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The growing sample size of genome-wide association studies has facilitated the discovery of gene-environment interactions (GxE). Here we propose a maximum likelihood method to estimate the contribution of GxE to continuous traits taking into account all interacting environmental variables, without the need to measure any. Extensive simulations demonstrate that our method provides unbiased interaction estimates and excellent coverage. We also offer strategies to distinguish specific GxE from general scale effects. Applying our method to 32 traits in the UK Biobank reveals that while the genetic risk score (GRS) of 376 variants explains 5.2% of body mass index (BMI) variance, GRSxE explains an additional 1.9%. Nevertheless, this interaction holds for any variable with identical correlation to BMI as the GRS, hence may not be GRS-specific. Still, we observe that the global contribution of specific GRSxE to complex traits is substantial for nine obesity-related measures (including leg impedance and trunk fat-free mass). Most gene-by-environment interaction methods rely on the availability of the interacting environment. Here, the authors propose a robust maximum likelihood method for estimating the overall statistical interaction between a genetic risk score for a continuous outcome and all environmental variables.

The gut microbiome is crucial in regulating overall physiology and communicates with the host through various microbial-derived metabolites, including secondary bile acids (BAs). However, mechanisms underlying the gut microbiome-BA crosstalk (gMxB) are still poorly understood. Here, we assess the postprandial cecal microbiome, BA levels, and colon transcriptome of male BXD mice fed with a chow or high-fat diet, and find that genetic and dietary factors shift microbiome composition and affect gMxB. Four diet-dependent co-mapping genetic loci associated with gMxB, including the interaction between Turicibacter sanguinis - plasma cholic acid, are identified using systems genetics approaches. By integrating human MiBioGen database, we prioritize PTGR1 and PTPRD as candidate genes potentially regulating identified gMxB. The human relevance of these candidates on metabolic health is investigated using data from the UK biobank, FinnGen, and million veteran program databases. Overall, this study illustrates potential modulators regulating gMxB and provides insights into gut microbiome-host communication. The authors show that genetic and dietary factors reshape the microbiome and influence gut microbiome–bile acid crosstalk. They also identify potential modulators and offer insights into microbiome–host communication.

Inflammatory gut disorders, including inflammatory bowel disease (IBD), can be impacted by dietary, environmental, and genetic factors. While the incidence of IBD is increasing worldwide, we still lack a complete understanding of the gene-by-environment interactions underlying inflammation and IBD. Here, we profiled the colon transcriptome of 52 BXD mouse strains fed with a chow or high-fat diet (HFD) and identified a subset of BXD strains that exhibit an IBD-like transcriptome signature on HFD, indicating that an interplay of genetics and diet can significantly affect intestinal inflammation. Using gene co-expression analyses, we identified modules that are enriched for IBD-dysregulated genes and found that these IBD-related modules share cis -regulatory elements that are responsive to the STAT2, SMAD3 , and REL transcription factors. We used module quantitative trait locus analyses to identify genetic loci associated with the expression of these modules. Through a prioritization scheme involving systems genetics in the mouse and integration with external human datasets, we identified Muc4 and Epha6 as the top candidates mediating differences in HFD-driven intestinal inflammation. This work provides insights into the contribution of genetics and diet to IBD risk and identifies two candidate genes, MUC4 and EPHA6 , that may mediate IBD susceptibility in humans.

Obesity is a major risk factor for a wide range of cardiometabolic diseases, however the impact of specific aspects of body morphology remains poorly understood. We combined the GWAS summary statistics of fourteen anthropometric traits from UK Biobank through principal component analysis to reveal four major independent axes: body size, adiposity, predisposition to abdominal fat deposition, and lean mass. Mendelian randomization analysis showed that although body size and adiposity both contribute to the consequences of BMI, many of their effects are distinct, such as body size increasing the risk of cardiac arrhythmia (b = 0.06, p = 4.2 ∗ 10−17) while adiposity instead increased that of ischemic heart disease (b = 0.079, p = 8.2 ∗ 10−21). The body mass-neutral component predisposing to abdominal fat deposition, likely reflecting a shift from subcutaneous to visceral fat, exhibited health effects that were weaker but specifically linked to lipotoxicity, such as ischemic heart disease (b = 0.067, p = 9.4 ∗ 10−14) and diabetes (b = 0.082, p = 5.9 ∗ 10−19). Combining their independent predicted effects significantly improved the prediction of obesity-related diseases (p < 10−10). The presented decomposition approach sheds light on the biological mechanisms underlying the heterogeneity of body morphology and its consequences on health and lifestyle.Jonathan Sulc et al. use principal component analysis and Mendelian randomization to conduct a comprehensive analysis of 14 body morphology metrics using data from the UK Biobank. Their results suggest that body size and adiposity have distinct impacts on obesity-related outcomes and highlight a BMI-neutral component affecting body fat distribution, providing further insight into the genetic basis of body shape and its consequences on human health and lifestyle.

Mitochondria are multifaceted organelles with crucial roles in energy generation, cellular signalling and a range of synthesis pathways. The study of mitochondrial biology is complicated by its own small genome, which is matrilineally inherited and not subject to recombination, and present in multiple, possibly different, copies. Recent methodological developments have enabled the analysis of mitochondrial DNA (mtDNA) in large-scale cohorts and highlight the far-reaching impact of mitochondrial genetic variation. Genome-editing techniques have been adapted to target mtDNA, further propelling the functional analysis of mitochondrial genes. Mitochondria are finely tuned signalling hubs, a concept that has been expanded by advances in methodologies for studying the function of mitochondrial proteins and protein complexes. Mitochondrial respiratory complexes are of dual genetic origin, requiring close coordination between mitochondrial and nuclear gene-expression systems (transcription and translation) for proper assembly and function, and recent findings highlight the importance of the mitochondria in this bidirectional signalling. Auwerx and colleagues review recent advances in mitochondrial genetics, proteomics and biochemistry that emphasize the far-reaching impact of mitochondrial genetic variation and the role of mitochondria as finely tuned signalling hubs.

Purpose of ReviewOur review provides a brief summary of the most recent advances towards the identification of the genetic basis of specific aspects of obesity and the quantification of their consequences on health. We also highlight the most promising avenues to be explored in the future.Recent FindingsWhile obesity has been demonstrated to lead to adverse cardio-metabolic consequences, the determinants of inter-individual variability remain largely unknown. The elucidation of the molecular underpinnings of this relationship is hampered by the extremely heterogeneous nature of obesity as a human trait. Recent technological advances have facilitated a more in-depth characterization of body composition at large-scale.SummaryAt the pace of current data acquisition and resolution, it is realistic to improve characterization of obesity and to advise individuals based on detailed body composition combined with tissue-specific molecular signatures. Individualized predictions of health implications would enable more personalized and effective public health interventions.

Inflammatory gut disorders, including inflammatory bowel disease (IBD), can be impacted by dietary, environmental, and genetic factors. While the incidence of IBD is increasing worldwide, we still lack a complete understanding of the gene-by-environment interactions underlying inflammation and IBD. Here, we profiled the colon transcriptome of 52 BXD mouse strains fed with a chow or high-fat diet (HFD) and identified a subset of BXD strains that exhibit an IBD-like transcriptome signature on HFD, indicating that an interplay of genetics and diet can significantly affect intestinal inflammation. Using gene co-expression analyses, we identified modules that are enriched for IBD-dysregulated genes and found that these IBD-related modules share cis -regulatory elements that are responsive to the STAT2, SMAD3 , and REL transcription factors. We used module quantitative trait locus analyses to identify genetic loci associated with the expression of these modules. Through a prioritization scheme involving systems genetics in the mouse and integration with external human datasets, we identified Muc4 and Epha6 as the top candidates mediating differences in HFD-driven intestinal inflammation. This work provides insights into the contribution of genetics and diet to IBD risk and identifies two candidate genes, MUC4 and EPHA6 , that may mediate IBD susceptibility in humans.

Lifespan is influenced by complex interactions between genetic and environmental factors. Studying those factors in model organisms of a single genetic background limits their translational value for humans. Here, we mapped lifespan determinants in 85 genetically diverse recombinant intercross advanced inbred lines (RIAILs). We assessed molecular profiles - transcriptome, proteome, and lipidome - and life-history traits, including lifespan, development, growth dynamics, and reproduction. RIAILs exhibited large variations in lifespan, which positively correlated with developmental time. Among the top candidates obtained from multi-omics data integration and QTL mapping, we validated known and novel longevity modulators, including , and . We translated their relevance to humans using UK Biobank data and showed that variants in and are associated with an elevated risk of age-related heart disease, dementia, diabetes, kidney, and liver diseases. We organized our dataset as a resource (https://lisp-lms.shinyapps.io/RIAILs/) that allows interactive explorations for new longevity targets.

Causal inference is a critical step in improving our understanding of biological processes and Mendelian randomisation (MR) has emerged as one of the foremost methods to efficiently interrogate diverse hypotheses using large-scale, observational data from biobanks. Although many extensions have been developed to address the three core assumptions of MR-based causal inference (relevance, exclusion restriction, and exchangeability), most approaches implicitly assume that any putative causal effect is linear. Here we propose PolyMR, an MR-based method which provides a polynomial approximation of an (arbitrary) causal function between an exposure and an outcome. We show that this method provides accurate inference of the shape and magnitude of causal functions with greater accuracy than existing methods. We applied this method to data from the UK Biobank, testing for effects between anthropometric traits and continuous health-related phenotypes and found most of these (84%) to have causal effects which deviate significantly from linear. These deviations ranged from slight attenuation at the extremes of the exposure distribution, to large changes in the magnitude of the effect across the range of the exposure (e.g. a 1 kg/m2 change in BMI having stronger effects on glucose levels if the initial BMI was higher), to non-monotonic causal relationships (e.g. the effects of BMI on cholesterol forming an inverted U shape). Finally, we show that the linearity assumption of the causal effect may lead to the misinterpretation of health risks at the individual level or heterogeneous effect estimates when using cohorts with differing average exposure levels. Competing Interest Statement The authors have declared no competing interest.

Inflammatory gut disorders, including inflammatory bowel disease (IBD), can be impacted by dietary, environmental and genetic factors. While the incidence of IBD is increasing worldwide, we still lack a complete understanding of the gene-by-environment interactions underlying inflammation and IBD. Here, we profiled the colon transcriptome of 52 BXD mouse strains fed with a chow or high-fat diet (HFD) and identified a subset of BXD strains that exhibit an IBD-like transcriptome signature on HFD, indicating that an interplay of genetics and diet can significantly affect intestinal inflammation. Using gene co-expression analyses, we identified modules that are enriched for IBD-dysregulated genes and found that these IBD-related modules share cis-regulatory elements that are responsive to the STAT2, SMAD3, and REL transcription factors. We used module quantitative trait locus (ModQTL) analyses to identify genetic loci associated with the expression of these modules. Through a prioritization scheme involving systems genetics in the mouse and integration with external human datasets, we identified Muc4 and Epha6 as the top candidates mediating differences in HFD-driven intestinal inflammation. This work provides insights into the contribution of genetics and diet to IBD risk and identifies two candidate genes, MUC4 and EPHA6, that may mediate IBD susceptibility in humans.