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1.
Cell ; 185(23): 4409-4427.e18, 2022 11 10.
Artículo en Inglés | MEDLINE | ID: mdl-36368308

RESUMEN

Fully understanding autism spectrum disorder (ASD) genetics requires whole-genome sequencing (WGS). We present the latest release of the Autism Speaks MSSNG resource, which includes WGS data from 5,100 individuals with ASD and 6,212 non-ASD parents and siblings (total n = 11,312). Examining a wide variety of genetic variants in MSSNG and the Simons Simplex Collection (SSC; n = 9,205), we identified ASD-associated rare variants in 718/5,100 individuals with ASD from MSSNG (14.1%) and 350/2,419 from SSC (14.5%). Considering genomic architecture, 52% were nuclear sequence-level variants, 46% were nuclear structural variants (including copy-number variants, inversions, large insertions, uniparental isodisomies, and tandem repeat expansions), and 2% were mitochondrial variants. Our study provides a guidebook for exploring genotype-phenotype correlations in families who carry ASD-associated rare variants and serves as an entry point to the expanded studies required to dissect the etiology in the ∼85% of the ASD population that remain idiopathic.


Asunto(s)
Trastorno del Espectro Autista , Trastorno Autístico , Humanos , Trastorno del Espectro Autista/genética , Predisposición Genética a la Enfermedad , Variaciones en el Número de Copia de ADN/genética , Genómica
2.
Cancer Res ; 77(21): e7-e10, 2017 11 01.
Artículo en Inglés | MEDLINE | ID: mdl-29092928

RESUMEN

The ISB Cancer Genomics Cloud (ISB-CGC) is one of three pilot projects funded by the National Cancer Institute to explore new approaches to computing on large cancer datasets in a cloud environment. With a focus on Data as a Service, the ISB-CGC offers multiple avenues for accessing and analyzing The Cancer Genome Atlas, TARGET, and other important references such as GENCODE and COSMIC using the Google Cloud Platform. The open approach allows researchers to choose approaches best suited to the task at hand: from analyzing terabytes of data using complex workflows to developing new analysis methods in common languages such as Python, R, and SQL; to using an interactive web application to create synthetic patient cohorts and to explore the wealth of available genomic data. Links to resources and documentation can be found at www.isb-cgc.org Cancer Res; 77(21); e7-10. ©2017 AACR.


Asunto(s)
Nube Computacional , Biología Computacional , Genómica , Neoplasias/genética , Conjuntos de Datos como Asunto , Genoma Humano , Humanos , Internet , National Cancer Institute (U.S.) , Investigación/tendencias , Programas Informáticos , Estados Unidos
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