Mendelian Randomization with Biomarker Associations for Causality with Outcomes
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README.md

README.md

MR BACOn 🐷

A Shiny application for Mendelian Randomization analysis of Biomarker Associations for Causality with Outcomes

Workflow

Usage:

R Shiny app User Manual

Back-end workflow

Data acquisition and filtering

LD pruning

Mendelian Randomization

Pathway analysis

What is MR?

mr_outline_figure

Mendelian Randomization (MR) is a method that tests for evidence of a causal association between a biomarker of interest and an outcome. It is based on the random assortment of alleles that occurs during meiosis (hence the allusion to Gregor Mendel). It is sometimes referred to as "Nature's Randomized Controlled Trial" and also has roots in instrumental variable theory. The core idea is that alleles are passed down at random and may have effects on a given outcome through a specific biomarker. Since these the assignment of an allele (and it's resultant effect on a biomarker) is given to an individual by random chance - these effects are uncorrelated with sources of error and confounding that traditionally plague association tests. The instrumental variables used for Mendelian Randomization are most often single nucleotide polymorphisms (SNPs) and for BACOn our biomarkers are metabolites. The SNP-metabolite associations can be combined with SNP-CAD associations to give an estimate of the causal effect between the metabolite and CAD. Modern MR analyses use data from two independent samples, one of which estimates the SNP-biomarker(metabolite) association and the other estimates the SNP-outcome(CAD) associations in independent cohorts. A good reference for MR is here

Shiny app User Interface

Example output:

Another example:



Team

Dr. Cavin Ward-Caviness, EPA
Nur Shahir, UNC - Chapel Hill
Nicholas Colaianni, UNC - Chapel Hill
Jayashree Kumar, UNC - Chapel Hill
Kevin Currin, UNC - Chapel Hill
Yue Hao, NC State

References

Burgess, S., Butterworth, A. and Thompson, S.G. 2013. Mendelian randomization analysis with multiple genetic variants using summarized data. Genetic Epidemiology 37(7), pp. 658–665.
Hemani, G., Zheng, J., Wade, K.H., Laurin, C., Elsworth, B., Burgess, S., Bowden, J., Langdon, R., Tan, V., Yarmolinsky, J., Shihab, H.A., Timpson, N., Evans, D.M., Relton, C., Martin, R.M., Davey Smith, G., Gaunt, T.R., Haycock, P.C. and The MR-Base Collaboration 2016. MR-Base: a platform for systematic causal inference across the phenome using billions of genetic associations. BioRxiv.
Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. and Tanabe, M. 2016. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Research 44(D1), pp. D457-62.
Nikpay, M., Goel, A., Won, H.-H., Hall, L.M., Willenborg, C., Kanoni, S., Saleheen, D., Kyriakou, T., Nelson, C.P., Hopewell, J.C., Webb, T.R., Zeng, L., Dehghan, A., Alver, M., Armasu, S.M., Auro, K., Bjonnes, A., Chasman, D.I., Chen, S., Ford, I. and Farrall, M. 2015. A comprehensive 1,000 Genomes-based genome-wide association meta-analysis of coronary artery disease. Nature Genetics 47(10), pp. 1121–1130.
Raffler, J., Friedrich, N., Arnold, M., Kacprowski, T., Rueedi, R., Altmaier, E., Bergmann, S., Budde, K., Gieger, C., Homuth, G., Pietzner, M., Römisch-Margl, W., Strauch, K., Völzke, H., Waldenberger, M., Wallaschofski, H., Nauck, M., Völker, U., Kastenmüller, G. and Suhre, K. 2015. Genome-Wide Association Study with Targeted and Non-targeted NMR Metabolomics Identifies 15 Novel Loci of Urinary Human Metabolic Individuality. PLoS Genetics 11(9), p. e1005487.
Shin, S.-Y., Fauman, E.B., Petersen, A.-K., Krumsiek, J., Santos, R., Huang, J., Arnold, M., Erte, I., Forgetta, V., Yang, T.-P., Walter, K., Menni, C., Chen, L., Vasquez, L., Valdes, A.M., Hyde, C.L., Wang, V., Ziemek, D., Roberts, P., Xi, L. and Soranzo, N. 2014. An atlas of genetic influences on human blood metabolites. Nature Genetics 46(6), pp. 543–550.

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