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Motivation: Haplotype reconstruction is an important tool for understanding the aetiology of human disease. Haplotyping infers the most likely phase of observed genotypes conditional on constraints imposed by the genotypes of other pedigree members. The results of haplotype reconstruction, when visualized appropriately, show which alleles are identical by descent despite the presence of untyped individuals. When used in concert with linkage analysis, haplotyping can help delineate a locus of interest and provide a succinct explanation for the transmission of the trait locus. Unfortunately, the design choices made by existing haplotype visualization programs do not scale to large numbers of markers. Indeed, following haplotypes from generation to generation requires excessive scrolling back and forth. In addition, the most widely used program for haplotype visualization produces inconsistent recombination artefacts for the X chromosome., Results: To resolve these issues, we developed HaploForge, a novel web application for haplotype visualization and pedigree drawing. HaploForge takes advantage of HTML5 to be fast, portable and avoid the need for local installation. It can accurately visualize autosomal and X-linked haplotypes from both outbred and consanguineous pedigrees. Haplotypes are coloured based on identity by descent using a novel A* search algorithm and we provide a flexible viewing mode to aid visual inspection. HaploForge can currently process haplotype reconstruction output from Allegro, GeneHunter, Merlin and Simwalk., Availability and Implementation: HaploForge is licensed under GPLv3 and is hosted and maintained via GitHub. https://github.com/mtekman/haploforge., Contact: r.kleta@ucl.ac.uk., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com)

Motivation: Linkage analysis remains an important tool in elucidating the genetic component of disease and has become even more important with the advent of whole exome sequencing, enabling the user to focus on only those genomic regions co-segregating with Mendelian traits. Unfortunately, methods to perform multipoint linkage analysis scale poorly with either the number of markers or with the size of the pedigree. Large pedigrees with many markers can only be evaluated with Markov chain Monte Carlo (MCMC) methods that are slow to converge and, as no attempts have been made to exploit parallelism, massively underuse available processing power. Here, we describe SWIFTLINK, a novel application that performs MCMC linkage analysis by spreading the computational burden between multiple processor cores and a graphics processing unit (GPU) simultaneously. SWIFTLINK was designed around the concept of explicitly matching the characteristics of an algorithm with the underlying computer architecture to maximize performance., Results: We implement our approach using existing Gibbs samplers redesigned for parallel hardware. We applied SWIFTLINK to a real-world dataset, performing parametric multipoint linkage analysis on a highly consanguineous pedigree with EAST syndrome, containing 28 members, where a subset of individuals were genotyped with single nucleotide polymorphisms (SNPs). In our experiments with a four core CPU and GPU, SWIFTLINK achieves a 8.5× speed-up over the single-threaded version and a 109× speed-up over the popular linkage analysis program SIMWALK., Availability: SWIFTLINK is available at https://github.com/ajm/swiftlink. All source code is licensed under GPLv3.