Phasing Algorithm That Incorporates Sequencing Read Data To Improve Haplotype Inference

Researchers in Dr. Carlos D. Bustamante's lab have developed a phasing algorithm that incorporates sequencing read information, population and individual genotype data to provide more accurate haplotype reconstruction. Humans carry two copies of every chromosome in their genome: one from each parent. Determining which alleles were inherited together from one parent or the other (phasing) is a critical component of many downstream medical and population studies. Methods have been developed to phase unrelated individuals in the absence of parental information but these methods restrict themselves to genotype, population, or read data alone when reconstructing haplotypes and obtaining better performance has become quite challenging. This algorithm overcomes this limitation by incorporating sequence read information to reconstruct more accurate haplotypes from the genomes of related or unrelated individuals using population level genotype and/or haplotype data (when available).

Stage of Research
A comparative study has demonstrated that this approach yields significantly more accurate results over existing methods. The inclusion of paired end read data has also been shown to be critical for the phasing of rare variants.