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Human DNA polymorphisms vary across geographic regions, with the most commonly observed variation reflecting distant ancestry differences. Here we investigate the geographic clustering of common genetic variants that influence complex traits in a sample of ~450,000 individuals from Great Britain. Of 33 traits analysed, 21 showed significant geographic clustering at the genetic level after controlling for ancestry, probably reflecting migration driven by socioeconomic status (SES). Alleles associated with educational attainment (EA) showed the most clustering, with EA-decreasing alleles clustering in lower SES areas such as coal mining areas. Individuals who leave coal mining areas carry more EA-increasing alleles on average than those in the rest of Great Britain. The level of geographic clustering is correlated with genetic associations between complex traits and regional measures of SES, health and cultural outcomes. Our results are consistent with the hypothesis that social stratification leaves visible marks in geographic arrangements of common allele frequencies and gene–environment correlations. Abdellaoui et al. examine the geographic distribution of human DNA differences in Great Britain, finding that the geographic distribution of polygenic scores for educational attainment and other complex traits resembles the geographic distribution of economic differences.

Gene–environment correlations affect associations between genetic variants and complex traits in genome-wide association studies (GWASs). Here we showed in up to 43,516 British siblings that educational attainment polygenic scores capture gene–environment correlations, and that migration extends these gene–environment correlations beyond the family to broader geographic regions. We then ran GWASs on 56 complex traits in up to 254,387 British individuals. Controlling for geographic regions significantly decreased the heritability for socioeconomic status (SES)-related traits, most strongly for educational attainment and income. For most traits, controlling for regions significantly reduced genetic correlations with educational attainment and income, most significantly for body mass index/body fat, sedentary behavior and substance use, consistent with gene–environment correlations related to regional socio-economic differences. The effects of controlling for birthplace and current address suggest both passive and active sources of gene–environment correlations. Our results show that the geographic clustering of DNA and SES introduces gene–environment correlations that affect GWAS results. Analyses of gene–environment correlations across geographic regions for 56 complex traits in UK Biobank suggest that both passive and active sources of gene–environment correlation affect genetic association signals.

Higher paternal age at offspring conception increases de novo genetic mutations. Based on evolutionary genetic theory we predicted older fathers' children, all else equal, would be less likely to survive and reproduce, i.e. have lower fitness. In sibling control studies, we find support for negative paternal age effects on offspring survival and reproductive success across four large populations with an aggregate N > 1.4 million. Three populations were pre-industrial (1670–1850) Western populations and showed negative paternal age effects on infant survival and offspring reproductive success. In twentieth-century Sweden, we found minuscule paternal age effects on survival, but found negative effects on reproductive success. Effects survived tests for key competing explanations, including maternal age and parental loss, but effects varied widely over different plausible model specifications and some competing explanations such as diminishing paternal investment and epigenetic mutations could not be tested. We can use our findings to aid in predicting the effect increasingly older parents in today's society will have on their children's survival and reproductive success. To the extent that we succeeded in isolating a mutation-driven effect of paternal age, our results can be understood to show that de novo mutations reduce offspring fitness across populations and time periods.

Human mate choice is central to individuals’ lives and to the evolution of the species, but the basis of variation in mate choice is not well understood. Here we looked at a large community-based sample of twins and their partners and parents ( individuals) to test for genetic and family environmental influences on mate choice, while controlling for and not controlling for the effects of assortative mating. Key traits were analyzed, including height, body mass index, age, education, income, personality, social attitudes, and religiosity. This revealed near-zero genetic influences on male and female mate choice over all traits and no significant genetic influences on mate choice for any specific trait. A significant family environmental influence was found for the age and income of females’ mate choices, possibly reflecting parental influence over mating decisions. We also tested for evidence of sexual imprinting, where individuals acquire mate-choice criteria during development by using their opposite-sex parent as the template of a desirable mate; there was no such effect for any trait. The main discernible pattern of mate choice was assortative mating; we found that partner similarity was due to initial choice rather than convergence and also at least in part to phenotypic matching.

Substance use disorders represent a significant public health concern with considerable socioeconomic implications worldwide. Twin and family-based studies have long established a heritable component underlying these disorders. In recent years, genome-wide association studies of large, broadly phenotyped samples have identified regions of the genome that harbour genetic risk variants associated with substance use disorders. These regions have enabled the discovery of putative causal genes and improved our understanding of genetic relationships among substance use disorders and other traits. Furthermore, the integration of these data with clinical information has yielded promising insights into how individuals respond to medications, allowing for the development of personalized treatment approaches based on an individual’s genetic profile. This review article provides an overview of recent advances in the genetics of substance use disorders and demonstrates how genetic data may be used to reduce the burden of disease and improve public health outcomes.

Cannabis is among the most widely consumed psychoactive substances worldwide. Individual differences in cannabis use phenotypes can partly be explained by genetic differences. Technical and methodological advances have increased our understanding of the genetic aetiology of cannabis use. This narrative review discusses the genetic literature on cannabis use, covering twin, linkage, and candidate-gene studies, and the more recent genome-wide association studies (GWASs), as well as the interplay between genetic and environmental factors. Not only do we focus on the insights that these methods have provided on the genetic aetiology of cannabis use, but also on how they have helped to clarify the relationship between cannabis use and co-occurring traits, such as the use of other substances and mental health disorders. Twin studies have shown that cannabis use is moderately heritable, with higher heritability estimates for more severe phases of use. Linkage and candidate-gene studies have been largely unsuccessful, while GWASs so far only explain a small portion of the heritability. Dozens of genetic variants predictive of cannabis use have been identified, located in genes such as CADM2 , FOXP2 , and CHRNA2 . Studies that applied multivariate methods (twin models, genetic correlation analysis, polygenic score analysis, genomic structural equation modelling, Mendelian randomisation) indicate that there is considerable genetic overlap between cannabis use and other traits (especially other substances and externalising disorders) and some evidence for causal relationships (most convincingly for schizophrenia). We end our review by discussing implications of these findings and suggestions for future work.

Crime poses a major burden for society. The heterogeneous nature of criminal behavior makes it difficult to unravel its causes. Relatively little research has been conducted on the genetic influences of criminal behavior. The few twin and adoption studies that have been undertaken suggest that about half of the variance in antisocial behavior can be explained by genetic factors. In order to identify the specific common genetic variants underlying this behavior, we conduct the first genome-wide association study (GWAS) on adult antisocial behavior. Our sample comprised a community sample of 4816 individuals who had completed a self-report questionnaire. No genetic polymorphisms reached genome-wide significance for association with adult antisocial behavior. In addition, none of the traditional candidate genes can be confirmed in our study. While not genome-wide significant, the gene with the strongest association (p-value = 8.7×10(-5)) was DYRK1A, a gene previously related to abnormal brain development and mental retardation. Future studies should use larger, more homogeneous samples to disentangle the etiology of antisocial behavior. Biosocial criminological research allows a more empirically grounded understanding of criminal behavior, which could ultimately inform and improve current treatment strategies.

Genome-wide association studies on human behavioural traits are producing large amounts of polygenic signals with significant predictive power and potentially useful biological clues. Behavioural traits are more distal and are less directly under biological control compared with physical characteristics, which makes the associated genetic effects harder to interpret. The results of genome-wide association studies for human behaviour are likely made up of a composite of signals from different sources. While sample sizes continue to increase, we outline additional steps that need to be taken to better delineate the origin of the increasingly stronger polygenic signals. In addition to genetic effects on the traits themselves, the major sources of polygenic signals are those that are associated with correlated traits, environmental effects and ascertainment bias. Advances in statistical approaches that disentangle polygenic effects from different traits as well as extending data collection to families and social circles with better geographical coverage will probably contribute to filling the gap of knowledge between genetic effects and behavioural outcomes. Genome-wide association studies of behavioural traits can generate predictive polygenic signals. Abdellaoui and Verweij review key developments in this field and explain how advances in methods and data can further our understanding of the relationship between genetic effects and human behaviour.

Abstract Individuals with schizophrenia have a reduced life-expectancy compared to the general population, largely due to an increased risk of cardiovascular disease (CVD). Clinical and epidemiological studies have been unable to unravel the nature of this relationship. We obtained summary-data of genome-wide-association studies of schizophrenia (N = 130 644), heart failure (N = 977 323), coronary artery disease (N = 332 477), systolic and diastolic blood pressure (N = 757 601), heart rate variability (N = 46 952), QT interval (N = 103 331), early repolarization and dilated cardiomyopathy ECG patterns (N = 63 700). We computed genetic correlations and conducted bi-directional Mendelian randomization (MR) to assess causality. With multivariable MR, we investigated whether causal effects were mediated by smoking, body mass index, physical activity, lipid levels, or type 2 diabetes. Genetic correlations between schizophrenia and CVD were close to zero (−0.02–0.04). There was evidence that liability to schizophrenia causally increases heart failure risk. This effect remained consistent with multivariable MR. There was also evidence that liability to schizophrenia increases early repolarization pattern, largely mediated by BMI and lipids. Finally, there was evidence that liability to schizophrenia increases heart rate variability, a direction of effect contrasting clinical studies. There was weak evidence that higher systolic blood pressure increases schizophrenia risk. Our finding that liability to schizophrenia increases heart failure is consistent with the notion that schizophrenia involves a systemic dysregulation of the body with detrimental effects on the heart. To decrease cardiovascular mortality among individuals with schizophrenia, priority should lie with optimal treatment in early stages of psychosis.

Across animal species, offspring of closely related mates exhibit lower fitness, a phenomenon called inbreeding depression. Inbreeding depression in humans is less well understood because mating between close relatives is generally rare and stigmatised, confounding investigation of its effect on fitness-relevant traits. Recently, the availability of high-density genotype data has enabled quantification of variation in distant inbreeding in 'outbred' human populations, but the low variance of inbreeding detected from genetic data in most outbred populations means large samples are required to test effects, and only a few traits have yet been studied. However, it is likely that isolated populations, or those with a small effective population size, have higher variation in inbreeding and therefore require smaller sample sizes to detect inbreeding effects. With a small effective population size and low immigration, Northern Finland is such a population. We make use of a sample of ∼5,500 'unrelated' individuals in the Northern Finnish Birth Cohort 1966 with known genotypes and measured phenotypes across a range of fitness-relevant physical and psychological traits, including birth length and adult height, body mass index (BMI), waist-to-hip ratio, blood pressure, heart rate, grip strength, educational attainment, income, marital status, handedness, health, and schizotypal features. We find significant associations in the predicted direction between individuals' inbreeding coefficient (measured by proportion of the genome in runs of homozygosity) and eight of the 18 traits investigated, significantly more than the one or two expected by chance. These results are consistent with inbreeding depression effects on a range of human traits, but further research is needed to replicate and test alternative explanations for these effects.