Extended Data Fig. 2: Effect of polygenicity and sample size of linkage studies on the correlation between predicted and observed linkage signals in simulated data.

The results are shown for 8 simulated genetic architectures (polygenicity = 0.1%-100%) with a genome-wide h² = 1. a-b, show the observed and predicted linkage signals (measured as variance explained) on chromosomes 1 (a) and 22 (b), respectively, for one simulation replicate. The simulated causal variants are depicted as green stars. The predicted signal, estimated as a weighted sum of simulated effects (Methods, equation (1)) is depicted by the black curve. The grey and yellow lines show the observed linkage signal from the analysis of 20,000 and 100,000 simulated sib-pairs, respectively, where the phenotypes were simulated using the same causal variants (green stars). The correlations \(\hat{\phi }\) for each polygenicity panel are the chromosome-wide estimates for each linkage sample size (yellow: n=20,000; grey: n=100,000). c, the summary of results across 100 replicates. \(\hat{\phi }\) is estimated per chromosome across the grid of 0.5 cM, then a chromosome length weighted average is calculated for each replicate. Each symbol represents a mean value across 100 simulation replicates and the error bars are standard deviation across replicates. The left-most enlarged symbols for each polygenicity panel indicate that the true simulated SNP effects were used predict linkage signal, that is, the expected prediction accuracy from polygenic scores (\({R}_{g}^{2}\)) using these causal variants = 1. To approximate estimation errors of SNP effects in a GWAS of finite sample, \(\hat{\phi }\) was also calculated using causal variants with \({R}_{g}^{2}\) <1 (regular symbols). For the numeric values see Supplementary Table 9. Estimated variance components were not constrained to ensure unbiasedness. Therefore, if a region of the genome does not explain any genetic variation, then 50% of the estimates are expected to be negative.