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..

We examined whether assortative mating for educational attainment (“like marries like”) can be detected in the genomes of ~ 1600 UK spouse pairs of European descent. Assortative mating on heritable traits like educational attainment increases the genetic variance and heritability of the trait in the population, which may increase social inequalities. We test for genetic assortative mating in the UK on educational attainment, a phenotype that is indicative of socio-economic status and has shown substantial levels of assortative mating. We use genome-wide allelic effect sizes from a large genome-wide association study on educational attainment (N ~ 300 k) to create polygenic scores that are predictive of educational attainment in our independent sample (r = 0.23, p < 2 × 10− 16). The polygenic scores significantly predict partners' educational outcome (r = 0.14, p = 4 × 10− 8 and r = 0.19, p = 2 × 10− 14, for prediction from males to females and vice versa, respectively), and are themselves significantly correlated between spouses (r = 0.11, p = 7 × 10− 6). Our findings provide molecular genetic evidence for genetic assortative mating on education in the UK

The protistan pathogen Bonamia ostreae was first detected in Ostrea edulis at Rossmore, Cork Harbour, on the south coast of Ireland in 1987. A selective breeding programme commenced in 1988 by Atlantic Shellfish Ltd. to produce B. ostreae-resistant oysters using 3 to 4 yr old survivors as broodstock for controlled spawning in land-based spatting ponds. On-growing of oyster spat settled on mussel cultch was carried out on designated beds within Cork Harbour. Oyster production subsequently increased successfully, resulting in 3 yr old Rossmore O. edulis being marketed from 1993 onwards and a record tonnage of 4 yr old oysters being produced in 1995 and 1996. O. edulis production, B. ostreae prevalence and oyster mortalities have been monitored and recorded at Rossmore for over 30 yr. The collation and analysis of this data from 52 samples and 3190 oysters demonstrate the introduction and progression of bonamiosis and subsequent interventions to ameliorate disease effects during this period at Rossmore. Results suggest that O. edulis mortalities are now negligible during the first 4 yr of growth, prevalence of B. ostreae infection is low, and no correlation exists between prevalence of infection and oyster mortalities. This study, when compared to other studies of bonamiosis-infected oyster populations, suggests that an intervention in the form of a selective breeding programme is required to reduce the impact of the disease.

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...

The honesty of people in an online panel from 15 countries was measured in two experiments: reporting a coin flip with a reward for "heads", and an online quiz with the possibility of cheating. There are large differences in honesty across countries. Average honesty is positively correlated with per capita GDP. This is driven mostly by GDP differences arising before 1950, rather than by GDP growth since 1950. A country's average honesty correlates with the proportion of its population that is Protestant. These facts suggest a long-run relationship between honesty and economic development. The experiment also elicited participants' expectations about different countries' levels of honesty. Expectations were not correlated with reality. Instead they appear to be driven by cognitive biases, including self-projection.• Average honesty of resident nationals of 15 countries was measured in two experiments• There are large cross-country differences in honesty• Honesty correlates at country level with GDP and Protestantism• Participants' expectations about honesty in different countries were also elicited• Expectations were not correlated to reality, but driven by cognitive biases 1 *Highlights (for review)