A General Approach for Haplotype Phasing across the Full Spectrum of Relatedness

Many existing cohorts contain a range of relatedness between genotyped individuals, either by design or by chance. Haplotype estimation in such cohorts is a central step in many downstream analyses. Using genotypes from six cohorts from isolated populations and two cohorts from non-isolated populations, we have investigated the performance of different phasing methods designed for nominally ‘unrelated’ individuals. We find that SHAPEIT2 produces much lower switch error rates in all cohorts compared to other methods, including those designed specifically for isolated populations. In particular, when large amounts of IBD sharing is present, SHAPEIT2 infers close to perfect haplotypes. Based on these results we have developed a general strategy for phasing cohorts with any level of implicit or explicit relatedness between individuals. First SHAPEIT2 is run ignoring all explicit family information. We then apply a novel HMM method (duoHMM) to combine the SHAPEIT2 haplotypes with any family information to infer the inheritance pattern of each meiosis at all sites across each chromosome. This allows the correction of switch errors, detection of recombination events and genotyping errors. We show that the method detects numbers of recombination events that align very well with expectations based on genetic maps, and that it infers far fewer spurious recombination events than Merlin. The method can also detect genotyping errors and infer recombination events in otherwise uninformative families, such as trios and duos. The detected recombination events can be used in association scans for recombination phenotypes. The method provides a simple and unified approach to haplotype estimation, that will be of interest to researchers in the fields of human, animal and plant genetics.Author Summary: Every individual carries two copies of each chromosome (haplotypes), one from each of their parents, that consist of a long sequence of alleles. Modern genotyping technologies do not measure haplotypes directly, but the combined sum (or genotype) of alleles at each site. Statistical methods are needed to infer (or phase) the haplotypes from the observed genotypes. Haplotype estimation is a key first step of many disease and population genetic studies. Much recent work in this area has focused on phasing in cohorts of nominally unrelated individuals. So called ‘long range phasing’ is a relatively recent concept for phasing individuals with intermediate levels of relatedness, such as cohorts taken from population isolates. Methods also exist for phasing genotypes for individuals within explicit pedigrees. Whilst high quality phasing techniques are available for each of these demographic scenarios, to date, no single method is applicable to all three. In this paper, we present a general approach for phasing cohorts that contain any level of relatedness between the study individuals. We demonstrate high levels of accuracy in all demographic scenarios, as well as the ability to detect (Mendelian consistent) genotyping error and recombination events in duos and trios, the first method with such a capability.