MRC-Epid/pGWAS_Olink_EPIC

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MRC-Epid/pGWAS_Olink_EPIC

MRC-Epid

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pGWAS_discovery

Code repository for Pietzner M, Wheeler E, et al. 2021 "Mapping the proteo-genomic convergence of human diseases"

MetabolomicsGWAS_INTERVAL_EPICNorfolk

This repository contains scripts related to Surendran P, Stewart I et al. Nature Medicine 2022 "Rare and common genetic causes of chemical individuality and their effects on human health"

pampro

Physical Activity Monitor Processing

Wave

crossplatform_mGWAS

Cross-platform genetic discovery of small molecule products of metabolism and application to clinical outcomes Luca A. Lotta, Maik Pietzner, Isobel D. Stewart, Laura B.L. Wittemans, Chen Li, Roberto Bonelli, Johannes Raffler, Emma K. Biggs, Clare Oliver-Williams, Victoria P.W. Auyeung, Jian’an Luan, Eleanor Wheeler, Ellie Paige, Praveen Surendran, Gregory A. Michelotti, Robert A. Scott, Stephen Burgess, Verena Zuber, Eleanor Sanderson, Albert Koulman, Fumiaki Imamura, Nita G. Forouhi, Kay-Tee Khaw, MacTel Consortium, Julian L. Griffin, Angela M. Wood, Gabi Kastenmüller, John Danesh, Adam S. Butterworth, Fiona M. Gribble, Frank Reimann, Melanie Bahlo, Eric Fauman, Nicholas J. Wareham, Claudia Langenberg bioRxiv 2020.02.03.932541; doi: https://doi.org/10.1101/2020.02.03.932541

cross_platform_pGWAS

This repository contains code related to Pietzner M, et al. 2021 Nature Communications "Synergistic insights into human health from aptamer- and antibody-based proteomic profiling."

qc_diagnostics

A standalone script for running a quality check on AX3 or GENEActiv files.

EPIC_Olink_protein_prediction

This repository contains code to derive prediction models for a total of 28 outcomes in the EPIC-Norfolk cohort (Day et al. 1997) using plasma proteomic data obtained with the Olink Explore platform.

GWAS_postchallenge_insulin

This repository contains code related to the publication "Genome-wide association study of postprandial glucose metabolism and functional characterisation identifies candidate genes for insulin-stimulated glucose uptake""

iigt_prediction_proteomics

This repository contains essential code templates to run the 3-stage machine learning framework proposed by Carrasco-Zanini et al. 2022