Performing genetic studies in multiple human populations can identify disease risk alleles that are common in one population but rare in others1, with the potential to illuminate pathophysiology, health disparities, and the population genetic origins of disease alleles. Here we analysed 9.2 million single nucleotide polymorphisms (SNPs) in each of 8,214 Mexicans and other Latin Americans: 3,848 with type 2 diabetes and 4,366 non-diabetic controls. In addition to replicating previous findings2,3,4, we identified a novel locus associated with type 2 diabetes at genome-wide significance spanning the solute carriers SLC16A11 and SLC16A13 (P = 3.9 × 10−13; odds ratio (OR) = 1.29). The association was stronger in younger, leaner people with type 2 diabetes, and replicated in independent samples (P = 1.1 × 10−4; OR = 1.20). The risk haplotype carries four amino acid substitutions, all in SLC16A11; it is present at ∼50% frequency in Native American samples and ∼10% in east Asian, but is rare in European and African samples. Analysis of an archaic genome sequence indicated that the risk haplotype introgressed into modern humans via admixture with Neanderthals. The SLC16A11 messenger RNA is expressed in liver, and V5-tagged SLC16A11 protein localizes to the endoplasmic reticulum. Expression of SLC16A11 in heterologous cells alters lipid metabolism, most notably causing an increase in intracellular triacylglycerol levels. Despite type 2 diabetes having been well studied by genome-wide association studies in other populations, analysis in Mexican and Latin American individuals identified SLC16A11 as a novel candidate gene for type 2 diabetes with a possible role in triacylglycerol metabolism.
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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.
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.
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This file contains Supplementary Methods, Supplementary Tables 1-14, Supplementary Notes, list of Subconsortia Authors and additional references.
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PAIRUP-MS: Pathway analysis and imputation to relate unknowns in profiles from mass spectrometry-based metabolite data
PLOS Computational Biology (2019)