Genome-to-phenome research in agriculture aims to improve crops through in silico predictions. Genome-wide association study (GWAS) is potent in identifying genomic loci that underlie important traits. As a statistical method, increasing the sample quantity, data quality, or diversity of the GWAS dataset positively impacts GWAS power. For more precise breeding, concrete candidate genes with exact functional variants must be discovered. Many post-GWAS methods have been developed to narrow down the associated genomic regions and, ideally, to predict candidate genes and causative mutations (CMs). Historical natural selection and breeding-related artificial selection both act to change the frequencies of different alleles of genes that control phenotypes. With higher diversity and more extensive GWAS datasets, there is an increased chance of multiple alleles with independent CMs in a single causal gene. This can be caused by the presence of samples from geographically isolated regions that arose during natural or artificial selection. This simple fact is a complicating factor in GWAS-driven discoveries. Currently, none of the existing association methods address this issue and need to identify multiple alleles and, more specifically, the actual CMs. Therefore, we developed a tool that computes a score for a combination of variant positions in a single candidate gene and, based on the highest score, identifies the best number and combination of CMs. The tool is publicly available as a Python package on GitHub, and we further created a web-based Multiple Alleles discovery (MADis) tool that supports soybean and is hosted in SoyKB (https://soykb.org/SoybeanMADisTool/). We tested and validated the algorithm and presented the utilization of MADis in a pod pigmentation L1 gene case study with multiple CMs from natural or artificial selection. Finally, we identified a candidate gene for the pod color L2 locus and predicted the existence of multiple alleles that potentially cause loss of pod pigmentation. In this work, we show how a genomic analysis can be employed to explore the natural and artificial selection of multiple alleles and, thus, improve and accelerate crop breeding in agriculture.

Christopher S Bond Life Sciences Center University of Missouri Columbia Columbia MO United States

Department of Biochemistry Faculty of Science Palacký University in Olomouc Olomouc Czechia

Department of Biomedical Informatics Biostatistics and Medical Epidemiology University of Missouri Columbia Columbia MO United States

Department of Electrical Engineering and Computer Science University of Missouri Columbia Columbia MO United States

MU Institute for Data Science and Informatics University of Missouri Columbia Columbia MO United States

United States Department of Agriculture Agricultural Research Service Plant Genetics Research Unit Columbia MO United States

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Bandillo N. B., Lorenz A. J., Graef G. L., Jarquin D., Hyten D. L., Nelson R. L., et al. (2017). Genome‐wide association mapping of qualitatively inherited traits in a Germplasm collection. Plant Genome 10 (2). 10.3835/plantgenome2016.06.0054 PubMed DOI

Batchelor D. W., Zeiss R. M., Pedigo P. L., Shibles M. R. (1997). Development of a model to predict soybean pod color distribution. Trans. ASAE Am. Soc. Agric. Eng. 40 (1), 221–227. 10.13031/2013.21234 DOI

Bernard R. L. (1967). Inheritance of pod color in soybeans. J. Hered. 58 (4), 165–and. 10.1093/oxfordjournals.jhered.a107575 DOI

Biová J., Dietz N., Chan Y.On, Joshi T., Bilyeu K., Škrabišová M. (2023). AccuCalc: a Python package for accuracy calculation in GWAS. Genes 14 (1), 123. 10.3390/genes14010123 PubMed DOI PMC

Chan Y.On, Dietz N., Zeng S., Wang J., Sherry Flint-GarciaNancy M. S.-V., Mária Š., et al. (2023). The Allele Catalog Tool: a web-based interactive tool for allele discovery and analysis. BMC Genomics 24 (1), 107–114. 10.1186/S12864-023-09161-3 PubMed DOI PMC

Chang F., Lv W., Lv P., Xiao Y., Yan W., Chen S., et al. (2021). Exploring genetic architecture for pod-related traits in soybean using image-based phenotyping. Mol. Breed. 41 (4), 28. 10.1007/s11032-021-01223-2 PubMed DOI PMC

Fang C., Ma Y., Wu S., Liu Z., Wang Z., Yang R., et al. (2017). Genome-wide association studies dissect the genetic networks underlying agronomical traits in soybean. Genome Biol. 18 (1), 161. 10.1186/s13059-017-1289-9 PubMed DOI PMC

Gillman J. D., Tetlow A., Lee J. D., Shannon J. G., Bilyeu K. (2011). Loss-of-function mutations affecting a specific Glycine max R2R3 MYB transcription factor result in brown hilum and brown seed coats. BMC Plant Biol. 11, 155. 10.1186/1471-2229-11-155 PubMed DOI PMC

Han Y., Cameron J. N., Wang L. Z., Beavis W. D., et al. (2017). The predicted cross value for genetic introgression of multiple alleles. Genetics 205 (4), 1409–1423. 10.1534/genetics.116.197095 PubMed DOI PMC

Hawkins C., Ginzburg D., Zhao K., Dwyer W., Xue Bo, Xu A., et al. (2021). Plant Metabolic Network 15: a resource of genome‐wide metabolism databases for 126 plants and algae. J. Integr. Plant Biol. 63 (11), 1888–1905. 10.1111/jipb.13163 PubMed DOI

He Q., Yang H., Xiang S., Dong T., Wang W., Zhao T., et al. (2015). Fine mapping of the genetic locus L1 conferring black pods using a chromosome segment substitution line population of soybean. Plant Breed. 134 (4), 437–445. 10.1111/pbr.12272 DOI

Joshi T., Fitzpatrick M. R., Chen S. Y., Liu Y., Zhang H. X., Endacott R. Z., et al. (2014). Soybean knowledge base (SoyKB): a web resource for integration of soybean translational genomics and molecular breeding. Nucleic Acids Res. 42 (D1), D1245–D1252. 10.1093/nar/gkt905 PubMed DOI PMC

Joshi T., Patil K., Fitzpatrick M. R., Franklin L. D., Yao Q., Cook J. R., et al. (2012). Soybean Knowledge Base (SoyKB): a web resource for soybean translational genomics. BMC Genomics 13 (S1), S15. 10.1186/1471-2164-13-s1-s15 PubMed DOI PMC

Joshi T., Wang J., Zhang H., Chen S., Zeng S., Xu B., et al. (2017). The evolution of soybean knowledge base (SoyKB). Methods Mol. Biol. 1533, 149–159. 10.1007/978-1-4939-6658-5_7 PubMed DOI

Lemay M.-A., De Ronne M., Bélanger R., Belzile F. (2023). k‐mer‐based GWAS enhances the discovery of causal variants and candidate genes in soybean. Plant Genome, e20374. 10.1002/tpg2.20374 PubMed DOI

Li D., Wang Qi, Tian Yu, Lyv X., Zhang H., Sun Y., et al. (2023). Transcriptome brings variations of gene expression, alternative splicing, and structural variations into gene-scale trait dissection in soybean. Available at: https://www.biorxiv.org/content/10.1101/2023.07.03.545230v1. DOI

Li M.-W., Muñoz N. B., Wong C.-F., Wong F.-L., Wong K.-S., Wong J.W.-H., et al. (2016). QTLs regulating the contents of antioxidants, phenolics, and flavonoids in soybean seeds share a common genomic region. Front. Plant Sci. 7, 854. JUNE2016. 10.3389/fpls.2016.00854 PubMed DOI PMC

Liu B., Watanabe S., Uchiyama T., Kong F., Kanazawa A., Xia Z., et al. (2010). The soybean stem growth habit gene Dt1 is an ortholog of Arabidopsis TERMINAL FLOWER1. Plant physiol. 153 (1), 198–210. 10.1104/pp.109.150607 PubMed DOI PMC

Liu Y., Du H., Liu Y., Du H., Li P., Shen Y., et al. (2020). Pan-genome of wild and cultivated soybeans. Cell 182, 162–176.e13. 10.1016/j.cell.2020.05.023 PubMed DOI

Lu S., Dong L., Fang C., Liu S., Kong L., Cheng Q., et al. (2020). Stepwise selection on homeologous PRR genes controlling flowering and maturity during soybean domestication. Nat. Genet. 52 (4), 428–436. 10.1038/s41588-020-0604-7 PubMed DOI

Lu S., Zhao X., Hu Y., Liu S., Nan H., Li X., et al. (2017). Natural variation at the soybean J locus improves adaptation to the tropics and enhances yield. Nat. Genet. 49 (5), 773–779. 10.1038/ng.3819 PubMed DOI

Lyu X., Li Y.-hui, Li Y., Li D., Chao H., Huilong H., et al. (2023). The domestication-associated L1 gene encodes an eucomic acid synthase pleiotropically modulating pod pigmentation and shattering in soybean. Mol. Plant, 1–14. 10.1016/j.molp.2023.06.003 PubMed DOI

McCaw Z. R., O'Dushlaine C., Somineni H., Bereket M., Klein C., Karaletsos T., et al. (2023). An allelic-series rare-variant association test for candidate-gene discovery. Am. J. Hum. Genet. 110 (8), 1330–1342. 10.1016/j.ajhg.2023.07.001 PubMed DOI PMC

Meyer R. S., Purugganan M. D. (2013). Evolution of crop species: genetics of domestication and diversification. Nat. Rev. Genet. 14 (12), 840–852. 10.1038/nrg3605 PubMed DOI

Mahmood A., Bilyeu K. D., Škrabišová M., Biová J., De Meyer E. J., Meinhardt C. G., et al. (2023). Cataloging SCN resistance loci in North American public soybean breeding programs. Frontiers in Plant Science 14. 10.3389/fpls.2023.1270546 PubMed DOI PMC

Nagai I. (1921). A genetic-physiological study on the formation of anthocyanin and brown pigments in plants. J. Coll. 4gr., Imp. Univ.Tokyo 8 (1).

Owen F. V. (1927). Hereditary and environmental factors that produce mottling in soybeans. J. Agric. Res. 34 (559).

Palmer R. G., Todd W. P., Glenn R. B., Kilen T. C. (2016). Qualitative genetics, 137–233.

Patil G. B., Lakhssassi N., Wan J., Song Li, Zhou Z., Klepadlo M., et al. (2019). Whole‐genome re‐sequencing reveals the impact of the interaction of copy number variants of the rhg1 and Rhg4 genes on broad‐based resistance to soybean cyst nematode. Plant Biotechnol. J. 17 (8), 1595–1611. 10.1111/pbi.13086 PubMed DOI PMC

Pickersgill B. (2018). Parallel vs. Convergent evolution in domestication and diversification of crops in the americas. Front. Ecol. Evol. 6. 10.3389/fevo.2018.00056 DOI

Punta M., Coggill P. C., Eberhardt R. Y., Mistry J., Tate J., Boursnell C., et al. (2012). The Pfam protein families database. Nucleic Acids Res. 40, D290–D301. D1. 10.1093/nar/gkr1065 PubMed DOI PMC

Sallam A. H., Conley E., Prakapenka D., Da Y., Anderson J. A. (2020). Improving prediction accuracy using multi-allelic haplotype prediction and training population optimization in wheat. G3-Genes Genomes Genet. 10 (7), 2265–2273. 10.1534/g3.120.401165 PubMed DOI PMC

Sachidanandam R., Weissman D., Schmidt S. C., Kakol J. M., Stein L. D., Marth G., et al. (2015). A map of human genome sequence variation containing 1.42 million single nucleotide polymorphisms. Nature 409 (6822), 928–933. 10.1038/35057149 PubMed DOI

Skrabišová M., Dietz N., Zeng S., Chan Y.On, Wang J., Liu Y., et al. (2022). A novel Synthetic phenotype association study approach reveals the landscape of association for genomic variants and phenotypes. J. Adv. Res. 42, 117–133. 10.1016/j.jare.2022.04.004 PubMed DOI PMC

Song Q. J., Marek L. F., Shoemaker R. C., Lark K. G., Concibido V. C., Delannay X., et al. (2004). A new integrated genetic linkage map of the soybean. Theor. Appl. Genet. 109 (1), 122–128. 10.1007/s00122-004-1602-3 PubMed DOI

Tian Z., Wang X., Lee R., Li Y., Specht J. E., Nelson R. L., et al. (2010). Artificial selection for determinate growth habit in soybean. Proc. Natl. Acad. Sci. U. S. A. 107 (19), 8563–8568. 10.1073/pnas.1000088107 PubMed DOI PMC

Tishkoff S. A., Reed F. A., Ranciaro A., Voight B. F., Babbitt C. C., Jesse S. S., et al. (2007). Convergent adaptation of human lactase persistence in Africa and Europe. Nat. Genet. 39 (1), 31–40. 10.1038/ng1946 PubMed DOI PMC

Torkamaneh D., Laroche J., Tardivel A., O'Donoughue L., Cober E., Rajcan I., et al. (2018). Comprehensive description of genomewide nucleotide and structural variation in short-season soya bean. Plant Biotechnol. J. 16 (3), 749–759. 10.1111/pbi.12825 PubMed DOI PMC

Tsubokura Y., Matsumura H., Xu M., Liu B., Nakashima H., Anai T., et al. (2013). Genetic variation in soybean at the maturity locus E4 is involved in adaptation to long days at high latitudes. Agronomy 3 (1), 117–134. 10.3390/agronomy3010117 DOI

Watanabe S., Xia Z., Hideshima R., Tsubokura Y., Sato S., Yamanaka N., et al. (2011). A map-based cloning strategy employing a residual heterozygous line reveals that the GIGANTEA gene is involved in soybean maturity and flowering. Genetics 188 (2), 395–407. 10.1534/GENETICS.110.125062 PubMed DOI PMC

Xia Z., Watanabe S., Yamada T., Tsubokura Y., Nakashima H., Zhai H., et al. (2012). Positional cloning and characterization reveal the molecular basis for soybean maturity locus E1 that regulates photoperiodic flowering. Proc. Natl. Acad. Sci. U. S. A. 109 (32), E2155–E2164. 10.1073/pnas.1117982109 PubMed DOI PMC

Yan F., Githiri S. M., Liu Y., Yu S., Wang Q., Takahashi R. (2020). Loss-of-Function mutation of soybean R2R3 MYB transcription factor dilutes tawny pubescence color. Front. Plant Sci. 10, 1809–1812. January. 10.3389/fpls.2019.01809 PubMed DOI PMC

Zhao G., Wang J., Yingpeng H., Weili T., Genlou S., Wenbin Li. (2008). Identification of QTL underlying the resistance of soybean to pod borer, Leguminivora glycinivorella (Mats.) obraztsov, and correlations with plant, pod and seed traits. Euphytica 164 (1), 275–282. 10.1007/S10681-008-9728-Z DOI

Zhou Z., Jiang Yu, Wang Z., Gou Z., Lyu J., Li W., et al. (2015). Resequencing 302 wild and cultivated accessions identifies genes related to domestication and improvement in soybean. Nat. Biotechnol. 33 (4), 408–414. 10.1038/nbt.3096 PubMed DOI