Large-scale analysis of non-synonymous coding region single nucleotide polymorphisms

Robert J. Clifford, Michael N. Edmonson, Cu Nguyen, Kenneth Buetow

Research output: Contribution to journalArticle

60 Citations (Scopus)

Abstract

Motivation: Single nucleotide polymorphisms (SNPs) are the most common form of genetic variant in humans. SNPs causing amino acid substitutions are of particular interest as candidates for loci affecting susceptibility to complex diseases, such as diabetes and hypertension. To efficiently screen SNPs for disease association, it is important to distinguish neutral variants from deleterious ones. Results: We describe the use of Pfam protein motif models and the HMMER program to predict whether amino acid changes in conserved domains are likely to affect protein function. We find that the magnitude of the change in the HMMER E-value caused by an amino acid substitution is a good predictor of whether it is deleterious. We provide internet-accessible display tools for a genomewide collection of SNPs, including 7391 distinct non-synonymous coding region SNPs in 2683 genes.

Original languageEnglish (US)
Pages (from-to)1006-1014
Number of pages9
JournalBioinformatics
Volume20
Issue number7
DOIs
StatePublished - May 1 2004
Externally publishedYes

Fingerprint

Single nucleotide Polymorphism
Nucleotides
Polymorphism
Single Nucleotide Polymorphism
Coding
Amino Acids
Amino acids
Amino Acid Substitution
Substitution
Substitution reactions
Proteins
Protein
Amino Acid Motifs
Hypertension
Diabetes
Medical problems
Internet
Susceptibility
Locus
Predictors

ASJC Scopus subject areas

  • Clinical Biochemistry
  • Computer Science Applications
  • Computational Theory and Mathematics

Cite this

Large-scale analysis of non-synonymous coding region single nucleotide polymorphisms. / Clifford, Robert J.; Edmonson, Michael N.; Nguyen, Cu; Buetow, Kenneth.

In: Bioinformatics, Vol. 20, No. 7, 01.05.2004, p. 1006-1014.

Research output: Contribution to journalArticle

Clifford, Robert J. ; Edmonson, Michael N. ; Nguyen, Cu ; Buetow, Kenneth. / Large-scale analysis of non-synonymous coding region single nucleotide polymorphisms. In: Bioinformatics. 2004 ; Vol. 20, No. 7. pp. 1006-1014.
@article{51425d259bd34d04a72d62f2cbb05a76,
title = "Large-scale analysis of non-synonymous coding region single nucleotide polymorphisms",
abstract = "Motivation: Single nucleotide polymorphisms (SNPs) are the most common form of genetic variant in humans. SNPs causing amino acid substitutions are of particular interest as candidates for loci affecting susceptibility to complex diseases, such as diabetes and hypertension. To efficiently screen SNPs for disease association, it is important to distinguish neutral variants from deleterious ones. Results: We describe the use of Pfam protein motif models and the HMMER program to predict whether amino acid changes in conserved domains are likely to affect protein function. We find that the magnitude of the change in the HMMER E-value caused by an amino acid substitution is a good predictor of whether it is deleterious. We provide internet-accessible display tools for a genomewide collection of SNPs, including 7391 distinct non-synonymous coding region SNPs in 2683 genes.",
author = "Clifford, {Robert J.} and Edmonson, {Michael N.} and Cu Nguyen and Kenneth Buetow",
year = "2004",
month = "5",
day = "1",
doi = "10.1093/bioinformatics/bth029",
language = "English (US)",
volume = "20",
pages = "1006--1014",
journal = "Bioinformatics",
issn = "1367-4803",
publisher = "Oxford University Press",
number = "7",

}

TY - JOUR

T1 - Large-scale analysis of non-synonymous coding region single nucleotide polymorphisms

AU - Clifford, Robert J.

AU - Edmonson, Michael N.

AU - Nguyen, Cu

AU - Buetow, Kenneth

PY - 2004/5/1

Y1 - 2004/5/1

N2 - Motivation: Single nucleotide polymorphisms (SNPs) are the most common form of genetic variant in humans. SNPs causing amino acid substitutions are of particular interest as candidates for loci affecting susceptibility to complex diseases, such as diabetes and hypertension. To efficiently screen SNPs for disease association, it is important to distinguish neutral variants from deleterious ones. Results: We describe the use of Pfam protein motif models and the HMMER program to predict whether amino acid changes in conserved domains are likely to affect protein function. We find that the magnitude of the change in the HMMER E-value caused by an amino acid substitution is a good predictor of whether it is deleterious. We provide internet-accessible display tools for a genomewide collection of SNPs, including 7391 distinct non-synonymous coding region SNPs in 2683 genes.

AB - Motivation: Single nucleotide polymorphisms (SNPs) are the most common form of genetic variant in humans. SNPs causing amino acid substitutions are of particular interest as candidates for loci affecting susceptibility to complex diseases, such as diabetes and hypertension. To efficiently screen SNPs for disease association, it is important to distinguish neutral variants from deleterious ones. Results: We describe the use of Pfam protein motif models and the HMMER program to predict whether amino acid changes in conserved domains are likely to affect protein function. We find that the magnitude of the change in the HMMER E-value caused by an amino acid substitution is a good predictor of whether it is deleterious. We provide internet-accessible display tools for a genomewide collection of SNPs, including 7391 distinct non-synonymous coding region SNPs in 2683 genes.

UR - http://www.scopus.com/inward/record.url?scp=2442694043&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=2442694043&partnerID=8YFLogxK

U2 - 10.1093/bioinformatics/bth029

DO - 10.1093/bioinformatics/bth029

M3 - Article

C2 - 14751981

AN - SCOPUS:2442694043

VL - 20

SP - 1006

EP - 1014

JO - Bioinformatics

JF - Bioinformatics

SN - 1367-4803

IS - 7

ER -