Exploration and comparison of methods for combining population- and family-based genetic association using the Genetic Analysis Workshop 17 mini-exome

BMC Proceedings

Proceedings

Jinze Liu¹  Lucia Mirea²  Patrick Breheny³  David W Fardo⁴  Anthony R Druen⁵  Claire Infante-Rivard⁶ 

[1] Center for Clinical and Translational Science, University of Kentucky, 800 Rose Street, Room C-300, 40536, Lexington, KY, USA;Department of Computer Science, University of Kentucky, 329 Rose Street, 40506, Lexington, KY, USA;Dalla Lana School of Public Health, University of Toronto, 155 College Street, Health Science Building, 6th floor, M5T 3M7, Toronto, ON, Canada;Samuel Lunenfeld Research Institute Mount Sinai Hospital Joseph and Wolf Lebovic Health Complex, 600 University Avenue, M5G 1X5, Toronto, ON, Canada;Department of Biostatistics, University of Kentucky College of Public Health, 121 Washington Avenue, 40536, Lexington, KY, USA;Department of Biostatistics, University of Kentucky College of Public Health, 121 Washington Avenue, 40536, Lexington, KY, USA;Division of Biomedical Informatics, University of Kentucky College of Public Health, 121 Washington Avenue, 40536, Lexington, KY, USA;Center for Clinical and Translational Science, University of Kentucky, 800 Rose Street, Room C-300, 40536, Lexington, KY, USA;Department of Computer Science, University of Kentucky, 329 Rose Street, 40506, Lexington, KY, USA;Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada Purvis Hall, 1020 Pine Avenue West, H3A 1A3, Montreal, QC, Canada;

关键词: Population Stratification;    Genetic Analysis Workshop;    Causal SNPs;    GAW17 Data;    Score Test Statistic;   

DOI  :  10.1186/1753-6561-5-S9-S28

来源: Springer

【 摘 要 】

We examine the performance of various methods for combining family- and population-based genetic association data. Several approaches have been proposed for situations in which information is collected from both a subset of unrelated subjects and a subset of family members. Analyzing these samples separately is known to be inefficient, and it is important to determine the scenarios for which differing methods perform well. Others have investigated this question; however, no extensive simulations have been conducted, nor have these methods been applied to mini-exome-style data such as that provided by Genetic Analysis Workshop 17. We quantify the empirical power and false-positive rates for three existing methods applied to the Genetic Analysis Workshop 17 mini-exome data and compare relative performance. We use knowledge of the underlying data simulation model to make these assessments.

【 授权许可】

CC BY   
© Fardo et al; licensee BioMed Central Ltd. 2011