Rapid genotype refinement for whole-genome sequencing data using multi-variate normal distributions | Zendy

Rudy Arthur | Zendy; Jared O’Connell | Zendy; Ole Schulz-Trieglaff | Zendy; Anthony J. Cox | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Rapid genotype refinement for whole-genome sequencing data using multi-variate normal distributions

Author(s) -

Rudy Arthur,

Jared O’Connell,

Ole Schulz-Trieglaff,

Anthony J. Cox

Publication year - 2016

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/btw097

Subject(s) - computer science , source code , random variate , multivariate normal distribution , linkage disequilibrium , multivariate statistics , gaussian , linkage (software) , markov chain , algorithm , data mining , genotype , statistics , biology , machine learning , mathematics , genetics , haplotype , random variable , physics , quantum mechanics , gene , operating system

Whole-genome low-coverage sequencing has been combined with linkage-disequilibrium (LD)-based genotype refinement to accurately and cost-effectively infer genotypes in large cohorts of individuals. Most genotype refinement methods are based on hidden Markov models, which are accurate but computationally expensive. We introduce an algorithm that models LD using a simple multivariate Gaussian distribution. The key feature of our algorithm is its speed.

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