Integrated small copy number variations and epigenome maps of disorders of sex development

被引：17
作者：
Amarillo I.E. [1 ,2 ]
Nievera I. [2 ]
Hagan A. [3 ]
Huchthagowder V. [1 ]
Heeley J. [2 ,4 ]
Hollander A. [2 ,4 ]
Koenig J. [2 ,5 ]
Austin P. [2 ,5 ]
Wang T. [6 ]
机构：
[1] Cytogenomics and Molecular Pathology Laboratory, Division of Laboratory and Genomic Medicine, Department of Pathology and Immunology, Washington University in St Louis School of Medicine, St Louis, MO
[2] Washington University in St Louis School of Medicine DSD Team, St Louis, MO
[3] Division of Biology and Biomedical Sciences, Washington University in St Louis, St Louis, MO
[4] Department of Pediatrics, Washington University in St Louis School of Medicine, St Louis, MO
[5] Department of Surgery, Washington University in St Louis School of Medicine, St Louis, MO
[6] Department of Genetics, Washington University in St Louis School of Medicine, St Louis, MO
来源：
Human Genome Variation | / 3卷 / 1期
关键词：
D O I：
10.1038/hgv.2016.12
中图分类号：
学科分类号：
摘要：
Small copy number variations (CNVs) have typically not been analyzed or reported in clinical settings and hence have remained underrepresented in databases and the literature. Here, we focused our investigations on these small CNVs using chromosome microarray analysis (CMA) data previously obtained from patients with atypical characteristics or disorders of sex development (DSD). Using our customized CMA track targeting 334 genes involved in the development of urogenital and reproductive structures and a less stringent analysis filter, we uncovered small genes with recurrent and overlapping CNVs as small as 1 kb, and small regions of homozygosity (ROHs), imprinting and position effects. Detailed analysis of these high-resolution data revealed CNVs and ROHs involving structural and functional domains, repeat elements, active transcription sites and regulatory regions. Integration of these genomic data with DNA methylation, histone modification and predicted RNA expression profiles in normal testes and ovaries suggested spatiotemporal and tissue-specific gene regulation. This study emphasized a DSD-specific and gene-targeted CMA approach that uncovered previously unanalyzed or unreported small genes and CNVs, contributing to the growing resources on small CNVs and facilitating the narrowing of the genomic gap for identifying candidate genes or regions. This high-resolution analysis tool could improve the diagnostic utility of CMA, not only in patients with DSD but also in other clinical populations. These integrated data provided a better genomic-epigenomic landscape of DSD and greater opportunities for downstream research.
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