Power analysis and sample size estimation for sequence-based association studies | Zendy

Gao T. Wang | Zendy; Biao Li | Zendy; Regie P. Lyn Santos-Cortez | Zendy; Bo Peng | Zendy; Suzanne M. Leal | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Power analysis and sample size estimation for sequence-based association studies

Author(s) -

Gao T. Wang,

Biao Li,

Regie P. Lyn Santos-Cortez,

Bo Peng,

Suzanne M. Leal

Publication year - 2014

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/btu296

Subject(s) - executable , sample size determination , genetic association , statistical power , computer science , data mining , association (psychology) , software , source code , documentation , statistics , computational biology , biology , genetics , mathematics , single nucleotide polymorphism , programming language , philosophy , epistemology , genotype , gene

Statistical methods have been developed to test for complex trait rare variant (RV) associations, in which variants are aggregated across a region, which is typically a gene. Power analysis and sample size estimation for sequence-based RV association studies are challenging because of the necessity to realistically model the underlying allelic architecture of complex diseases within a suitable analytical framework to assess the performance of a variety of RV association methods in an unbiased manner.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research