A curated catalogue of human genomic structural variation




Variant Details

Variant: dgv3e203



Internal ID20126228
Landmark
Location Information
TypeCoordinatesAssemblyOther Links
chr1:109676386..109710508hg38UCSC Ensembl
chr1:110219008..110253130hg19UCSC Ensembl
chr1:110020531..110054653hg18UCSC Ensembl
Cytoband1p13.3
Allele length
AssemblyAllele length
hg3834123
hg1934123
hg1834123
Variant TypeCNV gain+loss
Copy Number
Allele State
Allele Origin
Probe Count
Validation Flag
Merged StatusM
Merged Variants
Supporting Variantsesv2764242, esv2763824
SamplesRW_0532, RW_0047, SW_1266, SW_1290, SW_1125, RW_0660, RW_0123, RW_0138, RW_0348, SW_0119, SW_1170, RW_0030, RW_0039, RW_0105, SW_1081, RW_0239, RW_0010, SW_0620, SW_0003, RW_0610, RW_0262, SW_0677, SW_0607, RW_0595, RW_0606, RW_0187, SW_1105, SW_0771, RW_0559, SW_0086, RW_0168, SW_1174, SW_0804, SW_1286, RW_0226, RW_0192, RW_0180, RW_0658, RW_0629, SW_0691, RW_0098, SW_0802, SW_0581, SW_0116, SW_0815, SW_0589, SW_0099, RW_0551, RW_0549, SW_0800, SW_0816, RW_0137, RW_0624, RW_0503, SW_0604, SW_1221, SW_1258, SW_0828, SW_1285, SW_1109, RW_0113, RW_0626, RW_0241, SW_1126, RW_0115, SW_0047, SW_1263, SW_0646, RW_0017, RW_0528, SW_0817, SW_0648, RW_0111, RW_0519, SW_0625, RW_0122, RW_0118, RW_0324, RW_0637, RW_0600, SW_0789, SW_1084, RW_0505, RW_0094, SW_0761, SW_0665, SW_1095, RW_0253, SW_0021, RW_0601, SW_0631, SW_1148, RW_0666, SW_0076, SW_0703, RW_0211, RW_0091, RW_0029, SW_0044, RW_0250, SW_1093, SW_1194, SW_0590, RW_0129, SW_0091, RW_0633, SW_1220, SW_1140, RW_0210, RW_0201, RW_0088, SW_1113, SW_0663, SW_1089, RW_0667, SW_0775, SW_1249, SW_1176, RW_0120, SW_1227, RW_0145, SW_1112, SW_0814, RW_0331, RW_0126, RW_0080, SW_0004, RW_0235, RW_0663, RW_0190, SW_1116, SW_0673, SW_0626, RW_0273, RW_0220, RW_0108, SW_0632, SW_0791, SW_0001, RW_0170, RW_0079, RW_0665, SW_0674, SW_1087, RW_0014, SW_1308, SW_1279, SW_1273, RW_0057, SW_1147, RW_0072, SW_1175, SW_0100, RW_0107, RW_0084, RW_0041, RW_0139
Known GenesGSTM1, GSTM2
MethodMerging
AnalysisTwo different algorithms (PennCNV and Birdseye) were applied to detect CNVs. Only congruent CNV events regarding direction of effect that were detected by both algorithms were merged using the outer borders of the event in a first step. In a second step only CNVs that were detected in at least two individuals were merged into a CNVR.
PlatformMerging
Comments
ReferenceVogler_et_al_2010
Pubmed ID21179565
Accession Number(s)dgv3e203
Frequency
Sample Size1109
Observed Gain3
Observed Loss154
Observed Complex0
Frequencyn/a


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