affected kindred analysis of human x chromosome exomes to identify novel x-linked intellectual disability genes.

affected kindred analysis of human x chromosome exomes to identify novel x-linked intellectual disability genes.

;Tejasvi S Niranjan;Cindy Skinner;Melanie May;Tychele Turner;Rebecca Rose;Roger Stevenson;Charles E Schwartz;Tao Wang
ensaio pesquisa em educação em ciências 2015 Vol. 10 pp. e0116454-
173
niranjan2015plosaffected

Abstract

X-linked Intellectual Disability (XLID) is a group of genetically heterogeneous disorders caused by mutations in genes on the X chromosome. Deleterious mutations in ~10% of X chromosome genes are implicated in causing XLID disorders in ~50% of known and suspected XLID families. The remaining XLID genes are expected to be rare and even private to individual families. To systematically identify these XLID genes, we sequenced the X chromosome exome (X-exome) in 56 well-established XLID families (a single affected male from 30 families and two affected males from 26 families) using an Agilent SureSelect X-exome kit and the Illumina HiSeq 2000 platform. To enrich for disease-causing mutations, we first utilized variant filters based on dbSNP, the male-restricted portions of the 1000 Genomes Project, or the Exome Variant Server datasets. However, these databases present limitations as automatic filters for enrichment of XLID genes. We therefore developed and optimized a strategy that uses a cohort of affected male kindred pairs and an additional small cohort of affected unrelated males to enrich for potentially pathological variants and to remove neutral variants. This strategy, which we refer to as Affected Kindred/Cross-Cohort Analysis, achieves a substantial enrichment for potentially pathological variants in known XLID genes compared to variant filters from public reference databases, and it has identified novel XLID candidate genes. We conclude that Affected Kindred/Cross-Cohort Analysis can effectively enrich for disease-causing genes in rare, Mendelian disorders, and that public reference databases can be used effectively, but cautiously, as automatic filters for X-linked disorders.

Citation

ID: 155515
Ref Key: niranjan2015plosaffected
Use this key to autocite in SciMatic or Thesis Manager

References

Blockchain Verification

Account:
NFT Contract Address:
0x95644003c57E6F55A65596E3D9Eac6813e3566dA
Article ID:
155515
Unique Identifier:
10.1371/journal.pone.0116454
Network:
Scimatic Chain (ID: 481)
Loading...
Blockchain Readiness Checklist
Authors
Abstract
Journal Name
Year
Title
5/5
Creates 1,000,000 NFT tokens for this article
Token Features:
  • ERC-1155 Standard NFT
  • 1 Million Supply per Article
  • Transferable via MetaMask
  • Permanent Blockchain Record
Blockchain QR Code
Scan with Saymatik Web3.0 Wallet

Saymatik Web3.0 Wallet