kumar
- Brandon Research and Development Centre, Agriculture and Agri-Food Canada, Brandon, MB R7A 5Y3, Canada.
| | - Braulio Soto-Cerda
- Department of Plant Science, University of Manitoba, Winnipeg, MB R3T 2N2, Canada.
- Agriaquaculture Nutritional Genomic Center, CGNA, Temuco 4871158, Chile.
| | - Scott D Duguid
- Morden Research and Development Centre, Agriculture and Agri-Food Canada, Morden, MB R6M 1Y5, Canada.
| | - Helen M Booker
- Crop Development Centre, University of Saskatchewan, Saskatoon, SK S7N 5A8, Canada.
| | - Khalid Y Rashid
- Morden Research and Development Centre, Agriculture and Agri-Food Canada, Morden, MB R6M 1Y5, Canada.
| | - Sylvie Cloutier
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada.
- Department of Plant Science, University of Manitoba, Winnipeg, MB R3T 2N2, Canada.
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