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Ortiz-Barrientos, D.

Publications and source records attributed to Ortiz-Barrientos, D..

18 recordsLinked to original sources

Non-convergent aridity adaptation despite pervasive linked selection in Eucalyptus

Whether independent lineages evolve similar genetic solutions when faced with the same environmental pressure is central to understanding how repeatable and predictable adaptation is. Answering this question is increasingly urgent as climate change intensifies drought and aridity worldwide, a shared pressure to which many species must independently adapt. Here we characterised genomic adaptation in Eucalyptus across three independent species pairs, each comprising two closely related lineages that have diverged from wetter into drier environments. Across all pairs we found concordant genome-wide landscapes of diversity and divergence, and strong evidence for pervasive linked selection. Because linked selection acts most strongly in the same conserved features of the genome, this shared architecture could concentrate differentiation in the same regions across lineages, creating an appearance of convergent adaptation. Despite this, the genomic regions associated with the transition to drier environments were largely non-convergent, with almost no sharing of outlier loci among pairs, demonstrating that each lineage adapted through a largely independent genetic route. Some convergence was instead evident at the level of biological function, indicating that lineages reached similar functional outcomes using different genes. We further identified large genomic islands of differentiation, which were dominated by the signature of linked selection rather than elevated divergence, though several harboured candidate genes within the drought and abscisic acid regulatory networks. Together, our results indicate that adaptation to aridity in Eucalyptus is complex and polygenic, and largely unpredictable at the level of individual loci.

evolutionary biology↗

Additive Channels in Curved Fitness Landscapes

Additive models of inheritance predict the short-term response to selection remarkably well, even when the underlying biology involves widespread dominance and gene-gene interaction. We argue that this success reflects a property of how fitness varies with the additive genetic component of a trait, not a property of the molecular architecture beneath. We make this quantitative through an additivity index, [A]g, that measures the fraction of local log-fitness variance captured by a linear approximation, with the remainder attributable to local curvature of the fitness surface. Under Gaussian/quadratic assumptions, [A]g equals the squared correlation between the linear approximation and local log-fitness, so 1- [A]g is the fraction of local log-fitness variance not captured by a purely linear predictor. We call regions of breeding-value space where [A]g is high additive channels and develop a coupled selection-inheritance framework that identifies when populations enter, persist in, and leave them. The framework predicts that stable, well-adapted populations can be those for which additive prediction is least informative for directional-response prediction. Article summaryGene interactions are common, yet additive genetic models often predict short-term evolution. We propose that additivity is a local property of where populations sit in breeding-value space on a curved fitness landscape, not a global property of the biology beneath. We define an index, [A]g, that compares slope variance to curvature variance and identifies populations in additive channels for which linear prediction performs well. The framework predicts that elite breeding pools enter such channels under sustained selection, while natural populations near a fitness optimum may leave them because the directional signal collapses.

evolutionary biology↗

Directional heritability and the geometry of multivariate constraint

Evolutionary responses to selection depend on how additive genetic variance is distributed across trait combinations. We focus on directional heritability--the fraction of phenotypic variance that is additive genetic along a given selection gradient--and treat its distribution across directions as a central object for describing multivariate constraint. Using a geometric transformation that rescales trait space so that phenotypic variance is isotropic, we show that directional heritability becomes a quadratic form in a whitened genetic matrix G* = P-1/2GP-1/2. Under uniformly distributed selection directions, the squared coefficient of variation of directional heritability satisfies CV2(h2) = (2/(p + 2)) Vrel(G*), where Vrel(G*) is the relative eigenvalue variance of the whitened matrix and p is the number of traits. Simulations show that alignment between the eigenvector systems of G and P has a larger effect on the spread of directional heritability than correlation strength. Analyses of 55 empirical G-P matrix pairs from 11 studies reveal wide variation across biological systems in how often selection encounters low-heritability directions: over two thirds of the populations examined had more than 25% of phenotypic directions with h2 < 0.25. The eigenvalue spectrum of G* provides a sufficient summary for characterising how matrix geometry shapes evolutionary constraint on the phenotypic scale.

evolutionary biology↗

Chimeric Reference Panels for Genomic Imputation

Despite transformative advances in genomic technologies, missing data remains a fundamental constraint that limits the full potential of genomic research across biological systems. Genotype imputation offers a remedy by inferring unobserved genotypes from observed data. However, conventional methods typically rely on external reference panels constructed from complete genome sequences of hundreds of individuals, a costly approach largely inaccessible for non-model organisms. Moreover, these methods generally overlook novel genomic positions not captured in existing panels. To overcome these limitations, we developed Retriever, a framework that bypasses the need for external reference panels. Retriever constructs a chimeric reference panel directly from the target samples using a sliding-window approach to identify and retrieve genomic partitions with complete data. By exploiting the complementary distribution of missing data across samples, Retriever assembles a panel that preserves local patterns of linkage disequilibrium and captures novel variants. Integrated with Beagle, Retriever achieves genotype imputation with accuracy consistently exceeding 95% across diverse datasets, including plants, animals, and fungi. By eliminating the need for costly external panels, Retriever offers an accessible, cost-effective solution that broadens the application of sophisticated genomic analyses across species.

bioinformatics↗

The genetic architecture of polygenic adaptation under a network-derived trait

The genetic architecture of adaptation varies across species, populations, and traits. While existing models capture aspects like the number of loci, effect sizes, and allele frequencies, they often overlook the molecular processes underlying trait expression. We investigated how gene regulatory networks influence quantitative variation during adaptation by examining the negative autoregulation (NAR) motif in two configurations: K+, with four evolving network components, and K-, with two components. Using forward-time simulations, we tracked populations adapting to a shifted phenotypic optimum under varying genetic architectures. We found that K+ populations maintained rapid adaptation despite low recombination, preserving high genetic variance through positive epistasis and stronger linkage disequilibrium. Under low recombination, K+ populations reached the optimum through diverse molecular configurations, while responses were more uniform under high recombination. In contrast, K- and additive models showed impaired adaptation under low recombination. Our findings demonstrate that network structure fundamentally influences the distribution of variation within the molecular architecture of traits, with certain networks providing unexpected robustness against low recombination rates. This suggests that molecular complexity may confer evolutionary advantages in natural populations.

evolutionary biology↗

Centromeres are Hotspots for Chromosomal Inversions and Breeding Traits in Mango

Chromosomal inversions can preserve combinations of favorable alleles by suppressing recombination. Simultaneously, they reduce the effectiveness of purifying selection enabling deleterious alleles to accumulate. This study explores how areas of low recombination, including centromeric regions and chromosomal inversions, contribute to the accumulation of deleterious and favorable loci in 225 Mangifera indica genomes from the Australian Mango Breeding Program. Here, we identify 17 chromosomal inversions that cover 7.7% (29.7Mb) of the M. indica genome: eight pericentric (inversion includes the centromere) and nine paracentric (inversion is on one arm of the chromosome). Our results show that these large pericentric inversions are accumulating deleterious loci, while the paracentric inversions show deleterious levels above and below the genome wide average. We find that despite their deleterious load, chromosomal inversions contain small effect loci linked to variation in crucial breeding traits, indicating that chromosomal inversions have likely facilitated their selection. The results from this study have important implications for selective breeding of favorable combinations of alleles in regions of low recombination. Significance StatementChromosomal inversions and other low recombination regions of the genome can drive trait evolution. Fewer recombination events can assist in maintaining favorable combinations of alleles, but it can also make disentangling favorable and deleterious alleles difficult. Understanding whether these low recombination regions contain favorable or deleterious loci could drive our decision to increase or decrease the frequency of these regions in target breeding populations. Breeding for large segments of the genome based on presence or absence of an inversion can rapidly drive large trait differences within few generations. Harnessing the impact of large low recombination regions of the genome could have major implications for future genetic improvement in breeding.

genomics↗

The distribution of fitness effects during adaptive walks using a simple genetic network

The tempo and mode of adaptation depends on the availability of beneficial alleles. Genetic interactions arising from gene networks can restrict this availability. However, the extent to which networks affect adaptation remains largely unknown. Current models of evolution consider additive genotype-phenotype relationships while often ignoring the contribution of gene interactions to phenotypic variance. In this study, we model a quantitative trait as the product of a simple gene regulatory network, the negative autoregulation motif. Using forward-time genetic simulations, we measure adaptive walks towards a phenotypic optimum in both additive and network models. A key expectation from adaptive walk theory is that the distribution of fitness effects of new beneficial mutations is exponential. We found that both models instead harbored distributions with fewer large-effect beneficial alleles than expected. The network model also had a complex and bimodal distribution of fitness effects among all mutations, with a considerable density at deleterious selection coefficients. This behavior is reminiscent of the cost of complexity, where correlations among traits constrain adaptation. Our results suggest that the interactions emerging from genetic networks can generate complex and multimodal distributions of fitness effects.

evolutionary biology↗

Hypergraphs and centrality measures identifying keyfeatures in gene expression data

Multidisciplinary approaches can significantly advance our understanding of complex systems. For instance, gene co-expression networks align prior knowledge of biological systems with studies in graph theory, emphasising pairwise gene to gene interactions. In this paper, we extend these ideas, promoting hypergraphs as an investigative tool for studying multi-way interactions in gene expression data. Additional freedoms are achieved by representing individual genes with hyperedges, and simultaneous testing each gene against many features/vertices. Further gene/hyperedge interactions can be captured and explored using the line graph representations, a techniques that also reduces the complexity of dense hypergraphs. Such an approach provides access to graph centrality measures, which in turn identify salient features within a data set, for instance dominant or hub-like hyperedges leading to key knowledge on gene expression. The validity of this approach is established through the study of gene expression data for the plant species Senecio lautus and results will be interpreted within this biological setting.

bioinformatics↗

The influence of genetic structure on phenotypic diversity in the Australian mango (Mangifera indica) gene pool

Genomic selection is a promising breeding technique for tree crops to accelerate the development of new cultivars. However, factors such as genetic structure can create spurious associations between genotype and phenotype due to the shared history between populations with different trait values. Genetic structure can therefore reduce the accuracy of the genotype to phenotype map, a fundamental requirement of genomic selection models. Here, we employed 272 single nucleotide polymorphisms from 208 Mangifera indica cultivars to explore whether the genetic structure of the Australian mango gene pool explained variation in tree size, fruit blush colour and intensity. Our results show that genetic structure is weak, but cultivars imported from Southeast Asia (mainly those from Thailand) were genetically differentiated across multiple population genetic analyses. We find that genetic structure was strongly associated with phenotypic diversity in M. indica, suggesting that the history of these cultivars could drive spurious associations between loci and key mango phenotypes in the Australian mango gene pool. Incorporating such genetic structure in associations between genotype and phenotype has the potential to improve the accuracy of genomic selection, which can assist the development of new cultivars.

evolutionary biology↗

easyfm : An easy software suite for file manipulation of Next Generation Sequencing data on desktops

Storing and manipulating Next Generation Sequencing (NGS) file formats is an essential but difficult task in biological data analysis. The easyfm (easy file manipulation) toolkit (https://github.com/TaekAndBrendan/easyfm) makes manipulating commonly used NGS files more accessible to biologists. It enables them to perform end-to-end reproducible data analyses using a free standalone desktop application (available on Windows, Mac and Linux). Unlike existing tools (e.g. Galaxy), the Graphical User Interface (GUI)-based easyfm is not dependent on any high-performance computing (HPC) system and can be operated without an internet connection. This specific benefit allow easyfm to seamlessly integrate visual and interactive representations of NGS files, supporting a wider scope of bioinformatics applications in the life sciences. Author summaryThe analysis and manipulation of NGS data for understanding biological phenomena is an increasingly important aspect in the life sciences. Yet, most methods for analysing, storing and manipulating NGS data require complex command-line tools in HPC or web-based servers and have not yet been implemented in comprehensive, easy-to-use software. This is a major hurdle preventing more general application in the field of NGS data analysis and file manipulation. Here we present easyfm, a free standalone Graphical User Interface (GUI) software with Python support that can be used to facilitate the rapid discovery of target sequences (or users interest) in NGS datasets for novice users. For user-friendliness and convenience, easyfm was developed with four work modules and a secondary GUI window (herein secondary window), covering different aspects of NGS data analysis (mainly focusing on FASTA files), including post-processing, filtering, format conversion, generating results, real-time log, and help. In combination with the executable tools (BLAST+ and BLAT) and Python, easyfm allows the user to set analysis parameters, select/extract regions of interest, examine the input and output results, and convert to a wide range of file formats. To help augment the functionality of existing web-based and command-line tools, easyfm, a self-contained program, comes with extensive documentation (hosted at https://github.com/TaekAndBrendan/easyfm) including a comprehensive step-by-step guide.

bioinformatics↗

Modelling selection response in plant breeding programs using crop models as mechanistic gene-to-phenotype (CGM-G2P) multi-trait link functions

Plant breeding programs are designed and operated over multiple cycles to systematically change the genetic makeup of plants to achieve improved trait performance for a Target Population of Environments (TPE). Within each cycle, selection applied to the standing genetic variation within a structured reference population of genotypes (RPG) is the primary mechanism by which breeding programs make the desired genetic changes. Selection operates to change the frequencies of the alleles of the genes controlling trait variation within the RPG. The structure of the RPG and the TPE has important implications for the design of optimal breeding strategies. The breeders equation, together with the quantitative genetic theory behind the equation, informs many of the principles for design of breeding programs. The breeders equation can take many forms depending on the details of the breeding strategy. Through the genetic changes achieved by selection, the cultivated varieties of crops (cultivars) are improved for use in agriculture. From a breeding perspective, selection for specific trait combinations requires a quantitative link between the effects of the alleles of the genes impacted by selection and the trait phenotypes of plants and their breeding value. This gene-to-phenotype link function provides the G2P map for one to many traits. For complex traits controlled by many genes, the infinitesimal model for trait genetic variation is the dominant G2P model of quantitative genetics. Here we consider motivations and potential benefits of using the hierarchical structure of crop models as CGM-G2P trait link functions in combination with the infinitesimal model for the design and optimisation of selection in breeding programs.

genetics↗

Metabolic scaling has diversified among species, despite an evolutionary constraint within species

Metabolic rate scales disproportionally with body mass, such that the energetic cost of living is relatively lower in larger organisms. Theory emphasises the importance of fixed physical constraints on metabolic scaling, yet empirical data are lacking with which to assess how evolutionary processes (e.g. mutation, drift, selection) contribute to the observed variation in metabolic scaling across the tree of life. Using a large-scale quantitative genetic study of growth in cockroaches, we show that ontogenetic metabolic scaling is evolutionarily constrained due to an absence of additive genetic variation in juvenile metabolic rate and mass. Using a phylogenetic analysis, we also show that ontogenetic metabolic scaling is more similar among closely related species than among distant relatives, suggesting that the constraints on metabolic scaling are subject to change during lineage diversification. Our results are consistent with growing evidence that there is strong stabilising selection on combinations of mass and metabolic rate within species.

physiology↗

Highly replicated evolution of parapatric ecotypes

Parallel evolution of ecotypes occurs when selection independently drives the evolution of similar traits across similar environments. The multiple origin of ecotypes is often inferred on the basis of a phylogeny which clusters populations according to geographic location and not by the environment they occupy. However, the use of phylogenies to infer parallel evolution in closely related populations is problematic because gene flow and incomplete lineage sorting can uncouple the genetic structure at neutral markers from the colonization history of populations. Here, we demonstrate multiple origins within ecotypes of an Australian wildflower, Senecio lautus. We observed strong genetic structure as well as phylogenetic clustering by geography and show that this is unlikely due to gene flow between parapatric ecotypes, which is surprisingly low. We further confirm this analytically by demonstrating that phylogenetic distortion due to gene flow often requires higher levels of migration than those observed in S. lautus. Our results imply that selection can repeatedly create similar phenotypes despite the perceived homogenizing effects of gene flow.

evolutionary biology↗

A framework to quantify phenotypic and genotypic parallel evolution

The independent and repeated adaptation of populations to similar environments often results in the evolution of similar forms. This phenomenon creates a strong correlation between phenotype and environment and is referred to as parallel evolution. However, we are still largely unaware of the dynamics of parallel evolution, as well as the interplay between phenotype and genotype within natural systems. Here, we examined phenotypic and genotypic parallel evolution in multiple parapatric Dune-Headland coastal ecotypes of an Australian wildflower, Senecio lautus. We observed a clear trait-environment association within the system, with all replicate populations having evolved along the same phenotypic evolutionary trajectory. Similar phenotypes have arisen via mutational changes occurring in different genes, although many share the same biological functions. Our results shed light on how replicated adaptation manifests at the phenotypic and genotypic levels within populations, and highlights S. lautus as one of the most striking cases of phenotypic parallel evolution in nature.

evolutionary biology↗

Divergence in hormone signalling links local adaptation and hybrid failure

Natural selection is a significant driver of speciation. Yet it remains largely unknown whether local adaptation can drive speciation through the evolution of hybrid sterility between populations. Here, we show that adaptive divergence in shoot gravitropism, the ability of a plants shoot to bend upwards in response to the downward pull of gravity, contributes to the evolution of hybrid sterility in an Australian wildflower, Senecio lautus. We find that shoot gravitropism has evolved multiple times in association with plant height between adjacent populations inhabiting contrasting environments, suggesting that these traits have evolved by natural selection. We directly tested this prediction using a hybrid population subjected to eight rounds of recombination and three rounds of selection in the field. It revealed that shoot gravitropism responds to natural selection in the expected direction of the locally adapted population. This provided an ideal platform to test whether genetic differences in gravitropism contribute to hybrid sterility in S. lautus. Using this advanced hybrid population, we discovered that crossing individuals with extreme differences in gravitropism reduce their ability to produce seed by 21%, providing strong evidence that this adaptive trait is genetically correlated with hybrid sterility. Our results suggest that natural selection can drive the evolution of locally adaptive traits that also create hybrid sterility, thus indicating an evolutionary connection between local adaptation and the origin of new species. Significance statementNew species originate as populations become reproductively isolated from one another. Despite recent progress in uncovering the genetic basis of reproductive isolation, it remains unclear whether intrinsic reproductive barriers, such as hybrid sterility, evolve as a by-product of local adaptation to contrasting environments or evolve through non-ecological processes, such as meiotic drive. Here, we show that differences in a plants response to the pull of gravity have repeatedly evolved amongst coastal populations of an Australian wildflower, thus implicating a role of natural selection in their evolution. We found a strong genetic correlation between variation in this adaptive trait and hybrid sterility, suggesting that intrinsic reproductive barriers contribute to the origin of new species as populations adapt to heterogeneous environments.

evolutionary biology↗

Evidence for mutation-order speciation between convergent ecotypes

Natural selection shapes how new species arise, yet the mechanisms that generate reproductive barriers remain debated. Although ecological divergence in contrasting environments and mutation-order processes in similar environments are often viewed as distinct speciation mechanisms, we show they act together as part of a continuum we call parallel mosaic speciation. In the Senecio lautus species complex, Dune and Headland ecotypes have evolved repeatedly along the Australian coastline. Through crossing experiments and field studies, we find that divergent natural selection promotes strong reproductive isolation between the Dune and Headland ecotypes. While uniform selection maintains reproductive compatibility among ecologically similar Dune populations, Headland populations have evolved reproductive barriers despite their convergent prostrate phenotypes, likely driven by adaptation to heterogeneous environments. To understand how habitat heterogeneity contributes to patterns of reproductive isolation, we extend previous theoretical work on the accumulation of hybrid incompatibilities to account for environmental gradients and polygenic adaptation. We show that the probability of reproductive isolation depends on three factors: how similar the environments are, how complex the genetic architecture is, and how selection coefficients are distributed among beneficial mutations. These theoretical findings explain how reproductive isolation arises in systems like Senecio, where multiple forms of selection jointly drive parallel speciation.

evolutionary biology↗

Connecting micro and macroevolution using genetic incompatibilities and natural selection on additive genetic variance

Testing whether local adaptation and intrinsic reproductive isolation share a genetic basis can reveal important connections between adaptation and speciation. Local adaptation arises as advantageous alleles spread through a population, but whether these same advantageous alleles fail on the genetic backgrounds of other populations remains largely unknown. We used a quantitative genetic breeding design to produce a late generation (F4) recombinant hybrid population by equally mating amongst four contrasting ecotypes of a native Australian daisy for four generations. We tracked fitness across generations and measured morphological traits in the glasshouse, and used a reciprocal transplant to quantify fitness in all four parental habitats. In the glasshouse, plants in the second generation showed a reduction in fitness as a loss of fertility, but this was fully recovered in the following generation. The F4 hybrid lacked extreme phenotypes present in the parental ecotypes, suggesting that genes reducing hybrid fitness were linked to traits specific to each ecotype. In the natural habitats, additive genetic variance for fitness was greatest for habitats that showed stronger native-ecotype advantage, suggesting that a loss of genetic variation present in the parental ecotypes were adaptive in the natural habitats. Reductions in genetic variance for fitness were associated with a loss of ecological trade-offs previously described in the parental ecotypes. Furthermore, natural selection on morphological traits differed amongst the parental habitats, but was not predicted to occur towards the morphology of the parental ecotypes. Together, these results suggest that intrinsic reproductive isolation removed adaptive genetic variation present in the parental ecotypes. The evolution of these distinct ecotypes was likely governed by genetic variation that resulted in both ecological trade-offs and intrinsic reproductive isolation among populations adapted to contrasting environments.

evolutionary biology↗

Evolution of genetic variance during adaptive radiation

Genetic correlations between traits can bias adaptation away from optimal phenotypes and constrain the rate of evolution. If genetic correlations between traits limit adaptation to contrasting environments, rapid adaptive divergence across a heterogeneous landscape may be difficult. However, if genetic variance can evolve and align with the direction of natural selection, then abundant allelic variation can promote rapid divergence during adaptive radiation. Here, we explored adaptive divergence among ecotypes of an Australian native wildflower by quantifying divergence in multivariate phenotypes of populations that occupy four contrasting environments. We investigated differences in multivariate genetic variance underlying morphological traits and examined the alignment between divergence in phenotype and divergence in genetic variance. We found that divergence in mean multivariate phenotype has occurred along two major axes represented by different combinations of plant architecture and leaf traits. Ecotypes also showed divergence in the level of genetic variance in individual traits, and the multivariate distribution of genetic variance among traits. Divergence in multivariate phenotypic mean aligned with divergence in genetic variance, with most of the divergence in phenotype among ecotypes associated with a change in trait combinations that had substantial levels of genetic variance in each ecotype. Overall, our results suggest that divergent natural selection acting on high levels of standing genetic variation might fuel ecotypic differentiation during the early stages of adaptive radiation.

evolutionary biology↗