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Polygenic indexes (PGIs) are DNA-based predictors. Their value for research in many scientific disciplines is growing rapidly. As a resource for researchers, we used a consistent methodology to construct PGIs for 47 phenotypes in 11 datasets. To maximize the PGIs' prediction accuracies, we constructed them using genome-wide association studies-some not previously published-from multiple data sources, including 23andMe and UK Biobank. We present a theoretical framework to help interpret analyses involving PGIs. A key insight is that a PGI can be understood as an unbiased but noisy measure of a latent variable we call the 'additive SNP factor'. Regressions in which the true regressor is this factor but the PGI is used as its proxy therefore suffer from errors-in-variables bias. We derive an estimator that corrects for the bias, illustrate the correction, and make a Python tool for implementing it publicly available.

Young people's life chances can be predicted by characteristics of their neighbourhood1. Children growing up in disadvantaged neighbourhoods exhibit worse physical and mental health and suffer poorer educational and economic outcomes than children growing up in advantaged neighbourhoods. Increasing recognition that aspects of social inequalities tend, in fact, to be geographical inequalities2-5 is stimulating research and focusing policy interest on the role of place in shaping health, behaviour and social outcomes. Where neighbourhood effects are causal, neighbourhood-level interventions can be effective. Where neighbourhood effects reflect selection of families with different characteristics into different neighbourhoods, interventions should instead target families or individuals directly. To test how selection may affect different neighbourhood-linked problems, we linked neighbourhood data with genetic, health and social outcome data for >7,000 European-descent UK and US young people in the E-Risk and Add Health studies. We tested selection/concentration of genetic risks for obesity, schizophrenia, teen pregnancy and poor educational outcomes in high-risk neighbourhoods, including genetic analysis of neighbourhood mobility. Findings argue against genetic selection/concentration as an explanation for neighbourhood gradients in obesity and mental health problems. By contrast, modest genetic selection/concentration was evident for teen pregnancy and poor educational outcomes, suggesting that neighbourhood effects for these outcomes should be interpreted with care.