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ABSTRACT Yellow monkeyflowers (Mimulus guttatuscomplex, Phrymaceae) are a powerful system for studying ecological adaptation, reproductive variation, and genome evolution. To initiate pan‐genomics in this group, we present four chromosome‐scale assemblies and annotations of accessions spanning a broad evolutionary spectrum: two from a singleM. guttatuspopulation, one from the closely related selfing speciesM. nasutus, and one from a more divergent speciesM. tilingii. All assemblies are highly complete and resolve centromeric and repetitive regions. Comparative analyses reveal such extensive structural variation in repeat‐rich, gene‐poor regions that large portions of the genome are unalignable across accessions. As a result, thisMimuluspan‐genome is primarily informative in genic regions, underscoring limitations of resequencing approaches in such polymorphic taxa. We document gene presence–absence, investigate the recombination landscape using high‐resolution linkage data, and quantify nucleotide diversity. Surprisingly, pairwise differences at fourfold synonymous sites are exceptionally high—even in regions of very low recombination—reaching ~3.2% within a singleM. guttatuspopulation, ~7% within the interfertileM. guttatusspecies complex (approximately equal to SNP divergence between great apes and Old World monkeys), and ~7.4% between that complex and the reproductively isolatedM. tilingii. Genome‐wide patterns of nucleotide variation show little evidence of linked selection, and instead suggest that the concentration of genes (and likely selected sites) in high‐recombination regions may buffer diversity loss. These assemblies, annotations, and comparative analyses provide a robust genomic foundation forMimulusresearch and offer new insights into the interplay of recombination, structural variation, and molecular evolution in highly diverse plant genomes. 

This article is a Commentary onAcoca‐Pidolleet al. (2024),242: 717–726. 

Gene expression can be influenced by genetic variants that are closely linked to the expressed gene (cis eQTLs) and variants in other parts of the genome (trans eQTLs). We created a multiparental mapping population by sampling genotypes from a single natural population ofMimulus guttatusand scored gene expression in the leaves of 1,588 plants. We find that nearly every measured gene exhibits cis regulatory variation (91% have FDR < 0.05). cis eQTLs are usually allelic series with three or more functionally distinct alleles. The cis locus explains about two thirds of the standing genetic variance (on average) but varies among genes and tends to be greatest when there is high indel variation in the upstream regulatory region and high nucleotide diversity in the coding sequence. Despite mapping over 10,000 trans eQTL / affected gene pairs, most of the genetic variance generated by trans acting loci remains unexplained. This implies a large reservoir of trans acting genes with subtle or diffuse effects. Mapped trans eQTLs show lower allelic diversity but much higher genetic dominance than cis eQTLs. Several analyses also indicate that trans eQTLs make a substantial contribution to the genetic correlations in expression among different genes. They may thus be essential determinants of “gene expression modules,” which has important implications for the evolution of gene expression and how it is studied by geneticists. 

Introduction:Heavy metal pollutants can have long lasting negative impacts on ecosystem health and can shape the evolution of species. The persistent and ubiquitous nature of heavy metal pollution provides an opportunity to characterize the genetic mechanisms that contribute to metal resistance in natural populations. Methods:We examined variation in resistance to copper, a common heavy metal contaminant, using wild collections of the model organismDrosophila melanogaster. Flies were collected from multiple sites that varied in copper contamination risk. We characterized phenotypic variation in copper resistance within and among populations using bulked segregant analysis to identify regions of the genome that contribute to copper resistance. Results and Discussion:Copper resistance varied among wild populations with a clear correspondence between resistance level and historical exposure to copper. We identified 288 SNPs distributed across the genome associated with copper resistance. Many SNPs had population-specific effects, but some had consistent effects on copper resistance in all populations. Significant SNPs map to several novel candidate genes involved in refolding disrupted proteins, energy production, and mitochondrial function. We also identified one SNP with consistent effects on copper resistance in all populations nearCG11825, a gene involved in copper homeostasis and copper resistance. We compared the genetic signatures of copper resistance in the wild-derived populations to genetic control of copper resistance in theDrosophilaSynthetic Population Resource (DSPR) and theDrosophilaGenetic Reference Panel (DGRP), two copper-naïve laboratory populations. In addition toCG11825, which was identified as a candidate gene in the wild-derived populations and previously in the DSPR, there was modest overlap of copper-associated SNPs between the wild-derived populations and laboratory populations. Thirty-one SNPs associated with copper resistance in wild-derived populations fell within regions of the genome that were associated with copper resistance in the DSPR in a prior study. Collectively, our results demonstrate that the genetic control of copper resistance is highly polygenic, and that several loci can be clearly linked to genes involved in heavy metal toxicity response. The mixture of parallel and population-specific SNPs points to a complex interplay between genetic background and the selection regime that modifies the effects of genetic variation on copper resistance. 

In the formation of species, adaptation by natural selection generates distinct combinations of traits that function well together. The maintenance of adaptive trait combinations in the face of gene flow depends on the strength and nature of selection acting on the underlying genetic loci. Floral pollination syndromes exemplify the evolution of trait combinations adaptive for particular pollinators. The North American wildflower genusPenstemondisplays remarkable floral syndrome convergence, with at least 20 separate lineages that have evolved from ancestral bee pollination syndrome (wide blue-purple flowers that present a landing platform for bees and small amounts of nectar) to hummingbird pollination syndrome (bright red narrowly tubular flowers offering copious nectar). Related taxa that differ in floral syndrome offer an attractive opportunity to examine the genomic basis of complex trait divergence. In this study, we characterized genomic divergence among 229 individuals from aPenstemonspecies complex that includes both bee and hummingbird floral syndromes. Field plants are easily classified into species based on phenotypic differences and hybrids displaying intermediate floral syndromes are rare. Despite unambiguous phenotypic differences, genome-wide differentiation between species is minimal. Hummingbird-adapted populations are more genetically similar to nearby bee-adapted populations than to geographically distant hummingbird-adapted populations, in terms of genome-wided_XY. However, a small number of genetic loci are strongly differentiated between species. These approximately 20 “species-diagnostic loci,” which appear to have nearly fixed differences between pollination syndromes, are sprinkled throughout the genome in high recombination regions. Several map closely to previously established floral trait quantitative trait loci (QTLs). The striking difference between the diagnostic loci and the genome as whole suggests strong selection to maintain distinct combinations of traits, but with sufficient gene flow to homogenize the genomic background. A surprisingly small number of alleles confer phenotypic differences that form the basis of species identity in this species complex. 

Selection component analyses (SCA) relate individual genotype to fitness components such as viability, fecundity and mating success. SCA are based on population genetic models and yield selection estimates directly in terms of predicted allele frequency change. This paper explores the statistical properties of gSCA: experiments that apply SCA to genome-wide scoring of SNPs in field sampled individuals. Computer simulations indicate that gSCA involving a few thousand genotyped samples can detect allele frequency changes of the magnitude that has been documented in field experiments on diverse taxa. To detect selection, imprecise genotyping from low-level sequencing of large samples of individuals provides much greater power than precise genotyping of smaller samples. The simulations also demonstrate the efficacy of ‘haplotype matching’, a method to combine information from a limited collection of whole genome sequence (the reference panel) with the much larger sample of field individuals that are measured for fitness. Pooled sequencing is demonstrated as another way to increase statistical power. Finally, I discuss the interpretation of selection estimates in relation to the Beavis effect, the overestimation of selection intensities at significant loci. 

Abstract We measured the floral bud transcriptome of 151 fully sequenced lines of Mimulus guttatus from one natural population. Thousands of single nucleotide polymorphisms (SNPs) are implicated as transcription regulators, but there is a striking difference in the allele frequency spectrum of cis-acting and trans-acting mutations. Cis-SNPs have intermediate frequencies (consistent with balancing selection) while trans-SNPs exhibit a rare-alleles model (consistent with purifying selection). This pattern only becomes clear when transcript variation is normalized on a gene-to-gene basis. If a global normalization is applied, as is typically in RNAseq experiments, asymmetric transcript distributions combined with “rarity disequilibrium” produce a superabundance of false positives for trans-acting SNPs. To explore the cause of purifying selection on trans-acting mutations, we identified gene expression modules as sets of coexpressed genes. The extent to which trans-acting mutations influence modules is a strong predictor of allele frequency. Mutations altering expression of genes with high “connectedness” (those that are highly predictive of the representative module expression value) have the lowest allele frequency. The expression modules can also predict whole-plant traits such as flower size. We find that a substantial portion of the genetic (co)variance among traits can be described as an emergent property of genetic effects on expression modules.