MixFit: Methodology for Computing Ancestry-Related Genetic Scores at the Individual Level and Its Application to the Estonian and Finnish Population Studies

Abstract

Ancestry information at the individual level can be a valuable resource for personalized medicine, medical, demographical and history research, as well as for tracing back personal history. We report a new method for quantitatively determining personal genetic ancestry based on genome-wide data. Numerical ancestry component scores are assigned to individuals based on comparisons with reference populations. These comparisons are conducted with an existing analytical pipeline making use of genotype phasing, similarity matrix computation and our addition—multidimensional best fitting by MixFit. The method is demonstrated by studying Estonian and Finnish populations in geographical context. We show the main differences in the genetic composition of these otherwise close European populations and how they have influenced each other. The components of our analytical pipeline are freely available computer programs and scripts one of which was developed in house (available at: www.geenivaramu.ee/en/tools/mixfit).

Haplotype sharing provides insights into fine-scale population history and disease in Finland

“…Migration rates within Sweden are most elevated in southern regions near the largest cities, including Stockholm and Uppsala. As speculated previously 38 , migration rates are generally elevated within Estonia, but depleted along the west coast and between Tallinn and Tartu; it is also depleted between the Estonia mainland and Finland/Sweden. The strongest barriers to migration in/near Sweden are in the northwest as well as along the northwestern Finnish border separating Finnish Lapland and Sweden, although there are notably few individuals either sampled or living there, resulting in increased noise.…”

“…We assessed how much sharing occurs within and between regions of Finland and neighboring countries and/or regions, including Sweden, Estonia, St. Petersburg, Russia, and Hungary. PCA recapitulates geographic boundaries and Finnish bottlenecks: PC1 separates Finland from non-Finnish Europeans, and PC2 separates non-Finnish European populations along a cline ( Figure 3B ) 38,39 . Birth regions also recapitulate expected trends; for example, southern Finns project closer in PCA space with northern Estonians than other regions of either country ( Figure 3B ).…”

“…Swedish genotype data are available upon application from the National Institute of Mental Health (NIMH) Genetics Repository at https://www.nimhgenetics.org/. Estonian samples are from the Estonian Genome Center, University of Tartu (EGCUT) 38 . Genotyping for individuals from St. Petersburg, Russia was performed as a part of Starvation Study ongoing at the Broad Institute on a cohort previously described in 48 .…”

Haplotype Sharing Provides Insights into Fine-Scale Population History and Disease in Finland

“…PCA recapitulates geographic boundaries and Finnish bottlenecks: PC1 separates Finland from non-Finnish Europeans, and PC2 separates non-Finnish European populations along a cline ( Figure 3B). 58,73 Birth regions also recapitulate expected trends; for example, southern Finns project closer in PCA space with northern Estonians than with individuals from other regions of either country ( Figure 3B). Hierarchical clustering of genetic divergence (F ST ) within and between regions and countries demonstrates that divergence is typically smallest within countries, with the exception of Finland and the northernmost Swedish region, Norrbotten, which neighbors Finnish Lapland.…”

“…Estonian samples are from the Estonian Genome Center, University of Tartu. 58 Genotyping for individuals from St. Petersburg, Russia was performed as a part of a starvation study ongoing at the Broad Institute on a cohort previously described in Rotar et al 59 Hungarian samples included in the study were genotyped as part of the Hungarian Transdanubian Biobank. 60 Genotyping details and sample sizes are shown in Table S2.…”