Systems genetics uncover new loci containing functional gene candidates in Mycobacterium tuberculosis-infected Diversity Outbred mice.

Mycobacterium tuberculosis infects two billion people across the globe, and results in 8-9 million new tuberculosis (TB) cases and 1-1.5 million deaths each year. Most patients have no known genetic basis that predisposes them to disease. Here, we investigate the complex genetic basis of pulmonary TB by modelling human genetic diversity with the Diversity Outbred mouse population. When infected with M. tuberculosis, one-third develop early onset, rapidly progressive, necrotizing granulomas and succumb within 60 days. The remaining develop non-necrotizing granulomas and survive longer than 60 days. Genetic mapping using immune and inflammatory mediators; and clinical, microbiological, and granuloma correlates of disease identified five new loci on mouse chromosomes 1, 2, 4, 16; and three known loci on chromosomes 3 and 17. Further, multiple positively correlated traits shared loci on chromosomes 1, 16, and 17 and had similar patterns of allele effects, suggesting these loci contain critical genetic regulators of inflammatory responses to M. tuberculosis. To narrow the list of candidate genes, we used a machine learning strategy that integrated gene expression signatures from lungs of M. tuberculosis-infected Diversity Outbred mice with gene interaction networks to generate scores representing functional relationships. The scores were used to rank candidates for each mapped trait, resulting in 11 candidate genes: Ncf2, Fam20b, S100a8, S100a9, Itgb5, Fstl1, Zbtb20, Ddr1, Ier3, Vegfa, and Zfp318. Although all candidates have roles in infection, inflammation, cell migration, extracellular matrix remodeling, or intracellular signaling, and all contain single nucleotide polymorphisms (SNPs), SNPs in only four genes (S100a8, Itgb5, Fstl1, Zfp318) are predicted to have deleterious effects on protein functions. We performed methodological and candidate validations to (i) assess biological relevance of predicted allele effects by showing that Diversity Outbred mice carrying PWK/PhJ alleles at the H-2 locus on chromosome 17 QTL have shorter survival; (ii) confirm accuracy of predicted allele effects by quantifying S100A8 protein in inbred founder strains; and (iii) infection of C57BL/6 mice deficient for the S100a8 gene. Overall, this body of work demonstrates that systems genetics using Diversity Outbred mice can identify new (and known) QTLs and functionally relevant gene candidates that may be major regulators of complex host-pathogens interactions contributing to granuloma necrosis and acute inflammation in pulmonary TB.

Quantitative trait loci mapping provides insights into the genetic regulation of dendritic cell numbers in mouse tissues.

To explore the influence of genetics on homeostatic regulation of dendritic cell (DC) numbers, we present a screen of DCs and their progenitors in lymphoid and non-lymphoid tissues in Collaborative Cross (CC) and Diversity Outbred (DO) mice. We report 30 and 71 loci with logarithm of the odds (LOD) scores >8.18 and ranging from 6.67 to 8.19, respectively. The analysis reveals the highly polygenic and pleiotropic architecture of this complex trait, including many of the previously identified genetic regulators of DC development and maturation. Two SNPs in genes potentially underlying variation in DC homeostasis, a splice variant in Gramd4 (rs235532740) and a missense variant in Orai3 (rs216659754), are confirmed by gene editing using CRISPR-Cas9. Gramd4 is a central regulator of DC homeostasis that impacts the entire DC lineage, and Orai3 regulates cDC2 numbers in tissues. Overall, the data reveal a large number of candidate genes regulating DC homeostasis in vivo.

B cells in perivascular and peribronchiolar granuloma-associated lymphoid tissue and B-cell signatures identify asymptomatic Mycobacterium tuberculosis lung infection in Diversity Outbred mice.

Because most humans resist Mycobacterium tuberculosis infection, there is a paucity of lung samples to study. To address this gap, we infected Diversity Outbred mice with M. tuberculosis and studied the lungs of mice in different disease states. After a low-dose aerosol infection, progressors succumbed to acute, inflammatory lung disease within 60 days, while controllers maintained asymptomatic infection for at least 60 days, and then developed chronic pulmonary tuberculosis (TB) lasting months to more than 1 year. Here, we identified features of asymptomatic M. tuberculosis infection by applying computational and statistical approaches to multimodal data sets. Cytokines and anti-M. tuberculosis cell wall antibodies discriminated progressors vs controllers with chronic pulmonary TB but could not classify mice with asymptomatic infection. However, a novel deep-learning neural network trained on lung granuloma images was able to accurately classify asymptomatically infected lungs vs acute pulmonary TB in progressors vs chronic pulmonary TB in controllers, and discrimination was based on perivascular and peribronchiolar lymphocytes. Because the discriminatory lesion was rich in lymphocytes and CD4 T cell-mediated immunity is required for resistance, we expected CD4 T-cell genes would be elevated in asymptomatic infection. However, the significantly different, highly expressed genes were from B-cell pathways (e.g., Bank1, Cd19, Cd79, Fcmr, Ms4a1, Pax5, and H2-Ob), and CD20+ B cells were enriched in the perivascular and peribronchiolar regions of mice with asymptomatic M. tuberculosis infection. Together, these results indicate that genetically controlled B-cell responses are important for establishing asymptomatic M. tuberculosis lung infection.

Into the Wild: A novel wild-derived inbred strain resource expands the genomic and phenotypic diversity of laboratory mouse models.

The laboratory mouse has served as the premier animal model system for both basic and preclinical investigations for over a century. However, laboratory mice capture only a subset of the genetic variation found in wild mouse populations, ultimately limiting the potential of classical inbred strains to uncover phenotype-associated variants and pathways. Wild mouse populations are reservoirs of genetic diversity that could facilitate the discovery of new functional and disease-associated alleles, but the scarcity of commercially available, well-characterized wild mouse strains limits their broader adoption in biomedical research. To overcome this barrier, we have recently developed, sequenced, and phenotyped a set of 11 inbred strains derived from wild-caught Mus musculus domesticus. Each of these "Nachman strains" immortalizes a unique wild haplotype sampled from one of five environmentally distinct locations across North and South America. Whole genome sequence analysis reveals that each strain carries between 4.73-6.54 million single nucleotide differences relative to the GRCm39 mouse reference, with 42.5% of variants in the Nachman strain genomes absent from current classical inbred mouse strain panels. We phenotyped the Nachman strains on a customized pipeline to assess the scope of disease-relevant neurobehavioral, biochemical, physiological, metabolic, and morphological trait variation. The Nachman strains exhibit significant inter-strain variation in >90% of 1119 surveyed traits and expand the range of phenotypic diversity captured in classical inbred strain panels. These novel wild-derived inbred mouse strain resources are set to empower new discoveries in both basic and preclinical research.

Multiple genetic loci influence vaccine-induced protection against Mycobacterium tuberculosis in genetically diverse mice.

Mycobacterium tuberculosis (M.tb.) infection leads to over 1.5 million deaths annually, despite widespread vaccination with BCG at birth. Causes for the ongoing tuberculosis endemic are complex and include the failure of BCG to protect many against progressive pulmonary disease. Host genetics is one of the known factors implicated in susceptibility to primary tuberculosis, but less is known about the role that host genetics plays in controlling host responses to vaccination against M.tb. Here, we addressed this gap by utilizing Diversity Outbred (DO) mice as a small animal model to query genetic drivers of vaccine-induced protection against M.tb. DO mice are a highly genetically and phenotypically diverse outbred population that is well suited for fine genetic mapping. Similar to outcomes in people, our previous studies demonstrated that DO mice have a wide range of disease outcomes following BCG vaccination and M.tb. challenge. In the current study, we used a large population of BCG-vaccinated/M.tb.-challenged mice to perform quantitative trait loci mapping of complex infection traits; these included lung and spleen M.tb. burdens, as well as lung cytokines measured at necropsy. We found sixteen chromosomal loci associated with complex infection traits and cytokine production. QTL associated with bacterial burdens included a region encoding major histocompatibility antigens that are known to affect susceptibility to tuberculosis, supporting validity of the approach. Most of the other QTL represent novel associations with immune responses to M.tb. and novel pathways of cytokine regulation. Most importantly, we discovered that protection induced by BCG is a multigenic trait, in which genetic loci harboring functionally-distinct candidate genes influence different aspects of immune responses that are crucial collectively for successful protection. These data provide exciting new avenues to explore and exploit in developing new vaccines against M.tb.

Genetic mapping of microbial and host traits reveals production of immunomodulatory lipids by Akkermansia muciniphila in the murine gut.

The molecular bases of how host genetic variation impacts the gut microbiome remain largely unknown. Here we used a genetically diverse mouse population and applied systems genetics strategies to identify interactions between host and microbe phenotypes including microbial functions, using faecal metagenomics, small intestinal transcripts and caecal lipids that influence microbe-host dynamics. Quantitative trait locus (QTL) mapping identified murine genomic regions associated with variations in bacterial taxa; bacterial functions including motility, sporulation and lipopolysaccharide production and levels of bacterial- and host-derived lipids. We found overlapping QTL for the abundance of Akkermansia muciniphila and caecal levels of ornithine lipids. Follow-up in vitro and in vivo studies revealed that A. muciniphila is a major source of these lipids in the gut, provided evidence that ornithine lipids have immunomodulatory effects and identified intestinal transcripts co-regulated with these traits including Atf3, which encodes for a transcription factor that plays vital roles in modulating metabolism and immunity. Collectively, these results suggest that ornithine lipids are potentially important for A. muciniphila-host interactions and support the role of host genetics as a determinant of responses to gut microbes.

QTLViewer: an interactive webtool for genetic analysis in the Collaborative Cross and Diversity Outbred mouse populations.

The Collaborative Cross and the Diversity Outbred mouse populations are related multiparental populations, derived from the same 8 isogenic founder strains. They carry >50 M known genetic variants, which makes them ideal tools for mapping genetic loci that regulate phenotypes, including physiological and molecular traits. Mapping quantitative trait loci requires statistical and computational training, which can present a barrier to access for some researchers. The QTLViewer software allows users to graphically explore Collaborative Cross and Diversity Outbred quantitative trait locus mapping and related analyses performed through the R/qtl2 package. Additionally, the QTLViewer website serves as a repository for published Collaborative Cross and Diversity Outbred studies, increasing the accessibility of these genetic resources to the broader scientific community.

Genome-wide association mapping of ethanol sensitivity in the Diversity Outbred mouse population.

BACKGROUND: A strong predictor for the development of alcohol use disorder (AUD) is altered sensitivity to the intoxicating effects of alcohol. Individual differences in the initial sensitivity to alcohol are controlled in part by genetic factors. Mice offer a powerful tool to elucidate the genetic basis of behavioral and physiological traits relevant to AUD, but conventional experimental crosses have only been able to identify large chromosomal regions rather than specific genes. Genetically diverse, highly recombinant mouse populations make it possible to observe a wider range of phenotypic variation, offer greater mapping precision, and thus increase the potential for efficient gene identification. METHODS: We have taken advantage of the Diversity Outbred (DO) mouse population to identify and precisely map quantitative trait loci (QTL) associated with ethanol sensitivity. We phenotyped 798 male J:DO mice for three measures of ethanol sensitivity: ataxia, hypothermia, and loss of the righting response. We used high-density MegaMUGA and GigaMUGA to obtain genotypes ranging from 77,808 to 143,259 SNPs. We also performed RNA sequencing in striatum to map expression QTLs and identify gene expression-trait correlations. We then applied a systems genetic strategy to identify narrow QTLs and construct the network of correlations that exists between DNA sequence, gene expression values, and ethanol-related phenotypes to prioritize our list of positional candidate genes. RESULTS: We observed large amounts of phenotypic variation with the DO population and identified suggestive and significant QTLs associated with ethanol sensitivity on chromosomes 1, 2, and 16. The implicated regions were narrow (4.5-6.9 Mb in size) and each QTL explained ~4-5% of the variance. CONCLUSIONS: Our results can be used to identify alleles that contribute to AUD in humans, elucidate causative biological mechanisms, or assist in the development of novel therapeutic interventions.

CXCL1: A new diagnostic biomarker for human tuberculosis discovered using Diversity Outbred mice.

More humans have died of tuberculosis (TB) than any other infectious disease and millions still die each year. Experts advocate for blood-based, serum protein biomarkers to help diagnose TB, which afflicts millions of people in high-burden countries. However, the protein biomarker pipeline is small. Here, we used the Diversity Outbred (DO) mouse population to address this gap, identifying five protein biomarker candidates. One protein biomarker, serum CXCL1, met the World Health Organization's Targeted Product Profile for a triage test to diagnose active TB from latent M.tb infection (LTBI), non-TB lung disease, and normal sera in HIV-negative, adults from South Africa and Vietnam. To find the biomarker candidates, we quantified seven immune cytokines and four inflammatory proteins corresponding to highly expressed genes unique to progressor DO mice. Next, we applied statistical and machine learning methods to the data, i.e., 11 proteins in lungs from 453 infected and 29 non-infected mice. After searching all combinations of five algorithms and 239 protein subsets, validating, and testing the findings on independent data, two combinations accurately diagnosed progressor DO mice: Logistic Regression using MMP8; and Gradient Tree Boosting using a panel of 4: CXCL1, CXCL2, TNF, IL-10. Of those five protein biomarker candidates, two (MMP8 and CXCL1) were crucial for classifying DO mice; were above the limit of detection in most human serum samples; and had not been widely assessed for diagnostic performance in humans before. In patient sera, CXCL1 exceeded the triage diagnostic test criteria (>90% sensitivity; >70% specificity), while MMP8 did not. Using Area Under the Curve analyses, CXCL1 averaged 94.5% sensitivity and 88.8% specificity for active pulmonary TB (ATB) vs LTBI; 90.9% sensitivity and 71.4% specificity for ATB vs non-TB; and 100.0% sensitivity and 98.4% specificity for ATB vs normal sera. Our findings overall show that the DO mouse population can discover diagnostic-quality, serum protein biomarkers of human TB.

Uncovering Modifier Genes of X-Linked Alport Syndrome Using a Novel Multiparent Mouse Model.

BACKGROUND: Mutations in COL4A5 are responsible for 80% of cases of X-linked Alport Syndrome (XLAS). Although genes that cause AS are well characterized, people with AS who have similar genetic mutations present with a wide variation in the extent of kidney impairment and age of onset, suggesting the activities of modifier genes. METHODS: We created a cohort of genetically diverse XLAS male and female mice using the Diversity Outbred mouse resource and measured albuminuria, GFR, and gene expression. Using a quantitative trait locus approach, we mapped modifier genes that can best explain the underlying phenotypic variation measured in our diverse population. RESULTS: Genetic analysis identified several loci associated with the variation in albuminuria and GFR, including a locus on the X chromosome associated with X inactivation and a locus on chromosome 2 containing Fmn1. Subsequent analysis of genetically reduced Fmn1 expression in Col4a5 knockout mice showed a decrease in albuminuria, podocyte effacement, and podocyte protrusions in the glomerular basement membrane, which support the candidacy of Fmn1 as a modifier gene for AS. CONCLUSION: With this novel approach, we emulated the variability in the severity of kidney phenotypes found in human patients with Alport Syndrome through albuminuria and GFR measurements. This approach can identify modifier genes in kidney disease that can be used as novel therapeutic targets.

Automatic discovery of clinically interpretable imaging biomarkers for Mycobacterium tuberculosis supersusceptibility using deep learning.

BACKGROUND: Identifying which individuals will develop tuberculosis (TB) remains an unresolved problem due to few animal models and computational approaches that effectively address its heterogeneity. To meet these shortcomings, we show that Diversity Outbred (DO) mice reflect human-like genetic diversity and develop human-like lung granulomas when infected with Mycobacterium tuberculosis (M.tb) . METHODS: Following M.tb infection, a "supersusceptible" phenotype develops in approximately one-third of DO mice characterized by rapid morbidity and mortality within 8 weeks. These supersusceptible DO mice develop lung granulomas patterns akin to humans. This led us to utilize deep learning to identify supersusceptibility from hematoxylin & eosin (H&E) lung tissue sections utilizing only clinical outcomes (supersusceptible or not-supersusceptible) as labels. FINDINGS: The proposed machine learning model diagnosed supersusceptibility with high accuracy (91.50 ± 4.68%) compared to two expert pathologists using H&E stained lung sections (94.95% and 94.58%). Two non-experts used the imaging biomarker to diagnose supersusceptibility with high accuracy (88.25% and 87.95%) and agreement (96.00%). A board-certified veterinary pathologist (GB) examined the imaging biomarker and determined the model was making diagnostic decisions using a form of granuloma necrosis (karyorrhectic and pyknotic nuclear debris). This was corroborated by one other board-certified veterinary pathologist. Finally, the imaging biomarker was quantified, providing a novel means to convert visual patterns within granulomas to data suitable for statistical analyses. IMPLICATIONS: Overall, our results have translatable implication to improve our understanding of TB and also to the broader field of computational pathology in which clinical outcomes alone can drive automatic identification of interpretable imaging biomarkers, knowledge discovery, and validation of existing clinical biomarkers. FUNDING: National Institutes of Health and American Lung Association.

Cross-Species Analyses Identify Dlgap2 as a Regulator of Age-Related Cognitive Decline and Alzheimer's Dementia.

Genetic mechanisms underlying age-related cognitive decline and dementia remain poorly understood. Here, we take advantage of the Diversity Outbred mouse population to utilize quantitative trait loci mapping and identify Dlgap2 as a positional candidate responsible for modifying working memory decline. To evaluate the translational relevance of this finding, we utilize longitudinal cognitive measures from human patients, RNA expression from post-mortem brain tissue, data from a genome-wide association study (GWAS) of Alzheimer's dementia (AD), and GWAS results in African Americans. We find an association between Dlgap2 and AD phenotypes at the variant, gene and protein expression, and methylation levels. Lower cortical DLGAP2 expression is observed in AD and is associated with more plaques and tangles at autopsy and faster cognitive decline. Results will inform future studies aimed at investigating the cross-species role of Dlgap2 in regulating cognitive decline and highlight the benefit of using genetically diverse mice to prioritize novel candidates.

A large-scale genome-lipid association map guides lipid identification.

Despite the crucial roles of lipids in metabolism, we are still at the early stages of comprehensively annotating lipid species and their genetic basis. Mass spectrometry-based discovery lipidomics offers the potential to globally survey lipids and their relative abundances in various biological samples. To discover the genetics of lipid features obtained through high-resolution liquid chromatography-tandem mass spectrometry, we analysed liver and plasma from 384 diversity outbred mice, and quantified 3,283 molecular features. These features were mapped to 5,622 lipid quantitative trait loci and compiled into a public web resource termed LipidGenie. The data are cross-referenced to the human genome and offer a bridge between genetic associations in humans and mice. Harnessing this resource, we used genome-lipid association data as an additional aid to identify a number of lipids, for example gangliosides through their association with B4galnt1, and found evidence for a group of sex-specific phosphatidylcholines through their shared locus. Finally, LipidGenie's ability to query either mass or gene-centric terms suggests acyl-chain-specific functions for proteins of the ABHD family.

Mapping the Effects of Genetic Variation on Chromatin State and Gene Expression Reveals Loci That Control Ground State Pluripotency.

Mouse embryonic stem cells (mESCs) cultured in the presence of LIF occupy a ground state with highly active pluripotency-associated transcriptional and epigenetic circuitry. However, ground state pluripotency in some inbred strain backgrounds is unstable in the absence of ERK1/2 and GSK3 inhibition. Using an unbiased genetic approach, we dissect the basis of this divergent response to extracellular cues by profiling gene expression and chromatin accessibility in 170 genetically heterogeneous mESCs. We map thousands of loci affecting chromatin accessibility and/or transcript abundance, including 10 QTL hotspots where genetic variation at a single locus coordinates the regulation of genes throughout the genome. For one hotspot, we identify a single enhancer variant ∼10 kb upstream of Lifr associated with chromatin accessibility and mediating a cascade of molecular events affecting pluripotency. We validate causation through reciprocal allele swaps, demonstrating the functional consequences of noncoding variation in gene regulatory networks that stabilize pluripotent states in vitro.

Nine quick tips for efficient bioinformatics curriculum development and training.

Biomedical research is becoming increasingly data driven. New technologies that generate large-scale, complex data are continually emerging and evolving. As a result, there is a concurrent need for training researchers to use and understand new computational tools. Here we describe an efficient and effective approach to developing curriculum materials that can be deployed in a research environment to meet this need.

Nitrosative Stress and Lipid Homeostasis as a Mechanism for Zileuton Hepatotoxicity and Resistance in Genetically Sensitive Mice.

Zileuton is an orally active inhibitor of leukotriene synthesis for maintenance treatment of asthma, for which clinical usage has been associated with idiosyncratic liver injury. Mechanistic understanding of zileuton toxicity is hampered by the rarity of the cases and lack of an animal model. A promising model for mechanistic study of rare liver injury is the Diversity Outbred (J:DO) mouse population, with genetic variation similar to that found in humans. In this study, female DO mice were administered zileuton or vehicle daily for 7 days (i.g.). Serum liver enzymes were elevated in the zileuton group, with marked interindividual variability in response. Zileuton exposure-induced findings in susceptible DO mice included microvesicular fatty change, hepatocellular mitosis, and hepatocellular necrosis. Inducible nitric oxide synthase and nitrotyrosine abundance were increased in livers of animals with necrosis and those with fatty change, implicating nitrosative stress as a possible injury mechanism. Conversely, DO mice lacking adverse liver pathology following zileuton exposure experienced decreased hepatic concentrations of resistin and increased concentrations of insulin and leptin, providing potential clues into mechanisms of toxicity resistance. Transcriptome pathway analysis highlighted mitochondrial dysfunction and altered fatty acid oxidation as key molecular perturbations associated with zileuton exposure, and suggested that interindividual differences in cytochrome P450 metabolism, glutathione-mediated detoxification, and farnesoid X receptor signaling may contribute to zileuton-induced liver injury (ZILI). Taken together, DO mice provided a platform for investigating mechanisms of toxicity and resistance in context of ZILI which may lead to targeted therapeutic interventions.

High-throughput sleep phenotyping produces robust and heritable traits in Diversity Outbred mice and their founder strains.

STUDY OBJECTIVES: This study describes high-throughput phenotyping strategies for sleep and circadian behavior in mice, including examinations of robustness, reliability, and heritability among Diversity Outbred (DO) mice and their eight founder strains. METHODS: We performed high-throughput sleep and circadian phenotyping in male mice from the DO population (n = 338) and their eight founder strains: A/J (n = 6), C57BL/6J (n = 14), 129S1/SvlmJ (n = 6), NOD/LtJ (n = 6), NZO/H1LtJ (n = 6), CAST/EiJ (n = 8), PWK/PhJ (n = 8), and WSB/EiJ (n = 6). Using infrared beam break systems, we defined sleep as at least 40 s of continuous inactivity and quantified sleep-wake amounts and bout characteristics. We developed assays to measure sleep latency in a new environment and during a modified Murine Multiple Sleep Latency Test, and estimated circadian period from wheel-running experiments. For each trait, broad-sense heritability (proportion of variability explained by all genetic factors) was derived in founder strains, while narrow-sense heritability (proportion of variability explained by additive genetic effects) was calculated in DO mice. RESULTS: Phenotypes were robust to different inactivity durations to define sleep. Differences across founder strains and moderate/high broad-sense heritability were observed for most traits. There was large phenotypic variability among DO mice, and phenotypes were reliable, although estimates of heritability were lower than in founder mice. This likely reflects important nonadditive genetic effects. CONCLUSIONS: A high-throughput phenotyping strategy in mice, based primarily on monitoring of activity patterns, provides reliable and heritable estimates of sleep and circadian traits. This approach is suitable for discovery analyses in DO mice, where genetic factors explain some proportion of phenotypic variation.

Genetic determinants of gut microbiota composition and bile acid profiles in mice.

The microbial communities that inhabit the distal gut of humans and other mammals exhibit large inter-individual variation. While host genetics is a known factor that influences gut microbiota composition, the mechanisms underlying this variation remain largely unknown. Bile acids (BAs) are hormones that are produced by the host and chemically modified by gut bacteria. BAs serve as environmental cues and nutrients to microbes, but they can also have antibacterial effects. We hypothesized that host genetic variation in BA metabolism and homeostasis influence gut microbiota composition. To address this, we used the Diversity Outbred (DO) stock, a population of genetically distinct mice derived from eight founder strains. We characterized the fecal microbiota composition and plasma and cecal BA profiles from 400 DO mice maintained on a high-fat high-sucrose diet for ~22 weeks. Using quantitative trait locus (QTL) analysis, we identified several genomic regions associated with variations in both bacterial and BA profiles. Notably, we found overlapping QTL for Turicibacter sp. and plasma cholic acid, which mapped to a locus containing the gene for the ileal bile acid transporter, Slc10a2. Mediation analysis and subsequent follow-up validation experiments suggest that differences in Slc10a2 gene expression associated with the different strains influences levels of both traits and revealed novel interactions between Turicibacter and BAs. This work illustrates how systems genetics can be utilized to generate testable hypotheses and provide insight into host-microbe interactions.