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Understanding how genetic variants influence cellular-level processes is animportant step towards understanding how they influence importantorganismal-level traits, or "phenotypes", including human diseasesusceptibility. To this end scientists are undertaking large-scale geneticassociation studies that aim to identify genetic variants associated withmolecular and cellular phenotypes, such as gene expression, transcriptionfactor binding, or chromatin accessibility. These studies use high-throughputsequencing assays (e.g. RNA-seq, ChIP-seq, DNase-seq) to obtain high-resolutiondata on how the traits vary along the genome in each sample. However, typicalassociation analyses fail to exploit these high-resolution measurements,instead aggregating the data at coarser resolutions, such as genes, or windowsof fixed length. Here we develop and apply statistical methods that betterexploit the high-resolution data. The key idea is to treat the sequence data asmeasuring an underlying "function" that varies along the genome, and then,building on wavelet-based methods for functional data analysis, test forassociation between genetic variants and the underlying function. Applyingthese methods to identify genetic variants associated with chromatinaccessibility (dsQTLs) we find that they identify substantially moreassociations than a simpler window-based analysis, and in total we identify 772novel dsQTLs not identified by the original analysis.

Wavelet-based genetic association analysis of functional phenotypes arising from high-throughput sequencing assays