Genomic data and disease forecasting: application to type 2 diabetes (T2D).

A general approach is presented for the extraction of a classifier of disease risk that is latent in large scale disease/control databases. Novel features are the following: (1) a data reorganization into a regularized standard form that emphasizes individual alleles instead of the single nucleotide...

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Autor principal:	Lawrence Sirovich
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ee6d0247e21f49c1ae3713971e3ad0f4
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id	oai:doaj.org-article:ee6d0247e21f49c1ae3713971e3ad0f4
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spelling	oai:doaj.org-article:ee6d0247e21f49c1ae3713971e3ad0f42021-11-18T08:37:27ZGenomic data and disease forecasting: application to type 2 diabetes (T2D).1932-620310.1371/journal.pone.0085684https://doaj.org/article/ee6d0247e21f49c1ae3713971e3ad0f42014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24465649/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203A general approach is presented for the extraction of a classifier of disease risk that is latent in large scale disease/control databases. Novel features are the following: (1) a data reorganization into a regularized standard form that emphasizes individual alleles instead of the single nucleotide polymorphism (Snp) allele pair to which they belong; (2) from this a procedure that significantly enhances the discovery of high value genomic loci; (3) an investigative analysis based on the hypothesis that disease represents a very small signal (small signal-to-noise) that is latent in the data. The resulting analyses applied to the FUSION T2D database leads to the polling of thousands of genomic loci to classify disease. This large genomic kernel of loci is shared by non-diabetics at nearly the same high level; but a small well defined separation exists and it is speculated that this might be due to unconventional disease mechanisms. Another analysis demonstrates that the FUSION database size limits its disease predictability, and only one third of the resulting classifier loci are estimated to relate to T2D. The remainder is associated with hidden features that might contrast the disease and control populations and that more data would eliminate.Lawrence SirovichPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 1, p e85684 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Lawrence Sirovich Genomic data and disease forecasting: application to type 2 diabetes (T2D).
description	A general approach is presented for the extraction of a classifier of disease risk that is latent in large scale disease/control databases. Novel features are the following: (1) a data reorganization into a regularized standard form that emphasizes individual alleles instead of the single nucleotide polymorphism (Snp) allele pair to which they belong; (2) from this a procedure that significantly enhances the discovery of high value genomic loci; (3) an investigative analysis based on the hypothesis that disease represents a very small signal (small signal-to-noise) that is latent in the data. The resulting analyses applied to the FUSION T2D database leads to the polling of thousands of genomic loci to classify disease. This large genomic kernel of loci is shared by non-diabetics at nearly the same high level; but a small well defined separation exists and it is speculated that this might be due to unconventional disease mechanisms. Another analysis demonstrates that the FUSION database size limits its disease predictability, and only one third of the resulting classifier loci are estimated to relate to T2D. The remainder is associated with hidden features that might contrast the disease and control populations and that more data would eliminate.
format	article
author	Lawrence Sirovich
author_facet	Lawrence Sirovich
author_sort	Lawrence Sirovich
title	Genomic data and disease forecasting: application to type 2 diabetes (T2D).
title_short	Genomic data and disease forecasting: application to type 2 diabetes (T2D).
title_full	Genomic data and disease forecasting: application to type 2 diabetes (T2D).
title_fullStr	Genomic data and disease forecasting: application to type 2 diabetes (T2D).
title_full_unstemmed	Genomic data and disease forecasting: application to type 2 diabetes (T2D).
title_sort	genomic data and disease forecasting: application to type 2 diabetes (t2d).
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/ee6d0247e21f49c1ae3713971e3ad0f4
work_keys_str_mv	AT lawrencesirovich genomicdataanddiseaseforecastingapplicationtotype2diabetest2d
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Genomic data and disease forecasting: application to type 2 diabetes (T2D).

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