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Detailed information for vg0408202291:

Variant ID: vg0408202291 (JBrowse)Variation Type: SNP
Chromosome: chr04Position: 8202291
Reference Allele: AAlternative Allele: C,T
Primary Allele: ASecondary Allele: C

Inferred Ancestral Allele : C (evidence from allele frequency in Oryza rufipogon: C: 0.58, A: 0.42, others allele: 0.00, population size: 50. )

Flanking Sequence (100 bp) in Reference Genome:


GAATCGCGTTATACGGTTTTGCAAGGTTCAGTGGAGCAATCATTCAGAAGAAGAAGCCACTTGGGAGCGAGAAGATGAATTGAAGTCAGCTCATCCTTAT[A/C,T]
TGTTCACCAGTTCTTCTGAATCTCGAGGACGAGATTCCGTTTAACGGGGGTAGGTTTGTCACGTCCTAAAAATCCTAAATATATAAACTGTGGTTTAAAT

Reverse complement sequence

ATTTAAACCACAGTTTATATATTTAGGATTTTTAGGACGTGACAAACCTACCCCCGTTAAACGGAATCTCGTCCTCGAGATTCAGAAGAACTGGTGAACA[T/G,A]
ATAAGGATGAGCTGACTTCAATTCATCTTCTCGCTCCCAAGTGGCTTCTTCTTCTGAATGATTGCTCCACTGAACCTTGCAAAACCGTATAACGCGATTC

Allele Frequencies:

Populations Population SizeFrequency of A(primary allele) Frequency of C(secondary allele) Frequency of N Frequency of DEL Frequency of others Allele
All  4726 42.60% 13.90% 33.98% 9.48% T: 0.06%
All Indica  2759 36.30% 3.80% 45.23% 14.61% T: 0.11%
All Japonica  1512 62.30% 32.50% 3.77% 1.46% NA
Aus  269 9.30% 5.20% 82.90% 2.60% NA
Indica I  595 19.20% 2.70% 53.45% 24.71% NA
Indica II  465 40.00% 2.40% 41.29% 15.91% T: 0.43%
Indica III  913 50.10% 5.60% 38.77% 5.59% NA
Indica Intermediate  786 31.00% 3.30% 48.85% 16.67% T: 0.13%
Temperate Japonica  767 96.00% 2.70% 0.91% 0.39% NA
Tropical Japonica  504 7.50% 80.40% 8.73% 3.37% NA
Japonica Intermediate  241 69.70% 27.00% 2.49% 0.83% NA
VI/Aromatic  96 4.20% 36.50% 52.08% 7.29% NA
Intermediate  90 44.40% 14.40% 31.11% 10.00% NA

Allele Effect:

Var ID Var Locus snpEff Annotation CooVar Annotation Chromatin Accessibility Score PolyPhen-2 Effect PolyPhen-2 Score SIFT Effect SIFT Score
vg0408202291 A -> C LOC_Os04g14640.1 upstream_gene_variant ; 3845.0bp to feature; MODIFIER silent_mutation Average:85.307; most accessible tissue: Minghui63 flag leaf, score: 98.944 N N N N
vg0408202291 A -> C LOC_Os04g14610.1 downstream_gene_variant ; 4434.0bp to feature; MODIFIER silent_mutation Average:85.307; most accessible tissue: Minghui63 flag leaf, score: 98.944 N N N N
vg0408202291 A -> C LOC_Os04g14630.1 downstream_gene_variant ; 2942.0bp to feature; MODIFIER silent_mutation Average:85.307; most accessible tissue: Minghui63 flag leaf, score: 98.944 N N N N
vg0408202291 A -> C LOC_Os04g14620.1 intron_variant ; MODIFIER silent_mutation Average:85.307; most accessible tissue: Minghui63 flag leaf, score: 98.944 N N N N
vg0408202291 A -> DEL N N silent_mutation Average:85.307; most accessible tissue: Minghui63 flag leaf, score: 98.944 N N N N
vg0408202291 A -> T LOC_Os04g14640.1 upstream_gene_variant ; 3845.0bp to feature; MODIFIER silent_mutation Average:85.307; most accessible tissue: Minghui63 flag leaf, score: 98.944 N N N N
vg0408202291 A -> T LOC_Os04g14610.1 downstream_gene_variant ; 4434.0bp to feature; MODIFIER silent_mutation Average:85.307; most accessible tissue: Minghui63 flag leaf, score: 98.944 N N N N
vg0408202291 A -> T LOC_Os04g14630.1 downstream_gene_variant ; 2942.0bp to feature; MODIFIER silent_mutation Average:85.307; most accessible tissue: Minghui63 flag leaf, score: 98.944 N N N N
vg0408202291 A -> T LOC_Os04g14620.1 intron_variant ; MODIFIER silent_mutation Average:85.307; most accessible tissue: Minghui63 flag leaf, score: 98.944 N N N N

Effects Predicted by Deep Convolutional Neural Networks

For each variant, we constructed two sequences that contain the variation site and the sequence around it, differing only in the variation site. We then used Basenji to predict the chromatin accessibility of each tissue for the two sequences, respectively, and scored the effect of the variant by comparing the changes in chromatin accessibility corresponding to the two genotypes in the 1 kb region around the variation site. The effect score was defined as the logarithmic ratio of the predicted chromatin accessibility of the alternative genotype to the value of the reference genotype.

Var ID Ref Alt Root (RT) Young Leaf (YL) Flag Leaf (FL) Young Panicle (YP) Lemma & Palea (LP) Stamen & Pistil (SP)
vg0408202291 A C 0.08 0.06 0.06 0.07 0.06 0.1
vg0408202291 A T -0.02 -0.02 -0.02 -0.02 -0.02 -0.02

Putative Genotype-Phenotype Associations:

Var ID LMM P-value LR P-value Trait Subpopulation Is leadSNP Publication
vg0408202291 NA 5.36E-06 mr1045 Jap_All Not Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism, Nat Genet, 46(7):714-21, PMID:24908251
vg0408202291 NA 2.60E-07 mr1077 Jap_All Not Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism, Nat Genet, 46(7):714-21, PMID:24908251
vg0408202291 NA 1.18E-06 mr1252 Jap_All Not Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism, Nat Genet, 46(7):714-21, PMID:24908251
vg0408202291 NA 3.18E-07 mr1271 Jap_All Not Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism, Nat Genet, 46(7):714-21, PMID:24908251
vg0408202291 NA 4.36E-10 mr1282 All Not Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism, Nat Genet, 46(7):714-21, PMID:24908251
vg0408202291 NA 2.07E-11 mr1650 All Not Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism, Nat Genet, 46(7):714-21, PMID:24908251
vg0408202291 NA 4.01E-07 mr1715 Jap_All Not Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism, Nat Genet, 46(7):714-21, PMID:24908251
vg0408202291 2.17E-06 2.17E-06 mr1778 Ind_All YES Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism, Nat Genet, 46(7):714-21, PMID:24908251
vg0408202291 NA 1.09E-07 mr1864 Jap_All Not Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism, Nat Genet, 46(7):714-21, PMID:24908251
vg0408202291 NA 2.01E-06 mr1993 All Not Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism, Nat Genet, 46(7):714-21, PMID:24908251
vg0408202291 NA 4.10E-08 mr1090_2 Jap_All Not Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism, Nat Genet, 46(7):714-21, PMID:24908251
vg0408202291 NA 5.37E-07 mr1121_2 Jap_All Not Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism, Nat Genet, 46(7):714-21, PMID:24908251
vg0408202291 NA 4.81E-08 mr1211_2 Jap_All Not Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism, Nat Genet, 46(7):714-21, PMID:24908251
vg0408202291 NA 5.77E-09 mr1378_2 All Not Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism, Nat Genet, 46(7):714-21, PMID:24908251