ArticleBioinformatics (Oxford, England)2013
A comprehensive SNP and indel imputability database.
Article in Bioinformatics (Oxford, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Multistage genome-wide association meta-analyses identified two new loci for bone mineral density.Human molecular genetics · 2014Pooled it
- Advances in haplotype phasing and genotype imputation.Nature reviews. Genetics · 2026Review
- Genomic platform specific polygenic risk scores impact breast cancer risk stratification.Communications medicine · 2025Article
- MagicalRsq: Machine-learning-based genotype imputation quality calibration.American journal of human genetics · 2022Article
- Evaluation of vicinity-based hidden Markov models for genotype imputation.BMC bioinformatics · 2022Article
- Innovative computational approaches shed light on genetic mechanisms underlying cognitive impairment among children born extremely preterm.Journal of neurodevelopmental disorders · 2022Article
- Ultrafast homomorphic encryption models enable secure outsourcing of genotype imputation.Cell systems · 2021Article
- SLE non-coding genetic risk variant determines the epigenetic dysfunction of an immune cell specific enhancer that controls disease-critical microRNA expression.Nature communications · 2021Article
- Article
- Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps.Nature genetics · 2016Article
- Empirical estimation of genome-wide significance thresholds based on the 1000 Genomes Project data set.Journal of human genetics · 2016Article
- Systematic assessment of imputation performance using the 1000 Genomes reference panels.Briefings in bioinformatics · 2015Article
- Discovery and Fine-Mapping of Glycaemic and Obesity-Related Trait Loci Using High-Density Imputation.PLoS genetics · 2015Article
- Article
- FISH: fast and accurate diploid genotype imputation via segmental hidden Markov model.Bioinformatics (Oxford, England) · 2014Article
- Association studies with imputed variants using expectation-maximization likelihood-ratio tests.PloS one · 2014Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
motivationGenotype imputation has become an indispensible step in genome-wide association studies (GWAS). Imputation accuracy, directly influencing downstream analysis, has shown to be improved using re-sequencing-based reference panels; however, this comes at the cost of high computational burden due to the huge number of potentially imputable markers (tens of millions) discovered through sequencing a large number of individuals. Therefore, there is an increasing need for access to imputation quality information without actually conducting imputation. To facilitate this process, we have established a publicly available SNP and indel imputability database, aiming to provide direct access to imputation accuracy information for markers identified by the 1000 Genomes Project across four major populations and covering multiple GWAS genotyping platforms.
resultsSNP and indel imputability information can be retrieved through a user-friendly interface by providing the ID(s) of the desired variant(s) or by specifying the desired genomic region. The query results can be refined by selecting relevant GWAS genotyping platform(s). This is the first database providing variant imputability information specific to each continental group and to each genotyping platform. In Filipino individuals from the Cebu Longitudinal Health and Nutrition Survey, our database can achieve an area under the receiver-operating characteristic curve of 0.97, 0.91, 0.88 and 0.79 for markers with minor allele frequency >5%, 3-5%, 1-3% and 0.5-1%, respectively. Specifically, by filtering out 48.6% of markers (corresponding to a reduction of up to 48.6% in computational costs for actual imputation) based on the imputability information in our database, we can remove 77%, 58%, 51% and 42% of the poorly imputed markers at the cost of only 0.3%, 0.8%, 1.5% and 4.6% of the well-imputed markers with minor allele frequency >5%, 3-5%, 1-3% and 0.5-1%, respectively. AVAILABILITY: http://www.unc.edu/∼yunmli/imputability.html
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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.