Quantifying unobserved protein-coding variants in human populations provides a roadmap for large-scale sequencing projects
Accurate estimations of the frequency distribution of rare variants are needed to quantify the discovery power and guide large-scale human sequencing projects. This study describes an algorithm called UnseenEst to estimate the distribution of genetic variations using tens of thousands of exomes.
Enregistré dans:
Auteurs principaux: | James Zou, Gregory Valiant, Paul Valiant, Konrad Karczewski, Siu On Chan, Kaitlin Samocha, Monkol Lek, Shamil Sunyaev, Mark Daly, Daniel G. MacArthur |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2016
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/030ddc69bcf0485d89f5ce1d1a30988b |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
STATISTICAL ESTIMATION OF UNOBSERVED ECONOMIC ACTIVITY
par: Galina V. Agentova
Publié: (2017) -
Phillips curve in Brazil: an unobserved components approach
par: Vicente da Gama Machado, et autres
Publié: (2014) -
Using ALoFT to determine the impact of putative loss-of-function variants in protein-coding genes
par: Suganthi Balasubramanian, et autres
Publié: (2017) -
Inferring collective dynamical states from widely unobserved systems
par: Jens Wilting, et autres
Publié: (2018) -
Post-conception heat exposure increases clinically unobserved pregnancy losses
par: Tamás Hajdu, et autres
Publié: (2021)