Single nucleotide polymorphisms (SNPs) are the most common form of genetic variation between human individuals. The majority of monogenic disease is mediated by this mechanism, and it is believed that susceptibility to polygenic diseases and individual response to medication can also be understood largely in these terms. Large scale SNP mapping is rapidly increasing the amount of data available on SNPs within the human population. To understand these data and exploit them for development of new therapies requires models of the link between SNPs and function. We propose to develop such a model for SNPs that act via effects on protein function. Most monogenic disease SNPs operate in this manner so it is expected that the results will have wide applicability for interpreting and utilizing human SNP information. The model is based on the large amount of data available on the effect of single residue mutations in vitro. Those data can be understood in terms of the situation of the mutation within the protein structure. The mutation data, together with the structural context, have been used to devise a set of rules that identify which coding region SNPs are potentially harmful in vivo. We have tested a first version of the model against a set of data on SNPs known to cause human disease and a sample of SNPs from the human population. The results provide insights into the mechanism of action of SNPs. We now propose to develop a set of software capable of performing a full analysis of all disease related and general population SNP data. The software will be used to perform an analysis of the extent of harmful SNPs in the human population, and to identify a subset of these likely to be involved in polygenetic diseases.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Research Project (R01)
Project #
1R01LM007174-01
Application #
6320208
Study Section
Special Emphasis Panel (ZLM1-SJP-Q (J2))
Program Officer
Ye, Jane
Project Start
2001-05-15
Project End
2006-05-14
Budget Start
2001-05-15
Budget End
2002-05-14
Support Year
1
Fiscal Year
2001
Total Cost
$266,800
Indirect Cost
Name
University of MD Biotechnology Institute
Department
Type
Organized Research Units
DUNS #
City
Baltimore
State
MD
Country
United States
Zip Code
21202
Pal, Lipika R; Yu, Chen-Hsin; Mount, Stephen M et al. (2015) Insights from GWAS: emerging landscape of mechanisms underlying complex trait disease. BMC Genomics 16 Suppl 8:S4
Cao, Chen; Moult, John (2014) GWAS and drug targets. BMC Genomics 15 Suppl 4:S5
Feiglin, Ariel; Moult, John; Lee, Byungkook et al. (2012) Neighbor overlap is enriched in the yeast interaction network: analysis and implications. PLoS One 7:e39662
Shi, Zhen; Sellers, Jenn; Moult, John (2012) Protein stability and in vivo concentration of missense mutations in phenylalanine hydroxylase. Proteins 80:61-70
Shi, Zhen; Moult, John (2011) Structural and functional impact of cancer-related missense somatic mutations. J Mol Biol 413:495-512
Gorlatova, Natalia; Chao, Kinlin; Pal, Lipika R et al. (2011) Protein characterization of a candidate mechanism SNP for Crohn's disease: the macrophage stimulating protein R689C substitution. PLoS One 6:e27269
Yue, Peng; Moult, John (2006) Identification and analysis of deleterious human SNPs. J Mol Biol 356:1263-74
Yue, Peng; Melamud, Eugene; Moult, John (2006) SNPs3D: candidate gene and SNP selection for association studies. BMC Bioinformatics 7:166
Yue, Peng; Li, Zhaolong; Moult, John (2005) Loss of protein structure stability as a major causative factor in monogenic disease. J Mol Biol 353:459-73
Wang, Zhen; Moult, John (2003) Three-dimensional structural location and molecular functional effects of missense SNPs in the T cell receptor Vbeta domain. Proteins 53:748-57