ancGWAS: a post genome-wide association study method for interaction, pathway and ancestry analysis in homogeneous and admixed populations.
BACKGROUND
Despite numerous successful Genome-wide Association Studies (GWAS), detecting variants that have low disease risk still poses a challenge. GWAS may miss disease genes with weak genetic effects or strong epistatic effects due to the single-marker testing approach commonly used. GWAS may thus generate false negative or inconclusive results, suggesting the need for novel methods to combine effects of single nucleotide polymorphisms within a gene to increase the likelihood of fully characterizing the susceptibility gene.


RESULTS
We developed ancGWAS, an algebraic graph-based centrality measure that accounts for linkage disequilibrium in identifying significant disease sub-networks by integrating the association signal from GWAS data sets into the human protein-protein interaction (PPI) network. We validated ancGWAS using an association study result from a breast cancer data set and the simulation of interactive disease loci in the simulation of a complex admixed population, as well as pathway-based GWAS simulation. This new approach holds promise for deconvoluting the interactions between genes underlying the pathogenesis of complex diseases. Results obtained yield a novel central breast cancer sub-network of the human interactome implicated in the proteoglycan syndecan-mediated signaling events pathway which is known to play a major role in mesenchymal tumor cell proliferation, thus providing further insights into breast cancer pathogenesis.


METHODS
The ancGWAS package and documents are available at http://www.cbio.uct.ac.za/~emile/software.html.
Computational Biology Group, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Medical School, 7925, Observatory, South Africa and.
Emile R
ER
Chimusa
Computational Biology Group, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Medical School, 7925, Observatory, South Africa and African Institute for Mathematical Sciences, 7945 Muizenberg, Cape Town, South Africa.
Mamana
M
Mbiyavanga
Computational Biology Group, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Medical School, 7925, Observatory, South Africa and African Institute for Mathematical Sciences, 7945 Muizenberg, Cape Town, South Africa.
Gaston K
GK
Mazandu
Computational Biology Group, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Medical School, 7925, Observatory, South Africa and.
Nicola J
NJ
Mulder
D001943:Breast Neoplasms / Q000453:epidemiology / Q000235:genetics / Q000473:pathology*; D005260:Female; D053263:Gene Regulatory Networks; D020022:Genetic Predisposition to Disease; D005828:Genetics, Population; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D015810:Linkage Disequilibrium; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D025941:Protein Interaction Mapping / Q000379:methods*; D015398:Signal Transduction; D012984:Software