A unifying framework for evaluating the predictive power of genetic variants based on the level of heritability explained.

An increasing number of genetic variants have been identified for many complex diseases. However, it is controversial whether risk prediction based on genomic profiles will be useful clinically. Appropriate statistical measures to evaluate the performance of genetic risk prediction models are requir...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Hon-Cheong So, Pak C Sham
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Genetics QH426-470
Acceso en línea:	https://doaj.org/article/e9fab85e9f174781bfed2ed4e8fe6bab
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:e9fab85e9f174781bfed2ed4e8fe6bab
record_format	dspace
spelling	oai:doaj.org-article:e9fab85e9f174781bfed2ed4e8fe6bab2021-11-18T06:17:53ZA unifying framework for evaluating the predictive power of genetic variants based on the level of heritability explained.1553-73901553-740410.1371/journal.pgen.1001230https://doaj.org/article/e9fab85e9f174781bfed2ed4e8fe6bab2010-12-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21151957/?tool=EBIhttps://doaj.org/toc/1553-7390https://doaj.org/toc/1553-7404An increasing number of genetic variants have been identified for many complex diseases. However, it is controversial whether risk prediction based on genomic profiles will be useful clinically. Appropriate statistical measures to evaluate the performance of genetic risk prediction models are required. Previous studies have mainly focused on the use of the area under the receiver operating characteristic (ROC) curve, or AUC, to judge the predictive value of genetic tests. However, AUC has its limitations and should be complemented by other measures. In this study, we develop a novel unifying statistical framework that connects a large variety of predictive indices together. We showed that, given the overall disease probability and the level of variance in total liability (or heritability) explained by the genetic variants, we can estimate analytically a large variety of prediction metrics, for example the AUC, the mean risk difference between cases and non-cases, the net reclassification improvement (ability to reclassify people into high- and low-risk categories), the proportion of cases explained by a specific percentile of population at the highest risk, the variance of predicted risks, and the risk at any percentile. We also demonstrate how to construct graphs to visualize the performance of risk models, such as the ROC curve, the density of risks, and the predictiveness curve (disease risk plotted against risk percentile). The results from simulations match very well with our theoretical estimates. Finally we apply the methodology to nine complex diseases, evaluating the predictive power of genetic tests based on known susceptibility variants for each trait.Hon-Cheong SoPak C ShamPublic Library of Science (PLoS)articleGeneticsQH426-470ENPLoS Genetics, Vol 6, Iss 12, p e1001230 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Genetics QH426-470
spellingShingle	Genetics QH426-470 Hon-Cheong So Pak C Sham A unifying framework for evaluating the predictive power of genetic variants based on the level of heritability explained.
description	An increasing number of genetic variants have been identified for many complex diseases. However, it is controversial whether risk prediction based on genomic profiles will be useful clinically. Appropriate statistical measures to evaluate the performance of genetic risk prediction models are required. Previous studies have mainly focused on the use of the area under the receiver operating characteristic (ROC) curve, or AUC, to judge the predictive value of genetic tests. However, AUC has its limitations and should be complemented by other measures. In this study, we develop a novel unifying statistical framework that connects a large variety of predictive indices together. We showed that, given the overall disease probability and the level of variance in total liability (or heritability) explained by the genetic variants, we can estimate analytically a large variety of prediction metrics, for example the AUC, the mean risk difference between cases and non-cases, the net reclassification improvement (ability to reclassify people into high- and low-risk categories), the proportion of cases explained by a specific percentile of population at the highest risk, the variance of predicted risks, and the risk at any percentile. We also demonstrate how to construct graphs to visualize the performance of risk models, such as the ROC curve, the density of risks, and the predictiveness curve (disease risk plotted against risk percentile). The results from simulations match very well with our theoretical estimates. Finally we apply the methodology to nine complex diseases, evaluating the predictive power of genetic tests based on known susceptibility variants for each trait.
format	article
author	Hon-Cheong So Pak C Sham
author_facet	Hon-Cheong So Pak C Sham
author_sort	Hon-Cheong So
title	A unifying framework for evaluating the predictive power of genetic variants based on the level of heritability explained.
title_short	A unifying framework for evaluating the predictive power of genetic variants based on the level of heritability explained.
title_full	A unifying framework for evaluating the predictive power of genetic variants based on the level of heritability explained.
title_fullStr	A unifying framework for evaluating the predictive power of genetic variants based on the level of heritability explained.
title_full_unstemmed	A unifying framework for evaluating the predictive power of genetic variants based on the level of heritability explained.
title_sort	unifying framework for evaluating the predictive power of genetic variants based on the level of heritability explained.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/e9fab85e9f174781bfed2ed4e8fe6bab
work_keys_str_mv	AT honcheongso aunifyingframeworkforevaluatingthepredictivepowerofgeneticvariantsbasedonthelevelofheritabilityexplained AT pakcsham aunifyingframeworkforevaluatingthepredictivepowerofgeneticvariantsbasedonthelevelofheritabilityexplained AT honcheongso unifyingframeworkforevaluatingthepredictivepowerofgeneticvariantsbasedonthelevelofheritabilityexplained AT pakcsham unifyingframeworkforevaluatingthepredictivepowerofgeneticvariantsbasedonthelevelofheritabilityexplained
_version_	1718424512817528832

A unifying framework for evaluating the predictive power of genetic variants based on the level of heritability explained.

Ejemplares similares