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Daigle, A.

Publications and source records attributed to Daigle, A..

4 recordsLinked to original sources

The distribution of fitness effects of new mutations in regulatory regions of the D. melanogaster genome

Although non-coding regions play important roles in gene regulation and contribute to individual fitness, the precise distribution of fitness effects (DFE) of new mutations in these regions remains poorly understood. Here, we carefully compile experimentally validated regulatory regions in non-coding regions in the Drosophila melanogaster genome and identify putatively neutral sites near them. Incorporating a realistic genomic architecture that mimics the placement of the regulatory and coding regions, as well as a realistic heterogeneity in recombination and mutation rates across the genome, we use forward-in-time simulations to assess the power and accuracy of population genetics approaches that infer the DFE of new mutations in these regions. While the parameters of DFEs primarily comprising moderately and strongly deleterious mutations are estimated accurately, those of a DFE comprising mostly mildly deleterious mutations are misinferred. Applying these insights to three African D. melanogaster populations, we find that a large fraction of new mutations in functionally important non-coding regions are moderately deleterious, as opposed to strongly deleterious in coding regions. While the fraction of beneficial substitutions in regulatory regions (0.25-0.45) was also lower in coding regions ([~]0.5), our results suggest that non-coding regions contribute a majority of new deleterious mutations and beneficial substitutions in D. melanogaster populations. By incorporating both the genomic distribution and the inferred DFE of non-coding regions, we demonstrate that the effects of background selection across the genome are more accurately captured than with coding regions alone, highlighting the importance of considering selection on non-coding regions when interpreting patterns of genomic variation.

evolutionary biology↗

GHIST 2024: The 1st Genomic History Inference Strategies Tournament

Evaluating population genetic inference methods is challenging due to the complexity of evolutionary histories, potential model misspecification, and unconscious biases in self-assessment. The Genomic History Inference Strategies Tournament (GHIST) is a community-driven competition designed to evaluate methods for inferring evolutionary history from population genomic data. The inaugural GHIST competition ran from July to November 2024 and featured four demographic history inference challenges of varying complexity: a bottleneck model, a split with isolation model, a secondary contact model with demographic complexity, and an archaic admixture model. Data were provided as error-free VCF files, and participants submitted numerical parameter estimates that were scored by relative root mean squared error. Approximately 60 participants competed, using diverse approaches. Results revealed the current dominance of methods based on site frequency spectra, while highlighting the advantages of flexible model-building approaches for complex demographic histories. We discuss insights regarding the competition and outline the next iteration, which is ongoing with expanded challenge diversity. By providing standardized benchmarks and highlighting areas for improvement, GHIST represents a substantial step toward more reliable inference of evolutionary history from genomic data.

evolutionary biology↗

Limitations of the inference of the distribution of fitness effects of new mutations in partially selfing populations with linkage

The accurate estimation of the distribution of fitness effects (DFE) of new mutations is critical for population genetic inference but remains a challenging task. While various methods have been developed for DFE inference using the site frequency spectrum of putatively neutral and selected sites, their applicability in species with diverse life history traits and complex demographic scenarios is not well understood. Selfing is common among eukaryotic species and can lead to decreased effective recombination rates, increasing the effects of selection at linked sites, including interference between selected alleles. We employ forward simulations to investigate the limitations of current DFE estimation approaches in the presence of selfing and other model violations, such as linkage, departures from semidominance, population structure, and uneven sampling. We find that distortions of the site frequency spectrum due to Hill- Robertson interference in highly selfing populations lead to mis-inference of the deleterious DFE of new mutations. Specifically, when inferring the distribution of selection coefficients, there is an overestimation of nearly neutral and strongly deleterious mutations and an underestimation of mildly deleterious mutations when interference between selected alleles is pervasive. In addition, the presence of cryptic population structure with low rates of migration and uneven sampling across subpopulations leads to the false inference of a deleterious DFE skewed towards effectively neutral/mildly deleterious mutations. Finally, the proportion of adaptive substitutions estimated at high rates of selfing is substantially overestimated. Our observations apply broadly to species and genomic regions with little/no recombination and where interference might be pervasive.

genetics↗

Characterization of the cluster MabR prophages of Mycobacterium abscessus and Mycobacterium chelonae

Mycobacterium abscessus is an emerging pathogen of concern in cystic fibrosis and immunocompromised patients and is considered one of the most drug-resistant mycobacteria. The majority of clinical M. abscessus isolates carry one or more prophages that are hypothesized to contribute to virulence and bacterial fitness. The prophage McProf was identified in the genome of the Bergey strain of M. chelonae, and is distinct from previously described prophages of M. abscessus. The McProf genome increases intrinsic antibiotic resistance of M. chelonae and drives expression of the intrinsic antibiotic resistance gene, whiB7, when superinfected by a second phage. The prevalence of McProf-like genomes was determined in sequenced mycobacterial genomes. Related prophage genomes were identified in the genomes of 25 clinical isolates of M. abscessus and assigned to the novel cluster, MabR. The MabR genomes share less than 10% gene content with previously described prophages; however, share features typical of prophages, including polymorphic toxin immunity (PT-Imm) systems.

genomics↗