Abstract
BackgroundCurrent sequencing technologies have provided for a more comprehensive genome-wide assessment and have increased genotyping accuracy of rare variants. Scan statistic approaches have previously been adapted to genetic sequencing data. Unlike currently-employed association tests, scan-statistic-based approaches can both localize clusters of disease-related variants and, subsequently, examine the phenotype association within the resulting cluster. In this study, we present a novel Quantitative Phenotype Scan Statistic (QPSS) that extends an approach for dichotomous phenotypes to continuous outcomes in order to identify genomic regions where rare quantitative-phenotype-associated variants cluster.ResultsWe demonstrate the performance and practicality of QPSS with extensive simulations and an application to a whole-genome sequencing (WGS) study of cerebrospinal fluid (CSF) biomarkers from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Using QPSS, we identify regions of rare variant enrichment associated with levels of AD-related proteins, CSF Aβ1–42 and p-tau181P.ConclusionsQPSS is implemented under the assumption that causal variants within a window have the same direction of effect. Typical self-contained tests employ a null hypothesis of no association between the target variant set and the phenotype. Therefore, an advantage of the proposed competitive test is that it is possible to refine a known region of interest to localize disease-associated clusters. The definition of clusters can be easily adapted based on variant function or annotation.
Highlights
Current sequencing technologies have provided for a more comprehensive genome-wide assessment and have increased genotyping accuracy of rare variants
Genome-wide association studies (GWAS) have been successful in interrogating genetic variants for association with disease, genome-wide association studies (GWAS) are performed under the “common disease – common variant” hypothesis positing that common traits are caused by the combination of common variants with a small to moderate
GWAS rely on genotyping an array of single nucleotide polymorphisms (SNPs) imputing ungenotyped variants based on local linkage disequilibrium (LD) derived from reference population haplotypes
Summary
Current sequencing technologies have provided for a more comprehensive genome-wide assessment and have increased genotyping accuracy of rare variants. Unlike currently-employed association tests, scan-statistic-based approaches can both localize clusters of disease-related variants and, subsequently, examine the phenotype association within the resulting cluster. We present a novel Quantitative Phenotype Scan Statistic (QPSS) that extends an approach for dichotomous phenotypes to continuous outcomes in order to identify genomic regions where rare quantitative-phenotype-associated variants cluster. Genome-wide association studies (GWAS) have been successful in interrogating genetic variants for association with disease, GWAS are performed under the “common disease – common variant” hypothesis positing that common traits are caused by the combination of common variants with a small to moderate effect [1, 2]. A frequently hypothesized culprit of this missing heritability is the role of rare variants [7, 8]
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