Biology subjects

Smith, T. Q.

Publications and source records attributed to Smith, T. Q..

4 recordsLinked to original sources

bioRxiv2025

EGGS: Empirical Genotype Generalizer for Samples

SummaryWe introduce Empirical Genotype Generalizer for Samples (EGGS) which accepts empirical genotypes with missing data and replicates the distribution of missing genotypes along the empirical segment in other replicates. The empirical segment must have a number of sites less than the replicate. In addition, EGGS can remove phase, remove polarization, simulate deamination, simulate sequencing error, create pseudohaploids, and convert between Variant Call Format (VCF), ms-style replicates, and EIGENSTRAT/ANCESTRYMAP. When producing VCF files, EGGS is not limited to biallelic sites and assumes all samples are diploid. Availability and ImplementationEGGS is written in the C programming language. Precompiled executables, source code, the manual, and the analysis conducted in the paper are available at https://github.com/TQ-Smith/EGGS

bioinformatics↗

bioRxiv2025

Selection scans and downstream analysis with selscan

Summary statistics based on Extended Haplotype Homozygosity (EHH) are widely used for inferring positive selection in genomes as a result of their ease of use, computational efficiency, and interpretability. These various summary statistics can be applied to single populations or to pairs of populations, can be used with a genetic recombination map or without, and can be applied to phased or unphased data. Although these statistics are straightforward to compute, there lacks clear descriptions on how they relate to one another, how they should be used, and how their resulting outputs should be interpreted. Here, we provide a comprehensive introduction to selection statistics as they are implemented in the widely used software, selscan. In addition to this detailed guide, we implement enhanced normalization procedures and support for gene-based analyses, enabling users to translate selection signals captured by these statistics into gene-level interpretations using BED annotation files, facilitating biologically meaningful insights. We demonstrate the behavior of such statistics on simulated data and highlight best practices by performing an example downstream analysis on data from the 1000 Genomes Project using new features in selscan v3.0. We hope these guidelines will foster reproducibility in the evolutionary genomics community. Precompiled executables and source code for selscan v3.0 can be found at https://github.com/szpiech/selscan.

evolutionary biology↗

bioRxiv2025

Evolutionary Influences on Local Patterns of Genetic Relatedness

Dimensionality reduction methods, such as Principal Components Analysis (PCA) or Multi-dimensional Scaling (MDS), when applied to genomic data, help to visualize the relatedness of individuals in lower dimensional space and are ubiquitous in population-genetic studies. These analyses use genome-wide patterns of variation to provide an "average" picture of genetic structure and relatedness. However, evolutionary processes result in different patterns of relationship among samples in local genomic regions as compared to the genome-wide aggregate. Recently, these local patterns of relatedness have been used to identify regions under selection and inverted segments. Here, we propose a unifying method to dissect these local deviations in genetic relatedness. Our method, Local Decomposition and Similarity to All Regions (LODESTAR), uses Procrustes Analysis to assess the similarity between local MDS results computed using pairwise allele sharing distances or a set of user-defined points. Given two sets of points, Procrustes Analysis computes the optimal rotation and scaling that fits one set of points onto the other, while maintaining the relative relationship between points within both sets. We use the Procrustes statistic to measure the similarity between the two sets of points. We show how this method can be used to explore local relatedness patterns that mirror sampling geography or population stratification by performing Procrustes analysis between local relatedness plots and coordinates representing sampling geography or between local relatedness plots and the genome-wide relatedness plot, respectively. In addition, we show how the variance of samples in lower-dimensional space can capture regions lacking population structure and inverted segments.

evolutionary biology↗

bioRxiv2025

Fast and Memory-Efficient Dynamic Programming Approach for Large-Scale EHH-Based Selection Scans

Haplotype-based statistics are widely used for finding genomic regions under positive selection. At the heart of many such statistics is the computation of extended haplotype homozygosity (EHH), which captures the decay of homozygosity away from a focal site. This computation, repeated for potentially millions of sites, is computationally demanding, as it involves tracking counts of unique haplotypes iteratively over long genomic distances and across many individuals. Because of these computational challenges, existing tools do not scale well when applied to large-scale population datasets, such as the 1000 Genomes Project, or the UK Biobank with 500,000 individuals. Optimizing computation becomes crucial when data sets grow large, especially when handling large sample sizes or generating training data for machine learning algorithms. https://github.com/szpiech/selscan Here, we propose a dynamic programming algorithm that substantially improves runtime and memory usage over existing tools on both real and simulated data. On real phased data, we achieve 5-50x speedup with minimal memory footprint. Our simulations show an even more pronounced performance gap with large populations (up to 15x speedup and 46x memory reduction). EHH-based statistics designed for unphased genotypes run an order of magnitude faster, and multi-parameter support results in 20x runtime improvement. Source code and binaries are available at https://github.com/szpiech/selscan as selscan v2.1.

genomics↗