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Original Paper

Free Access

Simulating Sequences of the Human Genome with Rare Variants

Peng B.a · Liu X.b

Author affiliations

aDepartment of Epidemiology, University of Texas MD Anderson Cancer Center, and bHuman Genetics Center, University of Texas School of Public Health, Houston, Tex., USA

Corresponding Author

Bo Peng

Department of Epidemiology

University of Texas MD Anderson Cancer Center

1155 Pressler Street MS1340, Houston, TX 77030 (USA)

Tel. +1 713 745 2726, Fax +1 713 792 8261, E-Mail bpeng@mdanderson.org

Related Articles for ""

Hum Hered 2010;70:287–291

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Objective: Simulated samples have been widely used in the development of efficient statistical methods identifying genetic variants that predispose to human genetic diseases. Although it is well known that natural selection has a strong influence on the number and diversity of rare genetic variations in human populations, existing simulation methods are limited in their ability to simulate multi-locus selection models with realistic distributions of the random fitness effects of newly arising mutants. Methods: We developed a computer program to simulate large populations of gene sequences using a forward-time simulation approach. This program is capable of simulating several multi-locus fitness schemes with arbitrary diploid single-locus selection models with random or locus-specific fitness effects. Arbitrary quantitative trait or disease models can be applied to the simulated populations from which individual- or family-based samples can be drawn and analyzed. Results: Using realistic demographic and natural selection models estimated from empirical sequence data, datasets simulated using our method differ significantly in the number and diversity of rare variants from datasets simulated using existing methods that ignore natural selection. Our program thus provides a useful tool to simulate datasets with realistic distributions of rare genetic variants for the study of genetic diseases caused by such variants.

© 2011 S. Karger AG, Basel

Article / Publication Details

First-Page Preview
Abstract of Original Paper

Received: July 26, 2010
Accepted: December 02, 2010
Published online: January 06, 2011
Issue release date: February 2011

Number of Print Pages: 5
Number of Figures: 2
Number of Tables: 1

ISSN: 0001-5652 (Print)
eISSN: 1423-0062 (Online)

For additional information: http://www.karger.com/HHE

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