Examination of all type 2 diabetes GWAS loci reveals HHEX-IDE as a locus influencing pediatric BMI

Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
Diabetes (Impact Factor: 8.1). 11/2009; 59(3):751-5. DOI: 10.2337/db09-0972
Source: PubMed


A number of studies have found that BMI in early life influences the risk of developing type 2 diabetes later in life. Our goal was to investigate if any type 2 diabetes variants uncovered through genome-wide association studies (GWAS) impact BMI in childhood.
Using data from an ongoing GWAS of pediatric BMI in our cohort, we investigated the association of pediatric BMI with 20 single nucleotide polymorphisms at 18 type 2 diabetes loci uncovered through GWAS, consisting of ADAMTS9, CDC123-CAMK1D, CDKAL1, CDKN2A/B, EXT2, FTO, HHEX-IDE, IGF2BP2, the intragenic region on 11p12, JAZF1, KCNQ1, LOC387761, MTNR1B, NOTCH2, SLC30A8, TCF7L2, THADA, and TSPAN8-LGR5. We randomly partitioned our cohort exactly in half in order to have a discovery cohort (n = 3,592) and a replication cohort (n = 3,592).
Our data show that the major type 2 diabetes risk-conferring G allele of rs7923837 at the HHEX-IDE locus was associated with higher pediatric BMI in both the discovery (P = 0.0013 and survived correction for 20 tests) and replication (P = 0.023) sets (combined P = 1.01 x 10(-4)). Association was not detected with any other known type 2 diabetes loci uncovered to date through GWAS except for the well-established FTO.
Our data show that the same genetic HHEX-IDE variant, which is associated with type 2 diabetes from previous studies, also influences pediatric BMI.

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    • "Interestingly, many of the loci that have been shown to be associated with T2D have been implicated in birth weight determination [43–47]. Similarly, it has also been shown that the T2D-associated locus in HHEX is associated with increased childhood body mass index (BMI) [48]. This means that the influence of these T2D risk loci may exert their effects early on in life. "
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    ABSTRACT: Elucidating the underlying genetic variations influencing various complex diseases is one of the major challenges currently facing clinical genetic research. Although these variations are often difficult to uncover, approaches such as genome-wide association studies (GWASs) have been successful at finding statistically significant associations between specific genomic loci and disease susceptibility. GWAS has been especially successful in elucidating genetic variants that influence type 2 diabetes (T2D) and obesity/body mass index (BMI). Specifically, several GWASs have confirmed that a variant in transcription factor 7-like 2 (TCF7L2) confers risk for T2D, while a variant in fat mass and obesity-associated protein (FTO) confers risk for obesity/BMI; indeed both of these signals are considered the most statistically associated loci discovered for these respective traits to date. The discovery of these two key loci in this context has been invaluable for providing novel insight into mechanisms of heritability and disease pathogenesis. As follow-up studies of TCF7L2 and FTO have typically lead the way in how to follow up a GWAS discovery, we outline what has been learned from such investigations and how they have implications for the myriad of other loci that have been subsequently reported in this disease context.
    International Journal of Endocrinology 02/2014; 2014(17):769671. DOI:10.1155/2014/769671 · 1.95 Impact Factor
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    • "Several studies have also investigated the relationship between HHEX polymorphisms and other metabolic diseases. Zhao et al.[24] found that the type 2 diabetes risk-associated G allele of rs7923837 was associated with higher pediatric BMI in European American children. Cruz et al.[25] analyzed the association between the HHEX rs5015480 and risk of metabolic syndrome (MS) in a case-control study from Mexico city and found that rs5015480 was significantly associated with MS. "
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    ABSTRACT: Plasma lipid abnormalities are implicated in the pathogenic process of type 2 diabetes. The IDE-KIF11-HHEX gene cluster on chromosome 10q23.33 has been identified as a susceptibility locus for type 2 diabetes. We hypothesized that genetic variants at 10q23.33 may be associated with plasma lipid concentrations. Seven tagging single nucleotide polymorphisms (SNPs: rs7923837, rs2488075, rs947591, rs11187146, rs5015480, rs4646957 and rs1111875) at 10q23.33 were genotyped in 3,281 subjects from a Han Chinese population, using the TaqMan OpenArray and Sequenom MassARRAY platforms. Multiple linear regression analyses showed that SNP rs7923837 in the 3'-flanking region of HHEX was significantly associated with triglyceride levels (P = 0.019, 0.031 mmol/L average decrease per minor G allele) and that rs2488075 and rs947591 in the downstream region of HHEX were significantly associated with total cholesterol levels (P = 0.041, 0.058 mmol/L average decrease per minor C allele and P = 0.018, 0.063 mmol/L average decrease per minor A allele, respectively). However, the other four SNPs (rs11187146, rs5015480, rs4646957 and rs1111875) were not significantly associated with any plasma lipid concentrations in this Chinese population. Our data suggest that genetic variants in the IDE-KIF11-HHEX gene cluster at 10q23.33 may partially explain the variation of plasma lipid levels in the Han Chinese population. Further studies are required to confirm these findings in other populations.
    01/2014; 28(1):53-8. DOI:10.7555/JBR.27.20120091
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    • "However, there are several reasons to be skeptical of this hypothesis. First, subjects with Mendelian disorders are typically, by design, excluded from GWAS (Zhao et al., 2010). Second, Mendelian diseases are rare and have overt clinical presentations, so the unintentional inclusion of such carriers in the studies is highly improbable. "
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    ABSTRACT: Although countless highly penetrant variants have been associated with Mendelian disorders, the genetic etiologies underlying complex diseases remain largely unresolved. By mining the medical records of over 110 million patients, we examine the extent to which Mendelian variation contributes to complex disease risk. We detect thousands of associations between Mendelian and complex diseases, revealing a nondegenerate, phenotypic code that links each complex disorder to a unique collection of Mendelian loci. Using genome-wide association results, we demonstrate that common variants associated with complex diseases are enriched in the genes indicated by this "Mendelian code." Finally, we detect hundreds of comorbidity associations among Mendelian disorders, and we use probabilistic genetic modeling to demonstrate that Mendelian variants likely contribute nonadditively to the risk for a subset of complex diseases. Overall, this study illustrates a complementary approach for mapping complex disease loci and provides unique predictions concerning the etiologies of specific diseases.
    Cell 09/2013; 155(1):70-80. DOI:10.1016/j.cell.2013.08.030 · 32.24 Impact Factor
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