Genome size and repeat content contribute to a complex architecture of flowering time in Amaranthus tuberculatus
Kreiner, J. M.; Hnatovska, S.; Stinchcombe, J. R.; Wright, S. I.
Show abstract
Genome size variation, largely driven by repeat content, is poorly understood within and among populations, limiting our understanding of its significance for adaptation. Here we characterize intraspecific variation in genome size and repeat content across 186 individuals of Amaranthus tuberculatus, a ubiquitous native weed that shows flowering time adaptation to climate across its range and in response to agriculture. K-mer based genome size estimates vary by up to 20% across individuals, with transposable elements, unknown repeats, and rDNAs being the primary contributors to this variability. The additive effect of this variation has important phenotypic consequences--individuals with more repeats, and thus larger genomes, show slower flowering times and growth rates. Compared to newly-characterized gene copy number and polygenic nucleotide changes underlying variation in flowering time, we show that genome size remains a modest but significant contributor to the genetic basis of flowering time. Differences in flowering time across sexes and habitats are not mirrored by genome size variation, but rather polygenic variation and a gene copy number variant within the ATP synthesis pathway. Repeat content nonetheless shows non-neutral distributions across the genome, and across latitudinal and environmental gradients, reflecting numerous governing processes that in turn influence quantitative genetic variation for phenotypes key to plant adaptation. Author SummaryThe remarkable and seemingly inconsequential variation in genome size across species has long been an enigma in evolutionary biology. Calling this viewpoint into question, correlations between genome size variation and traits linked to fitness are increasingly uncovered. While this suggests that DNA content itself may be a source of adaptive genetic variation, repeat elements that propagate at the cost of the host are known to largely mediate this variation and may thus limit adaptive potential. Here we look to disentangle these multi-level dynamics, characterizing repeat dynamics across the genome and among individuals across diverse collections of a widespread agricultural weed, linking repeat content to genome size variation, and characterizing the relative importance of its phenotypic consequences. In Amaranthus tuberculatus, we find non-neutral repeat distributions across individuals across the range, and while this repeat variation underlies both variation in genome size and flowering time, we show that it makes a relatively minor contribution to variation in a fitness-related trait across the landscape relative to monogenic and polygenic features. Together, this work broadens our perspective on the complex selective dynamics that govern intraspecific variation in genome size and traits key to fitness in plants.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Chromatin organization in early land plants reveals an ancestral association between H3K27me3, transposons, and constitutive heterochromatin 94%
- The genetic architecture of the sexually selected sword ornament and its evolution in hybrid populations 93%
- Virgin Birth: A genetic basis for facultative parthenogenesis 92%
Similar papers in this journal
- Whole-genome sequence of Potamopyrgus antipodarum: a model system for the maintenance of sexual reproduction reveals a recent whole-genome duplication 96%
- Within-population genome size variation is mediated by multiple genomic elements that segregate independently during meiosis 95%
- Evolutionary Consequences of Unusually Large Pericentric TE-rich Regions in the Genome of a Neotropical Fig Wasp 93%
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.