Study ID	Study Name	Release Date	Nb Participants	Study Design	Project	Diseases	Ancestor ID	Ancestor Name	Molecular Data Type	Tumor Type	UID
phs001356.v1.p2	Exome Chip Genotyping: The Jackson Heart Study	2019-05-10	2788	Prospective Longitudinal Cohort	National Heart, Lung, Blood Institute	Cardiovascular Diseases, Hypertension, Diabetes Mellitus	phs000286.v6.p2	The Jackson Heart Study (JHS)	SNP Genotypes (Array)	germline	1692088
phs001098.v2.p2	T2D-GENES Multi-Ethnic Exome Sequencing Study: Jackson Heart Study	2019-05-10	1029	Case-Control	NHLBI GO-ESP	Diabetes Mellitus, Type 2	phs000286.v6.p2	The Jackson Heart Study (JHS)	SNP/CNV Genotypes (NGS), WXS	germline	1597258
phs000499.v4.p2	NHLBI Jackson Heart Study Candidate Gene Association Resource (CARe)	2019-05-10	3352	Prospective Longitudinal Cohort	NHLBI CARe	Longitudinal Studies	phs000286.v6.p2	The Jackson Heart Study (JHS)	SNP Genotypes (Array)	germline, unspecified	1597257
phs000498.v4.p2	Jackson Heart Study Allelic Spectrum Project	2019-05-10	1983	Prospective Longitudinal Cohort	National Heart, Lung, Blood Institute	Cardiovascular Diseases	phs000286.v6.p2	The Jackson Heart Study (JHS)	SNP Genotypes (NGS), WXS	germline	1597256
phs000286.v6.p2	The Jackson Heart Study (JHS)	2019-05-10	3889	Prospective Longitudinal Cohort	National Heart, Lung, Blood Institute, NHLBI GO-ESP, NHLBI CARe	Cardiovascular Diseases, Coronary Artery Disease, Diabetes Mellitus, Type 2, Obesity, Hypertension, Kidney Failure, Chronic, Stroke, Heart Failure, Peripheral Vascular Diseases, Arrhythmias, Cardiac				germline, unspecified	1597254
phs000964.v3.p1	NHLBI TOPMed: The Jackson Heart Study	2018-05-18	3596	Prospective Longitudinal Cohort	National Human Genome Research Institute	Cardiovascular Diseases, Hypertension, Diabetes Mellitus			SNP/CNV Genotypes (NGS), WGS	germline	1768620

\n\n\n\n##### dbGaP returns the list of the studies related to your term. As you see, there are 6 studies associated with the \"Jackson Heart Study\" (JHS). One of these study is the main one a.k.a the \"parent study\", whereas the other ones are substudies. In this case, phs000286.v5.p1 is the parent study. Firslty, we can use the phs.version() function in order to be sure that this is the latest version of the study. We can abbreviate the phs name by giving just the digit, or we can use the full dbGaP id.\n\n\n```R\nphs.version(\"286\")\n```\n\n\n'phs000286.v6.p2'\n\n\n##### The is.parent() function is usefull to test if a study is a parent study or a substudy\n\n\n```R\nis.parent(\"000286\") # JHS main cohort\nis.parent(\"phs499\") # substudy \"CARe\" for JHS\n```\n\n\nTRUE\n\n\n\nFALSE\n\n\n##### If you don't know the parent study of a substudy, try parent.study()\n\n\n```R\nparent.study(\"phs000499\")\n```\n\n\n

'phs000286.v6.p2'
'Jackson Heart Study (JHS) Cohort'

\n\n\n\n##### On the other side, use sub.study() to get the name and IDs of the substudies from a parent one\n\n\n```R\nsub.study(\"286\")\n```\n\n\n\n\n\n\t\n\t\n\t\n\t\n\t\n\t\n\n

phs	name
phs001356.v1.p2	Exome Chip Genotyping: The Jackson Heart Study
phs000498.v4.p2	Jackson Heart Study Allelic Spectrum Project
phs001069.v1.p2	MIGen_ExS: JHS
phs000402.v4.p2	NHLBI GO-ESP: Heart Cohorts Exome Sequencing Project (JHS)
phs000499.v4.p2	NHLBI Jackson Heart Study Candidate Gene Association Resource (CARe)
phs001098.v2.p2	T2D-GENES Multi-Ethnic Exome Sequencing Study: Jackson Heart Study

\n\n\n\n##### If you want to get the name of a study from its dbGaP id, use study.name()\n\n\n```R\nstudy.name(\"286\")\n```\n\n\n'Jackson Heart Study (JHS) Cohort'\n\n\n##### Finally, you can watch your study on dbGaP with browse.dbgap().\n##### If a website exists for this study, you can browse it using browse.study()\n\n\n```R\nbrowse.dbgap(\"286\", no.browser = TRUE)\nbrowse.study(\"286\", no.browser = TRUE)\n```\n\n\n'https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000286.v6.p2'\n\n\n\n'https://www.jacksonheartstudy.org'\n\n\n## Explore the characteristics of your study\n##### For each dbGaP study, there can be multiple consent groups that will have there specificities. Use consent.groups to know the number and the name of the consent groups in the study that you are exploring. Let's keep focusing on JHS.\n\n\n```R\nJHS <- \"phs000286\"\nconsent.groups(JHS)\n```\n\n\n\n\n\n\t\n\t\n\t\n\t\n\t\n\n

	shortName	longName
0	NRUP	Subjects did not participate in the study, did not complete a consent document and are included only for the pedigree structure and/or genotype controls, such as HapMap subjects
1	HMB-IRB-NPU	Health/Medical/Biomedical (IRB, NPU)
2	DS-FDO-IRB-NPU	Disease-Specific (Focused Disease Only, IRB, NPU)
3	HMB-IRB	Health/Medical/Biomedical (IRB)
4	DS-FDO-IRB	Disease-Specific (Focused Disease Only, IRB)

\n\n\n\n##### Use n.pop() to know the number of patient included in each groups\n\n\n```R\nn.pop(JHS)\nn.pop(JHS, consentgroups = FALSE)\n```\n\n\n\n\n\n\t\n\t\n\t\n\t\n\t\n\t\n\n

consent_group	male	female	total
HMB-IRB	2409	3046	5885
HMB-IRB-NPU	265	511	883
DS-FDO-IRB-NPU	63	109	201
HMB-IRB	793	1249	2289
DS-FDO-IRB	174	295	516
TOTAL	3704	5210	9774

\n\n\n\n\n9774\n\n\n##### Use n.tables() and n.variables() to get the number of datatables in your study and the total number of variables\n\n\n```R\nn.tables(JHS)\nn.variables(JHS)\n```\n\n\n112\n\n\n\n4856\n\n\n##### datatables.dict() will return a data frame with the datatables IDs (phtxxxxxx) and description of your study\n\n\n```R\ntablesdict <- datatables.dict(JHS)\nhead(tablesdict)\n```\n\n\n\n\n\n\t\n\t\n\t\n\t\n\t\n\t\n\n

pht	dt_study_name	dt_label
pht008811.v1	MIGen_JHS_AA_Subject_Phenotypes	Subject ID, age, sex, cohort, consortium, T2D affection status, weight, BMI, waist circumference, height, LDL, HDL, total cholesterol, blood pressure, adiponectin, debates age, creatinine, fasting glucose, fasting insulin, HbA1C, leptin, triglycerides, and medication of participants involved in the \"Myocardial Infarction Genetics Exome Sequencing Consortium: Jackson Heart Study\" project.
pht008783.v1	sbpc	sbpc
pht008727.v1	allevthf	allevthf
pht001959.v2	loca	loca
pht001945.v2	cena	cena
pht001957.v2	hcaa	hcaa

\n\n\n\n##### variables.dict() will return a data frame with the variables IDs (phvxxxxxx), their name in the study, the datatable where they come from and their description\n\n\n```R\nvardict <- variables.dict(JHS)\nhead(vardict)\n```\n\n\n\n\n\n\t\n\t

dt_study_name	phv	var_name	var_desc
MIGen_JHS_AA_Subject_Phenotypes	phv00404354.v1	SUBJECT_ID	De-identified Subject ID
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