Cargando…
Leveraging Gene-Level Prediction as Informative Covariate in Hypothesis Weighting Improves Power for Rare Variant Association Studies
Gene-based rare variant association studies (RVASs) have low power due to the infrequency of rare variants and the large multiple testing burden. To correct for multiple testing, traditional false discovery rate (FDR) procedures which depend solely on P-values are often used. Recently, Independent H...
Autores principales: | Ji, Ying, Chen, Rui, Wang, Quan, Wei, Qiang, Tao, Ran, Li, Bingshan |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8872452/ https://www.ncbi.nlm.nih.gov/pubmed/35205424 http://dx.doi.org/10.3390/genes13020381 |
Ejemplares similares
-
Leveraging blood serotonin as an endophenotype to identify de novo and rare variants involved in autism
por: Chen, Rui, et al.
Publicado: (2017) -
Leveraging biological and statistical covariates improves the detection power in epigenome-wide association testing
por: Huang, Jinyan, et al.
Publicado: (2020) -
A Bayesian framework to integrate multi-level genome-scale data for Autism risk gene prioritization
por: Ji, Ying, et al.
Publicado: (2022) -
Fast and covariate-adaptive method amplifies detection power in large-scale multiple hypothesis testing
por: Zhang, Martin J., et al.
Publicado: (2019) -
Openness weighted association studies: leveraging personal genome information to prioritize non-coding variants
por: Song, Shuang, et al.
Publicado: (2021)