Davide Piffer

Clark (2007, 2025) posits that the Industrial Revolution of the eighteenth and nineteenth century was partly facilitated by a gradual evolutionary shift in the distribution of income-enhancing traits such as cognitive ability, thrift, and future-oriented behaviour. Using 4,902 genotyped DNA samples from Europe, we find a statistically significant positive time trend in educational attainment polygenic index scores during the period from about 12,000 years ago to the nineteenth century. This replicates and extends the findings in Woodley et al. (2017), Kuijpers et al. (2022), Piffer and Kirkegaard (2024) and Akbari et al. (2026). The positive trend is large enough to serve as a contributory factor to the Industrial Revolution. In Great Britain, the trend accelerated after the Black Death of the 14th century; the evidence is not conclusive whether this post-Black Death trend acceleration occurred more widely.

Using surname distributions as proxies for historical migration, the authors study how ancestral geography relates to contemporary educational outcomes across Italian provinces and 2,244 municipalities. A directional TF-IDF framework quantifies the prevalence of Southern-origin surnames. Regions with higher Southern surname shares perform systematically worse on standardized assessments, net of economic controls. At the provincial level, Southern surname prevalence strongly predicts scores ( β = −0.815 to −1.028, p < 0.001); municipal effects are attenuated yet significant ( β = −0.220 to −0.485, p < 0.01). Latitude strongly predicts performance at both levels. The authors introduce surname-weighted provincial scores for Mathematical Olympiad outcomes, attributing performance to paternal ancestral provinces. In this setting, latitude significantly predicts surname-weighted scores ( β = 0.460, p < 0.001), with a role for GDP ( β = 0.311, p < 0.015). Results suggest persistent regional differences shaped by migration, cultural transmission, ancestry, and socioeconomic stratification.

This study examines how geography and socioeconomic factors affect educational performance in Italian municipalities. Using mixed-effects modeling and structural equation modeling, we found a strong link between latitude and standardized test scores (INVALSI scores, β = 0.72–0.89), with northern regions outperforming southern ones. This association remains significant even after accounting for income, regional clustering, and other variables, suggesting that latitude acts as a proxy for unmeasured factors like access to resources, community investment in education, or genetic differences. Income showed a robust correlation with educational outcomes (r = 0.71), but exceptions existed, such as tourist-heavy areas. A notable gap in test scores was observed between native Italians and immigrants, though immigrants’ scores were less strongly tied to latitude than those of natives. Additionally, areas with higher net migration saw declines in test scores over time.

The paper is thorough and well-argued. One minor point I’d flag is the assumption that a 1 SD difference in polygenic scores (PGS) equates to a 1 SD difference in IQ between populations. While I’ve used similar simplifications in past work, this rema...

This study explores directional selection on physical and psychosocial phenotypes in Eastern Eurasian populations, utilizing a dataset of 1245 ancient genomes. By analyzing polygenic scores (PGS) for traits including height, educational attainment (EA), IQ, autism, schizophrenia, and others, we observed significant temporal trends spanning the Holocene era. The results suggest positive selection for cognitive-related traits such as IQ, EA and autism spectrum disorder (ASD), alongside negative selection for anxiety and depression. The results for height were mixed and showed nonlinear relationships with Years Before Present (BP). These trends were partially mediated by genetic components linked to distinct ancestral populations. Regression models incorporating admixture, geography, and temporal variables were used to account for biases in population composition over time. Latitude showed a positive effect on ASD PGS, EA and height, while it had a negative effect on skin pigmentation scores. Additionally, latitude exhibited significant nonlinear effects on multiple phenotypes. The observed patterns highlight the influence of climate-mediated selection pressures on trait evolution. Spline regression revealed that several polygenic scores had nonlinear relationships with years BP. The findings provide evidence for complex evolutionary dynamics, with distinct selective pressures shaping phenotypic diversity across different timescales and environments.

We analyzed whole-genome sequencing (WGS) data from 51 populations and combined WGS and array data from 89 populations. Multiple types of polygenic scores (PGS) were employed, derived from multi-ancestry, between-family genome-wide association study (GWAS; MIX-Height), European-ancestry, between-family GWAS (EUR-Height), and European-ancestry siblings GWAS (SIB-Height). Our findings demonstrate that both genetic and environmental factors significantly influence adult body height between populations. Models that included both genetic and environmental predictors best explained population differences in adult body height, with the MIX-Height PGS and environmental factors (Human Development Index [HDI] + per capita caloric intake) achieving an R² of .83. Our findings shed light on Deaton’s ‘African paradox’, which noted the relatively tall stature of African populations despite poor nutrition and childhood health. Contrary to Deaton’s hypotheses, we demonstrate that both genetic differences and environmental factors significantly influence body height in countries with high infant mortality rates. This suggests that the observed tall stature in African populations can be attributed, in part, to a high genetic predisposition for body height. Furthermore, tests of divergent selection based on the Q_ST (i.e., standardized measure of the genetic differentiation of a quantitative trait among populations) and F_ST (neutral marker loci) measures exceeded neutral expectations, reaching statistical significance (p < .01) with the MIX-Height PGS but not with the SIB-Height PGS. This result indicates potential selective pressures on body height-related genetic variants across populations.