Yang Liu

Bio

Dr. Yang Liu is an evolutionary geneticist, currently working at the University of Queensland (UQ) as a Research Fellow. Prior to UQ, he obtained a PhD from the University of British Columbia (UBC) and did a postdoc research at UBC and University of Cambridge. He is broadly interested in the eco-evolutionary dynamics of plant populations that have undergone environmental heterogeneity over spatiotemporal scales. The goal of his research is to increase our understanding of the impacts of major episodes in plant demography and life histories on trait evolution and to foster sustainability. He tackles research questions at the interface between ecology and evolutionary biology with the integration of population genetics and quantitative genomics to elucidate the ecological and genetic basis of phenotypic traits and biological adaptation.

Currently, he leverages available Arabidopsis natural accessions across its geographic distribution range, coupled with their genomic data, to perform common-garden and divergent selection experiments. From these he aims to dissect features of the genetic architecture of traits and to reveal their relationships to environmental conditions. He is focusing on the shoot branching phenotype and its associated traits including flowering timing.

ECO-EVO-GENOMICS TEAM

Ongoing Projects

Three PhD positions available in 2023-2025

Project 1: Unification of selection and inheritance informs adaptive potential for generations to come (Applications open in 2023; CLOSED)

Natural selection acts on phenotypes and produces immediate phenotypic effects within a generation. In this short-term process, some phenotypes are more successful than others. Use of single traits for selection analysis could generate opposing outcomes and cannot predict how selection operates on an organism. In contrast, multivariate selection in trait combinations utilizes the attribute of functional integrations to reveal how selection works in a multi-dimensional trait space. Selection is an important force driving evolution but not equal to evolution; the latter leads to changes in genetic variation. Only through assessment of the evolutionary responses of phenotypes can we understand the transmission of such selection from one generation to the next. How does selection occurring within a generation affect evolution across generations? In the project, we aim to address the question by unifying the two processes to forecast evolutionary potential in relation to selection. To that end, we partition genetic variance into components based on an experimental design, employ experimental evolution to estimate additive genetic variance-covariances (G) on quantitative scales and evaluate G-matrix evolution. We eventually hope to elucidate how populations subjected to artificial selection move along evolutionary trajectories and whether there are genetic constraints making the fitness optimum evolutionarily inaccessible.

Project 2: Genetic and ecological bases of shoot branching divergence across Arabidopsis species-wide accessions (Applications open in 2024)

Spatial patterns of genetic variation are shaped by environmental factors, topological features, and dispersal barriers. As a result, we often can identify population genetic structure stratified by geographic locations or ecological niches, the drivers of population isolation by distance or the environment, clinal genetic variation over space in alignment with gradually varying environment gradients, and adaptive genetic variation in relation to environmental variables. At the ecological level, assembly rules uncover the coordination of phenotypic traits along environmental clines. Tradeoffs between traits represent the consequence of environmental filters and reflect adaptation to environmental heterogeneity. For example, three fundamental adaptive strategies are delineated by a CSR theory, that is, Competitors, Stress-tolerators, and Ruderals. As such, ways of genetic and phenotypic assemblage over space and throughout time point to a role for natural selection driven by spatially varying environmental conditions to maintain genetic variation that confers natural variation in phenotypes. In this project, we focus on an important agronomic trait – shoot branching – due to its important contribution to the overall shoot architecture of a plant and being a potential target for yield optimization. We aim to dissect features of the genetic architecture of the trait and to reveal its relationships to environmental conditions. We integrate geographic, environmental, and genomic data from the 1001 Arabidopsis Genomes Project, coupled with the branching phenotype measured in selected accessions and then forecasted for the rest of the 1001 accessions using machine-learning models, to investigate the ecological relevance and genetic underpinnings of branching divergence across the Arabidopsis species-wide accessions. Our study has implications for enhancing our understanding of the genetic and ecological basis of shoot branching divergence and the potential for generating novel knowledge for improving phenotypic predictability.

Project 3: Dimensionality, modularity, and integration: Insights from the architecture features of pan-genomes, pan-transcriptome, pan-epigenomes, and pan-chromatin (applications open in 2025) Application Portal ALSO ACCEPTING EXPRESSION OF INTEREST FROM INTERNATIONAL APPLICANTS

Organisms are functionally integrated systems, where interactions among phenotypic traits make the whole more than the sum of its parts. How is a suite of traits assembled into an adaptive module? How is an intramodule rewired to form a regulatory network? What is the persistence and stability of a module under exposures to perturbations triggered by altered interactions between the response to disparate environmental conditions or between the responses of multiple traits to the same environment? What constrains modules to vary independently, reflecting the integration and canalization of evolutionary trajectories? In this project, we utilize a compilation of pan-genomes, pan-transcriptome, pan-epigenomes, and pan-chromatin resources of Arabidopsis thaliana to uncover how dimensionality, modularity, and integration are organized at different omics levels including genetic polymorphisms, structural variants, RNA isoforms, expression abundance, epigenetic imprinting, and chromatin accessibility. Ultimately, we apply such functional elements to multivariate genomic selection, in the hope of enhancing multilayered omics-enabled prediction.