Abstract
Using individuals’ genetic data researchers can generate Polygenic Scores (PS) that are able to predict risk for diseases, variability in different behaviors as well as anthropomorphic measures. This is achieved by leveraging models learned from previously published large Genome-Wide Association Studies (GWASs) associating locations in the genome with a phenotype of interest. Previous GWASs have predominantly been performed in European ancestry individuals. This is of concern as PS generated in samples with a different ancestry to the original training GWAS have been shown to have lower performance and limited portability, and many efforts are now underway to collect genetic databases on individuals of diverse ancestries. In this study, we compare multiple methods of generating PS, including pruning and thresholding and Bayesian continuous shrinkage models, to determine which of them is best able to overcome these limitations. To do this we use the ABCD Study, a longitudinal cohort with deep phenotyping on individuals of diverse ancestry. We generate PS for anthropometric and psychiatric phenotypes using previously published GWAS summary statistics and examine their performance in three subsamples of ABCD: African ancestry individuals (n = 811), European ancestry Individuals (n = 6703), and admixed ancestry individuals (n = 3664). We find that the single ancestry continuous shrinkage method, PRScs (CS), and the multi ancestry meta method, PRScsx Meta (CSx Meta), show the best performance across ancestries and phenotypes.
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Data availability
Information about data availability can be found in Supplementary Table 1.
Code availability
The code used in this analysis can be found on the following websites: PRScsx: https://github.com/getian107/PRScsx. PRScs: https://github.com/getian107/PRScs. PRSice2: https://choishingwan.github.io/PRS-Tutorial/prsice/#:~:text=PRSice%2D2%20is%20one%20of,the%20standard%20 C%2BT%20method. TOPMED imputation scripts: https://github.com/robloughnan/TOPMED_Imputation_Scripts. PC-AIR scripts: https://github.com/robloughnan/ABCD_GeneticPCs_and_Relatedness. Plink 2.0: https://www.cog-genomics.org/plink/2.0/. SNPweights: https://mybiosoftware.com/tag/snpweights. Gamm4: https://cran.r-project.org/web/packages/gamm4/index.html.
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Acknowledgements
The authors wish to thank the youth and families participating in the Adolescent Brain Cognitive Development (ABCD) Study and all ABCD staff. Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9–10 and follow them over 10 years into early adulthood. The ABCD Study is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041022, U01DA041028, U01DA041048, U01DA041089, U01DA041106, U01DA041117, U01DA041120, U01DA041134, U01DA041148, U01DA041156, U01DA041174, U24DA041123, U24DA041147, U01DA041093, and U01DA041025. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/Consortium_Members.pdf. ABCD consortium investigators designed and implemented the study and/or provided data but did not all necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators. The ABCD data repository grows and changes over time. The data were downloaded from the NIMH Data Archive ABCD Collection Release 2.0.1 (https://doi.org/10.15154/1504041).
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This work was supported by Grant R01MH122688 and RF1MH120025 funded by the National Institute for Mental Health.
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Ahern, J., Thompson, W., Fan, C.C. et al. Comparing Pruning and Thresholding with Continuous Shrinkage Polygenic Score Methods in a Large Sample of Ancestrally Diverse Adolescents from the ABCD Study®. Behav Genet 53, 292–309 (2023). https://doi.org/10.1007/s10519-023-10139-w
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DOI: https://doi.org/10.1007/s10519-023-10139-w