Colocalization analysis takes into account five hypotheses: H0 (no association between the locus and either trait), H1 (locus has an association with first trait only), H2 (locus has an association with second trait only), H3 (locus has an association with both traits but driven by different SNPs which are not in linkage disequilibrium (LD)), H4 (locus has an association with both traits driven by same SNPs). For Project 3: Colocalization pipeline, we considered colocalization analysis with a posterior probability of colocalization in H4 (PPH4) greater than 0.8 to be significant. We utilized the coloc R package21 ,22 (link) and summary statistics from ref. 7 (link). We used eQTL data from a cerebellar cortical meta-analysis of four cohorts23 (link), publicly available from the AMP-AD Knowledge Portal24 . As an example for our pipeline, we extracted the region ± 500 kb around DYRK1A, nominated in Nalls et al. 2019, from the GWAS summary statistics and eQTL data. To visualize the results, we employed the eQTpLot R package25 (link), which can generate different plots for GWAS and eQTL signal colocalization, as well as the correlation between their p values and enrichment of eQTLs among variants and LD of loci of interest, allowing efficient and intuitive visualization of gene expression and trait interaction. We used our previously generated results for DYRK1A and whole brain eQTL as an example for creating visualizations using this package. (Fig. 3a).
Results from the downstream analysis of genetic variation projects.
a Displays the locus of interest, in this case, ±500 kb from DYRK1A, and the horizontal line depicts the GWAS significance threshold of P = 5 × 10–8. Displays the genes in the locus of interest. b Depicts the Leiden gene networks and correlations for significant eQTLs for PD controls and PD cases. c Depicts the general workflow for the variant interaction pipeline.
Leonard H.L., Murtadha R., Martinez-Carrasco A., Jama A., Müller-Nedebock A.C., Gil-Martinez A.L., Illarionova A., Moore A., Bustos B.I., Jadhav B., Huxford B., Storm C., Towns C., Vitale D., Chetty D., Yu E., Grenn F.P., Salazar G., Rateau G., Iwaki H., Elsayed I., Foote I.F., Jansen van Rensburg Z., Kim J.J., Yuan J., Lake J., Brolin K., Senkevich K., Wu L., Tan M.M., Periñán M.T., Makarious M.B., Ta M., Pillay N.S., Betancor O.L., Reyes-Pérez P.R., Alvarez Jerez P., Saini P., al-Ouran R., Sivakumar R., Real R., Reynolds R.H., Hu R., Abrahams S., Rao S.C., Antar T., Leal T.P., Iankova V., Scotton W.J., Song Y., Singleton A., Nalls M.A., Dey S., Bandres-Ciga S., Blauwendraat C, & Noyce A.J. (2023). The IPDGC/GP2 Hackathon - an open science event for training in data science, genomics, and collaboration using Parkinson’s disease data. NPJ Parkinson's Disease, 9, 33.
Corresponding Organization : National Institutes of Health
Other organizations :
National Institute of Neurological Disorders and Stroke, University College London, National Hospital for Neurology and Neurosurgery, Stellenbosch University, German Center for Neurodegenerative Diseases, Northwestern University, Child Health and Development Institute, Queen Mary University of London, McGill University, Institut du Cerveau, University of Gezira, Brigham and Women's Hospital, Harvard University, Lund University, Hospital Universitario Virgen del Rocío, Instituto de Biomedicina de Sevilla, Universidad de Sevilla, South African Medical Research Council, University of the Western Cape, South African National Biodiversity Institute, VA Puget Sound Health Care System, Universidad Nacional Autónoma de México, Baylor College of Medicine, University of Southern California, Cleveland Clinic, Ludwig-Maximilians-Universität München
Posterior probability of colocalization in H4 (PPH4)
control variables
Posterior probability of colocalization in H4 (PPH4) > 0.8 considered significant
Used coloc R package and summary statistics from previous study
Used eQTL data from a cerebellar cortical meta-analysis of four cohorts
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