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Cosenza F, Shrestha A, Van Inghelandt D, Casale FA, Wu PY, Weisweiler M, Li J, Wespel F, Stich B. Genetic mapping reveals new loci and alleles for flowering time and plant height using the double round-robin population of barley. J Exp Bot 2024; 75:2385-2402. [PMID: 38330219 PMCID: PMC11016846 DOI: 10.1093/jxb/erae010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 02/07/2024] [Indexed: 02/10/2024]
Abstract
Flowering time and plant height are two critical determinants of yield potential in barley (Hordeum vulgare). Despite their role in plant physiological regulation, a complete overview of the genetic complexity of flowering time and plant height regulation in barley is still lacking. Using a double round-robin population originated from the crossings of 23 diverse parental inbred lines, we aimed to determine the variance components in the regulation of flowering time and plant height in barley as well as to identify new genetic variants by single and multi-population QTL analyses and allele mining. Despite similar genotypic variance, we observed higher environmental variance components for plant height than flowering time. Furthermore, we detected new QTLs for flowering time and plant height. Finally, we identified a new functional allelic variant of the main regulatory gene Ppd-H1. Our results show that the genetic architecture of flowering time and plant height might be more complex than reported earlier and that a number of undetected, small effect, or low-frequency genetic variants underlie the control of these two traits.
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Affiliation(s)
- Francesco Cosenza
- Institute for Quantitative Genetics and Genomics of Plants, Heinrich Heine University, 40225 Düsseldorf, Germany
| | - Asis Shrestha
- Institute for Quantitative Genetics and Genomics of Plants, Heinrich Heine University, 40225 Düsseldorf, Germany
| | - Delphine Van Inghelandt
- Institute for Quantitative Genetics and Genomics of Plants, Heinrich Heine University, 40225 Düsseldorf, Germany
| | - Federico A Casale
- Institute for Quantitative Genetics and Genomics of Plants, Heinrich Heine University, 40225 Düsseldorf, Germany
| | - Po-Ya Wu
- Institute for Quantitative Genetics and Genomics of Plants, Heinrich Heine University, 40225 Düsseldorf, Germany
| | - Marius Weisweiler
- Institute for Quantitative Genetics and Genomics of Plants, Heinrich Heine University, 40225 Düsseldorf, Germany
| | - Jinquan Li
- Max Planck Institute for Plant Breeding Research, 50829 Köln, Germany
| | - Franziska Wespel
- Saatzucht Josef Breun GmbH Co. KG, Amselweg 1, 91074 Herzogenaurach, Germany
| | - Benjamin Stich
- Institute for Quantitative Genetics and Genomics of Plants, Heinrich Heine University, 40225 Düsseldorf, Germany
- Max Planck Institute for Plant Breeding Research, 50829 Köln, Germany
- Cluster of Excellence on Plant Sciences (CEPLAS), Heinrich Heine University, 40225 Düsseldorf, Germany
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Weisweiler M, Arlt C, Wu PY, Van Inghelandt D, Hartwig T, Stich B. Structural variants in the barley gene pool: precision and sensitivity to detect them using short-read sequencing and their association with gene expression and phenotypic variation. Theor Appl Genet 2022; 135:3511-3529. [PMID: 36029318 PMCID: PMC9519679 DOI: 10.1007/s00122-022-04197-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 08/03/2022] [Indexed: 06/15/2023]
Abstract
Structural variants (SV) of 23 barley inbreds, detected by the best combination of SV callers based on short-read sequencing, were associated with genome-wide and gene-specific gene expression and, thus, were evaluated to predict agronomic traits. In human genetics, several studies have shown that phenotypic variation is more likely to be caused by structural variants (SV) than by single nucleotide variants. However, accurate while cost-efficient discovery of SV in complex genomes remains challenging. The objectives of our study were to (i) facilitate SV discovery studies by benchmarking SV callers and their combinations with respect to their sensitivity and precision to detect SV in the barley genome, (ii) characterize the occurrence and distribution of SV clusters in the genomes of 23 barley inbreds that are the parents of a unique resource for mapping quantitative traits, the double round robin population, (iii) quantify the association of SV clusters with transcript abundance, and (iv) evaluate the use of SV clusters for the prediction of phenotypic traits. In our computer simulations based on a sequencing coverage of 25x, a sensitivity > 70% and precision > 95% was observed for all combinations of SV types and SV length categories if the best combination of SV callers was used. We observed a significant (P < 0.05) association of gene-associated SV clusters with global gene-specific gene expression. Furthermore, about 9% of all SV clusters that were within 5 kb of a gene were significantly (P < 0.05) associated with the gene expression of the corresponding gene. The prediction ability of SV clusters was higher compared to that of single-nucleotide polymorphisms from an array across the seven studied phenotypic traits. These findings suggest the usefulness of exploiting SV information when fine mapping and cloning the causal genes underlying quantitative traits as well as the high potential of using SV clusters for the prediction of phenotypes in diverse germplasm sets.
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Affiliation(s)
- Marius Weisweiler
- Institute for Quantitative Genetics and Genomics of Plants, Universitätsstraße 1, 40225, Düsseldorf, Germany
| | - Christopher Arlt
- Institute for Quantitative Genetics and Genomics of Plants, Universitätsstraße 1, 40225, Düsseldorf, Germany
| | - Po-Ya Wu
- Institute for Quantitative Genetics and Genomics of Plants, Universitätsstraße 1, 40225, Düsseldorf, Germany
| | - Delphine Van Inghelandt
- Institute for Quantitative Genetics and Genomics of Plants, Universitätsstraße 1, 40225, Düsseldorf, Germany
| | - Thomas Hartwig
- Institute for Molecular Physiology, Universitätsstraße 1, 40225, Düsseldorf, Germany
| | - Benjamin Stich
- Institute for Quantitative Genetics and Genomics of Plants, Universitätsstraße 1, 40225, Düsseldorf, Germany.
- Cluster of Excellence on Plant Sciences, From Complex Traits towards Synthetic Modules, Universitätsstraße 1, 40225, Düsseldorf, Germany.
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Casale F, Van Inghelandt D, Weisweiler M, Li J, Stich B. Genomic prediction of the recombination rate variation in barley - A route to highly recombinogenic genotypes. Plant Biotechnol J 2022; 20:676-690. [PMID: 34783155 PMCID: PMC8989500 DOI: 10.1111/pbi.13746] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 10/06/2021] [Accepted: 11/07/2021] [Indexed: 06/13/2023]
Abstract
Meiotic recombination is not only fundamental to the adaptation of sexually reproducing eukaryotes in nature but increased recombination rates facilitate the combination of favourable alleles into a single haplotype in breeding programmes. The main objectives of this study were to (i) assess the extent and distribution of the recombination rate variation in cultivated barley (Hordeum vulgare L.), (ii) quantify the importance of the general and specific recombination effects, and (iii) evaluate a genomic selection approach's ability to predict the recombination rate variation. Genetic maps were created for the 45 segregating populations that were derived from crosses among 23 spring barley inbreds with origins across the world. The genome-wide recombination rate among populations ranged from 0.31 to 0.73 cM/Mbp. The crossing design used in this study allowed to separate the general recombination effects (GRE) of individual parental inbreds from the specific recombination effects (SRE) caused by the combinations of parental inbreds. The variance of the genome-wide GRE was found to be about eight times the variance of the SRE. This finding indicated that parental inbreds differ in the efficiency of their recombination machinery. The ability to predict the chromosome or genome-wide recombination rate of an inbred ranged from 0.80 to 0.85. These results suggest that a reliable screening of large genetic materials for their potential to cause a high extent of genetic recombination in their progeny is possible, allowing to systematically manipulate the recombination rate using natural variation.
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Affiliation(s)
- Federico Casale
- Institute of Quantitative Genetics and Genomics of PlantsHeinrich Heine UniversityDüsseldorfGermany
| | - Delphine Van Inghelandt
- Institute of Quantitative Genetics and Genomics of PlantsHeinrich Heine UniversityDüsseldorfGermany
| | - Marius Weisweiler
- Institute of Quantitative Genetics and Genomics of PlantsHeinrich Heine UniversityDüsseldorfGermany
| | - Jinquan Li
- Max Planck Institute for Plant Breeding ResearchKölnGermany
- Strube D&S GmbHSöllingenGermany
| | - Benjamin Stich
- Institute of Quantitative Genetics and Genomics of PlantsHeinrich Heine UniversityDüsseldorfGermany
- Max Planck Institute for Plant Breeding ResearchKölnGermany
- Cluster of Excellence on Plant SciencesFrom Complex Traits Towards Synthetic ModulesDüsseldorfGermany
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Wu PY, Stich B, Weisweiler M, Shrestha A, Erban A, Westhoff P, Inghelandt DV. Improvement of prediction ability by integrating multi-omic datasets in barley. BMC Genomics 2022; 23:200. [PMID: 35279073 PMCID: PMC8917753 DOI: 10.1186/s12864-022-08337-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 01/20/2022] [Indexed: 11/10/2022] Open
Abstract
Background Genomic prediction (GP) based on single nucleotide polymorphisms (SNP) has become a broadly used tool to increase the gain of selection in plant breeding. However, using predictors that are biologically closer to the phenotypes such as transcriptome and metabolome may increase the prediction ability in GP. The objectives of this study were to (i) assess the prediction ability for three yield-related phenotypic traits using different omic datasets as single predictors compared to a SNP array, where these omic datasets included different types of sequence variants (full-SV, deleterious-dSV, and tolerant-tSV), different types of transcriptome (expression presence/absence variation-ePAV, gene expression-GE, and transcript expression-TE) sampled from two tissues, leaf and seedling, and metabolites (M); (ii) investigate the improvement in prediction ability when combining multiple omic datasets information to predict phenotypic variation in barley breeding programs; (iii) explore the predictive performance when using SV, GE, and ePAV from simulated 3’end mRNA sequencing of different lengths as predictors. Results The prediction ability from genomic best linear unbiased prediction (GBLUP) for the three traits using dSV information was higher than when using tSV, all SV information, or the SNP array. Any predictors from the transcriptome (GE, TE, as well as ePAV) and metabolome provided higher prediction abilities compared to the SNP array and SV on average across the three traits. In addition, some (di)-similarity existed between different omic datasets, and therefore provided complementary biological perspectives to phenotypic variation. Optimal combining the information of dSV, TE, ePAV, as well as metabolites into GP models could improve the prediction ability over that of the single predictors alone. Conclusions The use of integrated omic datasets in GP model is highly recommended. Furthermore, we evaluated a cost-effective approach generating 3’end mRNA sequencing with transcriptome data extracted from seedling without losing prediction ability in comparison to the full-length mRNA sequencing, paving the path for the use of such prediction methods in commercial breeding programs. Supplementary Information The online version contains supplementary material available at (10.1186/s12864-022-08337-7).
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Stich B, Van Inghelandt D. Prospects and Potential Uses of Genomic Prediction of Key Performance Traits in Tetraploid Potato. Front Plant Sci 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] [What about the content of this article? (0)] [Affiliation(s)] [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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Van Inghelandt D, Melchinger AE, Martinant JP, Stich B. Genome-wide association mapping of flowering time and northern corn leaf blight (Setosphaeria turcica) resistance in a vast commercial maize germplasm set. BMC Plant Biol 2012; 12:56. [PMID: 22545925 PMCID: PMC3511189 DOI: 10.1186/1471-2229-12-56] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2011] [Accepted: 03/30/2012] [Indexed: 05/18/2023]
Abstract
BACKGROUND Setosphaeria turcica is a fungal pathogen that causes northern corn leaf blight (NCLB) which is a serious foliar disease in maize. In order to unravel the genetic architecture of the resistance against this disease, a vast association mapping panel comprising 1487 European maize inbred lines was used to (i) identify chromosomal regions affecting flowering time (FT) and northern corn leaf blight (NCLB) resistance, (ii) examine the epistatic interactions of the identified chromosomal regions with the genetic background on an individual molecular marker basis, and (iii) dissect the correlation between NCLB resistance and FT. RESULTS The single marker analyses performed for 8 244 single nucleotide polymorphism (SNP) markers revealed seven, four, and four SNP markers significantly (α=0.05, amplicon wise Bonferroni correction) associated with FT, NCLB, and NCLB resistance corrected for FT, respectively. These markers explained individually between 0.36 and 14.29% of the genetic variance of the corresponding trait. CONCLUSIONS The very well interpretable pattern of SNP associations observed for FT suggested that data from applied plant breeding programs can be used to dissect polygenic traits. This in turn indicates that the associations identified for NCLB resistance might be successfully used in marker-assisted selection programs. Furthermore, the associated genes are also of interest for further research concerning the mechanism of resistance to NCLB and plant diseases in general, because some of the associated genes have not been mentioned in this context so far.
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Affiliation(s)
- Delphine Van Inghelandt
- Institute of Plant Breeding, Seed Science, and Population Genetics, University of Hohenheim, Germany
- Current address: Limagrain GmbH, Breeding Station, Schönburg 6, Germany
| | - Albrecht E Melchinger
- Institute of Plant Breeding, Seed Science, and Population Genetics, University of Hohenheim, Germany
| | | | - Benjamin Stich
- Institute of Plant Breeding, Seed Science, and Population Genetics, University of Hohenheim, Germany
- Max Planck Institute for Plant Breeding Research, Carl-von-Linne-Weg 10, Germany
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Van Inghelandt D, Reif JC, Dhillon BS, Flament P, Melchinger AE. Extent and genome-wide distribution of linkage disequilibrium in commercial maize germplasm. Theor Appl Genet 2011; 123:11-20. [PMID: 21404061 DOI: 10.1007/s00122-011-1562-3] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2010] [Accepted: 02/15/2011] [Indexed: 05/02/2023]
Abstract
Association mapping is based on linkage disequilibrium (LD) resulting from historical recombinations and helps understanding the genetic basis of complex traits. Many factors affect LD and, therefore, it must be determined empirically in the germplasm under investigation to examine the prospects of successful genome-wide association mapping. The objectives of our study were to (1) examine the extent of LD with simple sequence repeat (SSR) and single nucleotide polymorphism (SNP) markers in 1,537 commercial maize inbred lines belonging to four heterotic pools, (2) compare the LD patterns determined by these two marker types, (3) evaluate the number of SNP markers needed to perform genome-wide association analyses, and (4) investigate temporal trends of LD. Mean values of the squared correlation coefficient ([Formula: see text]) were almost identical for unlinked, linked, and adjacent SSR marker pairs. In contrast, [Formula: see text] values were lowest for the unlinked SNP loci and highest for the SNPs within amplicons. LD decay varied across the different heterotic pools and the individual chromosomes. The SSR markers employed in the present study are not adequate for association analysis, because of insufficient marker density for the germplasm evaluated. Based on the decay of LD in the various heterotic pools, we would need between 4,000 and 65,000 SNP markers to detect with a reasonable power associations with rather large quantitative trait loci (QTL). A much higher marker density is required to identify QTL with smaller effects. However, not only the total number of markers but also their distribution among and along the chromosomes are primordial for undertaking powerful association analyses.
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Affiliation(s)
- Delphine Van Inghelandt
- Institute of Plant Breeding, Seed Science, and Population Genetics, University of Hohenheim, 70593, Stuttgart, Germany.
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Van Inghelandt D, Melchinger AE, Lebreton C, Stich B. Population structure and genetic diversity in a commercial maize breeding program assessed with SSR and SNP markers. Theor Appl Genet 2010; 120:1289-99. [PMID: 20063144 PMCID: PMC2854351 DOI: 10.1007/s00122-009-1256-2] [Citation(s) in RCA: 112] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2009] [Accepted: 12/21/2009] [Indexed: 05/18/2023]
Abstract
Information about the genetic diversity and population structure in elite breeding material is of fundamental importance for the improvement of crops. The objectives of our study were to (a) examine the population structure and the genetic diversity in elite maize germplasm based on simple sequence repeat (SSR) markers, (b) compare these results with those obtained from single nucleotide polymorphism (SNP) markers, and (c) compare the coancestry coefficient calculated from pedigree records with genetic distance estimates calculated from SSR and SNP markers. Our study was based on 1,537 elite maize inbred lines genotyped with 359 SSR and 8,244 SNP markers. The average number of alleles per locus, of group specific alleles, and the gene diversity (D) were higher for SSRs than for SNPs. Modified Roger's distance (MRD) estimates and membership probabilities of the STRUCTURE matrices were higher for SSR than for SNP markers but the germplasm organization in four heterotic pools was consistent with STRUCTURE results based on SSRs and SNPs. MRD estimates calculated for the two marker systems were highly correlated (0.87). Our results suggested that the same conclusions regarding the structure and the diversity of heterotic pools could be drawn from both markers types. Furthermore, although our results suggested that the ratio of the number of SSRs and SNPs required to obtain MRD or D estimates with similar precision is not constant across the various precision levels, we propose that between 7 and 11 times more SNPs than SSRs should be used for analyzing population structure and genetic diversity.
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Affiliation(s)
- Delphine Van Inghelandt
- Institute of Plant Breeding, Seed Science, and Population Genetics, University of Hohenheim, 70593 Stuttgart, Germany
- Limagrain Verneuil Holding, Ferme de l’Étang, BP3, 77390 Verneuil l’Étang, France
| | - Albrecht E. Melchinger
- Institute of Plant Breeding, Seed Science, and Population Genetics, University of Hohenheim, 70593 Stuttgart, Germany
| | - Claude Lebreton
- Limagrain Verneuil Holding, Ferme de l’Étang, BP3, 77390 Verneuil l’Étang, France
| | - Benjamin Stich
- Max Planck Institute for Plant Breeding Research, Carl-von-Linné-Weg 10, 50829 Cologne, Germany
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