Genomic

Dataset Information

0

CCDG- Neuropsychiatric: Autism- Study of Autism Genetics Exploration (SAGE)


ABSTRACT:

In this study, we address the enormous challenges common complex diseases pose for genomic analysis and the enormous opportunities surmounting them offers for advancing healthcare. The common genetic disorders proposed for study here are believed to have extreme locus heterogeneity, requiring the analysis of large numbers of samples to comprehensively identify the genomic variants underlying them. We propose that a combination of deep population studies and joint analysis of SNPs, indels, and structural variants both in coding and noncoding regions will provide the next level of understanding of common genetic disorders. Whole genome sequencing (WGS) will be critical to this next-generation approach to the genomics of complex disease. WGS will need to be accompanied by the technical ability to generate and handle very large data sets, a particular focus and strength of NYGC. WGS will also need to be accompanied by new statistical tools and algorithms, which will be developed by the strong core group committed to this proposal.

An overarching goal of this proposal, one that capitalizes on the power of WGS, is to identify disease-associated variants at the individual nucleotide level. In many cases pathogenic mutations fall in noncoding regions of the genome, which can only be fruitfully explored with WGS. A major effort was put into building new computational strategies to functionally annotate noncoding transcribed sequences, and to build new datasets to enable such strategies, opening new frontiers of understanding of disease-related regulatory variants.

PROVIDER: phs001740 | dbGaP |

REPOSITORIES: dbGaP

Dataset's files

Source:
Action DRS
GapExchange_phs001740.v1.p1.xml Xml
dbGaPEx2.1.5.xsd Other
Study_Report.phs001740.CCDG_SAGE.v1.p1.MULTI.pdf Pdf
manifest_phs001740.CCDG_SAGE.v1.p1.c2.DS-ASD-RD-IRB.pdf Pdf
datadict_v2.xsl Other
Items per page:
1 - 5 of 17

Similar Datasets

2018-11-25 | E-MTAB-7351 | biostudies-arrayexpress
2024-12-25 | GSE285273 | GEO
2024-12-25 | GSE285270 | GEO
| EGAD00001011295 | EGA
2021-05-17 | GSE155945 | GEO
| EGAS00001002682 | EGA
2021-04-08 | GSE171452 | GEO
2020-03-02 | GSE117693 | GEO
2020-03-02 | GSE117692 | GEO
2020-03-02 | GSE116057 | GEO