Table 1 |.

Main phenotypes related to SUDs investigated in the largest genome-wide association studies available

SUD outcome	Description	GWS loci	SNP-based heritability	Sample size	Refs
Alcohol
Alcohol dependence	Alcohol dependence (AD) diagnosis based on DSM-IV or DSM-III-R criteria	1	0.090±0.019	14,904/37,944	8
Alcohol use disorder	AUD diagnosis based on ICD-9 and ICD-10 codes	10	0.056±0.004	55,584 /218,807	14
Problematic alcohol use	Meta-analysis of cohorts assessed using different phenotypic definitions including AD, AUD, and AUDIT-P	42	0.068±0.004	300,789*	13
Alcohol use disorder identification test:consumption	AUDIT-C scores were derived from electronic health records	13	0.068±0.005	272,842	14
Drinks per week	Average number of standard drinks a subject reports consuming each week	99	0.042±0.002	941,280	15
Tobacco smoking
Nicotine dependence	Nicotine dependence category (mild, moderate, severe) defined on the basis of the Fagerström Test for Nicotine Dependence (FTND) scores	5	0.086±0.012	58,000	185
Age of initiation of regular smoking	The age at which an individual first became a regular smoker	10	0.047±0.003	341,427	15
Cigarettes per day	Average number of cigarettes smoked per day binned in categories	55	0.08±0.008	337,334	15
Smoking initiation	Binary phenotype with ever regular smokers coded as cases and never regular smokers coded as controls	378	0.078±0.002	557,337/ 674,754	15
Smoking cessation	Binary phenotype with current smokers coded as cases and former smokers coded as controls	24	0.046±0.002	139,453/407,766	15
Smoking trajectories	Longitudinal trajectory groups for smoking status based on electronic health records	20	0.187±0.01 0.058±0.005	286,118	28
Opioids
Opioid dependence	Opioid dependence (OD) diagnosis based on DSM-IV criteria. Opioid exposure defined as being exposed to opioids at least once	0/1	NS 0.28±0.1	4,503/4,173/32,500	41
Opioid use disorder	OUD diagnosis and opioid exposure based on ICD-9 and ICD-10 codes	1	0.113±0.018	15,756/99,039	44
Opioid exposure	Opioid exposure defined as being exposed to opioids at least once	1	NS	4,173/32,500	41
Cannabis
Cannabis use	Self-reported information on whether the participants had ever used cannabis during their lifetimes	8	0.11±0.01	180,934*	34
Cannabis use disorder	CUD diagnosis based on DSM-IV or DSM-III-R criteria or ICD-10 codes	2	0.067±0.006	20,916/363,116	37
Age at first cannabis use	Age at first cannabis use assessed from questionnaires or clinical interviews	5	NS	24,953	35
Cocaine
Cocaine dependence	DSM-IV symptom count for cocaine dependence DSM-IV cocaine dependence diagnosis	1/0	NA 0.30±0.06	9,760 2,085/4,293	46,47

For each trait, we report a brief description, the number of genome-wide significant (GWS) loci, the SNP-based heritability, and the sample size. For binary traits, we reported the number of cases and controls (i.e., cases/controls). For traits meta-analysing different types of phenotypic definitions, we reported the effective sample size (*). For smoking trajectories, we report the SNP-based heritability calculated for contrast I (current versus never) and contrast II (current versus mixed). With respect to opioid dependence, the case–control analysis was conducted considering opioid-exposed controls and opioid-unexposed controls separately. For this phenotype, we reported information (i.e., GWS loci, SNP-based heritability, and sample size) for both analyses (cases/opioid-exposed controls/opioid-unexposed controls). With respect to cocaine dependence, we reported results from two studies. AUD, alcohol use disorder; AUDIT, Alcohol Use Disorders Identification Test for consumption (AUDIT-C) or problems (AUDIT-P); CUD, cannabis use disorder; DSM, Diagnostic and Statistical Manual of Mental Disorders; ICD, International Classification of Diseases; NA, not available in the referenced publication; NS, not significant (p>0.05); SUD, substance use disorder.