populations, we therefore assumed that effect sizes of causal variants are perfectly correlated across populations, i.e. ρb = 1 and that heritability is constant across populations, i.e. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$h_2^2 = h_1^2 = h^2$$\end{document}h22=h12=h2. Note that in practice these assumptions are likely to be violated. However, our theoretical framework (Eqs. (1, 2)) allows us to study the contribution of MAF and LD and that of heritability and genetic correlation separately. We relax this assumption below. In total, we simulated six scenarios corresponding to three values of the number of causal variants MC = 1000, 5000 and 10,000; and two values of trait heritability h2 = 0.25 and 0.5 (Methods).
