ArticleThe annals of applied statistics2015
WAVELET-BASED GENETIC ASSOCIATION ANALYSIS OF FUNCTIONAL PHENOTYPES ARISING FROM HIGH-THROUGHPUT SEQUENCING ASSAYS.
Article in The annals of applied statistics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
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- fSuSiE enables fine-mapping of QTLs from genome-scale molecular profiles.bioRxiv : the preprint server for biology · 2025Article
- Peak-agnostic high-resolution cis-regulatory circuitry mapping using single cell multiome data.Nucleic acids research · 2024Article
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- Wavelet Screening: a novel approach to analyzing GWAS data.BMC bioinformatics · 2021Article
- Wavelet Screening identifies regions highly enriched for differentially methylated loci for orofacial clefts.NAR genomics and bioinformatics · 2021Article
- A fast wavelet-based functional association analysis replicates several susceptibility loci for birth weight in a Norwegian population.BMC genomics · 2021Article
- Data Integration in Poplar: 'Omics Layers and Integration Strategies.Frontiers in genetics · 2019Review
- Promoter shape varies across populations and affects promoter evolution and expression noise.Nature genetics · 2017Article
- Simultaneous detection and estimation of trait associations with genomic phenotypes.Biostatistics (Oxford, England) · 2017Article
- Prediction of ribosome footprint profile shapes from transcript sequences.Bioinformatics (Oxford, England) · 2016Article
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- Identification of differentially methylated loci using wavelet-based functional mixed models.Bioinformatics (Oxford, England) · 2016Article
- WAVELET-BASED GENETIC ASSOCIATION ANALYSIS OF FUNCTIONAL PHENOTYPES ARISING FROM HIGH-THROUGHPUT SEQUENCING ASSAYS.The annals of applied statistics · 2015Article
- Article
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2 authors.
Funding
Abstract
Understanding how genetic variants influence cellular-level processes is an important step toward understanding how they influence important organismal-level traits, or "phenotypes," including human disease susceptibility. To this end, scientists are undertaking large-scale genetic association studies that aim to identify genetic variants associated with molecular and cellular phenotypes, such as gene expression, transcription factor binding, or chromatin accessibility. These studies use high-throughput sequencing assays (e.g., RNA-seq, ChIP-seq, DNase-seq) to obtain high-resolution data on how the traits vary along the genome in each sample. However, typical association analyses fail to exploit these high-resolution measurements, instead aggregating the data at coarser resolutions, such as genes, or windows of fixed length. Here we develop and apply statistical methods that better exploit the high-resolution data. The key idea is to treat the sequence data as measuring an underlying "function" that varies along the genome, and then, building on wavelet-based methods for functional data analysis, test for association between genetic variants and the underlying function. Applying these methods to identify genetic variants associated with chromatin accessibility (dsQTLs), we find that they identify substantially more associations than a simpler window-based analysis, and in total we identify 772 novel dsQTLs not identified by the original analysis.
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