Influence of genetic ancestry and socioeconomic status on type 2 diabetes in the diverse Colombian populations of Chocó and Antioquia

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1 Supplementary Information For: Influence of genetic ancestry and socioeconomic status on type 2 diabetes in the diverse Colombian populations of Chocó and Antioquia Aroon T. Chande 1,2,3, Jessica Rowell 1, Lavanya Rishishwar 1,2,3, Andrew B. Conley 2, Emily T. Norris 1,2,3, Augusto Valderrama-Aguirre 3,4, Miguel A. Medina-Rivas 3,5 and I. King Jordan 1,2,3, * 1 School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA 2 IHRC-Georgia Tech Applied Bioinformatics Laboratory, Atlanta, Georgia, USA 3 PanAmerican Bioinformatics Institute, Cali, Valle del Cauca, Colombia 4 Biomedical Research Institute, Universidad Libre, Cali, Valle del Cauca, Colombia 5 Centro de Investigación en Biodiversidad y Hábitat, Universidad Tecnológica del Chocó, Quibdó, Chocó, Colombia *Corresponding author: I. King Jordan School of Biological Sciences Georgia Institute of Technology 950 Atlantic Drive Atlanta, Georgia USA king.jordan@biology.gatech.edu 1

2 Supplementary Figures and Table Supplementary Figure 1. Relief map of Colombia showing the locations of the administrative departments (i.e., states) of Chocó and Antioquia. Map adapted from edited to highlight the states of interest. The image file is licensed under the Creative Commons Attribution-Share Alike 3.0 Unported license 2

3 Supplementary Figure 2. Admixture bar chart showing the percentage of African (blue), European (orange) and Native American (red) ancestry for the individuals from Antioquia Chocó analyzed here. Admixture was run with K=3 clusters, corresponding to the three continental ancestry groups, using the global putative ancestral source populations shown here 2. 3

4 Supplementary Figure 3. Validation of the SNP imputation process for the Chocó genotypes via comparison of genetic ancestry patterns before (original genotypes) and after (imputed genotypes) imputation. Pairwise genomic distances between individuals from Chocó and a panel of global reference populations (see color key) characterized via whole genome sequencing as part of the 1KGP are shown before and after imputation. 4

5 Supplementary Table 1. Type 2 diabetes (T2D) associated SNPs analyzed in this study. 165 T2Dassociated SNPs, corresponding to 29 studies, were taken from the NHRGI-EBI GWAS database 1. rsid 1 Chr 2 Pos 2 Risk Allele 3 Gene(s) 4 PubMed ID 5 rs X A DUSP rs G FAF rs A TGFBR rs C SLC44A3, F rs T NOTCH2, ADAM rs G PROX rs C LYPLAL rs A RHOU rs A PCNXL rs T intergenic rs C TMEM rs T intergenic rs G GALNT14, CAPN rs T THADA rs T BCL11A rs A BCL11A rs G LIMS rs T TMEM rs C RBM43, RND rs A GRB rs C IRS rs G SYN2, PPARG rs A PPARG rs C PPARG rs G UBE2E rs C UBE2E rs c PSMD rs C ADAMTS rs A ZPLD rs A ADCY rs T ADCY rs C PLS rs A PEX5L rs T IGF2BP rs C IGF2BP rs C IGF2BP rs T IGF2BP rs C ST64GAL

6 rs C LPP rs C MAEA rs G WFS rs T WFS rs T NR rs C TMEM rs A ARL rs A MAP3K rs G ZBED rs C PCBD rs T intergenic rs G CDKAL rs G CDKAL rs G CDKAL rs C CDKAL rs C CDKAL rs G CDKAL rs A CDKAL rs G CDKAL rs C CDKAL rs G HLA-B rs A HLA-DQA rs C ZFAND rs T KCNK rs T VEGFA rs G C6orf rs G C6orf rs A IL20RA rs A intergenic rs G ETV rs T DGKB rs T JAZF rs A JAZF rs G JAZF rs T HECW rs A ACHE rs G PAX4, GCC rs G ARF5, PAX4, SND rs A MIR129, LEP rs G KLF rs C ANK

7 rs T CPA rs G INTS rs T TP53INP rs T TP53INP rs C SLC30A rs G SLC30A rs C TMEM rs T WISP rs T KCNJ rs A CDKN2A, CDKN2B rs T CDC rs A CDC123, CAMK1D rs G CDC123, CAMK1D rs C C10orf rs A ZMIZ rs A PTEN rs G KIF rs C HHEX rs C HHEX rs C TCF7L rs C TCF7L rs T TCF7L rs C GRK rs C PLEKHA rs G TCERG1L rs A INS-IGF rs G ASCL2, TH rs G KCNQ rs T KCNQ rs C KCNQ rs C KCNQ rs G KCNQ rs A KCNQ rs C KCNQ rs C KCNQ rs C GALNTL4, LOC rs C KCNJ rs C intergenic rs A CENTD rs T MTNR1B rs G MTNR1B

8 rs C TMEM45B, BARX rs C KLHDC rs G NR rs A DCD rs C HMGA rs T HMGA rs T HMGA rs G NR rs A LGR5, TSPAN rs C LGR5, TSPAN rs C HNF1A rs G HNF1A rs T HNF1A rs C MPHOSPH rs G RNF rs G SPRY rs G C14orf rs T RASGRP rs A ANXA rs C C2CD4A rs A C2CD4A rs C C2CD4B rs G HMG20A rs T HMG20A rs G ZFAND rs C AP3S rs A PRC rs G PRC rs A FTO rs C FTO rs A FTO rs A FTO rs T WWOX rs T NXN rs G SLC16A rs G HNF1B, TCF rs C LPIN rs G LAMA rs T OSBPL1A rs A MC4R rs A PEPD

9 rs G FITM2, R3HDML, HNF4A rs A HNF4A CTCFL, RBM38, HMG1L1, rs C PCK1 rs A HUNK T2D-associated SNP identifier numbers, from the NCBI Single Nucleotide Polymorphism database (dbsnp 2 Chromosomal locations for the T2D-associated SNPS, corresponding to the GRh37/h19 version of the human genome reference sequence. 3 Identity of the risk alleles (nucleotide variants) that are associated with higher risk of T2D for each SNP. Risk allele identities are shown here for the positive DNA strand. 4 HGNC gene symbols for the genes considered to be influenced by the T2D-associated SNPs, according to each study. 5 NCBI PubMed identifier numbers for the publications where each T2D-associated SNP was reported. 9

10 Supplementary Figure 4. Distributions of T2D SNP OR values along with control analysis distributions. The distribution of OR values for the 165 individual T2D-associated SNPs is shown in gray, and the observed T2D meta-analysis OR value is indicated with a dashed line. The bootstrap T2D SNP set OR value distribution is shown in red, and the random disease-associated SNP set OR value distribution is shown in blue. Mean values for all three distributions are shown. 10

11 Supplementary Figure 5. Type 2 diabetes polygenic risk score distributions for European-American (orange) and African-American (green) populations from the US 3. The significance of the difference between the two distributions, based on the Student s t-test, is shown. 11

12 Supplementary Figure 6. Age pyramids for Chocó and Antioquia. The percentages of males and females in each population are shown for different age groups 4. 12

13 Supplementary Figure 7. Economic development and disease prevalence reporting in Chocó and Antioquia. (A) Regression of the human development index (HDI, y-axis) against diabetes prevalence estimates (x-axis) for Colombian administrative departments (i.e., states). The linear trend line is shown in blue with 95% CI in gray. The values of r and P for the Pearson correlation coefficient of the regression are shown. (B) Disease prevalence estimate differences (Chocó Antioquia) for 43 reported diseases. The values of W and P for a binomial sign test of consistent differences between pairs of disease prevalence estimates are shown. Note that the x-axis breaks at the two highest values for malaria, which has far higher prevalence in Chocó. 13

14 Supplementary References 1. Welter D, et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res 42, D (2014). 2. Medina-Rivas MA, et al. Choco, Colombia: a hotspot of human biodiversity. Rev Biodivers Neotrop 6, (2016). 3. Genomes Project C, et al. A global reference for human genetic variation. Nature 526, (2015). 4. Uribe Vélez A, Maldonado Gómez H, Fernández Ayala PJ, Vargas Bad A, Serna Ríos C. Censo General 2005 Departamento Administrativo Nacional de Estadística (DANE) (2006). 14