GWAS findings improved genomic prediction accuracy of lipid profile traits: Tehran Cardiometabolic Genetic Study

Abstract In recent decades, ongoing GWAS findings discovered novel therapeutic modifications such as whole-genome risk prediction in particular. Here, we proposed a method based on integrating the traditional genomic best linear unbiased prediction (gBLUP) approach with GWAS information to boost gen...

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Autores principales:	Mahdi Akbarzadeh, Saeid Rasekhi Dehkordi, Mahmoud Amiri Roudbar, Mehdi Sargolzaei, Kamran Guity, Bahareh Sedaghati-khayat, Parisa Riahi, Fereidoun Azizi, Maryam S. Daneshpour
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3beb39ab856f44bea8e96c98fd97889a
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spelling	oai:doaj.org-article:3beb39ab856f44bea8e96c98fd97889a2021-12-02T11:35:52ZGWAS findings improved genomic prediction accuracy of lipid profile traits: Tehran Cardiometabolic Genetic Study10.1038/s41598-021-85203-82045-2322https://doaj.org/article/3beb39ab856f44bea8e96c98fd97889a2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85203-8https://doaj.org/toc/2045-2322Abstract In recent decades, ongoing GWAS findings discovered novel therapeutic modifications such as whole-genome risk prediction in particular. Here, we proposed a method based on integrating the traditional genomic best linear unbiased prediction (gBLUP) approach with GWAS information to boost genetic prediction accuracy and gene-based heritability estimation. This study was conducted in the framework of the Tehran Cardio-metabolic Genetic study (TCGS) containing 14,827 individuals and 649,932 SNP markers. Five SNP subsets were selected based on GWAS results: top 1%, 5%, 10%, 50% significant SNPs, and reported associated SNPs in previous studies. Furthermore, we randomly selected subsets as large as every five subsets. Prediction accuracy has been investigated on lipid profile traits with a tenfold and 10-repeat cross-validation algorithm by the gBLUP method. Our results revealed that genetic prediction based on selected subsets of SNPs obtained from the dataset outperformed the subsets from previously reported SNPs. Selected SNPs’ subsets acquired a more precise prediction than whole SNPs and much higher than randomly selected SNPs. Also, common SNPs with the most captured prediction accuracy in the selected sets caught the highest gene-based heritability. However, it is better to be mindful of the fact that a small number of SNPs obtained from GWAS results could capture a highly notable proportion of variance and prediction accuracy.Mahdi AkbarzadehSaeid Rasekhi DehkordiMahmoud Amiri RoudbarMehdi SargolzaeiKamran GuityBahareh Sedaghati-khayatParisa RiahiFereidoun AziziMaryam S. DaneshpourNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mahdi Akbarzadeh Saeid Rasekhi Dehkordi Mahmoud Amiri Roudbar Mehdi Sargolzaei Kamran Guity Bahareh Sedaghati-khayat Parisa Riahi Fereidoun Azizi Maryam S. Daneshpour GWAS findings improved genomic prediction accuracy of lipid profile traits: Tehran Cardiometabolic Genetic Study
description	Abstract In recent decades, ongoing GWAS findings discovered novel therapeutic modifications such as whole-genome risk prediction in particular. Here, we proposed a method based on integrating the traditional genomic best linear unbiased prediction (gBLUP) approach with GWAS information to boost genetic prediction accuracy and gene-based heritability estimation. This study was conducted in the framework of the Tehran Cardio-metabolic Genetic study (TCGS) containing 14,827 individuals and 649,932 SNP markers. Five SNP subsets were selected based on GWAS results: top 1%, 5%, 10%, 50% significant SNPs, and reported associated SNPs in previous studies. Furthermore, we randomly selected subsets as large as every five subsets. Prediction accuracy has been investigated on lipid profile traits with a tenfold and 10-repeat cross-validation algorithm by the gBLUP method. Our results revealed that genetic prediction based on selected subsets of SNPs obtained from the dataset outperformed the subsets from previously reported SNPs. Selected SNPs’ subsets acquired a more precise prediction than whole SNPs and much higher than randomly selected SNPs. Also, common SNPs with the most captured prediction accuracy in the selected sets caught the highest gene-based heritability. However, it is better to be mindful of the fact that a small number of SNPs obtained from GWAS results could capture a highly notable proportion of variance and prediction accuracy.
format	article
author	Mahdi Akbarzadeh Saeid Rasekhi Dehkordi Mahmoud Amiri Roudbar Mehdi Sargolzaei Kamran Guity Bahareh Sedaghati-khayat Parisa Riahi Fereidoun Azizi Maryam S. Daneshpour
author_facet	Mahdi Akbarzadeh Saeid Rasekhi Dehkordi Mahmoud Amiri Roudbar Mehdi Sargolzaei Kamran Guity Bahareh Sedaghati-khayat Parisa Riahi Fereidoun Azizi Maryam S. Daneshpour
author_sort	Mahdi Akbarzadeh
title	GWAS findings improved genomic prediction accuracy of lipid profile traits: Tehran Cardiometabolic Genetic Study
title_short	GWAS findings improved genomic prediction accuracy of lipid profile traits: Tehran Cardiometabolic Genetic Study
title_full	GWAS findings improved genomic prediction accuracy of lipid profile traits: Tehran Cardiometabolic Genetic Study
title_fullStr	GWAS findings improved genomic prediction accuracy of lipid profile traits: Tehran Cardiometabolic Genetic Study
title_full_unstemmed	GWAS findings improved genomic prediction accuracy of lipid profile traits: Tehran Cardiometabolic Genetic Study
title_sort	gwas findings improved genomic prediction accuracy of lipid profile traits: tehran cardiometabolic genetic study
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/3beb39ab856f44bea8e96c98fd97889a
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GWAS findings improved genomic prediction accuracy of lipid profile traits: Tehran Cardiometabolic Genetic Study

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