Precise diagnosis of three top cancers using dbGaP data

Abstract The challenge of decoding information about complex diseases hidden in huge number of single nucleotide polymorphism (SNP) genotypes is undertaken based on five dbGaP studies. Current genome-wide association studies have successfully identified many high-risk SNPs associated with diseases,...

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Autores principales:	Xu-Qing Liu, Xin-Sheng Liu, Jian-Ying Rong, Feng Gao, Yan-Dong Wu, Chun-Hua Deng, Hong-Yan Jiang, Xiao-Feng Li, Ye-Qin Chen, Zhi-Guo Zhao, Yu-Ting Liu, Hai-Wen Chen, Jun-Liang Li, Yu Huang, Cheng-Yao Ji, Wen-Wen Liu, Xiao-Hu Luo, Li-Li Xiao
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/24e7d0fce6ac404098fc1a383345b51d
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spelling	oai:doaj.org-article:24e7d0fce6ac404098fc1a383345b51d2021-12-02T14:12:08ZPrecise diagnosis of three top cancers using dbGaP data10.1038/s41598-020-80832-x2045-2322https://doaj.org/article/24e7d0fce6ac404098fc1a383345b51d2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-80832-xhttps://doaj.org/toc/2045-2322Abstract The challenge of decoding information about complex diseases hidden in huge number of single nucleotide polymorphism (SNP) genotypes is undertaken based on five dbGaP studies. Current genome-wide association studies have successfully identified many high-risk SNPs associated with diseases, but precise diagnostic models for complex diseases by these or more other SNP genotypes are still unavailable in the literature. We report that lung cancer, breast cancer and prostate cancer as the first three top cancers worldwide can be predicted precisely via 240–370 SNPs with accuracy up to 99% according to leave-one-out and 10-fold cross-validation. Our findings (1) confirm an early guess of Dr. Mitchell H. Gail that about 300 SNPs are needed to improve risk forecasts for breast cancer, (2) reveal an incredible fact that SNP genotypes may contain almost all information that one wants to know, and (3) show a hopeful possibility that complex diseases can be precisely diagnosed by means of SNP genotypes without using phenotypical features. In short words, information hidden in SNP genotypes can be extracted in efficient ways to make precise diagnoses for complex diseases.Xu-Qing LiuXin-Sheng LiuJian-Ying RongFeng GaoYan-Dong WuChun-Hua DengHong-Yan JiangXiao-Feng LiYe-Qin ChenZhi-Guo ZhaoYu-Ting LiuHai-Wen ChenJun-Liang LiYu HuangCheng-Yao JiWen-Wen LiuXiao-Hu LuoLi-Li XiaoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xu-Qing Liu Xin-Sheng Liu Jian-Ying Rong Feng Gao Yan-Dong Wu Chun-Hua Deng Hong-Yan Jiang Xiao-Feng Li Ye-Qin Chen Zhi-Guo Zhao Yu-Ting Liu Hai-Wen Chen Jun-Liang Li Yu Huang Cheng-Yao Ji Wen-Wen Liu Xiao-Hu Luo Li-Li Xiao Precise diagnosis of three top cancers using dbGaP data
description	Abstract The challenge of decoding information about complex diseases hidden in huge number of single nucleotide polymorphism (SNP) genotypes is undertaken based on five dbGaP studies. Current genome-wide association studies have successfully identified many high-risk SNPs associated with diseases, but precise diagnostic models for complex diseases by these or more other SNP genotypes are still unavailable in the literature. We report that lung cancer, breast cancer and prostate cancer as the first three top cancers worldwide can be predicted precisely via 240–370 SNPs with accuracy up to 99% according to leave-one-out and 10-fold cross-validation. Our findings (1) confirm an early guess of Dr. Mitchell H. Gail that about 300 SNPs are needed to improve risk forecasts for breast cancer, (2) reveal an incredible fact that SNP genotypes may contain almost all information that one wants to know, and (3) show a hopeful possibility that complex diseases can be precisely diagnosed by means of SNP genotypes without using phenotypical features. In short words, information hidden in SNP genotypes can be extracted in efficient ways to make precise diagnoses for complex diseases.
format	article
author	Xu-Qing Liu Xin-Sheng Liu Jian-Ying Rong Feng Gao Yan-Dong Wu Chun-Hua Deng Hong-Yan Jiang Xiao-Feng Li Ye-Qin Chen Zhi-Guo Zhao Yu-Ting Liu Hai-Wen Chen Jun-Liang Li Yu Huang Cheng-Yao Ji Wen-Wen Liu Xiao-Hu Luo Li-Li Xiao
author_facet	Xu-Qing Liu Xin-Sheng Liu Jian-Ying Rong Feng Gao Yan-Dong Wu Chun-Hua Deng Hong-Yan Jiang Xiao-Feng Li Ye-Qin Chen Zhi-Guo Zhao Yu-Ting Liu Hai-Wen Chen Jun-Liang Li Yu Huang Cheng-Yao Ji Wen-Wen Liu Xiao-Hu Luo Li-Li Xiao
author_sort	Xu-Qing Liu
title	Precise diagnosis of three top cancers using dbGaP data
title_short	Precise diagnosis of three top cancers using dbGaP data
title_full	Precise diagnosis of three top cancers using dbGaP data
title_fullStr	Precise diagnosis of three top cancers using dbGaP data
title_full_unstemmed	Precise diagnosis of three top cancers using dbGaP data
title_sort	precise diagnosis of three top cancers using dbgap data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/24e7d0fce6ac404098fc1a383345b51d
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Precise diagnosis of three top cancers using dbGaP data

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