A fast and robust Bayesian nonparametric method for prediction of complex traits using summary statistics.

Genetic prediction of complex traits has great promise for disease prevention, monitoring, and treatment. The development of accurate risk prediction models is hindered by the wide diversity of genetic architecture across different traits, limited access to individual level data for training and par...

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Autores principales:	Geyu Zhou, Hongyu Zhao
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Genetics QH426-470
Acceso en línea:	https://doaj.org/article/b85ff74f5d3149dab6e1685113d36324
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spelling	oai:doaj.org-article:b85ff74f5d3149dab6e1685113d363242021-12-02T20:02:55ZA fast and robust Bayesian nonparametric method for prediction of complex traits using summary statistics.1553-73901553-740410.1371/journal.pgen.1009697https://doaj.org/article/b85ff74f5d3149dab6e1685113d363242021-07-01T00:00:00Zhttps://doi.org/10.1371/journal.pgen.1009697https://doaj.org/toc/1553-7390https://doaj.org/toc/1553-7404Genetic prediction of complex traits has great promise for disease prevention, monitoring, and treatment. The development of accurate risk prediction models is hindered by the wide diversity of genetic architecture across different traits, limited access to individual level data for training and parameter tuning, and the demand for computational resources. To overcome the limitations of the most existing methods that make explicit assumptions on the underlying genetic architecture and need a separate validation data set for parameter tuning, we develop a summary statistics-based nonparametric method that does not rely on validation datasets to tune parameters. In our implementation, we refine the commonly used likelihood assumption to deal with the discrepancy between summary statistics and external reference panel. We also leverage the block structure of the reference linkage disequilibrium matrix for implementation of a parallel algorithm. Through simulations and applications to twelve traits, we show that our method is adaptive to different genetic architectures, statistically robust, and computationally efficient. Our method is available at https://github.com/eldronzhou/SDPR.Geyu ZhouHongyu ZhaoPublic Library of Science (PLoS)articleGeneticsQH426-470ENPLoS Genetics, Vol 17, Iss 7, p e1009697 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Genetics QH426-470
spellingShingle	Genetics QH426-470 Geyu Zhou Hongyu Zhao A fast and robust Bayesian nonparametric method for prediction of complex traits using summary statistics.
description	Genetic prediction of complex traits has great promise for disease prevention, monitoring, and treatment. The development of accurate risk prediction models is hindered by the wide diversity of genetic architecture across different traits, limited access to individual level data for training and parameter tuning, and the demand for computational resources. To overcome the limitations of the most existing methods that make explicit assumptions on the underlying genetic architecture and need a separate validation data set for parameter tuning, we develop a summary statistics-based nonparametric method that does not rely on validation datasets to tune parameters. In our implementation, we refine the commonly used likelihood assumption to deal with the discrepancy between summary statistics and external reference panel. We also leverage the block structure of the reference linkage disequilibrium matrix for implementation of a parallel algorithm. Through simulations and applications to twelve traits, we show that our method is adaptive to different genetic architectures, statistically robust, and computationally efficient. Our method is available at https://github.com/eldronzhou/SDPR.
format	article
author	Geyu Zhou Hongyu Zhao
author_facet	Geyu Zhou Hongyu Zhao
author_sort	Geyu Zhou
title	A fast and robust Bayesian nonparametric method for prediction of complex traits using summary statistics.
title_short	A fast and robust Bayesian nonparametric method for prediction of complex traits using summary statistics.
title_full	A fast and robust Bayesian nonparametric method for prediction of complex traits using summary statistics.
title_fullStr	A fast and robust Bayesian nonparametric method for prediction of complex traits using summary statistics.
title_full_unstemmed	A fast and robust Bayesian nonparametric method for prediction of complex traits using summary statistics.
title_sort	fast and robust bayesian nonparametric method for prediction of complex traits using summary statistics.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/b85ff74f5d3149dab6e1685113d36324
work_keys_str_mv	AT geyuzhou afastandrobustbayesiannonparametricmethodforpredictionofcomplextraitsusingsummarystatistics AT hongyuzhao afastandrobustbayesiannonparametricmethodforpredictionofcomplextraitsusingsummarystatistics AT geyuzhou fastandrobustbayesiannonparametricmethodforpredictionofcomplextraitsusingsummarystatistics AT hongyuzhao fastandrobustbayesiannonparametricmethodforpredictionofcomplextraitsusingsummarystatistics
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A fast and robust Bayesian nonparametric method for prediction of complex traits using summary statistics.

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