ArticleAmerican journal of human genetics2019
Dynamic Scan Procedure for Detecting Rare-Variant Association Regions in Whole-Genome Sequencing Studies.
Article in American journal of human genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 2 of them syntheses that pooled it.
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Who cites it
35 citing papers in PubMed, 2 syntheses or guidelines pooled it, 66 citations in OpenAlex.
- Assessment of the functionality and usability of open-source rare variant analysis pipelines.Briefings in bioinformatics · 2025Pooled it
- Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies.Nature genetics · 2023Pooled it
- Whole-genome sequencing implicates rare, low-frequency and structural non-coding variation at themedRxiv : the preprint server for health sciences · 2026Article
- An empirical Bayes framework for burden and dispersion association tests helps prioritize rare variants associated with Alzheimer's disease.medRxiv : the preprint server for health sciences · 2026Article
- Genealogy-based trait association with LOCATER boosts power at loci with allelic heterogeneity.Genome research · 2026Article
- Genetic Architecture of N-Terminal Pro-B-Type Natriuretic Peptide in a Multiancestry Study Population.Circulation. Genomic and precision medicine · 2026Article
- Genetic determinants and genomic consequences of non-leukemogenic somatic point mutations.Nature communications · 2025Article
- Leveraging functional annotations to map rare variants associated with Alzheimer disease with gruyere.American journal of human genetics · 2025Article
- Noncoding rare variant associations with blood traits in 166,740 UK Biobank genomes.Nature genetics · 2025Article
- RetroFun-RVS: A Retrospective Family-Based Framework for Rare Variant Analysis Incorporating Functional Annotations.Genetic epidemiology · 2025Article
- A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies.Nature computational science · 2025Article
- Whole-genome sequencing in 333,100 individuals reveals rare non-coding single variant and aggregate associations with height.Nature communications · 2024Article
- The Genetic Determinants and Genomic Consequences of Non-Leukemogenic Somatic Point Mutations.medRxiv : the preprint server for health sciences · 2024Article
- Enhancing prediction accuracy of coronary artery disease through machine learning-driven genomic variant selection.Journal of translational medicine · 2024Article
- DYNATE: Localizing rare-variant association regions via multiple testing embedded in an aggregation tree.Genetic epidemiology · 2024Article
- Whole-genome sequencing reveals rare variants associated with gout in Taiwanese males.Frontiers in genetics · 2024Article
- FiMAP: A fast identity-by-descent mapping test for biobank-scale cohorts.PLoS genetics · 2023Article
- A statistical framework for powerful multi-trait rare variant analysis in large-scale whole-genome sequencing studies.bioRxiv : the preprint server for biology · 2023Article
- The hidden factor: accounting for covariate effects in power and sample size computation for a binary trait.Bioinformatics (Oxford, England) · 2023Article
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Authors and funding
9 authors at 4 institutions in 1 country.
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Abstract
Whole-genome sequencing (WGS) studies are being widely conducted in order to identify rare variants associated with human diseases and disease-related traits. Classical single-marker association analyses for rare variants have limited power, and variant-set-based analyses are commonly used by researchers for analyzing rare variants. However, existing variant-set-based approaches need to pre-specify genetic regions for analysis; hence, they are not directly applicable to WGS data because of the large number of intergenic and intron regions that consist of a massive number of non-coding variants. The commonly used sliding-window method requires the pre-specification of fixed window sizes, which are often unknown as a priori, are difficult to specify in practice, and are subject to limitations given that the sizes of genetic-association regions are likely to vary across the genome and phenotypes. We propose a computationally efficient and dynamic scan-statistic method (Scan the Genome [SCANG]) for analyzing WGS data; this method flexibly detects the sizes and the locations of rare-variant association regions without the need to specify a prior, fixed window size. The proposed method controls for the genome-wise type I error rate and accounts for the linkage disequilibrium among genetic variants. It allows the detected sizes of rare-variant association regions to vary across the genome. Through extensive simulated studies that consider a wide variety of scenarios, we show that SCANG substantially outperforms several alternative methods for detecting rare-variant-associations while controlling for the genome-wise type I error rates. We illustrate SCANG by analyzing the WGS lipids data from the Atherosclerosis Risk in Communities (ARIC) study.
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