Statistical models and computational tools for predicting complex traits and diseases

Wonil Chung

doi:10.5808/gi.21053

journal article Open Access Dec 31, 2021

Statistical models and computational tools for predicting complex traits and diseases

Wonil Chung

Genomics & Informatics Vol. 19 No. 4 pp. e36 · Korea Genome Organization

View at Publisher Save 10.5808/gi.21053

Abstract

Predicting individual traits and diseases from genetic variants is critical to fulfilling the promise of personalized medicine. The genetic variants from genome-wide association studies (GWAS), including variants well below GWAS significance, can be aggregated into highly significant predictions across a wide range of complex traits and diseases. The recent arrival of large-sample public biobanks enables highly accurate polygenic predictions based on genetic variants across the whole genome. Various statistical methodologies and diverse computational tools have been introduced and developed to computed the polygenic risk score (PRS) more accurately. However, many researchers utilize PRS tools without a thorough understanding of the underlying model and how to specify the parameters for the best performance. It is advantageous to study the statistical models implemented in computational tools for PRS estimation and the formulas of parameters to be specified. Here, we review a variety of recent statistical methodologies and computational tools for PRS computation.

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Details

Published: Dec 31, 2021
Vol/Issue: 19(4)
Pages: e36
License: View

Authors

W

Wonil Chung

Department of Epidemiology, Harvard T.H. Chan School of Public Health , 655 Huntington Avenue, Building II 2nd Floor, Boston, MA 02115, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health , 655 Huntington Avenue, Building II 4th Floor, Boston, MA 02115, USA

Cite This Article

Wonil Chung (2021). Statistical models and computational tools for predicting complex traits and diseases. Genomics & Informatics, 19(4), e36. https://doi.org/10.5808/gi.21053

Statistical models and computational tools for predicting complex traits and diseases

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