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

BMC Proceedings

Proceedings

Yoonhee Kim¹  Juanliang Cai¹  Heejong Sung¹  Alexa JM Sorant¹  Alexander F Wilson¹  Brian C Perry²  Cheryl D Cropp²  Claire L Simpson²  Joan E Bailey-Wilson²  Qing Li² 

[1] Genometrics Section, Inherited Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, 333 Cassell Drive, 21224, Baltimore, MD, USA;Statistical Genetics Section, Inherited Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, 333 Cassell Drive, 21224, Baltimore, MD, USA;

关键词: Rare Variant;    Unrelated Individual;    Causal Variant;    Nonsynonymous Variant;    Genetic Analysis Workshop;   

DOI  :  10.1186/1753-6561-5-S9-S15

来源: Springer

【 摘 要 】

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.

【 授权许可】

Unknown   
© Sung et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. 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.