Forward-time simulation of realistic samples for genome-wide association studies

BMC Bioinformatics

Methodology Article

Christopher I Amos¹  Bo Peng¹ 

[1] Department of Epidemiology, The University of Texas M. D. Anderson Cancer Center, 77030, Houston, Texas, USA;

关键词: Linkage Disequilibrium;    Selective Sweep;    Simulated Population;    Linkage Disequilibrium Pattern;    HapMap Sample;   

DOI  :  10.1186/1471-2105-11-442

 received in 2010-04-08, accepted in 2010-09-01,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundForward-time simulations have unique advantages in power and flexibility for the simulation of genetic samples of complex human diseases because they can closely mimic the evolution of human populations carrying these diseases. However, a number of methodological and computational constraints have prevented the power of this simulation method from being fully explored in existing forward-time simulation methods.ResultsUsing a general-purpose forward-time population genetics simulation environment, we developed a forward-time simulation method that can be used to simulate realistic samples for genome-wide association studies. We examined the properties of this simulation method by comparing simulated samples with real data and demonstrated its wide applicability using four examples, including a simulation of case-control samples with a disease caused by multiple interacting genetic and environmental factors, a simulation of trio families affected by a disease-predisposing allele that had been subjected to either slow or rapid selective sweep, and a simulation of a structured population resulting from recent population admixture.ConclusionsOur algorithm simulates populations that closely resemble the complex structure of the human genome, while allows the introduction of signals of natural selection. Because of its flexibility to generate different types of samples with arbitrary disease or quantitative trait models, this simulation method can simulate realistic samples to evaluate the performance of a wide variety of statistical gene mapping methods for genome-wide association studies.

【 授权许可】

CC BY   
© Peng and Amos; licensee BioMed Central Ltd. 2010