ArticleGenetic epidemiology2013
Detecting rare variant effects using extreme phenotype sampling in sequencing association studies.
Article in Genetic epidemiology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers, 2 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.
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.
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Who cites it
82 citing papers in PubMed, 2 syntheses or guidelines pooled it, 143 citations in OpenAlex.
- Pooled it
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- Hypometric genetics: Improved power in genetic discovery by incorporating quality control flags.American journal of human genetics · 2024Article
- Low-frequency inherited complement receptor variants are associated with purpura fulminans.Blood · 2024Article
- The role of genetic testing in diagnosis and care of inherited cardiac conditions in a specialised multidisciplinary clinic.Genome medicine · 2022Article
- Towards a global view of multiple sclerosis genetics.Nature reviews. Neurology · 2022Review
- Contribution of rare variants in monogenic diabetes-genes to early-onset type 2 diabetes.Diabetes & metabolism · 2022Article
- Genomic architecture of phenotypic extremes in a wild cervid.BMC genomics · 2022Article
- Comparison of mixed model based approaches for correcting for population substructure with application to extreme phenotype sampling.BMC genomics · 2022Article
- Polygenic power calculator: Statistical power and polygenic prediction accuracy of genome-wide association studies of complex traits.Frontiers in genetics · 2022Article
- Rare Genetic Variants Associated With Myocardial Fibrosis: Multi-Ethnic Study of Atherosclerosis.Frontiers in cardiovascular medicine · 2022Article
- High-impact rare genetic variants in severe schizophrenia.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Article
- Impact of rare and common genetic variation in the interleukin-1 pathway on human cytokine responses.Genome medicine · 2021Article
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- Discovery and Validation of a Recessively Inherited Major-Effect QTL Conferring Resistance to Maize Lethal Necrosis (MLN) Disease.Frontiers in genetics · 2021Article
- Polygenic score modifies risk for Alzheimer's disease inAlzheimer's & dementia (Amsterdam, Netherlands) · 2021Article
22 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
In the increasing number of sequencing studies aimed at identifying rare variants associated with complex traits, the power of the test can be improved by guided sampling procedures. We confirm both analytically and numerically that sampling individuals with extreme phenotypes can enrich the presence of causal rare variants and can therefore lead to an increase in power compared to random sampling. Although application of traditional rare variant association tests to these extreme phenotype samples requires dichotomizing the continuous phenotypes before analysis, the dichotomization procedure can decrease the power by reducing the information in the phenotypes. To avoid this, we propose a novel statistical method based on the optimal Sequence Kernel Association Test that allows us to test for rare variant effects using continuous phenotypes in the analysis of extreme phenotype samples. The increase in power of this method is demonstrated through simulation of a wide range of scenarios as well as in the triglyceride data of the Dallas Heart Study.
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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.