The value of isolated populations for the discovery of genetic modifiers of diseases or quantitative traits is discussed controversially [2-6]. On the one hand, reduced genetic and environmental variability of isolated populations could increase genotypic relative risks [7,8]. In combination with the generally higher degree of linkage disequilibrium (LD) in isolated populations, this could improve the power of genetic association studies [5,6,9-11]. On the other hand, studies in isolated populations are often limited in size and, therefore, cannot match modern genome-wide association studies and meta-analyses comprising several tens of thousands of individuals.

Nowadays, it is common practice to combine all available genotyped and phenotyped populations in large-scale, whole genome meta-analyses or pooled analyses in order to identify even very small genetic effects as commonly observed for complex traits. Spurious associations caused by the genetic sub-structures of combined populations are the most serious concern of this approach [12-15], implying the need for appropriate adjustment strategies [16,17]. This is especially true if evidence from isolated and outbred populations is combined as this approach necessitates a thorough comparison of populations by population genetic means in order to determine their "degree of isolation" [6]. For this purpose, different methods have been proposed in the literature. For example, length and number of runs of homozygosity (ROHs) are discussed as an appropriate measure of isolation since they measure the degree of parental consanguinity [18]. LD is estimated to be higher in isolated populations because of lower generation numbers resulting in fewer recombination events [5,6]. Due to the smaller size of the founder population, it can also be expected that there is a lower number of polymorphisms in isolated populations [6,19,20]. Other markers of population structure such as F-statistics [21] are related to the measures mentioned above. Furthermore, genetic distances between populations can be determined by principal components analysis (PCA), allowing to quantify how closely populations are related [22]. By this technique genetic information can be mapped to topographic maps [14] allowing the assessment of a new indicator of isolation in the sense that an isolated population could be genetically far away from their geographic location. So far there appears to be no single measure sufficient to characterize the isolation of a population.

Another characteristic feature of isolated populations is the putatively higher degree of cryptic relatedness in randomly drawn samples. This is a serious concern in genetic association analysis and needs to be addressed with appropriate statistical methods [17,23-25]. Relatedness of individuals could also interact with the above mentioned measures of isolation of populations. Thus, when comparing two populations with different degrees of cryptic relatedness, it is not easy to decide whether differences in these measures can be traced back to different degrees of isolation or simply to over-sampling of related subjects.

The degree of isolation of the Sorbs has been studied in the past by the analysis of Y-chromosomal markers [26]. Recently, we compared a subset of about 200 Sorbs with other European isolates using 30,000 SNPs measured by microarrays [1]. In this analysis, the Sorbs expressed only moderate signs of isolation. Here, we analyse a sample of N = 977 Sorbs, which is currently included in several genome-wide association studies e.g. [27,28], and compare the Sorbs with the German outbred population of the KORA study [29]. Using the KORA study (N = 1644) and a larger sample of Sorbs (N = 977) provides more power than previous studies for comparing population genetic patterns between Sorbs and their neighbours. For this purpose, we assess the above mentioned population genetic characteristics: PCA, number of rare SNPs, F-statistics, ROHs, and LD. All analyses are based on genome-wide SNP array data. We also aim to separate effects of cryptic relatedness from effects of genetic isolation.

Genotype data from a sample of 977 Sorbs were available from genotyping with 500 k and 1000 k Affymetrix SNP chips. While SNP markers come with certain drawbacks (ascertainment bias, need for careful QC), they have proven useful for detecting subtle population structures.

For comparison with a German mixed population, we used the KORA F3 sample (N = 1644) and corresponding genotypes from 500 k Affymetrix SNP chips. Observed differences between regions of Germany are typically an order of magnitude lower than differences observed between Sorbs and KORA [53]. Publicly available European-American HapMap samples were also included in the analysis.

A major goal of our study was to distinguish effects of genetic isolation from simple over-sampling of families in the Sorbs. Since most of the population genetic measures used to compare populations assume independence of individuals, over-sampling of families in certain samples may introduce a source of bias which is difficult to control. Indeed, we discovered a large number of closely related individuals within the Sorbs sample. Therefore, we repeated all analyses for a sub-group of Sorbs for which all relationships with relatedness estimates greater than 0.2 were removed. This does not completely resolve the problem of increased relatedness within the Sorbs sample but provides a trend for potential biases introduced by over-sampling of families. Indeed, such biases could be detected in our data but it is not substantial at least for the population genetic measures studied.

Since relatedness cannot be completely removed from the samples, a cut-off of 0.2 for the relatedness estimate seems to be feasible to study the effect of relatedness and to keep the sample size at an acceptable level. We also studied a cut-off of 0.1 reducing the sample size to N = 414. Results can be found in Additional file 6. Although tending slightly towards zero, results are essentially the same as those obtained for the cut-off of 0.2.

For some analyses such as determination of rare SNPs and LD it is known that sample size can introduce bias [39,44,54]. Therefore, for most comparisons we used randomly drawn subsamples of KORA which are of the same size as the Sorbs samples.

PCA is a proven means to detect even very small genetic differences between populations with high power. For European populations, it was demonstrated that the first two appropriately scaled principal components can map individuals to their geographic origin on the European continent with high precision, when all four grandparents are from the same location [14]. Our PCA results showed clear distances between KORA, Sorbs, and individuals from Tuscany. Using individuals from KORA and Tuscany to roughly orient the PCA graph on a map of Europe, Sorbs are positioned towards the East. KORA individuals are very close to the CEU HapMap population, while the distance to Tuscan/TSI individuals is much larger.

We conclude that the Slavonic origin of the Sorbs is still clearly genetically detectable. The analysis revealed that there is a west to east sequence of the clusters of KORA individuals born in Germany, KORA individuals born in Poland or Czech Republic, Half-Sorbs, and finally, Full-Sorbs. Although birthplace is not a stringent indicator of ethnicity, it is a commonly used surrogate in genetic epidemiologic studies if more detailed information cannot be ascertained. On the other hand, most of the KORA individuals born in Poland or Czech Republic are descendents from German minorities of these countries. Hence, on the basis of our data we cannot conclude that the Sorbs are genetically more distant from Germany than a random sample from Poland or Czech Republic. Half-Sorbs can be assumed to be closer to the German population than Full-Sorbs due to mating with German neighbours. This is clearly reflected by the localization of Half-Sorbs between KORA individuals and Full-Sorbs. There is a trend that the Sorbs are closer to the KORA individuals born in Poland than to the KORA individuals born in Czech Republic which is in agreement with a recently stated hypothesis that the Sorbs are genetically closer to Polish than to Czech [1].

Since it has been suggested that genetic diversity is lower in isolated populations [6], we analysed the number of rare SNPs. Indeed, we found a higher number of rare SNPs in the Sorbs sample compared to the KORA sample. Although significant, the difference is small in size.

The F_ST statistics between KORA and Sorbs were an order of magnitude higher than usually observed between different regions of Germany [53]. Thus, variance between KORA and Sorbs is much higher than expected for different regions in Germany. Surprisingly, the F_IS statistic was positive for KORA but negative for Sorbs. Such a phenomenon has also been observed for other isolated populations, suggesting that there may be signs of recent isolation breaking in the Sorbs [44]. Another indicator of isolation breaking is the relatively high number of Half-Sorbs (N = 160) in the present sample, i.e. subjects who claim to have less than four Sorbian grandparents. It should be remarked that the F_IS statistic is a population based measure rather than an individual based measure of inbreeding studied in [1].

ROH analysis was proposed to detect signs of isolation by estimation of inbreeding [18]. Despite the simplicity of this concept, calculation of ROH depends on many variable parameter settings such as SNP density or allowed numbers of missings or heterozygous markers, which heavily influence the results. Parameter settings are extensively discussed in McQuillan et al [18]. For our analysis, we used the default settings of PLINK except for two parameters: The threshold for homozygous segments was 500 kb (PLINK default is 1000 kb) and the splitting of homozygous segments can occur if two neighbouring SNPs are 100 kb apart (PLINK default is 1000 kb). Hence, we used the same settings as in McQuillan et al. except for the minimum number of contiguous homozygous SNPs constituting a ROH, for which we kept the PLINK default (N = 100). The results of ROH analysis also depend on allelic frequencies of populations and SNP-selections used by different genotyping technologies. Since McQuillan et al. [18] used a different genotyping platform (Illumina Infinium HumanHap300v2), the latter modification was necessary to obtain similar results.

We found that Sorbs have enriched ROHs of intermediate length (between 2.5 Mb and 5 Mb) compared to KORA, CEU, and TSI. This effect is much less pronounced for longer ROHs. Accordingly, the coverage of the genome by ROHs is higher in the Sorbian population. Following the argumentation of McQuillan et al., we conclude that there is a lack of recent parental relatedness in the Sorbs (no differences for long range ROHs) but that there are signs of ancient parental relatedness or the existence of autozygous segments of older pedigree structures (differences for ROHs of intermediate range). The lack of direct parental relatedness is in accordance with our estimates of F_IS.

Furthermore, we compared the LD structure of chromosome 22 between the KORA and the Sorbs population. We used the newly proposed LD measure η₁ for the comparison of KORA and Sorbs. In contrast to the more popular measures r and D', the measure η₁ is independent of allelic frequencies [42]. In our opinion, this property is desirable when comparing LD structure between populations of potentially differing allelic frequencies. However, the results obtained by the three measures are very similar (data not shown).

An expected small upward bias caused by smaller sample size in KORA₅₃₂ compared to KORA₉₇₇ could be clearly detected. In contrast, the results for Sorbs₉₇₇ and Sorbs₅₃₂ are virtually identical. We conclude that the expected upward bias of the reduced Sorbs₅₃₂ sample is nullified by the elimination of relationships. This interpretation is supported by the fact that a random sample of N = 532 individuals from Sorbs₉₇₇ resulted in the same sample size bias as observed for KORA (data not shown). That is, LD is upwardly biased by the relatedness structure in the Sorbs. Nevertheless, even if relationships are eliminated to a reasonable degree (first and second degree relationships), Sorbs show generally higher LD at longer distances than is observed in KORA. It has been already shown in the literature that LD excess at longer ranges is a characteristic of isolated populations [5,9-11]. However, the effect is moderate in size which is also in agreement with several other populations considered as isolated [44,55-57].

Since LD structure directly influences the coverage of a SNP technology, and with it, the power of genome-wide association studies, we performed power analyses in the Sorbs and KORA samples. For this purpose, we defined a fixed genetic effect of an arbitrary SNP at chromosome 22. Explained variance was used as a measure of effect in order to adjust for differences in allelic frequencies. For this SNP, we analysed the best proxy SNP available on chromosome 22 in order to mimic a situation in which an unobserved causative variant is detected via a marker in LD. We derived an analytical formula for our model for the case of negligible heritability for which individuals can be considered as independent. This formula also applies to situations where correction for relatedness effects has been performed, for instance with a GRAMMAR approach [17]. Power was calculated for all SNPs on chromosome 22 and the resulting distribution was compared between the Sorbs and KORA samples with and without relatives. No differences regarding power were detected. We conclude that there is no gain in power due to higher LD in the Sorbs.

Since relatedness structure is often neglected in genetic association studies, we also analysed the influence of present relatedness structure on the power of an uncorrected analysis. This analysis is done via simulations of a linear mixed model comprising a fixed effect of a SNP and random polygenetic and non-genetic effects. We showed that the variance of the β-estimator is inflated under relatedness and high heritability. This results in a gain in power for higher p-value thresholds and a loss of power for lower p-value thresholds in the Sorbs₉₇₇, irrespective of the size of the genetic effect considered. The explanation is that normal distributions with different variances are overlapping.

We conclude that relatedness in the Sorbs₉₇₇ sample influences the power of uncorrected genetic association studies. Influence of relatedness on power is highest under maximum heritability of the phenotype. However, directions of power differences depend on the size of the genetic effect in combination with the significance threshold chosen.

We gratefully acknowledge the contributions of P. Lichtner, G. Eckstein, Guido Fischer, T. Strom and all other members of the Helmholtz Centre Munich genotyping staff in generating the SNP dataset as well as the contribution of all members of field staffs who were involved in the planning and conduct of the MONICA/KORA Augsburg studies. The KORA group consists of H.E. Wichmann (speaker), A. Peters, C. Meisinger, T. Illig, R. Holle, J. John and their co-workers who are responsible for the design and conduct of the KORA studies.

We thank Maelle Salmon for helping with data quality control. We thank Karsten Krug and Lars Thielecke for their technical assistance.

Finally, we express our appreciation to all participants of the Sorb and the KORA study for donating their blood and time.