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We conducted genome-wide association studies (GWAS) of relative intake from the macronutrients fat, protein, carbohydrates, and sugar in over 235,000 individuals of European ancestries. We identified 21 unique, approximately independent lead SNPs. Fourteen lead SNPs are uniquely associated with one macronutrient at genome-wide significance (P

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 statistics from genome-wide association studies of 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 fitted 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 broken down into distinct categories of differential relevance for internalizing psychopathology. They also demonstrate how measured genetic markers can be used to inform theories of psychology and personality.

Risk factors that contribute to inter-individual differences in the age-of-onset of allergic diseases are poorly understood. The aim of this study was to identify genetic risk variants associated with the age at which symptoms of allergic disease first develop, considering information from asthma, hay fever and eczema. Self-reported age-of-onset information was available for 117,130 genotyped individuals of European ancestry from the UK Biobank study. For each individual, we identified the earliest age at which asthma, hay fever and/or eczema was first diagnosed and performed a genome-wide association study (GWAS) of this combined age-of-onset phenotype. We identified 50 variants with a significant independent association (P

Fox Insight is an online, longitudinal health study of people with and without Parkinson's disease with targeted enrollment set to at least 125,000 individuals. Fox Insight data is a rich data set facilitating discovery, validation, and reproducibility in Parkinson's disease research. The dataset is generated through routine longitudinal assessments (health and medical questionnaires evaluated at regular cycles), one-time questionnaires about environmental exposure and healthcare preferences, and genetic data collection. Qualified Researchers can explore, analyze, and download patient-reported outcomes (PROs) data and Parkinson's disease- related genetic variants at https://foxden.michaeljfox.org. The full Fox Insight genetic data set, including approximately 600,000 single nucleotide polymorphisms (SNPs), can be requested separately with institutional review and are described outside of this data descriptor.

BackgroundThe rise of direct-to-consumer genetic testing has enabled many to learn of their possible increased risk for rare diseases, some of which may be suitable for gene-targeted therapies. However, recruiting a large and representative population for rare diseases or genetically defined sub-populations of common diseases is slow, difficult, and expensive.ObjectiveTo assess the feasibility of recruiting and retaining a cohort of individuals who carry a genetic mutation linked to Parkinson's disease (G2019S variant of LRRK2); to characterize this cohort relative to the characteristics of traditional, in-person studies; and to evaluate this model's ability to create an engaged study cohort interested in future clinical trials of gene-directed therapies.MethodsThis single-site,3-year national longitudinal observational study will recruit between 250 to 350 LRRK2 carriers without Parkinson's disease and approximately 50 with the condition. Participants must have undergone genetic testing by the personal genetics company, 23andMe, Inc., have knowledge of their carrier status, and consent to be contacted for research studies. All participants undergo standardized assessments, including video-based cognitive and motor examination, and complete patient-reported outcomes on an annual basis.Results263 individuals living in 33 states have enrolled. The cohort has a mean (SD) age of 56.0 (15.9) years, 59% are female, and 76% are of Ashkenazi Jewish descent. 233 have completed the baseline visit: 47 with self-reported Parkinson's disease and 186 without.ConclusionsThis study establishes a promising model for developing a geographically dispersed and well-characterized cohort ready for participation in future clinical trials of gene-directed therapies.

Basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) are the most common skin cancers and have genetic overlap. Here, the authors use a multi-trait genetic and phenotypic analysis to reveal susceptibility loci for BCC and SCC, and report an optimised polygenic risk score for risk stratification.

Humans are social animals that experience intense suffering when they perceive a lack of social connection. Modern societies are experiencing an epidemic of loneliness. Although 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. Although it is intuitive that circumstantial factors such as marital status and age influence loneliness, there is also compelling evidence of a genetic predisposition toward loneliness. To better understand the genetic architecture of loneliness and its relationship with associated outcomes, we extended the genome-wide association study meta-analysis of loneliness to 511 280 subjects, and detect 19 significant genetic variants from 16 loci, including four novel loci, as well as 58 significantly associated genes. We investigated the genetic overlap with a wide range of physical and mental health traits by computing genetic correlations and by building loneliness polygenic scores in an independent sample of 18 498 individuals with EHR data to conduct a PheWAS with. A genetic predisposition toward loneliness was associated with cardiovascular, psychiatric, and metabolic disorders and triglycerides and high-density lipoproteins. Mendelian randomization analyses showed evidence of a causal, increasing, the effect of both BMI and body fat on loneliness. Our results provide a framework for future studies of the genetic basis of loneliness and its relationship to mental and physical health.

Several occurrences of the word 'schizophrenia' have been re-worded as 'liability to schizophrenia' or 'schizophrenia risk', including in the title, which should have been "GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal effect of schizophrenia liability," as well as in Supplementary Figures 1-10 and Supplementary Tables 7-10, to more accurately reflect the findings of the work.

The author list was in the wrong order in the HTML version of the original article and in the HTML version of the original correction notice. This has been corrected to show the 23andMe Research Team as the fourth author and Abraham A. Palmer as the last author in both places.

Autism spectrum disorder (ASD) is a highly heritable and heterogeneous group of neurodevelopmental phenotypes diagnosed in more than 1% of children. Common genetic variants contribute substantially to ASD susceptibility, but to date no individual variants have been robustly associated with ASD. With a marked sample-size increase from a unique Danish population resource, we report a genome-wide association meta-analysis of 18,381 individuals with ASD and 27,969 controls that identified five genome-wide-significant loci. Leveraging GWAS results from three phenotypes with significantly overlapping genetic architectures (schizophrenia, major depression, and educational attainment), we identified seven additional loci shared with other traits at equally strict significance levels. Dissecting the polygenic architecture, we found both quantitative and qualitative polygenic heterogeneity across ASD subtypes. These results highlight biological insights, particularly relating to neuronal function and corticogenesis, and establish that GWAS performed at scale will be much more productive in the near term in ASD.

ObjectiveAlcohol use disorders are common conditions that have enormous social and economic consequences. Genome-wide association analyses were performed to identify genetic variants associated with a proxy measure of alcohol consumption and alcohol misuse and to explore the shared genetic basis between these measures and other substance use, psychiatric, and behavioral traits.MethodThis study used quantitative measures from the Alcohol Use Disorders Identification Test (AUDIT) from two population-based cohorts of European ancestry (UK Biobank [N=121,604] and 23andMe [N=20,328]) and performed a genome-wide association study (GWAS) meta-analysis. Two additional GWAS analyses were performed, a GWAS for AUDIT scores on items 1-3, which focus on consumption (AUDIT-C), and for scores on items 4-10, which focus on the problematic consequences of drinking (AUDIT-P).ResultsThe GWAS meta-analysis of AUDIT total score identified 10 associated risk loci. Novel associations localized to genes including JCAD and SLC39A13; this study 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 DSM-IV alcohol dependence (rg=0.33-0.63). AUDIT-P and AUDIT-C scores showed significantly different patterns of association across a number of traits, including psychiatric disorders. AUDIT-P score was significantly positively genetically correlated with schizophrenia (rg=0.22), major depressive disorder (rg=0.26), and attention deficit hyperactivity disorder (rg=0.23), whereas AUDIT-C score was significantly negatively genetically correlated with major depressive disorder (rg=-0.24) and ADHD (rg=-0.10). This study also used the AUDIT data in the UK Biobank to identify thresholds for dichotomizing AUDIT total score that optimize genetic correlations with DSM-IV alcohol dependence. Coding individuals with AUDIT total scores ≤4 as control subjects and those with scores ≥12 as case subjects produced a significant high genetic correlation with DSM-IV alcohol dependence (rg=0.82) while retaining most subjects.ConclusionsAUDIT scores ascertained in population-based cohorts can be used to explore the genetic basis of both alcohol consumption and alcohol use disorders.

Tobacco and alcohol use are leading causes of mortality that influence risk for many complex diseases and disorders1. They are heritable2,3 and etiologically related4,5 behaviors that have been resistant to gene discovery efforts6-11. In sample sizes up to 1.2 million individuals, we discovered 566 genetic variants in 406 loci associated with multiple stages of tobacco use (initiation, cessation, and heaviness) as well as alcohol use, with 150 loci evidencing pleiotropic association. Smoking phenotypes were positively genetically correlated with many health conditions, whereas alcohol use was negatively correlated with these conditions, such that increased genetic risk for alcohol use is associated with lower disease risk. We report evidence for the involvement of many systems in tobacco and alcohol use, including genes involved in nicotinic, dopaminergic, and glutamatergic neurotransmission. The results provide a solid starting point to evaluate the effects of these loci in model organisms and more precise substance use measures.

Genetic factors contribute to the risk for developing alcohol use disorder (AUD). In collaboration with the genetics company 23andMe, Inc., we performed a genome-wide association study of the alcohol use disorder identification test (AUDIT), an instrument designed to screen for alcohol misuse over the past year. Our final sample consisted of 20 328 research participants of European ancestry (55.3% females; mean age = 53.8, SD = 16.1) who reported ever using alcohol. Our results showed that the 'chip-heritability' of AUDIT score, when treated as a continuous phenotype, was 12%. No loci reached genome-wide significance. The gene ADH1C, which has been previously implicated in AUD, was among our most significant associations (4.4 × 10-7 ; rs141973904). We also detected a suggestive association on chromosome 1 (2.1 × 10-7 ; rs182344113) near the gene KCNJ9, which has been implicated in mouse models of high ethanol drinking. Using linkage disequilibrium score regression, we identified positive genetic correlations between AUDIT score, high alcohol consumption and cigarette smoking. We also observed an unexpected positive genetic correlation between AUDIT and educational attainment and additional unexpected negative correlations with body mass index/obesity and attention-deficit/hyperactivity disorder. We conclude that conducting a genetic study using responses to an online questionnaire in a population not ascertained for AUD may represent a cost-effective strategy for elucidating aspects of the etiology of AUD.

Attention deficit/hyperactivity disorder (ADHD) is a highly heritable childhood behavioral disorder affecting 5% of children and 2.5% of adults. Common genetic variants contribute substantially to ADHD susceptibility, but no variants have been robustly associated with ADHD. We report a genome-wide association meta-analysis of 20,183 individuals diagnosed with ADHD and 35,191 controls that identifies variants surpassing genome-wide significance in 12 independent loci, finding important new information about the underlying biology of ADHD. Associations are enriched in evolutionarily constrained genomic regions and loss-of-function intolerant genes and around brain-expressed regulatory marks. Analyses of three replication studies: a cohort of individuals diagnosed with ADHD, a self-reported ADHD sample and a meta-analysis of quantitative measures of ADHD symptoms in the population, support these findings while highlighting study-specific differences on genetic overlap with educational attainment. Strong concordance with GWAS of quantitative population measures of ADHD symptoms supports that clinical diagnosis of ADHD is an extreme expression of continuous heritable traits.

Liability to alcohol dependence (AD) is heritable, but little is known about its complex polygenic architecture or its genetic relationship with other disorders. To discover loci associated with AD and characterize the relationship between AD and other psychiatric and behavioral outcomes, we carried out the largest genome-wide association study to date of DSM-IV-diagnosed AD. Genome-wide data on 14,904 individuals with AD and 37,944 controls from 28 case-control and family-based studies were meta-analyzed, stratified by genetic ancestry (European, n = 46,568; African, n = 6,280). Independent, genome-wide significant effects of different ADH1B variants were identified in European (rs1229984; P = 9.8 × 10-13) and African ancestries (rs2066702; P = 2.2 × 10-9). Significant genetic correlations were observed with 17 phenotypes, including schizophrenia, attention deficit-hyperactivity disorder, depression, and use of cigarettes and cannabis. The genetic underpinnings of AD only partially overlap with those for alcohol consumption, underscoring the genetic distinction between pathological and nonpathological drinking behaviors.

Cannabis use is a heritable trait that has been associated with adverse mental health outcomes. In the largest genome-wide association study (GWAS) for lifetime cannabis use to date (N = 184,765), we identified eight genome-wide significant independent single nucleotide polymorphisms in six regions. All measured genetic variants combined explained 11% of the variance. Gene-based tests revealed 35 significant genes in 16 regions, and S-PrediXcan analyses showed that 21 genes had different expression levels for cannabis users versus nonusers. The strongest finding across the different analyses was CADM2, which has been associated with substance use and risk-taking. Significant genetic correlations were found with 14 of 25 tested substance use and mental health-related traits, including smoking, alcohol use, schizophrenia and risk-taking. Mendelian randomization analysis showed evidence for a causal positive influence of schizophrenia risk on cannabis use. Overall, our study provides new insights into the etiology of cannabis use and its relation with mental health.

Cannabis use is a heritable trait that has been associated with adverse mental health outcomes. In the largest genome-wide association study (GWAS) for lifetime cannabis use to date (N = 184,765), we identified eight genome-wide significant independent single nucleotide polymorphisms in six regions. All measured genetic variants combined explained 11% of the variance. Gene-based tests revealed 35 significant genes in 16 regions, and S-PrediXcan analyses showed that 21 genes had different expression levels for cannabis users versus nonusers. The strongest finding across the different analyses was CADM2, which has been associated with substance use and risk-taking. Significant genetic correlations were found with 14 of 25 tested substance use and mental health-related traits, including smoking, alcohol use, schizophrenia and risk-taking. Mendelian randomization analysis showed evidence for a causal positive influence of schizophrenia risk on cannabis use. Overall, our study provides new insights into the etiology of cannabis use and its relation with mental health.

Allergic rhinitis is the most common clinical presentation of allergy, affecting 400 million people worldwide, with increasing incidence in westernized countries1,2. To elucidate the genetic architecture and understand the underlying disease mechanisms, we carried out a meta-analysis of allergic rhinitis in 59,762 cases and 152,358 controls of European ancestry and identified a total of 41 risk loci for allergic rhinitis, including 20 loci not previously associated with allergic rhinitis, which were confirmed in a replication phase of 60,720 cases and 618,527 controls. Functional annotation implicated genes involved in various immune pathways, and fine mapping of the HLA region suggested amino acid variants important for antigen binding. We further performed genome-wide association study (GWAS) analyses of allergic sensitization against inhalant allergens and nonallergic rhinitis, which suggested shared genetic mechanisms across rhinitis-related traits. Future studies of the identified loci and genes might identify novel targets for treatment and prevention of allergic rhinitis.

In the version of this article initially published, the consortium authorship was not presented correctly. The 23andMe Research Team was listed as the last author, rather than the fourth, and a line directing readers to the Supplementary Note for a list of members did appear but was not directly associated with the consortium name. Also, the Supplementary Note description stated that both member names and affiliations were included; in fact, only names are given. Finally, the URL for S-PrediXcan was given in the Methods as https://github.com/hakyimlab/S-PrediXcan; the correct URL is https://github.com/hakyimlab/MetaXcan. The errors have been corrected in the HTML and PDF versions of the article.

We introduce multi-trait analysis of GWAS (MTAG), a method for joint analysis of summary statistics from genome-wide association studies (GWAS) of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms (N eff = 354,862), neuroticism (N = 168,105), and subjective well-being (N = 388,538). As compared to the 32, 9, and 13 genome-wide significant loci identified in the single-trait GWAS (most of which are themselves novel), MTAG increases the number of associated loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yield more informative bioinformatics analyses and increase the variance explained by polygenic scores by approximately 25%, matching theoretical expectations.

Delay discounting (DD), the tendency to discount the value of delayed versus current rewards, is elevated in a constellation of diseases and behavioral conditions. We performed a genome-wide association study of DD using 23,127 research participants of European ancestry. The most significantly associated single-nucleotide polymorphism was rs6528024 (P = 2.40 × 10-8), which is located in an intron of the gene GPM6B. We also showed that 12% of the variance in DD was accounted for by genotype and that the genetic signature of DD overlapped with attention-deficit/hyperactivity disorder, schizophrenia, major depression, smoking, personality, cognition and body weight.

Puan and San Luis et al. find that FUT6, encoding a fucosyltransferase, is required for the “rolling” behavior of certain white blood cells that enables them to move from blood vessels to tissues. They show that FUT6 deficiency leads to a loss of the tetrasaccharide sLex on the surface of basophils, resulting in cells that are less sticky and therefore less able to form the necessary adhesions for exiting the blood vessel to drive the allergic reaction.

Abstract Depression is the most prevalent mental disorder with substantial morbidity and mortality. Although genome-wide association studies (GWASs) have identified multiple risk variants for depression, due to the complicated gene regulatory mechanisms and complexity of linkage disequilibrium (LD), the biological mechanisms by which the risk variants exert their effects on depression remain largely unknown. Here, we perform a transcriptome-wide association study (TWAS) of depression by integrating GWAS summary statistics from 807,553 individuals (246,363 depression cases and 561,190 controls) and summary-level gene-expression data (from the dorsolateral prefrontal cortex (DLPFC) of 1003 individuals). We identified 53 transcriptome-wide significant (TWS) risk genes for depression, of which 23 genes were not implicated in risk loci of the original GWAS. Seven out of 53 risk genes (B3GALTL, FADS1, TCTEX1D1, XPNPEP3, ZMAT2, ZNF501 and ZNF502) showed TWS associations with depression in two independent brain expression quantitative loci (eQTL) datasets, suggesting that these genes may represent promising candidates. We further conducted conditional analyses and identified the potential risk genes that driven the TWAS association signal in each locus. Finally, pathway enrichment analysis revealed biologically pathways relevant to depression. Our study identified new depression risk genes whose expression dysregulation may play a role in depression. More importantly, we translated the GWAS associations into risk genes and relevant pathways. Further mechanistic study and functional characterization of the TWS depression risk genes will facilitate the diagnostics and therapeutics for depression.

Abstract Background Defining clinical phenotypes provides opportunities for new diagnostics and may provide insights into early intervention and disease prevention. There is increasing evidence that patient-derived health data may contain information that complements traditional methods of clinical phenotyping. The utility of these data for defining meaningful phenotypic groups is of great interest because social media and online resources make it possible to query large cohorts of patients with health conditions. Methods We evaluated the degree to which patient-reported categorical data is useful for discovering subclinical phenotypes and evaluated its utility for discovering new measures of disease severity, treatment response and genetic architecture. Specifically, we examined the responses of 1961 patients with inflammatory bowel disease to questionnaires in search of sub-phenotypes. We applied machine learning methods to identify novel subtypes of Crohn’s disease and studied their associations with drug responses. Results Using the patients’ self-reported information, we identified two subpopulations of Crohn’s disease; these subpopulations differ in disease severity, associations with smoking, and genetic transmission patterns. We also identified distinct features of drug response for the two Crohn’s disease subtypes. These subtypes show a trend towards differential genotype signatures. Conclusion Our findings suggest that patient-defined data can have unplanned utility for defining disease subtypes and may be useful for guiding treatment approaches.

The association between blood pressure (BP) and migraine is poorly understood. Here, the authors explore this relationship using summary-level GWAS data for BP and migraine. Cross-trait meta-analysis reveals shared loci between BP and migraine, while Mendelian randomization suggests that diastolic BP specifically plays a key role in migraine susceptibility.

Family-based study designs have been applied to resolve confounding by population stratification, dynastic effects and assortative mating in genetic association analyses. Here, Brumpton et al. describe theory and simulations for overcoming such biases in Mendelian randomization through within-family studies.

The aggregation of Electronic Health Records (EHR) and personalized genetics leads to powerful discoveries relevant to population health. Here we perform genome-wide association studies (GWAS) and accompanying phenome-wide association studies (PheWAS) to validate phenotype-genotype associations of BMI, and to a greater extent, severe Class 2 obesity, using comprehensive diagnostic and clinical data from the EHR database of our cohort. Three GWASs of 500,000 variants on the Illumina platform of 6,645 Healthy Nevada participants identified several published and novel variants that affect BMI and obesity. Each GWAS was followed with two independent PheWASs to examine associations between extensive phenotypes (incidence of diagnoses, condition, or disease), significant SNPs, BMI, and incidence of extreme obesity. The first GWAS examines associations with BMI in a cohort with no type 2 diabetics, focusing exclusively on BMI. The second GWAS examines associations with BMI in a cohort that includes type 2 diabetics. In the second GWAS, type 2 diabetes is a comorbidity, and thus becomes a covariate in the statistical model. The intersection of significant variants of these two studies is surprising. The third GWAS is a case vs. control study, with cases defined as extremely obese (Class 2 or 3 obesity), and controls defined as participants with BMI between 18.5 and 25. This last GWAS identifies strong associations with extreme obesity, including established variants in the FTO and NEGR1 genes, as well as loci not yet linked to obesity. The PheWASs validate published associations between BMI and extreme obesity and incidence of specific diagnoses and conditions, yet also highlight novel links. This study emphasizes the importance of our extensive longitudinal EHR database to validate known associations and identify putative novel links with BMI and obesity.

Uterine leiomyomata (UL) or fibroids are neoplasms of the uterine smooth muscle associated with heavy menstrual bleeding and other female reproductive tract morbidity. Here, the authors identify eight previously undescribed genetic loci for UL and further look into genetic overlap with heavy menstrual bleeding and endometriosis.

Basal cell carcinoma (BCC) is one of the most common malignancies worldwide, yet its genetic determinants are incompletely defined. We perform a European ancestry genome-wide association (GWA) meta-analysis and a Hispanic/Latino ancestry GWA meta-analysis and meta-analyze both in a multi-ancestry GWAS meta-analysis of BCC, totaling 50,531 BCC cases and 762,234 controls from four cohorts (GERA, Mass-General Brigham Biobank, UK Biobank, and 23andMe research cohort). Here we identify 122 BCC-associated loci, of which 36 were novel, and subsequently fine-mapped these associations. We also identify an association of the well-known pigment gene SLC45A2 as well as associations at RCC2 and CLPTM1L with BCC in Hispanic/Latinos. We examine these BCC loci for association with cutaneous squamous cell carcinoma (cSCC) in 16,407 SCC cases and 762,486 controls of European ancestry, and 33 SNPs show evidence of association. Our study findings provide important insights into the genetic basis of BCC and cSCC susceptibility. A large multi-ancestry genome-wide meta-analysis identifies novel basal cell carcinoma loci and shared genetic effects with squamous cell carcinoma, providing insight into the genetic basis of these serious forms of cancer. [ABSTRACT FROM AUTHOR]