Environment dominates over host genetics in shaping human gut microbiota

Nature

Volumn 555, Issue 7695, 2018, Pages 210-215

  Rothschild, Daphna ^a   Weissbrod, Omer ^a   Barkan, Elad ^a   Kurilshikov, Alexander ^b   Korem, Tal ^a   Zeevi, David ^a   Costea, Paul I ^a   Godneva, Anastasia ^a   Kalka, Iris N ^a   Bar, Noam ^a   Shilo, Smadar ^a   Lador, Dar ^a   Vila, Arnau Vich ^b   Zmora, Niv ^a,c,d   Pevsner Fischer, Meirav ^a   Israeli, David ^e   Kosower, Noa ^a   Malka, Gal ^a   Wolf, Bat Chen ^a   Avnit Sagi, Tali ^a   Lotan Pompan, Maya ^a   Weinberger, Adina ^a   Halpern, Zamir ^c,d   Carmi, Shai ^f   Fu, Jingyuan ^b   Wijmenga, Cisca ^b,g   Zhernakova, Alexandra ^b   Elinav, Eran ^a   Segal, Eran ^a   more..

^a WEIZMANN INSTITUTE OF SCIENCE   (Israel)

^b UNIVERSITY MEDICAL CENTER GRONINGEN   (Netherlands)

^c TEL AVIV SOURASKY MEDICAL CENTER   (Israel)

^d TEL AVIV UNIVERSITY   (Israel)

^e Kfar Shaul Hospital   (Israel)

^f HEBREW UNIVERSITY OF JERUSALEM   (Israel)

^g UNIVERSITY OF OSLO   (Norway)

Author keywords

[No Author keywords available]

Indexed keywords

ANCESTRY; COMMUNITY COMPOSITION; DIGESTIVE SYSTEM; GENETIC ANALYSIS; GENETIC VARIATION; GENETICS; GENOTYPE; GLUCOSE; MICROORGANISM; OBESITY;

ADULT; AGED; ANCESTRY GROUP; ARTICLE; DIETARY INTAKE; ENVIRONMENTAL FACTOR; FECES ANALYSIS; FEMALE; GENETIC ANALYSIS; GENETIC ASSOCIATION; GENETIC VARIABILITY; GENOTYPE; GENOTYPE ENVIRONMENT INTERACTION; GLUCOSE BLOOD LEVEL; HUMAN; HUMAN EXPERIMENT; HUMAN GENETICS; INTESTINE FLORA; MALE; METAGENOMICS; NONHUMAN; NORMAL HUMAN; OBESITY; PHENOTYPIC VARIATION; POPULATION GENETICS; PRIORITY JOURNAL; SINGLE NUCLEOTIDE POLYMORPHISM; ADOLESCENT; COHORT ANALYSIS; DIET; ENVIRONMENT; FAMILY SIZE; GENETICS; HEREDITY; ISRAEL; LIFESTYLE; METABOLISM; MIDDLE AGED; PHENOTYPE; REPRODUCIBILITY; STATISTICS AND NUMERICAL DATA; TWIN STUDY; TWINS; VERY ELDERLY; YOUNG ADULT;

BACTERIAL RNA; GLUCOSE; RNA 16S;

ADOLESCENT; ADULT; AGED; AGED, 80 AND OVER; COHORT STUDIES; DIET; ENVIRONMENT; FAMILY CHARACTERISTICS; FEMALE; GASTROINTESTINAL MICROBIOME; GENE-ENVIRONMENT INTERACTION; GLUCOSE; HEALTHY VOLUNTEERS; HEREDITY; HUMANS; ISRAEL; LIFE STYLE; MALE; MIDDLE AGED; OBESITY; PHENOTYPE; POLYMORPHISM, SINGLE NUCLEOTIDE; REPRODUCIBILITY OF RESULTS; RNA, BACTERIAL; RNA, RIBOSOMAL, 16S; TWIN STUDIES AS TOPIC; TWINS; YOUNG ADULT;

EID: 85043351358     PISSN: 00280836     EISSN: 14764687     Source Type: Journal    
DOI: 10.1038/nature25973     Document Type: Article

References (62)

1. Le Chatelier, E. et al. Richness of human gut microbiome correlates with metabolic markers. Nature 500, 541-546 (2013).
2. 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).
3. Kurilshikov, A., Wijmenga, C., Fu, J. &Zhernakova, A. Host genetics and gut microbiome: challenges and perspectives. Trends Immunol. 38, 633-647 (2017).
4. Goodrich, J. K. K. et al. Human genetics shape the gut microbiome. Cell 159, 789-799 (2014).
5. Turpin, W. et al. Association of host genome with intestinal microbial composition in a large healthy cohort. Nat. Genet. 48, 1413-1417 (2016).
6. Goodrich, J. K. et al. Genetic determinants of the gut microbiome in UK twins. Cell Host Microbe 19, 731-743 (2016).
7. Goodrich, J. K., Davenport, E. R., Clark, A. G. &Ley, R. E. The relationship between the human genome and microbiome comes into view. Annu. Rev. Genet. 51, 413-433 (2017)
8. Bonder, M. J. et al. The effect of host genetics on the gut microbiome. Nat. Genet. 48, 1407-1412 (2016).
9. Wang, J. et al. Genome-wide association analysis identifies variation in vitamin D receptor and other host factors influencing the gut microbiota. Nat. Genet. 48, 1396-1406 (2016).
10. Blekhman, R. et al. Host genetic variation impacts microbiome composition across human body sites. Genome Biol. 16, 191 (2015).
11. Xie, H. et al. Shotgun metagenomics of 250 adult twins reveals genetic and environmental impacts on the gut microbiome. Cell Syst. 3, 572-584 (2016).
12. Zeevi, D. et al. Personalized nutrition by prediction of glycemic responses. Cell 163, 1079-1094 (2015).
13. Zhernakova, A. et al. Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. Science 352, 565-569 (2016).
14. Falony, G. et al. Population-level analysis of gut microbiome variation. Science 352, 560-564 (2016).
15. Fu, J. et al. The gut microbiome contributes to a substantial proportion of the variation in blood lipids. Circ. Res. 117, 817-824 (2015).
16. Behar, D. M.et al. The genome-wide structure of the Jewish people. Nature 466, 238-242 (2010).
17. Legendre P. &Legendre. L. Numerical Ecology Vol. 24, 3rd edn (Elsevier, 2012).
18. Visscher, P. M. &Goddard, M. E. A general unified framework to assess the sampling variance of heritability estimates using pedigree or marker-based relationships. Genetics 199, 223-232 (2015).
19. Rühlemann, M. C. et al. Application of the distance-based F test in an mGWAS investigating β diversity of intestinal microbiota identifies variants in SLC9A8 (NHE8) and 3 other loci. Gut Microbes https://doi.org/10.1080/19490976.201 7.1356979 (2017).
20. Song, S. J. et al. Cohabiting family members share microbiota with one another and with their dogs. eLife 2, e00458 (2013).
21. McArdle, B. H. &Anderson, M. J. Fitting multivariate models to community data: a comment on distance-based redundancy analysis. Ecology 82, 290-297 (2001).
22. Visscher, P. M., Hill, W. G. &Wray, N. R. Heritability in the genomics era-concepts and misconceptions. Nat. Rev. Genet. 9, 255-266 (2008).
23. Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42, 565-569 (2010).
24. Yang, J., Lee, S. H., Goddard, M. E. &Visscher, P. M. GCTA: a tool for genomewide complex trait analysis. Am. J. Hum. Genet. 88, 76-82 (2011).
25. Schweiger, R. et al. in RECOMB 2017: Research in Computational Molecular Biology (ed. Sahinalp, S.) 241-256 (Springer, 2017).
26. Genetic Analysis of Psoriasis Consortium &the Wellcome Trust Case Control Consortium 2. A genome-wide association study identifies new psoriasis susceptibility loci and an interaction between HLA-C and ERAP1. Nat. Genet. 42, 985-990 (2010).
27. Shi, H., Kichaev, G. &Pasaniuc, B. Contrasting the genetic architecture of 30 complex traits from summary association data. Am. J. Hum. Genet. 99, 139-153 (2016).
28. Speed, D., Cai, N., Johnson, M. R., Nejentsev, S. &Balding, D. J. Reevaluation of SNP heritability in complex human traits. Nat. Genet. 49, 986-992 (2017).
29. Ge, T., Chen, C. Y., Neale, B. M., Sabuncu, M. R. &Smoller, J. W. Phenome-wide heritability analysis of the UK Biobank. PLoS Genet. 13, e1006711 (2017).
30. Zaitlen, N. et al. Using extended genealogy to estimate components of heritability for 23 quantitative and dichotomous traits. PLoS Genet. 9, e1003520 (2013).
31. Vattikuti, S., Guo, J. &Chow, C. C. Heritability and genetic correlations explained by common SNPs for metabolic syndrome traits. PLoS Genet. 8, e1002637 (2012).
32. Arpegård, J. et al. Comparison of heritability of cystatin C-and creatininebased estimates of kidney function and their relation to heritability of cardiovascular disease. J. Am. Heart Assoc. 4, e001467 (2015).
33. Xia, C. et al. Pedigree-and SNP-associated genetics and recent environment are the major contributors to anthropometric and cardiometabolic trait variation. PLoS Genet. 12, e1005804 (2016).
34. Heckerman, D. et al. Linear mixed model for heritability estimation that explicitly addresses environmental variation. Proc. Natl Acad. Sci. USA 113, 7377-7382 (2016).
35. Antonopoulos, D. A. et al. Reproducible community dynamics of the gastrointestinal microbiota following antibiotic perturbation. Infect. Immun. 77, 2367-2375 (2009).
36. Caporaso, J. G. et al. Moving pictures of the human microbiome. Genome Biol. 12, R50 (2011).
37. Lozupone, C. A., Stombaugh, J. I., Gordon, J. I., Jansson, J. K. &Knight, R. Diversity, stability and resilience of the human gut microbiota. Nature 489, 220-230 (2012).
38. Widmer, C. et al. Further improvements to linear mixed models for genomewide association studies. Sci. Rep. 4, 6874 (2014).
39. Bolger, A. M., Lohse, M. &Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114-2120 (2014).
40. Langmead, B. &Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357-359 (2012).
41. Segata, N. et al. Metagenomic microbial community profiling using unique clade-specific marker genes. Nat. Methods 9, 811-814 (2012).
42. Loh, P.-R. et al. Reference-based phasing using the Haplotype Reference Consortium panel. Nat. Genet. 48, 1443-1448 (2016).
43. Howie, B. N., Donnelly, P. &Marchini, J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009).
44. Auton, A. et al. A global reference for human genetic variation. Nature 526, 68-74 (2015).
45. Carmi, S. et al. Sequencing an Ashkenazi reference panel supports populationtargeted personal genomics and illuminates Jewish and European origins. Nat. Commun. 5, 4835 (2014).
46. Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335-336 (2010).
47. Marco-Sola, S., Sammeth, M., Guigó, R. &Ribeca, P. The GEM mapper: fast, accurate and versatile alignment by filtration. Nat. Methods 9, 1185-1188 (2012).
48. Li, J. et al. An integrated catalog of reference genes in the human gut microbiome. Nat. Biotechnol. 32, 834-841 (2014).
49. Hong, C. et al. PathoScope 2.0: a complete computational framework for strain identification in environmental or clinical sequencing samples. Microbiome 2, 33 (2014).
50. Conomos, M. P., Reiner, A. P., Weir, B. S. &Thornton, T. A. Model-free estimation of recent genetic relatedness. Am. J. Hum. Genet. 98, 127-148 (2016).
51. Conomos, M. P., Miller, M. B. &Thornton, T. A. Robust inference of population structure for ancestry prediction and correction of stratification in the presence of relatedness. Genet. Epidemiol. 39, 276-293 (2015).
52. Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867-2873 (2010).
53. Conomos, M. P. et al. Genetic diversity and association studies in US Hispanic/Latino populations: applications in the Hispanic Community Health Study/Study of Latinos. Am. J. Hum. Genet. 98, 165-184 (2016).
54. Oksanen, J. et al. vegan: community ecology package. https://cran.r-project. org/web/packages/vegan/index.html (2017).
55. Hastie, T., Tibshirani, R. &Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer, 2009).
56. Chen, T. &Guestrin, C. Xgboost: A scalable tree boosting system. In Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785-794 (ACM, 2016).
57. Listgarten, J. et al. Improved linear mixed models for genome-wide association studies. Nat. Methods 9, 525-526 (2012).
58. Yang, J., Zaitlen, N. A., Goddard, M. E., Visscher, P. M. &Price, A. L. Advantages and pitfalls in the application of mixed-model association methods. Nat. Genet. 46, 100-106 (2014).
59. Ingram, C. J. E., Mulcare, C. A., Itan, Y., Thomas, M. G. &Swallow, D. M. Lactose digestion and the evolutionary genetics of lactase persistence. Hum. Genet. 124, 579-591 (2009).
60. Schweiger, R. et al. RL-SKAT: an exact and efficient score test for heritability and set tests. Genetics 207, 1275-1283 (2017).
61. de los Campos, G., Vazquez, A. I., Fernando, R., Klimentidis, Y. C. &Sorensen, D. Prediction of complex human traits using the genomic best linear unbiased predictor. PLoS Genet. 9, e1003608 (2013).
62. Zhao, N. et al. Testing in microbiome-profiling studies with MiRKAT, the microbiome regression-based kernel association test. Am. J. Hum. Genet. 96, 797-807 (2015).