Diverse environmental perturbations reveal the evolution and context-dependency of genetic effects on gene expression levels

There is increasing appreciation that human complex traits are determined by poorly understood interactions between our genomes and daily environments. These "genotype x environment" (GxE) interactions remain difficult to map at the organismal level, but can be uncovered using molecular phenotypes. To do so at large-scale, we profiled transcriptomes across 12 cellular environments using 544 immortalized B cell lines from the 1000 Genomes Project. We mapped the genetic basis of gene expression across environments and revealed a context-dependent genetic architecture: the average heritability of gene expression levels increased in treatment relative to control conditions and, on average, each treatment revealed expression quantitative trait loci (eQTL) at 11% of genes. In total, 22% of all eQTL were context-dependent, and this group was enriched for trait- and disease-associated loci. Further, evolutionary analyses revealed that positive selection has shaped GxE loci involved in responding to immune challenges and hormones, but not man-made chemicals, suggesting there is reduced opportunity for selection to act on responses to molecules recently introduced into human environments. Together, our work highlights the importance of considering an exposure's evolutionary history when studying and interpreting GxE interactions, and provides new insight into the evolutionary mechanisms that maintain GxE loci in human populations.

Combinatorial G x G x E CRISPR screening and functional analysis highlights SLC25A39 in mitochondrial GSH transport

The SLC25 carrier family consists of 53 transporters that shuttle nutrients and co-factors across mitochondrial membranes1-3. The family is highly redundant and their transport activities are coupled to metabolic state. Here, we introduce a pooled, dual CRISPR screening strategy that knocks out pairs of transporters in four metabolic states — glucose, galactose, OXPHOS inhibition, and absence of pyruvate — designed to unmask the inter-dependence of these genes. In total, we screened 63 genes in four metabolic states, corresponding to 2016 single and pair-wise genetic perturbations. We recovered 19 gene-by-environment (GxE) interactions and 9 gene-by-gene (GxG) interactions. One GxE interaction hit illustrated that the fitness defect in the mitochondrial folate carrier (SLC25A32) KO cells were genetically buffered in galactose due to a lack of substrate in de novo purine biosynthesis. Another GxE interaction hit revealed non-equivalence of the paralogous ATP/ADP exchangers (ANTs) with ANT2 specifically required during OXPHOS inhibition. GxG analysis highlighted a buffering interaction between the iron transporter SLC25A37 and the poorly characterized SLC25A39. Mitochondrial metabolite profiling, organelle transport assays, and structure-guided mutagenesis suggest SLC25A39 is critical for mitochondrial glutathione (GSH) transport. Our work underscores the importance of systematically investigating family-wide genetic interactions between mitochondrial transporters across many metabolic environments.

Combinatorial G x G x E CRISPR screening and functional analysis highlights SLC25A39 in mitochondrial GSH transport

The SLC25 carrier family consists of 53 transporters that shuttle nutrients and co-factors across mitochondrial membranes. The family is highly redundant and their transport activities coupled to metabolic state. Here, we introduce a pooled, dual CRISPR screening strategy that knocks out pairs of transporters in four metabolic states- glucose, galactose, OXPHOS inhibition, and absence of pyruvate-designed to unmask the inter-dependence of these genes. In total, we screened 63 genes in four metabolic states, corresponding to 2016 single and pair-wise genetic perturbations. We recovered 19 gene-by-environment (GxE) interactions and 9 gene-by-gene (GxG) interactions. One GxE interaction hit illustrated that the fitness defect in the mitochondrial folate carrier (SLC25A32) KO cells was genetically buffered in galactose due to a lack of substrate in de novo purine biosynthesis. Another GxE interaction hit revealed non-equivalence of the paralogous ATP/ADP exchangers (ANTs) with ANT2 specifically required during OXPHOS inhibition. GxG analysis highlighted a buffering interaction between the iron transporter SLC25A37 and the poorly characterized SLC25A39. Mitochondrial metabolite profiling, organelle transport assays, and structure-guided mutagenesis suggests SLC25A39 is critical for mitochondrial glutathione (GSH) transport. Our work underscores the importance of systemetically investigating family-wide genetic interactions between mitochondrial transporters across many metabolic environments.

Can We Harness “Enviromics” to Accelerate Crop Improvement by Integrating Breeding and Agronomy?

The diverse consequences of genotype-by-environment (GxE) interactions determine trait phenotypes across levels of biological organization for crops, challenging our ambition to predict trait phenotypes from genomic information alone. GxE interactions have many implications for optimizing both genetic gain through plant breeding and crop productivity through on-farm agronomic management. Advances in genomics technologies have provided many suitable predictors for the genotype dimension of GxE interactions. Emerging advances in high-throughput proximal and remote sensor technologies have stimulated the development of “enviromics” as a community of practice, which has the potential to provide suitable predictors for the environment dimension of GxE interactions. Recently, several bespoke examples have emerged demonstrating the nascent potential for enhancing the prediction of yield and other complex trait phenotypes of crop plants through including effects of GxE interactions within prediction models. These encouraging results motivate the development of new prediction methods to accelerate crop improvement. If we can automate methods to identify and harness suitable sets of coordinated genotypic and environmental predictors, this will open new opportunities to upscale and operationalize prediction of the consequences of GxE interactions. This would provide a foundation for accelerating crop improvement through integrating the contributions of both breeding and agronomy. Here we draw on our experience from improvement of maize productivity for the range of water-driven environments across the US corn-belt. We provide perspectives from the maize case study to prioritize promising opportunities to further develop and automate “enviromics” methodologies to accelerate crop improvement through integrated breeding and agronomic approaches for a wider range of crops and environmental targets.

Abstract P177: Sex- And Strain- Specific Metabolic Effects Of Postnatal Bpf Exposure In Heterogeneous Stock Rat Founding Inbred Strains

Bisphenol F (BPF) is increasing substituting bisphenol A (BPA), an endocrine disruptor associated with cardiometabolic disease, in manufacturing polycarbonates and consumer products. Interindividual variation in bisphenol levels suggests that gene x environment (GxE) interactions influence cardiometabolic disease risk from bisphenol exposure. Studies show that BPF is a potent endocrine disruptor with effects on thyroid, reproductive health, and neuroendocrine functions. Traditional in vivo toxicity studies are performed in isogenic or genetically undefined outbred rodents, leading to conflicting results possibly due to GxE interactions. The N/NIH Heterogeneous Stock (HS) rats are a genetically heterogeneous population amenable to genetic study. Our overall study hypothesis is that BPF-induced cardiometabolic disease has underlying genetic risk, which can be identified using the HS rat and its founding inbred strains. We previously demonstrated that five weeks of postnatal BPF exposure significantly impacts body growth and adiposity in male HS rats. The goal of this project was to evaluate the metabolic health impact of postnatal BPF exposure in HS founding inbred strains. Weanling littermate pairs of male and female ACI/EurMcwi (ACI), BN/NHsdMcwi (BN), F344/Stm (F344), and WKY/NCrl (WKY) rats were randomly exposed to either vehicle (0.1% EtOH) or 1.125 mg BPF/L in 0.1% EtOH for ten weeks in drinking water. Cardiometabolic measures, tissues, urine, and feces were taken. Our studies determined BPF exposure in ACI female rats significantly increased feeding efficiency (0.54 ± 0.07 vs 0.62 ± 0.06 vs, p=0.04), suggesting a possible decrease in metabolic rate. BPF exposure impacted males more often than females, with ACI males showing significantly increased thyroid mass (0.045 ± 0.004 mg/g vs 0.051 ± 0.003 mg/g, p<0.01), BN males showing a trend in increased pituitary mass (0.026 ± 0.003 mg/g vs 0.029 ± 0.001 mg/g, p=0.07), and WKY males showing increased adrenal mass (0.164 ± 0.010 mg/g vs 0.176 ± 0.005 mg/g, p<0.01). Our preliminary data suggests that the ACI strain and male BN and WKY are susceptible to metabolic effects of BPF exposure. This work indicates that the HS rat will be a useful model for dissecting GxBPF interactions on metabolic health.

Wheat genotypes evaluated for GxE interactions in Central Zone of the country by AMMI analysis

Highly significant effects of environments, GxE interaction and genotypes were observed for cropping years 2017-18 and 2018-19. Further analysis of interactions sum of squares bifurcated into seven significant multiplicative interactions principal components to assess the performances of genotypes as per AMMI based measures. For the first year of study wheat genotypes (G5, G6, G7) had top ranked by EV2, D2, ASV, ASV1 and ASTAB2 measures. MASV & MASV1 pointed towards G7, G8, G6 wheat genotypes. Association among these measures displayed graphically in a biplot analysis. Largest cluster comprised of D2, D3, D5, D7, ASV, ASV1, ASTAB2, EV2, EV3, EV5, ASTAB3, ASTAB5, ASTAB7 measures. Wheat genotypes (G1, G11, G3) pointed by EV2, D2, ASV, ASV1 and ASTAB2 values for the second year. MASV settled for G11, G7, G13 whereas MASV1 pointed towards G11, G7, G2. Biplot analysis based on first two PC’s observed largest group had clubbed measures D2, ASV, ASTAB2, EV5, MASV, MASV1, EV3, D3, D5, D7, EV7, ASTAB3 ASTAB5, ASTAB7. AMMI based measures would be useful to identify and recommend genotypes with high, stable and predictable yield across environments.

Testing Gene-Environment Interactions in the Presence of Confounders and Mismeasured Environmental Exposures

Interest in investigating gene-environment (GxE) interactions has rapidly increased over the last decade. Although GxE interactions have been extremely investigated in large studies, few such effects have been identified and replicated, highlighting the need to develop statistical GxE tests with greater statistical power. The reverse test has been proposed for testing the interaction effect between a continuous exposure and genetic variants in relation to a binary disease outcome, which leverages the idea of linear discriminant analysis, significantly increasing statistical power comparing to the standard logistic regression approach. However, this reverse approach did not take into consideration adjustment for confounders. Since GxE interaction studies are inherently non-experimental, adjusting for potential confounding effects is critical for valid evaluation of GxE interactions. In this paper, we extend the reverse test to allow for confounders. The proposed reverse test also allows for exposure measurement errors as typically occurs. Extensive simulation experiments demonstrated that the proposed method not only provides greater statistical power under most simulation scenarios but also provides substantive computational efficiency, which achieves a computation time that is more than sevenfold less than that of the standard logistic regression test. In an illustrative example, we applied the proposed approach to the Veterans Aging Cohort Study (VACS) to search for genetic susceptibility loci modifying the smoking–HIV status association.

Perspectives on Applications of Hierarchical Gene-To-Phenotype (G2P) Maps to Capture Non-stationary Effects of Alleles in Genomic Prediction

Genomic prediction of complex traits across environments, breeding cycles, and populations remains a challenge for plant breeding. A potential explanation for this is that underlying non-additive genetic (GxG) and genotype-by-environment (GxE) interactions generate allele substitution effects that are non-stationary across different contexts. Such non-stationary effects of alleles are either ignored or assumed to be implicitly captured by most gene-to-phenotype (G2P) maps used in genomic prediction. The implicit capture of non-stationary effects of alleles requires the G2P map to be re-estimated across different contexts. We discuss the development and application of hierarchical G2P maps that explicitly capture non-stationary effects of alleles and have successfully increased short-term prediction accuracy in plant breeding. These hierarchical G2P maps achieve increases in prediction accuracy by allowing intermediate processes such as other traits and environmental factors and their interactions to contribute to complex trait variation. However, long-term prediction remains a challenge. The plant breeding community should undertake complementary simulation and empirical experiments to interrogate various hierarchical G2P maps that connect GxG and GxE interactions simultaneously. The existing genetic correlation framework can be used to assess the magnitude of non-stationary effects of alleles and the predictive ability of these hierarchical G2P maps in long-term, multi-context genomic predictions of complex traits in plant breeding.

Increasing temperature weakens the positive effect of genetic diversity on population growth

Genetic diversity and temperature increases associated with global climate change, are known to independently influence population growth and extinction risk. Whether increasing temperature may influence the effect of genetic diversity on population growth, however, is not known. We address this issue in the model protist system Tetrahymena thermophila. We test the hypothesis that at temperatures closer to the species thermal optimum (i.e., the temperature at which population growth is maximal), genetic diversity should have a weaker effect on population growth compared to temperatures away from the thermal optimum. To do so, we grew populations of T. thermophila with varying levels of genetic diversity at increasingly warmer temperatures and quantified their intrinsic population growth rate, r. We found that genetic diversity increases population growth at cooler temperatures, but that as temperature increases, this effect almost completely disappears. We also show that a combination of changes in the amount of expressed genetic diversity (G), plastic changes in population growth across temperatures (E), and strong GxE interactions, underlie this temperature effect. Our results uncover important but largely overlooked temperature effects that have implications for the management of small populations with depauperate genetic stocks in an increasingly warming world.

A versatile, fast and unbiased method for estimation of gene-by-environment interaction effects on biobank-scale datasets

Current methods to evaluate gene-by-environment (GxE) interactions on biobank-scale datasets are limited. MonsterLM enables multiple linear regression on genome-wide datasets, does not rely on parameters specification and provides unbiased estimates of variance explained by GxE interaction effects. We applied MonsterLM to the UK Biobank for eight blood biomarkers (N=325,991), identifying significant genome-wide interaction variance with waist-to-hip ratio for five biomarkers, with variance explained by interactions ranging from 0.11 to 0.58. 48% to 94% of GxE interaction variance can be attributed to variants without significant marginal association with the phenotype of interest. Conversely, for most traits, >40% of interaction variance was explained by less than 5% of genetic variants. We observed significant improvements in polygenic score prediction with incorporation of GxE interactions in four biomarkers. Our results imply an important contribution of GxE interaction effects, driven largely by a restricted set of variants distinct from loci with strong marginal effects.