scholarly journals Genetic Diversity of Insulin Resistance and Metabolic Syndrome

2020 ◽  
Author(s):  
Sanghoo Lee ◽  
Jinwoo Ahn ◽  
Jimyeong Park ◽  
Hyeonkyun Na ◽  
Youngkee Lee ◽  
...  
Keyword(s):  
Insulin Resistance ◽  
Genetic Diversity ◽  
Metabolic Syndrome ◽  
Genetic Variants ◽  
Association Studies ◽  
Public Health Problem ◽  
Genome Wide Association Studies ◽  
Global Public Health ◽  
Genome Wide ◽  
Rare Genetic Variants

A key in the etiology of a cluster of metabolic syndrome such as hyperglycemia, dyslipidemia, and obesity is known for insulin resistance, which is becoming a major global public health problem. Extensive studies have revealed many genetic factors for both insulin resistance and the components of metabolic syndrome. Advanced modern genotyping methods including genome-wide association studies and next-generation sequencing have allowed for the identification of both common and rare genetic variants related to these chronic disease-associated traits. Multiple genotype–phenotype studies are also needed to identify new and accurate genetic biomarkers in these conditions. The purpose of this chapter is to present genetic variants related to the pathogenesis of metabolic syndrome and insulin resistance and is to review the relevance between insulin resistance and metabolic syndrome clusters in terms of genetic diversity.

scholarly journals M88. EVIDENCE FOR INFLAMMATION AS A PUTATIVE SHARED MECHANISM FOR INSULIN RESISTANCE AND SCHIZOPHRENIA

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S168-S168
Author(s):  
Benjamin Perry ◽  
Stephen Burgess ◽  
Hannah Jones ◽  
Stanley Zammit ◽  
Rachel Upthegrove ◽  
...  
Keyword(s):  
Risk Factors ◽  
Insulin Resistance ◽  
Genetic Variants ◽  
Cardiometabolic Risk ◽  
Association Studies ◽  
Density Lipoprotein ◽  
Cardiometabolic Risk Factors ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Cardiometabolic Disorders

Abstract Background Insulin Resistance (IR) predisposes to cardiometabolic disorders, which are common in schizophrenia and are associated with excess morbidity and mortality. The mechanisms of association remain unknown. We aimed 1) To use genetic data to examine the direction of association between IR and related cardiometabolic risk factors, and schizophrenia; 2) To examine whether inflammation could be a shared mechanism for IR and schizophrenia. Methods We used two-sample uni-variable Mendelian randomization (MR) to examine whether genetically-predicted IR-related cardiometabolic risk factors (Fasting insulin (FI), high-density lipoprotein (HDL), triglycerides (TG), low-density lipoprotein, fasting plasma glucose, glycated haemoglobin, leptin, body mass index, glucose tolerance and type 2 diabetes) may be causally associated with schizophrenia. We used the most recent summary statistics for genetic variants associated with schizophrenia and IR-related cardiometabolic risk factors from publicly-available large genome-wide association studies (GWAS). We used bi-directional MR to examine direction of association. To examine whether inflammation could be a shared mechanism for IR and schizophrenia, we first conducted a sensitivity analysis by performing MR using only cardiometabolic genetic variants that were also associated with inflammation, at genome-wide significance. Second, we used multi-variable MR (MVMR) to examine associations between cardiometabolic risk factors and schizophrenia after adjusting for genetically-predicted levels of C-reactive protein. Results In analyses using all associated genetic variants, genetically predicted levels of leptin were associated with risk of schizophrenia (OR=2.54 per SD increase in leptin; 95% CI, 1.02–6.31). In analyses using inflammation-related variants, genetically predicted levels of FI (OR=2.76 per SD increase in FI; 95% C.I., 1.31–6.17), TG (OR=2.90 per SD increase in TG; 95% C.I., 1.36–6.17), and HDL (OR=0.56 per SD increase in HDL; 95% C.I., 0.37–0.83) were associated with schizophrenia. The associations completely attenuated in MVMR analyses controlling for CRP. There was no evidence of an association between genetically-predicted schizophrenia liability and cardiometabolic factors. Discussion The IR phenotype of FI, TG and HDL could be associated with schizophrenia over and above common sociodemographic and lifestyle factors. This association is likely explained by a common inflammatory mechanism. Interventional studies are required to test whether inflammation could represent a putative therapeutic target for the treatment and prevention of cardiometabolic disorders in schizophrenia.

P4470Identification of four genes as novel susceptibility loci for early-onset type 2 diabetes mellitus, metabolic syndrome, or hyperuricemia in Japanese

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
Y Yamase ◽  
H Horibe ◽  
K Kato ◽  
M Oguri ◽  
T Fujimaki ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Metabolic Syndrome ◽  
Genetic Variants ◽  
Early Onset ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Susceptibility Loci ◽  
Genome Wide

Abstract Background Given that early-onset type 2 diabetes mellitus (T2DM), metabolic syndrome, and hyperuricemia have been shown to have strong genetic components, statistical power of a genetic association study may be increased by focusing on early-onset subjects with these conditions. Although genome-wide association studies have identified various genes and loci significantly associated with T2DM, metabolic syndrome, and hyperuricemia, genetic variants that contribute to predisposition to these conditions in Japanese individuals remain to be identified definitively. Purpose The purpose of the study was to identify genetic variants that confer susceptibility to early-onset T2DM, metabolic syndrome, or hyperuricemia in Japanese. We have now performed exome-wide association studies (EWASs) for early-onset subjects with T2DM, metabolic syndrome, or hyperuricemia and corresponding controls. Methods A total of 8102 individuals aged ≤65 years was enrolled in the study. The EWAS for T2DM was performed with 7407 subjects (1696 cases, 5711 controls), that for metabolic syndrome with 4215 subjects (2296 cases, 1919 controls), and that for hyperuricemia with 7919 subjects (1365 cases, 6554 controls). Single nucleotide polymorphisms (SNPs) were genotyped with Illumina Human Exome-12 DNA Analysis BeadChip or Infinium Exome-24 BeadChip arrays. The relation of allele frequencies for 31,210, 31,521, or 31,142 SNPs that passed quality control to T2DM, metabolic syndrome, or hyperuricemia, respectively, was examined with Fisher's exact test. To compensate for multiple comparisons of genotypes with T2DM, metabolic syndrome, or hyperuricemia, we applied Bonferroni's correction for statistical significance of association. Results The EWAS of allele frequencies revealed that four, six, or nine SNPs were significantly associated with T2DM (P<1.60 × 10–6), metabolic syndrome (P<1.59 × 10–6), or hyperuricemia (P<1.61 × 10–6), respectively. Multivariable logistic regression analysis with adjustment for age and sex revealed that three, six, or nine SNPs were significantly related to T2DM (P<0.0031), metabolic syndrome (P<0.0021), or hyperuricemia (P<0.0014). After examination of the association of identified SNPs to T2DM-, metabolic syndrome-, or hyperuricemia-related traits, linkage disequilibrium of the SNPs, and results of previous genome-wide association studies, we have newly identified ZNF860 and OR4F6 as susceptibility loci for T2DM, OR52E4 and OR4F6 for metabolic syndrome, and HERPUD2 for hyperuricemia. Conclusion Given that OR4F6 was significantly associated with both T2DM and metabolic syndrome, we thus newly identified four genes (ZNF860, OR4F6, OR52E4, HERPUD2) that confer susceptibility to early-onset T2DM, metabolic syndrome, or hyperuricemia. Determination of genotypes for the SNPs in these genes may prove informative for assessment of the genetic risk for T2DM, metabolic syndrome, or hyperuricemia in Japanese.

Migraine: Genetic Variants and Clinical Phenotypes

2019 ◽  
Vol 26 (34) ◽  
pp. 6207-6221 ◽  
Author(s):  
Innocenzo Rainero ◽  
Alessandro Vacca ◽  
Flora Govone ◽  
Annalisa Gai ◽  
Lorenzo Pinessi ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Association Studies ◽  
Mthfr Gene ◽  
Genome Wide Association Studies ◽  
Clinical Phenotypes ◽  
Network Analyses ◽  
Genome Wide ◽  
Genomic Studies ◽  
The Common ◽  
The Relationship

Migraine is a common, chronic neurovascular disorder caused by a complex interaction between genetic and environmental risk factors. In the last two decades, molecular genetics of migraine have been intensively investigated. In a few cases, migraine is transmitted as a monogenic disorder, and the disease phenotype cosegregates with mutations in different genes like CACNA1A, ATP1A2, SCN1A, KCNK18, and NOTCH3. In the common forms of migraine, candidate genes as well as genome-wide association studies have shown that a large number of genetic variants may increase the risk of developing migraine. At present, few studies investigated the genotype-phenotype correlation in patients with migraine. The purpose of this review was to discuss recent studies investigating the relationship between different genetic variants and the clinical characteristics of migraine. Analysis of genotype-phenotype correlations in migraineurs is complicated by several confounding factors and, to date, only polymorphisms of the MTHFR gene have been shown to have an effect on migraine phenotype. Additional genomic studies and network analyses are needed to clarify the complex pathways underlying migraine and its clinical phenotypes.

scholarly journals Editing GWAS: experimental approaches to dissect and exploit disease-associated genetic variation

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Shuquan Rao ◽  
Yao Yao ◽  
Daniel E. Bauer
Keyword(s):  
Genome Editing ◽  
Genetic Variants ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Functional Studies ◽  
Functional Genetics ◽  
Genome Wide ◽  
Causal Variants ◽  
Experimental Approaches

AbstractGenome-wide association studies (GWAS) have uncovered thousands of genetic variants that influence risk for human diseases and traits. Yet understanding the mechanisms by which these genetic variants, mainly noncoding, have an impact on associated diseases and traits remains a significant hurdle. In this review, we discuss emerging experimental approaches that are being applied for functional studies of causal variants and translational advances from GWAS findings to disease prevention and treatment. We highlight the use of genome editing technologies in GWAS functional studies to modify genomic sequences, with proof-of-principle examples. We discuss the challenges in interrogating causal variants, points for consideration in experimental design and interpretation of GWAS locus mechanisms, and the potential for novel therapeutic opportunities. With the accumulation of knowledge of functional genetics, therapeutic genome editing based on GWAS discoveries will become increasingly feasible.

scholarly journals CAUSALdb: a database for disease/trait causal variants identified using summary statistics of genome-wide association studies

2019 ◽  
Author(s):  
Jianhua Wang ◽  
Dandan Huang ◽  
Yao Zhou ◽  
Hongcheng Yao ◽  
Huanhuan Liu ◽  
...  
Keyword(s):  
Fine Mapping ◽  
Genetic Variants ◽  
Association Studies ◽  
Complex Trait ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Genome Wide ◽  
Credible Sets ◽  
Causal Variants

Abstract Genome-wide association studies (GWASs) have revolutionized the field of complex trait genetics over the past decade, yet for most of the significant genotype-phenotype associations the true causal variants remain unknown. Identifying and interpreting how causal genetic variants confer disease susceptibility is still a big challenge. Herein we introduce a new database, CAUSALdb, to integrate the most comprehensive GWAS summary statistics to date and identify credible sets of potential causal variants using uniformly processed fine-mapping. The database has six major features: it (i) curates 3052 high-quality, fine-mappable GWAS summary statistics across five human super-populations and 2629 unique traits; (ii) estimates causal probabilities of all genetic variants in GWAS significant loci using three state-of-the-art fine-mapping tools; (iii) maps the reported traits to a powerful ontology MeSH, making it simple for users to browse studies on the trait tree; (iv) incorporates highly interactive Manhattan and LocusZoom-like plots to allow visualization of credible sets in a single web page more efficiently; (v) enables online comparison of causal relations on variant-, gene- and trait-levels among studies with different sample sizes or populations and (vi) offers comprehensive variant annotations by integrating massive base-wise and allele-specific functional annotations. CAUSALdb is freely available at http://mulinlab.org/causaldb.

scholarly journals A transcriptome-wide Mendelian randomization study to uncover tissue-dependent regulatory mechanisms across the human phenome

2019 ◽  
Author(s):  
Tom G Richardson ◽  
Gibran Hemani ◽  
Tom R Gaunt ◽  
Caroline L Relton ◽  
George Davey Smith
Keyword(s):  
Gene Expression ◽  
Genetic Variants ◽  
Complex Traits ◽  
Mendelian Randomization ◽  
Drug Repositioning ◽  
Association Studies ◽  
Thyroid Tissue ◽  
Genome Wide Association Studies ◽  
Tissue Specific ◽  
Genome Wide

AbstractBackgroundDeveloping insight into tissue-specific transcriptional mechanisms can help improve our understanding of how genetic variants exert their effects on complex traits and disease. By applying the principles of Mendelian randomization, we have undertaken a systematic analysis to evaluate transcriptome-wide associations between gene expression across 48 different tissue types and 395 complex traits.ResultsOverall, we identified 100,025 gene-trait associations based on conventional genome-wide corrections (P < 5 × 10−08) that also provided evidence of genetic colocalization. These results indicated that genetic variants which influence gene expression levels in multiple tissues are more likely to influence multiple complex traits. We identified many examples of tissue-specific effects, such as genetically-predicted TPO, NR3C2 and SPATA13 expression only associating with thyroid disease in thyroid tissue. Additionally, FBN2 expression was associated with both cardiovascular and lung function traits, but only when analysed in heart and lung tissue respectively.We also demonstrate that conducting phenome-wide evaluations of our results can help flag adverse on-target side effects for therapeutic intervention, as well as propose drug repositioning opportunities. Moreover, we find that exploring the tissue-dependency of associations identified by genome-wide association studies (GWAS) can help elucidate the causal genes and tissues responsible for effects, as well as uncover putative novel associations.ConclusionsThe atlas of tissue-dependent associations we have constructed should prove extremely valuable to future studies investigating the genetic determinants of complex disease. The follow-up analyses we have performed in this study are merely a guide for future research. Conducting similar evaluations can be undertaken systematically at http://mrcieu.mrsoftware.org/Tissue_MR_atlas/.

scholarly journals GWASdb: a database for human genetic variants identified by genome-wide association studies

2011 ◽  
Vol 40 (D1) ◽  
pp. D1047-D1054 ◽  
Author(s):  
Mulin Jun Li ◽  
Panwen Wang ◽  
Xiaorong Liu ◽  
Ee Lyn Lim ◽  
Zhangyong Wang ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

The power of genetic diversity in genome-wide association studies of lipids

Nature ◽  
2021 ◽  
Author(s):  
Sarah E. Graham ◽  
Shoa L. Clarke ◽  
Kuan-Han H. Wu ◽  
Stavroula Kanoni ◽  
Greg J. M. Zajac ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

Statistical Genetics

2018 ◽  
pp. 57-69 ◽  
Author(s):  
Till F. M. Andlauer ◽  
Bertram Müller-Myhsok ◽  
Stephan Ripke
Keyword(s):  
Genetic Variants ◽  
Association Studies ◽  
Meta Analysis ◽  
Power Calculation ◽  
Genome Wide Association Studies ◽  
Psychiatric Genetics ◽  
Factors Influencing ◽  
Genome Wide ◽  
Basic Concepts ◽  
Study Designs

Over more than the last decade, hypothesis-free genome-wide association studies (GWAS) have been widely used to detect genetic factors influencing phenotypes of interest. The basic principle of GWAS has been unchanged since the beginning: a series of univariate tests is conducted on all genetic variants available across the genome. We present study designs and commonly used methods for genome-wide studies, with a focus on the analysis of common variants. The basic concepts required for an application of GWAS in psychiatric genetics are introduced, from power calculation to meta-analysis. This chapter will help the reader in gaining the knowledge required for participation in and realization of GWAS of both qualitative and quantitative traits.

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