High-dimensional variance partitioning reveals the modular genetic basis of adaptive divergence in gene expression during reproductive character displacement

Trans regulation of an odorant binding protein by a proto-Y chromosome affects male courtship in house fly

The male-limited inheritance of Y chromosomes favors alleles that increase male fitness, often at the expense of female fitness. Determining the mechanisms underlying these sexually antagonistic effects is challenging because it can require studying Y-linked alleles while they still segregate as polymorphisms. We used a Y chromosome polymorphism in the house fly, Musca domestica, to address this challenge. Two male determining Y chromosomes (YM and IIIM) segregate as stable polymorphisms in natural populations, and they differentially affect multiple traits, including male courtship performance. We identified differentially expressed genes encoding odorant binding proteins (in the Obp56h family) as candidate agents for the courtship differences. Through network analysis and allele-specific expression measurements, we identified multiple genes on the house fly IIIM chromosome that could serve as trans regulators of Obp56h gene expression. One of those genes is homologous to Drosophila melanogaster CG2120, which encodes a transcription factor that binds near Obp56h. Upregulation of CG2120 in D. melanogaster nervous tissues reduces copulation latency, consistent with this transcription factor acting as a negative regulator of Obp56h expression. The transcription factor gene, which we name speed date, demonstrates a molecular mechanism by which a Y-linked gene can evolve male-beneficial effects.

Mega-scale Bayesian regression methods for genome-wide prediction and association studies with thousands of traits

Large-scale phenotype data are expected to increase the accuracy of genome-wide prediction and the power of genome-wide association analyses. However, genomic analyses of high-dimensional, highly correlated traits are challenging. We developed a method for implementing high-dimensional Bayesian multivariate regression to simultaneously analyze genetic variants underlying thousands of traits. As a demonstration, we implemented the BayesC prior in the R package MegaLMM. Applied to Genomic Prediction, MegaBayesC effectively integrated hyperspectral reflectance data from 620 hyperspectral wavelengths to improve the accuracy of genetic value prediction on grain yield in a wheat dataset. Applied to Genome-Wide Association Studies, we used simulations to show that MegaBayesC can accurately estimate the effect sizes of QTL across a range of genetic architectures and causes of correlations among traits. To apply MegaBayesC to a realistic scenario involving whole-genome marker data, we developed a 2-stage procedure involving a preliminary step of candidate marker selection prior to multivariate regression. We then used MegaBayesC to identify genetic associations with flowering time in Arabidopsis thaliana, leveraging expression data from 20,843 genes. MegaBayesC selected 15 single nucleotide polymorphisms as important for flowering time, with 13 located within 100 kb of known flowering-time related genes, a higher validation rate than achieved by a single-stage analysis using only the flowering time data itself. These results demonstrate that MegaBayesC can efficiently and effectively leverage high-dimensional phenotypes in genetic analyses.

A lot of convergence, a bit of divergence: environment and interspecific interactions shape body color patterns in Lissotriton newts

Coexistence with related species poses evolutionary challenges to which populations may react in diverse ways. When exposed to similar environments, sympatric populations of two species may adopt similar phenotypic trait values. However, selection may also favor trait divergence as a way to reduce competition for resources or mates. The characteristics of external body parts, such as coloration and external morphology, are involved to varying degrees in intraspecific signaling as well as in the adaptation to the environment, and consequently may be diversely affected by interspecific interactions in sympatry. Here, we studied the effect of sympatry on various color and morphological traits in males and females of two related newt species Lissotriton helveticus and L. vulgaris. Importantly, we did not only estimate how raw trait differences between species respond to sympatry, but also the marginal responses after controlling for environmental variation. We found that dorsal and caudal coloration converged in sympatry, likely reflecting their role in adaptation to local environments, especially concealment from predators. In contrast, aspects of male and female ventral coloration, which harbours sexual signals in both species, diverged in sympatry. This divergence may reduce opportunities for interspecific sexual interactions and the associated loss of energy, suggesting reproductive character displacement (RCD). Our study emphasizes the contrasting patterns of traits involved in different functions and calls for the need to consider this diversity in evolutionary studies.

Restoring ancestral phenotypes is a general pattern in gene expression evolution during adaptation to new environments in Tribolium castaneum

Plasticity and evolution are two processes allowing populations to respond to environmental changes, but how both are related and impact each other remains controversial. We studied plastic and evolutionary responses in gene expression of Tribolium castaneum after exposure of the beetles to new environments that differed from ancestral conditions in temperature, humidity or both. Using experimental evolution with 10 replicated lines per condition, we were able to demonstrate adaptation after 20 generations. We measured whole-transcriptome gene expression with RNA-sequencing to infer evolutionary and plastic changes. We found more evidence for changes in mean expression (shift in the intercept of reaction norms) in adapted lines than for changes in plasticity (shifts in slopes). Plasticity was mainly preserved in selected lines and was responsible for a large part of the phenotypic divergence in expression between ancestral and new conditions. However, we found that genes with the largest evolutionary changes in expression also evolved reduced plasticity and often showed expression levels closer to the ancestral stage. Results obtained in the three different conditions were similar, suggesting that restoration of ancestral expression levels during adaptation is a general evolutionary pattern. With a larger sample in the most stressful condition, we were able to detect a positive correlation between the proportion of genes with reversion of the ancestral plastic response and mean fitness per selection line.

Structure and stability of genetic variance-covariance matrices: A Bayesian sparse factor analysis of transcriptional variation in the three-spined stickleback

The genetic variance-covariance matrix (G) is a quantity of central importance in evolutionary biology due to its influence on the rate and direction of multivariate evolution. However, the predictive power of empirically estimated G-matrices is limited for two reasons. First, phenotypes are high-dimensional, whereas traditional statistical methods are tuned to estimate and analyse low-dimensional matrices. Second, the stability of G to environmental effects and over time remains poorly understood. Using Bayesian sparse factor analysis (BSFG) designed to estimate high-dimensional G-matrices, we analysed levels variation and covariation in 10,527 expressed genes in a large (n = 563) half-sib breeding design of three-spined sticklebacks subject to two temperature treatments. We found significant differences in the structure of G between the treatments: heritabilities and evolvabilities were higher in the warm than in the low-temperature treatment, suggesting more and faster opportunity to evolve in warm (stressful) conditions. Furthermore, comparison of G and its phenotypic equivalent P revealed the latter is a poor substitute of the former. Most strikingly, the results suggest that the expected impact of G on evolvability-as well as the similarity among G-matrices-may depend strongly on the number of traits included into analyses. In our results, the inclusion of only few traits in the analyses leads to underestimation in the differences between the G-matrices and their predicted impacts on evolution. While the results highlight the challenges involved in estimating G, they also illustrate that by enabling the estimation of large G-matrices, the BSFG method can improve predicted evolutionary responses to selection.

Dissecting high-dimensional phenotypes with bayesian sparse factor analysis of genetic covariance matrices

Quantitative genetic studies that model complex, multivariate phenotypes are important for both evolutionary prediction and artificial selection. For example, changes in gene expression can provide insight into developmental and physiological mechanisms that link genotype and phenotype. However, classical analytical techniques are poorly suited to quantitative genetic studies of gene expression where the number of traits assayed per individual can reach many thousand. Here, we derive a Bayesian genetic sparse factor model for estimating the genetic covariance matrix (G-matrix) of high-dimensional traits, such as gene expression, in a mixed-effects model. The key idea of our model is that we need consider only G-matrices that are biologically plausible. An organism's entire phenotype is the result of processes that are modular and have limited complexity. This implies that the G-matrix will be highly structured. In particular, we assume that a limited number of intermediate traits (or factors, e.g., variations in development or physiology) control the variation in the high-dimensional phenotype, and that each of these intermediate traits is sparse - affecting only a few observed traits. The advantages of this approach are twofold. First, sparse factors are interpretable and provide biological insight into mechanisms underlying the genetic architecture. Second, enforcing sparsity helps prevent sampling errors from swamping out the true signal in high-dimensional data. We demonstrate the advantages of our model on simulated data and in an analysis of a published Drosophila melanogaster gene expression data set.

A sexual ornament in chickens is affected by pleiotropic alleles at HAO1 and BMP2, selected during domestication

Domestication is one of the strongest forms of short-term, directional selection. Although selection is typically only exerted on one or a few target traits, domestication can lead to numerous changes in many seemingly unrelated phenotypes. It is unknown whether such correlated responses are due to pleiotropy or linkage between separate genetic architectures. Using three separate intercrosses between wild and domestic chickens, a locus affecting comb mass (a sexual ornament in the chicken) and several fitness traits (primarily medullary bone allocation and fecundity) was identified. This locus contains two tightly-linked genes, BMP2 and HAO1, which together produce the range of pleiotropic effects seen. This study demonstrates the importance of pleiotropy (or extremely close linkage) in domestication. The nature of this pleiotropy also provides insights into how this sexual ornament could be maintained in wild populations.

The genetic analysis of phenotypes and the identification of the causative underlying genes remain central to molecular and evolutionary biology. By utilizing the domestication process, it is possible to exploit the large differences between domesticated animals and their wild counterparts to study both this and the mechanism of domestication itself. Domestication has been central to the advent of modern civilization; and yet, despite domesticated animals displaying similar adaptations in morphology, physiology, and behaviour, the genetic basis of these changes are unknown. In addition, though sexual selection theory has been the subject of a vast amount of study, very little is known about which genes are underpinning such traits. We have generated multiple intercrosses and advanced intercrosses based on wild-derived and domestic chickens to fine-map genomic regions affecting a sexual ornament. These regions have been over-laid with putative selective sweeps identified in domestic chickens and found to be significantly associated with them. By using expression QTL analysis, we show that two genes in one region, HAO1 and BMP2, are controlling multiple aspects of the domestication phenotype, from a sexual ornament to multiple life history traits. This demonstrates the importance of pleiotropy (or extremely close linkage) in controlling these genetic changes.

Development and evolution of character displacement

Character displacement occurs when competition for either resources or successful reproduction imposes divergent selection on interacting species, causing divergence in traits associated with resource use or reproduction. Here, we describe how character displacement can be mediated either by genetically canalized changes (i.e., changes that reflect allelic or genotype frequency changes) or by phenotypic plasticity. We also discuss how these two mechanisms influence the tempo of character displacement. Specifically, we suggest that, under some conditions, character displacement mediated by phenotypic plasticity might occur more rapidly than that mediated by genetically canalized changes. Finally, we describe how these two mechanisms may act together and determine character displacement's mode, such that it proceeds through an initial phase in which trait divergence is environmentally induced to a later phase in which divergence becomes genetically canalized. This plasticity-first hypothesis predicts that character displacement should be generally mediated by ancestral plasticity and that it will arise similarly in multiple, independently evolving populations. We conclude by highlighting future directions for research that would test these predictions.