RAINBOWR

RAINBOWR performs haplotype-based genome-wide association studies by treating haplotype blocks as SNP-sets and applying kernel-based SNP-set tests to detect rare and complex causal variants.

Key Features:

• SNP-Set Methodology: Treats a haplotype block as an SNP-set and applies kernel-based methods to test multiple SNPs simultaneously without requiring prior haplotype knowledge.
• Control of False Positives: Manages false positive rates to improve the reliability of association results.
• Detection Without Linkage Disequilibrium Dependence: Can identify causal variants when those variants are directly genotyped without relying on linkage disequilibrium.
• Increased Power for Close Causal Variants: Increases power to detect closely located causal variants with opposite effects that conventional methods often miss.
• Detection of Rare and Complex Mechanisms: Detects rare variants and genes with complex genetic architectures involving multiple causal variants.
• Implementation: Implemented as an R package.

Scientific Applications:

• Complex genetic architecture analysis: Applied to studies where understanding intricate genetic architectures is crucial.
• Simulation studies: Applied to simulated phenotypic data to evaluate method performance.
• Plant genomics: Applied to real marker genotype data of Oryza sativa subsp. indica.

Methodology:

Treats haplotype blocks as SNP-sets and applies kernel-based SNP-set tests to simultaneously evaluate multiple SNPs.