The ever-growing genetic cohorts lead to an increase in scale of molecular Quantitative Trait Loci (QTL) studies, creating opportunities for more extensive two samples Mendelian randomization (MR) investigations aiming to identify causal relationships between molecular traits and diseases. This increase led to the identification of multiple causal candidates and potential drug targets over time. However, the increase in scale of such studies and higher dimension multi-omic data come with computational challenges. We present "LArge SCAle MOLecular Mendelian Randomization with Julia" (LaScaMolMR.jl), an open-sourced integrated Julia package optimized for Omic-wide Mendelian Randomization (OWMR) Studies. This versatile package eliminates the two-language problem and implements fast algorithms for instrumental variable selection approaches with both cis and trans instruments and performs the most popular regression estimators for MR studies with molecular exposures. It reduces the compute time via meta-programming allowing easy deployment of multi-threaded approach and the internalization of linkage disequilibrium investigation of potential instrumental variables. Via its integrated approach and high-computational performance, LaScaMolMR.jl allows users who have minimal programming experience to perform large scale OWMR studies.

The authors have declared no competing interest.

Work of the authors is supported by the Canadian Institutes of Health Research grants to P.M. (FRN159697, FRN191807) and the Quebec Heart and Lung Institute Fund. Y.B. holds a Canada Research Chair in Genomics of Heart and Lung Diseases P.M. is the recipient of the Joseph C. Edwards Foundation granted to Universite Laval. S.M. and M.B. are supported by the Fonds de Recherche du Quebec secteur Sante through a scholarship.

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GTEx V8 : https://www.gtexportal.org/home/downloads/adult-gtex/qtl Fenland pQTLs : https://www.synapse.org/Synapse:syn51761394/wiki/622766 CAD GWAS : https://www.ebi.ac.uk/gwas/studies/GCST90132314

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