ArticlePlant physiology2023
Effective use of legacy data in a genome-wide association studies improves the credibility of quantitative trait loci detection in rice.
Article in Plant physiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Identification of candidate genes for deep-sowing tolerance in rice by genome-wide association study and transcriptome sequencing.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Photoperiodic flowering regulators are required for nitrogen-dependent flowering delay in rice under long-day condition.Nature communications · 2025Article
- RGB imaging-based evaluation of waterlogging tolerance in cultivated and wild chrysanthemums.Plant phenomics (Washington, D.C.) · 2025Article
- Local haplotyping reveals insights into the genetic control of flowering time variation in wild and domesticated soybean.The plant genome · 2024Article
- How have breeders adapted rice flowering to the growing region?Journal of integrative plant biology · 2024Article
- Redefining awn development in rice through the breeding history of Japanese awn reduction.Frontiers in plant science · 2024Article
- Low mutation rate of spontaneous mutants enables detection of causative genes by comparing whole genome sequences.Frontiers in plant science · 2024Article
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Authors and funding
10 authors.
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Abstract
Genome-wide association studies (GWASs) are used to detect quantitative trait loci (QTL) using genomic and phenotypic data as inputs. While genomic data are obtained with high throughput and low cost, obtaining phenotypic data requires a large amount of effort and time. In past breeding programs, researchers and breeders have conducted a large number of phenotypic surveys and accumulated results as legacy data. In this study, we conducted a GWAS using phenotypic data of temperate japonica rice (Oryza sativa) varieties from a public database. The GWAS using the legacy data detected several known agriculturally important genes, indicating reliability of the legacy data for GWAS. By comparing the GWAS using legacy data (L-GWAS) and a GWAS using phenotypic data that we measured (M-GWAS), we detected reliable QTL for agronomically important traits. These results suggest that an L-GWAS is a strong alternative to replicate tests to confirm the reproducibility of QTL detected by an M-GWAS. In addition, because legacy data have often been accumulated for many traits, it is possible to evaluate the pleiotropic effect of the QTL identified for the specific trait that we focused on with respect to various other traits. This study demonstrates the effectiveness of using legacy data for GWASs and proposes the use of legacy data to accelerate genomic breeding.
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