Precise diagnosis of three top cancers using dbGaP data

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 preci...

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Published in	Scientific reports Vol. 11; no. 1; pp. 823 - 8
Main Authors	Liu, Xu-Qing, Liu, Xin-Sheng, Rong, Jian-Ying, Gao, Feng, Wu, Yan-Dong, Deng, Chun-Hua, Jiang, Hong-Yan, Li, Xiao-Feng, Chen, Ye-Qin, Zhao, Zhi-Guo, Liu, Yu-Ting, Chen, Hai-Wen, Li, Jun-Liang, Huang, Yu, Ji, Cheng-Yao, Liu, Wen-Wen, Luo, Xiao-Hu, Xiao, Li-Li
Format	Journal Article
Language	English
Published	London Nature Publishing Group UK 12.01.2021 Nature Publishing Group Nature Portfolio
Subjects	631/67/1347 631/67/1612 631/67/589/466 692/699/67/1347 692/699/67/1612 692/699/67/589/466 Algorithms Breast cancer Breast Neoplasms - diagnosis Breast Neoplasms - genetics Computational Biology Computer Simulation Databases, Genetic Female Gene polymorphism Genome-wide association studies Genome-Wide Association Study - methods Genomes Genotype Genotypes Humanities and Social Sciences Humans Lung cancer Lung Neoplasms - diagnosis Lung Neoplasms - genetics Male multidisciplinary Phenotype Polymorphism, Single Nucleotide Prostate cancer Prostatic Neoplasms - diagnosis Prostatic Neoplasms - genetics Science Science (multidisciplinary) Single-nucleotide polymorphism
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ISSN	2045-2322 2045-2322
DOI	10.1038/s41598-020-80832-x

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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.
AbstractList	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. 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. 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.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.
ArticleNumber	823
Author	Xiao, Li-Li Liu, Yu-Ting Jiang, Hong-Yan Huang, Yu Ji, Cheng-Yao Chen, Hai-Wen Li, Jun-Liang Rong, Jian-Ying Chen, Ye-Qin Deng, Chun-Hua Liu, Xin-Sheng Li, Xiao-Feng Zhao, Zhi-Guo Liu, Wen-Wen Liu, Xu-Qing Wu, Yan-Dong Gao, Feng Luo, Xiao-Hu
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Snippet	The challenge of decoding information about complex diseases hidden in huge number of single nucleotide polymorphism (SNP) genotypes is undertaken based on... Abstract The challenge of decoding information about complex diseases hidden in huge number of single nucleotide polymorphism (SNP) genotypes is undertaken...
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SubjectTerms	631/67/1347 631/67/1612 631/67/589/466 692/699/67/1347 692/699/67/1612 692/699/67/589/466 Algorithms Breast cancer Breast Neoplasms - diagnosis Breast Neoplasms - genetics Computational Biology Computer Simulation Databases, Genetic Female Gene polymorphism Genome-wide association studies Genome-Wide Association Study - methods Genomes Genotype Genotypes Humanities and Social Sciences Humans Lung cancer Lung Neoplasms - diagnosis Lung Neoplasms - genetics Male multidisciplinary Phenotype Polymorphism, Single Nucleotide Prostate cancer Prostatic Neoplasms - diagnosis Prostatic Neoplasms - genetics Science Science (multidisciplinary) Single-nucleotide polymorphism
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Title	Precise diagnosis of three top cancers using dbGaP data
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