Michael Feolo

The NCBI dbGaP database of genotypes and phenotypes

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.

NCBI’s Database of Genotypes and Phenotypes: 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.

HLA Diversity in the 1000 Genomes Dataset

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.

Assessing and managing risk when sharing aggregate genetic variant data.

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.

Novel Sequence Feature Variant Type Analysis of the HLA Genetic Association in Systemic Sclerosis

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 sequencing-based typing tool of dbMHC: typing highly polymorphic gene sequences

The dbMHC resource (http://www.ncbi.nlm.nih.gov/mhc/sbt.cgi?cmd=main) at the National Center for Biotechnology Information (NCBI) has developed an online tool for evaluating the allelic composition of sequencing-based typing (SBT) results of cDNA or genomic sequences. Whether the samples are heterozygous, haploid or a combination of the two, they can be compared with two up-to-date databases of all known alleles of several human leukocyte antigen (HLA) and killer cell immunoglobulin-like receptor (KIR) loci. The results of the submission are returned as a table of potential allele hits, along with the respective base changes and an interactive sequence viewer for close examination of the alignment.

Supplementing high-density SNP microarrays for additional coverage of disease-related genes: addiction as a paradigm.

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.

Integrated genome-wide analysis of expression quantitative trait loci aids interpretation of genomic association studies

BackgroundIdentification 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.ResultsWe 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) < 5%. Of previously published significant GWAS SNPs, 48% are identified to be significant eQTLs in our study. Some trans-eQTLs point toward novel mechanistic explanations for the association of the SNP with the GWAS-related phenotype. We also identify 59 distinct blocks or clusters of trans-eQTLs, each targeting the expression of sets of six to 229 distinct trans-eGenes. Ten of these sets of target genes are significantly enriched for microRNA targets (FDR < 5%). Many of these clusters are associated in GWAS with multiple phenotypes.ConclusionsThese findings provide insights into the molecular regulatory patterns involved in human physiology and pathophysiology. We illustrate the value of our eQTL database in the context of a recent GWAS meta-analysis of coronary artery disease and provide a list of targeted eGenes for 21 of 58 GWAS loci.

Linkage analysis of HLA and candidate genes for celiac disease in a North American family-based study

BackgroundCeliac 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.MethodsDNA 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.ResultsThe 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.ConclusionsOur 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.

A strategy for high throughput HLA-DQ typing

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.