Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Selecting causal genes from genome-wide association studies via functionally coherent subnetworks

Abstract

Genome-wide association (GWA) studies have linked thousands of loci to human diseases, but the causal genes and variants at these loci generally remain unknown. Although investigators typically focus on genes closest to the associated polymorphisms, the causal gene is often more distal. Reliance on published work to prioritize candidates is biased toward well-characterized genes. We describe a 'prix fixe' strategy and software that uses genome-scale shared-function networks to identify sets of mutually functionally related genes spanning multiple GWA loci. Using associations from 100 GWA studies covering ten cancer types, our approach outperformed the common alternative strategy in ranking known cancer genes. As more GWA loci are discovered, the strategy will have increased power to elucidate the causes of human disease.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Overview of the prix fixe strategy.
Figure 2: Functional connectivity patterns in prostate cancer.
Figure 3: Rank-based analysis of SCGC prioritization.
Figure 4: Prix fixe gene-score distribution and functional enrichment.

Similar content being viewed by others

References

  1. Bodmer, W. & Bonilla, C. Common and rare variants in multifactorial susceptibility to common diseases. Nat. Genet. 40, 695–701 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Risch, N. & Merikangas, K. The future of genetic studies of complex human diseases. Science 273, 1516–1517 (1996).

    Article  CAS  PubMed  Google Scholar 

  3. Chakravarti, A., Clark, A.G. & Mootha, V.K. Distilling pathophysiology from complex disease genetics. Cell 155, 21–26 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Gilman, S.R. et al. Rare de novo variants associated with autism implicate a large functional network of genes involved in formation and function of synapses. Neuron 70, 898–907 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Raychaudhuri, S. et al. Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions. PLoS Genet. 5, e1000534 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Rossin, E.J. et al. Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology. PLoS Genet. 7, e1001273 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Han, S. et al. Integrating GWASs and human protein interaction networks identifies a gene subnetwork underlying alcohol dependence. Am. J. Hum. Genet. 93, 1027–1034 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Vanunu, O., Magger, O., Ruppin, E., Shlomi, T. & Sharan, R. Associating genes and protein complexes with disease via network propagation. PLoS Comput. Biol. 6, e1000641 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Das, J. & Yu, H. HINT: high-quality protein interactomes and their applications in understanding human disease. BMC Syst. Biol. 6, 92 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Venkatesan, K. et al. An empirical framework for binary interactome mapping. Nat. Methods 6, 83–90 (2009).

    Article  CAS  PubMed  Google Scholar 

  11. Rolland, T. et al. A Proteome-scale map of the human interactome network. Cell 159, 1212–1226 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Hirschhorn, J.N. Genomewide association studies—illuminating biologic pathways. N. Engl. J. Med. 360, 1699–1701 (2009).

    Article  CAS  PubMed  Google Scholar 

  13. Cantor, R.M., Lange, K. & Sinsheimer, J.S. Prioritizing GWAS results: a review of statistical methods and recommendations for their application. Am. J. Hum. Genet. 86, 6–22 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Lee, I., Date, S.V., Adai, A.T. & Marcotte, E.M. A probabilistic functional network of yeast genes. Science 306, 1555–1558 (2004).

    Article  CAS  PubMed  Google Scholar 

  15. Wang, P.I. & Marcotte, E.M. It's the machine that matters: predicting gene function and phenotype from protein networks. J. Proteomics 73, 2277–2289 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Hwang, S., Rhee, S.Y., Marcotte, E.M. & Lee, I. Systematic prediction of gene function in Arabidopsis thaliana using a probabilistic functional gene network. Nat. Protoc. 6, 1429–1442 (2011).

    Article  CAS  PubMed  Google Scholar 

  17. Peña-Castillo, L. et al. A critical assessment of Mus musculus gene function prediction using integrated genomic evidence. Genome Biol. 9 (suppl. 1), S2 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Mostafavi, S. & Morris, Q. Fast integration of heterogeneous data sources for predicting gene function with limited annotation. Bioinformatics 26, 1759–1765 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Tas¸an, M. et al. A resource of quantitative functional annotation for Homo sapiens genes. G3 (Bethesda) 2, 223–233 (2012).

    Article  CAS  Google Scholar 

  20. Huttenhower, C. et al. Exploring the human genome with functional maps. Genome Res. 19, 1093–1106 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Franke, L. et al. Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes. Am. J. Hum. Genet. 78, 1011–1025 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Lee, I., Blom, U.M., Wang, P.I., Shim, J.E. & Marcotte, E.M. Prioritizing candidate disease genes by network-based boosting of genome-wide association data. Genome Res. 21, 1109–1121 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Hindorff, L.A. et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl. Acad. Sci. USA 106, 9362–9367 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Warde-Farley, D. et al. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 38, W214–W220 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Goldberg, D.E. Genetic Algorithms in Search, Optimization, and Machine Learning (Addison-Wesley, 1989).

  26. de Resende, M.F. et al. Prognostication of OCT4 isoform expression in prostate cancer. Tumour Biol. 34, 2665–2673 (2013).

    Article  CAS  PubMed  Google Scholar 

  27. Hu, Y.L. et al. HNF1b is involved in prostate cancer risk via modulating androgenic hormone effects and coordination with other genes. Genet. Mol. Res. 12, 1327–1335 (2013).

    Article  CAS  PubMed  Google Scholar 

  28. Futreal, P.A. et al. A census of human cancer genes. Nat. Rev. Cancer 4, 177–183 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Berriz, G.F., Beaver, J.E., Cenik, C., Tasan, M. & Roth, F.P. Next generation software for functional trend analysis. Bioinformatics 25, 3043–3044 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Memarzadeh, S. et al. Enhanced paracrine FGF10 expression promotes formation of multifocal prostate adenocarcinoma and an increase in epithelial androgen receptor. Cancer Cell 12, 572–585 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Heinlein, C.A. & Chang, C. Androgen receptor in prostate cancer. Endocr. Rev. 25, 276–308 (2004).

    Article  CAS  PubMed  Google Scholar 

  32. Bhatia-Gaur, R. et al. Roles for Nkx3.1 in prostate development and cancer. Genes Dev. 13, 966–977 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Gao, W. Androgen receptor as a therapeutic target. Adv. Drug Deliv. Rev. 62, 1277–1284 (2010).

    Article  CAS  PubMed  Google Scholar 

  34. Katoh, M. & Nakagama, H. FGF receptors: cancer biology and therapeutics. Med. Res. Rev. 34, 280–300 (2014).

    Article  CAS  PubMed  Google Scholar 

  35. Kandoth, C. et al. Mutational landscape and significance across 12 major cancer types. Nature 502, 333–339 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. King, O.D. et al. Predicting phenotype from patterns of annotation. Bioinformatics 19 (suppl. 1), i183–i189 (2003).

    Article  PubMed  Google Scholar 

  37. Liu, J.Z. et al. A versatile gene-based test for genome-wide association studies. Am. J. Hum. Genet. 87, 139–145 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Lee, D.-S. et al. The implications of human metabolic network topology for disease comorbidity. Proc. Natl. Acad. Sci. USA 105, 9880–9885 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Vandin, F., Upfal, E. & Raphael, B.J. De novo discovery of mutated driver pathways in cancer. Genome Res. 22, 375–385 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Manolio, T.A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Amberger, J., Bocchini, C.A., Scott, A.F. & Hamosh, A. McKusick's Online Mendelian Inheritance in Man (OMIM). Nucleic Acids Res. 37, D793–D796 (2009).

    Article  CAS  PubMed  Google Scholar 

  42. Hunter, S. et al. InterPro in 2011: new developments in the family and domain prediction database. Nucleic Acids Res. 40, D306–D312 (2012).

    Article  CAS  PubMed  Google Scholar 

  43. Gunsalus, K.C., Yueh, W.-C., MacMenamin, P. & Piano, F. RNAiDB and PhenoBlast: web tools for genome-wide phenotypic mapping projects. Nucleic Acids Res. 32, D406–D410 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Karolchik, D. et al. The UCSC Genome Browser database: 2014 update. Nucleic Acids Res. 42, D764–D770 (2014).

    Article  CAS  PubMed  Google Scholar 

  45. Östlund, G. et al. InParanoid 7: new algorithms and tools for eukaryotic orthology analysis. Nucleic Acids Res. 38, D196–D203 (2010).

    Article  CAS  PubMed  Google Scholar 

  46. Su, A.I. et al. A gene atlas of the mouse and human protein-encoding transcriptomes. Proc. Natl. Acad. Sci. USA 101, 6062–6067 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Breiman, L. Random Forests. Mach. Learn. 45, 5–32 (2001).

    Article  Google Scholar 

  48. Tas¸an, M. et al. An en masse phenotype and function prediction system for Mus musculus. Genome Biol. 9 (suppl. 1), S8 (2008).

    Article  CAS  Google Scholar 

  49. Mostafavi, S., Ray, D., Warde-Farley, D., Grouios, C. & Morris, Q. GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function. Genome Biol. 9 (suppl. 1), S4 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Musso, G. et al. Novel cardiovascular gene functions revealed via systematic phenotype prediction in zebrafish. Development 141, 224–235 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Tian, W. et al. Combining guilt-by-association and guilt-by-profiling to predict Saccharomyces cerevisiae gene function. Genome Biol. 9 (suppl. 1), S7 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. The International HapMap Consortium. A haplotype map of the human genome. Nature 437, 1299–1320 (2005).

  53. Lango Allen, H. et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 467, 832–838 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Ferrari, S. & Cribari-Neto, F. Beta regression for modelling rates and proportions. J. Appl. Stat. 31, 799–815 (2004).

    Article  Google Scholar 

  55. Hill, W.G. & Robertson, A. Linkage disequilibrium in finite populations. Theor. Appl. Genet. 38, 226–231 (1968).

    Article  CAS  PubMed  Google Scholar 

  56. Sved, J.A. Linkage disequilibrium and homozygosity of chromosome segments in finite populations. Theor. Popul. Biol. 2, 125–141 (1971).

    Article  CAS  PubMed  Google Scholar 

  57. Franceschini, A. et al. STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 41, D808–D815 (2013).

    Article  CAS  PubMed  Google Scholar 

  58. Voight, B.F. et al. Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat. Genet. 42, 579–589 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. THE SIGMA Type 2 Diabetes Consortium. Sequence variants in SLC16A11 are a common risk factor for type 2 diabetes in Mexico. Nature 506, 97–101 (2014).

  60. Hara, K. et al. Genome-wide association study identifies three novel loci for type 2 diabetes. Hum. Mol. Genet. 23, 239–246 (2014).

    Article  CAS  PubMed  Google Scholar 

  61. Boj, S.F. et al. Diabetes risk gene and Wnt effector Tcf7l2/TCF4 controls hepatic response to perinatal and adult metabolic demand. Cell 151, 1595–1607 (2012).

    Article  CAS  PubMed  Google Scholar 

  62. Savic, D. et al. Alterations in TCF7L2 expression define its role as a key regulator of glucose metabolism. Genome Res. 21, 1417–1425 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Bingham, C. & Hattersley, A.T. Renal cysts and diabetes syndrome resulting from mutations in hepatocyte nuclear factor-1β. Nephrol. Dial. Transplant. 19, 2703–2708 (2004).

    Article  CAS  PubMed  Google Scholar 

  64. Farmer, S.R. Molecular determinants of brown adipocyte formation and function. Genes Dev. 22, 1269–1275 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Coppari, R. & Bjørbæk, C. Leptin revisited: its mechanism of action and potential for treating diabetes. Nat. Rev. Drug Discov. 11, 692–708 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Zhang, J., McKenna, L.B., Bogue, C.W. & Kaestner, K.H. The diabetes gene Hhex maintains δ-cell differentiation and islet function. Genes Dev. 28, 829–834 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Li, B., Ruotti, V., Stewart, R.M., Thomson, J.A. & Dewey, C.N. RNA-Seq gene expression estimation with read mapping uncertainty. Bioinformatics 26, 493–500 (2010).

    Article  CAS  PubMed  Google Scholar 

  68. Robinson, M.D., McCarthy, D.J. & Smyth, G.K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  69. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Series B Stat. Methodol. 57, 289–300 (1995).

    Google Scholar 

  70. Maglott, D., Ostell, J., Pruitt, K.D. & Tatusova, T. Entrez Gene: gene-centered information at NCBI. Nucleic Acids Res. 39, D52–D57 (2011).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank members of the Roth lab and the Center for Cancer Systems Biology (CCSB) at the Dana-Farber Cancer Institute (DFCI) for helpful comments and discussion; the lab of Q. Morris for assistance with GeneMANIA data; and M. Çokol and J. Mellor for useful conversations and advice during manuscript preparation. This work was primarily supported by Center of Excellence in Genomic Science (CEGS) grant P50 (HG004233) from the NHGRI awarded to M.V. and F.P.R. F.P.R. is additionally supported by US National Institutes of Health (NIH) grants (HG003224 and HL107440), the Krembil and Avon Foundations, a Canadian Ontario Research Fund Research Excellence Award and the Canada Excellence Research Chairs Program. C.A.M. was supported in this work by an NIH grant (HL098938), the Leducq Foundation and the Harvard Stem Cell Institute. M.T. was supported by an NIH grant (HG004098).

Author information

Authors and Affiliations

Authors

Contributions

M.T., G.M., C.A.M. and F.P.R. conceived of the project. M.T., G.M. and T.H. performed computational analyses. M.T., G.M., C.A.M. and F.P.R. wrote the manuscript. M.V., C.A.M. and F.P.R. oversaw and guided the research effort.

Corresponding authors

Correspondence to Murat Taşan or Frederick P Roth.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Much of the human proteome is unrepresented in protein-protein interaction databases.

The proteome as defined here consists of all 20484 “protein-coding” genes in the NCBI GENE database. Interactions, both binary and co-complex, are taken from the HINT high-quality amortized protein-protein interaction database.

Supplementary Figure 2 Historical investigation bias in the literature.

Genes characterized at earlier dates continue to appear in publications at higher rates than more-recently characterized genes. Circle positions indicate mean publication rate (y-axis) for genes first characterized during or before each year from 1990–2012 (x-axis). Circle sizes indicate the (cumulative) number of genes characterized during or before each year.

Supplementary Figure 3 Cofunction network (CFN) coverage in terms of genes and gene pairs.

(a,b) Coverage shown for genes (a) and gene pairs (b). “HumanFunc” and “GeneMania” are computed as described in Online Methods. NCBI Gene data downloaded on 2013-07-17.

Supplementary Figure 4 Efficient enrichment for dense prix fixe subnetworks using a prostate cancer case study.

Boxplot shows candidate prix fixe subnetwork fitness evolution over 20 generations, circles within boxes indicated mean fitness, whiskers extend cover the full range of observed fitnesses. Marginal histogram (on right) indicates distribution of final generations’ mean fitnesses for 1000 random trials (see Online Methods). Empirical P-value for final generation’s subnetwork enrichment is computed against this marginal distribution (dashed line).

Supplementary Figure 5 Prix fixe scores are uncorrelated with LD (r2) values.

Each scatter plot point is a candidate breast cancer gene. Correlation is computed using Kendall’s τ rank coefficient. Blue genes indicate significantly differentially-expressed mRNA levels in matched case-control TCGA breast cancer (BRCA) samples, while red genes indicate no evidence of cancer-dependent differential expression. Flanking boxplots indicate score distributions of differentially- and not-differentially-expressed genes. Boxplot whiskers extend to 1.5×IQR; outliers not shown. Boxplots compared by one-sided Wilcoxon rank sum tests.

Supplementary Figure 6 Prix fixe score robustness with respect to varying LD (r2) thresholds.

Each histogram represents a collection of Kendall’s τ rank correlation coefficients. Each single correlation coefficient represents a comparison of prix fixe rank orders for a single analyzed trait when the method is repeated using two different r2 thresholds. (a) “Pure” replication (to measure stochastic variance) of the primary analyses using the identical r2 ≥ 0.50 threshold. (b) Comparison of scores between primary analyses (r2 ≥ 0.50) and a ‘permissive’ (r2 ≥ 0.25) threshold. (c) Comparison of scores between primary analyses (r2 ≥ 0.50) and a ‘restrictive’ (r2 ≥ 0.75) threshold.

Supplementary Figure 7 Rank-based analysis of Sanger cancer gene census (SCGC) prioritization when using a ‘permissive’ LD threshold of r2 ≥0.25.

Genes are ranked within each cancer-associated locus and normalized ranks of known cancer (i.e. SCGC) genes are shown as dots for prix fixe-based (“PF”, left) and LD-based (“r2”, right) rankings (100 is highest ranked, 0 is lowest). Average relative rank of SCGC genes (for both methods) within each locus identified by horizontal bars; number of multigenic loci shown above as “n”. Right-most plot (“Union”) shows pooled results across all cancer-associated loci. PF SCGC ranks significantly outperform LD-based SCGC ranks (P = 0.025, one-sided paired Wilcoxon signed-rank test).

Supplementary Figure 8 Rank-based analysis of Sanger cancer gene census (SCGC) prioritization when using a ‘restrictive’ LD threshold of r2 ≥0.75.

Genes are ranked within each cancer-associated locus and normalized ranks of known cancer (i.e. SCGC) genes are shown as dots for prix fixe-based (“PF”, left) and LD-based (“r2”, right) rankings (100 is highest ranked, 0 is lowest). Average relative rank of SCGC genes (for both methods) within each locus identified by horizontal bars; number of multigenic loci shown above as “n”. Right-most plot (“Union”) shows pooled results across all cancer-associated loci. PF SCGC ranks significantly outperform LD-based SCGC ranks (P = 0.028, one-sided paired Wilcoxon signed-rank test).

Supplementary Figure 9 Prix fixe score robustness with respect to varying cofunction networks (CFNs).

Each histogram represents a collection of Kendall’s τ rank correlation coefficients. Each single correlation coefficient represents a comparison of prix fixe rank orders for a single analyzed trait when the method is repeated using two different CFNs. (a) Comparison of scores between primary analyses’ CFN (HF GM) and the HF-alone CFN. (b) Comparison of scores between primary analyses’ CFN (HF GM) and the GM-alone CFN. (c) Comparison of scores between primary analyses’ CFN (HF GM) and a STRING-augmented CFN (HF GM STRING).

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–9 (PDF 891 kb)

Supplementary Table 1

GWAS data This table provides all of the input data originating in published GWA studies. The majority of the data here originate directly from the NHGRI GWAS catalog [2]. Each ‘sheet’ represents a single study in this work. Column data is taken directly from the catalog. Each row represents a single trait-associated tagSNP. (XLSX 159 kb)

Supplementary Table 2

Analysis Scores Run results for all studies examined in this work. Each ‘sheet’ corresponds to a single trait. (XLSX 665 kb)

Supplementary Table 3

Functional enrichment results GO term functional enrichment results for all traits. (XLSX 373 kb)

Supplementary Table 4

Extended summary table GO term functional enrichment results for all traits. (XLSX 33 kb)

Supplementary Table 5

T2D replication results Results for the replication experiment using type-II diabetes (T2D) loci (identified independently from the T2D analysis included in our primary study). Three sheets hold (i) genes and prix fixe scores, (ii) annotations for genes having causal links to diabetes, and (iii) ‘ordered’ functional enrichment results. (XLSX 34 kb)

Prix fixe software

Includes the R package to run a prix fixe analysis (beta version), a reference manual, and an R/Bioconductor vignette for the package. (ZIP 280 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Taşan, M., Musso, G., Hao, T. et al. Selecting causal genes from genome-wide association studies via functionally coherent subnetworks. Nat Methods 12, 154–159 (2015). https://doi.org/10.1038/nmeth.3215

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nmeth.3215

This article is cited by

Search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research