Biology subjects

Rodrigues, M. F.

Publications and source records attributed to Rodrigues, M. F..

12 recordsLinked to original sources

bioRxiv2026

GPU accelerated population genetics statistics using pg_gpu

Population genetics summary statistics--diversity, divergence, linkage disequilibrium, selection scans, and dimensionality reduction--are fundamental across human, agricultural, and ecological genomics. As whole-genome sequencing datasets have grown to hundreds of thousands of individuals, the cost of computing these statistics on conventional CPU implementations has become a major bottleneck: windowed scans of a single chromosome arm can take hours to days, and computation of pairwise linkage-disequilibrium statistics useful for demographic inference scales as O(n2) in sample size, often exceeding wall-clock budgets entirely. We present pg_gpu, a Python library implementing a comprehensive catalog of population-genetics summary statistics as fused CUDA kernels on NVIDIA GPUs. pg_gpu covers eleven categories spanning diversity and neutrality tests, divergence, admixture, the site-frequency spectrum, linkage disequilibrium, haplotype-based selection scans, dimensionality reduction (PCA, randomized PCA, local PCA / lostruct), distance distributions, relatedness, resampling, and a generalized weighted-SFS framework for custom{omega} estimators. On the full Ag1000G Phase 3 chromosome 3R arm (2,940 haplotypes, 10.9 million variants) pg_gpu agrees with scikit-allel and PLINK2 to machine precision while delivering a median 139x and maximum 1,096x speedup. For the multi-population LD statistics used by moments for demographic inference, pg_gpu is a drop-in replacement that yields a ~1,750-fold speedup over the native implementation. Whole chromosome arm scans, lostruct screens, and calculation of LD statistics complete on a single NVIDIA A100 in seconds to a few minutes.

evolutionary biology↗

bioRxiv2026

Census and genetic analysis of the United States marmoset population

The common marmoset (Callithrix jacchus), a small monkey native to Brazil, has been used as a biomedical model in the United States (US) since the 1950s, yet the origins, genomic diversity, and population structure of current colonies remain poorly defined. Through the NIH Marmoset Coordinating Center, we registered and sampled most US research marmosets ([~]2,300 living animals) and assembled pedigrees and historical records for >10,000 individuals. We present a resource of >800 whole-genome sequences, largely from US colonies. These data reveal an unexpected population structure that predates the establishment of research colonies. Indeed, this population structure mirrors variation found in marmosets across Brazil. Leveraging sequenced families, we generate the first pedigree-based recombination map and improved estimates of de novo mutation processes for this species. Our insights into genetic diversity, structure, and inbreeding will guide colony management, inform disease modelling and strengthen the marmosets standing as a biomedical model. Further, this work demonstrates how coordinated efforts across colonies can enable a self-sustaining "living laboratory", supporting data sharing and well-powered studies beyond the reach of single institutions.

genetics↗

bioRxiv2026

A Complete Genome for the Common Marmoset

The common marmoset is a New World monkey (NWM) commonly used as a model organism to investigate questions in primate evolution and human disease, including Alzheimers and other neurodegenerative diseases, as well as neuropsychiatric disorders. Here we present the first telomere-to-telomere (T2T) reference genome for the common marmoset, adding over 88 Mb of sequence and resolving challenging genomic regions. An additional near-T2T assembly from a second unrelated individual yields a total of four high-quality haplotypes for analysis. The improved contiguity and accuracy of these assemblies enable unprecedented insights into complex and rapidly evolving genomic regions such as centromeres, sex chromosomes, ribosomal DNA (rDNA) structure, and the major histocompatibility complex (MHC). We fully resolved all marmoset centromeres, uncovering dimeric alpha satellites with chromosomal specificity and stratified inactive layers documenting ancestral centromere turnover. We assembled six acrocentric autosomes with gene-poor, satellite-rich short arms and provide evidence that most of them can harbor rDNA and all of them share large pseudo-homologous regions (PHRs). The Y chromosome, but not the X chromosome, carries active rDNA and PHRs, and the rDNA copy number is sexually dimorphic. Chromosomes that share PHRs also share closely related centromeric satellite DNA, supporting a model of ongoing recombinational exchange between heterologous chromosomes facilitated by rDNA. We discovered multiple novel, marmoset-specific MHC genes that are predicted to protect against pathogens encountered in its environment. Leveraging this complete reference, we further identified over 500 transcribed genes with transcript models or expansions specific to the marmoset lineage. Together with additional long-read marmoset assemblies, these genomes were used to construct a marmoset pangenome, providing a robust reference framework for short-read mapping across diverse individuals. This resource will improve the utility of the common marmoset as a biomedical model organism and fill key gaps in our understanding of primate evolution.

genomics↗

bioRxiv2025

Gene flow weakens genomic clines while selection maintains adaptive loci: two decades of evolution in Drosophila melanogaster natural populations

Spatial genetic clines suggest local adaptation, but whether they persist or erode over evolutionary time remains largely unknown. Here, we use a unique genomic time series collected over several decades to quantify how gene flow and selection jointly shape spatial genomic structure. We analyzed pooled whole-genome sequencing from 16 natural populations of Drosophila melanogaster sampled along the North American east coast between 1997 and 2023, spanning hundreds of generations. We find pronounced homogenization of populations over time, accompanied by a genome-wide loss of clinal variants. Polymorphisms that remained clinal were less steep on chromosome 3R, reflecting declines in the frequency and clinality of major chromosomal inversions. Genomic context modulates this process: clinal SNPs are preferentially lost in regions of high recombination, whereas remaining clines are increasingly concentrated in coding and splice-associated sites. Despite widespread erosion of spatial structure, we detect signatures of selection shared across space at detoxification loci, including cytochrome P450 genes linked to insecticide resistance. Together, our results show widespread homogenization of neutral spatial variation consistent with gene flow, while spatially varying selection has preserved a subset of functionally important loci.

evolutionary biology↗

bioRxiv2025

Bridging forward-in-time and coalescent simulations using pyslim

Ancestral Recombination Graphs (ARGs) provide an expressive and compact way to represent genetic variation data generated by simulations embedded within its genealogical history, and it can dramatically speed up simulations. The fact that the ARG records genealogical as well as genomic information opens up the possibility for a number of new analysis and simulation techniques. Here, we aim to introduce the reader to this deep source of information as produced by the forwards simulator SLiM. SLiM records the ARG using the tree sequence format, which can be manipulated using the tskit and pyslim python packages. We first describe the information that SLiM records in the tree sequence, then provide several examples that use the tree sequence as a format to losslessly pass population states between simulators: recapitation of a forwards simulation with coalescent simulation; initiation of a forwards simulation using the results of a coalescent simulation; and parallelization of simulations, first across branches of a phylogenetic tree, and then across the populations of parasites infecting different hosts.

evolutionary biology↗

bioRxiv2025

SimHumanity: Using SLiM 5.0 to run whole-genome simulations of human evolution

The reconstruction of human evolutionary history has undergone repeated advances, each made possible by methodological innovations. In recent decades, genetic and genomic data played a central role in the reconstruction of major evolutionary events such as the out-of-Africa migration, and genetic simulations of human evolutionary history have come to play a major role in testing more specific hypotheses including proposed patterns of migration and admixture with archaic hominins. Increasing computational power has allowed human evolutionary history to be modeled at ever-larger scales, but simulations that encompass the complete human genome, including sex chromosomes and mitochondrial DNA, have been difficult due to the lack of support for whole-genome models in commonly used evolutionary simulation frameworks. With the recent introduction of SLiM 5 such simulations are now straightforward to construct, allowing the easy simulation of humans at whole-genome scale under different demographic models and evolutionary dynamics. We here present three versions of a reusable, customizable, open-source SLiM 5 model for simulating the molecular evolution of the full human genome. We also show some simple analyses of results from the model, to illustrate its utility. We hope this model, which we have nicknamed "SimHumanity" in jest, will facilitate further progress in the field of human evolutionary simulations.

evolutionary biology↗

bioRxiv2025

Accessible, realistic genome simulation with selection using stdpopsim

Selection is a fundamental evolutionary force that shapes patterns of genetic variation across species. However, simulations incorporating realistic selection along heterogeneous genomes in complex demographic histories are challenging, limiting our ability to benchmark statistical methods aimed at detecting selection and to explore theoretical predictions. stdpopsim is a community-maintained simulation library that already provides an extensive catalog of species-specific population genetic models. Here we present a major extension to the stdpopsim framework that enables simulation of various modes of selection, including background selection, selective sweeps, and arbitrary distributions of fitness effects (DFE) acting on annotated subsets of the genome (for instance, exons). This extension maintains stdpopsims core principles of reproducibility and accessibility while adding support for species-specific genomic annotations and published DFE estimates. We demonstrate the utility of this framework by comparing methods for demographic inference, DFE estimation, and selective sweep detection across several species and scenarios. Our results demonstrate the robustness of demographic inference methods to selection on linked sites, reveal the sensitivity of DFE-inference methods to model assumptions, and show how genomic features, like recombination rate and functional sequence density, influence power to detect selective sweeps. This extension to stdpopsim provides a powerful new resource for the population genetics community to explore the interplay between selection and other evolutionary forces in a reproducible, user-friendly framework.

evolutionary biology↗

bioRxiv2023

Shared evolutionary processes shape landscapes of genomic variation in the great apes

For at least the past five decades population genetics, as a field, has worked to describe the precise balance of forces that shape patterns of variation in genomes. The problem is challenging because modelling the interactions between evolutionary processes is difficult, and different processes can impact genetic variation in similar ways. In this paper, we describe how diversity and divergence between closely related species change with time, using correlations between landscapes of genetic variation as a tool to understand the interplay between evolutionary processes. We find strong correlations between landscapes of diversity and divergence in a well sampled set of great ape genomes, and explore how various processes such as incomplete lineage sorting, mutation rate variation, GC-biased gene conversion and selection contribute to these correlations. Through highly realistic, chromosome-scale, forward-in-time simulations we show that the landscapes of diversity and divergence in the great apes are too well correlated to be explained via strictly neutral processes alone. Our best fitting simulation includes both deleterious and beneficial mutations in functional portions of the genome, in which 9% of fixations within those regions is driven by positive selection. This study provides a framework for modelling genetic variation in closely related species, an approach which can shed light on the complex balance of forces that have shaped genetic variation.

evolutionary biology↗

bioRxiv2022

Expanding the stdpopsim species catalog, and lessons learned forrealistic genome simulations

Simulation is a key tool in population genetics for both methods development and empirical research, but producing simulations that recapitulate the main features of genomic data sets remains a major obstacle. Today, more realistic simulations are possible thanks to large increases in the quantity and quality of available genetic data, and to the sophistication of inference and simulation software. However, implementing these simulations still requires substantial time and specialized knowledge. These challenges are especially pronounced for simulating genomes for species that are not well-studied, since it is not always clear what information is required to produce simulations with a level of realism sufficient to confidently answer a given question. The community-developed framework stdpopsim seeks to lower this barrier by facilitating the simulation of complex population genetic models using up-to-date information. The initial version of stdpopsim focused on establishing this framework using six well-characterized model species (Adrion et al., 2020). Here, we report on major improvements made in the new release of stdpopsim (version 0.2), which includes a significant expansion of the species catalog and substantial additions to simulation capabilities. Features added to improve the realism of the simulated genomes include non-crossover recombination and provision of species-specific genomic annotations. Through community-driven efforts, we expanded the number of species in the catalog more than three-fold and broadened coverage across the tree of life. During the process of expanding the catalog, we have identified common sticking points and developed best practices for setting up genome-scale simulations. We describe the input data required for generating a realistic simulation, suggest good practices for obtaining the relevant information from the literature, and discuss common pitfalls and major considerations. These improvements to stdpopsim aim to further promote the use of realistic whole-genome population genetic simulations, especially in non-model organisms, making them available, transparent, and accessible to everyone.

bioinformatics↗

bioRxiv2021

The origin and evolution of loqs2: a gene encoding an antiviral dsRNA binding protein in Aedes mosquitoes

Mosquito borne viruses such as dengue, Zika, yellow fever and Chikungunya cause millions of infections every year. These viruses are mostly transmitted by two urban-adapted mosquito species, Aedes aegypti and Aedes albopictus, that appear to be more permissive to arbovirus infections compared to closely related species, although mechanistic understanding remains unknown. Aedes mosquitoes may have evolved specialized antiviral mechanisms that potentially contribute to the low impact of viral infection. Recently, we reported the identification of an Aedes specific double-stranded RNA binding protein (dsRBP), named Loqs2, that is involved in the control of infection by dengue and Zika viruses in Ae. aegypti. Loqs2 interacts with two important co-factors of the RNA interference (RNAi) pathway, Loquacious (Loqs) and R2D2, and seems to be a strong regulator of the antiviral defense. However, the origin and evolution of loqs2 remains unclear. Here, we describe that loqs2 likely originated from two independent duplications of the first dsRNA binding domain (dsRBD) of loquacious that occurred before the radiation of the Aedes Stegomyia subgenus. After its origin, our analyses suggest that loqs2 evolved by relaxed positive selection towards neofunctionalization. In fact, loqs2 is evolving at a faster pace compared to other RNAi components such as loquacious, r2d2 and Dicer-2 in Aedes mosquitoes. Unlike loquacious, transcriptomic analysis showed that loqs2 expression is tightly regulated, almost restricted to reproductive tissues in Ae. aegypti and Ae. albopictus. Transgenic mosquitoes engineered to ubiquitously express loqs2 show massive dysregulation of stress response genes and undergo developmental arrest at larval stages. Overall, our results uncover the possible origin and neofunctionalization of a novel antiviral gene, loqs2, in Aedes mosquitoes that ultimately may contribute to their effectiveness as vectors for arboviruses.

evolutionary biology↗

bioRxiv2021

Efficient ancestry and mutation simulation with msprime 1.0

Stochastic simulation is a key tool in population genetics, since the models involved are often analytically intractable and simulation is usually the only way of obtaining ground-truth data to evaluate inferences. Because of this necessity, a large number of specialised simulation programs have been developed, each filling a particular niche, but with largely overlapping functionality and a substantial duplication of effort. Here, we introduce msprime version 1.0, which efficiently implements ancestry and mutation simulations based on the succinct tree sequence data structure and tskit library. We summarise msprimes many features, and show that its performance is excellent, often many times faster and more memory efficient than specialised alternatives. These high-performance features have been thoroughly tested and validated, and built using a collaborative, open source development model, which reduces duplication of effort and promotes software quality via community engagement.

genetics↗

bioRxiv2020

Natural selection and parallel clinal and seasonal changes in Drosophila melanogaster

Spatial and seasonal variation in the environment are ubiquitous. Environmental heterogeneity can affect natural populations and lead to covariation between environment and allele frequencies. Drosophila melanogaster is known to harbor polymorphisms that change both with latitude and seasons. Identifying the role of selection in driving these changes is not trivial, because non-adaptive processes can cause similar patterns. Given the environment changes in similar ways across seasons and along the latitudinal gradient, one promising approach may be to look for parallelism between clinal and seasonal change. Here, we test whether there is a genome-wide correlation between clinal and seasonal change, and whether the pattern is consistent with selection. Allele frequency estimates were obtained from pooled samples from seven different locations along the east coast of the US, and across seasons within Pennsylvania. We show that there is a genome-wide correlation between clinal and seasonal variation, which cannot be explained by linked selection alone. This pattern is stronger in genomic regions with higher functional content, consistent with natural selection. We derive a way to biologically interpret these correlations and show that around 3.7% of the common, autosomal variants could be under parallel seasonal and spatial selection. Our results highlight the contribution of natural selection in driving fluctuations in allele frequencies in natural fly populations and point to a shared genomic basis to climate adaptation which happens over space and time in D. melanogaster.

evolutionary biology↗