ARTICLE https://doi.org/10.1038/s41467-019-11952-w OPEN Gene-diet interactions associated with complex trait variation in an advanced intercross outbred mouse line Artem Vorobyev1,2,15, Yask Gupta1,15, Tanya Sezin2,15, Hiroshi Koga 1,12, Yannic C. Bartsch3, Meriem Belheouane4,5, Sven Künzel4, Christian Sina6, Paul Schilf1, Heiko Körber-Ahrens1,13, Foteini Beltsiou 1, Anna Lara Ernst 1, Stanislav Khil’chenko 1, Hassanin Al-Aasam 1, Rudolf A. Manz 7, Sandra Diehl8, Moritz Steinhaus3, Joanna Jascholt1, Phillip Kouki1, Wolf-Henning Boehncke9, Tanya N. Mayadas10, Detlef Zillikens 2, Christian D. Sadik 2, Hiroshi Nishi10,14, Marc Ehlers 3, Steffen Möller 11, 1234567890():,; Katja Bieber 1, John F. Baines4,5, Saleh M. Ibrahim1 & Ralf J. Ludwig 1 Phenotypic variation of quantitative traits is orchestrated by a complex interplay between the environment (e.g. diet) and genetics. However, the impact of gene-environment interactions on phenotypic traits mostly remains elusive. To address this, we feed 1154 mice of an autoimmunity-prone intercross line (AIL) three different diets. We find that diet substantially contributes to the variability of complex traits and unmasks additional genetic susceptibility quantitative trait loci (QTL). By performing whole-genome sequencing of the AIL founder strains, we resolve these QTLs to few or single candidate genes. To address whether diet can also modulate genetic predisposition towards a given trait, we set NZM2410/J mice on similar dietary regimens as AIL mice. Our data suggest that diet modifies genetic suscept- ibility to lupus and shifts intestinal bacterial and fungal community composition, which precedes clinical disease manifestation. Collectively, our study underlines the importance of including environmental factors in genetic association studies. 1 Lübeck Institute of Experimental Dermatology and Center for Research on Inflammation of the Skin, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany. 2 Department of Dermatology and Center for Research on Inflammation of the Skin, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany. 3 Laboratories of Immunology and Antibody Glycan Analysis, Institute for Nutritional Medicine, University of Lübeck and University Medical Center Schleswig-Holstein, Ratzeburger Allee 160, 23562 Lübeck, Germany. 4 Max Planck Institute for Evolutionary Biology, August-Thienemann- Straße 2, 24306 Plön, Germany. 5 Institute for Experimental Medicine, Kiel University, Christian-Albrechts-Platz 4, 24118 Kiel, Germany. 6 Institute of Nutritional Medicine, Molecular Gastroenterology, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany. 7 Institute for Systemic Inflammation Research, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany. 8 Department of Dermatology, Venereology and Allergology, Goethe University, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany. 9 Divison of Dermatology and Venereology, Geneva University Hospitals, and Department of Pathology and Immunology, University of Geneva, Rue Gabrielle-Perret-Gentil 4, 1205 Genève, Switzerland. 10 Center for Excellence in Vascular Biology, Department of Pathology, Brigham and Women’s Hospital and Harvard Medical School, 75 Francis St, Boston, MA 02115, USA. 11 Institute for Biostatistics and Informatics in Medicine and Ageing Research, Ernst-Heydemann-Str. 8, 18057 Rostock University of Rostock, Germany. 12Present address: Department of Dermatology, Kurume University School of Medicine, 67 Asahimachi, Kurume, Fukuoka 830-0011, Japan. 13Present address: Department of Urology, University Medical Center Goettingen, Robert-Koch-Strasse 40, 37075 Goettingen, Germany. 14Present address: Department of Nephrology and Endocrinology, University of Tokyo, 7 Chome-3-1 Hongo, Bunkyo City, Tokyo 113-8654, Japan. 15These authors contributed equally: Artem Vorobyev, Yask Gupta, Tanya Sezin. Correspondence and requests for materials should be addressed to R.J.L. (email: [email protected]) NATURE COMMUNICATIONS | (2019) 10:4097 | https://doi.org/10.1038/s41467-019-11952-w | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-11952-w n humans, genome-wide association studies (GWAS) have analysis of their intestinal micro- and mycobiota, as well as RNA- Iidentified hundreds of genetic variants associated with com- sequencing (RNA-Seq) of their spleens. This reveals that diet- plex human diseases and traits, providing detailed insights into induced changes in the intestinal micro- and mycobiota precede their genetic architecture1. However, depending on the pheno- clinical disease manifestation in NZM2410/J mice, and are asso- typic trait, only 5–50% of the variation is explained by host ciated with ANA production. Furthermore, by associating diet genetics while rest remains unexplained2,3. Recently, attention and pathophysiological traits (e.g., lupus and ANA) with the has shifted on the environment and its interaction with host RNA-Seq data, we identify dysregulated genes and biological genetics as a key regulator of complex traits4. Gene-by- pathways that predispose NZM2410/J mice to disease onset and environment interactions (GxEs) occur when environmental production of ANA. Finally, we use our multi-omics data to fine- factors and genetic variation have a joint impact on disease sus- map QTL for ANA production in AIL mice. ceptibility, thus deconstructing their individual contributions4. These interactions are thought to explain a large proportion of Results the unexplained variance in heritability5. For instance, the Impact of diet and host genetics on complex traits. A large interaction of genetics (e.g., the HLA locus) with environment cohort of male and female mice from an autoimmunity-prone (e.g., smoking) exemplifies the joint genetic and environmental AIL was fed three different diets (caloric restriction, control-, and control of the risk of developing rheumatoid arthritis (RA). Thus, Western-diet) until an age of 24 weeks. Thereafter, mice were while both presence of the HLA-DRB1 haplotype and smoking genotyped and phenotyped. In total, we quantified 55 phenotypes confer a similar risk of developing RA, the risk increases fourfold that were defined as either physiological or pathophysiological. if both factors are present6. Furthermore, dietary or microbe- Phenotypes and assessment methods are listed in Supplementary derived metabolites can induce inflammation by modulating Table 1. Physiological phenotypes were further categorized into specific receptor responses in the gut, further suggesting that the metabolic, hematological, immunoglobulin, glycosylation pattern, environment contributes to complex physiological traits7. and other phenotypes. Ultimately, the effects of host genetics, With diet being a major constituent of an organism’s envir- diet, and sex on the phenotypic variation of each trait were onment, we hypothesized that diet alone and its interaction with analyzed as stated in “Methods” (Fig. 1a, b). Diet accounted for host genetics may account for a considerable proportion of the largest proportion of the phenotypic variation in metabolic phenotypic variability in complex traits. Our interest in this topic (for example, 48% of the phenotypic variability for final body was further provoked by the clinical observation of metabolic and weight is explained by diet), immunoglobulin (up to 37% for IgA/ cardiovascular comorbidity in chronic inflammatory diseases8. IgM ratio), and pathophysiological traits (up to 46% NAFLD One school of thought considers inflammation a key driver of ballooning; Supplementary Table 1, Supplementary Data 1). metabolic and cardiovascular comorbidity, while the other sug- In contrast, diet had little impact on phenotypic variability of gests that this comorbidity may be a result of a joint genetic differential blood counts (up to 6% for eosinophils). For IgG control. Meta-analysis of GWAS data, however, has documented glycosylation traits, specifically composition of the biantennary little overlap of risk alleles among inflammatory, metabolic, and sugar residue at Asn297, comparable contributions were observed cardiovascular diseases9. In contrast, increased food intake has for both host genetics (up to 11% for the ratio between sialic acid been suggested as a more probable risk factor for developing these and galactose) and diet (up to 12.5% for G1). Other known diseases10. Nevertheless, little experimental evidence exists in covariates, such as sex, only explain a small magnitude of the favor of either hypothesis. To address this controversy and, phenotypic variation in the studied traits (up to 8% for body unravel the impact of diet on complex traits, we expose a large weight at 2 months of age), Supplementary Table 1, Supplemen- colony of an advanced intercross outbred mouse line (AIL) to tary Data 1). three different diets: caloric restriction, Western diet, and control Next, to more deeply characterize the impact of genetics, and diet. The overall experimental rationale is to mimic dietary life- identify genomic loci that co-vary with the investigated styles in their extremes, such as normal control diet, Western diet phenotypes, we performed QTL mapping. At the genome-wide mimicking the food of the modern Western countries, as well as level (α < 0.05), we identified 21 QTL, corresponding to 18 deficit of food intake in developing countries. A total of 1154 mice gw phenotypes (Fig. 1b, Supplementary Data 1). In addition, are genotyped and phenotyped for 55 physiological and