HAPPI_GWAS

HAPPI_GWAS provides an R-based pipeline for pre-GWAS preprocessing, genome-wide association analysis, and post-GWAS haploblock and candidate gene identification to detect SNP associations with phenotypic traits.

Key Features:

• Implementation: Implemented in the R programming language.
• Pre-GWAS Analysis: Includes outlier removal, data transformation, and calculation of Best Linear Unbiased Predictions (BLUPs) or Best Linear Unbiased Estimates (BLUEs).
• Integrated Pipeline: Integrates pre-GWAS preprocessing, GWAS analysis, and post-GWAS steps into a single automated pipeline.
• Post-GWAS Analysis: Performs haploblock analysis and candidate gene identification to identify linkage disequilibrium regions and potential causal genes.
• Scalability: Capable of handling large datasets suitable for high-throughput genomic studies.

Scientific Applications:

• SNP–trait association mapping: Identification of associations between single nucleotide polymorphisms (SNPs) and phenotypic traits.
• Candidate gene discovery: Prioritization of genes located in haploblocks or regions of interest identified post-GWAS.
• Complex trait analysis: Investigation of genetic variants contributing to complex trait architecture.
• High-throughput sequencing studies: Application to large-scale sequencing datasets for genome-wide association analyses.

Methodology:

Performs outlier removal, data transformation, calculation of BLUPs or BLUEs, GWAS analysis, haploblock analysis, and candidate gene identification and is implemented in R with capability to handle large datasets.