Cataloging SCN resistance loci in North American public soybean breeding programs

Mahmood, Anser; Bilyeu, Kristin D.; Škrabišová, Mária; Biová, Jana; De Meyer, Elizabeth J.; Meinhardt, Clinton G.; Usovsky, Mariola; Song, Qijian; Lorenz, Aaron J.; Mitchum, Melissa G.; Shannon, Grover; Scaboo, Andrew M.

doi:10.3389/fpls.2023.1270546

Cited by 1 publication

(1 citation statement)

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“…This led us to finding that accuracy values can be near perfection (100%) or that they can be decreased to some extent or close to randomness (approximately 50%). We further utilized SPAS and accuracy to investigate non-causative mutations (prior-founder effect emergence), multiple independent CMs, and tagging low-density genotyping markers for soybean cyst nematode resistance genes ( Mahmood et al, 2023 ), and discovered a pattern that is specific to genes with multiple CMs when the average accuracy is perplexing but nearly perfect for the WT phenotype.…”

Section: Introductionmentioning

confidence: 99%

Natural and artificial selection of multiple alleles revealed through genomic analyses

Biová,

Kaňovská,

Chan

et al. 2024

Front. Genet.

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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.

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Section: Introductionmentioning

confidence: 99%