ArticleNature genetics2016
Fast and accurate long-range phasing in a UK Biobank cohort.
Article in Nature genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 234 papers, 15 of them syntheses that pooled it.
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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.
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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.
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Who cites it
234 citing papers in PubMed, 15 syntheses or guidelines pooled it.
- Large-scale cross-ancestry genome-wide meta-analysis of serum urate.Nature communications · 2024Pooled it
- GWAS for systemic sclerosis identifies six novel susceptibility loci including one in the Fcγ receptor region.Nature communications · 2024Pooled it
- Genetic architecture of alcohol consumption identified by a genotype-stratified GWAS and impact on esophageal cancer risk in Japanese people.Science advances · 2024Pooled it
- Genetic architecture distinguishes tinnitus from hearing loss.Nature communications · 2024Pooled it
- Multi-ancestry genome-wide association meta-analysis of Parkinson's disease.Nature genetics · 2024Pooled it
- GWAS meta-analysis of over 29,000 people with epilepsy identifies 26 risk loci and subtype-specific genetic architecture.Nature genetics · 2023Pooled it
- The contribution of common and rare genetic variants to variation in metabolic traits in 288,137 East Asians.Nature communications · 2022Pooled it
- Meta-imputation: An efficient method to combine genotype data after imputation with multiple reference panels.American journal of human genetics · 2022Pooled it
- Blood copper and risk of cardiometabolic diseases: a Mendelian randomization study.Human molecular genetics · 2022Pooled it
- GWAS and genomic prediction of milk urea nitrogen in Australian and New Zealand dairy cattle.Genetics, selection, evolution : GSE · 2022Pooled it
- Pooled it
- TIGER: The gene expression regulatory variation landscape of human pancreatic islets.Cell reports · 2021Pooled it
- Population-specific and trans-ancestry genome-wide analyses identify distinct and shared genetic risk loci for coronary artery disease.Nature genetics · 2020Pooled it
- RICOPILI: Rapid Imputation for COnsortias PIpeLIne.Bioinformatics (Oxford, England) · 2020Pooled it
- Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure.Nature communications · 2020Pooled it
- Predicting Benefit From Evolocumab Therapy in Patients With Atherosclerotic Disease Using a Genetic Risk Score: Results From the FOURIER Trial.Circulation · 2020Trial
- A founder population reveals genetic variation with major effect on serious mental illness.medRxiv : the preprint server for health sciences · 2026Article
- ABCC9/SUR2 has a Complex Expression Pattern in Human Brain Gliovascular Unit Cells, Including Astrocytes.Journal of molecular neuroscience : MN · 2026Article
- Benchmarking copy number alteration inference methods for spatial transcriptomics.Nature communications · 2026Article
- Genetic Associations with Temporal Modeling of Alzheimer's Disease Progression Supports a Novel Paradigm for Disease Risk.medRxiv : the preprint server for health sciences · 2026Article
174 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Recent work has leveraged the extensive genotyping of the Icelandic population to perform long-range phasing (LRP), enabling accurate imputation and association analysis of rare variants in target samples typed on genotyping arrays. Here we develop a fast and accurate LRP method, Eagle, that extends this paradigm to populations with much smaller proportions of genotyped samples by harnessing long (>4-cM) identical-by-descent (IBD) tracts shared among distantly related individuals. We applied Eagle to N ≈ 150,000 samples (0.2% of the British population) from the UK Biobank, and we determined that it is 1-2 orders of magnitude faster than existing methods while achieving similar or better phasing accuracy (switch error rate ≈ 0.3%, corresponding to perfect phase in a majority of 10-Mb segments). We also observed that, when used within an imputation pipeline, Eagle prephasing improved downstream imputation accuracy in comparison to prephasing in batches using existing methods, as necessary to achieve comparable computational cost.
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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.