MADloy

MADloy detects mosaic loss of chromosome Y (mLOY) from SNP-array intensity data by modeling reference populations and integrating B-deviation to provide robust mLOY calling.

Key Features:

• Integration of existing methods: Incorporates previously established methods for detecting mLOY to leverage prior algorithms and metrics.
• Reference-population modeling: Optimizes mLOY calling by accurately modeling the reference population without mLOY status.
• B-deviation integration: Integrates B-deviation information into the calling procedure to improve discrimination of mLOY signal.
• Improved accuracy: Experimental validation reported superior accuracy in mLOY detection compared with earlier methods.
• Enhanced statistical power: Simulation studies and analyses of real datasets demonstrated increased power to detect associations between mLOY and phenotypes.
• Longitudinal cellularity detection: Detects changes in mLOY cellularity over time, exemplified by tracking increases in blood samples from 18 individuals over three years.
• Tissue-specific assessment: Identifies sub-optimal detection in saliva (41% optimal) and highlights suitability differences between blood and saliva matrices.
• Gene and pathway analysis support: Facilitates identification of down-regulated genes on chromosome Y in tumors such as kidney and bladder cancers and supports pathway analyses.

Scientific Applications:

• Large epidemiological studies: Provides robust mLOY calls for population-scale analyses of male-specific genomic variation.
• Association studies: Enables detection of associations between mLOY and diseases, age-related disorders, cancer, and male mortality outcomes.
• Longitudinal monitoring: Supports longitudinal assessment of mLOY cellularity changes in blood samples over time.
• Tumor genomics and transcriptomics: Facilitates study of down-regulated chromosome Y genes and pathway alterations in cancers such as kidney and bladder.
• Tissue-specific evaluation: Allows comparison of mLOY detectability across matrices, including blood versus saliva.

Methodology:

Analyzes SNP-array intensity data, models a reference population without mLOY status, integrates B-deviation information, incorporates previously established mLOY detection methods, and applies simulation studies and real-dataset analyses for validation and power assessment.