A survey on haplotyping algorithms for tightly linked markers.

Area of Science:

• Genomics
• Bioinformatics
• Human Genetics

Background:

• The postgenomic era faces challenges in understanding human genetic variation and its role in diseases and drug responses.
• Single nucleotide polymorphisms (SNPs) are key markers for human variation, and haplotype-based methods are promising for association mapping.
• Directly collected genotype data, not haplotype data, is practical, necessitating computational haplotype reconstruction.

Purpose of the Study:

• To review computational methods for haplotype reconstruction.
• To discuss algorithms for inferring haplotypes from genotype data.
• To cover haplotyping strategies for various sample types.

Main Methods:

• Review of statistical haplotyping algorithms.
• Review of combinatorial haplotyping algorithms.
• Analysis of methods using pedigree data, unrelated individuals, and pooled samples.

Main Results:

• Haplotype reconstruction is essential for leveraging genotype data in genetic studies.
• Various statistical and combinatorial algorithms exist for haplotyping.
• The choice of method depends on the available data (pedigree, unrelated, or pooled samples).

Conclusions:

• Accurate haplotype reconstruction is critical for advancing our understanding of human genetic variation and disease.
• Computational methods for haplotyping are actively researched and vital for postgenomic studies.
• This review provides an overview of current haplotyping algorithms and their applications.