A survey of current software for haplotype phase inference

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• Explosion in genetic data from single nucleotide polymorphism (SNP) maps and high-throughput genotyping.
• Emergence of numerous algorithms and software for haplotype phase inference.
• Challenges in obtaining accurate haplotype data through in vitro methods or pedigree analysis.

Purpose of the Study:

• To provide a snapshot of the current state of haplotype phase inference.
• To review algorithms and software, primarily for diallelic SNP data.
• To compare inference methods with alternative approaches like genotype-level analysis.

Main Methods:

• Focus on algorithms for single nucleotide polymorphism (SNP) genotyping data.
• Review of existing and emerging software for haplotype phase inference.
• Consideration of diallelic data due to its current predominance.

Main Results:

• A wide array of new algorithms and software have become available for haplotype inference.
• Haplotype inference offers potential analytic insights compared to genotype-level analysis.
• In vitro or pedigree-based haplotype resolution is often expensive and not always feasible.

Conclusions:

• Haplotype phase inference is a rapidly evolving field crucial for genetic analysis.
• The review highlights the current landscape of SNP-based haplotype inference methods.
• Researchers must weigh the benefits of inferred haplotypes against potential inaccuracies.