The Sorbs, resident in Lusatia, Germany, are an ethnic minority of Slavonic origin. Using genome-wide SNP array techniques, we aimed to compare this putatively isolated population with a German mixed population (KORA study) by various population genetic means. The Sorbs were compared recently with other European populations or isolates on the basis of a limited set of genetic markers and a limited set of unrelated individuals [1, 52]. In the present analysis, we studied the Sorbs from the perspective of ongoing genome-wide association studies. That is, we compared the population with a German mixed population on the basis of complete sets of genotyped individuals, and a large number of genotyped SNPs. We also aimed to separate the effect of isolation from potential effects caused by over-sampling of relatives in the Sorbs. Finally, we studied the implications of observed differences between KORA and Sorbs for the analysis, and especially, the power of genome-wide association studies.
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 . 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 . 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 .
Since it has been suggested that genetic diversity is lower in isolated populations , 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.
statistics between KORA and Sorbs were an order of magnitude higher than usually observed between different regions of Germany . Thus, variance between KORA and Sorbs is much higher than expected for different regions in Germany. Surprisingly, the F
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 . 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
statistic is a population based measure rather than an individual based measure of inbreeding studied in .
ROH analysis was proposed to detect signs of isolation by estimation of inbreeding . 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 . 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.  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
Furthermore, we compared the LD structure of chromosome 22 between the KORA and the Sorbs population. We used the newly proposed LD measure η
1 for the comparison of KORA and Sorbs. In contrast to the more popular measures r and D', the measure η
1 is independent of allelic frequencies . 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 . 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.
In our simulations we did not observe a scenario resulting in a clear power benefit in the Sorbs977 sample. However, this does not rule out that there might be a higher power in the Sorbs due to increased effect sizes caused, e.g., by higher environmental homogeneity or lower number of causative variants [7, 8].