Summary To gain further insight into the genetic architecture of psoriasis, we conducted a meta-analysis of three genome-wide association studies (GWAS) and two independent datasets genotyped on the Immunochip, involving 10,588 cases and 22,806 controls in total. We identified 15 new disease susceptibility regions, increasing the number of psoriasis-associated loci to 36 for Caucasians. Conditional analyses identified five independent signals within previously known loci. The newly identified shared disease regions encompassed a number of genes whose products regulate T-cell function (e.g. RUNX3, TAGAP and STAT3). The new psoriasis-specific regions were notable for candidate genes whose products are involved in innate host defense, encoding proteins with roles in interferon-mediated antiviral responses (DDX58), macrophage activation (ZC3H12C), and NF-κB signaling (CARD14 and CARM1). These results portend a better understanding of shared and distinctive genetic determinants of immune-mediated inflammatory disorders and emphasize the importance of the skin in innate and acquired host defense.

StraplineThe National Center for Biotechnology Information has created the dbGaP public repository for individual-level phenotype, exposure, genotype, and sequence data, and the associations between them. dbGaP assigns stable, unique identifiers to studies and subsets of information from those studies, including documents, individual phenotypic variables, tables of trait data, sets of genotype data, computed phenotype-genotype associations and groups of study subjects who have given similar consents for use of their data. IntroductionThe technical advances and declining costs for high-throughput genotyping afford investigators fresh opportunities to do increasingly complex analyses of genetic associations with phenotypic and disease characteristics. The leading candidates for such genome wide association studies (GWAS) are existing large-scale cohort and clinical studies that collected rich sets of phenotype data. To support investigator access to data from these initiatives at the National Institutes of Health (NIH) and elsewhere, the National Center for Biotechnology Information (NCBI) has created a database of Genotypes and Phenotypes (dbGaP) with stable identifiers that make it possible for published studies to discuss or cite the primary data in a specific and uniform way. dbGaP provides unprecedented access to the large-scale genetic and phenotypic datasets required for GWAS designs, including public access to study documents linked to summary data on specific phenotype variables, statistical overviews of the genetic information, position of published associations on the genome, and authorized access to individual-level data.The purposes of this description of dbGaP are three-fold: (1) to describe dbGaP's functionality for users and submitters; (2) to describe dbGaP's design and operational processes for database methodologists to emulate or improve upon; and (3) to reassure the lay and scientific public that individual-level phenotype and genotype data are securely and responsibly managed. dbGaP accommodates studies of varying design. It contains four basic types of data: (1) Study documentation, including study descriptions, protocol documents, and data collection instruments, such as questionnaires; (2) Phenotypic data for each variable assessed, at both an individual level and in summary form; (3) Genetic data, including study subjects' individual genotypes, pedigree information, fine mapping results, and resequencing traces; and (4) Statistical results, including association and linkage analyses, when available.Address editorial correspondence to: Stephen Sherry, PhD, National Center for Biotechnology Information, 8600 Rockville Pike, MSC 3804, Bethesda, MD 20894-3804, phone: 301-435-7799, fax: 301-480-5789, e-mail: sherry@ncbi.nlm To protect the confidentiality of study subjects, dbGaP accepts only de-identified data and requires investigators to go through an authorization process in order to access individual-level phenotype and genotype datasets. Summary phenotype and genotype data, as well as stu...

Parkinson disease (PD) is a common disorder that leads to motor and cognitive disability. We performed a genome-wide association study (GWAS) with 2000 PD and 1986 control Caucasian subjects from NeuroGenetics Research Consortium.1–5 We confirmed SNCA2,6–8 and MAPT3,7–9; replicated GAK9 (PPankratz+NGRC=3.2×10−9); and detected a novel association with HLA (PNGRC=2.9×10−8) which replicated in two datasets (PMeta-analysis=1.9×10−10). We designate the new PD genes PARK17 (GAK) and PARK18 (HLA). PD-HLA association was uniform across genetic and environmental risk strata, and strong in sporadic (P=5.5×10−10) and late-onset (P=2.4×10−8) PD. The association peak was at rs3129882, a non-coding variant in HLA-DRA. Two studies suggested rs3129882 influences expression of HLA-DR and HLA-DQ.10,11 PD brains exhibit up-regulation of DR antigens and presence of DR-positive reactive microglia.12 Moreover, non-steroidal anti-inflammatory drugs (NSAID) reduce PD risk.4,13 The genetic association with HLA coalesces the evidence for involvement of the immune system and offers new targets for drug development and pharmacogenetics.

Purpose Copy number variants (CNVs) have emerged as a major cause of human disease such as autism and intellectual disabilities. Because CNVs are common in normal individuals, determining the functional and clinical significance of rare CNVs in patients remains challenging. The adoption of whole-genome chromosomal microarray analysis (CMA) as a first-tier diagnostic test for individuals with unexplained developmental disabilities provides a unique opportunity to obtain large CNV datasets generated through routine patient care. Methods A consortium of diagnostic laboratories was established [the International Standards for Cytogenomic Arrays (ISCA) consortium] to share CNV and phenotypic data in a central, public database. We present the largest CNV case-control study to date comprising 15,749 ISCA cases and 10,118 published controls, focusing our initial analysis on recurrent deletions and duplications involving 14 CNV regions. Results Compared to controls, fourteen deletions, and seven duplications were significantly overrepresented in cases, providing a clinical diagnosis as pathogenic. Conclusion Given the rapid expansion of clinical CMA testing, very large datasets will be available to determine the functional significance of increasingly rare CNVs. This data will provide an evidenced-based guide to clinicians across many disciplines involved in the diagnosis, management, and care of these patients and their families.

We have designed and developed a data integration and visualization platform that provides evidence about the association of known and potential drug targets with diseases. The platform is designed to support identification and prioritization of biological targets for follow-up. Each drug target is linked to a disease using integrated genome-wide data from a broad range of data sources. The platform provides either a target-centric workflow to identify diseases that may be associated with a specific target, or a disease-centric workflow to identify targets that may be associated with a specific disease. Users can easily transition between these target- and disease-centric workflows. The Open Targets Validation Platform is accessible at https://www.targetvalidation.org.

There are few better examples of the need for data sharing than in the rare disease community, where patients, physicians, and researchers must search for “the needle in a haystack” to uncover rare, novel causes of disease within the genome. Impeding the pace of discovery has been the existence of many small siloed datasets within individual research or clinical laboratory databases and/or disease-specific organizations, hoping for serendipitous occasions when two distant investigators happen to learn they have a rare phenotype in common and can “match” these cases to build evidence for causality. However, serendipity has never proven to be a reliable or scalable approach in science. As such, the Matchmaker Exchange (MME) was launched to provide a robust and systematic approach to rare disease gene discovery through the creation of a federated network connecting databases of genotypes and rare phenotypes using a common application programming interface (API). The core building blocks of the MME have been defined and assembled. Three MME services have now been connected through the API and are available for community use. Additional databases that support internal matching are anticipated to join the MME network as it continues to grow.

Genome-wide scans of nucleotide variation in human subjects are providing an increasing number of replicated associations with complex disease traits. Most of the variants detected have small effects and, collectively, they account for a small fraction of the total genetic variance. Very large sample sizes are required to identify and validate findings. In this situation, even small sources of systematic or random error can cause spurious results or obscure real effects. The need for careful attention to data quality has been appreciated for some time in this field, and a number of strategies for quality control and quality assurance (QC/QA) have been developed. Here we extend these methods and describe a system of QC/QA for genotypic data in genome-wide association studies. This system includes some new approaches that (1) combine analysis of allelic probe

To identify bipolar disorder (BD) genetic susceptibility factors, we conducted two genome-wide association (GWA) studies: one involving a sample of individuals of European ancestry (EA; n = 1001 cases; n = 1033 controls), and one involving a sample of individuals of African ancestry (AA; n = 345 cases; n = 670 controls). For the EA sample, single-nucleotide polymorphisms (SNPs) with the strongest statistical evidence for association included rs5907577 in an intergenic region at Xq27.1 (P = 1.6 Â 10 À6) and rs10193871 in NAP5 at 2q21.2 (P = 9.8 Â 10 À6). For the AA sample, SNPs with the strongest statistical evidence for association included rs2111504 in DPY19L3 at 19q13.11 (P = 1.5 Â 10 À6) and rs2769605 in NTRK2 at 9q21.33 (P = 4.5 Â 10 À5 ). We also investigated whether we could provide support for three regions previously associated with BD, and we showed that the ANK3 region replicates in our sample, along with some support for C15Orf53; other evidence implicates BD candidate genes such as SLITRK2. We also tested the hypothesis that BD susceptibility variants exhibit genetic background-dependent effects. SNPs with the strongest statistical evidence for genetic background effects included rs11208285 in ROR1 at 1p31.3 (P = 1.4 Â 10 À6 ), rs4657247 in RGS5 at 1q23.3 (P = 4.1 Â 10 À6 ), and rs7078071 in BTBD16 at 10q26.13 (P = 4.5 Â 10 À6). This study is the first to conduct GWA of BD in individuals of AA and suggests that genetic variations that contribute to BD may vary as a function of ancestry.