Search

Inferring subject ancestry using genetic data is an important step in genetic association studies, required for dealing with population stratification. It has become more challenging to infer subject ancestry quickly and accurately since large amounts of genotype data, collected from millions of subjects by thousands of studies using different methods, are accessible to researchers from repositories such as the database of Genotypes and Phenotypes (dbGaP) at the National Center for Biotechnology Information (NCBI). Study-reported populations submitted to dbGaP are often not harmonized across studies or may be missing. Widely-used methods for ancestry prediction assume that most markers are genotyped in all subjects, but this assumption is unrealistic if one wants to combine studies that used different genotyping platforms. To provide ancestry inference and visualization across studies, we developed a new method, GRAF-pop, of ancestry prediction that is robust to missing genotypes and allows researchers to visualize predicted population structure in color and in three dimensions. When genotypes are dense, GRAF-pop is comparable in quality and running time to existing ancestry inference methods EIGENSTRAT, FastPCA, and FlashPCA2, all of which rely on principal components analysis (PCA). When genotypes are not dense, GRAF-pop gives much better ancestry predictions than the PCA-based methods. GRAF-pop employs basic geometric and probabilistic methods; the visualized ancestry predictions have a natural geometric interpretation, which is lacking in PCA-based methods. Since February 2018, GRAF-pop has been successfully incorporated into the dbGaP quality control process to identify inconsistencies between study-reported and computationally predicted populations and to provide harmonized population values in all new dbGaP submissions amenable to population prediction, based on marker genotypes. Plots, produced by GRAF-pop, of summary population predictions are available on dbGaP study pages, and the software, is available at https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/Software.cgi.

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)

The goal of the International HapMap Project is to determine the common patterns of DNA sequence variation in the human genome and to make this information freely available in the public domain. An international consortium is developing a map of these patterns across the genome by determining the genotypes of one million or more sequence variants, their frequencies and the degree of association between them, in DNA samples from populations with ancestry from parts of Africa, Asia and Europe. The HapMap will allow the discovery of sequence variants that affect common disease, will facilitate development of diagnostic tools, and will enhance our ability to choose targets for therapeutic intervention. © 2003 Nature Publishing Group.

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.

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.

This chapter provides an introduction to the major, freely available, Internet-accessible databases in human and medical genetics used by healthcare providers in the diagnosis, management, and genetic counseling of persons with inherited disorders and their families, as well as by researchers for gene discovery, recording allelic variants, and cataloging genotype-phenotype relationships. Databases discussed include: GeneTests (view: www.genetests.org); Online Mendelian Inheritance in Man (view: www.ncbi.nlm.nih.gov/Omim); locus specific databases (LSDBs) identified at the Human Genome Variation Society (HGVS) web site (http://www.HGVS.org/dblist.html); DatabasE of Chromosome Imbalance and Phenotype in Humans using Ensembl Resources (view: http://decipher.sanger.ac.uk); Entrez Gene (view: ncbi.nlm.nih.gov/gene); dbGap: Database of Genotype and Phenotype (view: ncbi.nlm.nih.gov/dbgap); and the Human Gene Mutation Database HGMD ® (view: http://www.hgmd.org).

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.

Certain immunologic features associated with idiopathic dilated cardiomyopathy (IDC) suggest an infectious and/or autoimmune etiology. In this regard, an association between the major histocompatibility complex class II allele, DR4, and increased risk for IDC was previously identified. In the present report, 43 additional patients with IDC and 236 control subjects were studied for major histocompatibility class II allele associations. DR alleles were identified by microcytotoxicity. No significant differences between control subjects and patients with IDC were seen, although the frequency of DR4 was increased among patients. DR4 subtyping (n = 9) was performed by “dot blot” hybridization of allele-specific oligonucleotide probes to PCR-amplified genomic deoxyribonucleic acid. The DRB1∗0401 and DRB1∗0404 alleles were each found in 44% (n = 4) of patients with IDC, and DRB1∗0407 was identified in 1 patient (11%). DQ and DP alleles were identified by restriction endonuclease codigestion of polymerase chain reaction-amplified deoxyribonucleic acid. The digested fragments were separated and identified by polyacrylamide gel electrophoresis. Differences between patients and control subjects were observed for DQA1∗0501 (11% of patients vs 28% of control subjects, p < 0.05) and DQB1∗0201 (13% patients vs 25% control subjects, p < 0.05). A modest difference was noted for DQA1∗0301 (35% patients vs 23% control subjects, p = 0.08). These findings suggest a complex immune-related etiology for IDC that cannot be explained solely by the presence or absence of a single class II allele. However, this and other studies continue to implicate genes within the class II region in determining the risk for IDC.

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)'.

Immunogeneticists have found significant associations between specific HLA alleles and a variety of medical conditions, including autoimmune diseases. Current disease association studies treat each HLA allele as a single complete unit, which does not illuminate the parts of the molecule associated with disease. We have developed a novel approach for genetic association analysis in which HLA genes and proteins are broken down into smaller sequence features. Sequence features are defined based on structural or functional information, and can be overlapping and continuous or discontinuous in the linear sequence. The extent of sequence variation is then assessed for each HLA sequence feature to define all variant types found in the human population. This allows for the independent analysis of disease association with any sequence feature variant type (SFVT). We tested this approach in the analysis of systemic sclerosis patients using HLA-DRB1 and HLA-DQB1 typing data. We identified a region of the HLA-DRB1 protein centered around peptide-binding pocket 7 that appears to be associated with disease risk; aromatic amino acids found in HLA-DRB1*1104 at positions 26, 28, 37 and 67 were associated with increased risk. The SFVT approach is a novel method for identifying the molecular determinants of disease association in highly polymorphic genes like the HLA. For DAIT-DISC; supported by NIAID N01AI40076