Beyond disease susceptibility-Leveraging genome-wide association studies for new insights into complex disease biology

Endometriosis is a complex disease which affects up to 10% of women in their reproductive years. Common symptoms include severe dysmenorrhea and pelvic pain. The disease is associated with subfertility and some malignancies. Genetic and environmental factors both influence endometriosis. The aim of our studies is to identify genetic variation contributing to endometriosis and define pathways leading to disease. We recruited a large cohort of affected sister pair (ASP) families where two sisters have had surgically confirmed disease and conducted a 10�cM genome scan. The results of the linkage analysis identified one chromosomal region with significant linkage and one region of suggestive linkage. The regions implicated by these studies are generally of the order of 20–30�cM and include several hundred genes. Locating the gene or genes contributing to disease within the region is a challenging task. The best approach to the problem is association studies using a high density of SNP markers. The recent development of human SNP maps and high throughput SNP genotyping platforms makes this task easier. We have developed high throughput SNP typing at QIMR using the Sequenom MassARRAY platform. The method allows multiple SNP assays to be genotyped on the same sample in a single experiment. Throughput and genotyping costs depend critically on this level of multiplexing and we routinely genotype 6–8 SNPs in a single assay. We are using bioinformatics and functional approaches to develop a priority list of genes to screen early in the project. SNP markers in these genes are being genotyped using the MassARRAY platform to search for genes contributing to endometriosis. In the future, genome wide association studies with our families may locate additional genes contributing to endometriosis.

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The druggable genome and support for target identification and validation in drug development

10.1101/066027 ◽

2016 ◽

Cited By ~ 1

Author(s):

Chris Finan ◽

Anna Gaulton ◽

Felix Kruger ◽

Tom Lumbers ◽

Tina Shah ◽

...

Keyword(s):

Drug Development ◽

Drug Target ◽

Complex Disease ◽

Target Identification ◽

Association Studies ◽

Target Selection ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Druggable Genome

Target identification (identifying the correct drug targets for each disease) and target validation (demonstrating the effect of target perturbation on disease biomarkers and disease end-points) are essential steps in drug development. We showed previously that biomarker and disease endpoint associations of single nucleotide polymorphisms (SNPs) in a gene encoding a drug target accurately depict the effect of modifying the same target with a pharmacological agent; others have shown that genomic support for a target is associated with a higher rate of drug development success. To delineate drug development (including repurposing) opportunities arising from this paradigm, we connected complex disease- and biomarker-associated loci from genome wide association studies (GWAS) to an updated set of genes encoding druggable human proteins, to compounds with bioactivity against these targets and, where these were licensed drugs, to clinical indications. We used this set of genes to inform the design of a new genotyping array, to enable druggable genome-wide association studies for drug target selection and validation in human disease.

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Identification of Novel Kawasaki Disease Susceptibility Genes by Genome-Wide Association Studies

Kawasaki Disease ◽

10.1007/978-4-431-56039-5_4 ◽

2016 ◽

pp. 23-29 ◽

Cited By ~ 1

Author(s):

Yoshihiro Onouchi

Keyword(s):

Kawasaki Disease ◽

Disease Susceptibility ◽

Association Studies ◽

Susceptibility Genes ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genome Wide

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Discovering genetic interactions bridging pathways in genome-wide association studies

Nature Communications ◽

10.1038/s41467-019-12131-7 ◽

2019 ◽

Vol 10 (1) ◽

Cited By ~ 8

Author(s):

Gang Fang ◽

Wen Wang ◽

Vanja Paunic ◽

Hamed Heydari ◽

Michael Costanzo ◽

...

Keyword(s):

Statistical Power ◽

Complex Disease ◽

Association Studies ◽

Genetic Network ◽

Genetic Interactions ◽

Gwas Data ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Cancer Breast ◽

Genome Wide

Abstract Genetic interactions have been reported to underlie phenotypes in a variety of systems, but the extent to which they contribute to complex disease in humans remains unclear. In principle, genome-wide association studies (GWAS) provide a platform for detecting genetic interactions, but existing methods for identifying them from GWAS data tend to focus on testing individual locus pairs, which undermines statistical power. Importantly, a global genetic network mapped for a model eukaryotic organism revealed that genetic interactions often connect genes between compensatory functional modules in a highly coherent manner. Taking advantage of this expected structure, we developed a computational approach called BridGE that identifies pathways connected by genetic interactions from GWAS data. Applying BridGE broadly, we discover significant interactions in Parkinson’s disease, schizophrenia, hypertension, prostate cancer, breast cancer, and type 2 diabetes. Our novel approach provides a general framework for mapping complex genetic networks underlying human disease from genome-wide genotype data.

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The power of genome-wide association studies of complex disease genes: statistical limitations of indirect approaches using SNP markers

Journal of Human Genetics ◽

10.1007/s100380170048 ◽

2001 ◽

Vol 46 (8) ◽

pp. 478-482 ◽

Cited By ~ 57

Author(s):

J. Ohashi ◽

K. Tokunaga

Keyword(s):

Complex Disease ◽

Association Studies ◽

Snp Markers ◽

Genome Wide Association ◽

Disease Genes ◽

Genome Wide Association Studies ◽

Genome Wide

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In Search of Complex Disease Risk through Genome Wide Association Studies

Mathematics ◽

10.3390/math9233083 ◽

2021 ◽

Vol 9 (23) ◽

pp. 3083

Author(s):

Lorena Alonso ◽

Ignasi Morán ◽

Cecilia Salvoro ◽

David Torrents

Keyword(s):

Genetic Variants ◽

Complex Disease ◽

Disease Risk ◽

Association Studies ◽

Personalised Medicine ◽

Genome Wide Association ◽

Phenotypic Traits ◽

Genome Wide Association Studies ◽

Treatment Protocols ◽

Genome Wide

The identification and characterisation of genomic changes (variants) that can lead to human diseases is one of the central aims of biomedical research. The generation of catalogues of genetic variants that have an impact on specific diseases is the basis of Personalised Medicine, where diagnoses and treatment protocols are selected according to each patient’s profile. In this context, the study of complex diseases, such as Type 2 diabetes or cardiovascular alterations, is fundamental. However, these diseases result from the combination of multiple genetic and environmental factors, which makes the discovery of causal variants particularly challenging at a statistical and computational level. Genome-Wide Association Studies (GWAS), which are based on the statistical analysis of genetic variant frequencies across non-diseased and diseased individuals, have been successful in finding genetic variants that are associated to specific diseases or phenotypic traits. But GWAS methodology is limited when considering important genetic aspects of the disease and has not yet resulted in meaningful translation to clinical practice. This review presents an outlook on the study of the link between genetics and complex phenotypes. We first present an overview of the past and current statistical methods used in the field. Next, we discuss current practices and their main limitations. Finally, we describe the open challenges that remain and that might benefit greatly from further mathematical developments.

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Harnessing the information contained within genome-wide association studies to improve individual prediction of complex disease risk

Human Molecular Genetics ◽

10.1093/hmg/ddp295 ◽

2009 ◽

Vol 18 (18) ◽

pp. 3525-3531 ◽

Cited By ~ 214

Author(s):

David M. Evans ◽

Peter M. Visscher ◽

Naomi R. Wray

Keyword(s):

Complex Disease ◽

Disease Risk ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genome Wide

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Discovering genetic interactions bridging pathways in genome-wide association studies

10.1101/182741 ◽

2017 ◽

Cited By ~ 1

Author(s):

Gang Fang ◽

Wen Wang ◽

Vanja Paunic ◽

Hamed Heydari ◽

Michael Costanzo ◽

...

Keyword(s):

Statistical Power ◽

Complex Disease ◽

Association Studies ◽

Genetic Networks ◽

Genetic Interactions ◽

Gwas Data ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Cancer Breast ◽

Genome Wide

AbstractGenetic interactions have been reported to underlie phenotypes in a variety of systems, but the extent to which they contribute to complex disease in humans remains unclear. In principle, genome-wide association studies (GWAS) provide a platform for detecting genetic interactions, but existing methods for identifying them from GWAS data tend to focus on testing individual locus pairs, which undermines statistical power. Importantly, the global genetic networks mapped for a model eukaryotic organism revealed that genetic interactions often connect genes between compensatory functional modules in a highly coherent manner. Taking advantage of this expected structure, we developed a computational approach called BridGE that identifies pathways connected by genetic interactions from GWAS data. Applying BridGE broadly, we discovered significant interactions in Parkinson’s disease, schizophrenia, hypertension, prostate cancer, breast cancer, and type 2 diabetes. Our novel approach provides a general framework for mapping complex genetic networks underlying human disease from genome-wide genotype data.

Download Full-text