Structural Variants Discovery from Exome Sequencing
Mi Yeong Hwang1, Sanghoon Moon1, Young Jin Kim1, Lyong Heo1, Yun Kyoung Kim1,
Bok-Ghee Han2, and Bong-Jo Kim1*
1
Division of Structural and Functional Genomics, Center for Genome Science, National Institute of
Health, Chungcheongbuk-do, 363-951, Korea, 2Center for Genome Science, National Institute of
Health, Chungcheongbuk-do, 363-951, Korea
Structural variations (SVs) including copy number variations (CNVs) and indels have a strong
functional relevance with complex diseases such as cancer, schizophrenia and obesity. Several years
ago, array-based platforms were mainly used to detect SVs. However, limitations such as noisy
signal and lower resolution had obstructed detection of small size of SVs.
Recently, next generation sequencing (NGS) technique is increasing rapidly owing to cost decline.
Moreover, exome-sequencing approach is being spotlight as means to find causal genes and variants
in complex diseases due to the inexpensive cost than whole-genome sequencing (WGS) approach.
The purpose of this study is to find SVs with exact break-point in genic regions and to examine a
potential relationship between SV and disease through finding SVs occurred frequently in the
particular gene. To do this, we genotyped 100 individuals (10 references and 90 tests) in populationbased cohort using whole exome-sequencing approach with Agilent SureSelect v2 44M/Illumina
Hiseq2000 (average depth of coverage 60x). GATK and ExomeDepth were used to discover indels
and CNVs, respectively. As a result, 2,460 SV regions were discovered and all of the detected SVs
were compared to CNV break-point of Database of Genomic Variants (DGV). About 50% of
detected SVs were evaluated by Agilent SurePrint G3 Human CGH Microarray 8x60K.