Chen et al. 2014, Nat Genet — Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism. A second replication ends with “_2”, for example “mr1002_2”.
Detailed information about mr1776 is available from the rice metabolite database.
Results are retained when the LR or LMM P-value is ≤ 1e-5 or its rank is within the top 20,000. The plot shows LMM results.
| Variation ID | Variation ID V6 | Chromosome | Position | LR P-value | LMM P-value |
|---|---|---|---|---|---|
| vg0916428508 | sf0916427507 | 9 | 16428508 | 9.37E-12 | NA |
| vg1002049704 | sf1002048681 | 10 | 2049704 | 9.72E-12 | NA |
| vg0623156256 | sf0623155259 | 6 | 23156256 | 1.72E-11 | NA |
| vg0327698923 | sf0327691976 | 3 | 27698923 | 1.84E-11 | NA |
| vg0500284719 | sf0500284719 | 5 | 284719 | 1.88E-11 | NA |
| vg1120838492 | sf1120372395 | 11 | 20838492 | 3.41E-11 | 3.90E-06 |
| vg0316990859 | sf0316989733 | 3 | 16990859 | 7.33E-11 | 3.30E-06 |
| vg0919712999 | sf0919712589 | 9 | 19712999 | 5.11E-09 | NA |
| vg0503050458 | sf0503050434 | 5 | 3050458 | 2.71E-08 | 1.56E-06 |
This page provides two coordinated modules for trait exploration. Use Phenotype to inspect accession-level measurements and population distributions, or switch to GWAS Results to inspect association signals, Manhattan plots, and lead variants.
Keeping the two workflows on one page makes it possible to move between observed trait variation and its genetic associations without mixing their search controls or result tables.
GWAS results were collected from Xie et al. (2015) and Chen et al. (2014), with additional peak-SNP information obtained from RiceNavi and Wei et al. (2021). Specifically, we collected GWAS peak SNPs linked to functionally characterized rice genes from six populations or panels analyzed by Wei et al. (2021), including Chinese rice accessions (Li et al., 2020), Japanese rice accessions (Yano et al., 2016), the 3K rice panel (Wang et al., 2018), hybrid rice F1 populations (Huang et al., 2015), the QTN library (Wei et al., 2021), and RDP1 (Zhao et al., 2011).