Abstract
Recent studies suggest that variation in complex disorders (e.g., schizophrenia) is explained by a large number of genetic variants with small effect size (Odds Ratio∼1.05–1.1). The statistical power to detect these genetic variants in Genome Wide Association (GWA) studies with large numbers of cases and controls (∼15,000) is still low. As it will be difficult to further increase sample size, we decided to explore an alternative method for analyzing GWA data in a study of schizophrenia, dramatically reducing the number of statistical tests. The underlying hypothesis was that at least some of the genetic variants related to a common outcome are collocated in segments of chromosomes at a wider scale than single genes. Our approach was therefore to study the association between relatively large segments of DNA and disease status. An association test was performed for each SNP and the number of nominally significant tests in a segment was counted. We then performed a permutation-based binomial test to determine whether this region contained significantly more nominally significant SNPs than expected under the null hypothesis of no association, taking linkage into account. Genome Wide Association data of three independent schizophrenia case/control cohorts with European ancestry (Dutch, German, and US) using segments of DNA with variable length (2 to 32 Mbp) was analyzed. Using this approach we identified a region at chromosome 5q23.3-q31.3 (128–160 Mbp) that was significantly enriched with nominally associated SNPs in three independent case-control samples. We conclude that considering relatively wide segments of chromosomes may reveal reliable relationships between the genome and schizophrenia, suggesting novel methodological possibilities as well as raising theoretical questions.
Highlights
The statistical power to detect genetic effects in Genome Wide Association (GWA) studies of complex disorders is hindered by multiple testing problems and small effect sizes of single SNPs [1]
Alternative methods for analyzing GWA data which consider larger scale relationships between phenotype and SNP or single genes may help address these problems, and may have biological implications concerning the organization of genetic information
The fact that broad regions of 4 to 32 Mbp widths were found to contain increased numbers of nominally significant associated single SNPs suggests that the combination of information over larger segments can increase the strength of the segment-wise association signal
Summary
The statistical power to detect genetic effects in Genome Wide Association (GWA) studies of complex disorders is hindered by multiple testing problems and small effect sizes of single SNPs [1]. In an effort to deal with this statistical problem, Moskvina and colleagues [4] used a gene-based approach to perform a GWA by determining the excess of nominally significant (P,.05; P,.01, and P,.001) disease associated SNPs within genes, observing that significantly more SNPs within genes showed evidence for association with schizophrenia than expected by chance. These results are important, a limitation of the method is the a priori exclusion of the genome outside the genic regions. True association signals may be located outside the boundaries of genes; for example, studies have shown the existence of long-range regulatory elements which suggest that the effects of functional gene domains may extend far beyond their transcription unit [5]
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