Genome partitioning of genetic variation for complex traits using common SNPs.

This figure consists of four scatter plots showing the variance explained by each chromosome (represented by numbered red circles) relative to chromosome length (Mb) for four traits: Height, BMI, vWF, and QTi. For Height and QTi, there is a positive correlation between chromosome length and variance explained, supported by significant p-values ($1.4 \times 10^{-6}$ and $1.1 \times 10^{-3}$, respectively) and higher $R^2$ values. In contrast, BMI and vWF show weak correlations with non-significant p-values ($0.214$ and $0.524$) and low $R^2$ values.

This figure consists of three stacked bar charts (a, b, and c) showing the variance explained by genic (blue) and intergenic (red) regions for height across chromosomes 1–22. The panels compare three different genic region definitions: $\pm 0\text{ Kb}$, $\pm 20\text{ Kb}$, and $\pm 50\text{ Kb}$. As the genic region definition expands from (a) to (c), the proportion of variance attributed to genic regions increases while the intergenic contribution decreases. Each panel includes summary statistics for total genic ($h_{Gg}^2$) and intergenic ($h_{Gi}^2$) variance, along with p-values for the goodness-of-fit test.

This figure consists of four scatter plots with linear regression lines showing the difference in variance explained by each chromosome ($h^2_{C2(sep)} - h^2_{C2}$) against chromosome length (Mb) for Height, BMI, vWF, and QTi. Each plot compares two groups: "All" individuals (red circles) and "Unrelated" individuals (blue squares). For Height and BMI, there is a positive correlation between chromosome length and the variance difference, while vWF shows no significant trend and QTi shows a positive correlation for both groups. Intercepts, slopes, and associated p-values for the regression lines are provided for each condition.

This is a scatter plot comparing the variance explained by each chromosome for height in a joint analysis (x-axis) against the sum of variance explained by GWAS-associated SNPs from the GIANT meta-analysis (y-axis). Each data point is labeled with a chromosome number (1–22), showing a positive correlation between the two measures. A red linear regression line is plotted through the data with a reported $R^2$ value of 0.511.