relatives. Conversely, the regression slope b1 appears to be due to population stratification because longer chromosomes are likely to have more ancestry informative markers (AIMs), assuming that the AIMs are randomly distributed across the genome. We then predicted that population stratification accounted for 6.9×10−5LC, and 7.2×10−6LC, −1.92×10−6LC (not significantly different from zero) and 2.3×10−5LC of variance for height, BMI, vWF and QTi, respectively, in the entire sample and a similar amount in the data set of unrelated individuals (Fig. 3). The difference between hC2(sep) and hC2 represents the overall effect of all the other 21 chromosomes on one chromosome. Therefore, the proportion of variance attributed to population structure (cryptic relatedness and population stratification) across the whole genome is approximately equal to b022/21+b1∑C=122LC/21, which is (1.6% + 0.91%), (0.088% + 0.095%), (0.23% + 0.0%) and (0.068% + 0.30%) for height, BMI, vWF and QTi, respectively, in the entire sample. Hence, we provide a simple approach to estimate and partition the variance attributed to population structure for complex traits. The variances due to cryptic relatedness and population stratification depend on the data structure in the sample. Therefore, the estimates we present above are specific for the data in this study.
