  Accurate identification of haplotypes in sequenced human genomes can provide
invaluable information about population demography and fine-scale correlations
along the genome, thus empowering both population genomic and medical
association studies. Yet phasing unrelated individuals remains a challenging
problem. Incorporating available data from high throughput sequencing into
traditional statistical phasing approaches is a promising avenue to alleviate
these issues. We present a novel statistical method that expands on an existing
graphical haplotype reconstruction method (shapeIT) to incorporate phasing
information from paired-end read data. The algorithm harnesses the haplotype
graph information estimated by shapeIT from genotypes across the population and
refines haplotype likelihoods for a given individual to be compatible with the
sequencing data. Applying the method to HapMap individuals genotyped on the
Affymetrix Axiom chip at 7,745,081 SNPs and on a trio sequenced by Complete
Genomics, we found that the inclusion of paired end read data significantly
improved phasing, with reductions in switch error on the order of 4-15% against
shapeIT across all panels. As expected, the improvements were found to be most
significant at sites harboring rare variants; furthermore, we found that longer
read sizes and higher throughput translated to greater decreases in switching
error, as did higher variance in the size of the insert separating the two
reads--suggesting that multi-platform next generation sequencing may be
exploited to yield particularly accurate haplotypes. Overall, the phasing
improvements afforded by this new method highlight the power of integrating
sequencing read information and population genotype data for reconstructing
haplotypes in unrelated individuals.
