QuantiSNP

QuantiSNP detects copy number variations and maps chromosomal copy-number changes from Illumina BeadArray whole-genome SNP genotyping data using an Objective Bayes Hidden-Markov Model (OB-HMM).

Key Features:

• Objective Bayes Hidden-Markov Model (OB-HMM): Implements an OB-HMM framework for state-based detection of CNVs and segmental aneuploidies.
• Objective Bayes hyperparameter selection: Uses Objective Bayes measures to set hyperparameters in prior distributions via a re-sampling framework.
• Type I error calibration: Calibrates the model to maintain a fixed Type I error rate.
• Probabilistic quantification: Provides probabilistic assessments of state classifications to quantify uncertainty in CNV calls and refine breakpoint mapping.
• Breakpoint validation: Demonstrates superior validation of breakpoint boundaries compared with Beadstudio and Illumina's proprietary software.
• Adaptability: Originally developed for Illumina BeadArray SNP data and adaptable to other genotyping platforms.
• Parameter optimization: Optimizes remaining parameters by maximum marginal likelihood using prior training data with known structures.

Scientific Applications:

• Clinical genetics: Detection and mapping of CNVs and segmental aneuploidies for diagnostic and research analyses in clinical genetics.
• Cancer research: Identification and mapping of chromosomal alterations and segmental aneuploidies relevant to tumor genomics.
• Disease association studies: Discovery and validation of novel and known CNVs for studies of genetic susceptibility and association.

Methodology:

Models SNP genotyping data with an Objective Bayes Hidden-Markov Model (OB-HMM); sets certain hyperparameters using Objective Bayes measures within a re-sampling framework; and optimizes other parameters by maximum marginal likelihood on prior training data with known structures.