Biology subjects

Sanchez-Roige, S.

Publications and source records attributed to Sanchez-Roige, S..

At least 19 recordsLinked to original sources

bioRxiv2026

MetaboXcan: A multiomic framework linking genetically predicted metabolites, gene expression, and complex traits

Genetically regulated molecular phenotypes, such as gene expression and metabolites, are widely thought to mediate the effect of disease-relevant genetic loci identified by genome-wide association studies (GWAS); however, molecular mechanisms connecting genetic variation to disease remain poorly understood. While transcriptome-wide association studies (TWAS) have identified disease-associated genes, strategies that integrate the metabolome remain underexplored. Here, we introduce MetaboXcan, a framework for predicting plasma metabolite levels from genetic data and associating them with complex traits using GWAS summary statistics. We trained lasso regression models on plasma metabolite and genotype data from the Metabolic Syndrome in Men Study (METSIM); these models outperform existing genetic metabolite predictors and generalize well across independent cohorts. MetaboXcan leverages these models to perform four complementary association analyses: (i) gene-to-trait (TWAS), (ii) gene-to-metabolite (M-TWAS), (iii) metabolite-to-trait (MWAS), and (iv) gene-expression-based metabolite-to-trait associations (g-MWAS). The framework further organizes results into metabolic pathways and gene-metabolite interaction networks to facilitate biological interpretation. Applied to chronic kidney disease (CKD), MetaboXcan identified known disease risk genes (PDILT/UMOD, SPATA5L1/GATM), as well as multiple CKD-relevant metabolites (e.g., glycine, homoarginine). Our integrated multiomic analysis enables biological interpretation by revealing glycine availability-centered biochemical axes spanning oxidative stress, cellular energetics, and vascular signaling, while nominating candidate mechanisms for downstream investigation. These results validate known disease-related biology with genetic evidence and generate new hypotheses for further investigation. MetaboXcan is publicly available and broadly applicable to any GWAS phenotype.

genetics↗

bioRxiv2025

Aberrant recursive splicing in a human disease locus

Recursive splice sites are rare motifs postulated to facilitate splicing across massive introns and shape isoform diversity, especially for long, brain-expressed genes. The necessity of this unique mechanism remains unsubstantiated, as does the role of recursive splicing (RS) in human disease. From analyses of rare copy number variants (CNVs) from almost one million individuals, we previously identified large, heterozygous deletions eliminating an RS site (RS1) in the first intron of CADM2 that conferred substantial risk for attention deficit hyperactivity disorder (ADHD) and other neurobehavioral traits. CADM2 encodes a neuronally expressed cell adhesion molecule that has repeatedly been associated with ADHD and numerous similar traits. To explore the molecular impact of RS ablation in CADM2, we used CRISPR to model patient deletions and to target a smaller region ([~]500 base pairs) containing RS1 in both human induced neurons (iNs) and rats. Transcriptome analyses in unedited iNs provided a catalog of CADM2 transcripts, including novel transcripts that retained RS exons. Intriguingly, ablating RS1 altered the gradient of RNA abundance across the first intron of CADM2, decreased the level of CADM2 expression, and impacted transcript usage. Decreased CADM2 expression was reflected in reduced exon usage downstream of the RS1 site and global alteration to genes involved in neuronal processes including synapse and axon development. Given the scale of our analyses and the widespread association of CADM2 with neurobehavioral traits, we sought to validate these findings using in vivo models and found that rodent models harboring Cadm2 RS1 deletions exhibited significant changes in relevant behaviors and functional brain connectivity. In summary, our analyses demonstrate a functional role for RS as a noncoding regulatory mechanism in a gene associated with a spectrum of neuropsychiatric and behavioral traits. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/666599v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@171156borg.highwire.dtl.DTLVardef@13553baorg.highwire.dtl.DTLVardef@bee7forg.highwire.dtl.DTLVardef@156f19e_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG

genomics↗

bioRxiv2025

Genomic structural equation modeling of impulsivity and risk-taking traits reveals three latent factors distinctly associated with brain structure and development

BackgroundImpulsivity is a multifaceted transdiagnostic trait that emerges in childhood. Research has identified genetic loci and brain systems associated with different facets of impulsivity and risk-taking. However, how these genetic underpinnings overlap across different facets, and how they are associated with brain development during childhood remain unknown. MethodsUsing genomic structural equation modeling on 17 impulsivity and risk-taking traits, we identified latent factors capturing overlapping genetic architecture. We then calculated polygenic scores for these factors using Adolescent Brain Cognitive Development Study data (N = 4,142) and examined their associations with brain structure, development, and behavior in children aged 9-14 years. Finally, we tested whether socioeconomic status modulated the associations between latent polygenic scores and brain structures. ResultsWe identified three distinct genetic latent factors, which we label lack of self-control, reward drive, and sensation seeking. In children, polygenic scores for the three factors showed associations with distinct brain patterns: lack of self-control associated with reduced prefrontal cortical thickness, reward drive with increased subcortical cellular density, and sensation seeking with increased cortical surface area and white matter integrity. Longitudinally, lack of self-control predicted slower white matter development. The association between polygenetic score for lack of self-control and white matter mean diffusivity was modulated by socioeconomic status. ConclusionsWe identified three genetically distinct dimensions of impulsivity and risk-taking with separable neurodevelopmental origins. These genetic predispositions manifested as distinct brain patterns as early as ages 9-10. Environmental experience modulated some of the genetic effects on brain development.

neuroscience↗

bioRxiv2024

A single-nucleus transcriptomic atlas of medium spiny neurons in the rat nucleus accumbens

Neural processing of rewarding stimuli involves several distinct regions, including the nucleus accumbens (NAc). The majority of NAc neurons are GABAergic projection neurons known as medium spiny neurons (MSNs). MSNs are broadly defined by dopamine receptor expression, but evidence suggests that a wider array of subtypes exist. To study MSN heterogeneity, we analyzed single-nucleus RNA sequencing data from the largest available rat NAc dataset. Analysis of 48,040 NAc MSN nuclei identified major populations belonging to the striosome and matrix compartments. Integration with mouse and human data indicated consistency across species and disease-relevance scoring using genome-wide association study results revealed potentially differential roles for MSN populations in substance use disorders. Additional high-resolution clustering identified 34 transcriptomically distinct subtypes of MSNs definable by a limited number of marker genes. Together, these data demonstrate the diversity of MSNs in the NAc and provide a basis for more targeted genetic manipulation of specific populations.

genetics↗

bioRxiv2024

Rare and Common Variants Associated with Alcohol Consumption Identify a Conserved Molecular Network

Genome-wide association studies (GWAS) have identified hundreds of common variants associated with alcohol consumption. In contrast, rare variants have only begun to be studied for their role in alcohol consumption. No studies have examined whether common and rare variants implicate the same genes and molecular networks. To address this knowledge gap, we used publicly available alcohol consumption GWAS summary statistics (GSCAN, N=666,978) and whole exome sequencing data (Genebass, N=393,099) to identify a set of common and rare variants for alcohol consumption. Gene-based analysis of each dataset have implicated 294 (common variants) and 35 (rare variants) genes, including ethanol metabolizing genes ADH1B and ADH1C, which were identified by both analyses, and ANKRD12, GIGYF1, KIF21B, and STK31, which were identified only by rare variant analysis, but have been associated with related psychiatric traits. We then used a network colocalization procedure to propagate the common and rare gene sets onto a shared molecular network, revealing significant overlap. The shared network identified gene families that function in alcohol metabolism, including ADH, ALDH, CYP, and UGT. 74 of the genes in the network were previously implicated in comorbid psychiatric or substance use disorders, but had not previously been identified for alcohol-related behaviors, including EXOC2, EPM2A, CACNB3, and CACNG4. Differential gene expression analysis showed enrichment in the liver and several brain regions supporting the role of network genes in alcohol consumption. Thus, genes implicated by common and rare variants identify shared functions relevant to alcohol consumption, which also underlie psychiatric traits and substance use disorders that are comorbid with alcohol use.

genetics↗

bioRxiv2023

Guidelines for Evaluating the Comparability of Down-Sampled GWAS Summary Statistics

Proprietary genetic datasets are valuable for boosting the statistical power of genome-wide association studies (GWASs), but their use can restrict investigators from publicly sharing the resulting summary statistics. Although researchers can resort to sharing down-sampled versions that exclude restricted data, down-sampling reduces power and might change the genetic etiology of the phenotype being studied. These problems are further complicated when using multivariate GWAS methods, such as genomic structural equation modeling (Genomic SEM), that model genetic correlations across multiple traits. Here, we propose a systematic approach to assess the comparability of GWAS summary statistics that include versus exclude restricted data. Illustrating this approach with a multivariate GWAS of an externalizing factor, we assessed the impact of down-sampling on (1) the strength of the genetic signal in univariate GWASs, (2) the factor loadings and model fit in multivariate Genomic SEM, (3) the strength of the genetic signal at the factor level, (4) insights from gene-property analyses, (5) the pattern of genetic correlations with other traits, and (6) polygenic score analyses in independent samples. For the externalizing GWAS, down-sampling resulted in a loss of genetic signal and fewer genome-wide significant loci, while the factor loadings and model fit, gene-property analyses, genetic correlations, and polygenic score analyses are robust. Given the importance of data sharing for the advancement of open science, we recommend that investigators who share down-sampled summary statistics report these analyses as accompanying documentation to support other researchers use of the summary statistics.

genetics↗

bioRxiv2022

A Multivariate Approach to Understanding the Genetic Overlap between Externalizing Phenotypes and Substance Use Disorders

Substance use disorders (SUDs) are phenotypically and genetically correlated with each other and with other psychological traits characterized by behavioral undercontrol, termed externalizing phenotypes. In this study, we used Genomic Structural Equation Modeling to explore the shared genetic architecture among six externalizing phenotypes and four SUDs used in two previous multivariate GWAS of an externalizing and an addiction risk factor, respectively. Using a preregistered set of criteria, we first evaluated the performance of five confirmatory factor analytic models, including a common factor model, alternative parameterizations of two-factor structures, and a bifactor model. We used a combination of model fit, factor reliability, and model characteristics to adjudicate among the models. We next explored the genetic correlations between factors identified in these models and other relevant psychological traits. We found that a common factor model, in which all externalizing phenotypes and SUDs were influenced by a single dimension of genetic risk best characterized the relationships among our phenotypes. Although two two-factor models also performed well, we found that the factors in those models were very highly correlated with each other (rgS > .87) and similarly genetically correlated with external criteria, suggesting they did not represent meaningfully distinct dimensions. Results from this study can be used to inform future efforts to characterize genetic liability for broad externalizing as well as specific externalizing phenotypes.

genetics↗

bioRxiv2022

Polygenic Transcriptome Risk Scores Can Translate Genetic Results Between Species

Genome-wide association studies (GWAS) have implicated specific alleles and genes as risk factors for numerous complex traits. However, translating GWAS results into biologically and therapeutically meaningful discoveries remains extremely challenging. Most GWAS results identify noncoding regions of the genome, suggesting that differences in gene regulation are the major driver of trait variability. To better integrate GWAS results with gene regulatory polymorphisms, we previously developed PrediXcan (also known as "transcriptome-wide association studies" or TWAS), which maps SNPs to predicted gene expression using GWAS data. In this study, we developed RatXcan, a framework that extends this methodology to outbred heterogeneous stock (HS) rats. RatXcan accounts for the close familial relationships among HS rats by modeling the relatedness with a random effect that encodes the genetic relatedness. RatXcan also corrects for polygenic-driven inflation because of the equivalence between a relatedness random effect and the infinitesimal polygenic model. To develop RatXcan, we trained transcript predictors for 8,934 genes using reference genotype and expression data from five rat brain regions. We found that the cis genetic architecture of gene expression in both rats and humans was sparse and similar across brain tissues. We tested the association between predicted expression in rats and two example traits (body length and BMI) using phenotype and genotype data from 5,401 densely genotyped HS rats and identified a significant enrichment between the genes associated with rat and human body length and BMI. Thus, RatXcan represents a valuable tool for identifying the relationship between gene expression and phenotypes across species and paves the way to explore shared biological mechanisms of complex traits. Author SummaryUnderstanding how genetic variation affects phenotypic variation is critical to leveraging the wealth of genetic studies to make biologically and therapeutically useful discoveries. Since most of the genetic loci associated with complex diseases are regulatory in nature--meaning that they do not alter protein coding but rather subtly affect gene expression--transcriptome-wide association studies have been developed. However, these apply only to human data where large samples of unrelated individuals are available. For animal models, relatedness is much higher, causing higher false-positive rates. We propose a computationally efficient method to address this problem and find shared biology between humans and rats. Taken together, our development paves the way to further explore shared biological mechanisms of complex traits across species.

genomics↗

bioRxiv2020

Multivariate genomic analysis of 1.5 million people identifies genes related to addiction, antisocial behavior, and health

Behaviors and disorders related to self-regulation, such as substance use, antisocial conduct, and ADHD, are collectively referred to as externalizing and have a shared genetic liability. We applied a multivariate approach that leverages genetic correlations among externalizing traits for genome-wide association analyses. By pooling data from ~1.5 million people, our approach is statistically more powerful than single-trait analyses and identifies more than 500 genetic loci. The identified loci were enriched for genes expressed in the brain and related to nervous system development. A polygenic score constructed from our results captures variation in a broad range of behavioral and medical outcomes that were not part of our genome-wide analyses, including traits that until now lacked well-performing polygenic scores, such as opioid use disorder, suicide, HIV infections, criminal convictions, and unemployment. Our findings are consistent with the idea that persistent difficulties in self-regulation can be conceptualized as a neurodevelopmental condition.

genetics↗

bioRxiv2020

Multivariate GWAS elucidates the genetic architecture of alcohol consumption and misuse, corrects biases, and reveals novel associations with disease

Genome-wide association studies (GWASs) of the Alcohol Use Disorder Identification Test (AUDIT), a ten-item screener for alcohol use disorder (AUD), have elucidated novel loci for alcohol consumption and misuse. However, these studies also revealed that GWASs can be influenced by numerous biases (e.g., measurement error, selection bias), which have led to inconsistent genetic correlations between alcohol involvement and AUD, as well as paradoxically negative genetic correlations between alcohol involvement and psychiatric disorders/medical conditions. To explore these unexpected differences in genetic correlations, we conducted the first item-level and largest GWAS of AUDIT items (N=160,824), and applied a multivariate framework to mitigate previous biases. In doing so, we identified novel patterns of similarity (and dissimilarity) among the AUDIT items, and found evidence of a correlated two-factor structure at the genetic level (Consumption and Problems, rg=.80). Moreover, by applying empirically-derived weights to each of the AUDIT items, we constructed an aggregate measure of alcohol consumption that is strongly associated with alcohol dependence (rg=.67) and several other psychiatric disorders, and no longer positively associated with health and positive socioeconomic outcomes. Lastly, by performing polygenic analyses in three independent cohorts that differed in their ascertainment and prevalence of AUD, we identified novel genetic associations between alcohol consumption, alcohol misuse, and human health. Our work further emphasizes the value of AUDIT for both clinical and genetic studies of AUD, and the importance of using multivariate methods to study genetic associations that are more closely related to AUD.

genetics↗

bioRxiv2020

The Latent Genetic Structure of Impulsivity and its Relation to Internalizing Psychopathology

Factor analyses suggest that impulsivity traits that capture tendencies to act prematurely or take risks tap partially distinct constructs. We applied genomic structure equation modeling to evaluate the genetic factor structure of two well-established impulsivity questionnaires, using published genome-wide association study statistics from up to 22,861 participants. We also tested the hypotheses that delay discounting would be genetically separable from other impulsivity factors, and that emotionally-triggered facets of impulsivity (urgency) would be those most strongly genetically correlated with an internalizing latent factor. A five-factor model best fit the impulsivity data. Delay discounting was genetically distinct from these five factors. As expected, the two urgency subscales were most strongly related to an Internalizing Psychopathology latent factor. These findings provide empirical genetic evidence that impulsivity can be disarticulated into distinct categories of differential relevance for internalizing psychopathology. They also demonstrate how measured genetic markers can be used to inform theories of psychology/personality.

genetics↗

bioRxiv2019

Meta-analysis of problematic alcohol use in 435,563 individuals identifies 29 risk variants and yields insights into biology, pleiotropy and causality

Problematic alcohol use (PAU) is a leading cause of death and disability worldwide. Although genome-wide association studies (GWASs) have identified PAU risk genes, the genetic architecture of this trait is not fully understood. We conducted a proxy-phenotype meta-analysis of PAU combining alcohol use disorder and problematic drinking in 435,563 European-ancestry individuals. We identified 29 independent risk variants, 19 of them novel. PAU was genetically correlated with 138 phenotypes, including substance use and psychiatric traits. Phenome-wide polygenic risk score analysis in an independent biobank sample (BioVU, n=67,589) confirmed the genetic correlations between PAU and substance use and psychiatric disorders. Genetic heritability of PAU was enriched in brain and in genomic conserved and regulatory regions. Mendelian randomization suggested causal effects on liability to PAU of substance use, psychiatric status, risk-taking behavior, and cognitive performance. In summary, this large PAU meta-analysis identified novel risk loci and revealed genetic relationships with numerous other outcomes.

genetics↗

bioRxiv2019

Genetic risk for major depressive disorder and loneliness in gender-specific associations with coronary artery disease

ImportanceEpidemiological evidence indicates that major depressive disorder (MDD) and loneliness both reduce life expectancies, but mechanisms underlying the excess morbidity are unclear. Electronic health records (EHRs) linked to genetic data offer new opportunities to address this knowledge gap.\n\nObjectiveTo determine the medical morbidity pattern associated with genetic risk factors for MDD and loneliness, two common psychological traits with adverse health outcomes.\n\nDesignPhenome-wide association study using EHRs spanning 1990 to 2017 from the Vanderbilt University Medical Center biobank, BioVU. Top associations with coronary artery disease (CAD) were replicated in the Atherosclerosis Risk in Communities (ARIC) cohort.\n\nSettingHospital-based EHR study, with replication in a population-based cohort study.\n\nParticipants18,385 genotyped adult patients in BioVU. Replication in ARIC included 7,197 genotyped participants. All participants were of European ancestry.\n\nExposuresPolygenic scores for MDD and loneliness were developed for each individual using previously published meta-GWAS summary statistics.\n\nMain Outcomes and MeasuresThe phenome-wide association study included 882 clinical diagnoses ascertained via billing codes in the EHR. ARIC included 1598 incident CAD cases.\n\nResultsBioVU patients had a median EHR length of 9.91 years. In the phenome-wide association study, polygenic scores for MDD and loneliness were significantly associated with psychiatric and cardiac phenotypes. Targeted analyses of CAD in 3,893 cases and 4,197 controls in BioVU found odds ratios of 1.11 (95% CI, 1.04-1.18; P=8.43x10-4) and 1.13 (95% CI, 1.07-1.20; P=4.51x10-6) per 1-SD increase in the polygenic scores for MDD and loneliness, respectively. Comparable hazard ratios in ARIC were 1.07 (95% CI, 0.99-1.14; P=0.07) and 1.07 (1.01-1.15; P=0.03). Across both studies, the increased risk persisted in women after adjusting for multiple conventional risk factors, a polygenic score for CAD, and psychiatric symptoms (available in BioVU). Controlling for genetic risk factors shared between MDD and loneliness, the polygenic score for loneliness conditioned on MDD remained associated with CAD risk, but the polygenic score for MDD conditioned on loneliness did not.\n\nConclusions and RelevanceGenetic risk factors for MDD and loneliness act pleiotropically to increase CAD risk in women. Continued research into the biological and clinical connections between the heart and mind is warranted.

genetics↗

bioRxiv2018

Phenome-wide investigation of health outcomes associated with genetic predisposition to loneliness

AO_SCPLOWBSTRACTC_SCPLOWHumans are social animals that experience intense suffering when they perceive a lack of social connection. Modern societies are experiencing an epidemic of loneliness. While the experience of loneliness is universally human, some people report experiencing greater loneliness than others. Loneliness is more strongly associated with mortality than obesity, emphasizing the need to understand the nature of the relationship between loneliness and health. While it is intuitive that circumstantial factors such as marital status and age influence loneliness, there is also compelling evidence of a genetic predisposition towards loneliness. To better understand the genetic architecture of loneliness and its relationship with associated outcomes, we conducted a genome-wide association (GWAS) meta-analysis of loneliness (N=475,661), report 12 associated loci (two novel) and significant genetic correlations with 34 other complex traits. The polygenic basis for loneliness was significantly enriched for evolutionary constrained genes and genes expressed in specific brain tissues: (frontal) cortex, cerebellum, anterior cingulate cortex, and substantia nigra. We built polygenic scores based on this GWAS meta-analysis to explore the genetic association between loneliness and health outcomes in an independent sample of 18,498 individuals for whom electronic health records were available. A genetic predisposition towards loneliness predicted cardiovascular, psychiatric, and metabolic disorders, and triglycerides and high-density lipoproteins. Mendelian randomization analyses showed evidence of a causal, increasing, effect of body fat on loneliness, and a similar weaker causal effect of BMI. Our results provide a framework for ongoing studies of the genetic basis of loneliness and its role in mental and physical health.

genetics↗

bioRxiv2018

Genome-wide association studies of impulsive personality traits (BIS-11 and UPPSP) and drug experimentation in up to 22,861 adult research participants

BackgroundImpulsive personality traits are complex heritable traits that are governed by frontal-subcortical circuits and are associated with numerous neuropsychiatric disorders, particularly drug abuse.\n\nMethodsIn collaboration with the genetics company 23andMe, Inc., we performed several genome-wide association studies (GWAS) on measures of impulsive personality traits (the short version of the UPPSP Impulsive Behavior Scale, and the Barratt Impulsiveness Scale [BIS-11]) and drug experimentation (the number of drug classes an individual has tried in their lifetime) in up to 22,861 male and female adult research participants of European ancestry.\n\nResultsImpulsive personality traits and drug experimentation showed SNP-heritabilities that ranged from 5 to 11%. Genetic variants in the CADM2 locus were significantly associated with the UPPSP Sensation Seeking subscale (P = 8.3 x 10-9, rs139528938) and showed a suggestive association with drug experimentation (P = 3.0 x 10-7, rs2163971; r2 = 0.68 with rs139528938); CADM2 has been previously associated with measures of risky behaviors and self-reported risk tolerance, cannabis initiation, alcohol consumption, as well as information speed processing, body mass index (BMI) variation and obesity. Furthermore, genetic variants in the CACNA1I locus were significantly associated with the UPPSP Negative Urgency subscale (P = 3.8 x 10-8, rs199694726). Multiple subscales from both UPPSP and BIS showed strong genetic correlations (>0.5) with drug experimentation and other substance use traits measured in independent cohorts, including smoking initiation, and lifetime cannabis use. Several UPPSP and BIS subscales were genetically correlated with attention-deficit/hyperactivity disorder (rg = 0.30-0.51, p < 8.69 x 10-3), supporting their validity as endophenotypes.\n\nConclusionsOur findings demonstrate a role for common genetic contributions to individual differences in impulsivity. Furthermore, our study is the first to provide a genetic dissection of the relationship between different types of impulsive personality traits and various psychiatric disorders.

genetics↗

bioRxiv2018

Genome-wide association study meta-analysis of the Alcohol Use Disorder Identification Test (AUDIT) in two population-based cohorts (N=141,958)

Alcohol use disorders (AUD) are common conditions that have enormous social and economic consequences. We obtained quantitative measures using the Alcohol Use Disorder Identification Test (AUDIT) from two population-based cohorts of European ancestry: UK Biobank (UKB; N=121,604) and 23andMe (N=20,328) and performed a genome-wide association study (GWAS) meta-analysis. We also performed GWAS for AUDIT items 1-3, which focus on consumption (AUDIT-C), and for items 4-10, which focus on the problematic consequences of drinking (AUDIT-P). The GWAS meta-analysis of AUDIT total score identified 10 associated risk loci. Novel associations localized to genes including JCAD and SLC39A13; we also replicated previously identified signals in the genes ADH1B, ADH1C, KLB, and GCKR. The dimensions of AUDIT showed positive genetic correlations with alcohol consumption (rg=0.76-0.92) and Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) alcohol dependence (rg=0.33-0.63). AUDIT-P and AUDIT-C showed significantly different patterns of association across a number of traits, including psychiatric disorders. AUDIT-P was positively genetically correlated with schizophrenia (rg=0.22, p=3.0x10-10), major depressive disorder (rg=0.26, p=5.6x10-3), and attention-deficit/hyperactivity disorder (ADHD; rg=0.23, p=1.1x10-5), whereas AUDIT-C was negatively genetically correlated with major depressive disorder (rg=-0.24, p=3.7x10-3) and ADHD (rg=-0.10, p=1.8x10-2). We also used the AUDIT data in the UKB to identify thresholds for dichotomizing AUDIT total score that optimize genetic correlations with DSM-IV alcohol dependence. Coding individuals with AUDIT total score of [≤]4 as controls and [≥]12 as cases produced a high genetic correlation with DSM-IV alcohol dependence (rg=0.82, p=3.2x10-6) while retaining most subjects. We conclude that AUDIT scores ascertained in population-based cohorts can be used to explore the genetic basis of both alcohol consumption and AUD.

genetics↗

bioRxiv2018

Genome-wide study identifies 611 loci associated with risk tolerance and risky behaviors

Humans vary substantially in their willingness to take risks. In a combined sample of over one million individuals, we conducted genome-wide association studies (GWAS) of general risk tolerance, adventurousness, and risky behaviors in the driving, drinking, smoking, and sexual domains. We identified 611 approximately independent genetic loci associated with at least one of our phenotypes, including 124 with general risk tolerance. We report evidence of substantial shared genetic influences across general risk tolerance and risky behaviors: 72 of the 124 general risk tolerance loci contain a lead SNP for at least one of our other GWAS, and general risk tolerance is moderately to strongly genetically correlated ([Formula] to 0.50) with a range of risky behaviors. Bioinformatics analyses imply that genes near general-risk-tolerance-associated SNPs are highly expressed in brain tissues and point to a role for glutamatergic and GABAergic neurotransmission. We find no evidence of enrichment for genes previously hypothesized to relate to risk tolerance.

genetics↗

bioRxiv2018

Genome-wide association analysis of lifetime cannabis use (N=184,765) identifies new risk loci, genetic overlap with mental health, and a causal influence of schizophrenia on cannabis use

Cannabis use is a heritable trait [1] that has been associated with adverse mental health outcomes. To identify risk variants and improve our knowledge of the genetic etiology of cannabis use, we performed the largest genome-wide association study (GWAS) meta-analysis for lifetime cannabis use (N=184,765) to date. We identified 4 independent loci containing genome-wide significant SNP associations. Gene-based tests revealed 29 genome-wide significant genes located in these 4 loci and 8 additional regions. All SNPs combined explained 10% of the variance in lifetime cannabis use. The most significantly associated gene, CADM2, has previously been associated with substance use and risk-taking phenotypes [2-4]. We used S-PrediXcan to explore gene expression levels and found 11 unique eGenes. LD-score regression uncovered genetic correlations with smoking, alcohol use and mental health outcomes, including schizophrenia and bipolar disorder. Mendelian randomisation analysis provided evidence for a causal positive influence of schizophrenia risk on lifetime cannabis use.

genetics↗