Coupling dense genotype data with new computational methods offers unprecedented opportunities for individual-level ancestry estimation once geographically precisely defined reference data sets become available. We study such a reference data set for Finland containing 2376 such individuals from the FINRISK Study survey of 1997 both of whose parents were born close to each other. This sampling strategy focuses on the population structure present in Finland before the 1950s. By using the recent haplotype-based methods ChromoPainter (CP) and FineSTRUCTURE (FS) we reveal a highly geographically clustered genetic structure in Finland and report its connections to the settlement history as well as to the current dialectal regions of the Finnish language. The main genetic division within Finland shows striking concordance with the 1323 borderline of the treaty of Nöteborg. In general, we detect genetic substructure throughout the country, which reflects stronger regional genetic differences in Finland compared to, for example, the UK, which in a similar analysis was dominated by a single unstructured population. We expect that similar population genetic reference data sets will become available for many more populations in the near future with important applications, for example, in forensic genetics and in genetic association studies. With this in mind, we report those extensions of the CP + FS approach that we found most useful in our analyses of the Finnish data.

Polygenic scores (PSs) are becoming a useful tool to identify individuals with high genetic risk for complex diseases, and several projects are currently testing their utility for translational applications. It is also tempting to use PSs to assess whether genetic variation can explain a part of the geographic distribution of a phenotype. However, it is not well known how the population genetic properties of the training and target samples affect the geographic distribution of PSs. Here, we evaluate geographic differences, and related biases, of PSs in Finland in a geographically well-defined sample of 2,376 individuals from the National FINRISK study. First, we detect geographic differences in PSs for coronary artery disease (CAD), rheumatoid arthritis, schizophrenia, waist-hip ratio (WHR), bodymass index (BMI), and height, but not for Crohn disease or ulcerative colitis. Second, we use height as a model trait to thoroughly assess the possible population genetic biases in PSs and apply similar approaches to the other phenotypes. Most importantly, we detect suspiciously large accumulations of geographic differences for CAD, WHR, BMI, and height, suggesting bias arising from the population's genetic structure rather than from a direct genotype-phenotype association. This work demonstrates how sensitive the geographic patterns of current PSs are for small biases even within relatively homogeneous populations and provides simple tools to identify such biases. A thorough understanding of the effects of population genetic structure on PSs is essential for translational applications of PSs.