Natural and artificial selection of multiple alleles revealed through genomic analyses

Authors: BIOVA, JANA - Palacky University; KANOVSKA, IVANA - Palacky University; CHAN, YEN ON - University Of Missouri; IMMADI, MANISH - University Of Missouri; JOSHI, TRUPTI - University Of Missouri; Bilyeu, Kristin; SKRABISOVA, MARIA - Palacky University

Submitted to: Frontiers in Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/17/2023
Publication Date: 1/8/2024
Citation: Biova, J., Kanovska, I., Chan, Y., Immadi, M.S., Joshi, T., Bilyeu, K.D., Skrabisova, M. 2024. Natural and artificial selection of multiple alleles revealed through genomic analyses. Frontiers in Genetics. 14. Article 1320652. https://doi.org/10.3389/fgene.2023.1320652.
DOI: https://doi.org/10.3389/fgene.2023.1320652

Interpretive Summary: One impediment to identifying some genes that control important plant traits is when there are multiple versions of individual genes that interfere with the current gene-finding methodology. This scenario often happens when there is some kind of selection for those traits. This work addressed the issue of multiple versions of genes being responsible for similar phenotypes by providing a new bioinformatics tool to test for this scenario, and research was conducted with soybean to demonstrate the utility of the tool in assisting with gene finding when there are multiple versions of genes impacting a trait. The impact of this work is an additional method and tool to address an important evolutionary signal of genetic selection that should empower researchers in different species to more effectively identify genes that control important traits.

Technical Abstract: 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.