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Summary Statistics

You can filter the data below or go straight to the results. Descriptions of the various cell types we distribute, such as CPLs and LCLs, can be found here. Note that CPLs are ideal for creating iPSCs. Information on ordering biomaterials can be found here.

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Disease Distributions

Subjects are grouped into distributions according to the way they were ascertained for their respective studies. This should not be confused with clinical diagnoses. Subjects in the Nicotine Dependence distribution, for example, may or may not be diagnosed with Nicotine Dependence. By default, all distributions are used.

Demographics, Biomaterials and Clinical Instruments

Sex:

Age:Between and

Race(s):

Has DNA:

Cell Types:

Cell Types Logic: OR AND

Cell type descriptions can be found here.

Clinical Instrument(s):

Clinical Diagnoses

Multiple Diagnosis Logic*: OR AND

*Example: when OR is selected, if you select both Nicotine and Alcohol dependence you get statistics for the combined set Nicotine-dependent subjects and Alcohol-dependent subjects. When AND is selected, you get statistics for subjects that are both Nicotine-dependent and Alcohol-dependent.

Opioid Dependence:

Cocaine Dependence:

Alcohol Dependence:

Nicotine Dependence:

Cannabis Dependence:

Stimulant Dependence:

Sedative Dependence:

Other Dependence:

Opioid Abuse:

Cocaine Abuse:

Alcohol Abuse:

Cannabis Abuse:

Stimulant Abuse:

Sedative Abuse:

Other Abuse:

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Results

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Breakdown by Disease Distribution

Cell type descriptions (LCLs, CPLs, etc) can be found here.

Distribution Subjects With DNA Plasma LCLs CPLs Whole Blood
Opioid Dependence 5.7 7,971 6,556 (82%) 2 ( 0%) 3,189 (40%) 4,240 (53%) 3,860 (48%)
Total 7,971 6,556 (82%) 2 ( 0%) 3,189 (40%) 4,240 (53%) 3,860 (48%)

Demographics, DNA and Clinical Instruments

Item Value
Subjects 7,971
With DNA 6,556 (82%)
Instrument: DSM-IV 6,153 (77%)
Instrument: DSM-III-R 22 (0%)
Instrument: Unknown 1,796 (23%)
Females / Males / Unknown 3,034 (38%) / 4,935 (62%) / 2 (0%)
Age Average: 38.7, Min: 17, Max: 111
Race: White 2,621 (33%)
Race: Hispanic 1,321 (17%)
Race: African-American 1,054 (13%)
Race: Asian 1,956 (25%)
Race: American Indian 6 (0%)
Race: Missing 208 (3%)
Race: Other 805 (10%)

Cell Types

Cell type descriptions (LCLs, CPLs, etc) can be found here.

Cell Type Subjects
LCL 3,189 (40%)
CPL 4,240 (53%)
LCL gDNA 3,127 (39%)
WB gDNA 4,055 (51%)
Plasma 2 ( 0%)
Whole Blood 3,860 (48%)

Diagnoses

Disease Affected Unaffected Other
Opioid Dependence 4,790 (60%) 1,385 (17%) 1,796 (23%)
Cocaine Dependence 1,780 (22%) 3,175 (40%) 3,016 (38%)
Alcohol Dependence 1,565 (20%) 4,240 (53%) 2,166 (27%)
Nicotine Dependence 1,102 (14%) 764 (10%) 6,105 (77%)
Cannabis Dependence 1,336 (17%) 1,856 (23%) 4,779 (60%)
Stimulant Dependence 824 (10%) 3,318 (42%) 3,829 (48%)
Sedative Dependence 720 ( 9%) 3,405 (43%) 3,846 (48%)
Other Drug Dependence 95 ( 1%) 548 ( 7%) 7,328 (92%)
Opioid Abuse 906 (11%) 3,392 (43%) 3,673 (46%)
Cocaine Abuse 981 (12%) 3,947 (50%) 3,043 (38%)
Alcohol Abuse 1,223 (15%) 4,680 (59%) 2,068 (26%)
Cannabis Abuse 686 ( 9%) 3,469 (44%) 3,816 (48%)
Stimulant Abuse 66 ( 1%) 1,325 (17%) 6,580 (83%)
Sedative Abuse 125 ( 2%) 1,257 (16%) 6,589 (83%)
Other Drug Abuse 66 ( 1%) 577 ( 7%) 7,328 (92%)

Breakdown by NIDA Studies

Cell type descriptions (LCLs, CPLs, etc) can be found here.

Opioid Dependence
Study Name PI Subjects With DNA Plasma LCLs CPLs Whole Blood
3 Molecular Genetics of Heroin Dependence in China Ming Tsuang 1,929 1,232 (64%) 0 ( 0%) 1,097 (57%) 3 ( 0%) 222 (12%)
5 Addictions, Genotypes, Polymorphisms, and Function Mary Jeanne Kreek 1,751 1,751 (100%) 2 ( 0%) 768 (44%) 1,708 (98%) 1,012 (58%)
14 Genome-Wide Analysis for Addiction Susceptibility Genes Herb Lachman 1,405 693 (49%) 0 ( 0%) 598 (43%) 640 (46%) 116 ( 8%)
17 Opioid Dependence Wade Berrettini 204 200 (98%) 0 ( 0%) 70 (34%) 191 (94%) 99 (49%)
18 Opioid Dependence: Candidate Genes and G x E Effects Elliot Nelson 1,896 1,895 (100%) 0 ( 0%) 500 (26%) 954 (50%) 1,627 (86%)
24 START Pharmacogenetics: Exploratory Genetic Studies in Starting Treatment with Agonist Replacement Therapies (START) Wade Berrettini 786 785 (100%) 0 ( 0%) 156 (20%) 744 (95%) 784 (100%)
Total 7,971 6,556 (82%) 2 ( 0%) 3,189 (40%) 4,240 (53%) 3,860 (48%)

cell_file version: 2023-11-14 #2