Assessing methods for assigning SNPs to genes in gene-based tests of association using common variants.

Gene-based tests of association are frequently applied to common SNPs (MAF>5%) as an alternative to single-marker tests. In this analysis we conduct a variety of simulation studies applied to five popular gene-based tests investigating general trends related to their performance in realistic situ...

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Autores principales:	Ashley Petersen, Carolina Alvarez, Scott DeClaire, Nathan L Tintle
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/709914c73ad649c48d9e2733da3eb768
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spelling	oai:doaj.org-article:709914c73ad649c48d9e2733da3eb7682021-11-18T07:43:42ZAssessing methods for assigning SNPs to genes in gene-based tests of association using common variants.1932-620310.1371/journal.pone.0062161https://doaj.org/article/709914c73ad649c48d9e2733da3eb7682013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23741293/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Gene-based tests of association are frequently applied to common SNPs (MAF>5%) as an alternative to single-marker tests. In this analysis we conduct a variety of simulation studies applied to five popular gene-based tests investigating general trends related to their performance in realistic situations. In particular, we focus on the impact of non-causal SNPs and a variety of LD structures on the behavior of these tests. Ultimately, we find that non-causal SNPs can significantly impact the power of all gene-based tests. On average, we find that the "noise" from 6-12 non-causal SNPs will cancel out the "signal" of one causal SNP across five popular gene-based tests. Furthermore, we find complex and differing behavior of the methods in the presence of LD within and between non-causal and causal SNPs. Ultimately, better approaches for a priori prioritization of potentially causal SNPs (e.g., predicting functionality of non-synonymous SNPs), application of these methods to sequenced or fully imputed datasets, and limited use of window-based methods for assigning inter-genic SNPs to genes will improve power. However, significant power loss from non-causal SNPs may remain unless alternative statistical approaches robust to the inclusion of non-causal SNPs are developed.Ashley PetersenCarolina AlvarezScott DeClaireNathan L TintlePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 5, p e62161 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ashley Petersen Carolina Alvarez Scott DeClaire Nathan L Tintle Assessing methods for assigning SNPs to genes in gene-based tests of association using common variants.
description	Gene-based tests of association are frequently applied to common SNPs (MAF>5%) as an alternative to single-marker tests. In this analysis we conduct a variety of simulation studies applied to five popular gene-based tests investigating general trends related to their performance in realistic situations. In particular, we focus on the impact of non-causal SNPs and a variety of LD structures on the behavior of these tests. Ultimately, we find that non-causal SNPs can significantly impact the power of all gene-based tests. On average, we find that the "noise" from 6-12 non-causal SNPs will cancel out the "signal" of one causal SNP across five popular gene-based tests. Furthermore, we find complex and differing behavior of the methods in the presence of LD within and between non-causal and causal SNPs. Ultimately, better approaches for a priori prioritization of potentially causal SNPs (e.g., predicting functionality of non-synonymous SNPs), application of these methods to sequenced or fully imputed datasets, and limited use of window-based methods for assigning inter-genic SNPs to genes will improve power. However, significant power loss from non-causal SNPs may remain unless alternative statistical approaches robust to the inclusion of non-causal SNPs are developed.
format	article
author	Ashley Petersen Carolina Alvarez Scott DeClaire Nathan L Tintle
author_facet	Ashley Petersen Carolina Alvarez Scott DeClaire Nathan L Tintle
author_sort	Ashley Petersen
title	Assessing methods for assigning SNPs to genes in gene-based tests of association using common variants.
title_short	Assessing methods for assigning SNPs to genes in gene-based tests of association using common variants.
title_full	Assessing methods for assigning SNPs to genes in gene-based tests of association using common variants.
title_fullStr	Assessing methods for assigning SNPs to genes in gene-based tests of association using common variants.
title_full_unstemmed	Assessing methods for assigning SNPs to genes in gene-based tests of association using common variants.
title_sort	assessing methods for assigning snps to genes in gene-based tests of association using common variants.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/709914c73ad649c48d9e2733da3eb768
work_keys_str_mv	AT ashleypetersen assessingmethodsforassigningsnpstogenesingenebasedtestsofassociationusingcommonvariants AT carolinaalvarez assessingmethodsforassigningsnpstogenesingenebasedtestsofassociationusingcommonvariants AT scottdeclaire assessingmethodsforassigningsnpstogenesingenebasedtestsofassociationusingcommonvariants AT nathanltintle assessingmethodsforassigningsnpstogenesingenebasedtestsofassociationusingcommonvariants
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Assessing methods for assigning SNPs to genes in gene-based tests of association using common variants.

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