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Genome-wide association in type 2 diabetes and its clinical application

How to cite this article: Esparza-Castro D, Andrade-Ancira FJ, Merelo-Arias CA, Cruz M, Valladares-Salgado A. Genome-wide association in type 2 diabetes and its clinical application. Rev Med Inst Mex Seguro Soc. 2015;53(5):592-9.



Received: June 30th 2014

Accepted: April 28th 2015

Genome-wide association in type 2 diabetes and its clinical application

Dagoberto Esparza-Castro,a Francisco Javier Andrade-Ancira,b Carlos Adrián Merelo-Arias,a Miguel Cruz,a Adán Valladares-Salgadoa

aUnidad de Investigación Médica en Bioquímica, Centro Médico Nacional Siglo XXI

bUnidad de Medicina Familiar 23

Instituto Mexicano del Seguro Social, Distrito Federal, México

Communication with: Adán Valladares-Salgado

Telephone: 56276900, extensión 21780


Diabetes mellitus is a complex and chronical disease, which represents one of the biggest health issues the world, with alarming numbers and constantly increasing it demands the creation of new diagnostic, therapeutic and preventive techniques. The complete Genome Wide Association (GWA) in type 2 diabetes (T2D) is a useful research tool for the characterization of genetic markers and physiopathogenic pathways, with potential clinical utility either as a T2D risk prediction or its complications. In Mexico is necessary to make a comprehensive dissection of the genetic background of T2D by the complex genetic mosaic of our population and increase the knowledge of the molecular and pathophysiological mechanisms that lead to this condition. There are several genetic studies for the Mexican population, linked to the 1000 genomes project, which have led to define some specific genetic markers for our population which are not described in European populations, until the moment, 78 loci have been associated with T2D. Recently in the global meta-analysis, with the participation of Mexico, we demonstrated at least 7 new variants associated with T2D.

Keywords: Type 2 diabetes mellitus; Genes; Genome-Wide Association Study

Diabetes mellitus is a chronic and complex disease that requires ongoing care with multifactorial risk reduction strategies beyond glycemic control. Type 2 diabetes (T2D) is a metabolic disease with progressive disorder in the secretion and / or action of insulin, according to the criteria of the American Diabetes Association (ADA).1 T2D is phenotypically and genetically heterogeneous and multifactorial, derived from the coexistence of genetic and environmental variables and individual habits that contribute to the development of the disease. Currently, the prevalence of T2D is constantly increasing due to changes in lifestyle and economic growth of both underdeveloped and developed countries. It is estimated that by 2030 the number of patients with T2D in the world will reach 439 million.2 This requires a new proposal of strategies for the diagnosis, treatment, and especially prevention of diabetes.  

Genomic linkage analysis focuses on the search for genetic variants linked to a phenotype or trait based on the genomic study of family members. Its aim is to identify loci that co-segregate with certain traits or phenotypes over generations. The resolving power of the genes of interest is generally considered low.3,4 Genomic analysis of association proposes the use of all known variants of single nucleotide polymorphisms or SNPs for the whole genome in an open population. The association of polymorphisms with any related trait already replicated in other populations is then evaluated. This approach has greater power, as it evaluates specific genes and can explore more subjects, since it does not require family relationships between them.4

The etiology of T2D is multifactorial, although genetic factors play a very important role in the development of this pathology. Initial efforts were based on analysis of linkage in families and studies of candidate genes, with modest results.5 Recently, gene identification and genome-wide association studies (GWAS) have successfully identified multiple genes that contribute to T2D susceptibility. Analysis of all these genetic loci of risk contribute significantly to the prediction of T2D, facilitating the adoption of preventive measures and early therapeutic strategies to reduce the growing morbidity.6-9 This review summarizes the research on T2D genetics in Mexico and the world, and discusses the context of the future and the clinical application of such studies.  

Genetic research on T2D

Developed countries that have seen increased prevalence of diabetes are the ones that started the genetic study of T2D, especially European populations that have been affected by this pandemic.10 This has facilitated the understanding of the pathophysiology of disease, the development of alternative treatments to those existing, and exploration of risk factors for monogenic diseases such as MODY, neonatal mitochondrial diabetes or insulin resistance syndromes and Wolfram.11-13 However, in the field of T2D identifying genetic risk factors currently shows no real clinical utility.

In 2006, an association study on a large scale identified TCF7L2 as an important genetic factor for T2D in Icelandic individuals.14 This association was later replicated in other populations such as US, European, Japanese and Latino. The TCF7L2 has been the most important gene for T2D susceptibility to date.

Other genes associated with T2D have been identified, such as gene variants in CAPN10,15 ENPP1,16 HNF4A,17 ADIPOQ (also called ACDC)18 and PPARg19 that play a key role in various cellular functions and differentiation of adipocytes. The PPARg Pro12Ala variant has been associated with increased sensitivity to insulin.20 The KCNJ11 gene has a role in glucose-dependent insulin secretion in beta cells of the pancreas,21 WFS1 and HNF1B also have been associated with T2D,21,22 the latter encoding the factors involved in hepatocyte metabolism, identified as causing MODY5.11

Genome-wide scanning in patients with T2D

With the advent of GWAS, exploring the genetic basis of susceptibility to T2D has made significant progress.10 Thanks to this type of study, in 2007 scientists managed to increase the number of loci to nine (PPARg, KCNJ11, TCF7L2, CDKAL1, CDKN2A / B, IGF2BP2, HHEX / IDE, FTO, and SLC30A8),23-28 which was the turning point for the identification of multiple risk loci for T2D mostly in European populations. 70 loci associated with this disease are broadly known, mostly in European populations (Table I).29 78 potential loci have recently been proposed by GWAS that can provide new insights into the etiology of T2D.30

Table I T2D susceptibility genes29
Gene Chromosome Odds ratio RAF Type of study Function and probable mechanism
ADAMTS9 3 1.09-1.05 0.68-0.81 MA Insulin and metalloproteinase action
ADCY5 3 1.12 0.78 MA Insulin action / adenylate cyclase
ANK1 8 1.09 0.76 MA, CC Cell stability / β cell function
ANKRD55 5 1.08 0.7 MA, CC Insulin action
ANKS1A 6 1.11 0.91 GWAS Path controller / unknown
ARAP1 11 1.08-1.14 0.81-0.88 GWAS, MA Actin cytoskeleton modulator / β cell function
BCAR1 16 1.12 0.89 MA, CC Docking protein / β cell function
BCL2 18 1.09 0.64 GWAS Regulator of cell death / β cell function
BCL11A 2 1.08-1.09 0.46 MA Zinc Finger / β cell function
CAMK1D 10 1.07-1.11 0.18 LA,MA Protein kinase / β cell function
CDC123 Mitotic protein / β cell function
CAPN10 2 1.09-1.18 0.73-0.96 MA Cysteine proteases calpain / insulin action
CDKAL1 6 1.10-1.20 0.27-0.31 GWAS, MA β-cell function
CDKN2A 9 1.19-1.20 0.82-0.83 GWAS Cyclin-dependent kinase inhibitor / β cell function
CENTD2 11 1.08-1.13 0.81-0.88 GWAS β-cell function
CHCHD9 9 1.11-1.20 0.93 MA Unknown
CILP2 19 1.13 0.08 MA, CC Unknown
DGKB 7 1.04-1.06 0.47-0.54 MA Diacylglycerol kinase / insulin action
DUSP9 X 1.09-1.27 0.12-0.77 MA Phosphatase
FOLH1 11 1.10 0.09 GWAS Transmembrane glycoprotein / unknown
FTO 16 1.06-1.27 0.38-0.41 GWAS, MA Metabolic regulator / insulin action
GATAD2A 19 1.12 0.08 GWAS Transcriptional repressor / unknown
GCK 7 1.07 0.20 MA Glucokinase / insulin action
GCKR 2 1.06-1.09 0.59-0.62 MA Glucokinase regulator / insulin action
GIPR 19 1.10 0.27 GWAS Receiver coupled to g-protein / unknown
GRB14 2 1.07 0.60 MA, CGS Adapter protein / insulin action
HFE 6 1.12 0.29 MA Membrane protein / unknown
HHEX 10 1.12-1.13 0.53-0.60 AL, Transcriptional repressor / intracellular insulin degradation / motor protein.
HMG20A 15 1.08 0.68 MA, CGS Chromatin-associated protein / unknown
HMGA1 6 1.34-15.8 0.10 CGS Transcriptional regulator / insulin action
HMGA2 12 1.10-1.20 0.09-0.10 MA Transcriptional regulator
HNF1A 12 1.07-1.14 0.77-0.85 MA Liver and pancreatic transcriptional activator
HNF1B 17 1.08-1.17 0.47-0.51 CGS, MA Transcription factor / β cell function
IGF2BP2 3 1.14 0.29-0.32 GWAS, MA Binding protein / β cell function
IRS1 2 1.09-1.12 0.64-0.67 GCS, MA Insulin signaling element / insulin action
JAZF1 7 1.10 0.52 MA Zinc finger / β cell function
KCNJ11 11 1.09-1.14 0.37-0.47 CGS, MA Potassium channels / β cell function
KCNQ1 11 1.08-1.23 0.44 GWAS Potassium channels / β cell function
KLF14 7 1.07-1.10 0.55 MA Transcription factor / insulin action
KLHDC5 12 1.10 0.80 MA, CC Mitotic progression and cytokinesis / unknown
LAMA1 18 1.13 0.38 GWAS Cell migration mediator / insulin action
MC4R 18 1.08 0.27 MA, CC Receiver coupled to g-protein / unknown
MTNR1B 11 1.05-1.08 0.28-0.30 GWAS, MA Melatonin receptor / β cell function
NOTCH2 1 1.06-1.13 0.10-0.11 MA Membrane receptor
PPARG 3 1.11-1.17 0.85-0.88 CGS, MA Nuclear receptor / insulin action
PRC1 15 1.07-1.10 0.22 MA Cytokinesis regulator
PROX1 1 1.07 0.50 MA Homeobox transcription factor / insulin action
PTPRD 9 1.57 0.10 GWAS Tyrosine phosphatase protein
RBMS1 2 1.11-1.08 0.79-0.83 MA DNA modulator / insulin action
SLC2A2 3 1.06 0.74 GWAS Glucose sensor / β cell function
SLC30A8 8 1.11-1.18 0.65-0.70 GWAS, MA β-cell function
SREBF1 17 1.07 0.38 GWAS Lipid transcriptional regulator / unknown
SRR 17 1.28 0.69 GWAS Serine racemase
TCF7L2 10 1.31-1.71 0.26-0.30 MA, GWAS Participant in signaling pathways / β cell function
THADA 2 1.15 0.90 MA Protein associated with thyroid adenoma / β cell function
TH/INS 11 1.14 0.39 GWAS Catecholamine synthesis / unknown
TLE1 9 1.07 0.57 MA, CC Transcriptional corepressor / unknown
TP53INP1 8 1.06 – 1.11 0.48 MA Proapoptotic protein / unknown
TSPAN8 12 1.06-1.09 0.27-0.71 MA Cell surface glycoprotein / β cell function
LGR5 Receiver coupled to g-protein / β cell function
WFS1 4 1.10-1.13 0.60-0.73 CGS Transmembrane protein / β cell function
ZBED3 5 1.08-1.16 0.26 MA Zinc finger / β cell function
ZFAND6 15 1.01-1.11 0.60-0.72 MA Zinc finger / β cell function
ZMIZ1 10 1.08 0.52 MA, CC Transcriptional regulator / unknown
Haplogroup B mtDNA 1.52 0.25 CGS
OriB mtDNA 1.10 0.30 MA
GWAS: Genome-wide association studies, MA: meta-analysis, CGS: candidate gene study, LA: linkage analysis, RAF: risk alleles frequencies

Genetic research of T2D in Mexico

In Mexico, the Encuesta Nacional de Salud y Nutrición 2012 (ENSANUT) showed alarming data with an overall prevalence of 9.2% of T2D, mostly present in men and women over 60 years. 6.4 million Mexican known diabetics were found, of which only 25% were found in metabolic control. The ENSANUT 2012 shows that 30.2% of the population is insured by the IMSS.31

Most GWAS in patients with T2D were performed in populations of European ancestry. In recent years various genetic variants associated with T2D in the Mexican population have been described, including TCF7L2 gene, the R230C variant of ABCA1 HDL receptor gene, and recently Gly972Arg polymorphism of the IRS1 gene.32

Early studies focused on understanding the genetic structure of the Mexican population were conducted in the Instituto Mexicano del Seguro Social by applying markers informative of ancestry,33 which helped to design the first GWAS in a sample of patients with T2D, in order to identify genetic risk markers in our population.34 Also, in collaboration with international groups, the first trans-ethnic meta-analysis was performed to characterize complete loci in populations with T2D with diverse ancestry.30 With these works, along with corroborating classic markers of T2D risk, several new signals in common variants were identified, but only seven reached the value of genomic signal.

Currently, the focus is identifying a new region with significant association with triglycerides near APOA5 gene, which is localized on chromosome 11; confirming of regions associated with T2D previously reported (HNF1A, KCNQ1, PTPRD, DGKB-TMEM19, CDKN2A / CDKN2B and IGF2BP2); identifying signals in regions that had not been identified in previous studies (Table II), but that may be relevant to T2D; and demonstrating that a variant located in the CIT gene has a regulatory effect on the WFS1 gene; all findings that are very relevant to T2D.

Table II New genetic markers associated with T2D
Locus SNP Chromosome Risk allele 1 Allele 2 OR (95% CI) [P]
TMEM154 rs6813195 4 C T 1.08(1.06–1.10) 4.1 × 10−14
SSR1-RREB1 rs9505118 6 A G 1.06(1.04–1.08) 1.4 × 10−9
FAF1 rs17106184 1 G A 1.10(1.07–1.14) 4.1 × 10−9
POU5F1-TCF19 rs3130501 6 G A 1.07 (1.04-1.09) 4.2 × 10−9
LPP rs6808574 3 C T 1.07 (1.04-1.09) 5.8 × 10−9
ARL15 rs702634 5 A G 1.06(1.04–1.09) 6.9 × 10−9
MPHOSPH9 rs4275659 12 C T 1.06(1.04–1.08) 9.5 × 10−9

Participation of the loci in the pathogenesis of T2D

GWAS are methodologies that identify the disease association with specific regions of chromosomes, called loci.35 A large number of loci related to the susceptibility of T2D have been mentioned above, derived from different studies (Table III). Most of them confer risk through the function of the beta cells of the pancreas, such as KCNJ11, TCF7L2, WFS1, HNF1B, IGF2BP2, CDKN2A-CDKN2B, CDKAL1, SLC30A8, HHEX / IDE, KCNQ1, THADA, TSPAN8 / LGR5, CDC123 / CAMK1D, JAZF1, MTNR1B, DGKB / TMEM195, GCK, PROX1, ADCY5, SRR, CENTD2, ST6GAL1, HNF4A, KCNK16, FITM2-R3HDML-HNF4A, GLIS3, GRB14, ANK1, BCAR1, RASGRP1 and TMEM163,16,17, 26,27 or those directly related to the action of insulin such as PPARg, ADAMTS9, IRS1, GCKR, RBMS1 / ITGB6, PTPRD, DUSP9, HMGA2, KLF14, GRB14, ANKRD55 and GRK536-39; although the FTO and MC4R are associated more with obesity, they are also known to influence the development of T2D.40,41

Table III Loci associated with T2D according to physiopathogenic mechanism
HbA1c Insulin-HOMA (FPG) fasting plasma glucose Insulin/FPG HbA1c /FPG HbA1c/FPG/insulin

TCF7L2 represents the gene most studied so far in the field of T2D, the risk allele of TCF7L2 is associated with increased expression of this gene in human islets, as well as alteration of insulin, and an altered effect on incretin in individuals carrying the risk allele has been observed. TCF7L2 has also been linked to impaired morphology of pancreatic islets.42

Clinical usefulness of genetic information

One of the most important clinical uses of genetic information is predicting the risk of developing T2D in non-diabetic patients, which would facilitate early intervention strategies to prevent or delay the onset of the disease. The prediction of T2D is the cornerstone in clinical practice, especially genetic information that might be proposed as a risk marker and / or associated with complications.10

Several recent studies have built models of genetic risk score. These models consider many genetic variants of susceptibility to T2D, such as ROC (Receiver Operating Characteristic) curves used to identify the probability of T2D. The areas under the curve (AUC) take values ​​from 0.5 to 1.0, where 0.5 represents AUC lack of discrimination, and AUC of 1.0 means perfect discrimination. A value above 0.75 is considered clinically useful.43 In a study of the Japanese population where 49 T2D susceptibility alleles were used, combined with phenotypic data such as age, gender, and body mass index, a modest but statistically significant effect of 0.773 was gained.44 This has also been observed in other populations.45,46 A recent study with data from GWAS has demonstrated that SNPs can explain more than 50% of the phenotypic variation in T2D.47


T2D is a multifactorial disease in which there are environmental and genetic risk factors. Recent genetic studies worldwide have shown that there are mutations or SNPs throughout the genome that lead to different forms of diabetes, to suffering complications or comorbidities. The dramatic increase and pandemic behavior of T2D, besides the high cost of the care of T2D patients, have led to the task of finding new ways to treat and prevent disease. Genomic studies represent a useful tool to determine risk factors in the population, so it is necessary to make a comprehensive dissection of the genetic background of T2D and its complications for the Mexican population and increase knowledge of the molecular and pathophysiological mechanisms leading to this condition.

Diabetes is a growing disease which urgently requires the creation of new strategies to support its prediction, early detection, and prevention. Currently we have identified more than 78 loci of risk for developing T2D, and although this represents a breakthrough in the field of medicine, they are not entirely useful, because so far they only account for a small amount of the estimated heritability of T2D, and the use of this genetic information is still in early stages. GWAS is a useful tool in identifying genetic risk factors for T2D and its heritability. To identify new genetic markers associated with the disease, new studies are needed such as exome sequencing or sequencing the entire genome, to give health benefits for the population at risk.

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Conflict of interest statement: The authors have completed and submitted the form translated into Spanish for the declaration of potential conflicts of interest of the International Committee of Medical Journal Editors, and none were reported in relation to this article.

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