Whole-genome haplotyping approaches and genomic medicine

Abstract

Genomic information reported as haplotypes rather than genotypes will be increasingly important for personalized medicine. Current technologies generate diploid sequence data that is rarely resolved into its constituent haplotypes. Furthermore, paradigms for thinking about genomic information are based on interpreting genotypes rather than haplotypes. Nevertheless, haplotypes have historically been useful in contexts ranging from population genetics to disease-gene mapping efforts. The main approaches for phasing genomic sequence data are molecular haplotyping, genetic haplotyping, and population-based inference. Long-read sequencing technologies are enabling longer molecular haplotypes, and decreases in the cost of whole-genome sequencing are enabling the sequencing of whole-chromosome genetic haplotypes. Hybrid approaches combining high-throughput short-read assembly with strategic approaches that enable physical or virtual binning of reads into haplotypes are enabling multi-gene haplotypes to be generated from single individuals. These techniques can be further combined with genetic and population approaches. Here, we review advances in whole-genome haplotyping approaches and discuss the importance of haplotypes for genomic medicine. Clinical applications include diagnosis by recognition of compound heterozygosity and by phasing regulatory variation to coding variation. Haplotypes, which are more specific than less complex variants such as single nucleotide variants, also have applications in prognostics and diagnostics, in the analysis of tumors, and in typing tissue for transplantation. Future advances will include technological innovations, the application of standard metrics for evaluating haplotype quality, and the development of databases that link haplotypes to disease.

Direct determination of diploid genome sequences

“…To correctly understand the biology of a diploid organism, these homologous chromosomes need to be separately represented (or phased), at least at the scale of genes (Muers 2011;Tewhey et al 2011;Glusman et al 2014;Snyder et al 2015). This is required to correctly understand allele-specific expression and compound heterozygosity.…”

SpecHap: a diploid phasing algorithm based on spectral graph theory

“…Recent computational methods for haplotype phase generally fall into three categories (Glusman et al, 2014): 1) haplotype assembly for individual genome (HASH (Bansal et al, 2008), LongRanger (Zheng et al, 2016), HapCut2 (Edge et al, 2017); 2) genetic analysis with inheritance related data (IBD analysis); 3) haplotype inference based on population data (SHAPEIT (Delaneau et al, 2008)). In this study, we only focus on the methodology of the haplotype assembly.…”

Overlap graph-based generation of haplotigs for diploids and polyploids

“…Distinguishing the two (in diploid organisms, such as in most vertebrates) or more than two (in polyploid organisms, such as many plants and some funghi) haplotypes plays an important role in various disciplines. Prominent examples are genetics, where assigning variants to ancestors is key (Tewhey et al, 2014), and medicine, because very often haplotype-specific combinations of variants establish clinically relevant effects, for example, when disease risks have been inherited (Glusman et al, 2014). In general, determining haplotypic sequence and thereby keeping track of ancestry based dependencies is instrumental in many settings.…”