Biology subjects

Postma, E.

Publications and source records attributed to Postma, E..

14 recordsLinked to original sources

bioRxiv2026

Evolutionary divergence and adaptive potential of scototaxis in juvenile Trinidadian Guppies

Habitat choice and resource acquisition represent an intrinsic trade-off, resulting in variation among individuals and potentially local adaptation among populations. Here, we quantify scototaxis, or preference for dark versus light backgrounds, in juvenile guppies. Scototaxis assays, in which individuals choose between safer dark and riskier light backgrounds, are widely used as a measure of boldness in small fishes, but they can equally be interpreted as choice between two habitats. By rearing and testing 586 fish descended from ten natural populations from Trinidad under common garden conditions, we first quantify (broad sense) heritable variation, i.e. evolutionary potential, within populations. Next, we test for evolutionary divergence among populations in mean preference, and if present, whether ancestral predation regime is a mediator of divergence. Finally, we ask whether families and/or populations differ in the amount of behavioural variation they contain. Preference varied among families (12% of total variance), consistent with broad-sense heritable variation. We also found that mean preference varied among populations (11% of total variance explained). Evolutionary divergence among-populations is partly explained by ancestral predation regime, with populations from low-predation sites showing a stronger preference for dark backgrounds than high-predation populations from the same river. Additionally, we find that within-population behavioural variation is greater in high-predation populations. We conclude that guppy populations contain heritable variation that could facilitate adaptive evolution if scototaxis is subject to natural selection. Furthermore, while genetic drift may also contribute to evolutionary divergence among-populations, observed patterns are qualitatively consistent with local adaption to predation regime. Our results suggests that high predation sites favour bolder habitat choice on average, but also that local predation regime shapes the evolutionary dynamics of variation, perhaps by maintaining shy-bold variation among individuals, or by favouring individuals with less-predictable behaviour.

evolutionary biology↗

bioRxiv2026

Nucleotide diversity is a poor predictor of short-term adaptive potential

Significance statementThe current paradigm in conservation genetics suggests that species with the lowest molecular genetic diversity have the lowest capacity to adapt. Despite previous concern that traditional measures of molecular genetic variation are not useful predictors of adaptive potential, the conflation of genetic diversity and adaptive potential remains prevalent in both scientific literature and global policy. By combining new theory with a large dataset of genetic variation across hundreds of species, we show that molecular sequence variation is weakly related to the level of heritable variation in traits across species. This demonstrates that genetic diversity does not reliably predict adaptive potential, and highlights the urgent need to move beyond simple measures when assessing the evolutionary resilience of populations. A capacity to adapt is essential for a population to avoid extinction in a changing world and is recognised as a global conservation priority. Adaptation requires additive (heritable) genetic variation for traits that influence survival and fecundity, but measuring this variation is difficult, particularly in species of conservation concern. Instead, molecular genetic diversity is often used to infer adaptive potential. However, previous research has cast doubt on the suitability of traditional molecular markers (allozymes and microsatellites) for this purpose given their weak relationship with heritability - a common measure of additive genetic variance. Recent advances in sequencing technology have since shifted focus towards nucleotide diversity and variation in functional regions, but their practicality for predicting adaptive potential remains debated and untested. Furthermore, heritability itself is a poor proxy for adaptive potential because it depends on environmental variance. We collated 2,113 published estimates of evolvability - a measure of additive genetic variance that avoids environmental confounding - across 193 eukaryotic species, and evaluated how well evolvability is predicted by molecular diversity. We find that microsatellite and nucleotide diversity are not significantly correlated to each other, and neither predict evolvability (nucleotide diversity explains 0.7% of interspecific differences in evolvability and doubling nucleotide diversity only corresponds to a 9.2% increase in evolvability). With new theoretical work, we show that such weak associations are expected. Together, our results suggest that simple molecular measures of genetic variation are insufficient for predicting adaptive potential and continued reliance on these metrics risks misinforming conservation management.

evolutionary biology↗

bioRxiv2025

Age- and sex-dependent sibling effects on early-life survival in preindustrial humans

Siblings are an important part of an individuals early-life environment and may therefore play an important role in shaping an individuals fitness. The quantification of sibling effects is challenging, especially in long-lived species with extended parental care and overlapping generations, such as humans. Here we quantify how the survival status, age, and sex of older siblings shape childhood survival across 2941 focal individuals born between 1750-1870 using historical parish data from Switzerland. While the total number of older siblings was not associated with an individuals childhood survival, distinguishing between siblings by their survival status, age, and sex revealed several associations, which in some cases also interacted with the sex of the focal individual: While older brothers close in age reduced the survival of girls but not boys, having more older sisters born close in age improved their younger siblings survival. Our results, therefore, show that sibling interactions play an important role in shaping early-life survival, and highlight that the strength and direction of these effects are context-dependent and can arise through biological and cultural factors. We encourage future studies on sibling interaction to consider siblings survival, age and sex, in both humans and other species.

evolutionary biology↗

bioRxiv2025

Genetic variation in life-history traits is not correlated with estrogen-receptor positive breast cancer risk

Estrogen receptor-positive breast cancer (ER+ BC) is one of the most prevalent cancers, but the evolutionary processes shaping genetic variation in ER+ BC risk are poorly understood. Both evolutionary life-history theory and evidence from studies of individual ER+ BC risk variants suggest that increased genetic ER+ BC risk is associated with faster maturation, earlier reproduction, and/or increased reproductive success (i.e., there is a trade-off), but it is unclear how well this pattern is replicated when considering the polygenic architecture of these traits after controlling for potential biases. Here, we estimate genome-wide genetic correlations between ER+ BC risk and three reproductive traits (age at menarche, age at first birth, and the number of children) using genomic restricted maximum-likelihood analyses on Lifelines biobank data and linkage disequilibrium score regressions on population and family-based genome-wide association study data. Regardless of the data or method used, genetic correlations were low and not statistically significant. Further analyses decomposing genome-wide genetic variance into local regions detected only three loci exhibiting significant pleiotropy between ER+ BC risk and age at menarche, suggesting little shared genetic architecture between ER+ BC risk and reproductive traits. Thus, the role of life-history trade-offs in shaping ER+ BC risk in European populations appears, at most, small, and the evolutionary processes giving rise to this life-threatening disease remain unclear. Future studies could examine the impact of evolutionary mismatches in shaping ER+ BC risk, where conducting longitudinal studies on populations transitioning to reproductive patterns observed in contemporary European populations would be most useful.

evolutionary biology↗

bioRxiv2024

Lifespan costs of reproduction increase during the Great Finnish Famine

Evolutionary theory of aging predicts that women with increased reproductive effort live shorter lives, but evidence is inconsistent. These inconsistencies could be because environmental conditions influence how much a mothers life span is reduced when having more children, i.e. their life-span cost of reproduction. Using a structural equation measurement model, we compare how reproductive effort affects the life span of 4,684 women exposed across different life-stages, or not at all, to the Great Finnish Famine. We find that life span costs of reproduction became higher in mothers exposed to the famine during reproduction, and for these mothers amounted to lower life expectancy of [~]0.5 years per child. Conversely, reproduction did not shape the life spans of mothers not exposed to the famine, or exposed post-reproduction or during development. This natural experiment reveals how environmental adversity can influence reproductive costs, providing a biological explanation for previous inconsistent findings, while showing how reproductive behavior has shaped the evolution of aging in humans. TEASERMothers with more children had shorter lives during a famine, suggesting that harsh conditions increase costs of reproduction.

evolutionary biology↗

bioRxiv2024

PhenoEncoder: A Discriminative Embedding Approach to Genomic Data Compression

AbstractExploring the heritability of complex genetic traits requires methods that can handle the genomes vast scale and the intricate re-lationships among genetic markers. Widely accepted association studies overlook non-linear effects (epistasis), prompting the adoption of deep neural networks (DNNs) for their scalability with large genetic datasets and ability to detect complex relationships. However, the curse of di-mensionality continues to limit the potential of DNNs, underscoring the critical need for dimensionality reduction for suitably sizing and shaping the genetic inputs, while preserving epistasis. Linkage disequilibrium (LD), a measure of correlation between genetic loci, offers a pathway to genome compression with minimal information loss. Using LD, the genome can be divided into smaller genomic regions, i.e., haplotype blocks, which can be locally compressed using deep au-toencoders. While autoencoders excel at preserving the main non-linear patterns, they still risk losing phenotype-relevant information when dom-inated by other sources of genetic variation. We propose a novel approach, PhenoEncoder, that incorporates pheno-typic variance directly into compression. This single nucleotide polymor-phism (SNP)-based pipeline employs multiple autoencoders, each dedi-cated to compressing a single haplotype block. The window-based spar-sity of the model eases the computational burden of simultaneously pro-cessing numerous SNPs. Concurrently, an auxiliary classifier predicts the phenotype from the compressed haplotype blocks. Epistasis is processed both within and between haplotype blocks by maintaining non-linearity in the autoencoders and the classifier. Through joint optimization of the compression and classification losses, PhenoEncoder ensures that disease-causing patterns are highlighted during compression. Applied to protein expression and simulated complex phenotype datasets, PhenoEncoder demonstrated enhanced generalizability in downstream classification tasks compared to standard autoencoder compression. By enabling phenotype-aware compression, PhenoEncoder emerges as a promis-ing approach for discriminative genomic feature extraction.

genomics↗

bioRxiv2023

Computing Linkage Disequilibrium Aware Genome Embeddings using Autoencoders

MotivationThe completion of the human genome has paved the way for genome-wide association studies (GWAS), which have already succeeded in explaining certain proportions of heritability. GWAS are not optimally suited to detect potential non-linear effects in disease risk, possibly hidden in non-additive interactions (epistasis). Alternative methods for epistasis detection using e.g. deep neural networks are currently under active development. Despite their promise to scale to high-dimensional data, deep neural networks (DNNs) are constrained by finite computational resources, which can be rapidly depleted due to increasing complexity with the sheer size of the genome. Besides, the curse of dimensionality complicates the task of capturing meaningful genetic patterns for DNNs; therefore calls for precaution in the form of dimensionality reduction. ResultsWe propose a method to compress genotyping data, involving single nucleotide polymorphisms (SNPs), while leveraging the LD structure in the genome and preserving non-linear relations among variants. This method involves clustering correlated SNPs into haplotype blocks and training per-block autoencoders that are able to learn a compressed representation of the blocks relevant genetic content. We provide an adjustable autoencoder design to accommodate diverse blocks and to bypass extensive hyperparameter tuning. We applied this method to genotyping data from Project MinE which involves a total of 23,209 ALS cases and 90,249 healthy controls. We compressed the haplotype blocks of an entire chromosome using our autoencoder-based approach, and show that this leads to more than 99% average test reconstruction accuracy - i.e. minimal information loss - while compressing the input to nearly 10% of the original size. We demonstrate that haplotype-block based autoencoders outperform their linear alternative Principal Component Analysis (PCA) by approximately 3% chromosome-wide accuracy of reconstructed variants. To the extent of our knowledge, our approach is the first to simultaneously leverage genome haplotype structure and deep neural networks for dimensionality reduction of genetic data. Availability and ImplementationData used in this study are available for academic use through the Project MinE Consortium at https://www.projectmine.com/research/data-sharing/. Data accessibility is contingent upon any terms or requirements specified by the source studies. Codes for data compression are available at https://github.com/gizem-tas/haploblock-autoencoders.

genomics↗

bioRxiv2023

Rapid decline of prenatal maternal effects with age is independent of postnatal environment in a precocial bird

Maternal effects are an important source of phenotypic variation with potentially large fitness consequences, but how their importance varies with the quality of the environment across an individuals ontogeny is poorly understood. We bred Japanese quail (Coturnix japonica) of known pedigree and experimentally manipulated the quality of offspring diet, to estimate the importance of prenatal maternal effects in shaping variation in body mass from hatching to adulthood. Maternal genetic effects on body mass at hatching were strong, and largely caused by variation in egg mass, but their importance rapidly declined with age. Whereas there was a large effect of diet on growth, this did not affect the decline of maternal effects variance. The importance of additive genetic and residual variance increased with age, with the latter being considerably larger in the poor diet treatment. Hence, we found no evidence for prenatal maternal effect by postnatal environment interactions, and that prenatal maternal effects are rapidly replaced by direct additive genetic and residual effects when offspring start to develop outside the egg. Thereby these results shed new light on the dynamics of the role of maternal versus offspring genes across ontogeny and environments.

evolutionary biology↗

bioRxiv2022

Lifetime reproductive success is an imprecise but largely unbiased predictor of long-term genetic contribution in historical humans

An individuals lifetime reproductive success (LRS) measures its realised genetic contributions to the next generation, but how well does it predict these over longer periods? Here we use human genealogical data to estimate expected individual genetic contributions (IGC) and quantify the degree to which LRS, relative to other fitness proxies, predicts IGC over longer periods in natural populations. This allows an identification of the life-history stages that are most important in shaping variation in IGC. We use historical genealogical data from two non-isolated local populations in Switzerland to estimate the stabilised IGC for 2,230 individuals ~10 generations after they were born. We find that LRS explains 30% less variation in IGC than the best predictor of IGC, the number of grandoffspring. However, albeit less precise than the number of grandoffspring, we show that LRS does provide an unbiased prediction of IGC and overall predicts IGC better than lifespan and similarly when accounting for offspring survival to adulthood. Overall, our findings demonstrate the value of human genealogy data to evolutionary biology and showing that reproduction - more than lifespan or offspring survival - impacts the long-term genetic contributions of historic humans, even in a population with appreciable migration.

evolutionary biology↗

bioRxiv2021

Low-coverage reduced representation sequencing reveals subtle within-island genetic structure in Aldabra giant tortoises

Aldabrachelys gigantea (Aldabra giant tortoise) is one of only two giant tortoise species left in the world and survives as a single wild population of over 100,000 individuals on Aldabra Atoll, Seychelles. Despite this large current population size, the species faces an uncertain future because of its extremely restricted distribution range and high vulnerability to the projected consequences of climate change. Captive-bred A. gigantea are increasingly used in rewilding programs across the region, where they are introduced to replace extinct giant tortoises in an attempt to functionally resurrect degraded island ecosystems. However, there has been little consideration of the current levels of genetic variation and differentiation within and among the islands on Aldabra. As previous microsatellite studies were inconclusive, we combined low-coverage and double digest restriction associated DNA (ddRAD) sequencing to analyze samples from 33 tortoises (11 from each main island). Using 5,426 variant sites within the tortoise genome, we detected patterns of population structure within two of the three studied islands, but no differentiation between the islands. These unexpected results highlight the importance of using genome-wide genetic markers to capture higher-resolution genetic structure to inform future management plans, even in a seemingly panmictic population. We show that low-coverage ddRAD sequencing provides an affordable alternative approach to conservation genomic projects of non-model species with large genomes.

ecology↗

bioRxiv2021

Modular, synthetic chromosomes as new tools for large scale engineering of metabolism

The construction of powerful cell factories requires intensive genetic engineering for the addition of new functionalities and the remodeling of native pathways and processes. The present study demonstrates the feasibility of extensive genome reprogramming using modular, specialized de novo-assembled neochromosomes in yeast. The in vivo assembly of linear and circular neochromosomes, carrying 20 native and 21 heterologous genes, enabled the first de novo production in a microbial cell factory of anthocyanins, plant compounds with a broad range pharmacological properties. Turned into exclusive expression platforms for heterologous and essential metabolic routes, the neochromosomes mimic native chromosomes regarding mitotic and genetic stability, copy number, harmlessness for the host and editability by CRISPR/Cas9. This study paves the way for future microbial cell factories with modular genomes in which core metabolic networks, localized on satellite, specialized neochromosomes can be swapped for alternative configurations and serve as landing pads for the addition of functionalities.

bioengineering↗

bioRxiv2021

Top-down, knowledge-based genetic reduction of yeast central carbon metabolism

Saccharomyces cerevisiae, whose evolutionary past includes a whole-genome duplication event, is characterised by a mosaic genome configuration with substantial apparent genetic redundancy. This apparent redundancy raises questions about the evolutionary driving force for genomic fixation of minor paralogs and complicates modular and combinatorial metabolic engineering strategies. While isoenzymes might be important in specific environments, they could be dispensable in controlled laboratory or industrial contexts. The present study explores the extent to which the genetic complexity of the central carbon metabolism (CCM) in S. cerevisiae, here defined as the combination of glycolysis, pentose phosphate pathway, tricarboxylic acid cycle and a limited number of related pathways and reactions, can be reduced by elimination of (iso)enzymes without major negative impacts on strain physiology. Cas9-mediated, groupwise deletion of 35 from the 111 genes yielded a minimal CCM strain, which despite the elimination of 32 % of CCM-related proteins, showed only a minimal change in phenotype on glucose-containing synthetic medium in controlled bioreactor cultures relative to a congenic reference strain. Analysis under a wide range of other growth and stress conditions revealed remarkably few phenotypic changes of the reduction of genetic complexity. Still, a well-documented context-dependent role of GPD1 in osmotolerance was confirmed. The minimal CCM strain provides a model system for further research into genetic redundancy of yeast genes and a platform for strategies aimed at large-scale, combinatorial remodelling of yeast CCM.

genetics↗

bioRxiv2020

A supernumerary designer chromosome for modular in vivo pathway assembly in Saccharomyces cerevisiae

The construction of microbial cell factories for sustainable production of chemicals and pharmaceuticals requires extensive genome engineering. Using Saccharomyces cerevisiae, this study proposes Synthetic Chromosomes (SynChs) as orthogonal expression platforms for rewiring native cellular processes and implementing new functionalities. Capitalizing the powerful homologous recombination capability of S. cerevisiae, modular SynChs of 50 and 100 Kb were fully assembled de novo from up to 44 transcriptional-unit-sized fragments in a single transformation. These assemblies were remarkably efficient and faithful to their in silico design. SynChs made of non-coding DNA were stably replicated and segregated irrespective of their size without affecting the physiology of their host. These non-coding SynChs were successfully used as landing pad and as exclusive expression platform for the essential glycolytic pathway. This work pushes the limit of DNA assembly in S. cerevisiae and paves the way for de novo designer chromosomes as modular genome engineering platforms in S. cerevisiae.

synthetic biology↗

bioRxiv2019

Artificial selection on male size depletes genetic variance but not covariance of life history traits in the yellow dung fly

The evolutionary potential of organisms depends on the presence of sufficient genetic variation for traits subject to selection, as well as on the genetic covariances among them. While genetic variation ultimately derives from mutation, theory predicts the depletion of genetic (co)variation under consistent directional or stabilizing selection in natural populations. We estimated and compared additive genetic (co)variances for several standard life history traits, including some for which this has never been assessed, before and after 24 generations of artificial selection on male size in the yellow dung fly Scathophaga stercoraria (Diptera: Scathophagidae) using a series of standard half-sib breeding experiments. As predicted, genetic variances (VA), heritabilities (h2) and evolvabilities (IA) of body size, development time, first clutch size, and female age at first clutch were lower after selection. As independent selection lines were crossed prior to testing, we can rule out that this reduction is due to genetic drift. In contrast to the variances, and against expectation, the additive genetic correlations between the sexes for development time and body size remained strong and positive (rA = 0.8-0.9), while the genetic correlation between these traits within the sexes tended to strengthen (but not significantly so). Our study documents that the effect of selection on genetic variance is predictable, whereas that on genetic correlations is not.

evolutionary biology↗