McKibben, M. T. W. | bioRxiv Science

bioRxiv2022

Chromosome-scale reference genome and RAD-based genetic map of yellow starthistle (Centaurea solstitialis) reveal putative structural variation and QTLs associated with invader traits

Invasive species offer outstanding opportunities to identify the genomic sources of variation that contribute to rapid adaptation, as well as the genetic mechanisms facilitating invasions. The Eurasian plant yellow starthistle (Centaurea solstitialis) is highly invasive in North and South American grasslands and known to have evolved increased growth and reproduction during invasion. Here we develop new genomic resources for C. solstitialis and map the genetic basis of invasiveness traits. We present a chromosome-scale (1N = 8) reference genome using PacBio CLR and Dovetail Omni-C technologies, and functional gene annotation using RNAseq. We find repeat structure typical of the family Asteraceae, with over 25% of gene content derived from ancestral whole genome duplications (paleologs). Using an F2 mapping population derived from a cross between native and invading parents, with a restriction site-associated DNA (RAD)-based genetic map, we validate the assembly and identify 13 QTL underpinning size traits that have evolved during invasion. We find evidence that large effect QTL may be associated with structural variants between native and invading genotypes, including a variant with an overdominant and pleiotropic effect on key invader traits. We also find evidence of significant paleolog enrichment under two QTL. Our results add to growing evidence of the importance of structural variants in evolution, and to understanding of the rapid evolution of invaders. Significance StatementInvasive species often evolve rapidly in new environments, potentially informing our understanding of the genomic basis of adaptation, but genomic studies of these non-model systems are limited. We provide a chromosome-scale reference genome, annotation, and genetic map for the invasive plant yellow starthistle, and we investigate the genetic basis of invader trait evolution in this system. We find regions of the genome with large effects on traits that differ between native and invading genotypes, and evidence suggesting genome structural variants and past genome duplications could play a role in rapid adaptation of invading populations. These genomic resources and evolutionary insights aid in our understanding of the sources of genomic variation for adaptation, and how their evolution facilitates invasion.

genomics↗

bioRxiv2021

Applying Machine Learning to Classify the Origins of Gene Duplications

Nearly all lineages of land plants have experienced at least one whole genome duplication (WGD) in their history. The legacy of these ancient WGDs is still observable in the diploidized genomes of extant plants. Genes originating from WGD--paleologs--can be maintained in diploidized genomes for millions of years. These paleologs have the potential to shape plant evolution through sub- and neofunctionalization, increased genetic diversity, and reciprocal gene loss among lineages. Current methods for classifying paleologs often rely on only a subset of potential genomic features, have varying levels of accuracy, and often require significant data and/or computational time. Here we developed a supervised machine learning approach to classify paleologs from a target WGD in diploidized genomes across a broad range of different duplication histories. We collected empirical data on syntenic block sizes and other genomic features from 27 plant species each with a different history of paleopolyploidy. Features from these genomes were used to develop simulations of syntenic blocks and paleologs to train a gradient boosted decision tree. Using this approach, Frackify (Fractionation Classify), we were able to accurately identify and classify paleologs across a broad range of parameter space, including cases with multiple overlapping WGDs. We then compared Frackify with other paleolog inference approaches in six species with paleotetraploid and paleohexaploid ancestries. Frackify provides a way to combine multiple genomic features to quickly classify paleologs while providing a high degree of consistency with existing approaches.

bioinformatics↗