Hereditas 144: 171Á180 (2007) The genetic population structure of northern Sweden and its implications for mapping genetic diseases ELISABET EINARSDOTTIR1, INEZ EGERBLADH2, LARS BECKMAN1,$, DAN HOLMBERG1 and STEFAN A. ESCHER1 1Medical and Clinical Genetics, Dept. of Medical Biosciences, Umea˚ University, Umea˚, Sweden 2Centre for Population Studies, Umea˚ University, Umea˚, Sweden $Deceased 5 October 2005 Einarsdottir, E., Egerbladh, I., Beckman, L., Holmberg, D. and Escher, S. A. 2007. The genetic population structure of northern Sweden and its implications for mapping genetic diseases. * Hereditas 144: 171Á180. Lund, Sweden. eISSN 1601- 5223. Received April 25, 2007. Accepted April 25, 2007 The northern Swedish population has a history of admixture of three ethnic groups and a dramatic population growth from a relatively small founder population. This has resulted in founder effects that together with unique resources for genealogical analyses provide excellent conditions for genetic mapping of monogenic diseases. Several recent examples of successful mapping of genetic factors underlying susceptibility to complex diseases have suggested that the population of northern Sweden may also be an important tool for efficient mapping of more complex phenotypes. A potential factor contributing to these effects may be population sub-isolates within the large river valleys, constituting a central geographic characteristic of this region. We here provide evidence that marriage patterns as well as the distribution of gene frequencies in a set of marker loci are compatible with this notion. The possible implications of this population structure on linkage- and association based strategies for identifying genes contributing risk to complex diseases are discussed. Stefan A. Escher, Department of Molecular Biology, Umea˚ University, SE-901 87 Umea˚, Sweden. E-mail: stefan.a.escher@ molbiol.umu.se Complex diseases have large repercussions on society, variants underlying complex phenotypes. To allow for but identification of the genetic risk factors underlying an optimal study design, it is thus important to them remains, in most cases, a major challenge. Large characterise in detail the population(s), revealing efforts and resources have been invested in this stratification possibilities and sub-isolates (HELGASON work, but the success rate to date has been limited et al. 2005). The population of northern Sweden has and most successful positional cloning projects been used extensively for genetic mapping of mono- focusing on complex diseases have been achieved genic diseases in particular (LUNDIN et al. 1997, using linkage-based approaches in populations with BURSTEDT et al. 1999, EINARSDOTTIR et al. 2004), reduced genetic heterogeneity (PELTONEN 1996, 2000, and lately also more complex traits (CARLSSON et al. SHEFFIELD et al. 1998, SHIFMAN and DARVASI 2001). 2002, VENKEN et al. 2005) and a detailed study of the More recently, large-scale association studies based population structure and hidden stratifications is on linkage disequilibrium (LD) mapping, made pos- essential to maximise future genetic mapping efforts sible through rapid technological development and in this population. efforts to establish a global haplotype map covering Northern Sweden is here defined as the two north- the genome, have come to light (HAPMAPPROJECT ernmost counties of Sweden, Va¨sterbotten and Norr- 2003). These efforts are likely to benefit from studies botten, with a total population of around 500 000 of different population isolates, due to their restricted individuals. Disease-mapping efforts in northern Swe- degree of genetic heterogeneity and extended regions den have been aided by several factors other than the of LD (JOHANSSON et al. 2005). genetic composition of the population itself. These Association-based mapping of genetic traits is include 1) a high-quality healthcare system, contribut- sensitive to hidden stratifications and confounding ing to a low frequency of undiagnosed cases and a factors within the population (BERGER et al. 2006), positive attitude towards genetic research; 2) large making accurate matching of controls essential to biobanks and disease registries further aiding the keep both false positive and false negative results to a study of diseases in this population; 3) a tradition of minimum. Founder effects and inbreeding may com- genealogical interest and comprehensive church regis- plicate association-based studies, but at the same time ters which have enabled the tracing of families to significantly reduce the genetic heterogeneity of a common founders and the identification of common population and aid in the identification of genetic founder mutations or haplotypes. DOI: 10.1111/j.2007.0018-0661.02007.x 172 E. Einarsdottir et al. Hereditas 144 (2007) Three main population-groups, Swedes, Finns and to a relative stability of the population from a genetic the Saami, make up the bulk of the northern Swedish point of view. population. The Saami are the endogenous population A number of genetic mapping projects have of northern Scandinavia, reaching northern Sweden provided evidence that founder effects, established as via Finland during the later Bronze Age. The Saami a result of the demographic history of the region, population lived in the inland areas of Sweden (SKO¨ LD underlie many of the diseases studied in the 2004), especially in the northernmost regions, but northern Swedish population (LUNDIN et al. 1997, became a minority group as the Swedes started to BURSTEDT et al. 1999, CARLSSON and FASTH 2001). move north. An early Finnish population settled down This, together with the geographic distribution of gene along the northern part of the Gulf of Bothnia, and frequencies of the disease-associated genes (BITTLES maintained frequent contacts with the population in and EGERBLADH 2005), suggests stratification in the northwestern Finland. A number of Finns also population and the potential existence of genetic sub- isolates. A plausible hypothesis, supported by histor- migrated to northern Sweden via the southeastern ical and demographic data, is that such sub-isolates parts of Sweden. A successive Swedish colonisation might have been formed in the large river valleys that took place in the coastal areas of northern Sweden, run from the mountains in the west to the coast in the strengthened by encouragements from the Swedish east and which are separated from each other by vast State in the early 14th century. It was not until forests. Each of these sub-isolates will have been much later, during the 19th century in particular, founded by different proportions of each of the three that colonisation took place in both the forested population-groups, contributing to initial differences areas near the coast and in the inland areas. In the of the sub-isolate populations (combined with the inland areas this process was characterised by an effects of genetic drift). However, each population will initial colonisation stemming from a relatively small subsequently have evolved into a distinct population, number of pioneers from the coastal parts within not characteristic for any of the three original the regions and from Finland, with subsequent population groups. settlements performed mainly by their descendants. Hence, immigration was small, and clusters of settle- ments representing several generations were formed METHODS (BYLUND 1960, 1994). To test the hypothesis that geographically defined sub- The size of the northern Swedish population was isolates exist in the population structure of northern around 15 000Á16 000 in 1571. Most people lived in the Sweden, we divided Norrbotten and Va¨sterbotten coastal parts of the region, 13%, at most, in the inland. counties into regions based on the major rivers in Almost 200 years later, the region had only 36 000 the area. These rivers run from the mountains in the inhabitants according to the population sta- west of Sweden bordering Norway, to the eastern tistics in 1751 Á reliable at least for the non-Saami Swedish coast on the Bothnic Sea (Fig. 2). Five such population in the inland areas. The Saami population river valley regions were identified and a sixth region was nomadic and moved between districts and coun- represented an inland area located between two major tries, making it difficult to count even in late 18th rivers in Norrbotten. The population of each of these century (HOLLSTEN 1777). river valley regions is likely to be a unique mix of The relatively high fertility and low mortality, Finnish, Saami and Swedish genetic contributions, but however, resulted in a notable growth after 1750; an founder effects and subsequent inbreeding within each increase by 250% during the following century and small founder population could be hypothesised to another 274% to 1950 (Fig. 1). have resulted in a relatively homogeneous population Contribution from net in-migration from other within each river valley region. parts of Sweden was mainly a result of the large- scale exploitation of iron ore fields in region E and F Demographic studies from the turn of the 20th century, which also meant As a first approach to test the hypothesis that the main a decade of net in-migration on the county level river valleys in northern Sweden contain sub-isolates, 1871Á1910 in contrast to the permanent net out- the frequencies of individuals choosing a spouse migration from Va¨sterbotten county observed at least within the same river valley region versus another in 1851Á1970 (HOFSTEN and LUNDSTRO¨ M 1976). region were scored. For this purpose, we analysed the More than 90% of the population in late 20th marriages of the descendants of a couple that settled century in the northernmost regions was inborn in Lycksele in 1650. All recorded marriages from (SVERIGES NATIONALATLAS (SNA) 1991), testifying generations 1Á4; and marriages in generations 5Á9of Hereditas 144 (2007) Genetic population structure of northern Sweden 173 Region A 160 000 Region B 150 000 Region C Region D 140 000 Region E 130 000 Region F 120 000 110 000 100 000 90 000 80 000 70 000 Population 60 000 50 000 40 000 30 000 20 000 10 000 0 1571 1699 1751 1810 1830 1850 1870 1890 1910 1930 1950 1970 1990 Year Fig.