Improved Identification of Large-effect Rare Genetic Variants using Haplotype Aggregated Allele-specific Expression...

Area of Science:

• Genomics
• Statistical Genetics

Background:

• Allele-specific expression (ASE) outlier detection identifies regulatory variants but faces challenges with data sparsity and noise in low-count genes.
• Genome phasing can aggregate ASE signals along haplotypes, mitigating sparsity and noise.
• Existing statistical tools lack robust methods for utilizing haplotype-level ASE data in rare variant interpretation.

Purpose of the Study:

• To introduce ANEVA-h, a novel statistical tool for quantifying genetic variation in gene expression from haplotype-level ASE data.
• To enable more accurate and comprehensive detection of regulatory effects by analyzing population-level haplotype ASE data.
• To facilitate the integration of haplotype-level ASE outlier testing into rare variant interpretation pipelines.

Main Methods:

• Development and application of ANEVA-h for analyzing haplotype-level ASE data.
• Integration with a compatible dosage outlier test.
• Application to GTEx project data and clinical cohorts (neuromuscular and congenital heart disease).
• Analysis of globally diverse populations to assess ancestry impact.

Main Results:

• ANEVA-h demonstrated over a 2-fold increase in testable genes compared to existing methods.
• Reduced spurious outlier calls and improved enrichment for rare, high-impact variants.
• Enhanced gene prioritization in clinical cohorts, identifying candidate diagnoses missed by other tools.
• Characterization of ancestry background effects on reference and test populations.

Conclusions:

• ANEVA-h significantly improves the detection of genetic regulatory variants by utilizing haplotype-level ASE data.
• The tool enhances gene prioritization and diagnostic capabilities in clinical settings.
• ANEVA-h provides essential tools and data for advancing rare variant interpretation pipelines, especially in diverse populations.