Human DNA varies across geographic regions, with most variation observed so far reflecting distant ancestry differences. Here, we investigate the geographic clustering of genetic variants that influence complex traits and disease risk in a sample of ~450,000 individuals from Great Britain. Out of 30 traits analyzed, 16 show significant geographic clustering at the genetic level after controlling for ancestry, likely reflecting recent migration driven by socio-economic status (SES). Alleles associated with educational attainment (EA) show most clustering, with EA-decreasing alleles clustering in lower SES areas such as coal mining areas. Individuals that leave coal mining areas carry more EA-increasing alleles on average than the rest of Great Britain. In addition, we leveraged the geographic clustering of complex trait variation to further disentangle regional differences in socio-economic and cultural outcomes through genome-wide association studies on publicly available regional measures, namely coal mining, religiousness, 1970/2015 general election outcomes, and Brexit referendum results. of reasons. They may be driven by the search for specific neighborhood, housing, and inhabitant characteristics, and/or socio-economic factors (e.g., education or job-related considerations), 9 such as the mass migrations from rural to industrial areas during the industrialization. 10 These geographic movements may coincide with regional clustering of heritable social outcomes such as socio-economic status and major group ideologies (e.g., religion 11 and political preference 12 ).Understanding what drives the geographic distribution of genome-wide complex trait variation is important for a variety of reasons. Studying regional differences of genetic variants associated with complex traits that reflect education, wealth, growth, health, and disease, may help understand why those traits are unevenly distributed across Great Britain. Besides the known regional differences in income and SES, significant regional differences have been reported for mental 13 and physical 14 health problems. Regional differences in wealth and health are likely linked to each other, [15][16][17] and have been shown to be partly driven by migration. 14,18 If genome-wide complex trait variation is geographically clustered, this should also be taken into account in certain genetically-informative study designs. Mendelian randomization for example uses genetic variants as instrumental variables to identify causality, under the assumption that the genetic instrument is not associated with confounders that influence the two traits under investigation. 19 Geographic clustering of genetic complex trait variation could introduce geneenvironment correlations that violate this assumption. 20 Such gene-environment correlations could also introduce bias in heritability estimates in twin and family studies, 21 and could affect signals from genomewide association studies (GWASs). Furthermore, studying the genetics of migration and geographically clustered cultural...

Mental health and cognitive achievement are partly heritable. To identify the underlying neural mechanisms, we associated genetic predispositions to various mental health and cognitive traits with a large set of structural and functional brain measures from the UK Biobank (N=36,799). We show that genetic predispositions to attention deficit hyperactivity disorder, smoking initiation, and cognitive traits have stronger associations with brain structure than with brain function, whereas genetic predispositions to most other psychiatric disorders have stronger associations with brain function than with brain structure. These results reveal that genetic predispositions to mental health and cognitive traits have distinct brain profiles.

Gene-environment correlations can bias associations between genetic variants and complex traits in genome-wide association studies (GWASs). Here, we control for geographic sources of gene-environment correlation in GWASs on 56 complex traits (N = 69,772–271,457). Controlling for geographic region significantly decreases heritability signals for SES-related traits, most strongly for educational attainment and income, indicating that socio-economic differences between regions induce gene-environment correlations that become part of the polygenic signal. For most other complex traits investigated, genetic correlations with educational attainment and income are significantly reduced, most significantly for traits related to BMI, sedentary behavior, and substance use. Controlling for current address has greater impact on the polygenic signal than birth place, suggesting both active and passive sources of gene-environment correlations. Our results show that societal sources of social stratification that extend beyond families introduce regional-level gene-environment correlations that affect GWAS results.

This phenomewide association study examined SNP and genebased associations of the CADM2 gene with 242 psychobehavioral traits (N=12,211 to 453,349). We found significant associations with 51 traits, many more than for other genes. We replicated previously reported associations with substance use, risk taking, and health behavior, and uncovered novel associations with sleep and dietary traits. Accordingly, CADM2 is involved in many psycho-behavioral traits, suggesting a common denominator in their biology.