ArticleThe plant genome2023
k-mer-based GWAS enhances the discovery of causal variants and candidate genes in soybean.
Article in The plant genome, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Extensive phenotyping and genome-wide analysis of grain number-related traits in a diverse wheat core collection.The plant genome · 2026Article
- Molecular polymorphisms that underlie trait variation in crops: Lessons learned from soybean.The plant genome · 2026Review
- Breeding jassid-resistant okra (Frontiers in plant science · 2026Review
- Genetic resource optimization: a core collection of tetraploid wheat from 2,254 NBRP-wheat accessions.BMC plant biology · 2025Article
- Individual Genomic Distinctness of Rice Germplasm as Measured with an Average Pairwise Dissimilarity of Genome-Wide SNPs and Structural Variants.Plants (Basel, Switzerland) · 2025Article
- Article
- A high-throughput pipeline for phenotyping, object detection and quantification of leaf trichomes.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Mem-based pangenome indexing for k-mer queries.Algorithms for molecular biology : AMB · 2025Article
- Inferring Staphylococcus aureus host species and cross-species transmission from a genome-based model.BMC genomics · 2025Article
- Application of machine learning and genomics for orphan crop improvement.Nature communications · 2025Review
- SNP-Based and Kmer-Based eQTL Analysis Using Transcriptome Data.Animals : an open access journal from MDPI · 2024Article
- Simple and accurate genomic classification model for distinguishing between human and pig Staphylococcus aureus.Communications biology · 2024Article
- MEM-based pangenome indexing forbioRxiv : the preprint server for biology · 2024Article
- k-mer-based GWAS enhances the discovery of causal variants and candidate genes in soybean.The plant genome · 2023Article
- Review
- Natural and artificial selection of multiple alleles revealed through genomic analyses.Frontiers in genetics · 2023Article
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4 authors.
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Abstract
Genome-wide association studies (GWAS) are powerful statistical methods that detect associations between genotype and phenotype at genome scale. Despite their power, GWAS frequently fail to pinpoint the causal variant or the gene controlling a given trait in crop species. Assessing genetic variants other than single-nucleotide polymorphisms (SNPs) could alleviate this problem. In this study, we tested the potential of structural variant (SV)- and k-mer-based GWAS in soybean by applying these methods as well as conventional SNP/indel-based GWAS to 13 traits. We assessed the performance of each GWAS approach based on loci for which the causal genes or variants were known from previous genetic studies. We found that k-mer-based GWAS was the most versatile approach and the best at pinpointing causal variants or candidate genes. Moreover, k-mer-based analyses identified promising candidate genes for loci related to pod color, pubescence form, and resistance to Phytophthora sojae. In our dataset, SV-based GWAS did not add value compared to k-mer-based GWAS and may not be worth the time and computational resources invested. Despite promising results, significant challenges remain regarding the downstream analysis of k-mer-based GWAS. Notably, better methods are needed to associate significant k-mers with sequence variation. Our results suggest that coupling k-mer- and SNP/indel-based GWAS is a powerful approach for discovering candidate genes in crop species.
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