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Comparison of scoring methods for the detection of causal genes with or without rare variants

Rare causal variants are believed to significantly contribute to the genetic basis of common diseases or quantitative traits. Appropriate statistical methods are required to discover the highest possible number of disease-relevant variants in a genome-wide screening study. The publicly available Gen...

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Autores principales: Scholz, Markus, Kirsten, Holger
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287886/
https://www.ncbi.nlm.nih.gov/pubmed/22373454
http://dx.doi.org/10.1186/1753-6561-5-S9-S49
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author Scholz, Markus
Kirsten, Holger
author_facet Scholz, Markus
Kirsten, Holger
author_sort Scholz, Markus
collection PubMed
description Rare causal variants are believed to significantly contribute to the genetic basis of common diseases or quantitative traits. Appropriate statistical methods are required to discover the highest possible number of disease-relevant variants in a genome-wide screening study. The publicly available Genetic Analysis Workshop 17 data set consists of 697 individuals and 24,487 genetic variants. It includes a simulated complex disease model with intermediate quantitative phenotypes. We compare four gene-wise scoring methods with respect to ranking of causal genes under variable allele frequency thresholds for collapsing of rare variants and considering whether or not rare variants were included. We also compare causal genes for which the ranks differ clearly between scoring methods regarding such characteristics as number and strength of causal variants. We corroborated our findings with additional simulations. We found that the maximum statistics method was superior in assigning high ranks to genes with a single strong causal variant. Hotelling’s T(2) test was superior for genes with several independent causal variants. This was consistent for all phenotypes and was confirmed by single-gene analyses and additional simulations. The multivariate analysis performed similarly to Hotelling’s T(2) test. The least absolute shrinkage and selection operator (LASSO) analysis was widely comparable with the maximum statistics method. We conclude that the maximum statistics method is a superior alternative to Hotelling’s T(2) test if one expects only one independent causal variant per gene with a dominating effect. Such a variant could also be a supermarker derived by collapsing rare variants. Because the true nature of the genetic effect is unknown for real data, both methods need to be taken into consideration.
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spelling pubmed-32878862012-02-28 Comparison of scoring methods for the detection of causal genes with or without rare variants Scholz, Markus Kirsten, Holger BMC Proc Proceedings Rare causal variants are believed to significantly contribute to the genetic basis of common diseases or quantitative traits. Appropriate statistical methods are required to discover the highest possible number of disease-relevant variants in a genome-wide screening study. The publicly available Genetic Analysis Workshop 17 data set consists of 697 individuals and 24,487 genetic variants. It includes a simulated complex disease model with intermediate quantitative phenotypes. We compare four gene-wise scoring methods with respect to ranking of causal genes under variable allele frequency thresholds for collapsing of rare variants and considering whether or not rare variants were included. We also compare causal genes for which the ranks differ clearly between scoring methods regarding such characteristics as number and strength of causal variants. We corroborated our findings with additional simulations. We found that the maximum statistics method was superior in assigning high ranks to genes with a single strong causal variant. Hotelling’s T(2) test was superior for genes with several independent causal variants. This was consistent for all phenotypes and was confirmed by single-gene analyses and additional simulations. The multivariate analysis performed similarly to Hotelling’s T(2) test. The least absolute shrinkage and selection operator (LASSO) analysis was widely comparable with the maximum statistics method. We conclude that the maximum statistics method is a superior alternative to Hotelling’s T(2) test if one expects only one independent causal variant per gene with a dominating effect. Such a variant could also be a supermarker derived by collapsing rare variants. Because the true nature of the genetic effect is unknown for real data, both methods need to be taken into consideration. BioMed Central 2011-11-29 /pmc/articles/PMC3287886/ /pubmed/22373454 http://dx.doi.org/10.1186/1753-6561-5-S9-S49 Text en Copyright ©2011 Scholz and Kirsten; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Proceedings
Scholz, Markus
Kirsten, Holger
Comparison of scoring methods for the detection of causal genes with or without rare variants
title Comparison of scoring methods for the detection of causal genes with or without rare variants
title_full Comparison of scoring methods for the detection of causal genes with or without rare variants
title_fullStr Comparison of scoring methods for the detection of causal genes with or without rare variants
title_full_unstemmed Comparison of scoring methods for the detection of causal genes with or without rare variants
title_short Comparison of scoring methods for the detection of causal genes with or without rare variants
title_sort comparison of scoring methods for the detection of causal genes with or without rare variants
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287886/
https://www.ncbi.nlm.nih.gov/pubmed/22373454
http://dx.doi.org/10.1186/1753-6561-5-S9-S49
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