cgmisc: enhanced genome-wide association analyses and visualization | Zendy

Marcin Kierczak | Zendy; Jagoda Jabłońska | Zendy; Simon K. G. Forsberg | Zendy; Matteo Bianchi | Zendy; Katarina Tengvall | Zendy; Mats E. Pettersson | Zendy; Veronika Scholz | Zendy; Jennifer R. S. Meadows | Zendy; Patric Jern | Zendy; Örjan Carlborg | Zendy; Kerstin LindbladToh | Zendy

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cgmisc: enhanced genome-wide association analyses and visualization

Author(s) -

Marcin Kierczak,

Jagoda Jabłońska,

Simon K. G. Forsberg,

Matteo Bianchi,

Katarina Tengvall,

Mats E. Pettersson,

Veronika Scholz,

Jennifer R. S. Meadows,

Patric Jern,

Örjan Carlborg,

Kerstin LindbladToh

Publication year - 2015

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/btv426

Subject(s) - genome wide association study , visualization , computer science , software , genetic association , r package , computational biology , data mining , data science , biology , genetics , single nucleotide polymorphism , genotype , programming language , gene

High-throughput genotyping and sequencing technologies facilitate studies of complex genetic traits and provide new research opportunities. The increasing popularity of genome-wide association studies (GWAS) leads to the discovery of new associated loci and a better understanding of the genetic architecture underlying not only diseases, but also other monogenic and complex phenotypes. Several softwares are available for performing GWAS analyses, R environment being one of them.

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