Chunk #79 — Methods — Notation and preliminaries

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Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics.

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Next we list the properties and definitions used in the derivation2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat \gamma _g = \frac{{\widehat {{\mathrm{Cov}}}(T_g,\,Y)}}{{\widehat {{\mathrm{Var}}}(T_g)}} = \frac{{\widehat {{\mathrm{Cov}}}(T_g,\,Y)}}{{\hat \sigma _g^2}}$$\end{document}γ^g=Cov^(Tg,Y)Var^(Tg)=Cov^(Tg,Y)σ^g2and3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\widehat {\beta _l} = \frac{{\widehat {{\mathrm{Cov}}}(X_l,\,Y)}}{{\widehat {{\mathrm{Var}}}(X_l)}} = \frac{{\widehat {{\mathrm{Cov}}}(X_l,\,Y)}}{{\hat \sigma _l^2}}$$\end{document}βl ^=Cov^(Xl,Y)Var^(Xl)=Cov^(Xl,Y)σ^l2