@article {70, title = {Redefining the Etiologic Landscape of Cerebellar Malformations.}, journal = {Am J Hum Genet}, volume = {105}, year = {2019}, month = {2019 Sep 05}, pages = {606-615}, abstract = {

Cerebellar malformations are diverse congenital anomalies frequently associated with developmental disability. Although genetic and prenatal non-genetic causes have been described, no systematic analysis has been performed. Here, we present a large-exome sequencing study of Dandy-Walker malformation (DWM) and cerebellar hypoplasia (CBLH). We performed exome sequencing in 282 individuals from 100 families with DWM or CBLH, and we established a molecular diagnosis in 36 of 100 families, with a significantly higher yield for CBLH (51\%) than for DWM (16\%). The 41 variants impact 27 neurodevelopmental-disorder-associated genes, thus demonstrating that CBLH and DWM are often features of monogenic neurodevelopmental disorders. Though only seven monogenic causes (19\%) were identified in more than one individual, neuroimaging review of 131 additional individuals confirmed cerebellar abnormalities in 23 of 27 genetic disorders (85\%). Prenatal risk factors were frequently found among individuals without a genetic diagnosis (30 of 64 individuals [47\%]). Single-cell RNA sequencing of prenatal human cerebellar tissue revealed gene enrichment in neuronal and vascular cell types; this suggests that defective vasculogenesis may disrupt cerebellar development. Further, de novo gain-of-function variants in PDGFRB, a tyrosine kinase receptor essential for vascular progenitor signaling, were associated with CBLH, and this discovery links genetic and non-genetic etiologies. Our results suggest that genetic defects impact specific cerebellar cell types and implicate abnormal vascular development as a mechanism for cerebellar malformations. We also confirmed a major contribution for non-genetic prenatal factors in individuals with cerebellar abnormalities, substantially influencing diagnostic evaluation and counseling regarding recurrence risk and prognosis.

}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2019.07.019}, author = {Aldinger, Kimberly A and Timms, Andrew E and Thomson, Zachary and Mirzaa, Ghayda M and Bennett, James T and Rosenberg, Alexander B and Roco, Charles M and Hirano, Matthew and Abidi, Fatima and Haldipur, Parthiv and Cheng, Chi V and Collins, Sarah and Park, Kaylee and Zeiger, Jordan and Overmann, Lynne M and Alkuraya, Fowzan S and Biesecker, Leslie G and Braddock, Stephen R and Cathey, Sara and Cho, Megan T and Chung, Brian H Y and Everman, David B and Zarate, Yuri A and Jones, Julie R and Schwartz, Charles E and Goldstein, Amy and Hopkin, Robert J and Krantz, Ian D and Ladda, Roger L and Leppig, Kathleen A and McGillivray, Barbara C and Sell, Susan and Wusik, Katherine and Gleeson, Joseph G and Nickerson, Deborah A and Bamshad, Michael J and Gerrelli, Dianne and Lisgo, Steven N and Seelig, Georg and Ishak, Gisele E and Barkovich, A James and Curry, Cynthia J and Glass, Ian A and Millen, Kathleen J and Doherty, Dan and Dobyns, William B} } @article {49, title = {Characterizing reduced coverage regions through comparison of exome and genome sequencing data across 10 centers.}, journal = {Genet Med}, volume = {20}, year = {2018}, month = {2018 08}, pages = {855-866}, abstract = {

PURPOSE: As massively parallel sequencing is increasingly being used for clinical decision making, it has become critical to understand parameters that affect sequencing quality and to establish methods for measuring and reporting clinical sequencing standards. In this report, we propose a definition for reduced coverage regions and describe a set of standards for variant calling in clinical sequencing applications.

METHODS: To enable sequencing centers to assess the regions of poor sequencing quality in their own data, we optimized and used a tool (ExCID) to identify reduced coverage loci within genes or regions of particular interest. We used this framework to examine sequencing data from 500 patients generated in 10 projects at sequencing centers in the National Human Genome Research Institute/National Cancer Institute Clinical Sequencing Exploratory Research Consortium.

RESULTS: This approach identified reduced coverage regions in clinically relevant genes, including known clinically relevant loci that were uniquely missed at individual centers, in multiple centers, and in all centers.

CONCLUSION: This report provides a process road map for clinical sequencing centers looking to perform similar analyses on their data.

}, keywords = {Base Sequence, Chromosome Mapping, Exome, Genome, Human, High-Throughput Nucleotide Sequencing, Humans, Sequence Analysis, DNA, Software, Whole Exome Sequencing, Whole Genome Sequencing}, issn = {1530-0366}, doi = {10.1038/gim.2017.192}, author = {Sanghvi, Rashesh V and Buhay, Christian J and Powell, Bradford C and Tsai, Ellen A and Dorschner, Michael O and Hong, Celine S and Lebo, Matthew S and Sasson, Ariella and Hanna, David S and McGee, Sean and Bowling, Kevin M and Cooper, Gregory M and Gray, David E and Lonigro, Robert J and Dunford, Andrew and Brennan, Christine A and Cibulskis, Carrie and Walker, Kimberly and Carneiro, Mauricio O and Sailsbery, Joshua and Hindorff, Lucia A and Robinson, Dan R and Santani, Avni and Sarmady, Mahdi and Rehm, Heidi L and Biesecker, Leslie G and Nickerson, Deborah A and Hutter, Carolyn M and Garraway, Levi and Muzny, Donna M and Wagle, Nikhil} }