ArticleNature communications2019
A genome-wide scan statistic framework for whole-genome sequence data analysis.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Atlas of glomerular disease-specific genetic effects on blood transcriptome.medRxiv : the preprint server for health sciences · 2026Article
- RetroFun-RVS: A Retrospective Family-Based Framework for Rare Variant Analysis Incorporating Functional Annotations.Genetic epidemiology · 2025Article
- Genomic microsatellite characterization and development of polymorphic microsatellites in Eospalax baileyi.Scientific reports · 2025Article
- CWAS-Plus: estimating category-wide association of rare noncoding variation from whole-genome sequencing data with cell-type-specific functional data.Briefings in bioinformatics · 2024Article
- CWAS-Plus: Estimating category-wide association of rare noncoding variation from whole-genome sequencing data with cell-type-specific functional data.medRxiv : the preprint server for health sciences · 2024Article
- DYNATE: Localizing rare-variant association regions via multiple testing embedded in an aggregation tree.Genetic epidemiology · 2024Article
- Maximum-scoring path sets on pangenome graphs of constant treewidth.Frontiers in bioinformatics · 2024Article
- Excalibur: A new ensemble method based on an optimal combination of aggregation tests for rare-variant association testing for sequencing data.PLoS computational biology · 2023Article
- Emerging Materials, Wearables, and Diagnostic Advancements in Therapeutic Treatment of Brain Diseases.Biosensors · 2022Review
- A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies.Nature methods · 2022Article
- Application of exome sequencing for prenatal diagnosis of fetal structural anomalies: clinical experience and lessons learned from a cohort of 1618 fetuses.Genome medicine · 2022Article
- Testing for association with rare variants in the coding and non-coding genome: RAVA-FIRST, a new approach based on CADD deleteriousness score.PLoS genetics · 2022Article
- Deep neural networks with controlled variable selection for the identification of putative causal genetic variants.Nature machine intelligence · 2022Article
- eSCAN: scan regulatory regions for aggregate association testing using whole-genome sequencing data.Briefings in bioinformatics · 2022Article
- Stochastic Epigenetic Mutations Influence Parkinson's Disease Risk, Progression, and Mortality.Journal of Parkinson's disease · 2022Article
- Powerful gene-based testing by integrating long-range chromatin interactions and knockoff genotypes.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Sequencing-based genome-wide association studies reporting standards.Cell genomics · 2021Article
- Advances and challenges in quantitative delineation of the genetic architecture of complex traits.Quantitative biology (Beijing, China) · 2021Article
- Identification of putative causal loci in whole-genome sequencing data via knockoff statistics.Nature communications · 2021Article
- A unifying framework for rare variant association testing in family-based designs, including higher criticism approaches, SKATs, and burden tests.Bioinformatics (Oxford, England) · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The analysis of whole-genome sequencing studies is challenging due to the large number of noncoding rare variants, our limited understanding of their functional effects, and the lack of natural units for testing. Here we propose a scan statistic framework, WGScan, to simultaneously detect the existence, and estimate the locations of association signals at genome-wide scale. WGScan can analytically estimate the significance threshold for a whole-genome scan; utilize summary statistics for a meta-analysis; incorporate functional annotations for enhanced discoveries in noncoding regions; and enable enrichment analyses using genome-wide summary statistics. Based on the analysis of whole genomes of 1,786 phenotypically discordant sibling pairs from the Simons Simplex Collection study for autism spectrum disorders, we derive genome-wide significance thresholds for whole genome sequencing studies and detect significant enrichments of regions showing associations with autism in promoter regions, functional categories related to autism, and enhancers predicted to regulate expression of autism associated genes.
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