This article is part of the supplement: Genetic Analysis Workshop 16
Proceedings
Region-based analysis in genome-wide association study of Framingham Heart Study blood lipid phenotypes
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* Corresponding author: Shelley B Bull bull@lunenfeld.ca
- † Equal contributors
1 Samuel Lunenfeld Research Institute of Mount Sinai Hospital, 60 Murray Street, Box 18, Toronto, Ontario M5T 3L9, Canada
2 Dalla Lana School of Public Health, University of Toronto, 155 College Street, Toronto M5T 3M7, Canada
3 Department of Statistics, University of Toronto, 100 St. George Street, Toronto M5S 3G3, Canada
BMC Proceedings 2009, 3(Suppl 7):S127 doi:
Published: 15 December 2009Abstract
Due to the high-dimensionality of single-nucleotide polymorphism (SNP) data, region-based methods are an attractive approach to the identification of genetic variation associated with a certain phenotype. A common approach to defining regions is to identify the most significant SNPs from a single-SNP association analysis, and then use a gene database to obtain a list of genes proximal to the identified SNPs. Alternatively, regions may be defined statistically, via a scan statistic. After categorizing SNPs as significant or not (based on the single-SNP association p-values), a scan statistic is useful to identify regions that contain more significant SNPs than expected by chance. Important features of this method are that regions are defined statistically, so that there is no dependence on a gene database, and both gene and inter-gene regions can be detected. In the analysis of blood-lipid phenotypes from the Framingham Heart Study (FHS), we compared statistically defined regions with those formed from the top single SNP tests. Although we missed a number of single SNPs, we also identified many additional regions not found as SNP-database regions and avoided issues related to region definition. In addition, analyses of candidate genes for high-density lipoprotein, low-density lipoprotein, and triglyceride levels suggested that associations detected with region-based statistics are also found using the scan statistic approach.