ArticleAmerican journal of human genetics2016
Determinants of Power in Gene-Based Burden Testing for Monogenic Disorders.
Article 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 26 papers.
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Who cites it
26 citing papers in PubMed.
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- PCDHA9 as a candidate gene for amyotrophic lateral sclerosis.Nature communications · 2024Article
- Single nucleotide polymorphism rs854560 in paraoxonase-1 regulates the cytodifferentiation of human periodontal ligament cells.Frontiers in dental medicine · 2024Article
- Excalibur: A new ensemble method based on an optimal combination of aggregation tests for rare-variant association testing for sequencing data.PLoS computational biology · 2023Article
- Whole exome sequencing reveals genetic landscape associated with left ventricular outflow tract obstruction in Chinese Han population.Frontiers in genetics · 2023Article
- Learning the kernel for rare variant genetic association test.Frontiers in genetics · 2023Article
- Whole exome sequencing study identifies candidate loss of function variants and locus heterogeneity in familial cholesteatoma.PloS one · 2023Article
- Human pluripotent stem cells for the modelling of retinal pigment epithelium homeostasis and disease: A review.Clinical & experimental ophthalmology · 2022Review
- Whole-Exome Sequencing in Congenital Hypothyroidism Due to Thyroid Dysgenesis.Thyroid : official journal of the American Thyroid Association · 2022Article
- Genetic Determinants of Sudden Unexpected Death in Pediatrics.Genetics in medicine : official journal of the American College of Medical Genetics · 2022Article
- Computational Genomics in the Era of Precision Medicine: Applications to Variant Analysis and Gene Therapy.Journal of personalized medicine · 2022Review
- Constitutively active SARM1 variants that induce neuropathy are enriched in ALS patients.Molecular neurodegeneration · 2022Article
- An exome-wide study of renal operational tolerance.Frontiers in medicine · 2022Article
- Novel Mutations and Genes That Impact on Growth in Short Stature of Undefined Aetiology: The EPIGROW Study.Journal of the Endocrine Society · 2020Article
- Germline Genetic Risk Variants for Progressive Multifocal Leukoencephalopathy.Frontiers in neurology · 2020Article
- Rare-variant collapsing analyses for complex traits: guidelines and applications.Nature reviews. Genetics · 2019Review
- Spectral Analysis of Codons in the DNA Sequence of Fragile X Syndrome.Journal of medical systems · 2019Article
- Exome-Based Rare-Variant Analyses in CKD.Journal of the American Society of Nephrology : JASN · 2019Article
- Revisiting Non-BRCA1/2 Familial Whole Exome Sequencing Datasets ImplicatesFrontiers in genetics · 2019Article
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6 authors.
Funding
Abstract
Whole-exome sequencing has enabled new approaches for discovering genes associated with monogenic disorders. One such approach is gene-based burden testing, in which the aggregate frequency of "qualifying variants" is compared between case and control subjects for each gene. Despite substantial successes of this approach, the genetic causes for many monogenic disorders remain unknown or only partially known. It is possible that particular genetic architectures lower rates of discovery, but the influence of these factors on power has not been rigorously evaluated. Here, we leverage large-scale exome-sequencing data to create an empirically based simulation framework to evaluate the impact of key parameters (background variation rates, locus heterogeneity, mode of inheritance, penetrance) on power in gene-based burden tests in the context of monogenic disorders. Our results demonstrate that across genes, there is a wide range in sample sizes needed to achieve power due to differences in the background rate of rare variants in each gene. Increasing locus heterogeneity results in rapid increases in sample sizes needed to achieve adequate power, particularly when individual genes contribute to less than 5% of cases under a dominant model. Interestingly, incomplete penetrance as low as 10% had little effect on power due to the low prevalence of monogenic disorders. Our results suggest that moderate incomplete penetrance is not an obstacle in this gene-based burden testing approach but that dominant disorders with high locus heterogeneity will require large sample sizes. Our simulations also provide guidance on sample size needs and inform study design under various genetic architectures.
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