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If you have questions regarding the PGC CP workgroup or projects that are currently being conducted, please contact the workgroup chairs.
For any questions or ideas related to research dissemination (e.g., via this webpage, social media, blogs, press outlets), please contact the workgroup outreach liaison.
If you have questions about how to access summary statistics or genotype-level data, or are interested to submit a secondary analysis proposal, please contact the workgroup data access committee representative.
The PGC Cross Population Special Interest Group (SIG) was formed in May of 2018 and it has 100+ members. Monthly meetings are held via Zoom calls, and we have in-person meetings yearly at the World Congress of Psychiatric Genetics (WCPG). The goal of the group is to support genetic analyses in diverse groups and ancestral populations through method development, empirical investigations, and support of collaborative projects. The need for this group was evident from the fact that -- as of 2018 -- greater than 80 percent of genomic analyses had been conducted in exclusively European ancestry populations. Moreover, many leading genetics analysis methods were not yet suitable for use in different populations. Fortunately, acknowledgement of these problems in genetics research has become widespread, and we formed this group as a means of accelerating progress in this important research area.
Speakers knowledgeable about methods suitable for use in ancestrally diverse populations often present on calls, and this provides a forum for discussion of best practices in the analysis of such data. Given that no single research group has the requisite expertise for genetics analyses in all populations, it is particularly valuable to have a venue for discussion of topics in this emerging research area.
Currently, we are looking for new collaborators with genotyping and depression data in order to increase our sample size for our next large-scale genome-wide meta-analyses.
The group’s flagship work product is a Best Practices paper for GWAS-based analyses in ancestrally diverse populations. Accepted as a Primer in Cell, this manuscript is the most comprehensive guide available to date, and it provides guidance about the relevant issues and practical solutions for analytical steps ranging from imputation and quality control to genetic correlation and other GWAS-based analyses. Members’ publications, of cross-ancestry analyses, have been published in Nature Genetics, Nature Communications, Molecular Psychiatry, Biological Psychiatry and elsewhere, and representative examples are given in the publications sections.
Peterson, R.E., Kuchenbaecker, K., Walters, R.K., Chen, C.-Y., Popejoy, A.B., Periyasamy, S., Lam, M., Iyegbe, C., Strawbridge, R.J., Brick, L., Carey, C.E., Martin, A.R., Meyers, J.L., Su, J., Chen, J., Edwards, A.C., Kalungi, A., Koen, N., Majara, L., Schwarz, E., Smoller, J.W., Stahl, E.A., Sullivan, P.F., Vassos, E., Mowry, B., Prieto, M.L., Cuellar-Barboza, A., Bigdeli, T.B., Edenberg, H.J., Huang, H., Duncan, L.E., 2019. Genome-wide Association Studies in Ancestrally Diverse Populations: Opportunities, Methods, Pitfalls, and Recommendations. Cell 0. https://doi.org/10.1016/j.cell.2019.08.051