ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2022
Genotype imputation for soybean nested association mapping population to improve precision of QTL detection.
Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 11 citations in OpenAlex.
- High-Density Genome-Wide Association Mapping Identifies Candidate Loci Associated with Maize Stalk Cell Wall Composition.Plants (Basel, Switzerland) · 2026Article
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- NAM-BSA: A Bulked Segregant Analysis Method Using a NAM Population.Plants (Basel, Switzerland) · 2026Article
- Korean Soybean Nested Association Mapping (K-SoyNAM) population: genetic linkage map, genetic mapping, and genotype imputation.BMC genomics · 2026Article
- Dissection of local haplotype diversity at soybean rust loci reveals resistance-associated and context-dependent variation patterns in diverse germplasm.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Integration and imputation of GBS-derived and DArTseq-derived SNP markers in assessing genetic diversity of bread wheat genotypes.BMC genomics · 2026Article
- Genome-Wide, High-Density Genotyping Approaches for Plant Germplasm Characterisation (Methods and Applications).International journal of molecular sciences · 2025Review
- High Imputation Accuracy Can Be Achieved Using a Small Reference Panel in a Natural Population With Low Genetic Diversity.Molecular ecology resources · 2025Article
- Characterization of a G. max × G. soja nested association mapping population and identification of loci controlling seed composition traits from wild soybean.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Genome-Wide Association Study on Imputed Genotypes of 180 Eurasian SoybeanPlants (Basel, Switzerland) · 2025Article
- Development and implementation of nested single-nucleotide polymorphism (SNP) assays for breeding and genetic research applications.The plant genome · 2024Article
- Genetic architecture of protein and oil content in soybean seed and meal.The plant genome · 2023Article
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Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
Abstract
key messageSoftware for high imputation accuracy in soybean was identified. Imputed dataset could significantly reduce the interval of genomic regions controlling traits, thus greatly improve the efficiency of candidate gene identification. Genotype imputation is a strategy to increase marker density of existing datasets without additional genotyping. We compared imputation performance of software BEAGLE 5.0, IMPUTE 5 and AlphaPlantImpute and tested software parameters that may help to improve imputation accuracy in soybean populations. Several factors including marker density, extent of linkage disequilibrium (LD), minor allele frequency (MAF), etc., were examined for their effects on imputation accuracy across different software. Our results showed that AlphaPlantImpute had a higher imputation accuracy than BEAGLE 5.0 or IMPUTE 5 tested in each soybean family, especially if the study progeny were genotyped with an extremely low number of markers. LD extent, MAF and reference panel size were positively correlated with imputation accuracy, a minimum number of 50 markers per chromosome and MAF of SNPs > 0.2 in soybean line were required to avoid a significant loss of imputation accuracy. Using the software, we imputed 5176 soybean lines in the soybean nested mapping population (NAM) with high-density markers of the 40 parents. The dataset containing 423,419 markers for 5176 lines and 40 parents was deposited at the Soybase. The imputed NAM dataset was further examined for the improvement of mapping quantitative trait loci (QTL) controlling soybean seed protein content. Most of the QTL identified were at identical or at similar position based on initial and imputed datasets; however, QTL intervals were greatly narrowed. The resulting genotypic dataset of NAM population will facilitate QTL mapping of traits and downstream applications. The information will also help to improve genotyping imputation accuracy in self-pollinated crops.
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