Hidden Markov models for the assessment of chromosomal alterations using high-throughput SNP arrays

TL;DR

This paper enhances Hidden Markov Models for analyzing high-throughput SNP array data to detect chromosomal alterations, integrating multiple data types and confidence measures, with implementation in the VanillaICE R package.

Contribution

It introduces an improved HMM framework that combines copy number, genotype calls, and uncertainty measures for better detection of chromosomal alterations.

Findings

Demonstrates improved detection accuracy with simulated and experimental data.

Shows how confidence scores effectively control smoothing in the analysis.

Provides software implementation in the VanillaICE R package.

Abstract

Chromosomal DNA is characterized by variation between individuals at the level of entire chromosomes (e.g., aneuploidy in which the chromosome copy number is altered), segmental changes (including insertions, deletions, inversions, and translocations), and changes to small genomic regions (including single nucleotide polymorphisms). A variety of alterations that occur in chromosomal DNA, many of which can be detected using high density single nucleotide polymorphism (SNP) microarrays, are linked to normal variation as well as disease and are therefore of particular interest. These include changes in copy number (deletions and duplications) and genotype (e.g., the occurrence of regions of homozygosity). Hidden Markov models (HMM) are particularly useful for detecting such alterations, modeling the spatial dependence between neighboring SNPs. Here, we improve previous approaches that utilize HMM frameworks for inference in high throughput SNP arrays by integrating copy number, genotype calls, and the corresponding measures of uncertainty when available. Using simulated and experimental data, we, in particular, demonstrate how confidence scores control smoothing in a probabilistic framework. Software for fitting HMMs to SNP array data is available in the R package VanillaICE.