This tutorial introduces the analytic estimation option in the userGWAS function from the GenomicSEM package. The analytic estimator uses a closed-form Generalized Least Squares (GLS) approach to perform multivariate GWAS, reducing run times by over 800 times compared to the standard iterative estimator while producing equivalent results. The tutorial covers installation, function arguments, model specification requirements, and a fully worked example using a 5-factor model of common psychiatric disorders across 13 phenotypes. Note that this feature is currently in alpha and under active development — results should be cross-validated against the iterative estimator before drawing scientific conclusions.

In this tutorial, we will guide you through the steps to run the QTrait function, a genetically informed method for assessing the external validity of common factors in the genomic space.

In this tutorial, we will guide you through the steps to run the rgmodel function. This function estimates the matrix of genetic correlations (R) and the corresponding matrix of their sampling covariances (V_R) from the output of the ldsc() function.

The **paLDSC** function allows to identify the number of non-spurious dimensions in exploratory genomic factor analysis. Our method adapts a classic method known as Parallel Analysis (Horn, 1965) to the genomic space. **paLDSC** compares the eigenvalues generated from the eigen decomposition of the LDSC genetic correlation matrix to the eigenvalues of a Monte-Carlo simulated null correlation matrix with random noise drawn from the multivariate LDSC sampling distribution. The suggested number of factors to be extracted corresponds with an eigenvalue exceeding a pre-specified percentile from the corresponding distribution of eigenvalues generated under the null.

Genomic SEM relaxed GWAX model tutorial for the joint analysis of direct GWAS and proxy GWAX summary data of complex diseases. The model relaxes standard assumptions and recovers unbiased estimates of common variant SNP heritability and of individual SNP effects under a variety of conditions.