ArticleBiometrics2015
Empirical Bayes scan statistics for detecting clusters of disease risk variants in genetic studies.
Article in Biometrics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Simultaneous Detection of Signal Regions Using Quadratic Scan Statistics With Applications to Whole Genome Association Studies.Journal of the American Statistical Association · 2022Article
- Dynamic Scan Procedure for Detecting Rare-Variant Association Regions in Whole-Genome Sequencing Studies.American journal of human genetics · 2019Article
- A subregion-based burden test for simultaneous identification of susceptibility loci and subregions within.Genetic epidemiology · 2018Article
- Rare RNF213 variants in the C-terminal region encompassing the RING-finger domain are associated with moyamoya angiopathy in Caucasians.European journal of human genetics : EJHG · 2017Article
- Autoencoder-Transformed Transcriptome Improves Genotype-Phenotype Association Studies.IEEE transactions on computational biology and bioinformaticsArticle
Corrections and comments
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Authors and funding
2 authors.
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Abstract
Recent developments of high-throughput genomic technologies offer an unprecedented detailed view of the genetic variation in various human populations, and promise to lead to significant progress in understanding the genetic basis of complex diseases. Despite this tremendous advance in data generation, it remains very challenging to analyze and interpret these data due to their sparse and high-dimensional nature. Here, we propose novel applications and new developments of empirical Bayes scan statistics to identify genomic regions significantly enriched with disease risk variants. We show that the proposed empirical Bayes methodology can be substantially more powerful than existing scan statistics methods especially so in the presence of many non-disease risk variants, and in situations when there is a mixture of risk and protective variants. Furthermore, the empirical Bayes approach has greater flexibility to accommodate covariates such as functional prediction scores and additional biomarkers. As proof-of-concept we apply the proposed methods to a whole-exome sequencing study for autism spectrum disorders and identify several promising candidate genes.
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Registered trials
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