Bayesian estimation of genetic regulatory effects in high-throughput reporter assays
Author(s) -
William H. Majoros,
YoungSook Kim,
Alejandro Barrera,
Fan Li,
Xingyan Wang,
Sarah J. Cunningham,
Graham D. Johnson,
Cong Guo,
William L. Lowe,
Denise Scholtens,
M. Geoffrey Hayes,
Timothy E. Reddy,
Andrew S. Allen
Publication year - 2019
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btz545
Subject(s) - linkage disequilibrium , computational biology , negative binomial distribution , bayesian probability , inference , computer science , biology , sample size determination , bayes' theorem , genetic association , multiple comparisons problem , statistical hypothesis testing , statistical power , allele , genetics , poisson distribution , gene , statistics , artificial intelligence , mathematics , haplotype , single nucleotide polymorphism , genotype
High-throughput reporter assays dramatically improve our ability to assign function to noncoding genetic variants, by measuring allelic effects on gene expression in the controlled setting of a reporter gene. Unlike genetic association tests, such assays are not confounded by linkage disequilibrium when loci are independently assayed. These methods can thus improve the identification of causal disease mutations. While work continues on improving experimental aspects of these assays, less effort has gone into developing methods for assessing the statistical significance of assay results, particularly in the case of rare variants captured from patient DNA.
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