RASQUAL

RASQUAL maps quantitative trait loci (QTLs) by jointly modeling population-level genotype effects and allele-specific (cis-haplotype) signals in high-throughput next-generation sequencing data (RNA-seq, DNaseI-seq, ChIP-seq, ATAC-seq) to improve localization of causal variants.

Key Features:

• Dual-Level QTL Mapping: Analyzes both population-level differences between individuals and allelic differences between cis-haplotypes within individuals.
• Integration with NGS Data Types: Processes RNA-seq, DNaseI-seq, ChIP-seq, and ATAC-seq data for QTL discovery across assays.
• Robust Bias Modeling: Incorporates robust modeling of biases inherent in next-generation sequencing data within a unified probabilistic framework.
• Improved Causal Variant Localization: Enhances precision of causal variant localization compared to existing methods.
• Mapping Chromatin Accessibility QTLs (caQTLs): Was used to map caQTLs from ATAC-seq in a European population, identifying 2,706 independent caQTLs at an FDR of 10%.
• Identification of Multipeak caQTLs: Detects multipeak caQTLs that span multiple independent open chromatin regions and links distal regulatory elements with gene promoters.

Scientific Applications:

• Functional Genomics: Improves interpretation of noncoding variation by integrating allele-specific and population signals.
• Disease Genetics: Enables finer mapping of disease-associated variants via improved causal variant localization.
• Regulatory Element Mapping: Facilitates linking regulatory elements with gene promoters and mapping chromatin accessibility QTLs.

Methodology:

Implements a unified probabilistic framework that integrates genetic effects and allele-specific quantitation while modeling biases in NGS data and performing dual-level (population and cis-haplotype) analysis.