Display Settings:

Format

Send to:

Choose Destination
See comment in PubMed Commons below
PLoS Comput Biol. 2008 Nov;4(11):e1000218. doi: 10.1371/journal.pcbi.1000218. Epub 2008 Nov 7.

Genome-wide analysis of human disease alleles reveals that their locations are correlated in paralogous proteins.

Author information

  • 1Eccles Institute of Human Genetics, University of Utah and School of Medicine, Salt Lake City, Utah, United States of America. myandell@genetics.utah.edu

Abstract

The millions of mutations and polymorphisms that occur in human populations are potential predictors of disease, of our reactions to drugs, of predisposition to microbial infections, and of age-related conditions such as impaired brain and cardiovascular functions. However, predicting the phenotypic consequences and eventual clinical significance of a sequence variant is not an easy task. Computational approaches have found perturbation of conserved amino acids to be a useful criterion for identifying variants likely to have phenotypic consequences. To our knowledge, however, no study to date has explored the potential of variants that occur at homologous positions within paralogous human proteins as a means of identifying polymorphisms with likely phenotypic consequences. In order to investigate the potential of this approach, we have assembled a unique collection of known disease-causing variants from OMIM and the Human Genome Mutation Database (HGMD) and used them to identify and characterize pairs of sequence variants that occur at homologous positions within paralogous human proteins. Our analyses demonstrate that the locations of variants are correlated in paralogous proteins. Moreover, if one member of a variant-pair is disease-causing, its partner is likely to be disease-causing as well. Thus, information about variant-pairs can be used to identify potentially disease-causing variants, extend existing procedures for polymorphism prioritization, and provide a suite of candidates for further diagnostic and therapeutic purposes.

PMID:
18989397
[PubMed - indexed for MEDLINE]
PMCID:
PMC2565504
Free PMC Article

Images from this publication.See all images (2)Free text

Figure 1
Figure 2
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Public Library of Science Icon for PubMed Central
    Loading ...
    Write to the Help Desk