Abstract Summary: We present a Mendelian randomization (MR) pipeline (MeRP) to facilitate rapid, causal inference analysis through automating key steps in developing and analyzing genetic instruments obtained from publicly available data. Our tool uses the National Human Genome Research Institute catalog of associations to generate instrumental variable trait files and provides methods for filtering of potential confounding associations as well as linkage disequilibrium. MeRP generates estimated causal effect scores via a MR-score analysis using summary data for disease endpoints typically found in the public domain. We utilize our pipeline to develop genetic instruments for seven traits and evaluate potential causal relationships with two disease endpoints, observing two putatively causal associations between blood pressure and bone-mineral density with type 2 diabetes. Our tool emphasizes the importance of careful but systematic screening of large datasets for discovery and systematic follow-up. Availability and implementation: MeRP is a free, open-source project and can be downloaded at http://github.com/py-merp/py-merp. Complete documentation can be found at http://py-merp.github.io. Requires Python 2.7, along with NumPy, SciPy. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

UNLABELLED: I present MR_predictor, a simulation engine designed to guide the development and interpretation of statistical tests of causality between phenotypes using genetic instruments. MR_predictor provides a framework to model either individual traits or complex scenarios where multiple phenotypes are correlated or dependent on each other. Crucially, MR_predictor can incorporate the effects of multiple biallelic loci (linked or unlinked) contributing genotypic variability to one or more simulated phenotypes. The software has a range of options for sample generation, and output files generated by MR_predictor port into commonly used analysis tools (e.g. PLINK, R), facilitating analyses germane for Mendelian Randomization studies. Benchmarks for speed and power calculations for summary statistic-based Mendelian Randomization analyses are presented and compared with analytical expectation. AVAILABILITY AND IMPLEMENTATION: The simulation engine is implemented in PERL, and the associated scripts can be downloaded from github.com, and online documentation, tutorial and example datasets are available at http://coruscant.itmat.upenn.edu/mr_predictor.

MOTIVATION: The question of how to best use information from known associated variants when conducting disease association studies has yet to be answered. Some studies compute a marginal P-value for each Several Nucleotide Polymorphisms independently, ignoring previously discovered variants. Other studies include known variants as covariates in logistic regression, but a weakness of this standard conditioning strategy is that it does not account for disease prevalence and non-random ascertainment, which can induce a correlation structure between candidate variants and known associated variants even if the variants lie on different chromosomes. Here, we propose a new conditioning approach, which is based in part on the classical technique of liability threshold modeling. Roughly, this method estimates model parameters for each known variant while accounting for the published disease prevalence from the epidemiological literature. RESULTS: We show via simulation and application to empirical datasets that our approach outperforms both the no conditioning strategy and the standard conditioning strategy, with a properly controlled false-positive rate. Furthermore, in multiple data sets involving diseases of low prevalence, standard conditioning produces a severe drop in test statistics whereas our approach generally performs as well or better than no conditioning. Our approach may substantially improve disease gene discovery for diseases with many known risk variants. AVAILABILITY: LTSOFT software is available online http://www.hsph.harvard.edu/faculty/alkes-price/software/.