Biology subjects

Wilkinson, M. J.

Publications and source records attributed to Wilkinson, M. J..

11 recordsLinked to original sources

bioRxiv2026

Improved Ensemble Performance by Weight Optimisation for the Genomic Prediction of Maize Flowering Time Traits

Ensembles of multiple genomic prediction models have demonstrated improved prediction performance over the individual models contributing to the ensemble. The outperformance of ensemble models is expected from the Diversity Prediction Theorem, which states that for ensembles constructed with diverse prediction models, the ensemble prediction error becomes lower than the mean prediction error of the individual models. While a naive ensemble-average model provides baseline performance improvement by aggregating all individual prediction models with equal weights, optimising weights for each individual model could further enhance ensemble prediction performance. The weights can be optimised based on their level of informativeness regarding prediction error and diversity. Here, we evaluated weighted ensemble-average models with three possible weight optimisation approaches (linear transformation, Nelder-Mead and Bayesian) using flowering time and tillering traits from two maize nested associated mapping (NAM) datasets; TeoNAM and MaizeNAM. The three proposed weighted ensemble-average approaches improved prediction performance in several of the prediction scenarios investigated. In particular, the weighted ensemble models enhanced prediction performance when the adjusted weights differed substantially from the equal weights used by the naive ensemble models. For performance comparisons among the weighted ensembles, there was no clear superiority among the proposed approaches in both prediction accuracy and error across the prediction scenarios. Weight optimisation for ensembles warrants further investigation to explore the opportunities to improve their prediction performance; for example, integration of a weighted ensemble with a simultaneous hyperparameter tuning process may offer a promising direction for further research.

bioinformatics↗

bioRxiv2026

Genomic and pedigree-based approaches to predict parental breeding values for nut and kernel traits in almond (Prunus dulcis Mill. D. A. Webb)

The self-incompatibility, perennial growth habit, large tree size, and long juvenility present challenges in applying traditional breeding approaches in almond (Prunus dulcis Mill. D. A. Webb). Moreover, nut and kernel traits in almond are mainly controlled by a large number of small-effect quantitative trait loci (QTLs) and improving complex traits through conventional breeding approaches is slow and often inefficient. Genome-wide selection represents a promising strategy to enhance the efficiency of cultivar identification and selection of superior parents in almond breeding programs by estimating the breeding values (BVs) at early maturity. The main aim of this study was to implement genomic (GBLUP) and pedigree-based (ABLUP) prediction approaches to estimate BVs to identify the superior parental candidates for improving nut and kernel traits in almond. Here, we estimated BVs for nine traits that are commonly used in the primary evaluation stage of the almond breeding using genomic data from 61 parents and phenotypic data of 15,281 progeny derived from 205 unique families. Breeding values obtained from both approaches showed a strong correlation (r [≥] 0.94) for all traits except shell seal (r = 0.87). The population structure analysis conducted using high-quality 90K single nucleotide polymorphisms (SNPs) indicated clear separation of the Californian, European and some old Australian almond cultivars, with considerable admixture across some cultivars. Following further validation, both prediction approaches could be useful in early identification of superior candidates. The slightly higher breeding values obtained using the GBLUP compared to the ABLUP approach suggest that accounting for within-family variations and realised genomic relationships can enhance prediction accuracy, reliability, and overall genomic prediction performance in almond.

genetics↗

bioRxiv2025

Ensembles of Graph Neural Networks Supervised by Genotype-to-Phenotype Structures Improved Genomic Prediction Performance

Accurate selection of favourable crop genotypes has motivated the exploration of diverse prediction algorithms for crop breeding applications. One genomic prediction method that has not been fully explored is graph attention networks (GAT). By directly analysing graphical data with the attention mechanism, GAT can incorporate the genotype-to-phenotype (G2P) structure to regularise predictions. As one potential G2P structure, a gene network can be inferred from interpretable machine learning models to effectively learn key features of prediction patterns, potentially improving prediction performance. Here, we investigated whether incorporating such data-driven prior knowledge into GAT improved prediction performance compared to GAT models representing a continuum of G2P structures, ranging from infinitesimal to fully connected. Applying the Diversity Prediction Theorem, we also combined these diverse G2P structures into an ensemble of GAT genomic prediction models to integrate complementary strengths of multiple models. The results for flowering time traits in two maize nested association mapping datasets showed a lack of consistent performance improvement in the data-driven prior knowledge GAT model. However, consistent outperformance was observed for the ensemble of GAT models. Improved predictions from the ensemble model may be driven by its ability to capture a more complete representation of the inferred gene network through the integration of information from diverse G2P structures. The observed results using the GAT methodology provided the foundation for potential performance improvement using GAT by integrating biological prior knowledge derived from omics data and empirically verified gene interactions in future research, thereby potentially enhancing the GAT ensemble performance.

genomics↗

bioRxiv2025

Ensemble-based genomic prediction for maize flowering time reveals novel insights into trait genetic architecture and improves prediction for breeding applications

While various genomic prediction models have been evaluated for their potential to accelerate genetic gain for multiple traits, no individual genomic prediction model has outperformed all others across all applications. As an alternative approach, ensembles of multiple individual genomic prediction models can be applied to utilise the complementary strengths of individual prediction models and offset the prediction errors of each. We used the EasiGP (Ensemble AnalySis with Interpretable Genomic Prediction) pipeline to investigate the performance of an ensemble approach, targeting flowering-time traits measured in two maize nested association mapping datasets. For both datasets, the ensemble-based prediction approach achieved higher prediction accuracy and lower prediction error across the flowering-time traits compared to each individual model. Multiple genomic regions known to contain key flowering-time related genes were repeatedly included as features across individual genomic prediction models, indicating the models successfully captured SNPs as features that are associated with genomic regions known to contain flowering-time genes. Although repeatability was high for some genomic regions, estimated marker effects varied across many genomic regions, suggesting that the models might also have captured different aspects of the genetic variation underlying the traits. The ensemble combination of the diverse views likely contributed to the improvement of prediction performance by the ensemble-based approach over the individual prediction models. Ensemble-based prediction can be applied to overcome limitations observed in the continuous exploration for the best individual genomic prediction models that can consistently achieve the highest prediction performance, thereby potentially contributing to improved prediction accuracy for applications in crop breeding. Article summaryThis study targets researchers interested in the performance of genomic prediction models. To demonstrate potential advantages of an ensemble of diverse individual genomic prediction models, we investigated the prediction of key flowering-time traits (days to anthesis and anthesis to silking interval) in two maize datasets. The ensemble approach consistently improved the prediction performance. The improvement was attributed to the offset of prediction errors by combining multiple different dimensions of trait genetic variation. Ensembles can lead to higher selection accuracy of desirable individuals for applications in crop breeding.

bioinformatics↗

bioRxiv2025

Circos Plots for Genome Level Interpretation of Genomic Prediction Models

Ensemble of multiple genomic prediction models have grown in popularity due to consistent prediction performance improvements in crop breeding. However, technical tools that analyse the predictive behaviour at the genome level are lacking. Here, we develop a computational tool called Ensemble AnalySis with Interpretable Genomic Prediction (EasiGP) that uses circos plots to visualise how different genomic prediction models quantify contributions of marker effects to trait phenotypes. As a demonstration of EasiGP, multiple genomic prediction models, spanning conventional statistical and machine learning algorithms, were used to infer the genetic architecture of days to anthesis (DTA) in a maize mapping population. The results indicate that genomic prediction models can capture different views of trait genetic architecture, even when their overall profiles of prediction accuracy are similar. Combinations of diverse views of the genetic architecture for the DTA trait in the TeoNAM study might explain the improved prediction performance achieved by ensembles, aligned with the implication of the Diversity Prediction Theorem. In addition to identifying well-known genomic regions contributing to the genetic architecture of DTA in maize, the ensemble of genomic prediction models highlighted several new genomic regions that have not been previously reported for DTA. Finally, different views of trait genetic architecture were observed across sub-populations, highlighting challenges for between-population genomic prediction. A deeper understanding of genomic prediction models with enhanced interpretability using EasiGP can reveal several critical findings at the genome level from the inferred genetic architecture, providing insights into the improvement of genomic prediction for crop breeding programs. Plain Language SummaryWhile an ensemble of genomic prediction models has been applied in crop breeding, the prediction mechanism has not been well-investigated due to the lack of a computational tool to interpret the predictive behaviour. It is critical to investigate prediction models at the genome level to understand how each model quantifies genomic marker effects contributing to the trait genetic architecture. Hence, we developed a computational tool, the Ensemble AnalySis with Interpretable Genomic Prediction (EasiGP), to investigate the genome features and predictive behaviours of the ensemble. Here, we demonstrate the utility of EasiGP using a maize breeding dataset. EasiGP visualised the genetic architecture from diverse interpretable genomic prediction models and identified several well-known key maize genes. EasiGP also revealed several potential new genomic regions for further investigation. EasiGP helps us investigate the trait genetic architecture that can be utilised to benefit crop breeding. Core ideasO_LIA new computational tool, EasiGP, was created to interpret multiple genomic prediction models at the genomic level C_LIO_LIEasiGP visualises the inferred trait genetic architecture from multiple genomic prediction models with circos plots C_LIO_LIAs a case study, EasiGP highlighted several well-known genes regulating the target trait, days to anthesis C_LIO_LIEasiGP can facilitate the discovery of novel genome regions underlying target traits for further investigation C_LI

bioinformatics↗

bioRxiv2024

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↗

bioRxiv2022

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↗

bioRxiv2020

Greenhouse spatial effects detected in the barley (Hordeum vulgare L.) epigenome underlie stochasticity of DNA methylation

Environmental cues are known to alter the methylation profile of genomic DNA, and thereby change the expression of some genes. A proportion of such modifications may become adaptive by adjusting expression of stress response genes but others been shown to be highly stochastic, even under controlled conditions. The influence of environmental flux on plants adds an additional layer of complexity that has potential to confound attempts to interpret interactions between environment, methylome and plant form. We therefore adopt a positional and longitudinal approach to study progressive changes to barley DNA methylation patterns in response to salt exposure during development under greenhouse conditions. Methylation-Sensitive Amplified Polymorphism (MSAP) and phenotypic analyses of nine diverse barley varieties were grown in a randomized plot design, under two salt treatments (0 mM and 75 mM NaCl). Combining environmental, phenotypic and epigenetic data analyses, we show that at least part of the epigenetic variability, previously described as stochastic, is linked to environmental micro-variations during plant growth. Additionally, we show that differences in methylation increase with time of exposure to micro-variations in environment. We propose that subsequent epigenetic studies take into account microclimate-induced epigenetic variability.

plant biology↗

bioRxiv2019

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↗

bioRxiv2019

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↗

bioRxiv2019

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↗