Alcohol milestones and internalizing, externalizing, and executive function: longitudinal and polygenic score associations.

BACKGROUND: Although the link between alcohol involvement and behavioral phenotypes (e.g. impulsivity, negative affect, executive function [EF]) is well-established, the directionality of these associations, specificity to stages of alcohol involvement, and extent of shared genetic liability remain unclear. We estimate longitudinal associations between transitions among alcohol milestones, behavioral phenotypes, and indices of genetic risk. METHODS: Data came from the Collaborative Study on the Genetics of Alcoholism (n = 3681; ages 11-36). Alcohol transitions (first: drink, intoxication, alcohol use disorder [AUD] symptom, AUD diagnosis), internalizing, and externalizing phenotypes came from the Semi-Structured Assessment for the Genetics of Alcoholism. EF was measured with the Tower of London and Visual Span Tasks. Polygenic scores (PGS) were computed for alcohol-related and behavioral phenotypes. Cox models estimated associations among PGS, behavior, and alcohol milestones. RESULTS: Externalizing phenotypes (e.g. conduct disorder symptoms) were associated with future initiation and drinking problems (hazard ratio (HR)⩾1.16). Internalizing (e.g. social anxiety) was associated with hazards for progression from first drink to severe AUD (HR⩾1.55). Initiation and AUD were associated with increased hazards for later depressive symptoms and suicidal ideation (HR⩾1.38), and initiation was associated with increased hazards for future conduct symptoms (HR = 1.60). EF was not associated with alcohol transitions. Drinks per week PGS was linked with increased hazards for alcohol transitions (HR⩾1.06). Problematic alcohol use PGS increased hazards for suicidal ideation (HR = 1.20). CONCLUSIONS: Behavioral markers of addiction vulnerability precede and follow alcohol transitions, highlighting dynamic, bidirectional relationships between behavior and emerging addiction.

Combining Transdiagnostic and Disorder-Level GWAS Enhances Precision of Psychiatric Genetic Risk Profiles in a Multi-Ancestry Sample.

The etiology of substance use disorders (SUDs) and psychiatric disorders reflects a combination of both transdiagnostic (i.e., common) and disorder-level (i.e., independent) genetic risk factors. We applied genomic structural equation modeling to examine these genetic factors across SUDs, psychotic, mood, and anxiety disorders using genome-wide association studies (GWAS) of European-(EUR) and African-ancestry (AFR) individuals. In EUR individuals, transdiagnostic genetic factors represented SUDs (143 lead single nucleotide polymorphisms [SNPs]), psychotic (162 lead SNPs), and mood/anxiety disorders (112 lead SNPs). We identified two novel SNPs for mood/anxiety disorders that have probable regulatory roles on FOXP1 , NECTIN3 , and BTLA genes. In AFR individuals, genetic factors represented SUDs (1 lead SNP) and psychiatric disorders (no significant SNPs). The SUD factor lead SNP, although previously significant in EUR- and cross-ancestry GWAS, is a novel finding in AFR individuals. Shared genetic variance accounted for overlap between SUDs and their psychiatric comorbidities, with second-order GWAS identifying up to 12 SNPs not significantly associated with either first-order factor in EUR individuals. Finally, common and independent genetic effects showed different associations with psychiatric, sociodemographic, and medical phenotypes. For example, the independent components of schizophrenia and bipolar disorder had distinct associations with affective and risk-taking behaviors, and phenome-wide association studies identified medical conditions associated with tobacco use disorder independent of the broader SUDs factor. Thus, combining transdiagnostic and disorder-level genetic approaches can improve our understanding of co-occurring conditions and increase the specificity of genetic discovery, which is critical for psychiatric disorders that demonstrate considerable symptom and etiological overlap.

A phenome-wide association and Mendelian randomisation study of alcohol use variants in a diverse cohort comprising over 3 million individuals.

BACKGROUND: Alcohol consumption is associated with numerous negative social and health outcomes. These associations may be direct consequences of drinking, or they may reflect common genetic factors that influence both alcohol consumption and other outcomes. METHODS: We performed exploratory phenome-wide association studies (PheWAS) of three of the best studied protective single nucleotide polymorphisms (SNPs) in genes encoding ethanol metabolising enzymes (ADH1B: rs1229984-T, rs2066702-A; ADH1C: rs698-T) using up to 1109 health outcomes across 28 phenotypic categories (e.g., substance-use, mental health, sleep, immune, cardiovascular, metabolic) from a diverse 23andMe cohort, including European (N ≤ 2,619,939), Latin American (N ≤ 446,646) and African American (N ≤ 146,776) populations to uncover new and perhaps unexpected associations. These SNPs have been consistently implicated by both candidate gene studies and genome-wide association studies of alcohol-related behaviours but have not been investigated in detail for other relevant phenotypes in a hypothesis-free approach in such a large cohort of multiple ancestries. To provide insight into potential causal effects of alcohol consumption on the outcomes significant in the PheWAS, we performed univariable two-sample and one-sample Mendelian randomisation (MR) analyses. FINDINGS: The minor allele rs1229984-T, which is protective against alcohol behaviours, showed the highest number of PheWAS associations across the three cohorts (N = 232, European; N = 29, Latin American; N = 7, African American). rs1229984-T influenced multiple domains of health. We replicated associations with alcohol-related behaviours, mental and sleep conditions, and cardio-metabolic health. We also found associations with understudied traits related to neurological (migraines, epilepsy), immune (allergies), musculoskeletal (fibromyalgia), and reproductive health (preeclampsia). MR analyses identified evidence of causal effects of alcohol consumption on liability for 35 of these outcomes in the European cohort. INTERPRETATION: Our work demonstrates that polymorphisms in genes encoding alcohol metabolising enzymes affect multiple domains of health beyond alcohol-related behaviours. Understanding the underlying mechanisms of these effects could have implications for treatments and preventative medicine. FUNDING: MVJ, NCK, SBB, SSR and AAP were supported by T32IR5226 and 28IR-0070. SSR was also supported by NIDA DP1DA054394. NCK and RBC were also supported by R25MH081482. ASH was supported by funds from NIAAA K01AA030083. JLMO was supported by VA 1IK2CX002095. JLMO and JJMM were also supported by NIDA R21DA050160. JJMM was also supported by the Kavli Postdoctoral Award for Academic Diversity. EGA was supported by K01MH121659 from the NIMH/NIH, the Caroline Wiess Law Fund for Research in Molecular Medicine and the ARCO Foundation Young Teacher-Investigator Fund at Baylor College of Medicine. MSA was supported by the Instituto de Salud Carlos III and co-funded by the European Union Found: Fondo Social Europeo Plus (FSE+) (P19/01224, PI22/00464 and CP22/00128).

Multi-ancestry meta-analysis of tobacco use disorder identifies 461 potential risk genes and reveals associations with multiple health outcomes.

Tobacco use disorder (TUD) is the most prevalent substance use disorder in the world. Genetic factors influence smoking behaviours and although strides have been made using genome-wide association studies to identify risk variants, most variants identified have been for nicotine consumption, rather than TUD. Here we leveraged four US biobanks to perform a multi-ancestral meta-analysis of TUD (derived via electronic health records) in 653,790 individuals (495,005 European, 114,420 African American and 44,365 Latin American) and data from UK Biobank (ncombined = 898,680). We identified 88 independent risk loci; integration with functional genomic tools uncovered 461 potential risk genes, primarily expressed in the brain. TUD was genetically correlated with smoking and psychiatric traits from traditionally ascertained cohorts, externalizing behaviours in children and hundreds of medical outcomes, including HIV infection, heart disease and pain. This work furthers our biological understanding of TUD and establishes electronic health records as a source of phenotypic information for studying the genetics of TUD.

Two common and distinct forms of variation in human functional brain networks.

The cortex has a characteristic layout with specialized functional areas forming distributed large-scale networks. However, substantial work shows striking variation in this organization across people, which relates to differences in behavior. While most previous work treats individual differences as linked to boundary shifts between the borders of regions, here we show that cortical 'variants' also occur at a distance from their typical position, forming ectopic intrusions. Both 'border' and 'ectopic' variants are common across individuals, but differ in their location, network associations, properties of subgroups of individuals, activations during tasks, and prediction of behavioral phenotypes. Border variants also track significantly more with shared genetics than ectopic variants, suggesting a closer link between ectopic variants and environmental influences. This work argues that these two dissociable forms of variation-border shifts and ectopic intrusions-must be separately accounted for in the analysis of individual differences in cortical systems across people.

Pleiotropy and genetically inferred causality linking multisite chronic pain to substance use disorders.

Individuals suffering from chronic pain develop substance use disorders (SUDs) more often than others. Understanding the shared genetic influences underlying the comorbidity between chronic pain and SUDs will lead to a greater understanding of their biology. Genome-wide association statistics were obtained from the UK Biobank for multisite chronic pain (MCP, Neffective = 387,649) and from the Million Veteran Program and the Psychiatric Genomics Consortium meta-analyses for alcohol use disorder (AUD, Neffective = 296,974), cannabis use disorder (CanUD, Neffective = 161,053), opioid use disorder (OUD, Neffective = 57,120), and problematic tobacco use (PTU, Neffective = 270,120). SNP-based heritability was estimated for each of the traits and genetic correlation (rg) analyses were performed to assess MCP-SUD pleiotropy. Bidirectional Mendelian Randomization analyses evaluated possible causal relationships. Finally, to identify and characterize individual loci, we performed a genome-wide pleiotropy analysis and a brain-wide analysis using imaging phenotypes available from the UK Biobank. MCP was positively genetically correlated with AUD (rg = 0.26, p = 7.55 × 10-18), CanUD (rg = 0.37, p = 8.21 × 10-37), OUD (rg = 0.20, p = 1.50 × 10-3), and PTU (rg = 0.29, p = 8.53 × 10-12). Although the MR analyses supported bi-directional relationships, MCP had larger effects on AUD (pain-exposure: beta = 0.18, p = 8.21 × 10-4; pain-outcome: beta = 0.07, p = 0.018), CanUD (pain-exposure: beta = 0.58, p = 2.70 × 10-6; pain-outcome: beta = 0.05, p = 0.014) and PTU (pain-exposure: beta = 0.43, p = 4.16 × 10-8; pain-outcome: beta = 0.09, p = 3.05 × 10-6) than the reverse. The genome-wide analysis identified two SNPs pleiotropic between MCP and all SUD investigated: IHO1 rs7652746 (ppleiotropy = 2.69 × 10-8), and CADM2 rs1248857 (ppleiotropy = 1.98 × 10-5). In the brain-wide analysis, rs7652746 was associated with multiple cerebellum and amygdala imaging phenotypes. When analyzing MCP pleiotropy with each SUD separately, we found 25, 22, and 4 pleiotropic variants for AUD, CanUD, and OUD, respectively. To our knowledge, this is the first large-scale study to provide evidence of potential causal relationships and shared genetic mechanisms underlying MCP-SUD comorbidity.

Genetic contribution to the comorbidity between attention-deficit/hyperactivity disorder and substance use disorders.

We characterized the genetic architecture of the attention-deficit hyperactivity disorder-substance use disorder (ADHD-SUD) relationship by investigating genetic correlation, causality, pleiotropy, and common polygenic risk. Summary statistics from genome-wide association studies (GWAS) were used to investigate ADHD (Neff = 51,568), cannabis use disorder (CanUD, Neff = 161,053), opioid use disorder (OUD, Neff = 57,120), problematic alcohol use (PAU, Neff = 502,272), and problematic tobacco use (PTU, Neff = 97,836). ADHD, CanUD, and OUD GWAS meta-analyses included cohorts with case definitions based on different diagnostic criteria. PAU GWAS combined information related to alcohol use disorder, alcohol dependence, and the items related to alcohol problematic consequences assessed by the alcohol use disorders identification test. PTU GWAS was generated a multi-trait analysis including information regarding Fagerström Test for Nicotine Dependence and cigarettes per day. Linkage disequilibrium score regression analyses indicated positive genetic correlation with CanUD, OUD, PAU, and PTU. Genomic structural equation modeling showed that these genetic correlations were related to two latent factors: one including ADHD, CanUD, and PTU and the other with OUD and PAU. The evidence of a causal effect of PAU and PTU on ADHD was stronger than the reverse in the two-sample Mendelian randomization analysis. Conversely, similar strength of evidence was found between ADHD and CanUD. CADM2 rs62250713 was a pleiotropic SNP between ADHD and all SUDs. We found seven, one, and twenty-eight pleiotropic variants between ADHD and CanUD, PAU, and PTU, respectively. Finally, OUD, CanUD, and PAU PRS were associated with increased odds of ADHD. Our findings demonstrated the contribution of multiple pleiotropic mechanisms to the comorbidity between ADHD and SUDs.

GenomeMUSter mouse genetic variation service enables multitrait, multipopulation data integration and analysis.

Hundreds of inbred mouse strains and intercross populations have been used to characterize the function of genetic variants that contribute to disease. Thousands of disease-relevant traits have been characterized in mice and made publicly available. New strains and populations including consomics, the collaborative cross, expanded BXD, and inbred wild-derived strains add to existing complex disease mouse models, mapping populations, and sensitized backgrounds for engineered mutations. The genome sequences of inbred strains, along with dense genotypes from others, enable integrated analysis of trait-variant associations across populations, but these analyses are hampered by the sparsity of genotypes available. Moreover, the data are not readily interoperable with other resources. To address these limitations, we created a uniformly dense variant resource by harmonizing multiple data sets. Missing genotypes were imputed using the Viterbi algorithm with a data-driven technique that incorporates local phylogenetic information, an approach that is extendable to other model organisms. The result is a web- and programmatically accessible data service called GenomeMUSter, comprising single-nucleotide variants covering 657 strains at 106.8 million segregating sites. Interoperation with phenotype databases, analytic tools, and other resources enable a wealth of applications, including multitrait, multipopulation meta-analysis. We show this in cross-species comparisons of type 2 diabetes and substance use disorder meta-analyses, leveraging mouse data to characterize the likely role of human variant effects in disease. Other applications include refinement of mapped loci and prioritization of strain backgrounds for disease modeling to further unlock extant mouse diversity for genetic and genomic studies in health and disease.

Multi-ancestry study of the genetics of problematic alcohol use in over 1 million individuals.

Problematic alcohol use (PAU), a trait that combines alcohol use disorder and alcohol-related problems assessed with a questionnaire, is a leading cause of death and morbidity worldwide. Here we conducted a large cross-ancestry meta-analysis of PAU in 1,079,947 individuals (European, N = 903,147; African, N = 122,571; Latin American, N = 38,962; East Asian, N = 13,551; and South Asian, N = 1,716 ancestries). We observed a high degree of cross-ancestral similarity in the genetic architecture of PAU and identified 110 independent risk variants in within- and cross-ancestry analyses. Cross-ancestry fine mapping improved the identification of likely causal variants. Prioritizing genes through gene expression and chromatin interaction in brain tissues identified multiple genes associated with PAU. We identified existing medications for potential pharmacological studies by a computational drug repurposing analysis. Cross-ancestry polygenic risk scores showed better performance of association in independent samples than single-ancestry polygenic risk scores. Genetic correlations between PAU and other traits were observed in multiple ancestries, with other substance use traits having the highest correlations. This study advances our knowledge of the genetic etiology of PAU, and these findings may bring possible clinical applicability of genetics insights-together with neuroscience, biology and data science-closer.

Associations Between Polygenic Scores for Cognitive and Non-cognitive Factors of Educational Attainment and Measures of Behavior, Psychopathology, and Neuroimaging in the Adolescent Brain Cognitive Development Study.

Background: Both cognitive and non-cognitive (e.g., traits like curiosity) factors are critical for social and emotional functioning and independently predict educational attainment. These factors are heritable and genetically correlated with a range of health-relevant traits and behaviors in adulthood (e.g., risk-taking, psychopathology). However, whether these associations are present during adolescence, and to what extent these relationships diverge, could have implications for adolescent health and well-being. Methods: Using data from 5,517 youth of European ancestry from the ongoing Adolescent Brain Cognitive DevelopmentSM Study, we examined associations between polygenic scores (PGS) for cognitive and non-cognitive factors and outcomes related to cognition, socioeconomic status, risk tolerance and decision-making, substance initiation, psychopathology, and brain structure. Results: Cognitive and non-cognitive PGSs were both positively associated with cognitive performance and family income, and negatively associated with ADHD and severity of psychotic-like experiences. The cognitive PGS was also associated with greater risk-taking, delayed discounting, and anorexia, as well as lower likelihood of nicotine initiation. The cognitive PGS was further associated with cognition scores and anorexia in within-sibling analyses, suggesting these results do not solely reflect the effects of assortative mating or passive gene-environment correlations. The cognitive PGS showed significantly stronger associations with cortical volumes than the non-cognitive PGS and was associated with right hemisphere caudal anterior cingulate and pars-orbitalis in within-sibling analyses, while the non-cognitive PGS showed stronger associations with white matter fractional anisotropy and a significant within-sibling association for right superior corticostriate-frontal cortex. Conclusions: Our findings suggest that PGSs for cognitive and non-cognitive factors show similar associations with cognition and socioeconomic status as well as other psychosocial outcomes, but distinct associations with regional neural phenotypes in this adolescent sample.

Prenatal cannabis exposure is associated with localized brain differences that partially mediate associations with increased adolescent psychopathology.

Prenatal cannabis exposure (PCE) is associated with mental health problems, but the neurobiological mechanisms remain unknown. We find that PCE is associated with localized differences across neuroimaging metrics that longitudinally mediate associations with mental health in adolescence (n=9,322-10,186). Differences in brain development may contribute to PCE-related variability in adolescent mental health.

GenomeMUSter mouse genetic variation service enables multi-trait, multi-population data integration and analyses.

Hundreds of inbred laboratory mouse strains and intercross populations have been used to functionalize genetic variants that contribute to disease. Thousands of disease relevant traits have been characterized in mice and made publicly available. New strains and populations including the Collaborative Cross, expanded BXD and inbred wild-derived strains add to set of complex disease mouse models, genetic mapping resources and sensitized backgrounds against which to evaluate engineered mutations. The genome sequences of many inbred strains, along with dense genotypes from others could allow integrated analysis of trait - variant associations across populations, but these analyses are not feasible due to the sparsity of genotypes available. Moreover, the data are not readily interoperable with other resources. To address these limitations, we created a uniformly dense data resource by harmonizing multiple variant datasets. Missing genotypes were imputed using the Viterbi algorithm with a data-driven technique that incorporates local phylogenetic information, an approach that is extensible to other model organism species. The result is a web- and programmatically-accessible data service called GenomeMUSter ( https://muster.jax.org ), comprising allelic data covering 657 strains at 106.8M segregating sites. Interoperation with phenotype databases, analytic tools and other resources enable a wealth of applications including multi-trait, multi-population meta-analysis. We demonstrate this in a cross-species comparison of the meta-analysis of Type 2 Diabetes and of substance use disorders, resulting in the more specific characterization of the role of human variant effects in light of mouse phenotype data. Other applications include refinement of mapped loci and prioritization of strain backgrounds for disease modeling to further unlock extant mouse diversity for genetic and genomic studies in health and disease.

Alcohol use and grey matter structure: Disentangling predispositional and causal contributions in human studies.

Alcohol use is a growing global health concern and economic burden. Alcohol involvement (i.e., initiation, use, problematic use, alcohol use disorder) has been reliably associated with broad spectrum grey matter differences in cross-sectional studies. These findings have been largely interpreted as reflecting alcohol-induced atrophy. However, emerging data suggest that brain structure differences also represent pre-existing vulnerability factors for alcohol involvement. Here, we review evidence from human studies with designs (i.e., family-based, genomic, longitudinal) that allow them to assess the plausibility that these correlates reflect predispositional risk factors and/or causal consequences of alcohol involvement. These studies provide convergent evidence that grey matter correlates of alcohol involvement largely reflect predisposing risk factors, with some evidence for potential alcohol-induced atrophy. These conclusions highlight the importance of study designs that can provide causal clues to cross-sectional observations. An integrative model may best account for these data, in which predisposition to alcohol use affects brain development, effects which may then be compounded by the neurotoxic consequences of heavy alcohol use.

Characteristics Associated With Cannabis Use Initiation by Late Childhood and Early Adolescence in the Adolescent Brain Cognitive Development (ABCD) Study.

This cohort study evaluates characteristics associated with early-onset cannabis use.

Multivariate genome-wide association meta-analysis of over 1 million subjects identifies loci underlying multiple substance use disorders.

Genetic liability to substance use disorders can be parsed into loci that confer general or substance-specific addiction risk. We report a multivariate genome-wide association meta-analysis that disaggregates general and substance-specific loci for published summary statistics of problematic alcohol use, problematic tobacco use, cannabis use disorder, and opioid use disorder in a sample of 1,025,550 individuals of European descent and 92,630 individuals of African descent. Nineteen independent SNPs were genome-wide significant (P < 5e-8) for the general addiction risk factor (addiction-rf), which showed high polygenicity. Across ancestries, PDE4B was significant (among other genes), suggesting dopamine regulation as a cross-substance vulnerability. An addiction-rf polygenic risk score was associated with substance use disorders, psychopathologies, somatic conditions, and environments associated with the onset of addictions. Substance-specific loci (9 for alcohol, 32 for tobacco, 5 for cannabis, 1 for opioids) included metabolic and receptor genes. These findings provide insight into genetic risk loci for substance use disorders that could be leveraged as treatment targets.

Multi-ancestry meta-analysis of tobacco use disorder prioritizes novel candidate risk genes and reveals associations with numerous health outcomes.

Tobacco use disorder (TUD) is the most prevalent substance use disorder in the world. Genetic factors influence smoking behaviors, and although strides have been made using genome-wide association studies (GWAS) to identify risk variants, the majority of variants identified have been for nicotine consumption, rather than TUD. We leveraged five biobanks to perform a multi-ancestral meta-analysis of TUD (derived via electronic health records, EHR) in 898,680 individuals (739,895 European, 114,420 African American, 44,365 Latin American). We identified 88 independent risk loci; integration with functional genomic tools uncovered 461 potential risk genes, primarily expressed in the brain. TUD was genetically correlated with smoking and psychiatric traits from traditionally ascertained cohorts, externalizing behaviors in children, and hundreds of medical outcomes, including HIV infection, heart disease, and pain. This work furthers our biological understanding of TUD and establishes EHR as a source of phenotypic information for studying the genetics of TUD.

A Phenome-Wide Association Study (PheWAS) of Late Onset Alzheimer Disease Genetic Risk in Children of European Ancestry at Middle Childhood: Results from the ABCD Study.

Genetic risk for Late Onset Alzheimer Disease (AD) has been associated with lower cognition and smaller hippocampal volume in healthy young adults. However, whether these and other associations are present during childhood remains unclear. Using data from 5556 genomically-confirmed European ancestry youth who completed the baseline session of the ongoing the Adolescent Brain Cognitive DevelopmentSM Study (ABCD Study®), our phenome-wide association study estimating associations between four indices of genetic risk for late-onset AD (i.e., AD polygenic risk scores (PRS), APOE rs429358 genotype, AD PRS with the APOE region removed (ADPRS-APOE), and an interaction between ADPRS-APOE and APOE genotype) and 1687 psychosocial, behavioral, and neural phenotypes revealed no significant associations after correction for multiple testing (all ps > 0.0002; all pfdr > 0.07). These data suggest that AD genetic risk may not phenotypically manifest during middle-childhood or that effects are smaller than this sample is powered to detect.

Inferring the Genetic Influences on Psychological Traits Using MRI Connectivity Predictive Models: Demonstration with Cognition.

Introduction: Genetic correlations between brain and behavioral phenotypes in analyses from major genetic consortia have been weak and mostly nonsignificant. fMRI models of systems-level brain patterns may help improve our ability to link genes, brains, and behavior by identifying reliable and reproducible endophenotypes. Work using connectivity-based predictive modeling has generated brain-based proxies of behavioral and neuropsychological variables. If such models capture activity in inherited brain systems, they may offer a more powerful link between genes and behavior. Method: As a proof of concept, we develop models predicting intelligence (IQ) based on fMRI connectivity and test their effectiveness as endophenotypes. We link brain and IQ in a model development dataset of N = 3,000 individuals and test the genetic correlations between brain models and measured IQ in a genetic validation sample of N = 13,092 individuals from the UK Biobank. We compare an additive connectivity-based model to multivariate LASSO and ridge models phenotypically and genetically. We also compare these approaches to single "candidate" brain areas. Results: We found that predictive brain models were significantly phenotypically correlated with IQ and showed much stronger correlations than individual edges. Further, brain models were more heritable (h2 = 0.155-0.181) than single brain regions (h2 = 0.038-0.118) and captured about half of the genetic variance in IQ (rG = 0.422-0.576), while rGs with single brain measures were smaller and nonsignificant. For the different approaches, LASSO and ridge were similarly predictive, with slightly weaker performance of the additive model. LASSO model weights were highly theoretically interpretable and replicated known brain IQ associations. Finally, functional connectivity models trained in midlife showed genetic correlations with early life correlates of IQ, suggesting some stability in the prediction of fMRI models. Conclusion: Multisystem predictive models hold promise as imaging endophenotypes that offer complex and theoretically relevant conclusions for future imaging genetics research.

Multi-omics cannot replace sample size in genome-wide association studies.

The integration of multi-omics information (e.g., epigenetics and transcriptomics) can be useful for interpreting findings from genome-wide association studies (GWAS). It has been suggested that multi-omics could circumvent or greatly reduce the need to increase GWAS sample sizes for novel variant discovery. We tested whether incorporating multi-omics information in earlier and smaller-sized GWAS boosts true-positive discovery of genes that were later revealed by larger GWAS of the same/similar traits. We applied 10 different analytic approaches to integrating multi-omics data from 12 sources (e.g., Genotype-Tissue Expression project) to test whether earlier and smaller GWAS of 4 brain-related traits (alcohol use disorder/problematic alcohol use, major depression/depression, schizophrenia, and intracranial volume/brain volume) could detect genes that were revealed by a later and larger GWAS. Multi-omics data did not reliably identify novel genes in earlier less-powered GWAS (PPV <0.2; 80% false-positive associations). Machine learning predictions marginally increased the number of identified novel genes, correctly identifying 1-8 additional genes, but only for well-powered early GWAS of highly heritable traits (i.e., intracranial volume and schizophrenia). Although multi-omics, particularly positional mapping (i.e., fastBAT, MAGMA, and H-MAGMA), can help to prioritize genes within genome-wide significant loci (PPVs = 0.5-1.0) and translate them into information about disease biology, it does not reliably increase novel gene discovery in brain-related GWAS. To increase power for discovery of novel genes and loci, increasing sample size is required.