Stephen F. Chenoweth

This study explores the impact of positive selection on the genetic composition of a Drosophila serrata population in eastern Australia through a comprehensive analysis of 110 whole genome sequences. Utilizing an advanced deep learning algorithm (partialS/HIC) and a range of inferred demographic histories, we identified that approximately 14% of the genome is directly affected by sweeps, with soft sweeps being more prevalent (10.6%) than hard sweeps (2.1%), and partial sweeps being uncommon (1.3%). The algorithm demonstrated robustness to demographic assumptions in classifying complete sweeps but faced challenges in distinguishing neutral regions from partial sweeps and linked regions under demographic misspecification. The findings reveal the indirect influence of sweeps on nearly two‐thirds of the genome through linkage, with an over‐representation of putatively deleterious variants suggesting that positive selection drags deleterious variants to higher frequency due to hitchhiking with beneficial loci. Gene ontology enrichment analysis further supported our confidence in the accuracy of sweep detection as several traits expected to be under positive selection due to evolutionary arms races (e.g. immunity) were detected in hard sweeps. This study provides valuable insights into the direct and indirect contributions of positive selection in shaping genomic variation in natural populations.

Diet elicits varied effects on longevity across a wide range of animal species where dietary discordance between an organisms' evolutionary and developmental dietary history is increasingly recognized to play a critical role in shaping lifespan. However, whether such changes, predominantly assessed in a single generation, lead to evolutionary shifts in lifespan remains unclear. In this study, we used an experimental evolution approach to test whether changes in an organisms' evolutionary and developmental dietary history, specifically carbohydrate content, causes lifespan evolution in Drosophila serrata . After 30 generations, we investigated the evolutionary potential of lifespan in response to four novel diets that varied systematically in their ratio of carbohydrate–protein content. We also examined developmental plasticity effects using a set of control populations that were raised on the four novel environments allowing us to assess the extent to which plastic responses of lifespan mirrored adaptive responses observed following experimental evolution. Both high- and low-carbohydrate diets elicited plastic effects on lifespan; however, the plastic responses for lifespan to developmental diets bore little resemblance to the evolved responses on evolutionary diets. Understanding the dietary conditions regulating the match/mismatch of plastic and evolved responses will be important in determining whether a particular match/mismatch combination is adaptive for lifespan. While the differences in evolutionary diet by developmental diet interactions are only beginning to be elucidated, this study lays the foundation for future investigations of carbohydrate contributions to evolved and plastic effects on health and lifespan.

Standing genetic variation, and capacity to adapt to environment change, will ultimately depend on the fitness effects of mutations across the range of environments experienced by contemporary, panmictic, populations. We investigated how mild perturbations in diet and temperature affect mutational (co)variances of traits that evolve under climatic adaptation, and contribute to individual fitness in Drosophila serrata. We assessed egg-to-adult viability, development time and wing size of 64 lines that had diverged from one another via spontaneous mutation over 30 generations of brother-sister mating. Our results suggested most mutations have directionally concordant (i.e., synergistic) effects in all environments and both sexes. However, elevated mutational variance under reduced macronutrient conditions suggested environment-dependent variation in mutational effect sizes for development time. We also observed evidence for antagonistic effects under standard versus reduced macronutrient conditions, where these effects were further contingent on temperature (for development time) or sex (for size). Diet also influenced the magnitude and sign of mutational correlations between traits, although this result was largely due to a single genotype (line), which may reflect a rare, large effect mutation. Overall, our results suggest environmental heterogeneity and environment-dependency of mutational effects could contribute to the maintenance of genetic variance.

Diet elicits varied effects on longevity across a wide range of animal species. For example, diets low in protein and high in carbohydrate typically extend lifespan while diets high in protein tend to reduce it. Although studies have also shown that diet-induced lifespan changes can persist through transgenerational plasticity, whether such changes lead to evolutionary shifts in lifespan remains unclear. In this study we combine experimental evolution and phenotypic plasticity assays to address this gap. Using Drosophila serrata , we investigated the evolutionary potential of lifespan in response to four novel diets spanning a carbohydrate-protein gradient. We also examined developmental plasticity effects using a set of control populations that were raised on the four novel environments. Our results show that although lifespan evolved in response to changes in dietary carbohydrate concentration, the plastic responses for lifespan differed from the evolved responses. The direction of the evolved response (increased lifespan) observed on low carbohydrate diets was in the opposite direction to the plastic response (decreased lifespan). Our results imply that plastic responses to low carbohydrates can be maladaptive for lifespan and misaligned with the evolved responses, laying the groundwork for future investigations of carbohydrate contributions to evolved and plastic effects on lifespan.

The interaction of evolutionary processes to determine quantitative genetic variation has implications for contemporary and future phenotypic evolution, as well as for our ability to detect causal genetic variants. While theoretical studies have provided robust predictions to discriminate among competing models, empirical assessment of these has been limited. In particular, theory highlights the importance of pleiotropy in resolving observations of selection and mutation, but empirical investigations have typically been limited to few traits. Here, we applied high-dimensional Bayesian Sparse Factor Genetic modeling to gene expression datasets in 2 species, Drosophila melanogaster and Drosophila serrata, to explore the distributions of genetic variance across high-dimensional phenotypic space. Surprisingly, most of the heritable trait covariation was due to few lines (genotypes) with extreme [>3 interquartile ranges (IQR) from the median] values. Intriguingly, while genotypes extreme for a multivariate factor also tended to have a higher proportion of individual traits that were extreme, we also observed genotypes that were extreme for multivariate factors but not for any individual trait. We observed other consistent differences between heritable multivariate factors with outlier lines vs those factors without extreme values, including differences in gene functions. We use these observations to identify further data required to advance our understanding of the evolutionary dynamics and nature of standing genetic variation for quantitative traits.

The interaction of evolutionary processes to determine quantitative genetic variation has implications for contemporary and future phenotypic evolution, as well as for our ability to detect causal genetic variants. While theoretical studies have provided robust predictions to discriminate among competing models, empirical assessment of these has been limited. In particular, theory highlights the importance of pleiotropy in resolving observations of selection and mutation, but empirical investigations have typically been limited to few traits. Here, we applied high dimensional Bayesian Sparse Factor Genetic modelling to 3,385 gene expression traits from Drosophila melanogaster and from D. serrata to explore how genetic variance is distributed across high-dimensional phenotypic space. Surprisingly, most of the heritable trait covariation was due to few lines (genotypes) with extreme (>3 IQR from the median) values. This observation, in the two independently sampled species, suggests that the House of Cards (HoC) model might apply not only to individual expression traits, but also to emergent co-expression phenotypes. Intriguingly, while genotypes extreme for a multivariate factor also tended to have a higher proportion of individual traits that were extreme, we also observed genotypes that were outliers for multivariate factors but not for any individual traits. We observed other consistent differences between heritable multivariate factors with outlier lines versus those factors that conformed to a Gaussian distribution of genetic effects, including differences in gene functions. We use these observations to identify further data required to advance our understanding of the evolutionary dynamics and nature of standing genetic variation for quantitative traits.

Understanding the extent to which microevolutionary adaptation relies on novel beneficial mutations, as opposed to previously neutral standing genetic variation, is an important goal of evolutionary genetics. Progress towards this goal has been enhanced during the genomic era through the study of selective sweeps. Selective sweeps fall into two categories: hard sweeps via new mutations and soft sweeps via pre-existing mutations. However, data are currently lacking on the relative frequency of these two types of selective sweep. In this study, we examined 110 whole genome sequences from Drosophila serrata sampled from eastern Australia and searched for hard and soft sweeps using a deep learning algorithm (diploS/HIC). Analyses revealed that approximately 15% of the D. serrata genome was directly impacted by soft sweeps, and that 46% of the genome was indirectly influenced via linkage to these soft sweeps. In contrast, hard sweep signatures were very rare, only accounting for 0.1% of the scanned genome. Gene ontology enrichment analysis further supported our confidence in the accuracy of sweep detection as several traits expected to be under frequent selection due to evolutionary arms races (e.g. immunity and sperm competition) were detected. Within soft sweep regions and those flanking them, there was an over-representation of SNPs with predicted deleterious effects, suggesting positive selection drags deleterious variants to higher frequency due to their linkage with beneficial loci. This study provides insight into the direct and indirect contributions of positive selection in shaping genomic variation in natural populations.

Sex differences in lifespan remain an intriguing puzzle in evolutionary biology. While explanations range from sex differences in selection to sex differences in the expression of recessive lifespan‐altering mutations (via X‐linkage), little consensus has been reached. One unresolved issue is the extent to which genetic influences on lifespan dimorphism are modulated by the environment. For example, studies have shown that sex differences in lifespan can either increase or decrease depending upon the social environment. Here, we took an experimental approach, manipulating multiple axes of the social environment across inbred long‐ and short‐lived genotypes and their reciprocal F1s in the fly Drosophila serrata. Our results reveal strong genetic effects and subtle yet significant genotype‐by‐environment interactions for male and female lifespan, specifically due to both population density and mating status. Further, our data do not support the idea that unconditional expression of deleterious X‐linked recessive alleles in heterogametic males accounts for lower male lifespan.

In evolutionary quantitative genetics, the genetic variance–covariance matrix, G , and the vector of directional selection gradients, β , are key parameters for predicting multivariate selection responses and genetic constraints. Historically, investigations of G and β have not overlapped with those dissecting the genetic basis of quantitative traits. Thus, it remains unknown whether these parameters reflect pleiotropic effects at individual loci. Here, we integrate multivariate genome-wide association study (GWAS) with G and β estimation in a well-studied system of multivariate constraint: sexual selection on male cuticular hydrocarbons (CHCs) in Drosophila serrata . In a panel of wild-derived re-sequenced lines, we augment genome-based restricted maximum likelihood to estimate G alongside multivariate single nucleotide polymorphism (SNP) effects, detecting 532 significant associations from 1 652 276 SNPs. Constraint was evident, with β lying in a direction of G with low evolvability. Interestingly, minor frequency alleles typically increased male CHC-attractiveness suggesting opposing natural selection on β . SNP effects were significantly misaligned with the major eigenvector of G , g _max , but well aligned to the second and third eigenvectors g ₂ and g ₃ . We discuss potential factors leading to these varied results including multivariate stabilizing selection and mutational bias. Our framework may be useful as researchers increasingly access genomic methods to study multivariate selection responses in wild populations.