  We present an alternative method for genome-wide association studies (GWAS)
that is more powerful than the regular GWAS method for locus detection. The
regular GWAS method suffers from a substantial multiple-testing burden because
of the millions of single nucleotide polymorphisms (SNPs) being tested
simultaneously. Furthermore, it does not consider the functional genetic effect
on the response variable; i.e., it ignores more complex joint effects of nearby
SNPs within a region. Our proposed method screens the entire genome for
associations using a sequential sliding-window approach based on wavelets. A
sequence of SNPs represents a genetic signal, and for every screened region, we
transform the genetic signal into the wavelet space. We then estimate the
proportion of wavelet coefficients associated with the phenotype at different
scales. The significance of a region is assessed via simulations, taking
advantage of a recent result on Bayes factor distributions. Our new approach
reduces the number of independent tests to be performed. Moreover, we show via
simulations that the Wavelet Screaming method provides a substantial gain in
power compared to the classic GWAS modeling when faced with more complex
signals than just single-SNP associations. To demonstrate feasibility, we
re-analyze data from the large Norwegian HARVEST cohort. Keywords: Bayes
factors, GWAS, SNP, Multiple testing, Polygenic
