PopCluster: an algorithm to identify genetic variants with ethnicity-dependent effects | Zendy

Anastasia Gurinovich | Zendy; Harold Bae | Zendy; John J. Farrell | Zendy; Stacy L. Andersen | Zendy; Stefano Monti | Zendy; Annibale Alessandro Puca | Zendy; Gil Atzmon | Zendy; Nir Barzilai | Zendy; Thomas T. Perls | Zendy; Paola Sebastiani | Zendy

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PopCluster: an algorithm to identify genetic variants with ethnicity-dependent effects

Author(s) -

Anastasia Gurinovich,

Harold Bae,

John J. Farrell,

Stacy L. Andersen,

Stefano Monti,

Annibale Alessandro Puca,

Gil Atzmon,

Nir Barzilai,

Thomas T. Perls,

Paola Sebastiani

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/btz017

Subject(s) - computer science , cluster analysis , genetic association , population , tree (set theory) , data mining , machine learning , biology , genotype , genetics , medicine , single nucleotide polymorphism , gene , mathematics , mathematical analysis , environmental health

Over the last decade, more diverse populations have been included in genome-wide association studies. If a genetic variant has a varying effect on a phenotype in different populations, genome-wide association studies applied to a dataset as a whole may not pinpoint such differences. It is especially important to be able to identify population-specific effects of genetic variants in studies that would eventually lead to development of diagnostic tests or drug discovery.

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