A curated catalogue of human genomic structural variation




Variant Details

Variant: dgv43e203



Internal ID18985020
Landmark
Location Information
TypeCoordinatesAssemblyOther Links
chr15:34386576..34570522hg38UCSC Ensembl
chr15:34678777..34862723hg19UCSC Ensembl
chr15:32466069..32650015hg18UCSC Ensembl
Cytoband15q14
Allele length
AssemblyAllele length
hg38183947
hg19183947
hg18183947
Variant TypeCNV gain+loss
Copy Number
Allele State
Allele Origin
Probe Count
Validation Flag
Merged StatusM
Merged Variants
Supporting Variantsesv2760383, esv2761988
SamplesSW_1334, SW_0787, SW_1162, SW_0569, SW_1377, SW_0046, SW_1539, SW_0086, SW_1096, SW_1097, RW_0183, SW_1303, SW_0311, SW_0802, SW_0270, SW_1463, SW_1064, SW_1170, SW_1240, RW_0348, SW_1180, SW_0891, SW_1465, SW_0118, SW_1148, RW_0045, SW_1266, SW_1468, SW_0352, SW_0241, SW_0570, SW_1243, SW_0590, SW_0872, SW_1478, RW_0270, RW_0273, SW_1281, SW_0230, RW_0033, SW_0369, SW_1506, RW_0521, SW_0790, RW_0272, SW_1447, SW_0057, RW_0226, RW_0028, RW_0152, RW_0344, SW_0379, RW_0505, RW_0140, SW_0647, SW_0675, SW_1236, RW_0098, RW_0544, SW_1452, RW_0508, SW_0820, SW_1295, SW_0855, SW_1381, RW_0224, SW_0690, SW_0091, RW_0592, SW_0047, RW_0143, SW_0673, SW_0016, SW_1326, RW_0191, RW_0637, SW_1084, RW_0522, RW_0312, SW_0836, SW_1157, SW_1392, RW_0043, RW_0255, RW_0627, SW_1304, SW_1443, SW_0584, SW_0691, SW_1119, SW_0505, SW_1201, RW_0178, RW_0230, SW_0884, SW_1357, RW_0552, SW_0843, SW_1233, RW_0336, SW_1278, RW_0136, RW_0576, RW_0560, SW_0603, SW_1134, SW_0609, SW_0805, SW_1045, RW_0659, SW_1142, SW_1429, SW_1063, SW_1008, RW_0065, RW_0078, RW_0175, SW_0191, SW_1389, SW_0775, SW_1060, SW_1120, SW_1476, SW_0187, SW_1332, SW_0058, SW_0146, RW_0266, SW_1325, SW_1053, RW_0527, SW_1305, RW_0235, SW_0103, RW_0525, RW_0139, SW_1203, SW_1455, SW_1376, SW_1417, RW_0553, RW_0335, SW_0313, SW_1066, RW_0079, SW_1118, SW_1143, SW_1232, RW_0032, RW_0586, SW_1043, RW_0606, SW_1480, SW_0101, SW_1475
Known GenesGOLGA8A, GOLGA8B, MIR1233-1, MIR1233-2
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)dgv43e203
Frequency
Sample Size1109
Observed Gain11
Observed Loss144
Observed Complex0
Frequencyn/a


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