Benjamin J. Keller

Privacy risks in recommender systems

Recommender system users who rate items across disjoint domains face a privacy risk analogous to the one that occurs with statistical database queries.

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-β1) 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.

Mining Electronic Health Records

Initial efforts to mine electronic health records are unlikely to yield many Eureka insights, but there are many opportunities for improving the delivery, efficiency, and effectiveness of healthcare.

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.

From SNP 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

A current challenge in interpretation of genome-wide association studies is to establish the mechanistic links between the measured genotype and observed phenotype. The integration of gene expression with disease genome-wide association studies is emerging as an important strategy for deciphering these regulatory mechanisms. For renal disease, the availability of both tissue- and disease-specific expression data makes the strategy a compelling option. In this review, three approaches of integrating single nucleotide polymorphism (SNP) genotypes with transcriptional regulation are discussed as follows: (1) interpreting the functional role of transcripts affected by a SNP, (2) identifying the mechanistic role of noncoding SNPs in regulation, and (3) identifying regulatory candidate SNPs with expression associations. Combining these strategies in an integrative manner should allow the discovery of more extensive regulatory information. Linking genetics to systems biology more directly promises the opportunity to explain how genetic variants contribute to disease in a truly holistic manner.

Scouts, promoters, and connectors: the roles of ratings in nearest neighbor collaborative filtering

Recommender systems aggregate individual user ratings into predictions of products or services that might interest visitors. The quality of this aggregation process crucially affects the user experience and hence the effectiveness of recommenders in e-commerce. We present a novel study that disaggregates global recommender performance metrics into contributions made by each individual rating, allowing us to characterize the many roles played by ratings in nearest neighbor collaborative filtering. In particular, we formulate three roles--scouts, promoters, and connectors--that capture how users receive recommendations, how items get recommended, and how ratings of these two types are themselves connected (resp.). These roles find direct uses in improving recommendations for users, in better targeting of items and, most importantly, in helping monitor the health of the system as a whole. For instance, they can be used to track the evolution of neighborhoods, to identify rating subspaces that do not contribute (or contribute negatively) to system performance, to enumerate users who are in danger of leaving, and to assess the susceptibility of the system to attacks such as shilling. We argue that the three rating roles presented here provide broad primitives to manage a recommender system and its community.

Scouts, promoters, and connectors: The roles of ratings in nearest-neighbor collaborative filtering

Recommender systems aggregate individual user ratings into predictions of products or services that might interest visitors. The quality of this aggregation process crucially affects the user experience and hence the effectiveness of recommenders in e-commerce. We present a characterization of nearest-neighbor collaborative filtering that allows us to disaggregate global recommender performance measures into contributions made by each individual rating. In particular, we formulate three roles---scouts, promoters, and connectors---that capture how users receive recommendations, how items get recommended, and how ratings of these two types are themselves connected, respectively. These roles find direct uses in improving recommendations for users, in better targeting of items and, most importantly, in helping monitor the health of the system as a whole. For instance, they can be used to track the evolution of neighborhoods, to identify rating subspaces that do not contribute (or contribute negatively) to system performance, to enumerate users who are in danger of leaving, and to assess the susceptibility of the system to attacks such as shilling. We argue that the three rating roles presented here provide broad primitives to manage a recommender system and its community.