HAPPI GWAS: Holistic Analysis with Pre- and Post-Integration GWAS | Zendy

Marianne L. Slaten | Zendy; Yen On Chan | Zendy; Vivek Shrestha | Zendy; Alexander E. Lipka | Zendy; Ruthie Angelovici | Zendy

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HAPPI GWAS: Holistic Analysis with Pre- and Post-Integration GWAS

Author(s) -

Marianne L. Slaten,

Yen On Chan,

Vivek Shrestha,

Alexander E. Lipka,

Ruthie Angelovici

Publication year - 2020

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/btaa589

Subject(s) - genome wide association study , computer science , pipeline (software) , identification (biology) , computational biology , data mining , data science , single nucleotide polymorphism , biology , genetics , gene , operating system , botany , genotype

Advanced publicly available sequencing data from large populations have enabled informative genome-wide association studies (GWAS) that associate SNPs with phenotypic traits of interest. Many publicly available tools able to perform GWAS have been developed in response to increased demand. However, these tools lack a comprehensive pipeline that includes both pre-GWAS analysis, such as outlier removal, data transformation and calculation of Best Linear Unbiased Predictions or Best Linear Unbiased Estimates. In addition, post-GWAS analysis, such as haploblock analysis and candidate gene identification, is lacking.

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