1 Clemente, J. C., Ursell, L. K., Parfrey, L. W. & Knight, R. The impact of the gut microbiota on human health: an integrative view. Cell 148, 1258–1270 (2012).

2 Collaborative Cross Consortium. The genome architecture of the Collaborative Cross mouse genetic reference population. Genetics 190, 389–401 (2012).

3 Kubinak, J. L. et al. MHC variation sculpts individualized microbial communities that control susceptibility to enteric infection. Nat. Commun. 6, 8642 (2015).

4 Goodrich, J. K. et al. Human genetics shape the gut microbiome. Cell 159, 789–799 (2014).

5 McKnite, A. M. et al. Murine gut microbiota is defined by host genetics and modulates variation of metabolic traits. PLoS ONE 7, e39191 (2012).

6 Benson, A. K. et al. Individuality in gut microbiota composition is a complex polygenic trait shaped by multiple environmental and host genetic factors. Proc. Natl Acad. Sci. USA 107, 18933–18938 (2010).

7 Benson, A. K. Host genetic architecture and the landscape of microbiome composition: humans weigh in. Genome Biol. 16, 203 (2015).

8 Anukam, K. C., Osazuwa, E. O., Osadolor, H. B., Bruce, A. W. & Reid, G. Yogurt containing probiotic Lactobacillus rhamnosus GR-1 and L. reuteri RC-14 helps resolve moderate diarrhea and increases CD4 count in HIV/AIDS patients. J. Clin. Gastroenterol. 42, 239–243 (2008).

9 Trois, L., Cardoso, E. M. & Miura, E. Use of probiotics in HIV-infected children: a randomized double-blind controlled study. J. Trop. Pediatr. 54, 19–24 (2008).

10 Bravo, J. A. et al. Ingestion of Lactobacillus strain regulates emotional behavior and central GABA receptor expression in a mouse via the vagus nerve. Proc. Natl Acad. Sci. USA 108, 16050–16055 (2011).

11 Mohamadzadeh, M. et al. Lactobacilli activate human dendritic cells that skew T cells toward T helper 1 polarization. Proc. Natl Acad. Sci. USA 102, 2880–2885 (2005).

12 Replication, D. I. G. et al. Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat. Genet. 46, 234–244 (2014).

13 Gong, Y. et al. PROX1 gene variant is associated with fasting glucose change after antihypertensive treatment. Pharmacotherapy 34, 123–130 (2014).

14 Yu, B. et al. Genome-wide association study of a heart failure related metabolomic profile among African Americans in the Atherosclerosis Risk in Communities (ARIC) study. Genet. Epidemiol. 37, 840–845 (2013).

15 Kim, H. J. et al. Combined linkage and association analyses identify a novel locus for obesity near PROX1 in Asians. Obesity 21, 2405–2412 (2013).

16 Manning, A. K. et al. A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance. Nat. Genet. 44, 659–669 (2012).

17 Alipour, B. et al. Effects of Lactobacillus casei supplementation on disease activity and inflammatory cytokines in rheumatoid arthritis patients: a randomized double-blind clinical trial. Int. J. Rheum. Dis. 17, 519–527 (2014).

18 Bordalo Tonucci, L. et al. Clinical application of probiotics in diabetes mellitus: therapeutics and new perspectives. Crit. Rev. Food Sci. Nutr. http://dx.doi.org/10.1080/10408398.2014.934438 (2015).

19 Hindorff, L. et al. A Catalog of Published Genome-Wide Association Studies; http://www.ebi.ac.uk/gwas

20 Kind, T. et al. Fiehnlib: mass spectral and retention index libraries for metabolomics based on quadrupole and time-of-flight gas chromatography/mass spectrometry. Anal. Chem. 81, 10038–10048 (2009).

21 Noecker, C. et al. Metabolic model-based integration of microbiome taxonomic and metabolomic profiles elucidates mechanistic links between ecological and metabolic variation. mSystems 1, e00013-15 (2016).

22 Welsh, C. E. et al. Status and access to the Collaborative Cross population. Mamm. Genome. 23, 706–712 (2012).

23 Iraqi, F. A., Churchill, G. & Mott, R. The Collaborative Cross, developing a resource for mammalian systems genetics: a status report of the Wellcome Trust cohort. Mamm. Genome 19, 379–381 (2008).

24 Morahan, G., Balmer, L. & Monley, D. Establishment of ‘The Gene Mine’: a resource for rapid identification of complex trait genes. Mamm. Genome. 19, 390–393 (2008).

25 Chesler, E. J. et al. The Collaborative Cross at Oak Ridge National Laboratory: developing a powerful resource for systems genetics. Mamm. Genome. 19, 382–389 (2008).

26 Caporaso, J. G. et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6, 1621–1624 (2012).

27 Walters, W. et al. Improved bacterial 16S rRNA gene (V4 and V4–5) and fungal internal transcribed spacer marker gene primers for microbial community surveys. mSystems 1, e00009-15 (2015).

28 Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).

29 Aronesty, E. ea-utils: Command-Line Tools for Processing Biological Sequencing Data (Expression Analysis, 2011); https://github.com/ExpressionAnalysis/ea-utils

30 Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).

31 Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011).

32 Rognes, T., Flouri, T. & Mahe, F. vsearch: VSEARCH Version 1.1.3 (2015); https://zenodo.org/record/16153#.VwwcqxMrKuM

33 McDonald, D. et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 6, 610–618 (2012).

34 Caporaso, J. G. et al. PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics 26, 266–267 (2010).

35 Price, M. N., Dehal, P. S. & Arkin, A. P. Fasttree 2—approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).

36 Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

37 Lozupone, C. & Knight, R. Unifrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235 (2005).

38 McMurdie, P. J. & Holmes, S. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).

39 Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2010).

40 R-Core-Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2016); http://www.R-project.org/

41 Mudge, J. M. & Harrow, J. Creating reference gene annotation for the mouse C57BL6/J genome assembly. Mamm. Genome 26, 366–378 (2015).

42 Eppig, J. T. et al. The Mouse Genome Database (MGD): facilitating mouse as a model for human biology and disease. Nucleic Acids Res. 43, D726–D736 (2015).

43 Yin, T., Cook, D. & Lawrence, M. Ggbio: an R package for extending the grammar of graphics for genomic data. Genome Biol. 13, R77 (2012).

44 Krzywinski, M. et al. Circos: an information aesthetic for comparative genomics. Genome Res. 19, 1639–1645 (2009).

45 Mao, J. H. et al. Identification of genetic factors that modify motor performance and body weight using Collaborative Cross mice. Sci. Rep. 5, 16247 (2015).

46 Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2, 18–22 (2002).

47 Walker, A. et al. Importance of sulfur-containing metabolites in discriminating fecal extracts between normal and type-2 diabetic mice. J. Proteome Res. 13, 4220–4231 (2014).

48 Kim, Y. M. et al. Salmonella modulates metabolism during growth under conditions that induce expression of virulence genes. Mol. Biosyst. 9, 1522–1534 (2013).

49 Hiller, K. et al. Metabolitedetector: comprehensive analysis tool for targeted and nontargeted GC/MS based metabolome analysis. Anal. Chem. 81, 3429–3439 (2009).

50 Sumner, L. W. et al. Proposed minimum reporting standards for chemical analysis. Metabolomics 3, 211–221 (2007).

51 Langille, M. G. et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotechnol. 31, 814–821 (2013).