SynthesisAmerican journal of human genetics2016
Imputing Phenotypes for Genome-wide Association Studies.
Synthesis in American journal of human genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 2 of them syntheses that pooled it.
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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
32 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Comparison of two multi-trait association testing methods and sequence-based fine mapping of six additive QTL in Swiss Large White pigs.BMC genomics · 2023Pooled it
- Lifestyle Risk Score: handling missingness of individual lifestyle components in meta-analysis of gene-by-lifestyle interactions.European journal of human genetics : EJHG · 2021Pooled it
- The association of a common HFE gene variant with stroke disability utilizing predictive machine learning and electronic health records.Scientific reports · 2026Article
- Genetic association studies using disease liabilities from deep neural networks.American journal of human genetics · 2025Article
- Rapid and accurate multi-phenotype imputation for millions of individuals.Nature communications · 2025Article
- Genome-wide associations spanning 194 in-hospital drug dosage change phenotypes highlight diverse genetic backgrounds in concurrent drug therapy.Computational and structural biotechnology journal · 2025Article
- Endophenotype 2.0: updated definitions and criteria for endophenotypes of psychiatric disorders, incorporating new technologies and findings.Translational psychiatry · 2024Review
- A novel phenotype imputation method with copula model.BMC bioinformatics · 2024Article
- Article
- Gene-environment interactions in human health.Nature reviews. Genetics · 2024Review
- Valid inference for machine learning-assisted genome-wide association studies.Nature genetics · 2024Article
- Synthetic surrogates improve power for genome-wide association studies of partially missing phenotypes in population biobanks.Nature genetics · 2024Article
- Statistical inference with large-scale trait imputation.Statistics in medicine · 2024Article
- Deep learning-based phenotype imputation on population-scale biobank data increases genetic discoveries.Nature genetics · 2023Article
- Phenotype integration improves power and preserves specificity in biobank-based genetic studies of major depressive disorder.Nature genetics · 2023Article
- Machine learning-based quantification for disease uncertainty increases the statistical power of genetic association studies.Bioinformatics (Oxford, England) · 2023Article
- Integrating GWAS summary statistics, individual-level genotypic and omic data to enhance the performance for large-scale trait imputation.Human molecular genetics · 2023Article
- Using GWAS summary data to impute traits for genotyped individuals.HGG advances · 2023Article
- Missingness adapted group informed clustered (MAGIC)-LASSO: a novel paradigm for phenotype prediction to improve power for genetic loci discovery.Frontiers in genetics · 2023Article
- GxEsum: a novel approach to estimate the phenotypic variance explained by genome-wide GxE interaction based on GWAS summary statistics for biobank-scale data.Genome biology · 2021Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Genome-wide association studies (GWASs) have been successful in detecting variants correlated with phenotypes of clinical interest. However, the power to detect these variants depends on the number of individuals whose phenotypes are collected, and for phenotypes that are difficult to collect, the sample size might be insufficient to achieve the desired statistical power. The phenotype of interest is often difficult to collect, whereas surrogate phenotypes or related phenotypes are easier to collect and have already been collected in very large samples. This paper demonstrates how we take advantage of these additional related phenotypes to impute the phenotype of interest or target phenotype and then perform association analysis. Our approach leverages the correlation structure between phenotypes to perform the imputation. The correlation structure can be estimated from a smaller complete dataset for which both the target and related phenotypes have been collected. Under some assumptions, the statistical power can be computed analytically given the correlation structure of the phenotypes used in imputation. In addition, our method can impute the summary statistic of the target phenotype as a weighted linear combination of the summary statistics of related phenotypes. Thus, our method is applicable to datasets for which we have access only to summary statistics and not to the raw genotypes. We illustrate our approach by analyzing associated loci to triglycerides (TGs), body mass index (BMI), and systolic blood pressure (SBP) in the Northern Finland Birth Cohort dataset.
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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.