Common Variants at 10 Genomic Loci Influence Hemoglobin A(1C) Levels via Glycemic and Nonglycemic Pathways

OBJECTIVE Glycated hemoglobin (HbA1c), used to monitor and diagnose diabetes, is influenced by average glycemia over a 2- to 3-month period. Genetic factors affecting expression, turnover, and abnormal glycation of hemoglobin could also be associated with increased levels of HbA1c. We aimed to identify such genetic factors and investigate the extent to which they influence diabetes classification based on HbA1c levels. RESEARCH DESIGN AND METHODS We studied associations with HbA1c in up to 46,368 nondiabetic adults of European descent from 23 genome-wide association studies (GWAS) and 8 cohorts with de novo genotyped single nucleotide polymorphisms (SNPs). We combined studies using inverse-variance meta-analysis and tested mediation by glycemia using conditional analyses. We estimated the global effect of HbA1c loci using a multilocus risk score, and used net reclassification to estimate genetic effects on diabetes screening. RESULTS Ten loci reached genome-wide significant association with HbA1c, including six new loci near FN3K (lead SNP/P value, rs1046896/P = 1.6 × 10−26), HFE (rs1800562/P = 2.6 × 10−20), TMPRSS6 (rs855791/P = 2.7 × 10−14), ANK1 (rs4737009/P = 6.1 × 10−12), SPTA1 (rs2779116/P = 2.8 × 10−9) and ATP11A/TUBGCP3 (rs7998202/P = 5.2 × 10−9), and four known HbA1c loci: HK1 (rs16926246/P = 3.1 × 10−54), MTNR1B (rs1387153/P = 4.0 × 10−11), GCK (rs1799884/P = 1.5 × 10−20) and G6PC2/ABCB11 (rs552976/P = 8.2 × 10−18). We show that associations with HbA1c are partly a function of hyperglycemia associated with 3 of the 10 loci (GCK, G6PC2 and MTNR1B). The seven nonglycemic loci accounted for a 0.19 (% HbA1c) difference between the extreme 10% tails of the risk score, and would reclassify ∼2% of a general white population screened for diabetes with HbA1c. CONCLUSIONS GWAS identified 10 genetic loci reproducibly associated with HbA1c. Six are novel and seven map to loci where rarer variants cause hereditary anemias and iron storage disorders. Common variants at these loci likely influence HbA1c levels via erythrocyte biology, and confer a small but detectable reclassification of diabetes diagnosis by HbA1c

Transcriptome analysis and comparison reveal divergence between two invasive whitefly cryptic species

<p>Abstract</p> <p>Background</p> <p>Invasive species are valuable model systems for examining the evolutionary processes and molecular mechanisms associated with their specific characteristics by comparison with closely related species. Over the past 20 years, two species of the whitefly <it>Bemisia tabaci </it>species complex, Middle East-Asia Minor 1 (MEAM1) and Mediterranean (MED), have both spread from their origin Middle East/Mediterranean to many countries despite their apparent differences in many life history parameters. Previously, we have sequenced the transcriptome of MED. In this study, we sequenced the transcriptome of MEAM1 and took a comparative genomic approach to investigate the transcriptome evolution and the genetic factors underlying the differences between MEAM1 and MED.</p> <p>Results</p> <p>Using Illumina sequencing technology, we generated 17 million sequencing reads for MEAM1. These reads were assembled into 57,741 unique sequences and 15,922 sequences were annotated with an E-value above 10^-5. Compared with the MED transcriptome, we identified 3,585 pairs of high quality orthologous genes and inferred their sequence divergences. The average differences in coding, 5' untranslated and 3' untranslated region were 0.83%, 1.66% and 1.43%, respectively. The level of sequence divergence provides additional support to the proposition that MEAM1 and MED are two species. Based on the ratio of nonsynonymous and synonymous substitutions, we identified 24 sequences that have evolved in response to positive selection. Many of those genes are predicted to be involved in metabolism and insecticide resistance which might contribute to the divergence of the two whitefly species.</p> <p>Conclusions</p> <p>Our data present a comprehensive sequence comparison between the two invasive whitefly species. This study will provide a road map for future investigations on the molecular mechanisms underlying their biological differences.</p

Genome-wide physical activity interactions in adiposity. A meta-analysis of 200,452 adults

Physical activity (PA) may modify the genetic effects that give rise to increased risk of obesity. To identify adiposity loci whose effects are modified by PA, we performed genome-wide interaction meta-analyses of BMI and BMI-adjusted waist circumference and waist-hip ratio from up to 200,452 adults of European (n = 180,423) or other ancestry (n = 20,029). We standardized PA by categorizing it into a dichotomous variable where, on average, 23% of participants were categorized as inactive and 77% as physically active. While we replicate the interaction with PA for the strongest known obesity-risk locus in the FTO gene, of which the effect is attenuated by similar to 30% in physically active individuals compared to inactive individuals, we do not identify additional loci that are sensitive to PA. In additional genome-wide meta-analyses adjusting for PA and interaction with PA, we identify 11 novel adiposity loci, suggesting that accounting for PA or other environmental factors that contribute to variation in adiposity may facilitate gene discovery.Peer reviewe

A Large-Scale Multi-ancestry Genome-wide Study Accounting for Smoking Behavior Identifies Multiple Significant Loci for Blood Pressure

Genome-wide association analysis advanced understanding of blood pressure (BP), a major risk factor for vascular conditions such as coronary heart disease and stroke. Accounting for smoking behavior may help identify BP loci and extend our knowledge of its genetic architecture. We performed genome-wide association meta-analyses of systolic and diastolic BP incorporating gene-smoking interactions in 610,091 individuals. Stage 1 analysis examined similar to 18.8 million SNPs and small insertion/deletion variants in 129,913 individuals from four ancestries (European, African, Asian, and Hispanic) with follow-up analysis of promising variants in 480,178 additional individuals from five ancestries. We identified 15 loci that were genome-wide significant (p <5 x 10(-8)) in stage 1 and formally replicated in stage 2. A combined stage 1 and 2 meta-analysis identified 66 additional genome-wide significant loci (13, 35, and 18 loci in European, African, and trans-ancestry, respectively). A total of 56 known BP loci were also identified by our results (p <5 x 10(-8)). Of the newly identified loci, ten showed significant interaction with smoking status, but none of them were replicated in stage 2. Several loci were identified in African ancestry, highlighting the importance of genetic studies in diverse populations. The identified loci show strong evidence for regulatory features and support shared pathophysiology with cardiometabolic and addiction traits. They also highlight a role in BP regulation for biological candidates such as modulators of vascular structure and function (CDKN1B, BCAR1-CFDP1, PXDN, EEA1), ciliopathies (SDCCAG8, RPGRIP1L), telomere maintenance (TNKS, PINX1, AKTIP), and central dopaminergic signaling MSRA, EBF2).Peer reviewe

The impact of transposable element activity on therapeutically relevant human stem cells

Human stem cells harbor significant potential for basic and clinical translational research as well as regenerative medicine. Currently ~ 3000 adult and ~ 30 pluripotent stem cell-based, interventional clinical trials are ongoing worldwide, and numbers are increasing continuously. Although stem cells are promising cell sources to treat a wide range of human diseases, there are also concerns regarding potential risks associated with their clinical use, including genomic instability and tumorigenesis concerns. Thus, a deeper understanding of the factors and molecular mechanisms contributing to stem cell genome stability are a prerequisite to harnessing their therapeutic potential for degenerative diseases. Chemical and physical factors are known to influence the stability of stem cell genomes, together with random mutations and Copy Number Variants (CNVs) that accumulated in cultured human stem cells. Here we review the activity of endogenous transposable elements (TEs) in human multipotent and pluripotent stem cells, and the consequences of their mobility for genomic integrity and host gene expression. We describe transcriptional and post-transcriptional mechanisms antagonizing the spread of TEs in the human genome, and highlight those that are more prevalent in multipotent and pluripotent stem cells. Notably, TEs do not only represent a source of mutations/CNVs in genomes, but are also often harnessed as tools to engineer the stem cell genome; thus, we also describe and discuss the most widely applied transposon-based tools and highlight the most relevant areas of their biomedical applications in stem cells. Taken together, this review will contribute to the assessment of the risk that endogenous TE activity and the application of genetically engineered TEs constitute for the biosafety of stem cells to be used for substitutive and regenerative cell therapiesS.R.H. and P.T.R. are funded by the Government of Spain (MINECO, RYC-2016- 21395 and SAF2015–71589-P [S.R.H.]; PEJ-2014-A-31985 and SAF2015–71589- P [P.T.R.]). GGS is supported by a grant from the Ministry of Health of the Federal Republic of Germany (FKZ2518FSB403)

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology.

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care

The trans-ancestral genomic architecture of glycemic traits

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 × 10−8), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution