Protocol for genetic discovery and fine-mapping of multivariate latent factors from high-dimensional traits

High-dimensional traits, like blood cell traits, are often analyzed using univariate genetic analysis approaches, ignoring trait relationships. Here, we present a protocol for using the flashfmZero software for analyses of latent factors that capture variation in observed traits generated by shared underlying biological mechanisms. We describe steps for calculating genome-wide association study (GWAS) summary statistics of latent factors from GWAS of observed traits, allowing for missing trait measurements. We then describe steps for jointly fine-mapping associations from multiple latent factors. For complete details on the use and execution of this protocol, please refer to Zhou et al.(1).