Association mapping in bambara groundnut [ <i>Vigna subterranea</i> (L.) Verdc.] reveals loci associated with agro-morphological traits

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Abstract

Background

Genome-wide association studies (GWAS) are important for the acceleration of crop improvement through knowledge of marker-trait association (MTA). This report used DArT SNP markers to successfully perform GWAS on agro-morphological traits using 270 bambara groundnut [ Vigna subterranea (L.) Verdc.] landraces sourced from diverse origins. The study aimed to identify marker traits association for nine agronomic traits using GWAS and their candidate genes. The experiment was conducted at two different locations laid out in alpha lattice design. The cowpea [ Vigna unguiculata (L.) Walp.] reference genome (i.e. legume genome most closely related to bambara groundnut) assisted in the identification of candidate genes.

Results

The analyses showed that linkage disequilibrium was found to decay rapidly with an average genetic distance of 148 kb. The broadsense heritability was relatively high and ranged from 48.39% (terminal leaf length) to 79.39% (number of pods per plant). The GWAS identified a total of 27 significant marker-trait associations (MTAs) for the nine studied traits explaining 5.27% to 24.86% of phenotypic variations. Among studied traits, the highest number of MTAs was obtained from seed coat colour (6) followed by days to flowering (5), while the least is days to maturity (1), explaining 5.76% to 11.03%, 14.5% to 19.49%, and 11.66% phenotypic variations, respectively. Also, a total of 17 candidate genes were identified, varying in number for different traits; seed coat colour (6), days to flowering (3), terminal leaf length (2), terminal leaf width (2), number of seed per pod (2), pod width (1) and days to maturity (1).

Conclusion

These results revealed the prospect of GWAS in identification of SNP variations associated with agronomic traits in bambara groundnut. Also, its present new opportunity to explore GWAS and marker assisted strategies in breeding of bambara groundnut for acceleration of the crop improvement.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-023-09684-9.

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Most cited references 74

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TASSEL: software for association mapping of complex traits in diverse samples.

P J Bradbury, Z. Zhang, D. E. Kroon … (2007)

Association analyses that exploit the natural diversity of a genome to map at very high resolutions are becoming increasingly important. In most studies, however, researchers must contend with the confounding effects of both population and family structure. TASSEL (Trait Analysis by aSSociation, Evolution and Linkage) implements general linear model and mixed linear model approaches for controlling population and family structure. For result interpretation, the program allows for linkage disequilibrium statistics to be calculated and visualized graphically. Database browsing and data importation is facilitated by integrated middleware. Other features include analyzing insertions/deletions, calculating diversity statistics, integration of phenotypic and genotypic data, imputing missing data and calculating principal components.

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Resequencing of 31 wild and cultivated soybean genomes identifies patterns of genetic diversity and selection.

Hon-Ming Lam, Xun Xu, Xin Liu … (2010)

We report a large-scale analysis of the patterns of genome-wide genetic variation in soybeans. We re-sequenced a total of 17 wild and 14 cultivated soybean genomes to an average of approximately ×5 depth and >90% coverage using the Illumina Genome Analyzer II platform. We compared the patterns of genetic variation between wild and cultivated soybeans and identified higher allelic diversity in wild soybeans. We identified a high level of linkage disequilibrium in the soybean genome, suggesting that marker-assisted breeding of soybean will be less challenging than map-based cloning. We report linkage disequilibrium block location and distribution, and we identified a set of 205,614 tag SNPs that may be useful for QTL mapping and association studies. The data here provide a valuable resource for the analysis of wild soybeans and to facilitate future breeding and quantitative trait analysis.

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Iterative Usage of Fixed and Random Effect Models for Powerful and Efficient Genome-Wide Association Studies

Xiaolei Liu, Meng-Lin Huang, Bin Fan … (2016)

False positives in a Genome-Wide Association Study (GWAS) can be effectively controlled by a fixed effect and random effect Mixed Linear Model (MLM) that incorporates population structure and kinship among individuals to adjust association tests on markers; however, the adjustment also compromises true positives. The modified MLM method, Multiple Loci Linear Mixed Model (MLMM), incorporates multiple markers simultaneously as covariates in a stepwise MLM to partially remove the confounding between testing markers and kinship. To completely eliminate the confounding, we divided MLMM into two parts: Fixed Effect Model (FEM) and a Random Effect Model (REM) and use them iteratively. FEM contains testing markers, one at a time, and multiple associated markers as covariates to control false positives. To avoid model over-fitting problem in FEM, the associated markers are estimated in REM by using them to define kinship. The P values of testing markers and the associated markers are unified at each iteration. We named the new method as Fixed and random model Circulating Probability Unification (FarmCPU). Both real and simulated data analyses demonstrated that FarmCPU improves statistical power compared to current methods. Additional benefits include an efficient computing time that is linear to both number of individuals and number of markers. Now, a dataset with half million individuals and half million markers can be analyzed within three days.

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Author and article information

Contributors

Charles U. Uba: charlesuba192@gmail.com

Journal

Journal ID (nlm-ta): BMC Genomics

Journal ID (iso-abbrev): BMC Genomics

Title: BMC Genomics

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2164

Publication date (Electronic): 6 October 2023

Publication date PMC-release: 6 October 2023

Publication date Collection: 2023

Volume: 24

Electronic Location Identifier: 593

Affiliations

[1 ]Department of Horticulture and Plant Science, Jimma University, ( https://ror.org/05eer8g02) Jimma, Ethiopia

[2 ]GRID grid.412141.3, ISNI 0000 0001 2033 5930, Ebonyi State University Abakalilki, ; Abakalilki, Nigeria

[3 ]International Institute of Tropical Agriculture, ( https://ror.org/00va88c89) Ibadan, Nigeria

Article

Publisher ID: 9684

DOI: 10.1186/s12864-023-09684-9

PMC ID: 10557193

PubMed ID: 37803263

SO-VID: af60316e-3b0e-4880-b504-3d6492d2743a

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 12 March 2023

Date accepted : 19 September 2023

Custom metadata

ScienceOpen disciplines: Genetics

Keywords: bambara groundnut,candidate genes,dart snp,linkage disequilibrium decay,loci,gwas,marker trait association

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ScienceOpen disciplines: Genetics

Keywords: bambara groundnut, candidate genes, dart snp, linkage disequilibrium decay, loci, gwas, marker trait association

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Association mapping in bambara groundnut [ Vigna subterranea (L.) Verdc.] reveals loci associated with agro-morphological traits

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Abstract

Background

Results

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Supplementary Information

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Genome Integrity

Most cited references 74

TASSEL: software for association mapping of complex traits in diverse samples.

Resequencing of 31 wild and cultivated soybean genomes identifies patterns of genetic diversity and selection.

Iterative Usage of Fixed and Random Effect Models for Powerful and Efficient Genome-Wide Association Studies

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