MOTIVATION: Across-chromosome phasing identifies which haplotypes of different chromosomes come from the same parent. This differs from within-chromosome phasing, which uses linkage disequilibrium patterns to determine which alleles were co-inherited within each chromosome but does not match haplotypes across different chromosomes. While across-chromosome phasing can be conducted using genotypes from parents or close relatives, current methods perform poorly for samples of unrelated individuals. Here, we introduce a novel approach for across-chromosome phasing that employs a window-based SNP-similarity metric, eliminating the need for data from close relatives or detection of identical-by-descent haplotypes. RESULTS: Using UK Biobank offspring with both parents genotyped as a gold standard, we evaluated the performance of our method by phasing the offspring without using parental data. In genomic data with no within-chromosome phase errors, our algorithm achieved a mean across-chromosome phasing accuracy of 95%, with 53% of individuals phased perfectly. When data was pre-phased computationally using a standard within-chromosome phasing algorithm, mean accuracy for across-chromosome phasing dropped to 83.1%. Thus, our method is limited primarily by the accuracy of within-chromosome phasing accuracy and can approach near-perfect across-chromosome phasing accuracy as within-chromosome phasing accuracy improves. AVAILABILITY AND IMPLEMENTATION: The implementation was executed within a multi-node computational environment of University of Colorado Boulder Research Computing (Blanca Cluster: https://www.colorado.edu/rc/resources/blanca), employing parallelization techniques in the C programming language. The source code has been made publicly accessible online at https://github.com/emmanuelsapin/AcrossChromosomesPhasing, thereby facilitating reproducibility of the results for researchers with authorized access to the UK Biobank dataset.

MOTIVATION: Pairwise comparison problems arise in many areas of science. In genomics, datasets are already large and getting larger, and so operations that require pairwise comparisons-either on pairs of SNPs or pairs of individuals-are extremely computationally challenging. We propose a generic algorithm for addressing pairwise comparison problems that breaks a large problem (of order n2 comparisons) into multiple smaller ones (each of order n comparisons), allowing for massive parallelization. RESULTS: We demonstrated that this approach is very efficient for calling identical by descent (IBD) segments between all pairs of individuals in the UK Biobank dataset, with a 250-fold savings in time and 750-fold savings in memory over the standard approach to detecting such segments across the full dataset. This efficiency should extend to other methods of IBD calling and, more generally, to other pairwise comparison tasks in genomics or other areas of science. AVAILABILITY AND IMPLEMENTATION: A GitHub page is available at https://github.com/emmanuelsapin with the code to generate data needed for the implementation.

MOTIVATION: Computer simulations are excellent tools for understanding the evolutionary and genetic consequences of complex processes that cannot be analytically predicted and for creating realistic genetic data. There are many software packages that simulate genetic data, but they are typically not fast or memory efficient enough to simulate realistic, individual-level genome-wide SNP/sequence data. RESULTS: GeneEvolve is a user-friendly and efficient population genetics simulator that handles complex evolutionary and life history scenarios and generates individual-level phenotypes and realistic whole-genome sequence or SNP data. GeneEvolve runs forward-in-time, which allows it to provide a wide range of scenarios for mating systems, selection, population size and structure, migration, recombination and environmental effects. The software is designed to use as input data from real or previously simulated phased haplotypes, allowing it to mimic very closely the properties of real genomic data. AVAILABILITY AND IMPLEMENTATION: GeneEvolve is freely available at https://github.com/rtahmasbi/GeneEvolve CONTACT: [email protected] information: Supplementary data are available at Bioinformatics online.