ArticleEuropean journal of human genetics : EJHG2016
Phenotypic extremes in rare variant study designs.
Article in European journal of human genetics : EJHG, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 2 of them syntheses that pooled it.
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Who cites it
40 citing papers in PubMed, 2 syntheses or guidelines pooled it, 66 citations in OpenAlex.
- Biological annotation of genetic loci associated with intelligence in a meta-analysis of 87,740 individuals.Molecular psychiatry · 2019Pooled it
- Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence.Nature genetics · 2018Pooled it
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- The biomedical landscape of genomic structural variation in the qatari population.Nature communications · 2026Article
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- Label-free quantitative proteomics in serum reveals candidate biomarkers associated with low bone mineral density in Mexican postmenopausal women.GeroScience · 2024Article
- Genetic architecture and biology of youth-onset type 2 diabetes.Nature metabolism · 2024Article
- Insights from rare variants into the genetic architecture and biology of youth-onset type 2 diabetes.Research square · 2023Article
- Neurotensin Gene rs2234762 C>G Variant Associates with Reduced Circulating Pro-NT Levels and Predicts Lower Insulin Resistance in Overweight/Obese Children.International journal of molecular sciences · 2023Article
- The role of genetic testing in diagnosis and care of inherited cardiac conditions in a specialised multidisciplinary clinic.Genome medicine · 2022Article
- Deconstructing a Syndrome: Genomic Insights Into PCOS Causal Mechanisms and Classification.Endocrine reviews · 2022Article
- Efficient permutation-based genome-wide association studies for normal and skewed phenotypic distributions.Bioinformatics (Oxford, England) · 2022Article
- Comparison of mixed model based approaches for correcting for population substructure with application to extreme phenotype sampling.BMC genomics · 2022Article
- Deep Resequencing of 9 Candidate Genes Identifies a Role for ARAP1 and IGF2BP2 in Modulating Insulin Secretion Adjusted for Insulin Resistance in Obese Southern Europeans.International journal of molecular sciences · 2022Article
- Applications of single-cell genomics and computational strategies to study common disease and population-level variation.Genome research · 2021Article
- A genomics resource for genetics, physiology, and breeding of West African sorghum.The plant genome · 2021Article
- Emerging roles of rare and low-frequency genetic variants in type 1 diabetes mellitus.Journal of medical genetics · 2021Review
- A state-based approach to genomics for rare disease and population screening.Genetics in medicine : official journal of the American College of Medical Genetics · 2021Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
Abstract
Currently, next-generation sequencing studies aim to identify rare and low-frequency variation that may contribute to disease. For a given effect size, as the allele frequency decreases, the power to detect genes or variants of interest also decreases. Although many methods have been proposed for the analysis of such data, study design and analytic issues still persist in data interpretation. In this study we present sequencing data for ABCA1 that has known rare variants associated with high-density lipoprotein cholesterol (HDL-C). We contrast empirical findings from two study designs: a phenotypic extreme sample and a population-based random sample. We found differing strengths of association with HDL-C across the two study designs (P=0.0006 with n=701 phenotypic extremes vs P=0.03 with n=1600 randomly sampled individuals). To explore this apparent difference in evidence for association, we performed a simulation study focused on the impact of phenotypic selection on power. We demonstrate that the power gain for an extreme phenotypic selection study design is much greater in rare variant studies than for studies of common variants. Our study confirms that studying phenotypic extremes is critical in rare variant studies because it boosts power in two ways: the typical increases from extreme sampling and increasing the proportion of relevant functional variants ascertained and thereby tested for association. Furthermore, we show that when combining statistical evidence through meta-analysis from an extreme-selected sample and a second separate population-based random sample, power is lower when a traditional sample size weighting is used compared with weighting by the noncentrality parameter.
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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.