Type 2 Diabetes Risk Alleles Are Associated With Reduced Size at Birth

OBJECTIVE: Low birth weight is associated with an increased risk of type 2 diabetes. The mechanisms underlying this association are unknown and may represent intrauterine programming or two phenotypes of one genotype. The fetal insulin hypothesis proposes that common genetic variants that reduce insulin secretion or action may predispose to type 2 diabetes and also reduce birth weight, since insulin is a key fetal growth factor. We tested whether common genetic variants that predispose to type 2 diabetes also reduce birth weight. RESEARCH DESIGN AND METHODS: We genotyped single-nucleotide polymorphisms (SNPs) at five recently identified type 2 diabetes loci (CDKAL1, CDKN2A/B, HHEX-IDE, IGF2BP2, and SLC30A8) in 7,986 mothers and 19,200 offspring from four studies of white Europeans. We tested the association between maternal or fetal genotype at each locus and birth weight of the offspring. RESULTS: We found that type 2 diabetes risk alleles at the CDKAL1 and HHEX-IDE loci were associated with reduced birth weight when inherited by the fetus (21 g [95% CI 11-31], P = 2 x 10(-5), and 14 g [4-23], P = 0.004, lower birth weight per risk allele, respectively). The 4% of offspring carrying four risk alleles at these two loci were 80 g (95% CI 39-120) lighter at birth than the 8% carrying none (P(trend) = 5 x 10(-7)). There were no associations between birth weight and fetal genotypes at the three other loci or maternal genotypes at any locus. CONCLUSIONS: Our results are in keeping with the fetal insulin hypothesis and provide robust evidence that common disease-associated variants can alter size at birth directly through the fetal genotype

Adult height variants affect birth length and growth rate in children

Previous studies identified 180 single nucleotide polymorphisms (SNPs) associated with adult height, explaining ∼10% of the variance. The age at which these begin to affect growth is unclear. We modelled the effect of these SNPs on birth length and childhood growth. A total of 7768 participants in the Avon Longitudinal Study of Parents and Children had data available. Individual growth trajectories from 0 to 10 years were estimated using mixed-effects linear spline models and differences in trajectories by individual SNPs and allelic score were determined. The allelic score was associated with birth length (0.026 cm increase per ‘tall’ allele, SE = 0.003, P = 1 × 10−15, equivalent to 0.017 SD). There was little evidence of association between the allelic score and early infancy growth (0–3 months), but there was evidence of association between the allelic score and later growth. This association became stronger with each consecutive growth period, per ‘tall’ allele per month effects were 0.015 SD (3 months–1 year, SE = 0.004), 0.023 SD (1–3 years, SE = 0.003) and 0.028 SD (3–10 years, SE = 0.003). By age 10, the mean height difference between individuals with ≤170 versus ≥191 ‘tall’ alleles (the top and bottom 10%) was 4.7 cm (0.8 SD), explaining ∼5% of the variance. There was evidence of associations with specific growth periods for some SNPs (rs3791675, EFEMP1 and rs6569648, L3MBTL3) and supportive evidence for previously reported age-dependent effects of HHIP and SOCS2 SNPs. SNPs associated with adult height influence birth length and have an increasing effect on growth from late infancy through to late childhood. By age 10, they explain half the height variance (∼5%) of that explained in adults (∼10%)

A common genetic variant in the 15q24 nicotinic acetylcholine receptor gene cluster (CHRNA5–CHRNA3–CHRNB4) is associated with a reduced ability of women to quit smoking in pregnancy

Maternal smoking during pregnancy is associated with low birth weight and adverse pregnancy outcomes. Women are more likely to quit smoking during pregnancy than at any other time in their lives, but some pregnant women continue to smoke. A recent genome-wide association study demonstrated an association between a common polymorphism (rs1051730) in the nicotinic acetylcholine receptor gene cluster (CHRNA5–CHRNA3–CHRNB4) and both smoking quantity and nicotine dependence. We aimed to test whether the same polymorphism that predisposes to greater cigarette consumption would also reduce the likelihood of smoking cessation in pregnancy. We studied 7845 pregnant women of European descent from the South-West of England. Using 2474 women who smoked regularly immediately pre-pregnancy, we analysed the association between the rs1051730 risk allele and both smoking cessation during pregnancy and smoking quantity. Each additional copy of the risk allele was associated with a 1.27-fold higher odds (95% CI 1.11–1.45) of continued smoking during pregnancy (P = 0.0006). Adjustment for pre-pregnancy smoking quantity weakened, but did not remove this association [odds ratio (OR) 1.20 (95% CI 1.03–1.39); P = 0.018]. The same risk allele was also associated with heavier smoking before pregnancy and in the first, but not the last, trimester [OR for smoking 10+ cigarettes/day versus 1–9/day in first trimester = 1.30 (95% CI 1.13–1.50); P = 0.0003]. To conclude, we have found strong evidence of association between the rs1051730 variant and an increased likelihood of continued smoking in pregnancy and have confirmed the previously observed association with smoking quantity. Our data support the role of genetic factors in influencing smoking cessation during pregnancy

Sequencing PDX1 (insulin promoter factor 1) in 1788 UK individuals found 5% had a low frequency coding variant, but these variants are not associated with Type 2 diabetes

OnlineOpen Article. This is a copy of an article published in Diabetic Medicine. This journal is available online at: http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1464-5491Genome-wide association studies have identified >30 common variants associated with Type 2 diabetes (>5% minor allele frequency). These variants have small effects on individual risk and do not account for a large proportion of the heritable component of the disease. Monogenic forms of diabetes are caused by mutations that occur in <1:2000 individuals and follow strict patterns of inheritance. In contrast, the role of low frequency genetic variants (minor allele frequency 0.1-5%) in Type 2 diabetes is not known. The aim of this study was to assess the role of low frequency PDX1 (also called IPF1) variants in Type 2 diabetes

Interrogating Type 2 Diabetes Genome-Wide Association Data Using a Biological Pathway-Based Approach

OBJECTIVE-Recent genome-wide association Studies have resulted in a dramatic increase in our knowledge of the genetic loci involved in type 2 diabetes. In a complementary approach to these single-marker studies, we attempted to identify biological pathways associated with type 2 diabetes. This approach could allow its to identify additional risk loci. RESEARCH DESIGN AND METHODS-We used individual level genotype data generated from the Wellcome Trust Case Control Consortium (WTCCC) type 2 diabetes study, consisting of 393,143 autosomal SNPs, genotyped across 1,924 case subjects and 2,938 control subjects. We sought additional evidence from summary level data available from the Diabetes Genetics Initiative (DGI) and the Finland-United States Investigation of NIDDM Genetics (FUSION) studies. Statistical analysis of pathways was performed using a modification of the Gene Set Enrichment Algorithm (GSEA). A total of 439 pathways were analyzed from the Kyoto Encyclopedia of Genes and Genomes, Gene Ontology, and BioCarta databases. RESULTS-After correcting for the number of pathways tested, we found no strong evidence for any pathway showing association with type 2 diabetes (top P-adj = 0.31). The candidate WNT-signaling pathway ranked top (nominal P = 0.0007, excluding TCF7L2; P = 0.002), containing a number of promising single gene associations. These include CCND2 (rs11833537; P = 0.003), SMAD3 (rs7178347; P = 0.0006), and PRICKLE1 (rs1796390; P = 0.001), all expressed in the pancreas. CONCLUSIONS-Common variants involved in type 2 diabetes risk are likely to occur in or near genes in multiple pathways. Pathway-based approaches to genome-wide association data may be more Successful for some complex traits than others, depending on the nature of the underlying disease physiology. Diabetes 58:1463-1467, 200

Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling

Abstract. We undertook a comprehensive evaluation of 22 gridded (quasi-)global (sub-)daily precipitation (P) datasets for the period 2000–2016. Thirteen non-gauge-corrected P datasets were evaluated using daily P gauge observations from 76 086 gauges worldwide. Another nine gauge-corrected datasets were evaluated using hydrological modeling, by calibrating the HBV conceptual model against streamflow records for each of 9053 small to medium-sized ( <  50 000 km2) catchments worldwide, and comparing the resulting performance. Marked differences in spatio-temporal patterns and accuracy were found among the datasets. Among the uncorrected P datasets, the satellite- and reanalysis-based MSWEP-ng V1.2 and V2.0 datasets generally showed the best temporal correlations with the gauge observations, followed by the reanalyses (ERA-Interim, JRA-55, and NCEP-CFSR) and the satellite- and reanalysis-based CHIRP V2.0 dataset, the estimates based primarily on passive microwave remote sensing of rainfall (CMORPH V1.0, GSMaP V5/6, and TMPA 3B42RT V7) or near-surface soil moisture (SM2RAIN-ASCAT), and finally, estimates based primarily on thermal infrared imagery (GridSat V1.0, PERSIANN, and PERSIANN-CCS). Two of the three reanalyses (ERA-Interim and JRA-55) unexpectedly obtained lower trend errors than the satellite datasets. Among the corrected P datasets, the ones directly incorporating daily gauge data (CPC Unified, and MSWEP V1.2 and V2.0) generally provided the best calibration scores, although the good performance of the fully gauge-based CPC Unified is unlikely to translate to sparsely or ungauged regions. Next best results were obtained with P estimates directly incorporating temporally coarser gauge data (CHIRPS V2.0, GPCP-1DD V1.2, TMPA 3B42 V7, and WFDEI-CRU), which in turn outperformed the one indirectly incorporating gauge data through another multi-source dataset (PERSIANN-CDR V1R1). Our results highlight large differences in estimation accuracy, and hence the importance of P dataset selection in both research and operational applications. The good performance of MSWEP emphasizes that careful data merging can exploit the complementary strengths of gauge-, satellite-, and reanalysis-based P estimates

Effects of the diabetes linked TCF7L2 polymorphism in a representative older population

BACKGROUND: A polymorphism in the transcription factor 7-like 2 (TCF7L2) gene has been found to be associated with type 2 diabetes in case-control studies. We aimed to estimate associations of the marker rs7903146 (C/T) polymorphism with fasting glucose, lipids, diabetes prevalence and complications in an older general population. METHODS: In total, 944 subjects aged ≥ 65 years from the population representative InCHIANTI study were enrolled in this study. Those with fasting blood glucose of ≥ 7 mmol/l or physician diagnosis were considered diabetic. Cut-off points for impaired fasting glucose (IFG) were ≥ 5.6 mmol/l to < 7 mmol/l. RESULTS: In the general population sample, minor (T) allele carriers of rs7903146 had higher fasting blood glucose (FBG) (p = 0.028) but lower fasting insulin (p = 0.030) and HOMA2b scores (p = 0.001), suggesting poorer beta-cell function. T allele carriers also had smaller waist circumference (p = 0.009), lower triglyceride levels (p = 0.006), and higher high-density lipoprotein cholesterol (p = 0.008). The prevalence of diabetes or IFG was 32.4% in TT carriers and 23.3% in CC carriers; adjusted OR = 1.67 (95% confidence interval 1.05 to 2.65, p = 0.031). Within the diabetic and IFG groups, fewer T allele carriers had metabolic syndrome features (p = 0.047) or had experienced a myocardial infarction (p = 0.037). Conversely, T allele carriers with diabetes had poorer renal function (reduced 24-hour creatinine clearance, p = 0.013), and possibly more retinopathy (p = 0.067). Physician-diagnosed dementia was more common in the T carriers (in diabetes p = 0.05, with IFG p = 0.024). CONCLUSION: The TCF7L2 rs7903146 polymorphism is associated with lower insulin levels, smaller waist circumference, and lower risk lipid profiles in the general elderly population. Patients with diabetes who are carriers of the minor allele are less likely to have metabolic-syndrome features, but may experience more microvascular complications, although the number of cases was small. If replicated, these findings may have implications for developing treatment approaches tailored by genotype

The functional "KL-VS" variant of KLOTHO is not associated with type 2 diabetes in 5028 UK Caucasians

BACKGROUND: Klotho has an important role in insulin signalling and the development of ageing-like phenotypes in mice. The common functional "KL-VS" variant in the KLOTHO (KL) gene is associated with longevity in humans but its role in type 2 diabetes is not known. We performed a large case-control and family-based study to test the hypothesis that KL-VS is associated with type 2 diabetes in a UK Caucasian population. METHODS: We genotyped 1793 cases, 1619 controls and 1616 subjects from 509 families for the single nucleotide polymorphism (SNP) F352V (rs9536314) that defines the KL-VS variant. Allele and genotype frequencies were compared between cases and controls. Family-based analysis was used to test for over- or under-transmission of V352 to affected offspring. RESULTS: Despite good power to detect odds ratios of 1.2, there were no significant associations between alleles or genotypes and type 2 diabetes (V352 allele: odds ratio = 0.96 (0.84–1.09)). Additional analysis of quantitative trait data in 1177 healthy control subjects showed no association of the variant with fasting insulin, glucose, triglycerides, HDL- or LDL-cholesterol (all P > 0.05). However, the HDL-cholesterol levels observed across the genotype groups showed a similar, but non-significant, pattern to previously reported data. CONCLUSION: This is the first large-scale study to examine the association between common functional variation in KL and type 2 diabetes risk. We have found no evidence that the functional KL-VS variant is a risk factor for type 2 diabetes in a large UK Caucasian case-control and family-based study

Using genetics to understand the causal influence of higher BMI on depression

Background: Depression is more common in obese than non-obese individuals, especially in women, but the causal relationship between obesity and depression is complex and uncertain. Previous studies have used genetic variants associated with BMI to provide evidence that higher body mass index (BMI) causes depression, but have not tested whether this relationship is driven by the metabolic consequences of BMI nor for differences between men and women. Methods: We performed a Mendelian randomization study using 48 791 individuals with depression and 291 995 controls in the UK Biobank, to test for causal effects of higher BMI on depression (defined using self-report and Hospital Episode data). We used two genetic instruments, both representing higher BMI, but one with and one without its adverse metabolic consequences, in an attempt to 'uncouple' the psychological component of obesity from the metabolic consequences. We further tested causal relationships in men and women separately, and using subsets of BMI variants from known physiological pathways. Results: Higher BMI was strongly associated with higher odds of depression, especially in women. Mendelian randomization provided evidence that higher BMI partly causes depression. Using a 73-variant BMI genetic risk score, a genetically determined one standard deviation (1 SD) higher BMI (4.9 kg/m2) was associated with higher odds of depression in all individuals [odds ratio (OR): 1.18, 95% confidence interval (CI): 1.09, 1.28, P = 0.00007) and women only (OR: 1.24, 95% CI: 1.11, 1.39, P = 0.0001). Meta-analysis with 45 591 depression cases and 97 647 controls from the Psychiatric Genomics Consortium (PGC) strengthened the statistical confidence of the findings in all individuals. Similar effect size estimates were obtained using different Mendelian randomization methods, although not all reached P < 0.05. Using a metabolically favourable adiposity genetic risk score, and meta-analysing data from the UK biobank and PGC, a genetically determined 1 SD higher BMI (4.9 kg/m2) was associated with higher odds of depression in all individuals (OR: 1.26, 95% CI: 1.06, 1.50], P = 0.010), but with weaker statistical confidence. Conclusions: Higher BMI, with and without its adverse metabolic consequences, is likely to have a causal role in determining the likelihood of an individual developing depression.This article is freely available via Open Access. Click on the Publisher URL to access it via the publisher's site.DH_/Department of Health/United Kingdom MC_PC_17228/MRC_/Medical Research Council/United Kingdom MC_QA137853/MRC_/Medical Research Council/United Kingdom 104150/Z/14/Z/WT_/Wellcome Trust/United Kingdom MR/M005070/1/MRC_/Medical Research Council/United Kingdom WT097835MF/WT_/Wellcome Trust/United Kingdompublished version, accepted version (12 month embargo), submitted versio

Combining Information from Common Type 2 Diabetes Risk Polymorphisms Improves Disease Prediction

BACKGROUND: A limited number of studies have assessed the risk of common diseases when combining information from several predisposing polymorphisms. In most cases, individual polymorphisms only moderately increase risk (~20%), and they are thought to be unhelpful in assessing individuals' risk clinically. The value of analyzing multiple alleles simultaneously is not well studied. This is often because, for any given disease, very few common risk alleles have been confirmed. METHODS AND FINDINGS: Three common variants (Lys23 of KCNJ11, Pro12 of PPARG, and the T allele at rs7903146 of TCF7L2) have been shown to predispose to type 2 diabetes mellitus across many large studies. Risk allele frequencies ranged from 0.30 to 0.88 in controls. To assess the combined effect of multiple susceptibility alleles, we genotyped these variants in a large case-control study (3,668 controls versus 2,409 cases). Individual allele odds ratios (ORs) ranged from 1.14 (95% confidence interval [CI], 1.05 to 1.23) to 1.48 (95% CI, 1.36 to 1.60). We found no evidence of gene-gene interaction, and the risks of multiple alleles were consistent with a multiplicative model. Each additional risk allele increased the odds of type 2 diabetes by 1.28 (95% CI, 1.21 to 1.35) times. Participants with all six risk alleles had an OR of 5.71 (95% CI, 1.15 to 28.3) compared to those with no risk alleles. The 8.1% of participants that were double-homozygous for the risk alleles at TCF7L2 and Pro12Ala had an OR of 3.16 (95% CI, 2.22 to 4.50), compared to 4.3% with no TCF7L2 risk alleles and either no or one Glu23Lys or Pro12Ala risk alleles. CONCLUSIONS: Combining information from several known common risk polymorphisms allows the identification of population subgroups with markedly differing risks of developing type 2 diabetes compared to those obtained using single polymorphisms. This approach may have a role in future preventative measures for common, polygenic diseases