ArticleBMC bioinformatics2021
Wavelet Screening: a novel approach to analyzing GWAS data.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- A wavelet-based approach generates quantitative, scale-free and hierarchical descriptions of 3D genome structures and new biological insights.PLoS computational biology · 2026Article
- M-band wavelet-based multi-view clustering of cells.PLoS computational biology · 2025Article
- Using expression quantitative trait loci data and graph-embedded neural networks to uncover genotype-phenotype interactions.Frontiers in genetics · 2022Article
- Wavelet Screening identifies regions highly enriched for differentially methylated loci for orofacial clefts.NAR genomics and bioinformatics · 2021Article
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Authors and funding
5 authors at 1 institution in 2 countries.
Funding
Abstract
backgroundTraditional methods for single-variant genome-wide association study (GWAS) incur a substantial multiple-testing burden because of the need to test for associations with a vast number of single-nucleotide polymorphisms (SNPs) simultaneously. Further, by ignoring more complex joint effects of nearby SNPs within a given region, these methods fail to consider the genomic context of an association with the outcome.
resultsTo address these shortcomings, we present a more powerful method for GWAS, coined 'Wavelet Screening' (WS), that greatly reduces the number of tests to be performed. This is achieved through the use of a sliding-window approach based on wavelets to sequentially screen the entire genome for associations. Wavelets are oscillatory functions that are useful for analyzing the local frequency and time behavior of signals. The signals can then be divided into different scale components and analyzed separately. In the current setting, we consider a sequence of SNPs as a genetic signal, and for each screened region, we transform the genetic signal into the wavelet space. The null and alternative hypotheses are modeled using the posterior distribution of the wavelet coefficients. WS is enhanced by using additional information from the regression coefficients and by taking advantage of the pyramidal structure of wavelets. When faced with more complex genetic signals than single-SNP associations, we show via simulations that WS provides a substantial gain in power compared to both the traditional GWAS modeling and another popular regional association test called SNP-set (Sequence) Kernel Association Test (SKAT). To demonstrate feasibility, we applied WS to a large Norwegian cohort (N=8006) with genotypes and information available on gestational duration.
conclusionsWS is a powerful and versatile approach to analyzing whole-genome data and lends itself easily to investigating various omics data types. Given its broader focus on the genomic context of an association, WS may provide additional insight into trait etiology by revealing genes and loci that might have been missed by previous efforts.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.