Biology subjects

Lotterhos, K. E.

Publications and source records attributed to Lotterhos, K. E..

13 recordsLinked to original sources

bioRxiv2026

A general model for the evolution of thermal performance curves with application to real time-series data

Thermal performance curves (TPCs) are widely used to investigate the effect of changes in body temperature on an organisms performance. Despite empirical evidence that temperature-dependent performance is ubiquitous across taxa, the field lacks models for how thermal performance evolves under realistic timeseries, genetic architectures, and physiological constraints. We address this gap by integrating a mathematical model with individual-based quantitative population genetic simulations. Our model can predict the evolutionary trajectory and shape of TPCs for any given thermal regime. Our model reproduces core properties of TPC evolution from previous studies such as the emergence of generalists in variable environments, but also explains why organisms may evolve TPCs that do not match their historical body temperature range. We uncover novel dynamics of adaptive tracking, the most notable being multi-generation lags between temperature and TPC parameters that can lead to unexpected correlations between the two. Our model predicts empirically observed patterns of adaptive tracking of critical thermal minimum in the invasive pest Drosophila suzukii, including individual-level variability and multi-generation lags with changing temperature. Our results also highlight the limitations of models that ignore factors that influence TPC evolution and individual variability, such as autocorrelation in temperature timeseries, effective population size, evolution of additive genetic correlation in TPC parameters, genetic architecture, and physiological constraints. Our flexible simulation model can incorporate these factors and help generate empirically testable hypotheses of how species will evolve in response to global climate change.

evolutionary biology↗

bioRxiv2026

Contrasting effects of geographic distance, environmental distance, and intraspecific diversity on the performance of a marine invertebrate in common gardens

Restoration and management of natural populations often assume that local genotypes are best suited for transplantation to their local environment. Prioritizing a single local genotype, however, contrasts with the framework of maximizing intraspecific diversity to increase population resilience to environmental change. Local populations may also become maladapted to a rapidly changing environment, motivating alternative frameworks that instead minimize environmental distance between source and transplantation sites. Here, we tested the predictive power of the local is best, maximize intraspecific diversity, and minimize environmental distance frameworks on the survival and growth of Eastern oyster (Crassostrea virginica) genotypes in field common gardens that differed in salinity and disease pressure. Although a genome scan revealed patterns of adaptation to disease, heat stress, and salinity among source populations, we did not find strong support for the local is best framework: geographically distant southern genotypes performed comparably to local selection lines and a local wild population. Higher genetic diversity within monocultures was associated with higher survival, yet highly diverse polycultures survived at lower rates than the best-performing monocultures, providing mixed support for the maximize intraspecific diversity framework. Temperature and salinity of the environments-of-origin of parents predicted the survival of their offspring in common gardens, but the relationship between survival and environmental distance was context-dependent, leading to mixed support for the minimize environmental distance framework. Together, these results demonstrate that no single framework reliably predicted transplantation success, suggesting that effective management strategies may need to integrate genomic and environmental lines of evidence to guide genotype selection.

evolutionary biology↗

bioRxiv2026

Phenotypic and Genomic Evidence of Adaptive Tracking in Thermal Tolerance of Wild Populations of an Invasive Drosophila.

Adaptive tracking is an evolutionary process in which allele frequencies and phenotypes shift in response to temporally fluctuating environments. Currently it is unclear whether adaptive tracking causes predictable evolution of complex traits such as thermal tolerance. We investigated seasonal adaptive tracking of critical thermal minimum (CTmin) and genome-wide allele frequencies over multiple years in the invasive fly Drosophila suzukii. CTmin increased throughout the growing season, showing a lag of several generations between increasing temperature and evolutionary change. Genetic analyses indicate CTmin is highly polygenic, with little overlap between alleles associated with CTmin and other seasonally fluctuating alleles. Thus, polygenic traits may track seasonal environments without leaving strong genomic signals. By contrast, there were strong seasonal genomic signatures for alleles associated with oligogenic traits as such pesticide resistance and olfactory behavior. These findings suggest that seasonal adaptive tracking shapes a broad suite of traits that contribute to D. suzukiis invasion success.

evolutionary biology↗

bioRxiv2025

GHIST 2024: The 1st Genomic History Inference Strategies Tournament

Evaluating population genetic inference methods is challenging due to the complexity of evolutionary histories, potential model misspecification, and unconscious biases in self-assessment. The Genomic History Inference Strategies Tournament (GHIST) is a community-driven competition designed to evaluate methods for inferring evolutionary history from population genomic data. The inaugural GHIST competition ran from July to November 2024 and featured four demographic history inference challenges of varying complexity: a bottleneck model, a split with isolation model, a secondary contact model with demographic complexity, and an archaic admixture model. Data were provided as error-free VCF files, and participants submitted numerical parameter estimates that were scored by relative root mean squared error. Approximately 60 participants competed, using diverse approaches. Results revealed the current dominance of methods based on site frequency spectra, while highlighting the advantages of flexible model-building approaches for complex demographic histories. We discuss insights regarding the competition and outline the next iteration, which is ongoing with expanded challenge diversity. By providing standardized benchmarks and highlighting areas for improvement, GHIST represents a substantial step toward more reliable inference of evolutionary history from genomic data.

evolutionary biology↗

bioRxiv2024

DNA methylation modulates transcriptional noise in response to elevated pCO2 in the eastern oyster (Crassostrea virginica)

Ocean acidification significantly affects marine calcifiers like oysters, warranting the study of molecular mechanisms like DNA methylation that contribute to adaptive plasticity in response to environmental change. However, a consensus has not been reached on the extent to which methylation modules gene expression, and in turn plasticity, in marine invertebrates. In this study, we investigated the impact of pCO2 on gene expression and DNA methylation in the eastern oyster, Crassostrea virginica. After a 30-day exposure to control (572 ppm) or elevated pCO2 (2,827 ppm), whole genome bisulfite sequencing (WGBS) and RNA-Seq data were generated from adult female gonad tissue and male sperm samples. Although differentially methylated loci (DML) were identified in females (89) and males (2,916), there were no differentially expressed genes, and only one differentially expressed transcript in females. However, gene body methylation impacted other forms of gene activity in sperm, such as the maximum number of transcripts expressed per gene and changes in the predominant transcript expressed. Elevated pCO2 exposure increased gene expression variability (transcriptional noise) in males but decreased noise in females, suggesting a sex-specific role of methylation in gene expression regulation. Functional annotation of genes with changes in transcript-level expression or containing DML revealed several enriched biological processes potentially involved in elevated pCO2 response, including apoptotic pathways and signal transduction, as well as reproductive functions. Taken together, these results suggest that DNA methylation may regulate gene expression variability to maintain homeostasis in elevated pCO2 conditions and could play a key role in environmental resilience in marine invertebrates.

physiology↗

bioRxiv2024

The limits of predicting maladaptation to future environments with genomic data

Anthropogenically driven changes in land use and climate patterns pose unprecedented challenges to species persistence. To understand the extent of these impacts, genomic offset methods have been used to forecast maladaptation of natural populations to future environmental change. However, while their use has become increasingly common, little is known regarding their predictive performance across a wide array of realistic and challenging scenarios. Here, we evaluate four offset methods (Gradient Forests, the Risk-Of-Non-Adaptedness, redundancy analysis, and LFMM2) using an extensive set of simulated datasets that vary demography, adaptive architecture, and the number and spatial patterns of adaptive environments. For each dataset, we train models using either all, adaptive, or neutral marker sets and evaluate performance using in silico common gardens by correlating known fitness with projected offset. Using over 4,850,000 of such evaluations, we find that 1) method performance is largely due to the degree of local adaptation across the metapopulation (LA{Delta}SA), 2) adaptive marker sets provide minimal performance advantages, 3) within-landscape performance is variable across gardens and declines when offset models are trained using additional non-adaptive environments, and 4) despite (1), performance declines more rapidly in novel climates for metapopulations with higher LA{Delta}SA than lower LA{Delta}SA. We discuss the implications of these results for management, assisted gene flow, and assisted migration.

genomics↗

bioRxiv2022

Nucleotide and structural polymorphisms of the eastern oyster genome paint a mosaic of divergence, selection, and human impacts

The eastern oyster, Crassostrea virginica, is a valuable fishery and aquaculture species that provides critical services as an ecosystem engineer. Oysters have a life-history that promotes high genetic diversity and gene flow while also occupying a wide range of habitats in variable coastal environments from the southern Gulf of Mexico to the southern waters of Atlantic Canada. To understand the interplay of genetic diversity, gene flow, and intense environmental selection, we used whole genome re-sequencing data from 90 individuals across the eastern United States and Gulf of Mexico, plus 5 selectively bred lines. Our data confirmed a large phylogeographic break between oyster populations in the Gulf of Mexico and the Atlantic coast of the USA. We also demonstrated that domestication has artificially admixed genetic material between the two ocean basins, and selected lines with admixed ancestry continue to maintain heterozygosity at these sites through several generations post admixture, possibly indicating relevance to desirable aquaculture traits. We found that genetic and structural variation are high in both wild and selected populations, but we also demonstrated that, when controlling for domestication admixture across ocean basins, wild populations do have significantly higher levels of nucleotide diversity and copy number variation than selected lines. Within the Atlantic coast, we detected subtle but distinct population structure, introgression of selected lines within wild individuals, an interaction between structural variation and putatively adaptive population structure, and evidence of candidate genes responding to selection from salinity. Our study highlights the potential for applying whole genome sequencing to highly polymorphic species and provides a road map for future work examining the genome variation of eastern oyster populations.

evolutionary biology↗

bioRxiv2022

A second unveiling: haplotig masking of the eastern oyster genome improves population-level inference

Genome assembly can be challenging for species that are characterized by high amounts of polymorphism, heterozygosity, and large effective population sizes. High levels of heterozygosity can result in genome mis-assemblies and a larger than expected genome size due to the haplotig versions of a single locus being assembled as separate loci. Here, we describe the first chromosome-level genome for the eastern oyster, Crassostrea virginica. Publicly released and annotated in 2017, the assembly has a scaffold N50 of 54 mb and is over 97.3% complete based on BUSCO analysis. The genome assembly for the eastern oyster is a critical resource for foundational research into molluscan adaptation to a changing environment and for selective breeding for the aquaculture industry. Subsequent resequencing data suggested the presence of haplotigs in the original assembly, and we developed a post hoc method to break up chimeric contigs and mask haplotigs in published heterozygous genomes and evaluated improvements to the accuracy of downstream analysis. Masking haplotigs had a large impact on SNP discovery and estimates of nucleotide diversity and had more subtle and nuanced effects on estimates of heterozygosity, population structure analysis, and outlier detection. We show that haplotig-masking can be a powerful tool for improving genomic inference, and we present an open, reproducible resource for the masking of haplotigs in any published genome.

genomics↗

bioRxiv2022

Does the paradigm of genotype-environment associations need to be re-assessed? The paradox of adaptive phenotypic clines with non-clinal patterns in causal alleles

Multivariate climate change presents an urgent need to understand how species adapt to complex environments. Population genetic theory predicts that loci under selection will form monotonic allele frequency clines with their selective environment, which has led to the wide use of genotype-environment associations (GEAs). This study elucidates the conditions under which allele frequency clines are more or less likely to evolve as multiple quantitative traits adapt to a multivariate environment. A novel set of simulations was created that all evolved similar phenotypic clines, but with varying proportions of causal alleles with clines. Phenotypic clines evolved mostly without clines in the causal allele frequencies under conditions that promoted unique combinations of mutations to achieve the multivariate optimum in different parts of the landscape. Although univariate and multivariate GEA methods failed to accurately infer the genetic basis of adaptation under a range of scenarios, individual multivariate traits could be accurately predicted from genotype and environmental data without any knowledge of the genetic architecture. This research challenges the utility of GEAs for understanding the genetic basis of adaptation to the environment, and instead suggests that multivariate trait predictions are a more fruitful approach for genomic forecasting and assisted gene flow efforts.

genomics↗

bioRxiv2021

Seeing the Forest for the trees: Assessing genetic offset predictions with Gradient Forest

Gradient Forest (GF) is increasingly being used to forecast climate change impacts, but remains mostly untested for this purpose. We explore its robustness to assumption violations, and relationship to measures of fitness, using SLiM simulations with explicit genome architecture and a spatial metapopulation. We evaluate measures of GF offset in: (1) a neutral model with no environmental adaptation; (2) a monogenic "population genetic" model with a single environmentally adapted locus; and (3) a polygenic "quantitative genetic" model with two adaptive traits, each adapting to a different environment. Although we found GF Offset to be broadly correlated with fitness offsets under both single locus and polygenic architectures. It could also be confounded by neutral demography, genomic architecture, and the nature of the adaptive environment. GF Offset is a promising tool, but it is important to understand its limitations and underlying assumptions, especially when used in the context of forecasting maladaptation.

evolutionary biology↗

bioRxiv2021

Invertebrate methylomes provide insight into mechanisms of environmental tolerance and reveal methodological biases

There is a growing focus on the role of DNA methylation in the ability of marine invertebrates to rapidly respond to changing environmental factors and anthropogenic impacts. However, genome-wide DNA methylation studies in non-model organisms are currently hampered by limited understanding of methodological biases. Here we compare three methods for quantifying DNA methylation at single base-pair resolution -- Whole Genome Bisulfite Sequencing (WGBS), Reduced Representation Bisulfite Sequencing (RRBS), and Methyl-CpG Binding Domain Bisulfite Sequencing (MBDBS) -- using multiple individuals from two reef-building coral species with contrasting environmental sensitivity. All methods reveal substantially greater methylation in Montipora capitata (11.4%) than the more sensitive Pocillopora acuta (2.9%). The majority of CpG methylation in both species occurs in gene bodies and flanking regions. In both species, MBDBS has the greatest capacity for detecting CpGs in coding regions at our sequencing depth, however MBDBS may be influenced by intra-sample methylation heterogeneity. RRBS yields robust information for specific loci albeit without enrichment of any particular genome feature and with significantly reduced genome coverage. Relative genome size strongly influences the number and location of CpGs detected by each method when sequencing depth is limited, illuminating nuances in cross-species comparisons. As genome-wide methylation differences, supported by data across bisulfite sequencing methods, may contribute to environmental sensitivity phenotypes in critical marine invertebrate taxa, these data provide a genomic resource for investigating the functional role of DNA methylation in environmental tolerance.

genomics↗

bioRxiv2020

General DNA methylation patterns and environmentally-induced differential methylation in the eastern oyster (Crassostrea virginica)

Epigenetic modification, specifically DNA methylation, is one possible mechanism for intergenerational plasticity. Before inheritance of methylation patterns can be characterized, we need a better understanding of how environmental change modifies the parental epigenome. To examine the influence of experimental ocean acidification on eastern oyster (Crassostrea virginica) gonad tissue, oysters were cultured in the laboratory under control (491 {+/-} 49 atm) or high (2550 {+/-} 211 atm) pCO2 conditions for four weeks. DNA from reproductive tissue was isolated from five oysters per treatment, then subjected to bisulfite treatment and DNA sequencing. Irrespective of treatment, DNA methylation was primarily found in gene bodies with approximately 22% of CpGs (2.7% of total cytosines) in the C. virginica genome predicted to be methylated. In response to elevated pCO2, we found 598 differentially methylated loci primarily overlapping with gene bodies. A majority of differentially methylated loci were in exons (61.5%) with less intron overlap (31.9%). While there was no evidence of a significant tendency for the genes with differentially methylated loci to be associated with distinct biological processes, the concentration of these loci in gene bodies, including genes involved in protein ubiquitination and biomineralization suggests DNA methylation may be important for transcriptional control in response to ocean acidification. Changes in gonad methylation also indicate potential for these methylation patterns to be inherited by offspring. Understanding how experimental ocean acidification conditions modify the oyster epigenome, and if these modifications are inherited, allows for a better understanding of how ecosystems will respond to environmental change.

genomics↗

bioRxiv2017

Expressed Exome Capture Sequencing (EecSeq): a method for cost-effective exome sequencing for all organisms with or without genomic resources

Exome capture is an effective tool for surveying the genome for loci under selection. However, traditional methods require annotated genomic resources. Here, we present a method for creating cDNA probes from expressed mRNA, which are then used to enrich and capture genomic DNA for exon regions. This approach, called \"EecSeq\", eliminates the need for costly probe design and synthesis. We tested EecSeq in the eastern oyster, Crassostrea virginica, using a controlled exposure experiment. Four adult oysters were heat shocked at 36{degrees} C for 1 hour along with four control oysters kept at 14{degrees} C. Stranded mRNA libraries were prepared for two individuals from each treatment and pooled. Half of the combined library was used for probe synthesis and half was sequenced to evaluate capture efficiency. Genomic DNA was extracted from all individuals, enriched via captured probes, and sequenced directly. We found that EecSeq had an average capture sensitivity of 86.8% across all known exons and had over 99.4% sensitivity for exons with detectable levels of expression in the mRNA library. For all mapped reads, over 47.9% mapped to exons and 37.0% mapped to expressed targets, which is similar to previously published exon capture studies. EecSeq displayed relatively even coverage within exons (i.e. minor \"edge effects\") and even coverage across exon GC content. We discovered 5,951 SNPs with a minimum average coverage of 80X, with 3,508 SNPs appearing in exonic regions. We show that EecSeq provides comparable, if not superior, specificity and capture efficiency compared to costly, traditional methods.

genomics↗