Comparison of results from tests of association in unrelated individuals with uncollapsed and collapsed sequence variants using tiled regression

Tiled regression is an approach designed to determine the set of independent genetic variants that contribute to the variation of a quantitative trait in the presence of many highly correlated variants. In this study, we evaluate the statistical properties of the tiled regression method using the Ge...

Full description

Bibliographic Details
Main Authors:	Sung, Heejong, Kim, Yoonhee, Cai, Juanliang, Cropp, Cheryl D, Simpson, Claire L, Li, Qing, Perry, Brian C, Sorant, Alexa JM, Bailey-Wilson, Joan E, Wilson, Alexander F
Format:	Online
Language:	English
Published:	BioMed Central 2011
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287849/

id	pubmed-3287849
recordtype	oai_dc
spelling	pubmed-32878492012-02-28 Comparison of results from tests of association in unrelated individuals with uncollapsed and collapsed sequence variants using tiled regression Sung, Heejong Kim, Yoonhee Cai, Juanliang Cropp, Cheryl D Simpson, Claire L Li, Qing Perry, Brian C Sorant, Alexa JM Bailey-Wilson, Joan E Wilson, Alexander F Proceedings Tiled regression is an approach designed to determine the set of independent genetic variants that contribute to the variation of a quantitative trait in the presence of many highly correlated variants. In this study, we evaluate the statistical properties of the tiled regression method using the Genetic Analysis Workshop 17 data in unrelated individuals for traits Q1, Q2, and Q4. To increase the power to detect rare variants, we use two methods to collapse rare variants and compare the results with those from the uncollapsed data. In addition, we compare the tiled regression method to traditional tests of association with and without collapsed rare variants. The results show that collapsing rare variants generally improves the power to detect associations regardless of method, although only variants with the largest allelic effects could be detected. However, for traditional simple linear regression, the average estimated type I error is dependent on the trait and varies by about three orders of magnitude. The estimated type I error rate is stable for tiled regression across traits. BioMed Central 2011-11-29 /pmc/articles/PMC3287849/ /pubmed/22373501 http://dx.doi.org/10.1186/1753-6561-5-S9-S15 Text en Copyright ©2011 Sung et al; 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.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Sung, Heejong Kim, Yoonhee Cai, Juanliang Cropp, Cheryl D Simpson, Claire L Li, Qing Perry, Brian C Sorant, Alexa JM Bailey-Wilson, Joan E Wilson, Alexander F
spellingShingle	Sung, Heejong Kim, Yoonhee Cai, Juanliang Cropp, Cheryl D Simpson, Claire L Li, Qing Perry, Brian C Sorant, Alexa JM Bailey-Wilson, Joan E Wilson, Alexander F Comparison of results from tests of association in unrelated individuals with uncollapsed and collapsed sequence variants using tiled regression
author_facet	Sung, Heejong Kim, Yoonhee Cai, Juanliang Cropp, Cheryl D Simpson, Claire L Li, Qing Perry, Brian C Sorant, Alexa JM Bailey-Wilson, Joan E Wilson, Alexander F
author_sort	Sung, Heejong
title	Comparison of results from tests of association in unrelated individuals with uncollapsed and collapsed sequence variants using tiled regression
title_short	Comparison of results from tests of association in unrelated individuals with uncollapsed and collapsed sequence variants using tiled regression
title_full	Comparison of results from tests of association in unrelated individuals with uncollapsed and collapsed sequence variants using tiled regression
title_fullStr	Comparison of results from tests of association in unrelated individuals with uncollapsed and collapsed sequence variants using tiled regression
title_full_unstemmed	Comparison of results from tests of association in unrelated individuals with uncollapsed and collapsed sequence variants using tiled regression
title_sort	comparison of results from tests of association in unrelated individuals with uncollapsed and collapsed sequence variants using tiled regression
description	Tiled regression is an approach designed to determine the set of independent genetic variants that contribute to the variation of a quantitative trait in the presence of many highly correlated variants. In this study, we evaluate the statistical properties of the tiled regression method using the Genetic Analysis Workshop 17 data in unrelated individuals for traits Q1, Q2, and Q4. To increase the power to detect rare variants, we use two methods to collapse rare variants and compare the results with those from the uncollapsed data. In addition, we compare the tiled regression method to traditional tests of association with and without collapsed rare variants. The results show that collapsing rare variants generally improves the power to detect associations regardless of method, although only variants with the largest allelic effects could be detected. However, for traditional simple linear regression, the average estimated type I error is dependent on the trait and varies by about three orders of magnitude. The estimated type I error rate is stable for tiled regression across traits.
publisher	BioMed Central
publishDate	2011
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287849/
_version_	1611508742539444224

Comparison of results from tests of association in unrelated individuals with uncollapsed and collapsed sequence variants using tiled regression

Similar Items