Genome-Wide Association and Trans-ethnic Meta-Analysis for Advanced Diabetic Kidney Disease: Family Investigation of Nephropathy and Diabetes (FIND).

Diabetic kidney disease (DKD) is the most common etiology of chronic kidney disease (CKD) in the industrialized world and accounts for much of the excess mortality in patients with diabetes mellitus. Approximately 45% of U.S.

Integrative biology identifies shared transcriptional networks in CKD.

A previous meta-analysis of genome-wide association data by the Cohorts for Heart and Aging Research in Genomic Epidemiology and CKDGen consortia identified 16 loci associated with eGFR. To define how each of these single-nucleotide polymorphisms (SNPs) could affect renal function, we integrated GFR-associated loci with regulatory pathways, producing a molecular map of CKD. In kidney biopsy specimens from 157 European subjects representing nine different CKDs, renal transcript levels for 18 genes in proximity to the SNPs significantly correlated with GFR.

From single nucleotide polymorphism to transcriptional mechanism: a model for FRMD3 in diabetic nephropathy.

Genome-wide association studies have proven to be highly effective at defining relationships between single nucleotide polymorphisms (SNPs) and clinical phenotypes in complex diseases. Establishing a mechanistic link between a noncoding SNP and the clinical outcome is a significant hurdle in translating associations into biological insight. We demonstrate an approach to assess the functional context of a diabetic nephropathy (DN)-associated SNP located in the promoter region of the gene FRMD3.

New susceptibility loci associated with kidney disease in type 1 diabetes.

Diabetic kidney disease, or diabetic nephropathy (DN), is a major complication of diabetes and the leading cause of end-stage renal disease (ESRD) that requires dialysis treatment or kidney transplantation. In addition to the decrease in the quality of life, DN accounts for a large proportion of the excess mortality associated with type 1 diabetes (T1D). Whereas the degree of glycemia plays a pivotal role in DN, a subset of individuals with poorly controlled T1D do not develop DN.

Linking variants from genome-wide association analysis to function via transcriptional network analysis.

We outline a strategy to use tissue-specific expression along with promoter module analysis to determine the putative functional context of candidate genes implicated in genome-wide association studies. First, genes are selected from candidate SNPs, followed by construction of a gene co-regulation network to expand the regulatory context of the candidate genes, functional analysis to determine putative functional roles, and subsequent analysis of regulatory elements. We describe these sub-strategies and variations, along with guidelines for alternatives in the overall analysis.

Formal concept analysis of disease similarity.

Previous work shows that gene associations and network properties common between pairs of diseases can provide molecular evidence of comorbidity, but relationships among diseases may extend to larger groups. Formal concept analysis allows the study of multiple diseases based on a concept lattice whose structure indicates gene set commonality. We use the concept lattice for gene associations to evaluate the complexity of the relationships among diseases, and to identify concepts whose gene sets are candidates for further functional analysis.

A systems view of genetics in chronic kidney disease.

A tight interplay of genetic predisposition and environmental factors define the onset and the rate of progression of chronic renal disease. We are seeing a rapid expansion of information about genetic loci associated with kidney function and complex renal disease. However, discovering the functional links that bridge the gap from genetic risk loci to disease phenotype is one of the main challenges ahead.

A cognitive task analysis of a visual analytic workflow: Exploring molecular interaction networks in systems biology.

Background: Bioinformatics visualization tools are often not robust enough to support biomedical specialists’ complex exploratory analyses. Tools need to accommodate the workflows that scientists actually perform for specific translational research questions. To understand and model one of these workflows, we conducted a case-based, cognitive task analysis of a biomedical specialist’s exploratory workflow for the question: What functional interactions among gene products of high throughput expression data suggest previously unknown mechanisms of a disease?

Results: From our cognitive task analysis four complementary representations of the targeted workflow were developed.

Identifying hypothetical genetic influences on complex disease phenotypes.

Background: Statistical interactions between disease-associated loci of complex genetic diseases suggest that genes from these regions are involved in a common mechanism impacting, or impacted by, the disease. The computational problem we address is to discover relationships among genes from these interacting regions that may explain the observed statistical interaction and the role of these genes in the disease phenotype.

Results: We describe a heuristic algorithm for generating hypothetical gene relationships from loci associated with a complex disease phenotype.

Modeling gene-by-environment interaction in comorbid depression with alcohol use disorders via an integrated bioinformatics approach.

Background: Comorbidity of Major Depressive Disorder (depression) and Alcohol Use Disorders (AUD) is well documented. Depression, AUD, and the comorbidity of depression with AUD show evidence of genetic and environmental influences on susceptibility. We used an integrated bioinformatics approach, mining available data in multiple databases, to develop and refine a model of gene-by-environment interaction consistent with this comorbidity.

Prioritizing Disease Genes by Analysis of Common Elements (PDG-ACE).

Complex diseases are characterized by multiple genetic and environmental influences on disease susceptibility. Since the multiple genetic influences converge on a single phenotype, some commonality may be evident among genes that influence the disease. We exploit this potential commonality among candidate disease genes to prioritize genes for further analysis and to pose novel, statistically significant, biologically plausible hypotheses on disease etiology.