Toni Ingolf Gossmann

Genetic diversity is central to evolutionary change, with both natural selection and random genetic drift depending on variation within a population. An individual in a diploid population carries two alleles per locus, yet the population as a whole can harbour many alleles, giving rise to a rich spectrum of homozygous and heterozygous genotypes. Such multiallelic variation is common at biologically and medically important loci such as the major histocompatibility complex, the ABO blood group system, and genes underlying monogenic diseases. However, much of population genetic theory and data analysis has focussed on biallelic loci. Here, we introduce a matrix representation of the genotypic selection acting at a multiallelic locus. This exploits the common mathematical structure underlying selection and drift, and separates the effects of genetic diversity and fitness. The representation accommodates diverse selection regimes, including additive, multiplicative, frequency-dependent, and temporally varying selection, as well as heterozygote advantage. We show how, under specific assumptions, genotype-specific fitness-effects can be estimated from allele frequency trajectories over microevolutionary timescales. Applying this estimation procedure to time-series data from experimental yeast evolution illustrates how multiallelic fitness interactions, including heterozygote advantage, may be characterised from haplotype frequency data. More broadly, this work provides a practical foundation for analysing evolutionary dynamics at multiallelic loci in experimental and natural populations.

Introgression redistributes genetic variation among diverging lineages, shaping evolution-ary trajectories and contributing to phenotypic evolution. Yet how localized introgressed genomic regions persist despite extensive genomic homogenization remains poorly under-stood. Here, we investigate the evolutionary history of the northeastern Iranian great tit ( Parus major intermedius ), a grey-plumaged member of the great tit complex occurring at the eastern range margin of the green- and yellow-plumaged major lineage, adjacent to the grey-plumaged Central Asian bokharensis lineage, and long regarded as a putative hybrid. We find that P. m. intermedius retains predominantly major -derived genomic ancestry despite its grey plumage, revealing extensive genomic homogenization across the genome. Surprisingly, a single localized introgressed haplotype on chromosome 24 retains bokharensis -derived ancestry, exhibits elevated genomic differentiation relative to the genomic background, and overlaps the carotenoid-processing gene BCO2 , a strong candidate underly-ing plumage pigmentation. Our findings provide a genomic explanation for the discordance between phenotype and genome-wide ancestry, demonstrating how localized introgression can preserve genomic regions associated with phenotypic divergence despite extensive genomic homogenization. This system illustrates how individual genomic regions can retain distinct evolutionary histories long after the surrounding genome has largely homogenized.

The anti-ageing response to Dietary Restriction (DR) is thought to be mechanistically ancient, reasoning from its phenotypic conservation. However, DR is implemented differently across species and evidence for conserved mechanisms remains limited. Here, we tested longevity and fecundity in response to DR across eight different Drosophila species, finding that DR is, on balance, phenotypically conserved. Next, we used comparative transcriptomics and found strongly concordant responses to DR. We studied the evolutionary history of the top concordantly differentially expressed orthologous genes and identified that many are “young” genes, suggesting that the genetic basis of DR is not widely conserved. To validate this hypothesis, we tested the longevity effects of the 15 most conserved genes that change in transcription in response to DR. Surprisingly, we found that 12 out of 15 genes tested had a lifespan phenotype, with 9 extending lifespan. Our findings suggest that while large parts of the DR response are taxonomically specific, some core mechanisms appear conserved. The comparative approaches we used here hold promise to identify shared mechanisms relevant to our own species.

Trainees in computational biology frequently inherit research plans whose aims, datasets, analytical strategies, and technical constraints were defined before their arrival. These plans often emerge from grants, collaborations, legacy codebases, shared high-performance computing environments, or partially completed analyses. While such plans provide a useful scaffold, they rarely specify all implementation details, prior assumptions, evaluation criteria, or dependencies needed for reliable execution. The transition from inheriting a partially articulated plan to producing reproducible results therefore creates an execution gap : a phase in which trainees must reconstruct what the project is, which elements are fixed, which remain negotiable, and which technical or organizational assumptions need to be tested before full-scale analysis begins. In this Ten Simple Rules article, we provide a practice-oriented framework for stabilizing inherited computational biology projects before workflows, benchmarks, and decision paths become entrenched. We do not claim that the individual practices described here are novel in isolation. Rather, our contribution is to organize familiar practices into a sequenced framework for a recurrent but under-articulated phase of computational research: inherited-plan execution. Computational biology makes this phase especially important because projects often combine heterogeneous datasets, fragile software environments, undocumented preprocessing choices, benchmarking assumptions, distributed collaborators, and asymmetrical access to contextual knowledge. By making this transition visible and operational, the rules aim to help trainees, supervisors, and collaborators reduce ambiguity, test feasibility, document decisions, and support reproducible and equitable project execution under real-world constraints.

Background: Biomedical research effort is distributed highly unevenly across human genes, with a small subset dominating the scientific literature while thousands remain sparsely studied. Whether this imbalance reflects intrinsic biological importance or historically reinforced research bias remains unclear. Understanding how research attention relates to gene properties is essential for more systematic exploration of the human genome. Results: Here, we quantify gene-level publication patterns and integrate sequence features, evolutionary constraint, gene age, expression, and disease associations across stratified gene sets. Using standardized MANE Select annotations, we show that publication counts follow a strongly heavy-tailed distribution. Highly studied genes cluster within a narrow GC-content regime and exhibit lower nonsynonymous substitution rates and lower dN/dS ratios, consistent with stronger long-term evolutionary constraint. In contrast, genes sampled from below rank 10,000 are enriched for evolutionarily younger loci and display moderately elevated dN and dN/dS values, reduced expression magnitude, and increased tissue specificity. At the disease level, research attention concentrates within a limited number of dominant domains, particularly cancer, respiratory, and vascular diseases, whereas congenital and rare disease categories remain comparatively underrepresented. Genes associated with orphan diseases show significantly reduced publication counts. Conclusions: Together, these results demonstrate that research attention is systematically structured across evolutionary, molecular, and disease dimensions. The least-studied genes represent a distinct and underexplored portion of the genome, characterized by features that may reduce experimental tractability. These findings highlight the need for bias-aware research prioritization strategies to broaden discovery and ensure more comprehensive characterization of human genes.

Advances in sequencing technologies have made whole‐genome data widely accessible, enabling research in population genetics, evolutionary biology, and conservation. However, analysing whole‐genome sequencing (WGS) data remains challenging, often requiring multiple specialised tools and substantial bioinformatics expertise. We present SwarmGenomics, a modular, user‐friendly command‐line pipeline for reference‐based genome assembly and individual‐based genetic analyses. The pipeline integrates seven modules: heterozygosity estimation, runs of homozygosity detection, Pairwise Sequentially Markovian Coalescent (PSMC) analysis, unmapped reads classification, repeat analysis, mitochondrial genome assembly, and nuclear mitochondrial DNA segment (NUMT) identification. Each module can be run independently or as part of a complete workflow. We demonstrate the pipeline's utility with a case study on the giant panda ( Ailuropoda melanoleuca ), revealing insights into genetic diversity, inbreeding history, historical population size changes, transposable element activity, and microbial contamination. SwarmGenomics lowers the entry barrier for genomic analysis of diploid, non‐model species, serving both as a research and teaching tool. The pipeline and documentation are available at https://github.com/AureKylmanen/Swarmgenomics .

Insertions and deletions (indels) are a ubiquitous form of genomic variation; yet, their contribution to adaptive evolution remains poorly understood. Technical challenges in identifying indels and integrating them into existing evolutionary models have limited their study, but previous work suggests that they may be subject to strong selective pressures. Here, we investigated selective signatures of indels in coding regions of the great tit ( Parus major ). We identified species-specific indels in protein-coding genes and analyzed the 300 bp flanking regions of indels using codeml to estimate dN, dS, and ω (dN/dS). Additionally, we performed enrichment analyses and protein structure modeling to assess functional consequences. We found that mutations in the flanking regions of indels are non-random and that selection strength varies with indel type and length. Genes including indels were enriched for functions related to protein and nucleic acid binding and linked to potentially adaptive traits such as vocal learning, migration, circadian rhythm, and environmental acclimation. Our results suggest that insertions and deletions may serve as hotspots of selection, influencing nearby sequences and contributing to adaptation in the great tit. Significance Statement: Small DNA changes called insertions and deletions (indels) are common in genomes, but their role in evolution is often overlooked because they are difficult to study. Using the genome of the great tit, a well-studied songbird, we identified thousands of indels in protein-coding regions and examined how they affect nearby DNA. We found that indels are not neutral accidents: they show signs of natural selection and may influence important traits. Some of the affected genes are linked to behaviors and adaptations that are crucial for survival, such as vocal learning, migration, and adaptation to new environments. These results suggest that indels can act as hotspots for evolutionary change and may help explain how species adapt to their surroundings. By high-lighting the evolutionary importance of these often-neglected mutations, our study opens new perspectives on the genetic basis of adaptation.

Advances in sequencing technologies have made whole-genome data widely accessible, enabling research in population genetics, evolutionary biology, and conservation. However, analyzing whole-genome sequencing (WGS) data remains challenging, often requiring multiple specialized tools and substantial bioinformatics expertise. We present SwarmGenomics, a modular, user-friendly command-line pipeline for reference-based genome assembly and individual-based genetic analyses. The pipeline integrates seven modules: heterozygosity estimation, runs of homozygosity detection, Pairwise Sequentially Markovian Coalescent (PSMC) analysis, unmapped reads classification, repeat analysis, mitochondrial genome assembly, and nuclear mitochondrial DNA segment (NUMT) identification. Each module can be run independently or as part of a complete workflow. We demonstrate the pipeline’s utility with a case study on the giant panda ( Ailuropoda melanoleuca ), revealing insights into genetic diversity, inbreeding history, historical population size changes, transposable element activity, and microbial contamination. SwarmGenomics lowers the entry barrier for genomic analysis of diploid, non-model species, serving both as a research and teaching tool. The pipeline and documentation are available at https://github.com/AureKylmanen/Swarmgenomics .

Sex‐specific genomic differentiation is a crucial yet frequently overlooked factor in population genetics. In this opinion piece, we leverage the substantial genomic resources available for the great tit (Parus major), including population‐scale data sets from many European populations, to investigate genomic differentiation between males and females. Unlike in some other species, where high‐quality genome assemblies exist but broad population sampling is lacking, the great tit offers a unique opportunity to study sex‐based differentiation at both the genomic and population level. We identify significant differentiation at an autosomal locus on chromosome 5, which we hypothesise originates from sex‐linked variation present on the sex chromosomes (Z and potentially W). By referencing genomic data from other songbirds with well‐assembled sex chromosomes, we illustrate how autosomal loci may exhibit high sequence similarity to sex‐linked regions. Our analyses demonstrate that uneven sex ratios in sampled populations can substantially bias differentiation metrics (e.g., F_ST), potentially resulting in false‐positive interpretations of adaptive differentiation. To mitigate such issues, we stress the importance of sex‐aware study designs, including balanced sex sampling and explicitly incorporating sex as a covariate. Furthermore, while optimal study designs would include high‐quality reference genomes from both sexes, we recommend, as a pragmatic and cost‐effective alternative for labs with limited resources, generating a reference genome from the heterogametic sex (females in birds) to ensure both sex chromosomes are represented in mapping and analysis. Finally, we emphasise the need for rigorous validation of candidate loci to ensure accurate and biologically meaningful outcomes in evolutionary genomic studies.