Exome sequencing of Finnish isolates enhances rare-variant association power


Exome-sequencing studies have generally been underpowered to identify deleterious alleles with a large effect on complex traits as such alleles are mostly rare. Because the population of northern and eastern Finland has expanded considerably and in isolation following a series of bottlenecks, individuals of these populations have numerous deleterious alleles at a relatively high frequency. Here, using exome sequencing of nearly 20,000 individuals from these regions, we investigate the role of rare coding variants in clinically relevant quantitative cardiometabolic traits. Exome-wide association studies for 64 quantitative traits identified 26 newly associated deleterious alleles. Of these 26 alleles, 19 are either unique to or more than 20 times more frequent in Finnish individuals than in other Europeans and show geographical clustering comparable to Mendelian disease mutations that are characteristic of the Finnish population. We estimate that sequencing studies of populations without this unique history would require hundreds of thousands to millions of participants to achieve comparable association power.

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Fig. 1: Characterization of associations.
Fig. 2: Allelic enrichment in the Finnish population and its effect on genetic discovery.
Fig. 3: Geographical clustering of associated variants.

Data availability

The sequencing data can be accessed through dbGaP (https://www.ncbi.nlm.nih.gov/gap/) using study numbers phs000756 and phs000752. Association results can be accessed at http://pheweb.sph.umich.edu/FinMetSeq/ and are searchable via the Type 2 Diabetes Knowledge Portal (http://www.type2diabetesgenetics.org/). Summary statistics are also available through the NHGRI-EBI GWAS Catalog at https://www.ebi.ac.uk/gwas/downloads/summary-statistics.


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We thank T. Teshiba for coordinating ethical permissions and samples; S. Kerminen, D. Lawson and G. Busby for discussions and providing scripts to run fineSTRUCTURE. S.R. was supported by the Academy of Finland Center of Excellence in Complex Disease Genetics (312062), Academy of Finland (285380), the Finnish Foundation for Cardiovascular Research, the Sigrid Juselius Foundation, Biocentrum Helsinki and University of Helsinki HiLIFE Fellow grant. V.R. acknowledges support by RFBR, research project 18-04-00789 A. V.S. was supported by the Finnish Foundation for Cardiovascular Research. C.S. and L.S. received funding from HG006695, HL113315 and MH105578. M.A.-K. is supported by a Senior Research Fellowship from the National Health and Medical Research Council (NHMRC) of Australia (APP1158958) and works in a unit that is supported by the University of Bristol and UK Medical Research Council (MC_UU_12013/1). The Baker Institute is supported in part by the Victorian Government’s Operational Infrastructure Support Program. A.U.J., D.R., L.J.S., H.M.S., R.W., P.Y., X.Y. and M.B. received funding from DK062370. S.K.S., C.W.K.C. and N.B.F. received funding from HL113315 and NS062691. The METSIM study was supported by grants from Academy of Finland (321428), the Sigrid Juselius Foundation, the Finnish Foundation for Cardiovascular Research, Kuopio University Hospital and the Centre of Excellence of Cardiovascular and Metabolic Diseases is supported by the Academy of Finland (M.L.). Sequencing was funded by 5U54HG003079. A.E.L., K.M.S., H.J.A., C.C.C., C.J.K., K.L.K., D.C.K., D.E.L., J.N., T.J.N., S.K.D., N.O.S., I.M.H. and R.K.W. were funded by 5U54HG003079 and 5UM1HG008853-03.

Author information

A.E.L., L.J.S., R.K.W., A. Palotie, V.S., M.L., S.R., M.B. and N.B.F. designed the study. A.E.L., K.M.S., H.J.A., R.S.F., D.C.K., D.E.L., J.N., T.J.N. and J.V. produced and quality-controlled the sequence data. A.E.L., A.S.H., A.U.J., A. Pietilä, H.M.S., M.A.-K., V.S. and M.L. collected, quality-controlled and/or prepared the clinical data for association analysis. A.E.L., K.M.S., C.W.K.C., S.K.S., A.S.H., L.S., M.P., C.C.C., A.U.J., C.J.K., K.L.K., V.R., D.R., J.V., R.W., P.Y. and X.Y. analysed data. A.S.H., J.G.E., M.A.-K., M.-R.J. and M.M. collected, quality-controlled and analysed replication data. H.L., S.K.D., N.O.S., I.M.H., C.S., S.R., M.B. and N.B.F. supervised experiments and analyses. A.E.L., K.M.S., C.W.K.C., S.K.S., C.S., M.B. and N.B.F. wrote the paper.

Correspondence to Michael Boehnke or Nelson B. Freimer.

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Competing interests

: V.S. has participated in a conference trip sponsored by Novo Nordisk and received a honorarium from the same source for participating in an advisory board meeting. He also has ongoing research collaboration with Bayer. H.L. is a member of the Nordic Expert group unconditionally supported by Gedeon Richter Nordics and has received an honorarium from Orion. All other authors have no competing interests.

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Peer review information Nature thanks Timothy Frayling, Alan Shuldiner, André G. Uitterlinden, Daniel E. Weeks for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Fig. 1 Allele frequency comparisons between FinMetSeq and NFE from gnomAD.

a, Distribution of allelic frequencies between FinMetSeq and gnomAD NFE. The comparison of allele frequencies shows the excess of variants at higher frequency in Finland as a result of the multiple bottlenecks experienced in Finnish population history. b, Proportional site frequency spectra between FinMetSeq and gnomAD NFE by variant annotation class. In general, we find a depletion of the variants in the rarest frequency class, as well as enrichment of variants in the intermediate to common frequency range. The site frequency spectra were down-sampled to 18,000 chromosomes for each data set. c, Comparison of MAFs for trait-associated variants in FinMetSeq and NFE gnomAD. Plotted in the grey background is a two-dimensional histogram of variants with non-zero allele frequencies in both gnomAD and FinMetSeq but no trait associations. Variants associated with at least one trait are coloured and scaled inversely proportional to the logarithm of the association P value. Variants >10× enriched in FinMetSeq compared to NFE are pink, those <10× enriched are in blue. The dashed line is the line of equal frequency. Two-sided uncorrected P values are from a regression of trait on the count of alternative allele at each variant. The number of independent individuals used in each point is listed in Supplementary Table 5.

Extended Data Fig. 2 Heritability of and correlations between traits.

ab, Traits are in the same order, clockwise in a, and left to right and top to bottom in b, following the trait group colour key. a, Heritability estimated in 13,342 unrelated individuals (for abbreviations see Supplementary Table 4; for details see Supplementary Table 6). b, Heat map of the absolute Pearson correlations of standardized trait values (top right triangle) and the absolute values of estimated pairwise genetic correlations (bottom left triangle). Genetic correlations are estimated in 13,342 unrelated individuals. Values in grey below the diagonal had trait heritability less than 1.5× the s.e. of heritability.

Extended Data Fig. 3 Properties of associations shared between traits.

a, Shared genomic associations by pairs of traits. For traits x and y, colour in row x and column y reflects the number of loci associated with both traits divided by the number of loci associated with trait x. Traits are presented in the same order as in Extended Data Fig. 2a, and the side and top colour bars reflect trait groups. b, Relationship between estimated genetic correlation and extent of sharing of genetic associations. For each trait pair, the extent of locus sharing is defined as the number of loci associated with both traits divided by the total number of loci associated with either trait. Analysis using the absolute value of the Pearson correlation of the residual series results in a very similar pattern. The number of trait pairs in each x-axis category is as follows: 0–1%, 819; 1–10%, 204; 11–20%, 102; 21–30%, 41; 31–40%, 29; 41–50%, 16; >50%, 13. The bar within each box is the median, the box represents the upper and lower quartiles, whiskers extend to 1.5× the interquartile range and points represent outliers.

Extended Data Fig. 4 Gene-based association of extremely rare variants in APOB with serum total cholesterol.

Top, the distribution of the covariate-adjusted and inverse-normal transformed phenotype. Bottom, the association statistics for each variant included in the gene-based test along with the trait value for minor allele carriers of each variant (orange triangles). SV.P is the P value from the analysis of each variant in a single-variant analysis. The number of independent individuals in the analysis is 19,291.

Extended Data Fig. 5 Gene-based association of rare variants in SECTM1 with HDL2 cholesterol.

Top, the distribution of the covariate-adjusted and inverse-normal transformed phenotype. Bottom, the association statistics for each variant included in the gene-based test, along with the trait value for minor allele carriers of each variant (orange triangles). SV.P is the P value from the analysis of each variant in a single-variant analysis. The number of independent individuals in the analysis is 10,984.

Extended Data Fig. 6 Gene-based association of extremely rare variants in ALDH1L1 with glycine levels.

Top, the distribution of the covariate-adjusted and inverse-normal transformed phenotype. Bottom, the association statistics for each variant included in the gene-based test, along with the trait value for minor allele carriers of each variant (orange triangles). SV.P is the P value from the analysis of each variant in a single-variant analysis. The number of independent individuals in the analysis is 8,206.

Extended Data Fig. 7 Population structure of the FinMetSeq dataset, by region.

Population structure, by region, from a principal component analysis of exome-sequencing variant data (MAF > 1%) for 14,874 unrelated individuals with known parental birthplaces. Colour indicates individuals with both parents born in the same region; grey indicates individuals with different parental birth regions or missing information for one parent. Ctf, Central Finland; COs, Central Ostrobothnia; Kai, Kainuu; Khm, Kanta-Hame; Kyl, Kymenlaakso; Lap, Lapland; Nka, Northern Karelia; NOs, Northern Ostrobothnia; NSv, Northern Savonia; Osb, Ostrobothnia; Phm, Paijat-Hame; Prk, Pirkanmaa; SKa, Southern Karelia; SOs, Southern Ostrobothnia; SSv, Southern Savonia; Stk, Satakunta; Swf, Southwest Finland; Usm, Uusimaa; X, split parental birthplaces. Large solid circles represent the centre of each region. A map of Finland with regions labelled is supplied for reference.

Extended Data Fig. 8 Hierarchical clustering tree produced by fineSTRUCTURE.

We identified 16 subpopulations within the FinMetSeq dataset by applying a haplotype-based clustering algorithm, fineSTRUCTURE, on 2,644 unrelated individuals born by 1955 whose parents were both born in the same municipality (Methods). Each subpopulation is named based on the most common parental birth location among its members. Kai, Kainuu; Lap, Lapland; NKa, North Karelia; NOs, North Ostrobothnia; NSv, North Savonia; SOs, South Ostrobothnia; SuK, Surrendered Karelia. A map of Finland with regions labelled is supplied for reference. If multiple subpopulations share the same location label, the subpopulation is further distinguished with a numeral. NSv3 is used as an internal reference for the enrichment analysis. See Supplementary Table 17 for more detailed demographic descriptions of each subpopulation.

Extended Data Fig. 9 Regional variation in allele frequencies by functional annotation.

Enrichment of variants by allelic class in regional subpopulations of late-settlement Finland (defined in Supplementary Table 17). Each bin represents the ratio of variants in the subpopulation compared to the reference subpopulation (NSv3), after down-sampling the frequency spectra of all populations to 200 chromosomes. Pink cells represent enrichment (ratio >1), blue cells represent depletion (ratio <1). Sample sizes and confidence intervals for each enrichment ratio and the associated P values are presented in Supplementary Table 18. The results are consistent with multiple bottlenecks in late-settlement Finland, particularly for populations in Lapland and Northern Ostrobothnia. *P < 0.05; **P < 0.01; ***P < 0.005.

Supplementary information

Supplementary Information

This file contains the Supplementary Results, Supplementary Methods, Supplementary References and a full list of members of FinnGen.

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This file contains Supplementary Tables 1–22 with a full guide.

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