Using Maximal Segmental Score in Genome-Wide Association Studies

Genome‐wide association studies (GWAS) have become the method of choice for identifying disease susceptibility genes in common disease genetics research. Despite successes in these studies, much of the heritability remains unexplained due to lack of power and low resolution. High‐density genotyping...

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Bibliographic Details
Published in:Genetic epidemiology 2012-09, Vol.36 (6), p.594-601
Main Authors: Lin, Ying-Chao, Hsiao, Ching-Lin, Hsieh, Ai-Ru, Lian, Ie-Bin, Fann, Cathy S.J.
Format: Article
Language:English
Subjects:
association test
Case-Control Studies
Computer Simulation
Data processing
Error detection
Gene Frequency
Genetic Markers
Genetic Predisposition to Disease
Genome-wide association studies
Genome-Wide Association Study - methods
Genomes
Genotyping
GWAS
Haplotypes - genetics
Heritability
Humans
maximal segmental score
Models, Genetic
Movement disorders
multiple testing
Neurodegenerative diseases
Parkinson Disease - genetics
Parkinson's disease
Software
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Summary:Genome‐wide association studies (GWAS) have become the method of choice for identifying disease susceptibility genes in common disease genetics research. Despite successes in these studies, much of the heritability remains unexplained due to lack of power and low resolution. High‐density genotyping arrays can now screen more than 5 million genetic markers. As a result, multiple comparison has become an important issue especially in the era of next‐generation sequencing. We propose to use a two‐stage maximal segmental score procedure (MSS) which uses region‐specific empirical P‐values to identify genomic segments most likely harboring the disease gene. We develop scoring systems based on Fisher's P‐value combining method to convert locus‐specific significance levels into region‐specific scores. Through simulations, our result indicated that MSS increased the power to detect genetic association as compared with conventional methods provided type I error was at 5%. We demonstrated the application of MSS on a publicly available case‐control dataset of Parkinson's disease and replicated the findings in the literature. MSS provides an efficient exploratory tool for high‐density association data in the current era of next‐generation sequencing. R source codes to implement the MSS procedure are freely available at http://www.csjfann.ibms.sinica.edu.tw/EAG/program/programlist.htm.
ISSN:0741-0395
1098-2272
DOI:10.1002/gepi.21652