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. patients with incident end-stage kidney disease (ESKD) have DKD. Independent of glycemic control, DKD aggregates in families and has higher incidence rates in African, Mexican, and American Indian ancestral groups relative to European populations. The Family Investigation of Nephropathy and Diabetes (FIND) performed a genome-wide association study (GWAS) contrasting 6,197 unrelated individuals with advanced DKD with healthy and diabetic individuals lacking nephropathy of European American, African American, Mexican American, or American Indian ancestry. A large-scale replication and trans-ethnic meta-analysis included 7,539 additional European American, African American and American Indian DKD cases and non-nephropathy controls. Within ethnic group meta-analysis of discovery GWAS and replication set results identified genome-wide significant evidence for association between DKD and rs12523822 on chromosome 6q25.2 in American Indians (P = 5.74x10-9). The strongest signal of association in the trans-ethnic meta-analysis was with a SNP in strong linkage disequilibrium with rs12523822 (rs955333; P = 1.31x10-8), with directionally consistent results across ethnic groups. These 6q25.2 SNPs are located between the SCAF8 and CNKSR3 genes, a region with DKD relevant changes in gene expression and an eQTL with IPCEF1, a gene co-translated with CNKSR3. Several other SNPs demonstrated suggestive evidence of association with DKD, within and across populations. These data identify a novel DKD susceptibility locus with consistent directions of effect across diverse ancestral groups and provide insight into the genetic architecture of DKD.

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. Furthermore, strong familial aggregation supports genetic susceptibility to DN. However, the genes and the molecular mechanisms behind the disease remain poorly understood, and current therapeutic strategies rarely result in reversal of DN. In the GEnetics of Nephropathy: an International Effort (GENIE) consortium, we have undertaken a meta-analysis of genome-wide association studies (GWAS) of T1D DN comprising ~2.4 million single nucleotide polymorphisms (SNPs) imputed in 6,691 individuals. After additional genotyping of 41 top ranked SNPs representing 24 independent signals in 5,873 individuals, combined meta-analysis revealed association of two SNPs with ESRD: rs7583877 in the AFF3 gene (P = 1.2 × 10(-8)) and an intergenic SNP on chromosome 15q26 between the genes RGMA and MCTP2, rs12437854 (P = 2.0 × 10(-9)). Functional data suggest that AFF3 influences renal tubule fibrosis via the transforming growth factor-beta (TGF-ß1) pathway. The strongest association with DN as a primary phenotype was seen for an intronic SNP in the ERBB4 gene (rs7588550, P = 2.1 × 10(-7)), a gene with type 2 diabetes DN differential expression and in the same intron as a variant with cis-eQTL expression of ERBB4. All these detected associations represent new signals in the pathogenesis of DN.

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. These 18 genes were mapped into their biologic context by testing coregulated transcripts for enriched pathways. A network of 97 pathways linked by shared genes was constructed and characterized. Of these pathways, 56 pathways were reported previously to be associated with CKD; 41 pathways without prior association with CKD were ranked on the basis of the number of candidate genes connected to the respective pathways. All pathways aggregated into a network of two main clusters comprising inflammation- and metabolism-related pathways, with the NRF2-mediated oxidative stress response pathway serving as the hub between the two clusters. In all, 78 pathways and 95% of the connections among those pathways were verified in an independent North American biopsy cohort. Disease-specific analyses showed that most pathways are shared between sets of three diseases, with closest interconnection between lupus nephritis, IgA nephritis, and diabetic nephropathy. Taken together, the network integrates candidate genes from genome-wide association studies into their functional context, revealing interactions and defining established and novel biologic mechanisms of renal impairment in renal diseases.

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. Risk loci are currently assigned to a putative context using the functional annotation of the closest genes via a guilt-by-proximity approach. These approaches can be extended by strategies integrating genetic risk loci with kidney-specific, genome-wide gene expression. Risk loci-associated transcripts can be assigned a putative disease-specific function using gene expression coregulation networks. Ultimately, genotype-phenotype dependencies postulated from these associative approaches in humans need to be tested via genetic modification in model organisms. In this review, we survey strategies that employ human tissue-specific expression and the use of model organisms to identify and validate the functional relationship between genotype and phenotype in renal disease. Strategies to unravel how genetic risk and environmental factors orchestrate renal disease manifestation can be the first steps toward a more integrated, holistic approach urgently needed for chronic renal diseases.

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. This approach, called Prioritizing Disease Genes by Analysis of Common Elements (PDG-ACE), mines biomedical keywords from text descriptions of genes and uses them to relate genes close to disease-associated loci. A keyword common to, and significantly over-represented in, a pair of gene descriptions may represent a preliminary hypothesis about the biological relationship between the genes, and suggest the role the genes play in the disease phenotype. CONCLUSION: Our experimentation shows that the approach finds previously published relationships, while failing to find relationships that don't exist. The results also indicate that the approach is robust to differences in keyword vocabulary. We outline a brief case study in which results from a recently published Type 2 Diabetes association study are used to identify potential hypotheses.

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. The approach integrates pathway analyses with transcriptional regulatory pattern-based promoter modeling and allows the identification of a transcriptional framework affected by the DN-associated SNP in the FRMD3 promoter. This framework provides a testable hypothesis for mechanisms of genomic variation and transcriptional regulation in the context of DN. Our model proposes a possible transcriptional link through which the polymorphism in the FRMD3 promoter could influence transcriptional regulation within the bone morphogenetic protein (BMP)-signaling pathway. These findings provide the rationale to interrogate the biological link between FRMD3 and the BMP pathway and serve as an example of functional genomics-based hypothesis generation.

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. For this, we define a heuristic on the lattice structure that allows the identification of concepts whose gene sets indicate strong relationships among the included diseases, which are distinguished from other diseases in the family. Applying this approach to a family of renal diseases we demonstrate that this approach finds gene sets that may be promising for studying common (and differing) mechanism among a family of comorbid or phenotypically related diseases.

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. They include: usage scenarios, flow diagrams, a cognitive task taxonomy, and a mapping between cognitive tasks and user-centered visualization requirements. The representations capture the flows of cognitive tasks that led a biomedical specialist to inferences critical to hypothesizing. We created representations at levels of detail that could strategically guide visualization development, and we confirmed this by making a trial prototype based on user requirements for a small portion of the workflow. CONCLUSIONS: Our results imply that visualizations should make available to scientific users â€Å“bundles of features” consonant with the compositional cognitive tasks purposefully enacted at specific points in the workflow. We also highlight certain aspects of visualizations that: (a) need more built-in flexibility; (b) are critical for negotiating meaning; and (c) are necessary for essential metacognitive support.

A genetic network model of cellular responses to lithium treatment and cocaine abuse in bipolar disorder.

BACKGROUND: Lithium is an effective treatment for Bipolar Disorder (BD) and significantly reduces suicide risk, though the molecular basis of lithium's effectiveness is not well understood. We seek to improve our understanding of this effectiveness by posing hypotheses based on new experimental data as well as published data, testing these hypotheses in silico, and posing new hypotheses for validation in future studies. We initially hypothesized a gene-by-environment interaction where lithium, acting as an environmental influence, impacts signal transduction pathways leading to differential expression of genes important in the etiology of BD mania. RESULTS: Using microarray and rt-QPCR assays, we identified candidate genes that are differentially expressed with lithium treatment. We used a systems biology approach to identify interactions among these candidate genes and develop a network of genes that interact with the differentially expressed candidates. Notably, we also identified cocaine as having a potential influence on the network, consistent with the observed high rate of comorbidity for BD and cocaine abuse. The resulting network represents a novel hypothesis on how multiple genetic influences on bipolar disorder are impacted by both lithium treatment and cocaine use. Testing this network for association with BD and related phenotypes, we find that it is significantly over-represented for genes that participate in signal transduction, consistent with our hypothesized-gene-by environment interaction. In addition, it models related pharmacogenomic, psychiatric, and chemical dependence phenotypes. CONCLUSIONS: We offer a network model of gene-by-environment interaction associated with lithium's effectiveness in treating BD mania, as well as the observed high rate of comorbidity of BD and cocaine abuse. We identified drug targets within this network that represent immediate candidates for therapeutic drug testing. Posing novel hypotheses for validation in future work, we prioritized SNPs near genes in the network based on functional annotation. We also developed a "concept signature" for the genes in the network and identified additional candidate genes that may influence the system because they are significantly associated with the signature.

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. METHODS: We established the validity of a genetic model via queries against NCBI databases, identifying and validating TNF (Tumor Necrosis Factor) and MTHFR (Methylenetetrahydrofolate Reductase) as candidate genes. We used the PDG-ACE algorithm (Prioritizing Disease Genes by Analysis of Common Elements) to show that TNF and MTHFR share significant commonality and that this commonality is consistent with a response to environmental exposure to ethanol. Finally, we used MetaCore from GeneGo, Inc. to model a gene-by-environment interaction consistent with the data. RESULTS: TNF Alpha Converting Enzyme (TACE) activity is suppressed by ethanol exposure, resulting in reduced TNF signaling. TNF binds to TNF receptors, initiating signal transduction pathways that activate MTHFR expression. MTHFR is an essential enzyme in folate metabolism and reduced folate levels are associated with both AUD and depression. Integrating these pieces of information our model shows how excessive alcohol use would be expected to lead to reduced TNF signaling, reduced MTHFR expression, and increased susceptibility to depression. CONCLUSION: The proposed model provides a novel hypothesis on the genetic etiology of comorbid depression with AUD, consistent with established clinical and biochemical data. This analysis also provides an example of how an integrated bioinformatics approach can maximize the use of available biomedical data to improve our understanding of complex disease.

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.