The R package clValid contains functions for validating the results of a clustering analysis. There are three main types of cluster validation measures available, "internal", "stability", and "biological". The user can choose from nine clustering algorithms in existing R packages, including hierarchical, K-means, self-organizing maps (SOM), and model-based clustering. In addition, we provide a function to perform the self-organizing tree algorithm (SOTA) method of clustering. Any combination of validation measures and clustering methods can be requested in a single function call. This allows the user to simultaneously evaluate several clustering algorithms while varying the number of clusters, to help determine the most appropriate method and number of clusters for the dataset of interest. Additionally, the package can automatically make use of the biological information contained in the Gene Ontology (GO) database to calculate the biological validation measures, via the annotation packages available in Bioconductor. The function returns an object of S4 class "clValid", which has summary, plot, print, and additional methods which allow the user to display the optimal validation scores and extract clustering results.

Reduced glomerular filtration rate defines chronic kidney disease and is associated with cardiovascular and all-cause mortality. We conducted a meta-analysis of genome-wide association studies for estimated glomerular filtration rate (eGFR), combining data across 133,413 individuals with replication in up to 42,166 individuals. We identify 24 new and confirm 29 previously identified loci. Of these 53 loci, nineteen associate with eGFR among individuals with diabetes. Using bioinformatics, we show that identified genes at eGFR loci are enriched for expression in kidney tissues and in pathways relevant for kidney development and transmembrane transporter activity, kidney structure, and regulation of glucose metabolism. Chromatin state mapping and DNase I hypersensitivity analyses across adult tissues demonstrate preferential mapping of associated variants to regulatory regions in kidney but not extra-renal tissues. These findings suggest that genetic determinants of eGFR are mediated largely through direct effects within the kidney and highlight important cell types and biologic pathways.

To dissect the genetic architecture of blood pressure and assess effects on target-organ damage, we analyzed 128,272 SNPs from targeted and genome-wide arrays in 201,529 individuals of European ancestry and genotypes from an additional 140,886 individuals were used for validation. We identified 66 blood pressure loci, of which 17 were novel and 15 harbored multiple distinct association signals. The 66 index SNPs were enriched for cis-regulatory elements, particularly in vascular endothelial cells, consistent with a primary role in blood pressure control through modulation of vascular tone across multiple tissues. The 66 index SNPs combined in a risk score showed comparable effects in 64,421 individuals of non-European descent. The 66-SNP blood pressure risk score was significantly associated with target-organ damage in multiple tissues, with minor effects in the kidney. Our findings expand current knowledge of blood pressure pathways and highlight tissues beyond the classic renal system in blood pressure regulation.

Numerous genetic loci influence systolic blood pressure (SBP) and diastolic blood pressure (DBP) in Europeans 1-3. We now report genome-wide association studies of pulse pressure (PP) and mean arterial pressure (MAP). In discovery (N=74,064) and follow-up studies (N=48,607), we identified at genome-wide significance (P= 2.7×10-8 to P=2.3×10-13) four novel PP loci (at 4q12 near CHIC2/PDGFRAI, 7q22.3 near PIK3CG, 8q24.12 in NOV, 11q24.3 near ADAMTS-8), two novel MAP loci (3p21.31 in MAP4, 10q25.3 near ADRB1) and one locus associated with both traits (2q24.3 near FIGN) which has recently been associated with SBP in east Asians. For three of the novel PP signals, the estimated effect for SBP was opposite to that for DBP, in contrast to the majority of common SBP- and DBP-associated variants which show concordant effects on both traits. These findings indicate novel genetic mechanisms underlying blood pressure variation, including pathways that may differentially influence SBP and DBP.

Background: Researchers in the field of bioinformatics often face a challenge of combining several ordered lists in a proper and efficient manner. Rank aggregation techniques offer a general and flexible framework that allows one to objectively perform the necessary aggregation. With the rapid growth of high-throughput genomic and proteomic studies, the potential utility of rank aggregation in the context of meta-analysis becomes even more apparent. One of the major strengths of rank-based aggregation is the ability to combine lists coming from different sources and platforms, for example different microarray chips, which may or may not be directly comparable otherwise.

Techniques based on randomized response enable the collection of potentially sensitive data from clients in a privacy-preserving manner with strong local differential privacy guarantees. A recent such technology, RAPPOR [12], enables estimation of the marginal frequencies of a set of strings via privacy-preserving crowdsourcing. However, this original estimation process relies on a known dictionary of possible strings; in practice, this dictionary can be extremely large and/or unknown. In this paper, we propose a novel decoding algorithm for the RAPPOR mechanism that enables the estimation of "unknown unknowns," i.e., strings we do not know we should be estimating. To enable learning without explicit dictionary knowledge, we develop methodology for estimating the joint distribution of multiple variables collected with RAPPOR. Our contributions are not RAPPOR-specific, and can be generalized to other local differential privacy mechanisms for learning distributions of string-valued random variables.

Randomized Aggregatable Privacy-Preserving Ordinal Response, or RAPPOR, is a technology for crowdsourcing statistics from end-user client software, anonymously, with strong privacy guarantees. In short, RAPPORs allow the forest of client data to be studied, without permitting the possibility of looking at individual trees. By applying randomized response in a novel manner, RAPPOR provides the mechanisms for such collection as well as for efficient, high-utility analysis of the collected data. In particular, RAPPOR permits statistics to be collected on the population of client-side strings with strong privacy guarantees for each client, and without linkability of their reports.This paper describes and motivates RAPPOR, details its differential-privacy and utility guarantees, discusses its practical deployment and properties in the face of different attack models, and, finally, gives results of its application to both synthetic and real-world data.

SUMMARYAutism is a multifactorial neurodevelopmental disorder affecting more males than females; consequently, under a multifactorial genetic hypothesis, females are affected only when they cross a higher biological threshold. We hypothesize that deleterious variants at conserved residues are enriched in severely affected patients arising from FEMFs (female-enriched multiplex families) with severe disease, enhancing the detection of key autism genes in modest numbers of cases. We show the utility of this strategy by identifying missense and dosage sequence variants in the gene encoding the adhesive junction-associated delta catenin protein (CTNND2) in FEMFs and demonstrating their loss-of-function effect by functional analyses in zebrafish embryos and cultured hippocampal neurons from wildtype and Ctnnd2 null mouse embryos. Finally, through gene expression and network analyses, we highlight a critical role for CTNND2 in neuronal development and an intimate connection to chromatin biology. Our data contribute to the understanding of the genetic architecture of autism and suggest that genetic analyses of phenotypic extremes, such as FEMFs, are of innate value in multifactorial disorders.