ArticleNature communications2021
Identification of putative causal loci in whole-genome sequencing data via knockoff statistics.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- GhostKnockoff inference empowers identification of putative causal variants in genome-wide association studies.Nature communications · 2022Pooled it
- Genome-wide analysis of common and rare variants via multiple knockoffs at biobank scale, with an application to Alzheimer disease genetics.American journal of human genetics · 2021Pooled it
- Robust Inference With Ghostknockoffs in Genome-Wide Association Studies With Sample Relatedness.Genetic epidemiology · 2026Article
- Atlas of glomerular disease-specific genetic effects on blood transcriptome.medRxiv : the preprint server for health sciences · 2026Article
- CIT-Lasso: a scalable approach beyond guilty by association for identifying causal variants from genome-wide summary statistics.Genome biology · 2026Article
- Comprehensive adjudication identifies 111 high-confidence loci for Alzheimer's disease and related dementias.medRxiv : the preprint server for health sciences · 2026Article
- Statistical knockoffs improve biomarker discovery from transcriptomic data.Briefings in bioinformatics · 2026Article
- Knockoff-ML: a knockoff machine learning framework for controlled variable selection and risk stratification in electronic health record data.NPJ digital medicine · 2025Article
- Reflection knockoffs via householder reflection: applications in proteomics and genetic fine mapping.Genetics · 2025Article
- PLSKO: a robust knockoff generator to control false discovery rate in omics variable selection.Bioinformatics (Oxford, England) · 2025Article
- It's a wrap: deriving distinct discoveries with FDR control after a GWAS pipeline.bioRxiv : the preprint server for biology · 2025Article
- Local genetic correlation via knockoffs reduces confounding due to cross-trait assortative mating.American journal of human genetics · 2024Article
- Second-order group knockoffs with applications to genome-wide association studies.Bioinformatics (Oxford, England) · 2024Article
- KnockoffHybrid: A knockoff framework for hybrid analysis of trio and population designs in genome-wide association studies.American journal of human genetics · 2024Article
- Article
- Key variants via the Alzheimer's Disease Sequencing Project whole genome sequence data.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Article
- Enhancing credit scoring accuracy with a comprehensive evaluation of alternative data.PloS one · 2024Article
- Article
- Estimating gene-level false discovery probability improves eQTL statistical fine-mapping precision.NAR genomics and bioinformatics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
The analysis of whole-genome sequencing studies is challenging due to the large number of rare variants in noncoding regions and the lack of natural units for testing. We propose a statistical method to detect and localize rare and common risk variants in whole-genome sequencing studies based on a recently developed knockoff framework. It can (1) prioritize causal variants over associations due to linkage disequilibrium thereby improving interpretability; (2) help distinguish the signal due to rare variants from shadow effects of significant common variants nearby; (3) integrate multiple knockoffs for improved power, stability, and reproducibility; and (4) flexibly incorporate state-of-the-art and future association tests to achieve the benefits proposed here. In applications to whole-genome sequencing data from the Alzheimer's Disease Sequencing Project (ADSP) and COPDGene samples from NHLBI Trans-Omics for Precision Medicine (TOPMed) Program we show that our method compared with conventional association tests can lead to substantially more discoveries.
Indexed as
Identifiers
What OpenQuestion holds
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.