ABSTRACT Impulsivity is a multidimensional heritable phenotype that broadly refers to the tendency to act prematurely and is associated with multiple forms of psychopathology, including

substance use disorders. We performed genome-wide association studies (GWAS) of eight impulsive personality traits from the Barratt Impulsiveness Scale and the short UPPS-P Impulsive

Personality Scale (_N_ = 123,509–133,517 23andMe research participants of European ancestry), and a measure of Drug Experimentation (_N_ = 130,684). Because these GWAS implicated the gene

_CADM2_, we next performed single-SNP phenome-wide studies (PheWAS) of several of the implicated variants in _CADM2_ in a multi-ancestral 23andMe cohort (_N_ = 3,229,317, European; _N_ = 

traits, and various psychiatric and medical traits. We identified significant associations proximal to genes such as _TCF4_ and _PTPRF_, and also identified nominal associations proximal to

_DRD2_ and _CRHR1_. PheWAS for _CADM2_ variants identified associations with 378 traits in European participants, and 47 traits in Latin American participants, replicating associations with

risky behaviors, cognition and BMI, and revealing novel associations including allergies, anxiety, irritable bowel syndrome, and migraine. Our MouseWAS recapitulated some of the associations

found in humans, including impulsivity, cognition, and BMI. Our results further delineate the role of _CADM2_ in impulsivity and numerous other psychiatric and somatic traits across

ancestries and species. SIMILAR CONTENT BEING VIEWED BY OTHERS UNCOVERING THE GENETIC ARCHITECTURE OF BROAD ANTISOCIAL BEHAVIOR THROUGH A GENOME-WIDE ASSOCIATION STUDY META-ANALYSIS Article

25 October 2022 SHARED GENETIC LIABILITY FOR ALCOHOL CONSUMPTION, ALCOHOL PROBLEMS, AND SUICIDE ATTEMPT: EVALUATING THE ROLE OF IMPULSIVITY Article Open access 10 March 2023 A GENOME-WIDE

INVESTIGATION INTO THE UNDERLYING GENETIC ARCHITECTURE OF PERSONALITY TRAITS AND OVERLAP WITH PSYCHOPATHOLOGY Article Open access 12 August 2024 INTRODUCTION Impulsivity is a multifaceted

psychological construct that has been broadly defined as thoughts or actions that are “poorly conceived, prematurely expressed, unduly risky or inappropriate to the situation, and that often

result in undesirable consequences” [1]. Impulsivity has been repeatedly associated with numerous psychiatric diseases, including ADHD and substance use disorders [2, 3]. We previously

performed genome-wide association studies (GWAS) of impulsive personality traits (_n_ = 21,806–22,861) using two of the most widely used impulsivity questionnaires, the Barratt Impulsiveness

Scale (BIS-11; 3 traits) and the Impulsive Personality Scale (UPPS-P; 5 traits), as well as a measure of Drug Experimentation [4]. These traits were partially genetically correlated,

suggesting that each impulsivity domain is governed by overlapping but distinct biological mechanisms [4, 5]. Our work also identified significant genetic correlations between impulsivity

and numerous psychiatric and substance use traits, in line with the NIMH Research Domain Criteria (RDoC), proposing impulsivity as a transdiagnostic endophenotype for psychopathology [6].

The cell adhesion molecule 2 (_CADM2_) gene, which was the most robustly implicated gene in our prior GWAS of impulsivity [4], has also been extensively implicated in other risky and

substance use behaviors [7]. _CADM2_ mediates synaptic plasticity and is enriched in the frontal cortex and striatum, which are regions that regulate reward and inhibitory processes. We and

others have implicated this gene in traits that may underlie disinhibition in humans, supporting the observed genetic correlations between impulsivity and personality [8], educational

attainment [9], cognition [10], risk-taking [11], substance use [4, 10, 12,13,14,15], externalizing psychopathology [16], neurodevelopmental disorders [17, 18], physical activity [19],

reproductive health [20, 21], metabolic traits [22], and BMI [23], among others (see GWAS Catalog www.ebi.ac.uk/gwas/). _Cadm2_ knockout mice have previously been assessed for body weight

and energy homeostasis [24] but have never been behaviorally characterized for measures of impulsivity or related behaviors. Here, we took three approaches to elucidate genetic factors

related to impulsivity. First, we collaborated with 23andMe, Inc., to extend upon our earlier GWAS of impulsivity [4] by increasing our sample size approximately 6-fold (_n_ = 

123,509–133,517). Second, we performed single-SNP phenome-wide studies (PheWAS) of the 5 single nucleotide polymorphisms (SNPs) in and around _CADM2_ that have been most strongly implicated

by the current and prior GWAS. PheWAS were conducted in three ancestral groups (_N_ = 3,229,317, European; _N_ = 579,623, Latin American; _N_ = 199,663, African American) from the 23andMe

research cohort, examining close to 1300 traits, most with no published GWAS. Finally, we performed a mouse-PheWAS (“MouseWAS”) by creating and phenotyping mice harboring a _Cadm2_ mutant

allele in a broad battery of behavioral tasks that included analogous human measures of risk-taking and impulsivity, substance use, cognition and BMI. MATERIALS AND METHODS HUMAN STUDIES

GWAS COHORT AND PHENOTYPES We analyzed data from a cohort of up to 133,517 male and female research participants of European ancestry, a subset of which were analyzed in our prior

publications [4, 13, 25, 26]. All participants were drawn from the research participant base of 23andMe, Inc., a direct-to-consumer genetics company, and were not compensated for their

participation. Participants provided informed consent and participated in the research online, under a protocol approved by the external AAHRPP-accredited IRB, Ethical & Independent

Review Services (www.eandireview.com). During 4 months in 2015 and 14 months from 2018–2020, participants responded to a survey that included up to 139 questions pertaining to aspects of

impulsivity and substance use and misuse. To measure impulsive personality, we used five subscales from the UPPS-P ([27, 28]; a 20-item that measures (lack of) Premeditation, (lack of)

Perseverance, Positive Urgency, Negative Urgency, and Sensation Seeking; Table S1). We also administered the BIS-11 ([29]; a 30-item questionnaire that measures Attentional, Motor, and

Nonplanning impulsiveness; Table S1). Lastly, we measured Drug Experimentation, defined as the number of substances an individual has used (adapted from the PhenX toolkit [30]; Table S1). We

scored UPPS-P, BIS-11 and Drug Experimentation as previously described [4]. We used quantile normalization, since some scores were not normally distributed (Figs. S1–3). Only individuals

identified as being of European ancestry based on empirical genotype data [31] were included in this study. Basic demographic information about this sample is presented in Table S2. We used

Pearson correlation coefficients (_r_) to measure the phenotypic relationships between impulsivity subscales and demographics. GENOME-WIDE ASSOCIATION AND SECONDARY ANALYSES DNA extraction

and genotyping were performed on saliva samples by CLIA-certified and CAP-accredited clinical laboratories of Laboratory Corporation of America. Quality control, imputation, and genome-wide

analyses were performed by 23andMe (Table S3; [32, 33]). 23andMe’s analysis pipeline performs linear regression assuming an additive model for allelic effects (Supplementary Material).

Covariates included age (inverse-normal transformed), sex, the top five principal genotype components, and indicator variables for genotyping platforms. _p_-values were corrected for genomic

control. We examined genotype*sex interactions for suggestive loci. In addition, for the top loci, we examined African American and Latin American 23andMe research participants who had

responded to the same survey. We used the FUMA web-based platform (version 1.3.6a) and MAGMA v1.08 [34, 35] to explore the functional consequences of the GWAS loci and to conduct gene-based

analyses. We used LDSC [36] to calculate genetic correlations (rg) between UPPS-P, BIS and Drug Experimentation, and 96 selected traits informed by prior literature. PHENOME-WIDE ASSOCIATION

SCAN (PHEWAS) IN 23ANDME We performed single-SNP PheWAS for 5 _CADM2_ SNPs (rs993137, rs62263923, rs11708632, rs818219, rs6803322) using up to 1291 well-curated self-reported phenotypes

from a separate cohort of 23andMe research participants of European (_N_ ≤ 3,229,317), Latin American (_N_ ≤ 579,623) and African American (_N_ ≤ 199,663) ancestries. We excluded traits with

<1000 responses, based on a prior simulation study for PheWAS power analysis [37]. Ancestry was determined by analyzing local ancestry ([31] Supplementary Material). The variants were

selected based on our GWAS results and previous literature (Table S4, and Supplementary Material). Genotyped and imputed variant statistics for the PheWAS are shown in Table S5. An overview

of the data collection process has been previously described [38]. All regression analyses were performed using R version 3.2.2. We assumed additive allelic effects and included covariates

for age (as determined by participant date of birth), sex, and the top five ancestry-specific principal components. We used a 5% FDR correction for multiple testing. MOUSEWAS SUBJECTS,

BEHAVIORAL CHARACTERIZATION, AND STATISTICAL ANALYSES Our _Cadm2_ mutant mice were produced at the University of California San Diego, Moores Cancer Center, Transgenic Mouse Core. We used

the JM8.N4 cryosperm line (CSD70565 KOMP), which carries a floxed null allele in the _Cadm2_ gene (Fig. S30), on a C57BL/6 N background. We crossed the floxed null allele line with a

constitutive CRE driver line (Stock# 014094; The Jackson Laboratory), yielding a global constitutive null allele. We used a heterozygous x heterozygous (HET) breeding scheme, which produced

homozygous (HOM) mutant _Cadm2_ mice and their HET and wildtype (WT) littermates. Mice were genotyped using allele-specific polymerase chain reaction on ear notch tissue followed by gel

electrophoresis [39]. CADM2 protein expression levels were quantified by western blotting (Fig. S31). Five separate cohorts of male and female mice were used for these studies. See

Supplementary Material for a more detailed description of the tasks and analyses of main variables. Procedures were approved by the University of California San Diego Institutional Animal

Care and Use Committee. The UCSD animal facility meets all federal and state requirements for animal care and was approved by the American Association for Accreditation of Laboratory Animal

Care. Procedures from cohort 2 were conducted in accordance with the Canadian Council on Animal Care and were approved by the University of Guelph Institutional Animal Care and Use

Committee. RESULTS GENOME-WIDE ASSOCIATION ANALYSES AND SECONDARY ANALYSES Self-reported impulsivity and drug experimentation scores are shown in Table S6. We found that ~6–11% of the

phenotypic variation of these traits can be explained by common variants (Table S7). We identified 21 genome-wide significant associations (_p_ < 5.0E-08) for UPPS-P (5 traits), BIS (3

traits), and Drug Experimentation (Figs. 1; S4–21; Table S8). Although we tested 9 traits, in keeping with the standards of the field, we did not adjust the significance threshold. We also

detected several nominal associations (_p_ < 1.0E-06, Table S8); we discuss some of them in the Supplementary Material. GWAS OF UPPS-P PREMEDITATION We detected one significant hit

(rs2958162, _p_ = 2.50E-10), located on chromosome 18 in the _TCF4_ gene, which encodes a helix-loop-helix transcription factor and is widely expressed throughout the body and during

development. Polymorphisms in _TCF4_ have been associated with risk-taking and adventurousness [15], alcohol consumption [40], schizophrenia [41], depression [42, 43], and neuroticism [44,

45] (Table S9); _TCF4_ is also a non-GWAS candidate gene for other psychiatric and neurological conditions [46]. PERSEVERANCE We detected one significant association (rs5943997, _p_ = 

1.50E-8) in the _POLA1_ gene on the X chromosome. _POLA1_ has been related to blood traits [46] and neurodevelopmental disorders [47], but its association with impulsivity is novel. POSITIVE

URGENCY We identified one significant hit (rs143987963, _p_ = 4.30E-08) on chromosome 12, near the genes _MDM1_ and _RAP1B_; however, inspection of the locus zoom plot (Fig. S9) does not

support a robust association. NEGATIVE URGENCY We detected three significant hits: rs4840542 (_p_ = 1.60E-09), on chromosome 8, in the _XKR6_ gene; rs5008475 (_p_ = 4.90E-09), on chromosome

5, near _TMEM161B_ and _MEF2C_; and rs7829975, on chromosome 8, near _SGK223_ and _CLDN23_ (_p_ = 5.00E-09). Variants in strong LD with rs4840542 and rs7829975 are highly pleiotropic, and

have been previously associated with several traits (Table S9), including body mass index (BMI) [48, 49], neuroticism [50, 51], depression [52], blood pressure, and alcohol consumption [53].

_XKR6_ was also implicated in a recent GWAS of externalizing [16], and a GWAS of anxiety and depression [52]. SENSATION SEEKING We detected 5 significant associations. First, we again

observed a previously reported [4] association with a SNP near _CADM2_ (rs11288859, _p_ = 2.10E-09) on chromosome 3. We also detected an association with a SNP in _TCF4_ (rs2958178, _p_ = 

3.80E-12). We identified a significant hit in _CACNA2D1_ (rs38547, _p_ = 2.10E-08) on chromosome 18. _CACNA2D1_ has been previously associated with feeling nervous [50], and levels of sex

hormone-binding globulin [54]. Furthermore, we found a significant association (rs1605379_, p_ = 3.80E-08) on chromosome 16, near _CYLD_ and _SALL1_. SNPs in strong LD with rs1605379 have

been previously identified for risk-taking, adventurousness, and smoking initiation (Table S9). Lastly, we found a significant association (rs12600879_, p_ = 4.10E-08) on chromosome 17, near

_TBX21_ and _OSBPL7_. Variants in strong LD with rs12600879 have been associated with BMI [55], but the finding in relation to impulsivity is novel. GWAS OF BIS-11 ATTENTIONAL We identified

one significant association (rs10196237, _p_ = 1.10E-08) on chromosome 2, near the genes _SPHKAP and PID1_. _SPHKAP_ has been previously associated with educational attainment [9], but the

association with impulsivity is novel. MOTOR We detected one significant association near _CADM2_ (rs35614735, _p_ = 3.20E-11). We also identified an association (rs111502401, _p_ = 

2.00E-08), on chromosome 19, near the genes _ZNF229_ and _ZNF180_; however, inspection of the regional association is not supportive of a strong association (Fig. S17). NONPLANNING We

detected 2 variants: rs35614735 (_p_ = 4.70E-12) near _CADM2_, which was the same SNP identified for Motor impulsivity; and rs6872863 (_p_ = 1.20E-08) in the gene _ELOVL7_ on chromosome 5.

Variants in strong LD with rs6872863 have been reported for a variety of traits including educational attainment, mathematical ability [9], household income [56], and brain morphology, such

as cortical surface area [57] (Table S9). However, there is extensive LD in this region, making the association difficult to interpret. GWAS OF DRUG EXPERIMENTATION We previously reported

[4] a suggestive association (rs2163971, _p_ = 3.00E-07) near the _CADM2_ gene. In the present study, we identified a nearby SNP that was genome-wide significant (rs35614735, _p_ = 

2.80E-15). We also report 4 novel hits (rs951740, _p_ = 9.70E-10, _PTPRF_ on chromosome 1; rs12713405, _p_ = 9.70E-09, _BLC11A_ on chromosome 2_;_ rs67660520, _p_ = 7.60E-09, _CADPS2_ on

chromosome 7_;_ rs7128648, _p_ = 2.50E-09, _NCAM1_ on chromosome 11). Intriguingly, _PTPRF_ has been recently associated with problematic prescription opioid use [25] and opioid use disorder

[58], as well as smoking initiation/cessation [59], cognition [60], and educational attainment [9] (Table S9). Variants in strong LD with rs67660520 have been associated with ADHD [61],

smoking initiation [59], number of sexual partners [15] and BMI [49] (Table S9). _NCAM1_ variants have been previously associated with alcohol, cannabis and smoking behaviors [59, 62],

mathematical ability [9], and anxiety and depression [52], among other traits. GENE-BASED ANALYSES Similar to the GWAS results, gene-based analyses using MAGMA identified an association

(Bonferroni _p_ < 2.53E-06; Table S10) between _CADM2_ and 6 of the 9 traits examined in this paper: Premeditation, Sensation Seeking (UPPS-P); Attentional, Motor and Nonplanning

(BIS-11); and Drug Experimentation. _TCF4_, which was significantly associated with Premeditation and Sensation Seeking in the GWAS, was significantly associated with these traits in the

gene-based analysis. _MAPT_, which has been previously associated with many traits including multiple alcohol-related behaviors [13], was implicated in Negative Urgency. Lastly, _KDM4A_,

which was recently related to problematic opioid use and interacts with selective serotonin reuptake inhibitors and dopaminergic agents [25], was significantly associated with Drug

Experimentation. PHENOTYPIC AND GENETIC CORRELATIONS A phenotypic and genetic correlation matrix of all 9 traits is shown in Fig. S22 and Tables S11–12. Consistent with the literature and

our prior work [4, 5, 63, 64], both phenotypic and genetic inter-correlations among the UPPS-P and BIS subscales were high and positive, with the exception of Sensation Seeking and

Perseverance, suggesting that these traits may represent relatively different constructs [5, 13, 63]. Drug experimentation was positively and significantly associated with all impulsive

personality traits. All impulsivity traits were phenotypically associated (_r_ = −0.34–0.11) with demographic variables (Table S12), impulsivity scores being greater in male and younger

research participants, compared to female and older participants; and in participants with higher BMI, lower household income, and fewer years of education, as we previously reported [13].

Figure 2 shows a genetic correlation matrix of BIS, UPPS-P, Drug Experimentation and several other phenotypes (full results in Table S13). As anticipated, we found positive moderate to high

genetic correlations (_r__g_ = 0.25–0.79) between virtually all UPPS-P (except Perseverance and Sensation Seeking) and BIS subscales, and Drug Experimentation, and substance use disorders

Table S13). We also observed moderate to strong associations between all impulsive subscales (except UPPS-P Perseverance) and other personality traits, such as risk-taking (_r__g_ = 

0.15–0.65), neuroticism (_r__g_ = −0.23–0.84), and loneliness (_r__g_ = 0.17–0.54), particularly for Positive and Negative Urgency. Extraversion was positively associated with Sensation

Seeking (_r__g_ = 0.34). Externalizing psychopathology, which represents disorders and behaviors characterized by deficits in inhibition, was strongly associated with all impulsivity facets

(_r__g_ = 0.28–0.92), except Perseverance. We also identified positive associations with an array of psychiatric phenotypes, including ADHD (_r__g_ = 0.20–0.47), depression (_r__g_ = 

−0.13–0.47) and anxiety (_r__g_ = −0.38–0.61) disorders, and cross-disorder (_r__g_ = 0.12–0.44). The associations were again primarily significant for all except Perseverance and Sensation

Seeking. Other disorders showed weaker associations (e.g., schizophrenia, _r__g_ = −0.09–0.15) or were only significantly associated with one impulsivity facet [e.g., anorexia nervosa

(Perseverance, _r__g_ = −0.16); bipolar disorder (Motor, _r__g_ = 0.22)]. Most impulsivity subscales were genetically correlated with lower socioeconomic variables [e.g., educational

attainment (_r__g_ = −0.49 to −0.16), income (_r__g_ = −0.38 to −0.16), Townsend index (_r__g_ = 0.18–0.58)]. Metabolic and medical phenotypes, such as BMI (_r__g_ = 0.18–0.28), chronic pain

(_r__g_ = 0.22–0.46), insomnia (_r__g_ = 0.20–0.42), and coronary artery disease (_r__g_ = 0.18–0.30), were genetically correlated with all impulsive subscales (except Perseverance and

Sensation Seeking). We also noted negative genetic associations with parental longevity (_r__g_ = −0.17 to −0.32). PHEWAS To explore the impact of specific variants in and around _CADM2_, we

performed single-SNP PheWAS using 5 of the most implicated SNPs, independently, against 1291 traits (Fig. 3). The list of PheWAS association results using the 23andMe cohort after 5% FDR

correction is available in Table S14 (summary), S15 (Europeans), S16 (Latin American) and S17 (African Americans). In European cohorts, _CADM2_ variants had been previously identified to be

significantly associated with numerous traits (Table S18). Most SNPs were highly correlated (R2 > 0.1) and tagged similar traits (Fig. S23), but the overlap was incomplete (Fig. S24 and

Table S19). rs993137, located at 85,449,885 bp on chromosome 3, showed the highest number of associations (378), which we describe below. We replicated all previously known associations in

23andMe participants of European ancestry, identifying signals across all categories tested (Table S15). These included negative associations with risky behavior (e.g., lower risk for

adventurousness [β = −0.05, _p_ = 1.33E-08], risk-taking tendencies [β = −0.02, _p_ = 1.13E-07]) and substance use behaviors (e.g., lower risk for alcohol consumption [β = −0.03, _p_ = 

2.05E-09] and tobacco initiation (β = −0.02, _p_ = 3.66E-12; but see packs per day, β = 0.01, _p_ = 1.05E-03), as well as negative associations with psychiatric disorders characterized by

deficits in impulsivity, such as lower risk for ADHD (β = −0.05, _p_ = 2.17E-41). Furthermore, we found positive associations with educational outcomes (e.g., higher educational attainment

(β = 0.03, _p_ = 1.67E-12). Novel findings included positive associations with allergies (β = 0.04, _p_ = 4.51E-03), anxiety (e.g., panic [β = 0.02, _p_ = 6.82E-08]), and medical conditions

(e.g., IBS [β = 0.02, _p_ = 8.89E-07]), anemia (β = 0.01, _p_ = 8.30E-74), hepatitis C (β = −0.06, _p_ = 8.36E-10). Intriguingly, we also detected positive associations with pain phenotypes

(β = 0.02, _p_ = 8.37E-12) and a need for a higher dose of pain medication (β = 0.01, _p_ = 1.02E-06). For the overlapping phenotypes, UK Biobank PheWAS results [65] largely supported the

23andMe PheWAS findings (except for smoking behaviors). For example, we identified associations with dietary traits (e.g., daily fruit and vegetable intake (β = −0.01, _p_ = 4.23E-11),

pastry frequency (β = 0.01, _p_ = 7.36E-06), sleep quality (β = −0.01, _p_ = 2.53E-03), and number of pregnancies (β = −0.01, _p_ = 7.69E-04), among others (Table S15, [12]). In the PheWAS

of the Latin American cohort, 47 traits were significantly associated with _CADM2_ variants (Table S16). The highest number of associations were again observed for rs993137 [66], which are

described below. Similarly, although some of the SNPs were correlated (R2 > 0.1; Fig. S24), the overlap was incomplete (Fig. S26; Table S20). The pattern of associations was consistent

with those described in the European cohort. The strongest associations were with risky behaviors, such as adventurousness (β = −0.04, _p_ = 1.76E-17), risk-taking (β = −0.02, _p_ = 

5.90E-07), alcohol consumption (β = −0.03, _p_ = 1.41E-12), and disorders characterized by high levels of impulsivity, such as ADHD (β = −0.04, _p_ = 4.74E-10). The novel findings were,

again, with multiple forms of allergies (e.g., seasonal allergies, β = 0.03, _p_ = 3.0E-04), migraine (β = 0.04, _p_ = 1.56E-04), sleep behaviors (e.g., sleep apnea, β = −0.03, _p_ = 

6.76E-04), among others. All findings that were in common between the European and Latin American cohorts showed the same direction of effect and similar effect sizes. We did not identify

FDR-significant associations in the African American cohort (Table S17). The effect sizes were generally extremely small (Figs. S27–28), as is expected for a single gene and complex traits.

MOUSEWAS Figure 4 summarizes the MouseWAS results across the five cohorts tested. Full statistics and additional secondary measures are described in the Supplementary Material and Table S20.

COHORT 1 - MOTIVATION, INHIBITION, AND RISK-TAKING BEHAVIOR No differences in motivation were found between WT and HET mice during the Progressive Breakpoint task [F(1,51) = 0.003, _p_ = 

9.57E-01; Fig. 4A]. However, we noted significant genotype differences in behavioral flexibility in the Probabilistic Reversal Learning (PRL) task, as indexed by the number of trials to

first reversal [F(1,42) = 4.27, _p_ = 4.50E-02; Fig. 4B], and risky behavior in the mouse Iowa Gambling Task (IGT; F(1,51) = 4.70, _p_ = 3.50E-02; Fig. 4C], HET mice requiring fewer trials

to reach criterion and choosing risky options less frequently than WT mice (_p_ < 0.05), respectively. The number of premature responses, on the contrary, were higher in HET mice [F(1,51)

 = 5.78, _p_ = 2.00E-02] compared to WT mice (_p_ < 0.05; Fig. 4D). In the Behavioral pattern monitor (BPM), HET mice exhibited greater exploratory behavior, as shown by an increase in

hole-pokes [F(1,53) = 4.88, _p_ = 3.20E-02; Fig. 4E], compared to WT mice (_p_ < 0.05), but general levels of activity, such as distance traveled (F(1,53) = 0.42, _p_ = 5.21E-01; Fig.

4F), were similar across the genotypes. Lastly, although the startle response was equal across the groups (Fig. 4G), prepulse inhibition (PPI) was larger in HET mice compared to WT mice (_p_

 < 0.05; Fig. 4H), particularly at ISI 25 and 100 in HET mice [F(1,53) = 8.23, _p_ = 6.00E-03, F(1,53) = 4.50, _p_ = 3.90E-02, respectively]. COHORT 2 - MOTORIC IMPULSIVITY The main

outcome tested in cohort 2 were premature responses via the 5-choice serial reaction time task (5CSRTT; Fig. 4J–M). Premature responses were lower in HOM (_p_ < 0.001) and WT (_p_ < 

0.02) mice compared to HET mice under standard conditions (F(2,36) = 8.74, _p_ = 8.06E-04; Fig. 4J), and compared to both HET (_p_ < 0.001) and WT (_p_ < 0.01) mice during a long ITI

session (H(2) = 16.10, _p_ = 3.19E-04; Fig. 4L). HOM mice were faster at learning the 5CSRTT, requiring fewer days for adequate baseline performance (F(2,36) = 7.42, _p_ = 2.00E-03; Fig.

4I), compared to WT mice (_p_ < 0.01). COHORT 3 - GENERAL LOCOMOTION, ANXIETY-LIKE BEHAVIOR, AND ETHANOL CONSUMPTION We found a significant effect of genotype on the distance traveled in

the Open Field [OF; F(2,70) = 7.525, _p_ = 1.00E-03; Fig. 4N], with HOM mice showing higher levels of locomotor activity than WT mice (_p_ = 1.40E-02). No differences in anxiety-like

behavior were detected across WT, HET or HOM mice in the Elevated Plus Maze (EPM) or Light-Dark Box (LDB) tests (Fig. 4O–P; Table S20). The total amount of ethanol consumed during the

drinking-in-the-dark (DID) paradigm did not differ between the groups ([F(2,78) = 1.084, _p_ = 3.44E-01]; Fig. 4Q). COHORT 4 - BODY WEIGHT Relative to WT mice, there was a significant

reduction in body weight in HOM mice from week 21 onwards (β = −3.74 ± 1.27, _p_ = 4.00E-03; Fig. 4R). COHORT 5 - DENDRITE MORPHOLOGY Quantitative analyses of MSN in the NAc revealed no

difference in dendritic spine density across the groups (Fig. 5S). DISCUSSION In this study, we performed the largest GWAS of impulsive personality traits to date, we conducted the first

multi-ancestral PheWAS exploring the role of _CADM2_ on a diverse array of traits, and we performed a corresponding MouseWAS using _Cadm2_ mutant mice to assess its role in impulsivity and

other relevant behaviors. We extended on prior findings [4, 5] showing that the genetic architectures of impulsivity facets only partially overlap, providing further support to the idea of

impulsivity being a multifaceted construct even at the genetic level. We identified positive genetic correlations across multiple domains, particularly substance use disorders, confirming

that NIMH RDoC transdiagnostic domains [6], or endophenotypes, such as impulsive personality traits, can be used to dissect the genetic basis of psychiatric illness and normal functioning.

RDoC or transdiagnostic traits are beneficial because they enable translational research and provide a more granular biological understanding of psychiatric disorders. Using mouse and human

correlates, we provided further evidence that _CADM2_ is a robust candidate gene for impulsivity and an important modulator of numerous other psychiatric and somatic traits. We increased the

sample size of our prior GWAS of impulsivity by almost 6-fold and identified 21 genome-wide significant loci implicated in impulsive personality and Drug Experimentation. For instance, SNPs

located in the gene _TCF4_ were implicated in 3 subscales; this gene is also highly pleiotropic for other psychiatric conditions. Furthermore, we identified associations with _NCAM1_,

which, intriguingly, is a critical member of the NTAD (_NCAM1-TTC12-ANKK1-DRD2_) gene cluster [67] and variants correlated with _NCAM1_ in that cluster have been associated with differences

in D2 receptor density [68]. We also detected associations near _XKR6_ and _AFF3_, which have been recently implicated in externalizing psychopathology [16], and _PTPRF_ and _KDM4A_,

recently implicated in problematic opioid use [25] and opioid use disorder [58]. Although in this report we focused on _CADM2_, functional studies of those genes are also warranted.

Furthermore, we found nominal evidence for candidate gene studies implicating monoamine neurotransmitters in impulsivity and Drug Experimentation (_DRD2_, _HTR3B_). High impulsivity depends

on a neural network that includes the ventral striatum (subsuming the NAc) with top-down control from prefrontal cortical regions, and is modulated by monoamine neurotransmitters including

dopamine and serotonin [69]; this is the first GWAS to implicate genes modulating these systems as robust candidate genes for impulsivity. Recent studies have implicated the _CADM2_ gene in

impulsivity and traits associated with reward sensitivity and multiple domains of human health. We confirmed numerous previously reported associations and extended our findings of variants

related to _CADM2_. _CADM2_ was significantly associated with 4 out of the 9 traits that we measured in GWAS and 6 out of the 9 traits that we measured in gene-based analyses. In the PheWAS,

_CADM2_ variants were associated with decreased risk for externalizing psychopathology, but also increased risk for internalizing psychopathology (anxiety, depression, OCD). We also

observed novel associations with migraines and various allergies. Using a similar approach with UK Biobank data, previous studies have found that this enrichment of associations is higher

than expected [12] compared to other genes. These results provide evidence that _CADM2_ variants are associated with broad health outcomes, but whether this gene affects human health via

disruptions in inhibitory control or reward systems, or whether it acts via multiple pathways [70], is still not fully understood. A relatively unique feature of our study is that, to follow

up on the _CADM2_ loci implicated in human studies, we generated a _Cadm2_ mutant mouse line and used it to perform a PheWAS-like study in mice, which we have termed a MouseWAS to emphasize

its conceptual similarity to human PheWAS studies. These functional experiments provided information about the causality and directionality of effects in its reported associations. We found

evidence that loss of _Cadm2_ resulted in _less_ risky behavior and _improved_ information processing, extending on prior work in humans [4, 10, 16, 68, 71]. _Cadm2_ expression may uniquely

contribute to the different domains of impulsivity. The IGT assays preference for high risk, high reward (disadvantageous) choices vs low risk, low reward (advantageous) choices [72]. HET

mice exhibited a greater preference for selecting the safe option vs their WT littermates. This finding can be contrasted with the _elevated_ premature responses in the 5CSRTT seen in HET vs

WT mice, reflective of motoric impulsivity. However, premature responses have also been linked to temporal discrimination, wherein mice and humans overestimating the passage of time exhibit

higher premature responses [73, 74]. The preference for less risky options of HET mice in the IGT could reflect their misjudgment of time – resulting in higher premature responses – and

thus avoidance of higher temporal punishment in the IGT. We also observed genotype differences in performance that could be indicative of _Cadm2_ function in information processing. HET mice

exhibiting _better_ PPI at the shortest temporal window (25 ms) supports the premise that these mice have faster processing speeds. HET mice also showed small increases in hole-poking in

the BPM test, which is thought to reflect exploration of the environment and information gathering. Finally, we observed that HOM mice acquired 5CSRTT faster than WT littermates. Taken

together, these results suggest that _Cadm2_ reduction may improve some facets of information processing. Findings from the 5CSRTT provide evidence that _Cadm2_ deletion improves some

information processing and impulsivity outcomes, while being detrimental to others. HET mice were the most likely to commit 5CSRTT premature responses, although HOM mice were surprisingly

the least likely to make premature responses. Interestingly, although not significant, there was a consistent elevation in the number of premature responses committed by the HOM mice as the

stimulus duration was reduced. This could suggest that HOM mice, like HET mice, may show motoric impulsivity deficits when performing tasks that require greater attentional demand. Compared

with WT, HOM mice also showed impaired accuracy performance under RSD conditions, in line with our human findings of _CADM2_ association with BIS Attentional, and cognitive function by

others [10]. The heterogeneity of performance outcomes in the HOM mice further supports a unique but overlapping contribution of genetics across impulsivity domains. In this paper, we

translated measures from human to mice. These studies begin the process of understanding the biological basis of associations identified by GWAS. The methods for measuring impulsivity in

humans and mice are fundamentally different. Despite these differences, our MouseWAS identified several measures of impulsivity that were influenced by _Cadm2_, consistent with our

observations in humans. Furthermore, _CADM2_ has been shown to be implicated in BMI in humans [24, 75] and energy homeostasis in mice [24]; extending on this, we found novel evidence of body

weight reductions in adult mutant mice. Interestingly, _Cadm2_ did not have more general effects on mouse behavior; for instance, we did not observe deficits in anxiety-like behavior or

general motivation, as some of the human PheWAS findings revealed. A few other measures were also inconsistent across species, particularly measures of alcohol consumption, where _CADM2_

showed a role in humans [7, 13, 76, 77] but not mice. Lastly, some measures identified by our human PheWAS (e.g., allergies and other medical conditions) were not examined in our MouseWAS.

This approach highlights the challenges of using mouse models to further investigate the role of specific genes in behavioral traits. _CADM2_ encodes the immunoglobulin adhesion protein

SynCAM 2, which is part of the family of synaptic adhesion molecules known as SynCAMs. Studies have shown the influence of SynCAMs on synaptogenesis [78,79,80,81,82], axon guidance [83], and

neuron myelination [84,85,86], processes that have direct effects on the pathology of neurodevelopmental diseases [56]. _CADM2_ is strongly expressed in the striatum and frontal cortex,

which are core regions that regulate impulsivity [69]. We did not observe changes in spine density in the Nac, which suggests that _Cadm2_ may not have a role as a postsynaptic organizer of

spines in this region, or may have redundant functions that are compensated in the mutant mice by other molecules. Based on _in-silico_ analyses in humans, _CADM2_ expression seems to be

greater at earlier stages of development (Fig. S29); whether _Cadm2_ may affect earlier stages of development (prenatal and early postnatal) that are compensated in adulthood has not been

investigated in this study. Several limitations of this study are worth noting. The discovery GWAS only includes male and female participants of European ancestry. While we provided

exploratory analyses of top variants in other ancestries and broken down by sex (Supplementary Table 22), larger sample sizes would be needed to perform GWAS separately in males and females.

Our results are also biased by potential ascertainment and characteristics of the sample; the 23andMe participant population is more educated and has higher socioeconomic status and lower

levels of drug use and impulsivity than the general US population [86]. Replication in additional cohorts with different characteristics is warranted. Moreover, although the traits we

studied are extracted via well-established questionnaires, they are self-reported measures, which are different from behavioral phenotypes [87, 88]. Another issue is that, although we tested

multiple variants in the _CADM2_ loci, further conditional analyses are required to determine if this signal and previously reported associations implicating _CADM2_ loci, including a large

non-coding rare deletion in the first intron of _CADM2_ [70], may tag the same underlying genetic effect. We are also unaware of the sequence of events, and whether there is true pleiotropy

or mediation effects has not been examined. The analyses were well-powered for moderate and large effect sizes. Still, for unclear reasons, despite similar minor allele frequencies and

imputation quality of the SNPs we tested across all ancestries, we identified no significant associations in the African American cohort. Finally, although our mouse studies detected some

discordant cross-species effects of _Cadm2_ on behavior, background strain effects [89] or subtle allelic variations (vs whole KO) may explain those differences. While some results are

suggestive of additive effects, we were unable to evaluate different genetic models due to lack of sufficient sample sizes for HOM mice. Future multivariate analyses examining paths of

commonality and specificity across impulsivity facets may provide further insights not herein examined. In conclusion, we show that impulsivity facets are extremely polygenic, but of very

high transdiagnostic significance. Genetic studies using research participants not ascertained for neuropsychiatric disorders may represent an efficient and cost-effective strategy for

elucidating the genetic basis and etiology of genetically complex psychiatric diseases. Using homologous measures of impulsivity in mice and humans across three ancestral backgrounds, we

provide evidence of the overarching role of _CADM2_ on impulsivity, and a much broader impact on human health. DATA AVAILABILITY We provide summary statistics for the top 10,000 SNPs (Tables

S23–31). Full GWAS summary statistics will be made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants.

Please visit https://research.23andme.com/collaborate/#dataset-access/ for more information and to apply to access the data. CODE AVAILABILITY All software used to generate results has been

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participants and employees of 23andMe for making this work possible. We would also like to thank The Externalizing Consortium for sharing the GWAS summary statistics of externalizing. The

Externalizing Consortium: Principal Investigators: Danielle M. Dick, Philipp Koellinger, K. Paige Harden, Abraham A. Palmer. Lead Analysts: Richard Karlsson Linnér, Travis T. Mallard, Peter

B. Barr, Sandra Sanchez-Roige. Significant Contributors: Irwin D. Waldman. The Externalizing Consortium has been supported by the National Institute on Alcohol Abuse and Alcoholism

(R01AA015416 -administrative supplement), and the National Institute on Drug Abuse (R01DA050721). Additional funding for investigator effort has been provided by K02AA018755, U10AA008401,

P50AA022537, as well as a European Research Council Consolidator Grant (647648 EdGe to Koellinger). The content is solely the responsibility of the authors and does not necessarily represent

the official views of the above funding bodies. The Externalizing Consortium would like to thank the following groups for making the research possible: 23andMe, Add Health, Vanderbilt

University Medical Center’s BioVU, Collaborative Study on the Genetics of Alcoholism (COGA), the Psychiatric Genomics Consortium’s Substance Use Disorders working group, UK10K Consortium, UK

Biobank, and Philadelphia Neurodevelopmental Cohort. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Department of Psychiatry, University of California San Diego, La Jolla, CA, USA Sandra

Sanchez-Roige, Mariela V. Jennings, Jazlene E. Mallari, Lieke C. van der Werf, Sevim B. Bianchi, Yuye Huang, Calvin Lee, Samuel A. Barnes, Jin Yi Wu, Amanda M. Barkley-Levenson, Ely C.

Boussaty, Cedric E. Snethlage, Danielle Schafer, Zeljana Babic, Jared W. Young & Abraham A. Palmer * Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

Sandra Sanchez-Roige * Department of Biomedical Sciences, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada Hayley H. A. Thorpe & Jibran Y. Khokhar * Psychiatric and

Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA Travis T. Mallard * Department of Psychology, University of Guelph, Guelph, ON,

Canada Boyer D. Winters * Department of Neuroscience, Tufts University School of Medicine, Boston, MA, USA Katherine E. Watters & Thomas Biederer * Department of Neurology, Yale School

of Medicine, New Haven, CT, USA Katherine E. Watters * Peter Boris Centre for Addictions Research, McMaster University and St. Joseph’s Healthcare Hamilton, Hamilton, ON, Canada and Homewood

Research Institute, Guelph, ON, Canada James Mackillop * Laboratory of Behavioural and Clinical Neuroscience, School of Psychology, University of Sussex, Brighton, UK David N. Stephens *

23andMe, Inc., Sunnyvale, CA, USA Stella Aslibekyan, Adam Auton, Elizabeth Babalola, Robert K. Bell, Jessica Bielenberg, Katarzyna Bryc, Emily Bullis, Daniella Coker, Gabriel Cuellar

Partida, Devika Dhamija, Sayantan Das, Teresa Filshtein, Kipper Fletez-Brant, Will Freyman, Karl Heilbron, Pooja M. Gandhi, Barry Hicks, David A. Hinds, Ethan M. Jewett, Yunxuan Jiang, 

Katelyn Kukar, Keng-Han Lin, Maya Lowe, Jey C. McCreight, Matthew H. McIntyre, Steven J. Micheletti, Meghan E. Moreno, Joanna L. Mountain, Priyanka Nandakumar, Elizabeth S. Noblin, Jared

O’Connell, Aaron A. Petrakovitz, G. David Poznik, Morgan Schumacher, Anjali J. Shastri, Janie F. Shelton, Jingchunzi Shi, Suyash Shringarpure, Vinh Tran, Joyce Y. Tung, Xin Wang, Wei Wang, 

Catherine H. Weldon, Peter Wilton, Alejandro Hernandez, Corinna Wong, Christophe Toukam Tchakouté, Sarah L. Elson & Pierre Fontanillas * Schulich School of Medicine and Dentistry,

Western University, London, ON, Canada Jibran Y. Khokhar * Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA Abraham A. Palmer Authors * Sandra

Sanchez-Roige View author publications You can also search for this author inPubMed Google Scholar * Mariela V. Jennings View author publications You can also search for this author inPubMed

 Google Scholar * Hayley H. A. Thorpe View author publications You can also search for this author inPubMed Google Scholar * Jazlene E. Mallari View author publications You can also search

for this author inPubMed Google Scholar * Lieke C. van der Werf View author publications You can also search for this author inPubMed Google Scholar * Sevim B. Bianchi View author

publications You can also search for this author inPubMed Google Scholar * Yuye Huang View author publications You can also search for this author inPubMed Google Scholar * Calvin Lee View

author publications You can also search for this author inPubMed Google Scholar * Travis T. Mallard View author publications You can also search for this author inPubMed Google Scholar *

Samuel A. Barnes View author publications You can also search for this author inPubMed Google Scholar * Jin Yi Wu View author publications You can also search for this author inPubMed Google

Scholar * Amanda M. Barkley-Levenson View author publications You can also search for this author inPubMed Google Scholar * Ely C. Boussaty View author publications You can also search for

this author inPubMed Google Scholar * Cedric E. Snethlage View author publications You can also search for this author inPubMed Google Scholar * Danielle Schafer View author publications You

can also search for this author inPubMed Google Scholar * Zeljana Babic View author publications You can also search for this author inPubMed Google Scholar * Boyer D. Winters View author

publications You can also search for this author inPubMed Google Scholar * Katherine E. Watters View author publications You can also search for this author inPubMed Google Scholar * Thomas

Biederer View author publications You can also search for this author inPubMed Google Scholar * James Mackillop View author publications You can also search for this author inPubMed Google

Scholar * David N. Stephens View author publications You can also search for this author inPubMed Google Scholar * Sarah L. Elson View author publications You can also search for this author

inPubMed Google Scholar * Pierre Fontanillas View author publications You can also search for this author inPubMed Google Scholar * Jibran Y. Khokhar View author publications You can also

search for this author inPubMed Google Scholar * Jared W. Young View author publications You can also search for this author inPubMed Google Scholar * Abraham A. Palmer View author

publications You can also search for this author inPubMed Google Scholar CONSORTIA 23ANDME RESEARCH TEAM * Stella Aslibekyan * , Adam Auton * , Elizabeth Babalola * , Robert K. Bell * , 

Jessica Bielenberg * , Katarzyna Bryc * , Emily Bullis * , Daniella Coker * , Gabriel Cuellar Partida * , Devika Dhamija * , Sayantan Das * , Teresa Filshtein * , Kipper Fletez-Brant * , 

Will Freyman * , Karl Heilbron * , Pooja M. Gandhi * , Karl Heilbron * , Barry Hicks * , David A. Hinds * , Ethan M. Jewett * , Yunxuan Jiang * , Katelyn Kukar * , Keng-Han Lin * , Maya Lowe

* , Jey C. McCreight * , Matthew H. McIntyre * , Steven J. Micheletti * , Meghan E. Moreno * , Joanna L. Mountain * , Priyanka Nandakumar * , Elizabeth S. Noblin * , Jared O’Connell * , 

Aaron A. Petrakovitz * , G. David Poznik * , Morgan Schumacher * , Anjali J. Shastri * , Janie F. Shelton * , Jingchunzi Shi * , Suyash Shringarpure * , Vinh Tran * , Joyce Y. Tung * , Xin

Wang * , Wei Wang * , Catherine H. Weldon * , Peter Wilton * , Alejandro Hernandez * , Corinna Wong *  & Christophe Toukam Tchakouté CORRESPONDING AUTHORS Correspondence to Sandra

Sanchez-Roige or Abraham A. Palmer. ETHICS DECLARATIONS COMPETING INTERESTS MVJ, SBB, YH, SSR and AAP were supported by funds from the California Tobacco-Related Disease Research Program

(TRDRP; Grant Number 28IR-0070, T29KT0526 and T32IR5226). SBB was also supported by P50DA037844, SSR was also supported by NIH/NIDA DP1DA054394. JM and SSR were supported by the Brain and

Behavior Foundation (grant 27676) and the Families for Borderline Personality Disorder Research (Beth and Rob Elliott) 2018 NARSAD Young Investigator Grant. The content is solely the

responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. SAB was supported by NIH/NIMH grants R01MH108653 and R21MH117518.

AMBL was supported by NIH/NIAAA grant K99AA027835. TB acknowledges support by NIH/NIDA R01 DA018928. JM is supported by the Peter Boris Chair in Addictions Research. HHAT is funded through a

Natural Science and Engineering Research Council PGS-D scholarship, and studies in Cohort 2 were supported by the Canadian Institutes of Health Research Project Grant (PJT-173442 to JYK).

PF and SLE are employees of 23andMe, Inc., and hold stock or stock options in 23andMe. JY reports having received grant support funding from Sunovion, Heptares, and Gilgamesh, as well as

honoraria from Marvel Biotech, none of which were involved in the current project. The other authors report no conflict of interest. ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer Nature

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ARTICLE Sanchez-Roige, S., Jennings, M.V., Thorpe, H.H.A. _et al._ _CADM2_ is implicated in impulsive personality and numerous other traits by genome- and phenome-wide association studies in

humans and mice. _Transl Psychiatry_ 13, 167 (2023). https://doi.org/10.1038/s41398-023-02453-y Download citation * Received: 04 April 2023 * Revised: 17 April 2023 * Accepted: 25 April

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