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Genome-wide association studies (GWAS) usually rely on the assumption that different samples are not from closely related individuals. Detection of duplicates and close relatives becomes more difficult both statistically and computationally when one wants to combine datasets that may have been genotyped on different platforms. The dbGaP repository at the National Center of Biotechnology Information (NCBI) contains datasets from hundreds of studies with over one million samples. There are many duplicates and closely related individuals both within and across studies from different submitters. Relationships between studies cannot always be identified by the submitters of individual datasets. To aid in curation of dbGaP, we developed a rapid statistical method called Genetic Relationship and Fingerprinting (GRAF) to detect duplicates and closely related samples, even when the sets of genotyped markers differ and the DNA strand orientations are unknown. GRAF extracts genotypes of 10,000 informative and independent SNPs from genotype datasets obtained using different methods, and implements quick algorithms that enable it to find all of the duplicate pairs from more than 880,000 samples within and across dbGaP studies in less than two hours. In addition, GRAF uses two statistical metrics called All Genotype Mismatch Rate (AGMR) and Homozygous Genotype Mismatch Rate (HGMR) to determine subject relationships directly from the observed genotypes, without estimating probabilities of identity by descent (IBD), or kinship coefficients, and compares the predicted relationships with those reported in the pedigree files. We implemented GRAF in a freely available C++ program of the same name. In this paper, we describe the methods in GRAF and validate the usage of GRAF on samples from the dbGaP repository. Other scientists can use GRAF on their own samples and in combination with samples downloaded from dbGaP.

The Database of Genotypes and Phenotypes (dbGap, http://www.ncbi.nlm.nih.gov/gap) is a National Institutes of Health-sponsored repository charged to archive, curate and distribute information produced by studies investigating the interaction of genotype and phenotype. Information in dbGaP is organized as a hierarchical structure and includes the accessioned objects, phenotypes (as variables and datasets), various molecular assay data (SNP and Expression Array data, Sequence and Epigenomic marks), analyses and documents. Publicly accessible metadata about submitted studies, summary level data, and documents related to studies can be accessed freely on the dbGaP website. Individual-level data are accessible via Controlled Access application to scientists across the globe.

Background Identification of single nucleotide polymorphisms (SNPs) associated with gene expression levels, known as expression quantitative trait loci (eQTLs), may improve understanding of the functional role of phenotype-associated SNPs in genome-wide association studies (GWAS). The small sample sizes of some previous eQTL studies have limited their statistical power. We conducted an eQTL investigation of microarray-based gene and exon expression levels in whole blood in a cohort of 5257 individuals, exceeding the single cohort size of previous studies by more than a factor of 2. Results We detected over 19,000 independent lead cis-eQTLs and over 6000 independent lead trans-eQTLs, targeting over 10,000 gene targets (eGenes), with a false discovery rate (FDR)

We describe a novel approach to genetic association analyses with proteins sub-divided into biologically relevant smaller sequence features (SFs), and their variant types (VTs). SFVT analyses are particularly informative for study of highly polymorphic proteins such as the human leukocyte antigen (HLA), given the nature of its genetic variation: the high level of polymorphism, the pattern of amino acid variability, and that most HLA variation occurs at functionally important sites, as well as its known role in organ transplant rejection, autoimmune disease development and response to infection. Further, combinations of variable amino acid sites shared by several HLA alleles (shared epitopes) are most likely better descriptors of the actual causative genetic variants. In a cohort of systemic sclerosis patients/controls, SFVT analysis shows that a combination of SFs implicating specific amino acid residues in peptide binding pockets 4 and 7 of HLA-DRB1 explains much of the molecular determinant of risk.

In addition to maintaining the GenBank® nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides analysis and retrieval resources for the data in GenBank and other biological data made available through the NCBI web site. NCBI resources include Entrez, the Entrez Programming Utilities, MyNCBI, PubMed, PubMed Central, Entrez Gene, the NCBI Taxonomy Browser, BLAST, BLAST Link (BLink), Electronic PCR, OrfFinder, Spidey, Splign, Reference Sequence, UniGene, HomoloGene, ProtEST, dbMHC, dbSNP, Cancer Chromosomes, Entrez Genomes and related tools, the Map Viewer, Model Maker, Evidence Viewer, Trace Archive, Sequence Read Archive, Retroviral Genotyping Tools, HIV-1/Human Protein Interaction Database, Gene Expression Omnibus, Entrez Probe, GENSAT, Online Mendelian Inheritance in Man, Online Mendelian Inheritance in Animals, the Molecular Modeling Database, the Conserved Domain Database, the Conserved Domain Architecture Retrieval Tool, Biosystems, Peptidome, Protein Clusters and the PubChem suite of small molecule databases. Augmenting many of the web applications are custom implementations of the BLAST program optimized to search specialized data sets. All these resources can be accessed through the NCBI home page at www.ncbi.nlm.nih.gov.

The 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.

The type 1 diabetes (T1D) component of the 13th International Histocompatibility Workshop (IHW) obtained microsatellite (msat) and human leukocyte antigen (HLA)-DR/DQ data on case/control and family samples through an international collaboration. The aim was to detect the effects of susceptibility loci on the HLA complex independent of the primary determinants in the class II region (HLA-DR/DQ). As part of the activity of the 14th International HLA and Immunogenetics Workshop (14th IHIWS), a T1D workshop was held to present analyses of the 13th IHW data and to discuss the current status of knowledge about the genetics of T1D. These data are now available online through dbMHC, a web-based resource established by the National Center for Biotechnology. Continuing work since the 13th IHW has resulted in published work showing heterogeneity of DR3 haplotypes in data sets from the 13th IHW and Human Biological Data Interchange (HBDI). In addition, we identified markers that define DRB1*1501 DQB1*0602 haplotypes conferring reduced protection from diabetes in a Swedish 13th IHW data set. Further analyses of the 13th IHW data set not only showed some significant results but also demonstrated extensive heterogeneity reminiscent of non-HLA genes. The haplotype analysis in HBDI families identified two msats with significant effects on susceptibility and statistically significant age of onset effects at class III markers that are not because of linkage disequilibrium, with class I alleles known to affect age of onset. The above studies underscore the importance of refining our understanding of susceptibility associated with genes in the HLA complex.

Major histocompatibility complex (MHC) region Microsatellites (Msat) have been extensively used in various applications, such as disease mapping, forensics, and population genetics. A comprehensive review of HLA Msat primers has been previously published based on literature and sequence analysis, but electronic tools are lacking to make it easily accessible and actually used by the community. We have integrated data from this review, with an overlapping set of 31 Msat markers used in the 13th International Histocompatibility and Immunogenetics Workshop (IHIWS) to create a public archive that will synchronize published descriptions to a common framework. http://www.ncbi.nlm.nih.gov/projects/mhc. Currently, the dbMHC contains 389 primer pairs across the extended MHC targeting 281 distinct repeat regions (approximately 1/45 kb). Literature review and analysis of the primers reveal that over 200 synonymous names have been published for these markers. Users may view or download specific Msat data sets using the portal. Query options include name or partial name, primer sequence, neighboring genes, and/or position. Query results include locus name(s), a graphic showing of the relative location of the marker in relation to the classical HLA genes, a listing of the constituent primer pairs and name, a link to UniSTS, aliases, allele range (bp), overlapping single nucleotide polymorphisms, a link to e-polymerase chain reaction, and physical mapping information. To increase the utility of this resource, researchers using Msat markers in the HLA region are encouraged by the authors to submit new primers to the dbMHC. The minimal Msat submission consists of primers sequences, a submitter's name and contact information. Additional information recommended but not required is the laboratory protocol(s), known allele size range (bp), known aliases, and an exemplar sequence. Assigned UniSTS numbers can be used for primer pair standard identification.

Inherited genetic variation has a critical but as yet largely uncharacterized role in human disease. Here we report a public database of common variation in the human genome: more than one million single nucleotide polymorphisms (SNPs) for which accurate and complete genotypes have been obtained in 269 DNA samples from four populations, including ten 500-kilobase regions in which essentially all information about common DNA variation has been extracted. These data document the generality of recombination hotspots, a block-like structure of linkage disequilibrium and low haplotype diversity, leading to substantial correlations of SNPs with many of their neighbours. We show how the HapMap resource can guide the design and analysis of genetic association studies, shed light on structural variation and recombination, and identify loci that may have been subject to natural selection during human evolution. © 2005 Nature Publishing Group., link_to_OA_fulltext

Elevated low-density lipoprotein cholesterol (LDL-C) is a treatable, heritable risk factor for cardiovascular disease. Genome-wide association studies (GWASs) have identified 157 variants associated with lipid levels but are not well suited to assess the impact of rare and low-frequency variants. To determine whether rare or low-frequency coding variants are associated with LDL-C, we exome sequenced 2,005 individuals, including 554 individuals selected for extreme LDL-C (>98(th) or

The 1000 Genomes Project aims to provide a deep characterization of human genome sequence variation by sequencing at a level that should allow the genome-wide detection of most variants with frequencies as low as 1%. However, in the major histocompatibility complex (MHC), only the top 10 most frequent haplotypes are in the 1% frequency range whereas thousands of haplotypes are present at lower frequencies. Given the limitation of both the coverage and the read length of the sequences generated by the 1000 Genomes Project, the highly variable positions that define HLA alleles may be difficult to identify. We used classical Sanger sequencing techniques to type the HLA-A, HLA-B, HLA-C, HLA-DRB1 and HLA-DQB1 genes in the available 1000 Genomes samples and combined the results with the 103,310 variants in the MHC region genotyped by the 1000 Genomes Project. Using pairwise identity-by-descent distances between individuals and principal component analysis, we established the relationship between ancestry and genetic diversity in the MHC region. As expected, both the MHC variants and the HLA phenotype can identify the major ancestry lineage, informed mainly by the most frequent HLA haplotypes. To some extent, regions of the genome with similar genetic or similar recombination rate have similar properties. An MHC-centric analysis underlines departures between the ancestral background of the MHC and the genome-wide picture. Our analysis of linkage disequilibrium (LD) decay in these samples suggests that overestimation of pairwise LD occurs due to a limited sampling of the MHC diversity. This collection of HLA-specific MHC variants, available on the dbMHC portal, is a valuable resource for future analyses of the role of MHC in population and disease studies. © 2014 Gourraud et al.

Access to genetic data across studies is an important aspect of identifying new genetic associations through genome-wide association studies (GWASs). Meta-analysis across multiple GWASs with combined cohort sizes of tens of thousands of individuals often uncovers many more genome-wide associated loci than the original individual studies; this emphasizes the importance of tools and mechanisms for data sharing. However, even sharing summary-level data, such as allele frequencies, inherently carries some degree of privacy risk to study participants. Here we discuss mechanisms and resources for sharing data from GWASs, particularly focusing on approaches for assessing and quantifying the privacy risks to participants that result from the sharing of summary-level data.

Commercial SNP microarrays now provide comprehensive and affordable coverage of the human genome. However, some diseases have biologically relevant genomic regions that may require additional coverage. Addiction, for example, is thought to be influenced by complex interactions among many relevant genes and pathways. We have assembled a list of 486 biologically relevant genes nominated by a panel of experts on addiction. We then added 424 genes that showed evidence of association with addiction phenotypes through mouse QTL mappings and gene co-expression analysis. We demonstrate that there are a substantial number of SNPs in these genes that are not well represented by commercial SNP platforms. We address this problem by introducing a publicly available SNP database for addiction. The database is annotated using numeric prioritization scores indicating the extent of biological relevance. The scores incorporate a number of factors such as SNP/gene functional properties (including synonymy and promoter regions), data from mouse systems genetics and measures of human/mouse evolutionary conservation. We then used HapMap genotyping data to determine if a SNP is tagged by a commercial microarray through linkage disequilibrium. This combination of biological prioritization scores and LD tagging annotation will enable addiction researchers to supplement commercial SNP microarrays to ensure comprehensive coverage of biologically relevant regions.

This paper describes the use of the program e-pcr to localize 687 known major histocompatability complex (MHC) microsatellite primer pairs to their sequence positions in several genomic assemblies across the MHC region. The sequences used were the Sequences of Sanger Institute's MHC Haplotype Project: COX, PGF, QBL, as well as the Celera, and Reference (PGF across extended MHC) sequences from the NCBI genomic build 36. More than 95% (664/687) of the markers mapped unambiguously to the Reference assembly sequence. All primer pairs used in this analysis, and those were previously unknown to UniSTS, have now been assigned permanent public UniSTS identifiers. Mapping and descriptive data for each primer pair are available at the publicly accessible dbMHC microsatellite resource: http://www.ncbi.nlm.nih.gov/projects/mhc/xslcgi.fcgi?cmd=mssearch.

A proposal for a standardized nomenclature for human leukocyte antigen (HLA) microsatellites is presented. It provides recommendations for Microsatellites as regards to locus name, primer names, and denominations for alleles.

In the attempt to understand human variation and the genetic basis of complex disease, a tremendous number of single nucleotide polymorphisms (SNPs) have been discovered and deposited into NCBI's dbSNP public database. More than 2.7 million SNPs in the database have genotype information. This data provides an invaluable resource for understanding the structure of human variation and the design of genetic association studies. The genotypes deposited to dbSNP are unphased, and thus, the haplotype information is unknown. We applied the phasing method HAP to obtain the haplotype information, block partitions, and tag SNPs for all publicly available genotype data and deposited this information into the dbSNP database. We also deposited the orthologous chimpanzee reference sequence for each predicted haplotype block computed using the UCSC BLASTZ alignments of human and chimpanzee. Using dbSNP, researchers can now easily perform analyses using multiple genotype data sets from the same genomic regions. Dense and sparse genotype data sets from the same region were combined to show that the number of common haplotypes is significantly underestimated in whole genome data sets, while the predicted haplotypes over the common SNPs are consistent between studies. To validate the accuracy of the predictions, we benchmarked HAP's running time and phasing accuracy against PHASE. Although HAP is slightly less accurate than PHASE, HAP is over 1000 times faster than PHASE, making it suitable for application to the entire set of genotypes in dbSNP.

The reagent database dbMHC was built by the National Center for Biotechnology Information (NCBI) as an open resource for registration and characterization of HLA DNA-typing kits and reagents. Each reagent is uniquely identified as sequence-specific oligonucleotide (SSO) or primer (SSP), SSO mix, or SSP mix. Computerized prediction of allele reactivities, based on annealing stringency, is performed on all submissions to the reagent database. User-specified allele reactivities may be added or deleted independently of the prediction algorithm. Updates of allele reactivities are performed in synchronization with the IMGT/HLA database, in order to account for newly discovered alleles. Probe and primer sequences aligned to allelic sequences can be displayed at any time. Reagents registered in the reagent database are grouped in typing kits. Each kit or kit batch is uniquely identified. Group-specific amplification of alleles can be specified for an entire kit or for sections of each kit. Kits designed to test multiple loci are supported. Kits can be entered and updated via the web or submitted as batches in extensible markup language (XML) format. A tool for online interpretation of typing results is available. Both the reagent database and the typing kit database have been designed to facilitate the exchange of HLA typing based on raw typing data using the unique identifiers of kits or individual reagents. In addition, batch-wise reinterpretation of previous typing data can be performed either using the NCBI web site or by locally using downloaded allele-reactivity lists. Reinterpretation by the NCBI requires submission of raw typing data in XML format.

Background Celiac disease has a strong genetic association with HLA. However, this association only explains approximately half of the sibling risk for celiac disease. Therefore, other genes must be involved in susceptibility to celiac disease. We tested for linkage to genes or loci that could play a role in pathogenesis of celiac disease. Methods DNA samples, from members of 62 families with a minimum of two cases of celiac disease, were genotyped at HLA and at 13 candidate gene regions, including CD4, CTLA4, four T-cell receptor regions, and 7 insulin-dependent diabetes regions. Two-point and multipoint heterogeneity LOD (HLOD) scores were examined. Results The highest two-point and multipoint HLOD scores were obtained in the HLA region, with a two-point HLOD of 3.1 and a multipoint HLOD of 5.0. For the candidate genes, we found no evidence for linkage. Conclusions Our significant evidence of linkage to HLA replicates the known linkage and association of HLA with CD. In our families, likely candidate genes did not explain the susceptibility to celiac disease.

We have developed a high throughput HLA typing methodology that is a modification of the standard sequence-specific primer method. This approach is distinct from other methods using an automated DNA analyzer, as more than one gene is typed in a single lane. We have optimized the method for use on an ABI 373 automated genotyping machine. Primers were designed to preferentially amplify DNA fragments of the generic allelic groups of the DQA1 and DQB1 loci. PCR products representing alleles at the DQA1 locus were amplified using a different fluorescent dye than the PCR products from the DQB1 locus. Only three PCR reactions are required for low resolution typing of DQA1 and DQB1. Use of different labeled primers enables genotyping for both loci in a single gel lane, allowing for 64 samples to be typed at low resolution for both DQA1 and DQB1 on a single gel. Automated allele assignments were determined based on DNA migration distance through a polyacrylamide gel using a standard genotype allele-calling program. Accuracy of this method is greater than 98% for both loci. The strategy described here may be adapted to include more loci or to produce higher resolution typing of alleles encoded by these loci. It can be readily optimized for use on other slab gel or capillary electrophoresis systems.

Nature Reviews Genetics 12, 730–736 (2011) In the above article, the incorrect link was provided for GWAS Central. The correct link should have been http://www.gwascentral.org. In the Further Information Box, the link to http://gwas.nih.gov was incorrectly described as 'GWAS Central (includes policy)'.