Haplotype is fundamental genetic information;it provides essential information for deciphering the functional and etiological roles of genetic variants. Current sequencing and genotyping technologies deliver only half of the genetic information of each individual. They cannot deliver the other half of the information;the structural conformations of alleles or the haplotypes. Existing haplotyping technologies cannot meet the market needs on cost, labor, throughput, accuracy, resolution, and phasing distance;therefore, haplotype information has been absent from all but a handful of genome studies. We propose a cost-efficient technology towards commercialization to meet the unmet market needs for experimental haplotype determination. The proposed research in this grant application is based on the single-chromosome isolation approach that we developed recently. The low-resolution caveat of the first version of this approach has been resolved by our recently published work in which a computational imputation component is incorporated into this pipeline. This Phase I proposal will focus on cost reduction prior to commercialization. Specifically, we will examine a cost-reduction strategy by coupling the single-chromosome isolation with low-depth high-throughput next-generation sequencing of single chromosomes. The minimal sequencing depth requirement of those single chromosomes will be titrated to achieve optimal effectiveness regarding the cost, throughput and data accuracy. The feasibility of this strategy has been demonstrated by our simulated data analysis and by recent publications on single sperm haplotyping and fosmid haplotyping. The objective is to establish and commercial the first technology or service for generating high-throughput, high- resolution and cost-effective chromosomal haplotypes. The commercialized technology will revolutionize genomic studies and genetic studies by providing complete genetic information with both nucleotide sequences and the allele structural conformations (allelic phases or haplotypes).
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|Li, Wenzhi; Xu, Wei; Li, Qiling et al. (2015) References for Haplotype Imputation in the Big Data Era. Mol Biol (Los Angel) 4:|