{"id":9660,"date":"2022-10-25T18:02:01","date_gmt":"2022-10-25T22:02:01","guid":{"rendered":"https:\/\/ncbiinsights.ncbi.nlm.nih.gov\/?p=9660"},"modified":"2022-10-27T13:17:50","modified_gmt":"2022-10-27T17:17:50","slug":"dbgap-intro-1","status":"publish","type":"post","link":"https:\/\/ncbiinsights.ncbi.nlm.nih.gov\/2022\/10\/25\/dbgap-intro-1\/","title":{"rendered":"dbGaP: Data and analyses from millions of study participants, samples, and trillions of genotypes!"},"content":{"rendered":"

Are you familiar with the well-known\u00a0Framingham Heart Study<\/a>, a multi-generation study of residents of Framingham, Massachusetts begun in 1948? Much of what is now known about the impact of genetics, lifestyle, and diet on cardiovascular health and disease has come from this research study. (See PMC4159698<\/a>\u00a0 for a historical perspective.) Did you know that\u00a0data from this study<\/a>\u00a0and\u00a0over 2,000 other studies<\/a> that demonstrate the relationship between genetic and medical outcomes and other phenotypes are available from NCBI\u2019s Database of Genotypes and Phenotypes (dbGaP)<\/a>?<\/p>\n

dbGaP was\u00a0established in 2007<\/a> as a repository of human data from large scale studies. You can access data from more than 2.8 million study participants who have provided over 3.3 million molecular samples. You can retrieve patient-level phenotypic (e.g.<\/em>, demographic, clinical, exposure) data and molecular (e.g.<\/em>, called genotypes omics, sequence) data, and the results of association analyses from genome-scale case-control and longitudinal studies of heritable diseases.<\/p>\n

What types of studies and data are available in dbGaP?<\/strong><\/p>\n

dbGaP contains a wide range of studies and types of data, all relating to human genetic and phenotypic measurements. Most dbGaP data are from NIH-funded research, but recently we have expanded to include non-NIH funded studies. An easy way to find dbGaP Studies, Phenotype and Molecular Datasets, Variables, Analyses and Documents is through the\u00a0dbGaP Advanced Search<\/a> (Figure 1). The interface allows you to filter results by different characteristics depending on the tab you choose.<\/p>\n

\"\"<\/p>\n

Figure 1. The dbGaP Advanced Search interface. Tabs that appear at the top of the web interface allow you to select the studies, datasets, analyses, etc. of interest. Filters (facets) appear on the left (see inset). Click on filters to select values to find Links on the study summary pages provide direct access to data. <\/em>Top panel:\u00a0 Studies tab and the corresponding filter categories.\u00a0 Bottom panel: Molecular data tab results with Study (Framingham SHARe), Markerset Source (Affymetrix) filters applied.\u00a0<\/em><\/p>\n

<\/p>\n

For example, selecting the Study Disease\/Focus filter in the dbGaP Advanced search allows you to filter studies by human diseases or conditions. Table 1 shows a sample of the dbGaP studies focused on human diseases.<\/span><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n
Study Focus<\/td>\nNumber<\/td>\n<\/tr>\n
Cardiovascular Diseases<\/td>\n66<\/a><\/td>\n<\/tr>\n
Breast Neoplasms<\/td>\n63<\/a><\/td>\n<\/tr>\n
Type II Diabetes<\/td>\n37<\/a><\/td>\n<\/tr>\n
Asthma<\/td>\n31<\/a><\/td>\n<\/tr>\n
Autism Spectrum Disorder<\/td>\n19<\/a><\/td>\n<\/tr>\n
Parkinson Disease<\/td>\n19<\/a><\/td>\n<\/tr>\n
Schizophrenia<\/td>\n15<\/a><\/td>\n<\/tr>\n
Hypertension<\/td>\n13<\/a><\/td>\n<\/tr>\n
COVID-19<\/td>\n9<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Table 1. Selected disease conditions and their counts that link to dbGaP study summaries from the <\/em>Advanced Search displays <\/em>.<\/em><\/p>\n

Study designs include longitudinal cohort, case, case-control, family\/twin\/trios, tumor vs. normal, clinical trial and many others. Molecular data (summary of types<\/a>) include whole genome, whole exome and transcriptome sequencing, gene expression analyses, SNP array genotyping, and many more. Phenotypic data may range from habits and lifestyle choices to presence\/absence of a disease diagnosis, to more specific clinical measurements such as blood pressure and blood glucose levels. These links list the molecular<\/a>\u00a0and\u00a0phenotype<\/a> datasets from the Framingham study to show some of the kinds of data available.<\/p>\n

How do I access data in dbGaP?<\/strong><\/p>\n

There are two access tiers in dbGaP 1) Public Access and 2) Controlled Access. Anyone may access public data without making a request. Public data includes study and variable metadata, summaries, and even some preliminary association analyses for studies. For example,\u00a0you may access the variable summary level data and data dictionary, and study documents, such as protocols and questionnaires, from the\u00a0public FTP site<\/a>, or browse these data from the public pages and advanced search (Figure 1). Access to association analysis data is available via the analysis pages or through the Phenotype Genotype Integrator (PheGenI)<\/a> a browser for variations, genes and the associated trait (phenotype) with P-values indicating the strength of the genotype to phenotype association. For many studies you can see\u00a0genetic population ancestry calculated from participant genotypes using GRAF-pop<\/a>. Figure 2 shows the\u00a0GRAF-pop analysis<\/a>\u00a0of genotypes for participants in the\u00a0Women\u2019s Health Initiative study<\/a>.<\/p>\n

\"\"<\/p>\n

Figure 2. GRAF-pop analysis of Women’s Health Initiative genotypes<\/a>. The colors are the participants’ reported ancestry. Note that many of the reported genotypes fall well outside the 95% range (yellow ellipses) for that ancestry in dbGaP.\u00a0<\/em><\/p>\n

The controlled-access portion of dbGaP provides de-identified individual-level genotype, sequence, omics and phenotype data. Once you find a study of interest you can follow the\u00a0Authorized Access link<\/a> on records to apply for access. Requirements for gaining access to these individual level data vary by study. See\u00a0Tips for Preparing a Successful Data Access Request<\/a>\u00a0for more details about applying for controlled access. Your request will be reviewed by an NIH Data Access Committee<\/a>\u00a0for compliance with any data use limitations. Once you have been granted access, you can retrieve controlled access data from the study including phenotype, omics, images and supporting next-gen sequencing data from the\u00a0Sequence Read Archive (SRA)<\/a>.<\/p>\n

How can I submit my own study data to dbGaP?<\/strong><\/p>\n

See the\u00a0Submission Flowchart<\/a>\u00a0for information on submitting your study data to dbGaP.<\/p>\n

Please\u00a0write to the dbGaP staff<\/a>\u00a0for more information on how to get started with access or to the submission staff<\/a> for help with submitting data.<\/p>\n

Stay tuned to learn more! We will provide more information about dbGaP in the coming months!<\/p>\n","protected":false},"excerpt":{"rendered":"

Are you familiar with the well-known\u00a0Framingham Heart Study, a multi-generation study of residents of Framingham, Massachusetts begun in 1948? Much of what is now known about the impact of genetics, lifestyle, and diet on cardiovascular health and disease has come from this research study. (See PMC4159698\u00a0 for a historical perspective.) Did you know that\u00a0data from … Continue reading dbGaP: Data and analyses from millions of study participants, samples, and trillions of genotypes!<\/span> →<\/span><\/a><\/p>\n","protected":false},"author":42204617,"featured_media":9869,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","enabled":false},"version":2}},"categories":[2248],"tags":[14750092,685466144,693060776,651448],"table_tags":[],"class_list":["post-9660","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-whats-new","tag-dbgap","tag-graf-pop","tag-phenotype-genotype-integrator-phegeni","tag-sra"],"jetpack_publicize_connections":[],"yoast_head":"\ndbGaP: Data and analyses from millions of study participants, samples, and trillions of genotypes! - NCBI Insights<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/ncbiinsights.ncbi.nlm.nih.gov\/2022\/10\/25\/dbgap-intro-1\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"dbGaP: Data and analyses from millions of study participants, samples, and trillions of genotypes! - NCBI Insights\" \/>\n<meta property=\"og:description\" content=\"Are you familiar with the well-known\u00a0Framingham Heart Study, a multi-generation study of residents of Framingham, Massachusetts begun in 1948? 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