Dynamic genetic architecture of yeast response to environmental perturbation shed light on origin of cryptic genetic variation

Does the definition of a novel environment affect the ability to detect cryptic genetic variation?

Anthropogenic change exposes populations to environments that have been rare or entirely absent from their evolutionary past. Such novel environments are hypothesized to release cryptic genetic variation, a hidden store of variance that can fuel evolution. However, support for this hypothesis is mixed. One possible reason is a lack of clarity in what is meant by 'novel environment', an umbrella term encompassing conditions with potentially contrasting effects on the exposure or concealment of cryptic variation. Here, we use a meta-analysis approach to investigate changes in the total genetic variance of multivariate traits in ancestral versus novel environments. To determine whether the definition of a novel environment could explain the mixed support for a release of cryptic genetic variation, we compared absolute novel environments, those not represented in a population's evolutionary past, to extreme novel environments, those involving frequency or magnitude changes to environments present in a population's ancestry. Despite sufficient statistical power, we detected no broad-scale pattern of increased genetic variance in novel environments, and finding the type of novel environment did not explain any significant variation in effect sizes. When effect sizes were partitioned by experimental design, we found increased genetic variation in studies based on broad-sense measures of variance, and decreased variation in narrow-sense studies, in support of previous research. Therefore, the source of genetic variance, not the definition of a novel environment, was key to understanding environment-dependant genetic variation, highlighting non-additive genetic variance as an important component of cryptic genetic variation and avenue for future research.

Role of genetic architecture in phenotypic plasticity

Phenotypic plasticity, the ability of an organism to display different phenotypes across environments, is widespread in nature. Plasticity aids survival in novel environments. Herein, we review studies from yeast that allow us to start uncovering the genetic architecture of phenotypic plasticity. Genetic variants and their interactions impact the phenotype in different environments, and distinct environments modulate the impact of genetic variants and their interactions on the phenotype. Because of this, certain hidden genetic variation is expressed in specific genetic and environmental backgrounds. A better understanding of the genetic mechanisms of phenotypic plasticity will help to determine short- and long-term responses to selection and how wide variation in disease manifestation occurs in human populations.

Genetic structure and triazole resistance among Aspergillus fumigatus populations from remote and undeveloped regions in Eastern Himalaya

Aspergillus fumigatus is a ubiquitous mold and a common human fungal pathogen. Recent molecular population genetic and epidemiological analyses have revealed evidence for long-distance gene flow and high genetic diversity within most local populations of A. fumigatus. However, little is known about the impact of regional landscape factors in shaping the population diversity patterns of this species. Here we sampled extensively and investigated the population structure of A. fumigatus from soils in the Three Parallel Rivers (TPR) region in Eastern Himalaya. This region is remote, undeveloped and sparsely populated, bordered by glaciated peaks more than 6,000 m above sea level, and contained three rivers separated by tall mountains over very short horizontal distances. A total of 358 A. fumigatus strains from 19 sites along the three rivers were isolated and analyzed at nine loci containing short tandem repeats. Our analyses revealed that mountain barriers, elevation differences, and drainage systems all contributed low but statistically significant genetic variations to the total A. fumigatus population in this region. We found abundant novel alleles and genotypes in the TPR population of A. fumigatus and significant genetic differentiation between this population and those from other parts of Yunnan and the globe. Surprisingly, despite limited human presence in this region, about 7% of the A. fumigatus isolates were resistant to at least one of the two medical triazoles commonly used for treating aspergillosis. Our results call for greater surveillance of this and other human fungal pathogens in the environment. IMPORTANCE The extreme habitat fragmentation and substantial environmental heterogeneity in the TPR region have long known to contribute to geographically shaped genetic structure and local adaptation in several plant and animal species. However, there have been limited studies of fungi in this region. Aspergillus fumigatus is a ubiquitous pathogen capable of long-distance dispersal and growth in diverse environments. In this study, using A. fumigatus as a model, we investigated how localized landscape features contribute to genetic variations in fungal populations. Our results revealed that elevation and drainage isolation rather than direct physical distances significantly impacted genetic exchange and diversity among the local A. fumigatus populations. Interestingly, within each local population, we found high allelic and genotypic diversities, and with evidence ~7% of all isolates being resistant to two medical triazoles, itraconazole and voriconazole. Given the high frequency of ARAF found in mostly natural soils of sparsely populated sites in the TPR region, close monitoring of their dynamics in nature and their effects on human health is needed.

Complex genetic architecture underlying the plasticity of maize agronomic traits

Phenotypic plasticity is the ability of a given genotype to produce multiple phenotypes in response to changing environmental conditions. Understanding the genetic basis of phenotypic plasticity and establishing a predictive model is highly relevant to future agriculture under a changing climate. Here we report findings on the genetic basis of phenotypic plasticity for 23 complex traits using a diverse maize population planted at five sites with distinct environmental conditions. We found that latitude-related environmental factors were the main drivers of across-site variation in flowering time traits but not in plant architecture or yield traits. For the 23 traits, we detected 109 quantitative trait loci (QTLs), 29 for mean values, 66 for plasticity, and 14 for both parameters, and 80% of the QTLs interacted with latitude. The effects of several QTLs changed in magnitude or sign, driving variation in phenotypic plasticity. We experimentally validated one plastic gene, ZmTPS14.1, whose effect was likely mediated by the compensation effect of ZmSPL6 from a downstream pathway. By integrating genetic diversity, environmental variation, and their interaction into a joint model, we could provide site-specific predictions with increased accuracy by as much as 9.9%, 2.2%, and 2.6% for days to tassel, plant height, and ear weight, respectively. This study revealed a complex genetic architecture involving multiple alleles, pleiotropy, and genotype-by-environment interaction that underlies variation in the mean and plasticity of maize complex traits. It provides novel insights into the dynamic genetic architecture of agronomic traits in response to changing environments, paving a practical way toward precision agriculture.

An exploration into the conversion of dominance to additive genetic variance in contrasting environments

PREMISE

The evolutionary response of a trait to environmental change depends upon the level of additive genetic variance. It has been long argued that sustained selection will tend to deplete additive genetic variance as favored alleles approach fixation. Non-additive genetic variance, due to interactions among alleles within and between loci, does not immediately contribute to an evolutionary response. However, shifts in the allele frequencies within and between interacting loci may convert non-additive variance into additive variance. Here we consider the possibility that an environmental shift may alter allelic interactions in ways that convert dominance into additive genetic variance.

METHODS

We grew a pedigreed population of Brassica rapa in greenhouse and field conditions. The field conditions mimicked agricultural conditions from which the base population was drawn, while the greenhouse featured benign conditions. We used Bayesian models to estimate the additive, dominance, and maternal components of quantitative genetic variance. We also estimated genetic correlations across environments using parental breeding values.

RESULTS

Although the additive genetic variance was elevated in the greenhouse condition, no consistent pattens emerged that would indicate a conversion of dominance variance. The unusually low genetic variance and broad confidence intervals for the variance estimates obtained through this analysis preclude definitive interpretations.

CONCLUSIONS

Further studies are needed to determine whether between-environment changes in additive genetic variance can be traced to conversion of dominance variance.

Low-coverage sequencing in a deep intercross of the Virginia body weight lines provides insight to the polygenic genetic architecture of growth: novel loci revealed by increased power and improved genome-coverage

Genetic dissection of highly polygenic traits is a challenge, in part due to the power necessary to confidently identify loci with minor effects. Experimental crosses are valuable resources for mapping such traits. Traditionally, genome-wide analyses of experimental crosses have targeted major loci using data from a single generation (often the F₂) with individuals from later generations being generated for replication and fine-mapping. Here, we aim to confidently identify minor-effect loci contributing to the highly polygenic basis of the long-term, bi-directional selection responses for 56-d body weight in the Virginia body weight chicken lines. To achieve this, a strategy was developed to make use of data from all generations (F₂-F₁₈) of the advanced intercross line, developed by crossing the low and high selected lines after 40 generations of selection. A cost-efficient low-coverage sequencing based approach was used to obtain high-confidence genotypes in 1Mb bins across 99.3% of the chicken genome for >3,300 intercross individuals. In total, 12 genome-wide significant, and 30 additional suggestive QTL reaching a 10% FDR threshold, were mapped for 56-d body weight. Only 2 of these QTL reached genome-wide significance in earlier analyses of the F₂ generation. The minor-effect QTL mapped here were generally due to an overall increase in power by integrating data across generations, with contributions from increased genome-coverage and improved marker information content. The 12 significant QTL explain >37% of the difference between the parental lines, three times more than 2 previously reported significant QTL. The 42 significant and suggestive QTL together explain >80%. Making integrated use of all available samples from multiple generations in experimental crosses are economically feasible using the low-cost, sequencing-based genotyping strategies outlined here. Our empirical results illustrate the value of this strategy for mapping novel minor-effect loci contributing to complex traits to provide a more confident, comprehensive view of the individual loci that form the genetic basis of the highly polygenic, long-term selection responses for 56-d body weight in the Virginia body weight chicken lines.

Mapping causal genes and genetic interactions for agronomic traits using a large F2 population in rice

Dissecting the genetic mechanisms underlying agronomic traits is of great importance for crop breeding. Agronomic traits are usually controlled by multiple quantitative trait loci (QTLs) and genetic interactions, and mapping the underlying causal genes is still labor-intensive and time-consuming. Here, we present a genetic tool for directly targeting the specific causal genes by using a single-gene resolution linkage map that was constructed from 3756 F2 rice plants via targeted sequencing technology and Tukey-Kramer multiple comparisons test. Three large- and moderate-effect QTLs, qHD6-2, qGL3-1, and qGW5-2, were successfully mapped to their specific causal genes, Hd1, GS3, and GW5, respectively. A complex genetic interaction network containing 30 QTL-QTL interactions was constructed, revealing that the alternative allele of hub QTL, qHD6-2, can hide or release the genetic contributions of the alleles at interacting loci. Moreover, arranging genetic interactions in the models lead to more accurate phenotypic predictions. These results provide a community resource and new feasible strategy for deciphering the genetic mechanisms of complex agronomic traits and accelerating crop breeding.

Associations between TUBB-WWOX SNPs, their haplotypes, gene-gene, and gene-environment interactions and dyslipidemia

In this study, we investigated associations between single nucleotide polymorphisms (SNPs) in the tubulin beta class I (TUBB) and WW domain-containing oxidoreductase (WWOX) genes, gene-gene interactions, and gene-environment interactions and dyslipidemia in the Chinese Maonan ethnic group. Four SNPs (rs3132584, rs3130685, rs2222896, and rs2548861) were genotyped in unrelated subjects with normal lipid levels (864) or dyslipidemia (1129). While 5.0% of Maonan subjects carried the rs3132584TT genotype, none of the Chinese Han in Beijing subjects did. Allele and genotype frequencies differed between the normal and dyslipidemia groups for three SNPs (rs3132584, rs3130685, and rs2222896). rs2222896G allele carriers in the normal group had higher low-density lipoprotein cholesterol and lower high-density lipoprotein cholesterol levels. The rs3132584GG, rs3130685CC+TT, and rs2222896GG genotypes as well as the rs2222896G-rs2548861G and rs2222896G-rs2548861T haplotypes were associated with an elevated risk of dyslipidemia; the rs2222896A-rs2548861T and rs2222896A-rs2548861G haplotypes were associated with a reduced risk of dyslipidemia. Among the thirteen TUBB-WWOX interaction types identified, rs3132584T-rs3130685T-rs2222896G-rs2548861T increased the risk of dyslipidemia 1.371-fold. Fourteen two- to four-locus optimal interactive models for SNP-SNP, haplotype-haplotype, gene-gene, and gene-environment interactions exhibited synergistic or contrasting effects on dyslipidemia. Finally, the interaction between rs3132584 and rs2222896 increased the risk of dyslipidemia 2.548-fold and predicted hypertension.

The complex role of genetic background in shaping the effects of spontaneous and induced mutations

Spontaneous and induced mutations frequently show different phenotypic effects across genetically distinct individuals. It is generally appreciated that these background effects mainly result from genetic interactions between the mutations and segregating loci. However, the architectures and molecular bases of these genetic interactions are not well understood. Recent work in a number of model organisms has tried to advance knowledge of background effects both by using large‐scale screens to find mutations that exhibit this phenomenon and by identifying the specific loci that are involved. Here, we review this body of research, emphasizing in particular the insights it provides into both the prevalence of background effects across different mutations and the mechanisms that cause these background effects.

A large fraction of mutations show different effects in distinct individuals.

These background effects are mainly caused by epistasis with segregating loci.

Mapping studies show a diversity of genetic architectures can be involved.

Genetically complex changes in gene expression are often, but not always, causative.

Fitness variation across subtle environmental perturbations reveals local modularity and global pleiotropy of adaptation

Building a genotype-phenotype-fitness map of adaptation is a central goal in evolutionary biology. It is difficult even when adaptive mutations are known because it is hard to enumerate which phenotypes make these mutations adaptive. We address this problem by first quantifying how the fitness of hundreds of adaptive yeast mutants responds to subtle environmental shifts. We then model the number of phenotypes these mutations collectively influence by decomposing these patterns of fitness variation. We find that a small number of inferred phenotypes can predict fitness of the adaptive mutations near their original glucose-limited evolution condition. Importantly, inferred phenotypes that matter little to fitness at or near the evolution condition can matter strongly in distant environments. This suggests that adaptive mutations are locally modular - affecting a small number of phenotypes that matter to fitness in the environment where they evolved - yet globally pleiotropic - affecting additional phenotypes that may reduce or improve fitness in new environments.

Gene-Environment Interaction in the Era of Precision Medicine - Filling the Potholes Rather Than Starting to Build a New Road

Gene-environment interaction is a key part of evolutionary biology, animal, and plant breeding, and a number of health sciences, like epidemiology and precision medicine. However, bottlenecks in models of gene-environment interaction have recently been made manifest, particularly in the field of medicine and, consequently, specific improvements have been explicitly requested-namely, an implementation of gene-environment interaction satisfactorily disentangled from gene-environment correlation. The present paper meets those demands by providing mathematical developments that implement classical models of genetic effects and bring them up to date with the prospects current available data bestow. These developments are shown to overcome the limitations of previous proposals through the analysis of illustrative examples on disease susceptibility, with special attention paid to precision medicine. Indeed, a number of misconceptions about the application of models of genetic/environmental effects to precision medicine are here identified and clarified. The theory here provided is argued to strengthen, in particular, the methodology required for high-precision characterization of strain virulence in the study of the COVID-19 pandemic.

Dissecting the Genetic Regulation of Yeast Growth Plasticity in Response to Environmental Changes

Variable individual responses to environmental changes, such as phenotype plasticity, are heritable, with some genotypes being robust and others plastic. This variation for plasticity contributes to variance in complex traits as genotype-by-environment interactions (G × E). However, the genetic basis of this variability in responses to the same external stimuli is still largely unknown. In an earlier study of a large haploid segregant yeast population, genotype-by-genotype-by-environment interactions were found to make important contributions to the release of genetic variation in growth responses to alterations of the growth medium. Here, we explore the genetic basis for heritable variation of different measures of phenotype plasticity in the same dataset. We found that the central loci in the environmentally dependent epistatic networks were associated with overall measures of plasticity, while the specific measures of plasticity identified a more diverse set of loci. Based on this, a rapid one-dimensional genome-wide association (GWA) approach to overall plasticity is proposed as a strategy to efficiently identify key epistatic loci contributing to the phenotype plasticity. The study thus provided both analytical strategies and a deeper understanding of the complex genetic regulation of phenotype plasticity in yeast growth.