Sequence variants in SLC16A11 are a common risk factor for type 2 diabetes in Mexico (original) (raw)

Accession codes

Data deposits

Genotype data have been deposited in dbGaP under accession number phs000683.v1.p1. Microarray data used in the ‘55k screen’ is publicly available through the NCBI Gene Expression Omnibus and the Cancer Cell Line Encyclopedia. A list of sample identities and accession numbers are available in the Supplementary Information.

References

  1. Rosenberg, N. A. et al. Genome-wide association studies in diverse populations. Nature Rev. Genet. 11, 356–366 (2010)
    Article CAS Google Scholar
  2. Grant, S. F. A. et al. Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. Nature Genet. 38, 320–323 (2006)
    Article CAS ADS Google Scholar
  3. Unoki, H. et al. SNPs in KCNQ1 are associated with susceptibility to type 2 diabetes in East Asian and European populations. Nature Genet. 40, 1098–1102 (2008)
    Article CAS Google Scholar
  4. Yasuda, K. et al. Variants in KCNQ1 are associated with susceptibility to type 2 diabetes mellitus. Nature Genet. 40, 1092–1097 (2008)
    Article CAS Google Scholar
  5. Villalpando, S. et al. Prevalence and distribution of type 2 diabetes mellitus in Mexican adult population: a probabilistic survey. Salud Publica Mex. 52, S19–S26 (2010)
    Article Google Scholar
  6. Patterson, N., Price, A. L. & Reich, D. Population structure and eigenanalysis. PLoS Genet. 2, e190 (2006)
    Article Google Scholar
  7. 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)
    Article Google Scholar
  8. Williams, A. L., Patterson, N., Glessner, J., Hakonarson, H. & Reich, D. Phasing of many thousands of genotyped samples. Am. J. Hum. Genet. 91, 238–251 (2012)
    Article CAS Google Scholar
  9. The 1000 Genomes Project Consortium An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012)
    Article Google Scholar
  10. Zaitlen, N. et al. Informed conditioning on clinical covariates increases power in case-control aAssociation Studies. PLoS Genet. 8 e1003032 http://dx.doi.org/10.1371/journal.pgen.1003032 (2012)
    Article CAS Google Scholar
  11. Voight, B. F. et al. Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nature Genet. 42, 579–589 (2010)
    Article CAS Google Scholar
  12. Halestrap, A. P. The monocarboxylate transporter family—Structure and functional characterization. IUBMB Life 64, 1–9 (2012)
    Article CAS Google Scholar
  13. Ng, P. C. & Henikoff, S. SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res. 31, 3812–3814 (2003)
    Article CAS Google Scholar
  14. Green, R. E. et al. A draft sequence of the Neandertal genome. Science 328, 710–722 (2010)
    Article CAS ADS Google Scholar
  15. Meyer, M. et al. A high-coverage genome sequence from an Archaic Denisovan individual. Science 338, 222–226 (2012)
    Article CAS ADS Google Scholar
  16. Mednikova, M. B. A proximal pedal phalanx of a Paleolithic hominin from denisova cave, Altai. Archaeol. Ethnol. Anthropol. Eurasia 39, 129–138 (2011)
    Article Google Scholar
  17. Max Planck Institute for Evolutionary Anthropology. A high-quality Neandertal genome sequence. http://www.eva.mpg.de/neandertal/ (2013)
  18. Hublin, J. J. The origin of Neandertals. Proc. Natl Acad. Sci. USA 106, 16022–16027 (2009)
    Article CAS ADS Google Scholar
  19. Halestrap, A. P. & Wilson, M. C. The monocarboxylate transporter family—Role and regulation. IUBMB Life 64, 109–119 (2012)
    Article CAS Google Scholar
  20. Garcia, C. K., Goldstein, J. L., Pathak, R. K., Anderson, R. G. W. & Brown, M. S. Molecular characterization of a membrane transporter for lactate, pyruvate, and other monocarboxylates: Implications for the Cori cycle. Cell 76, 865–873 (1994)
    Article CAS Google Scholar
  21. Fu, S., Watkins, S. M. & Hotamisligil, G. S. The role of endoplasmic reticulum in hepatic lipid homeostasis and stress signaling. Cell Metab. 15, 623–634 (2012)
    Article CAS Google Scholar
  22. Rhee, E. P. et al. Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans. J. Clin. Invest. 121, 1402–1411 (2011)
    Article CAS Google Scholar
  23. Savage, D. B. & Semple, R. K. Recent insights into fatty liver, metabolic dyslipidaemia and their links to insulin resistance. Curr. Opin. Lipidol. 21, 329–336 (2010)
    Article CAS Google Scholar
  24. Samuel, V. T. & Shulman, G. I. Mechanisms for insulin resistance: Common threads and missing links. Cell 148, 852–871 (2012)
    Article CAS Google Scholar
  25. Florez, J. et al. Strong association of socioeconomic status with genetic ancestry in Latinos: implications for admixture studies of type 2 diabetes. Diabetologia 52, 1528–1536 (2009)
    Article CAS Google Scholar
  26. Purcell, S. et al. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007)
    Article CAS Google Scholar
  27. Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664 (2009)
    Article CAS Google Scholar
  28. Risch, N. & Merikangas, K. The future of genetic studies of complex human diseases. Science. 273, 1516–1517 (1996)
    Article CAS ADS Google Scholar
  29. Hara, K. et al. Genome-wide association study identifies three novel loci for type 2 diabetes. Hum. Mol. Genet. http://dx.doi.org/10.1093/hmg/ddt399 (14 August 2013)

Download references

Acknowledgements

We thank M. Daly, V. Mootha, E. Lander and K. Estrada for comments on the manuscript, B. Voight, A. Segre, J. Pickrell and the Scientific Advisory Board of the SIGMA Project (especially C. Bustamante) for useful discussions, and A. Subramanian and V. Rusu for assistance with expression analyses. This work was conducted as part of the Slim Initiative for Genomic Medicine, a joint US–Mexico project funded by the Carlos Slim Health Institute. The UNAM/INCMNSZ Diabetes Study was supported by Consejo Nacional de Ciencia y Tecnología grants 138826, 128877, CONACyT- SALUD 2009-01-115250, and a grant from Dirección General de Asuntos del Personal Académico, UNAM, IT 214711. The Diabetes in Mexico Study was supported by Consejo Nacional de Ciencia y Tecnología grant 86867 and by Instituto Carlos Slim de la Salud, A.C. The Mexico City Diabetes Study was supported by National Institutes of Health (NIH) grant R01HL24799 and by the Consejo Nacional de Ciencia y Tenologia grants 2092, M9303, F677-M9407, 251M and 2005-C01-14502, SALUD 2010-2-151165. The Multiethnic Cohort was supported by NIH grants CA164973, CA054281 and CA063464. The Singapore Chinese Health Study was funded by the National Medical Research Council of Singapore under its individual research grant scheme and by NIH grants R01 CA55069, R35 CA53890, R01 CA80205 and R01 CA144034. The Type 2 Diabetes Genetic Exploration by Next-generation sequencing in multi-Ethnic Samples (T2D-GENES) project was supported by NIH grant U01DK085526. The San Antonio Mexican American Family Studies (SAMAFS) were supported by R01 DK042273, R01 DK047482, R01 DK053889, R01 DK057295, P01 HL045522 and a Veterans Administration Epidemiologic grant to R.A.D. A.L.W. was supported by National Institutes of Health Ruth L. Kirschstein National Research Service Award number F32 HG005944.

Author information

Authors and Affiliations

  1. Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, 02142, Massachusetts, USA
    Amy L. Williams, Suzanne B. R. Jacobs, Claire Churchhouse, Noël P. Burtt, Jose C. Florez, David Altshuler, Amy L. Williams, Stephan Ripke, Alisa K. Manning, Benjamin Neale, Noël P. Burtt, David Reich, David Altshuler, Jose C. Florez, Nick Patterson, Jose C. Florez, Noël P. Burtt, Jacquelyn Murphy, Monkol Lek, Sriram Sankararaman, Amy L. Williams, Nick Patterson, Daniel G. MacArthur, David Reich, Suzanne B. R. Jacobs, Claire Churchhouse, David Altshuler, Jason Flannick, Pierre Fontanillas, Noël P. Burtt, Noël P. Burtt, David Altshuler & Jose C. Florez
  2. Department of Genetics, Harvard Medical School, Boston, 02115, Massachusetts, USA
    Amy L. Williams, David Altshuler, Amy L. Williams, David Reich, David Altshuler, Sriram Sankararaman, Amy L. Williams, David Reich, David Altshuler & David Altshuler
  3. Universidad Autonoma Metropolitana, Tlalpan 14387, Mexico City, Mexico.,
    Hortensia Moreno-Macías & Hortensia Moreno-Macías
  4. Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Sección XVI, Tlalpan, 14000 Mexico City, Mexico.,
    Alicia Huerta-Chagoya, María José Gómez-Vázquez, Carlos A. Aguilar-Salinas, Teresa Tusié-Luna, Alicia Huerta-Chagoya, María José Gómez-Vázquez, Carlos A. Aguilar-Salinas, Teresa Tusié-Luna, María Luisa Ordóñez-Sánchez, Rosario Rodríguez-Guillén, Ivette Cruz-Bautista, Maribel Rodríguez-Torres, Linda Liliana Muñoz-Hernández, Tamara Sáenz, Donají Gómez, Ulices Alvirde, Carlos A. Aguilar-Salinas & Teresa Tusié-Luna
  5. Instituto de Investigaciones Biomédicas, UNAM. Unidad de Biología Molecular y Medicina Genómica, UNAM/INCMNSZ, Coyoacán, 04510 Mexico City, Mexico.,
    Alicia Huerta-Chagoya, Teresa Tusié-Luna, Alicia Huerta-Chagoya, Teresa Tusié-Luna, Laura Riba & Teresa Tusié-Luna
  6. Instituto Nacional de Medicina Genómica, Tlalpan, 14610 Mexico City, Mexico.,
    Carla Márquez-Luna, Humberto García-Ortíz, Lorena Orozco, Carla Márquez-Luna, Humberto García-Ortíz, Juan Carlos Fernández-López, Sandra Romero-Hidalgo, Irma Aguilar-Delfín, Angélica Martínez-Hernández, Federico Centeno-Cruz, Elvia Mendoza-Caamal, Emilio Córdova, Xavier Soberón, Lorena Orozco & Lorena Orozco
  7. Universidad Autónoma de Nuevo León, San Nicolás de los Garza, Nuevo León 66451, México.,
    María José Gómez-Vázquez & María José Gómez-Vázquez
  8. Centro de Estudios en Diabetes, Unidad de Investigacion en Diabetes y Riesgo Cardiovascular, Centro de Investigacion en Salud Poblacional, Instituto Nacional de Salud Publica, 01120 Mexico City, Mexico.,
    Clicerio González-Villalpando, Clicerio González-Villalpando, María Elena González-Villalpando & Clicerio González-Villalpando
  9. Center for Human Genetic Research and Diabetes Research Center (Diabetes Unit), Massachusetts General Hospital, Boston, 02114, Massachusetts, USA
    Jose C. Florez, David Altshuler, David Altshuler, Jose C. Florez, Jose C. Florez, David Altshuler, Jason Flannick, David Altshuler & Jose C. Florez
  10. Department of Medicine, Harvard Medical School, Boston, 02115, Massachusetts, USA
    Jose C. Florez, David Altshuler, David Altshuler, Jose C. Florez, Jose C. Florez, David Altshuler, David Altshuler & Jose C. Florez
  11. Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, 90089, California, USA
    Christopher A. Haiman, Daniel O. Stram, Christopher A. Haiman, Christopher A. Haiman, Brian E. Henderson, Kristine Monroe, Daniel O. Stram, Christopher A. Haiman, Brian E. Henderson, Kristine Monroe, Christopher A. Haiman & Brian E. Henderson
  12. Center for Human Genetic Research, Massachusetts General Hospital, Boston, 02114, Massachusetts, USA
    David Altshuler, David Altshuler, David Altshuler & David Altshuler
  13. Department of Molecular Biology, Harvard Medical School, Boston, 02114, Massachusetts, USA
    David Altshuler, David Altshuler, David Altshuler & David Altshuler
  14. Department of Biology, Massachusetts Institute of Technology, Cambridge, 02139, Massachusetts, USA
    David Altshuler, David Altshuler, David Altshuler & David Altshuler
  15. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, 02114, Massachusetts, USA
    Stephan Ripke, Benjamin Neale, Monkol Lek & Daniel G. MacArthur
  16. Unidad de Investigación Médica en Enfermedades Metabólicas, Instituto Mexicano del Seguro Social SXXI, Cuauhtémoc, 06720 Mexico City, Mexico.,
    Cristina Revilla-Monsalve & Sergio Islas-Andrade
  17. Instituto de Seguridad y Servicios Sociales para los Trabajadores del Estado, Álvaro Obregón, 01030 Mexico City, Mexico.,
    Eunice Rodríguez-Arellano
  18. Epidemiology Program, University of Hawaii Cancer Center, Honolulu, 96813, Hawaii, USA
    Lynne Wilkens, Laurence N. Kolonel, Loic Le Marchand, Lynne Wilkens, Laurence N. Kolonel & Loic Le Marchand
  19. The Genomics Platform, The Broad Institute of Harvard and MIT, Cambridge, 02142, Massachusetts, USA
    Robert C. Onofrio, Wendy M. Brodeur, Diane Gage, Jennifer Franklin, Scott Mahan, Kristin Ardlie, Andrew T. Crenshaw, Wendy Winckler, Timothy Fennell, Yossi Farjoun & Stacey Gabriel
  20. Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, D-04103 Leipzig, Germany.,
    Kay Prüfer, Susanna Sawyer, Udo Stenzel, Janet Kelso & Svante Pääbo
  21. Palaeolithic Department, Institute of Archaeology and Ethnography, Russian Academy of Sciences, Siberian Branch, 630090 Novosibirsk, Russia.,
    Michael V. Shunkov & Anatoli P. Derevianko
  22. The Metabolite Profiling Platform, The Broad Institute of Harvard and MIT, Cambridge, 02142, Massachusetts, USA
    Shuba Gopal, James A. Grammatikos, Kevin H. Bullock, Amy A. Deik, Amanda L. Souza, Kerry A. Pierce & Clary B. Clish
  23. Cancer Biology Program, The Broad Institute of Harvard and MIT, Cambridge, 02142, Massachusetts, USA
    Ian C. Smith
  24. University of Minnesota, Minneapolis, 55455, Minnesota, USA
    Myron D. Gross & Mark A. Pereira
  25. University of California San Francisco, San Francisco, 94143, California, USA
    Mark Seielstad
  26. Duke National University of Singapore Graduate Medical School, Singapore 169857, Singapore.,
    Woon-Puay Koh, E-Shyong Tai & E-Shyong Tai
  27. Saw Swee Hock School of Public Health, National University of Singapore, Singapore 117597, Singapore.,
    Woon-Puay Koh, E-Shyong Tai & E-Shyong Tai
  28. Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore.,
    E-Shyong Tai & E-Shyong Tai
  29. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK.,
    Andrew Morris
  30. Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan 48109, USA.,
    Tanya M. Teslovich
  31. Department of Medicine, Department of Genetics, Albert Einstein College of Medicine, Bronx, 10461, New York, USA
    Gil Atzmon
  32. Department of Genetics, Texas Biomedical Research Institute, San Antonio, 78227, Texas, USA
    John Blangero, Ravindranath Duggirala, Sobha Puppala, Vidya S. Farook, Joanne E. Curran, John Blangero & Ravindranath Duggirala
  33. Department of Biochemistry, Department of Internal Medicine, Center for Genomics and Personalized Medicine Research, Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, 27157, North Carolina, USA
    Donald W. Bowden
  34. Department of Epidemiology and Biostatistics, Imperial College London, London SW7 2AZ, UK.,
    John Chambers
  35. Imperial College Healthcare NHS Trust, London W2 1NY, UK.,
    John Chambers & Jaspal Kooner
  36. Ealing Hospital National Health Service (NHS) Trust, Middlesex UB1 3HW, UK.,
    John Chambers & Jaspal Kooner
  37. Department of Biomedical Science, Hallym University, Chuncheon, Gangwon-do, 200-702 South Korea.,
    Yoon Shin Cho
  38. Endocrinology and Metabolism Service, Hadassah-Hebrew University Medical School, Jerusalem 91120, Israel.,
    Benjamin Glaser
  39. Israel Diabetes Research Group (IDRG), Diabetes Unit, The E. Wolfson Medical Center, Holon 58100, Israel.,
    Benjamin Glaser
  40. Human Genetics Center, University of Texas Health Science Center at Houston, Houston, 77030, Texas, USA
    Craig Hanis
  41. National Heart and Lung Institute (NHLI), Imperial College London, Hammersmith Hospital, London W12 0HS, UK.,
    Jaspal Kooner
  42. Department of Medicine, University of Eastern Finland, Kuopio Campus and Kuopio University Hospital, FI-70211 Kuopio, Finland.,
    Markku Laakso
  43. Center for Genome Science, Korea National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-do 363-951, South Korea.,
    Jong-Young Lee
  44. Department of Epidemiology and Public Health, National University of Singapore, Singapore 117597, Singapore.,
    Yik Ying Teo
  45. Centre for Molecular Epidemiology, National University of Singapore, Singapore 117456, Singapore.,
    Yik Ying Teo
  46. Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore 138672, Singapore.,
    Yik Ying Teo
  47. Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore 117456, Singapore.,
    Yik Ying Teo
  48. Department of Statistics and Applied Probability, National University of Singapore, Singapore 117546, Singapore.,
    Yik Ying Teo
  49. Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, 39216, Mississippi, USA
    James G. Wilson
  50. Division of Nephrology, Department of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, 78229, Texas, USA
    Farook Thameem & Hanna E. Abboud
  51. Division of Diabetes, Department of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, 78229, Texas, USA
    Ralph A. DeFronzo & Christopher P. Jenkinson
  52. Division of Clinical Epidemiology, Department of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, 78229, Texas, USA
    Donna M. Lehman
  53. Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA.,
    Maria L. Cortes

Consortia

The SIGMA Type 2 Diabetes Consortium

Contributions

See the author list for details of author contributions.

A list of participants and affiliations for the T2D-GENES Consortium and the Broad Genomics Platform is available in the Supplementary Information.

Corresponding authors

Correspondence toTeresa Tusié-Luna, David Altshuler, David Altshuler, Teresa Tusié-Luna, David Altshuler, David Altshuler or Teresa Tusié-Luna.

Ethics declarations

Competing interests

The author declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Principal component analysis (PCA) projection of SIGMA samples onto principal components calculated using data from samples collected by the Human Genome Diversity Project (HGDP) and 1000 Genomes Project.

a, b, PCA projection of SIGMA onto HGDP Yoruba, French, Karitiana and Han (Chinese) populations before ancestry quality control filters were applied (a), with cohort centroids as indicated, and after all quality control filters were applied (b), with case and control centroids as indicated. c, d, Principal components 3 and 4 before filtering samples on ancestry (a small number of samples in the MEC show East Asian admixture) (c), and after all quality control filters were applied (d). e, f, Additional plots as in b but separating cases (e) and controls (f). g, SIGMA samples projected onto the 1000 Genomes Project Omni2.5 genotype data. 1000 Genomes samples are labelled by their continental ancestry group: AFR, African; AMR, Native American descent; ASN, east Asian; EUR, European.

Extended Data Figure 2 Regional plot for signal at TCF7L2.

Point colour indicates _r_2 to the most strongly associated site (rs7903146) and recombination rate is also shown, both based on the 1000 Genomes ASN population.

Extended Data Figure 3 Conditional analyses reveal multiple independent signals at INS–IGF2 and KCNQ1.

a–d, Regional plots are shown for the interval spanning INS–IGF2 and KCNQ1 without conditioning (a), conditional on rs2237897 at KCNQ1 (b), conditional on rs2237897 and rs139647931 (both at KCNQ1) (c), and conditional on rs2237897 and rs139647931 (both at KCNQ1), and rs11564732 (the top associated variant in the INS–IGF2–TH region) (d). The top SNPs in 11p15.5 and KCNQ1 are ∼700 kb away from each other, but despite this proximity, there is a strong residual signal of association at INS–IGF2 after analysis conditional on genotype at KCNQ1. Point colour indicates _r_2 to rs11564732 and recombination rate is also shown, both based on the 1000 Genomes ASN population.

Extended Data Figure 4 Regional plots for SLC16A11 conditional on associated missense variants of that gene.

a–e, Association signal at chromosome 17p13 without conditioning (a), or conditional on the four missense SNPs in SLC16A11: rs117767867 (b), rs13342692 (c), rs75418188 (d) and rs75493593 (e). Point colour indicates _r_2 to the most strongly associated SNP (rs13342232) and recombination rate is also shown, both based on the 1000 Genomes ASN population.

Extended Data Figure 5 Cases with risk haplotype develop type 2 diabetes younger and at a lower BMI than non-carriers.

a, Distribution of age-of-onset in type 2 diabetes cases based on genotype at rs13342232, binned every 5 years with upper bounds indicated (carriers n = 1,126; non-carriers n = 594). b, Distribution of BMI in type 2 diabetes cases for carriers and non-carriers of rs13342232, binned every 2.5 kg m−2 with upper bounds indicated (carriers n = 2,161; non-carriers n = 1,647). P values from two-sample _t_-test between type 2 diabetes risk haplotype carriers and type 2 diabetes non-carriers.

Extended Data Figure 6 Frequency distribution of the risk haplotype and dendrogram depicting clustering with Neanderthal haplotypes.

a, Allele frequency of missense SNP rs117767867 (tag for risk haplotype) in the 1000 Genomes Phase I data set. b, Dendrogram generated from haplotypes across the 73-kb Neanderthal introgressed region. Nodes for modern human haplotypes are labelled in red or blue with the 1000 Genomes population in which the corresponding haplotype resides. Archaic Neanderthal sequences are labelled in black and include the low-coverage Neanderthal sequence14 (labelled Vindija), and the unpublished Neanderthal sequence that is homozygous for the 5 SNP risk haplotype[17](/articles/nature12828#ref-CR17 "Max Planck Institute for Evolutionary Anthropology. A high-quality Neandertal genome sequence. http://www.eva.mpg.de/neandertal/

             (2013)") (Altai). H1 includes haplotypes from MXL and FIN, and H2 and H3 both include haplotypes from CLM, MXL, CHB and ASW. Modern human sequences included are all 1000 Genomes Phase I samples that are homozygous for the 5 SNP risk haplotype (_n_ \= 15), and 16 non-risk haplotypes—four haplotypes (from two randomly selected individuals) from each of the CLM (Colombian in Medellin, Colombia), MXL (Mexican Ancestry in Los Angeles, California), CHB (Han Chinese in Beijing, China) and FIN (Finnish in Finland) 1000 Genomes populations (the populations with carriers of the 5 SNP haplotype). The red subtree depicts the Neanderthal clade, with all risk haplotypes clustering with the Altai and Vindija sequences. In blue are all other modern human haplotypes. The dendrogram was generated by the R function hclust using a complete linkage clustering algorithm on a distance matrix measuring the fraction of SNPs called in the 1000 Genomes project at which a pair of haplotypes differs (the _y_ axis represents this distance). Because haplotypes are unavailable for the archaic samples, we picked a random allele to compute the distance matrix.

Extended Data Figure 7 Analysis of gene expression for SLC16A11, SLC16A13 and SLC16A1 in 30 human tissues.

Data measured using nCounter are shown as mean, normalized mRNA counts per 200 ng RNA ± s.e.m. Threshold for background (nonspecific) binding is indicated by the red line. Sample size for each tissue (n): pancreas (5); adipose, brain, colon, liver, skeletal muscle and thyroid (3); adrenal, fetal brain, breast, heart, kidney, lung, placenta, prostate, small intestine, spleen, testes, thymus and trachea (2); bladder, cervix, oesophagus, fetal liver, ovary, salivary gland, fetal skeletal muscle, skin, umbilical cord and uterus (1).

Source data

Extended Data Figure 8 Microarray-based analysis of SLC16A11 expression in human tissues.

a, Results from the ‘55k screen’, a survey of gene expression in 55,269 samples profiled on the Affymetrix U133 plus 2.0 array, are shown as the fraction of samples of a given tissue in which SLC16A11 is expressed. Sample size for each tissue (n): adipose (394), adrenal (69), brain (1,990), breast (4,104), heart (178), kidney (675), liver (721), lung (1,442), pancreas (150), placenta (107), prostate (578), salivary gland (26), skeletal muscle (793), skin (947), testis (102), thyroid (108). b, Histograms show the expression level distribution of SLC16A11 and other well-studied liver genes in 721 liver samples from the ‘55k screen’. INS is shown as reference for a gene not expressed in liver. On the basis of negative controls, a normalized log2 expression of 4 is considered baseline and log2 expression values greater than 6 are considered expressed.

Source data

Extended Data Figure 9 SLC16A13 localizes to Golgi apparatus.

a, b, HeLa cells transiently expressing C terminus, V5-tagged SLC16A13 (a) or BFP (b) were immunostained for SLC16A13 or BFP expression (anti-V5) along with specific markers for the endoplasmic reticulum (anti-calnexin), cis-Golgi apparatus (anti-Golph4) and mitochondria (MitoTracker). Representative images from multiple independent transfections are shown. Owing to heterogeneity in expression levels of overexpressed proteins and endogenous organelle markers, imaging of each protein was optimized for clarity of localization and varied across images; therefore, images are not representative of relative expression levels of each protein as compared to the other proteins.

Extended Data Figure 10 Pathway and class-based metabolic changes induced by SLC16A11 expression.

Changes in metabolite levels in HeLa cells expressing SLC16A11–V5 compared to control-transfected cells are plotted in groups according to metabolic pathway or class. Data shown are the combined results from three independent experiments, each of which included 12 biological replicates each for SLC16A11 and control. Pathways shown include all KEGG pathways from the human reference set for which metabolites were measured as well as eight additional classes of metabolites covering carnitines and lipid subtypes. Each point within a pathway or class shows the fold change of a single metabolite within that pathway or class. For each pathway or class with at least six measured metabolites, enrichment was computed as described in Supplementary Methods. Asterisks indicate pathways with P ≤ 0.05 and FDR ≤ 0.25. Supplementary Table 14 shows additional details from the enrichment analysis.

Supplementary information

PowerPoint slides

Source data

Rights and permissions

About this article

Cite this article

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). https://doi.org/10.1038/nature12828

Download citation