Update README.md

README.md

@@ -311,7 +311,7 @@ Results of univariate analysis:
 * ``['univariate'][0]['qq_plot_bins_data'][<index>]`` - a 3x3 matrix of QQ plots, partitioned by MAF and LD score; ``<index>`` can take values 0 to 8; each QQ plot follows the format defined above; the ranges of MAF and LD score for each bin is specified in ``['univariate'][0]['qq_plot_bins_data'][<index>]['title']``.
 
 Results of bivariate analysis:
-* ``['bivariate']['params'][<parameter>]`` - point estimates of model parameters. Here ``<parameter>`` can be one of the following: ``pi_vec`` - polygenicty of each component (fraction of variants specific to the first trait, specific to the second trait, and shared across traits); ``rho_beta`` - correlation of effect sizes within each component (first two values are zeros); ``rho_zero`` - correlation of residuals; ``sig2_beta`` - 2x3 matrix, variance of effect sizes for each trait and within each component; ``sig2_zero`` - variance distortion in each trait. 
-* ``['bivariate']['ci'][<parameter>][<measure>]`` - uncertainty of parameter estimates. Here ``<parameter>`` can be one of the following: ``h2_T1``, ``h2_T2`` - heritability of the first and the second traits; ``pi1u``, ``pi2u`` total polygenicity of the first and of the second trait; ``pi_vec_C1``, ``pi_vec_C2``, ``pi_vec_C3`` - polygenicity of the three components in the model; ``rg`` - genetic correlation; ``rho_beta`` - correlation of effect sizes within shared polygenic component; ``rho_zero`` - correlation of residuals; ``sig2_beta_T1``, ``sig2_beta_T2`` - variance of effect sizes in the first and in the second trait; ``sig2_zero_T1``, ``sig2_zero_T1`` - variance distortion in the first and in the second trait.)
+* ``['bivariate']['params'][<parameter>]`` - point estimates of model parameters. Here ``<parameter>`` can be one of the following: ``pi_vec`` - polygenicty of each component (fraction of variants specific to the first trait, specific to the second trait, and shared across traits); ``rho_beta`` - correlation of effect sizes within each component (first two values are zeros); ``rho_zero`` - covariance inflation parameter; ``sig2_beta`` - 2x3 matrix, variance of effect sizes for each trait and within each component; ``sig2_zero`` - variance distortion in each trait. 
+* ``['bivariate']['ci'][<parameter>][<measure>]`` - uncertainty of parameter estimates. Here ``<parameter>`` can be one of the following: ``h2_T1``, ``h2_T2`` - heritability of the first and the second traits; ``pi1u``, ``pi2u`` total polygenicity of the first and of the second trait; ``pi_vec_C1``, ``pi_vec_C2``, ``pi_vec_C3`` - polygenicity of the three components in the model; ``rg`` - genetic correlation; ``rho_beta`` - correlation of effect sizes within shared polygenic component; ``rho_zero`` - covariance inflation parameter; ``sig2_beta_T1``, ``sig2_beta_T2`` - variance of effect sizes in the first and in the second trait; ``sig2_zero_T1``, ``sig2_zero_T1`` - variance distortion in the first and in the second trait.)
 * ``['bivariate']['stratified_qq_plot_fit_data'][<trait>][<index>]`` - data for stratified QQ plots. Here ``<trait>`` can be either ``'trait1'`` or ``'trait2'``; ``<index>`` can be ``0``, ``1``, ``2`` or ``3``. Stratified QQ plots are made both ways (i.e. ``trait1|trait2``, and ``trait2||trait1``; hense the ``<trait>`` defines which trait is primary (so that stratified QQ plots are conditioned on the other trait. ``<index>`` equal to ``0`` means a stratum of all SNPs, ``1``, ``2`` and ``3`` are increased levels of association on the secondary trait. Each QQ plot has format defined above (see results of univariate analysis).