Biology subjects

Kelly, J. K.

Publications and source records attributed to Kelly, J. K..

14 recordsLinked to original sources

bioRxiv2026

Soil microbiome effects on quantitative floral traits of the annual plant Mimulus guttatus

Plant flowering is a critical determinant of fitness and supports pollination services to ecosystems. Flowers are energetically expensive and require nutrients that plants might otherwise use for growth. Soil microorganisms can alter plant development in a variety of ways, but the quantitative importance of microbiomes in determining floral variation has yet to be established. In particular, it is unclear the extent to which genotypic differences in flowering phenotypes are mediated through the microbiome. We used a two-generation plant-soil feedback design to test whether flowering traits in a short-lived annual plant Mimulus guttatus drive differentiation in rhizosphere microbiomes. Second, we tested whether these microbiomes subsequently affected flowering traits of progeny plants. We found that different plant genotypes did not generate differences in rhizosphere microbiome structure or diversity after eight weeks of growth. When these microbiomes were inoculated onto new plants, flowering traits were mainly driven by the progeny plants genotype and not the genotype of the parental plant that trained the inoculated microbiome. Longer microbial training, as would necessarily happen in perennial plants, may produce stronger effects, but our results suggest microbiome effects contribute mainly to the environmental noise in flower traits in the short-lived annual Mimulus guttatus.

plant biology↗

bioRxiv2026

GCM: metric-guided clustering by genetic algorithm for correlation-defined modules

Gene co-expression analyses identify "good" modules by a correlation criterion. However, standard pipelines detect modules with greedy algorithms that optimize other quantities and only measure correlation afterwards. We present a method called Genetic Clustering by Metric(GCM hereafter), an open-source Python tool that closes this gap by treating module detection as maximum-likelihood inference and solving it globally. In GCM, the correlation objective is, up to a constant and the sample-size factor, the profile log-likelihood of an explicit generative model: a block-diagonal one-factor Gaussian in which each module is a single regulator with equal-magnitude {+/-} loadings. This bases model selection on a principled footing through a genuine BIC/AIC in correlation space. GCM maximizes this likelihood with a memetic genetic algorithm: a population-based search hybridized with a greedy local refinement that reassigns genes after the fact, a move the agglomerative clustering at the core of co-expression pipelines cannot make. Across a replicated noise sweep, GCM reproducibly surpasses hierarchical correlation clustering and k-means with the lowest variance, and an ablation shows the local-search step is responsible; the advantage persists when the number of modules is unknown and when unstructured genes must be ignored. GCM faithfully optimizes geometric indices on the Iris benchmark dataset. For a breast-cancer RNA-seq it recovers coherent modules that predict tumor-versus-normal status. GCM depends only on NumPy and SciPy and exposes one swappable-metric interface with single- and multi-objective modes. Author summaryWhen biologists group genes by how similarly they are expressed, they usually run a standard clustering method and then score the result with a separate quality measure. The method, however, was never trying to do well on that measure because it optimizes its own internal objective. We built a tool, GCM, that removes this gap: the user picks the quality measure they actually care about, and the tool searches directly for the grouping that scores best on it. The search is performed by a genetic algorithm, a population-based optimizer that mixes and mutates candidate groupings over many generations. GCM includes a purpose-built score for "modules" of co-expressed genes, as well as several widely used geometric scores, and it can balance two competing scores at once to choose how many groups the data support. We show on synthetic data with a known answer, on a textbook dataset, and on real expression data that the tool recovers the intended structure and lets researchers make explicit, and optimize for, their own definition of a good cluster.

bioinformatics↗

bioRxiv2026

Neo-sex Chromosomes Anchor a Sex-Limited Polymorphism Under Gene Flow

How polymorphic traits are maintained despite the homogenizing forces of gene flow and recombination is a central question in evolutionary biology. While the accumulation of locally adaptive alleles within chromosomal inversions is well established, the role of sex chromosomes in local adaptation has received comparatively less empirical support. Here, we show that a male-limited color polymorphism in an Anolis distichus contact zone is anchored by a neo-sex chromosome. Across a narrow environmental gradient, an abrupt transition between yellow and orange dewlaps--extensible throat fans used for signaling--is driven by distinct pigmentation profiles we characterize via histological and chromatographic analyses. Integrating genomics and association mapping, we demonstrate this divergence relies on an additive, oligogenic architecture. Alternative neo-Y haplotypes track the phenotypic cline and combine additively with autosomal variants near a putative ketolase and a lipid regulator to determine color, jointly explaining a significant portion of the phenotypic variance. Furthermore, we identify a copy number variant near an X-linked visual processing gene, indicating simultaneous sensory divergence. This concerted evolution suggests the local light environment drives adaptation of the communication system. Ultimately, this study demonstrates that degrading neo-sex chromosomes act as non-recombining hubs for locally adaptive alleles, preserving phenotypic diversity under gene flow.

evolutionary biology↗

bioRxiv2026

The dynamics of silent variation in Mimulus guttatus: Codon usage bias and linked selection

O_LISynonymous nucleotide variation, which is remarkably high in Mimulus guttatus, can be affected by both codon usage selection (translational efficiency) and linked selection (hitchhiking effects). C_LIO_LICodon usage reflects a genome-wide tug-of-war between mutational pressure toward A/T-ending codons and weak selection favoring G/C-ending codons. The outcome is determined largely by gene expression level and localized variation in recombination rate. C_LIO_LIUsing both mechanistic (ROC-SEMPPR) and population genetic models, we find that most genes are weakly selected for codon usage, about 76% yielding scaled selection coefficients (S = 4Nes) in the range of 0 to 1. Additionally, 4029 genes, primarily involved in photosynthesis, translation, defense, and phosphate scavenging, experience strong selection (S > 1). C_LIO_LILevels of nucleotide variation within genes indicate a strong effect of linked selection. Non-synonymous polymorphism declines in genes with strong purifying selection, and as the rate of (intra-genic) recombination declines. Levels of synonymous polymorphism usually track non-synonymous (owing to background selection), except in genes under the strongest translational selection. C_LIO_LICounterintuitively, we find that codon usage selection has a generally positive effect on synonymous nucleotide diversity at 4-fold degenerate positions. Since mutation strongly disfavors the optimal base in M. guttatus, codon selection in the range of 0 < S < 2 evens the balance (between selection and mutation) and thus inflates heterozygosity. C_LI

evolutionary biology↗

bioRxiv2025

Evolve and resequence provides a granular view of micro-evolution under different mating systems in Mimulus guttatus

We performed 10 generations of experimental evolution in Mimulus guttatus and measured genome-wide change in replicated populations that were compelled to reproduce entirely by self-fertilization, entirely by outcrossing, or by a mixture of the two. We developed a novel testing framework based on ancestral haplotype inference to locate mating system loci. Our results confirm several outstanding theoretical predictions: Selfing populations showed increased homozygosity, widespread hitch-hiking, and higher stochastic changes in allele frequencies compared to outcrossing populations. Despite this variability, approximately 20 genomic regions (QTLs) demonstrated parallel evolution across treatments. We identified candidate genes within QTLs using RNA sequencing data from the ancestral lines. In several instances, we found closely linked candidate genes, suggesting that by inhibiting recombination inbreeding can allow for selection on favorable gene combinations. We observed a general down-regulation of candidate genes in selfing populations, mirroring known transcriptome differences between established selfing and outcrossing sister species. This suggests that gene expression is a significant component of the "selfing syndrome."

evolutionary biology↗

bioRxiv2025

Adaptive Transgenerational Plasticity through Priming in Response to Neighbor Density in Mimulus platycalyx

Transgenerational plasticity (TGP) allows organisms to epigenetically alter offspring phenotypes based on their own environmental experience. TGP is adaptive if offspring perform better when their conditions match those experienced by parents. TGP is now a well-established phenomenon in plants, with current research focusing on the range of environmental causes and the mechanisms that epigenetically translate environmental experiences into phenotypic responses. This study tests neighbor density as an environmental driver of TPG, either through induction (response determined only by parental experience) or priming (response determined by the combination of parental and offspring experience). Replicate individuals of a single genotype of Mimulus platycalyx were grown to maturity under three conditions: No neighbors, Medium neighbor density, or High neighbor density. Offspring produced by selfing plants within each parental treatment were grown under both crowded and uncrowded conditions and measured for height, plant size traits, and flower number. Offspring growing under crowded conditions were relatively taller, had bigger leaves, and produced more flowers if their parents experienced neighbor competition than if their parents did not. In contrast, offspring grown alone performed relatively better when their parents also grew alone and did not experience competition. This pattern of performance variation, where plants did best when their environmental conditions matched those of their parents, suggests adaptive transgenerational priming in this species.

evolutionary biology↗

bioRxiv2024

Waves of Colonization and Gene Flow in a Great Speciator

Secondary contact between previously allopatric lineages offers a test of reproductive isolating mechanisms that may have accrued in isolation. Such instances of contact can produce stable hybrid zones--where reproductive isolation can further develop via reinforcement or phenotypic displacement--or result in the lineages merging. Ongoing secondary contact is most visible in continental systems, where steady input from parental taxa can occur readily. In oceanic island systems, however, secondary contact between closely related species of birds is relatively rare. When observed on sufficiently small islands, relative to population size, secondary contact likely represents a recent phenomenon. Here, we examine the dynamics of a group of birds whose apparent widespread hybridization influenced Ernst Mayrs foundational work on allopatric speciation: the whistlers of Fiji (Aves: Pachycephala). We demonstrate two clear instances of secondary contact within the Fijian archipelago, one resulting in a hybrid zone on a larger island, and the other resulting in a wholly admixed population on a smaller, adjacent island. We leveraged low genome-wide divergence in the hybrid zone to pinpoint a single genomic region associated with observed phenotypic differences. We use genomic data to present a new hypothesis that emphasizes rapid plumage evolution and post-divergence gene flow.

evolutionary biology↗

bioRxiv2023

The quantitative genetics of gene expression in Mimulus guttatus

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 of Mimulus guttatus and 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) and that 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 eQTL 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 also how it is studied by geneticists. Author summaryMimulus guttatus (yellow monkeyflower) is a model for the study of quantitative trait evolution in natural populations. Research has focused mainly on whole organism traits like flower size or herbivore resistance, but the level of expression of a gene is also a quantitative trait. In this study, we dissect leaf transcriptome variation using a breeding design that estimates the contribution of individual loci to expression variation (eQTLs). We find at least rough agreement to the "oligogenic model" where a major locus (the cis regulatory region) generates much of the genetic variation in the population. While associations studies routinely characterize genetic effects as binary (the two alternatives at a single nucleotide polymorphism or "SNP"), this description proves insufficient for Mimulus. Most loci exhibit multiple, and in some cases, a continuum of alleles. We find that trans eQTLs have different features than cis eQTLs, both in terms of the diversity and genetic dominance of alleles. These genetic features of eQTLs are critical determinants of the "G matrix", the genetic variances and covariances among all genes. The G matrix determines how gene expression will evolve under selection in response to changing environmental conditions. Our finding of large effect sizes and high allelic diversity suggests that the G matrix may be surprisingly malleable, even on ecological timescales.

evolutionary biology↗

bioRxiv2021

Genome-wide association mapping of transcriptome variation in Mimulus guttatus indicates differing patterns of selection on cis- versus trans-acting mutations

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 (AFS) 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 super-abundance 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 co-expressed 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.

genetics↗

bioRxiv2020

Predicting evolutionary change at the DNA level in a natural Mimulus population

Evolution by natural selection occurs when the frequencies of genetic variants change because individuals differ in Darwinian fitness components such as survival or reproductive success. Differential fitness has been demonstrated in field studies of many organisms, but our ability to quantitatively predict allele frequency changes from fitness measurements remains unclear. Here, we characterize natural selection on millions of Single Nucleotide Polymorphisms (SNPs) across the genome of the annual plant Mimulus guttatus. We use fitness estimates to calibrate population genetic models that effectively predict observed allele frequency changes into the next generation. Hundreds of SNPs experienced "male selection" in 2013 with one allele at each SNP elevated in frequency among successful male gametes relative to the entire population of adults. In the following generation, allele frequencies at these SNPs consistently shifted in the predicted direction. A second year of study revealed that SNPs had effects on both viability and reproductive success with pervasive trade-offs between fitness components. SNPs favored by male selection were, on average, detrimental to survival. These trade-offs (antagonistic pleiotropy and temporal fluctuations in fitness) may be essential to the long-term maintenance of alleles undergoing substantial changes from generation to generation. Despite the challenges of measuring selection in the wild, the strong correlation between predicted and observed allele frequency changes suggests that population genetic models have a much greater role to play in forward-time prediction of evolutionary change. Author summaryFor the last 100 years, population geneticists have been deriving equations for {Delta}p, the change in allele frequency owing to mutation, selection, migration, and genetic drift. Seldom are these equations used directly, to match a prediction for {Delta}p to an observation of {Delta}p. Here, we apply genomic sequencing technologies to samples from natural populations, obtaining millions of observations of {Delta}p. We estimate natural selection on SNPs in a natural population of yellow monkeyflowers and find extensive evidence for selection through differential male success. We use the SNP-specific fitness estimates to calibrate a population genetic model that predicts observed {Delta}p into the next generation. We find that when male selection favored one nucleotide at a SNP, that nucleotide increased in frequency in the next generation. Since neither observed nor predicted {Delta}p are generally large in magnitude, we developed a novel method called "haplotype matching" to improve prediction accuracy. The method leverages intensive whole genome sequencing of a reference panel (187 individuals) to infer sequence-specific selection in thousands of field individuals sequenced at much lower coverage. This method proved essential to accurately predicting {Delta}p in this experiment and further development may facilitate population genetic prediction more generally.

evolutionary biology↗

bioRxiv2018

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

Copy number variation (CNV) is a major part of the genetic diversity segregating within populations, but remains poorly understood relative to single nucleotide variation. Here, we report on a tRNA ligase gene (Migut.N02091; RLG1a) exhibiting unprecedented, and fitness-relevant, CNV within an annual population of the yellow monkeyflower Mimulus guttatus. RLG1a variation was associated with multiple traits in pooled population sequencing (PoolSeq) scans of phenotypic and phenological cohorts. Resequencing of inbred lines revealed intermediate frequency three-copy variants of RLG1a (trip+; 5/35 = 14%), and trip+ lines exhibited elevated RLG1a expression under multiple conditions. trip+ carriers, in addition to being over-represented in late-flowering and large-flowered PoolSeq populations, flowered later under stressful conditions in a greenhouse experiment (P < 0.05). In wild population samples, we discovered an additional rare RLG1a variant (high+) that carries 250-300 copies of RLG1a totaling [~]5.7Mb (20-40% of a chromosome). In the progeny of a high+ carrier, Mendelian segregation of diagnostic alleles and qPCR-based copy counts indicate that high+ is a single tandem array unlinked from the single copy RLG1a locus. In the wild, high+ carriers had highest fitness in two particularly dry and/or hot years (2015 and 2017; both p < 0.01), while single copy individuals were twice as fecund as either CNV type in a lush year (2016: p < 0.005). Our results demonstrate fluctuating selection on CNVs affecting phenological traits in a wild population, suggest that plant tRNA ligases mediate stress-responsive life-history traits, and introduce a novel system for investigating the molecular mechanisms of gene amplification.

evolutionary biology↗

bioRxiv2018

An examination of the evolve-and-resequence method using Drosophila simulans

We develop a set of analytical and simulation tools for Evolve-and-Resequence (E&R) experiments and apply them to a new study of rapid evolution in Drosophila simulans. Likelihood based test statistics applied to pooled population sequencing data suggest parallel evolution of 138 polymorphisms (SNPs) across the genome. This number is reduced by orders of magnitude from previous studies (thousands or tens of thousands), owing to differences in both experimental design and statistical analysis. Whole genome simulations calibrated from several Drosophila genetic datasets support the contention that the observed genome-wide response could be generated by as few as 30 loci under strong directional selection, with a corresponding hitch-hiking effect. Finally, the SNPs that showed strong parallel evolution in the novel laboratory environment exhibit an (initial) allele frequency spectrum indicative of balancing in nature. These loci also exhibit elevated differentiation among natural populations of D. simulans.

evolutionary biology↗

bioRxiv2018

A synthesis of mapping experiments reveals extensive genomic structural diversity in the Mimulus guttatus species complex

Understanding genomic structural variation such as inversions and translocations is a key challenge in evolutionary genetics. In this paper, we tackle this challenge by developing a novel statistical approach to comparative genetic mapping. The procedure couples a Hidden Markov Model with a Genetic Algorithm to detect large-scale structural variation using low-level sequencing data from multiple genetic mapping populations. We demonstrate the method using five distinct crosses within the flowering plant genus Mimulus. The synthesis of data from these experiments is first used to correct numerous errors (misplaced sequences) in the M. guttatus reference genome. Second, we confirm and/or detect eight large inversions polymorphic within the M. guttatus species complex. Finally, we show how this method can be applied in genomic scans to improve the accuracy and resolution of Quantitative Trait Locus (QTL) mapping.\n\nAUTHOR SUMMARYGenome sequences have proved to be a critical experimental resource for genetic research in many species. However, in some species there is considerable variation in genomic organization, making a single reference genome sequence inadequate. This variation can cause issues in interpreting genomic signals, such as those coming from trait mapping. We introduce a new statistical method and computational tools that use linkage information to reorganize a single reference genome to 1) repair genome assembly errors, and 2) identify variation between individuals or populations of the same species. Using this method we can create a new genome order that improves upon the reference genome. We apply this method to five crosses among plants in the Mimulus guttatus species complex. In this system we detect eight large chromosomal inversions and improve the resolution of a trait mapping study. This work highlights the utility of our method, and indicates how others studying diverse species might use them to improve their own research.

genetics↗

bioRxiv2017

The genomic architecture of flowering time varies across space and time in Mimulus guttatus

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.

genomics↗