Type 2 Diabetes TCF7L2 Risk Genotypes Alter Birth Weight: A Study of 24,053 Individuals

The role of genes in normal birth-weight variation is poorly understood, and it has been suggested that the genetic component of fetal growth is small. Type 2 diabetes genes may influence birth weight through maternal genotype, by increasing maternal glycemia in pregnancy, or through fetal genotype, by altering fetal insulin secretion. We aimed to assess the role of the recently described type 2 diabetes gene TCF7L2 in birth weight. We genotyped the polymorphism rs7903146 in 15,709 individuals whose birth weight was available from six studies and in 8,344 mothers from three studies. Each fetal copy of the predisposing allele was associated with an 18-g (95% confidence interval [CI] 7–29 g) increase in birth weight (P=.001) and each maternal copy with a 30-g (95% CI 15–45 g) increase in offspring birth weight (P=2.8×10(-5)). Stratification by fetal genotype suggested that the association was driven by maternal genotype (31-g [95% CI 9–48 g] increase per allele; corrected P=.003). Analysis of diabetes-related traits in 10,314 nondiabetic individuals suggested the most likely mechanism is that the risk allele reduces maternal insulin secretion (disposition index reduced by ∼0.15 standard deviation; P=1×10(-4)), which results in increased maternal glycemia in pregnancy and hence increased offspring birth weight. We combined information with the other common variant known to alter fetal growth, the −30G→A polymorphism of glucokinase (rs1799884). The 4% of offspring born to mothers carrying three or four risk alleles were 119 g (95% CI 62–172 g) heavier than were the 32% born to mothers with none (for overall trend, P=2×10(-7)), comparable to the impact of maternal smoking during pregnancy. In conclusion, we have identified the first type 2 diabetes–susceptibility allele to be reproducibly associated with birth weight. Common gene variants can substantially influence normal birth-weight variation

A common variation in deiodinase 1 gene DIO1 is associated with the relative levels of free thyroxine and triiodothyronine

Introduction: Genetic factors influence circulating thyroid hormone levels, but the common gene variants involved have not been conclusively identified. The genes encoding the iodothyronine deiodinases are good candidates because they alter the balance of thyroid hormones. We aimed to thoroughly examine the role of common variation across the three deiodinase genes in relation to thyroid hormones. Methods: We used HapMap data to select single-nucleotide polymorphisms (SNPs) that captured a large proportion of the common genetic variation across the three deiodinase genes. We analyzed these initially in a cohort of 552 people on T-4 replacement. Suggestive findings were taken forward into three additional studies in people not on T-4 (total n = 2513) and metaanalyzed for confirmation. Results: A SNP in the DIO1 gene, rs2235544, was associated with the free T-3 to free T-4 ratio with genome-wide levels of significance (P = 3.6 x 10(-13)). The C-allele of this SNP was associated with increased deiodinase 1 (D1) function with resulting increase in free T-3/T-4 ratio and free T-3 and decrease in free T-4 and rT(3). There was no effect on serum TSH levels. None of the SNPs in the genes coding for D2 or D3 had any influence on hormone levels. Conclusions: This study provides convincing evidence that common genetic variation in DIO1 alters deiodinase function, resulting in an alteration in the balance of circulating free T-3 to free T-4. This should prove a valuable tool to assess the relative effects of circulating free T-3 vs. free T-4 on a wide range of biological parameters

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

Abstract: Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers

HLA-DQA1-HLA-DRB1 variants confer susceptibility to pancreatitis induced by thiopurine immunosuppressants

Pancreatitis occurs in approximately 4% of patients treated with the thiopurines azathioprine or mercaptopurine. Its development is unpredictable and almost always leads to drug withdrawal. We identified patients with inflammatory bowel disease (IBD) who had developed pancreatitis within 3 months of starting these drugs from 168 sites around the world. After detailed case adjudication, we performed a genome-wide association study on 172 cases and 2,035 controls with IBD. We identified strong evidence of association within the class II HLA region, with the most significant association identified at rs2647087 (odds ratio 2.59, 95% confidence interval 2.07-3.26, P = 2 x 10(-16)). We replicated these findings in an independent set of 78 cases and 472 controls with IBD matched for drug exposure. Fine mapping of the HLA region identified association with the HLA-DQA1*02:01-HLA-DRB1*07:01 haplotype. Patients heterozygous at rs2647087 have a 9% risk of developing pancreatitis after administration of a thiopurine, whereas homozygotes have a 17% risk

Genome-wide association study of ulcerative colitis identifies three new susceptibility loci, including the HNF4A region

Ulcerative colitis is a common form of inflammatory bowel disease with a complex etiology. As part of the Wellcome Trust Case Control Consortium 2, we performed a genome-wide association scan for ulcerative colitis in 2,361 cases and 5,417 controls. Loci showing evidence of association at P < 1 10-5 were followed up by genotyping in an independent set of 2,321 cases and 4,818 controls. We find genome-wide significant evidence of association at three new loci, each containing at least one biologically relevant candidate gene, on chromosomes 20q13 (HNF4A; P = 3.2 10-17), 16q22 (CDH1 and CDH3; P = 2.8 10-8) and 7q31 (LAMB1; P = 3.0 10-8). Of note, CDH1 has recently been associated with susceptibility to colorectal cancer, an established complication of longstanding ulcerative colitis. The new associations suggest that changes in the integrity of the intestinal epithelial barrier may contribute to the pathogenesis of ulcerative colitis