The genomic architecture of flowering time varies across space and time inMimulus guttatus

Abstract

The degree to which genomic architecture varies across space and time is central to the evolution of genomes in response to natural selection. Bulked-segregant mapping combined with pooled sequencing provides an efficient method to estimate the effect of genetic variants on quantitative traits. We develop a novel likelihood framework to identify segregating variation within multiple populations and generations while accommodating estimation error on a sample- and SNP-specific basis. We use this method to map loci for flowering time within natural populations of Mimulus guttatus, collecting the early and late flowering plants from each of three neighboring populations and two consecutive generations. We find appreciable variation in genetic effects on flowering time across both time and space; the greatest differences evident between populations. Structural variants, such as inversions, and genes from multiple flowering time pathways exhibit the strongest associations with flowering time. It is also clear that genotype-by-environment interactions are an important influence on flowering time variation.

Extreme copy number variation at a tRNA ligase affecting phenology and fitness in yellow monkeyflowers

“…We focus here on the well-studied Iron Mountain (IM, Oregon) high-elevation annual population, from which the inbred line (IM62) defining the M. guttatus reference genome was derived. This population exhibits high levels of standing genetic variation for fitness-related traits (including flowering time and flower size) (Kelly et al 2013; Monnahan & Kelly 2017) as well as multiple segregating inversions with opposing effects on fitness (Fishman & Kelly 2015; Lee et al 2016). These features reflect a very large population (census size in 100,000s each year) without internal genetic structure (Sweigart et al 1999) and dominated by natural selection rather than drift (Puzey et al 2017).…”

“…Such antagonistic pleiotropy across environments is key to the models for the selective maintenance of standing variation in space (i.e., local adaptation in the face of gene flow) and time (Wittmann et al 2017; Brown & Kelly 2018). More recently, we used PoolSeq to identify SNPs and chromosomal structural variants associated with flower size evolution under artificial selection (Kelly et al 2013) and with flowering time in the field in two populations (Monnahan et al 2015; Monnahan & Kelly 2017). These studies point to climatic fluctuations as a likely factor in the maintenance of standing variation within annual M. guttatus , but captured only a subset of the life-history traits potentially under selection.…”

“…In this study, we conducted CNV-focused reanalyses of existing population sequencing (PoolSeq) datasets for flower size (Kelly et al 2013) and flowering time (Monnahan & Kelly 2017) and bulked segregant experiments (also using PoolSeq) for vernalization requirement and germination time in the field. Both of these traits may mediate the timing of reproduction (a key trait for plant fitness), and also affect the probability of survival to flowering under winter-and drought-stress.…”

Determining the evolutionary forces shaping G × E

“…Alternatively, traits measured in different environments can be mapped independently, allowing the identification of loci in which allelic effects differ across environments (Atwell et al, 2010;Zhao et al, 2011;Filiault & Maloof, 2012;Consortium et al, 2016). In addition, methods have been developed to use bulk segregant analysis to compare effects measured across environments (Monnahan & Kelly, 2017). The first approach will identify loci associated with trait responses but will not provide information about how effects differ across environments, while the second approach can identify loci with effects in specific environments of interest (Maranville et al, 2012).…”

Evolutionary and ecological functional genomics, from lab to the wild

“…Examples of these species are the model plant Arabidopsis thaliana (Ehrenreich et al ., ), crops such as maize (Buckler et al ., ), rice (Zhou et al ., ) and soybeans (Diers et al ., ), and ecologically and evolutionarily interesting species such as Mimulus spp. (Monnahan and Kelly, ) and Helianthus spp. (Anderson et al ., ).…”