Construction and analysis of genome-wide SNP networks

The study of gene regulatory network (GRN) and protein protein interaction network (PPI) is believed to be fundamental to the understanding of molecular processes and functions in system biology and therefore, attracted more and more attentions in past few years. However, there is little focus about network construction in single nucleotide polymorphism (SNP) level, which may provide a direct insight into mutations among individuals, potentially leading to new pathogenesis discovery and diagnostics. In this paper, we present a novel method to mine, model and evaluate a SNP sub-network from SNP-SNP interactions. Specifically, based on logistic regression between two SNPs, we first construct a genome-wide SNP-SNP interaction network. Then by using gene information, selected SNP seeds are employed to detect SNP sub-networks with a maximal modularity. Finally to identify functional role of each SNP sub-network, its gene association network is constructed and their functional similarity values are calculated to show the biological relevance. Results show that our method is effective in SNP sub-network extraction and gene function prediction.

[1]  Michael K. Ng,et al.  Shrunken methodology to genome-wide SNPs selection and construction of SNPs networks , 2010, BMC Systems Biology.

[2]  Luonan Chen,et al.  Biomolecular Networks: Methods and Applications in Systems Biology , 2009 .

[3]  T. Barrette,et al.  Probabilistic model of the human protein-protein interaction network , 2005, Nature Biotechnology.

[4]  L. Hood,et al.  A Genomic Regulatory Network for Development , 2002, Science.

[5]  Aidong Zhang.  Protein Interaction Networks: Introduction , 2009 .

[6]  Luonan Chen,et al.  Quantitative function for community detection. , 2008 .

[7]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[8]  Jesús A. Izaguirre,et al.  Predicting Protein-Protein Interactions from Protein Domains Using a Set Cover Approach , 2007, IEEE ACM Trans. Comput. Biol. Bioinform..

[9]  Laura J. Scott,et al.  SNP Function Portal: a web database for exploring the function implication of SNP alleles , 2006, ISMB.

[10]  Pablo Moscato,et al.  The Gene Interaction Miner: a new tool for data mining contextual information for protein-protein interaction analysis , 2010, Bioinform..

[11]  Hagit Shatkay,et al.  Functionally informative tag SNP selection using a pareto-optimal approach: playing the game of life , 2009, BMC Bioinformatics.

[12]  B. Horne,et al.  Principal component analysis for selection of optimal SNP‐sets that capture intragenic genetic variation , 2004, Genetic epidemiology.

[13]  Manuel A. R. Ferreira,et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses. , 2007, American journal of human genetics.

[14]  N. Schork,et al.  Single nucleotide polymorphisms and the future of genetic epidemiology , 2000, Clinical genetics.

[15]  Aidong Zhang,et al.  Protein Interaction Networks: Computational Analysis , 2009 .

[16]  Holger Schwender,et al.  Identification of SNP interactions using logic regression. , 2008, Biostatistics.

[17]  M. Gustafsson,et al.  Constructing and analyzing a large-scale gene-to-gene regulatory network Lasso-constrained inference and biological validation , 2005, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[18]  Aidong Zhang.  Protein Interaction Networks: Distance-Based Modularity Analysis , 2009 .

[19]  Hagit Shatkay,et al.  An integrative scoring system for ranking SNPs by their potential deleterious effects , 2009, Bioinform..

[20]  Kazuyuki Aihara,et al.  Modeling Biomolecular Networks in Cells , 2010 .

[21]  A. E. Hirsh,et al.  Evolutionary Rate in the Protein Interaction Network , 2002, Science.

[22]  M. Daly,et al.  Genome-wide association studies for common diseases and complex traits , 2005, Nature Reviews Genetics.

[23]  N Risch,et al.  The Future of Genetic Studies of Complex Human Diseases , 1996, Science.

[24]  H. Kitano.  Systems Biology: A Brief Overview , 2002, Science.

[25]  Qiang Yang,et al.  Predictive rule inference for epistatic interaction detection in genome-wide association studies , 2010, Bioinform..

[26]  A. Brookes.  The essence of SNPs. , 1999, Gene.

[27]  Hamid Pezeshk,et al.  BMC Bioinformatics BioMed Central Methodology article Global haplotype partitioning for maximal associated SNP pairs , 2009 .