Combining haplotypers

TL;DR

This paper explores combining multiple haplotype reconstruction methods to improve accuracy and robustness in gene mapping, addressing the challenge of selecting the best method for different population samples.

Contribution

It introduces several techniques for combining haplotype predictions and demonstrates their effectiveness on real data, outperforming individual methods.

Findings

Combined methods often outperform single methods in accuracy.

Techniques provide robustness against outliers.

Combining methods helps circumvent method selection issues.

Abstract

Statistically resolving the underlying haplotype pair for a genotype measurement is an important intermediate step in gene mapping studies, and has received much attention recently. Consequently, a variety of methods for this problem have been developed. Different methods employ different statistical models, and thus implicitly encode different assumptions about the nature of the underlying haplotype structure. Depending on the population sample in question, their relative performance can vary greatly, and it is unclear which method to choose for a particular sample. Instead of choosing a single method, we explore combining predictions returned by different methods in a principled way, and thereby circumvent the problem of method selection. We propose several techniques for combining haplotype reconstructions and analyze their computational properties. In an experimental study on real-world haplotype data we show that such techniques can provide more accurate and robust reconstructions, and are useful for outlier detection. Typically, the combined prediction is at least as accurate as or even more accurate than the best individual method, effectively circumventing the method selection problem.