Email updates

Keep up to date with the latest news and content from BMC Proceedings and BioMed Central.

This article is part of the supplement: Genetic Analysis Workshop 17: Unraveling Human Exome Data

Open Access Proceedings

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

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

Author Affiliations

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

2 Statistical Genetics Section, Inherited Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, 333 Cassell Drive, Baltimore, MD 21224, USA

For all author emails, please log on.

BMC Proceedings 2011, 5(Suppl 9):S15  doi:10.1186/1753-6561-5-S9-S15

Published: 29 November 2011

Abstract

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.