Browse
Submit Data
Databases
API
Help

Dataset Information

17 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Genetic aetiology of glycaemic traits: approaches and insights.

ABSTRACT: Glycaemic traits such as fasting and post-challenge glucose and insulin measures, as well as glycated haemoglobin (HbA1c), are used to diagnose and monitor diabetes. These traits are risk factors for cardiovascular disease even below the diabetic threshold, and their study can additionally yield insights into the pathophysiology of type 2 diabetes. To date, a diverse set of genetic approaches have led to the discovery of over 97 loci influencing glycaemic traits. In this review, we will focus on recent advances in the genetic aetiology of glycaemic traits, and the resulting biological insights. We will provide a brief overview of results ranging from common, to low- and rare-frequency variant-trait association studies, studies leveraging the diversity across populations, and studies harnessing the power of genetic and genomic approaches to gain insights into the biological underpinnings of these traits.

SUBMITTER: Wheeler E

PROVIDER: S-EPMC5886471 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Genetic aetiology of glycaemic traits: approaches and insights.

Wheeler Eleanor E Marenne Gaëlle G Barroso Inês I

Human molecular genetics 20171001 R2

Glycaemic traits such as fasting and post-challenge glucose and insulin measures, as well as glycated haemoglobin (HbA1c), are used to diagnose and monitor diabetes. These traits are risk factors for cardiovascular disease even below the diabetic threshold, and their study can additionally yield insights into the pathophysiology of type 2 diabetes. To date, a diverse set of genetic approaches have led to the discovery of over 97 loci influencing glycaemic traits. In this review, we will focus on ...[more]

PMID: 28977447

Similar Datasets

Genetic and environmental aetiology of the dimensions of Callous-Unemotional traits.

Project description:BackgroundA Callous-Unemotional trait specifier (termed 'Limited Prosocial Emotions') was added to the diagnosis of conduct disorder in DSM-5. The Inventory of Callous-Unemotional Traits (ICU) is a comprehensive measure of these traits assessing three distinct, yet correlated dimensions--Callousness, Uncaring, and Unemotional--all thought to reflect the general Callous-Unemotional construct. The present study was the first to examine the degree to which the aetiology of these dimensions is shared v. independent.MethodParent-reported ICU data from 5092 16-year-old twin pairs from the Twins Early Development Study were subjected to confirmatory factor analysis. Multivariate genetic modelling was applied to the best-fitting structure.ResultsA general-specific structure, retaining a general factor and two uncorrelated specific factors (Callousness-Uncaring, Unemotional), provided the best fit to the data. The general factor was substantially heritable (h2 = 0.58, 95% CI 0.51-0.65). Unusually, shared environmental influences were also important in accounting for this general factor (c2 = 0.26, 95% CI 0.22-0.31), in addition to non-shared environmental influences. The Unemotional dimension appeared phenotypically and genetically distinct as shown by the substantial loadings of unemotional items on a separate dimension and a low genetic correlation between Unemotional and Callousness-Uncaring.ConclusionsA general factor, indicative of a shared phenotypic structure across the dimensions of the ICU was under substantial common genetic and more modest shared environment influences. Our findings also suggest that the relevance of the Unemotional dimension as part of a comprehensive assessment of CU traits should be investigated further.

| S-EPMC4682480 | biostudies-literature

Genetic insights into morphometric inflorescence traits of wheat.

Project description:KEY MESSAGE:Modifying morphometric inflorescence traits is important for increasing grain yield in wheat. Mapping revealed nine QTL, including new QTL and a new allele for the q locus, controlling wheat spike morphometric traits. To identify loci controlling spike morphometric traits, namely spike length (SL), internode length (IL), node number per spike (NPS), and node density (ND), we studied 146 Recombinant Inbred Lines of tetraploid wheat (Triticum turgidum L.) derived from standard spike and spike-branching mutant parents. Phenotypic analyses of spike morphometric traits showed low genetic coefficients of variation, resulting in high heritabilities. The phenotypic correlation between NPS with growing degree days (GDD) suggested the importance of GDD in the determination of node number in wheat. The major effect QTL for GDD or heading date was mapped to chromosome 7BS carrying the flowering time gene, Vrn3-B1. Mapping also identified nine QTL controlling spike morphometric traits. Most of these loci controlled more than a single trait, suggesting a close genetic interrelationship among spike morphometric traits. For example, this study identified a new QTL, QND.ipk-4AL, controlling ND (up to 17.6% of the phenotypic variance), IL (up to 11% of the phenotypic variance), and SL (up to 20.8% of the phenotypic variance). Similarly, the major effect QTL for IL was mapped to the q locus. Sequencing of the Q/q gene further revealed a new q allele, qdel-5A, in spike-branching accessions possessing a six base pair deletion close to the miR172 target site. The identification of qdel-5A suggested that the spike-branching tetraploid wheats are double mutants for the spikelet meristem (SM) identity gene, i.e., branched headt (TtBHt), and the q gene, which is believed to be involved in the SM indeterminacy complex in wheat.

| S-EPMC6531419 | biostudies-literature

Insights into the aetiology of snoring from observational and genetic investigations in the UK Biobank.

Project description:Although snoring is common in the general population, its aetiology has been largely understudied. Here we report a genetic study on snoring (n ~ 408,000; snorers ~ 152,000) using data from the UK Biobank. We identify 42 genome-wide significant loci, with an SNP-based heritability estimate of ~10% on the liability scale. Genetic correlations with body mass index, alcohol intake, smoking, schizophrenia, anorexia nervosa and neuroticism are observed. Gene-based associations identify 173 genes, including DLEU7, MSRB3 and POC5, highlighting genes expressed in the brain, cerebellum, lungs, blood and oesophagus. We use polygenic scores (PGS) to predict recent snoring and probable obstructive sleep apnoea (OSA) in an independent Australian sample (n ~ 8000). Mendelian randomization analyses suggest a potential causal relationship between high BMI and snoring. Altogether, our results uncover insights into the aetiology of snoring as a complex sleep-related trait and its role in health and disease beyond it being a cardinal symptom of OSA.

| S-EPMC7021827 | biostudies-literature

The importance of increasing population diversity in genetic studies of type 2 diabetes and related glycaemic traits.

Project description:Type 2 diabetes has a global prevalence, with epidemiological data suggesting that some populations have a higher risk of developing this disease. However, to date, most genetic studies of type 2 diabetes and related glycaemic traits have been performed in individuals of European ancestry. The same is true for most other complex diseases, largely due to use of 'convenience samples'. Rapid genotyping of large population cohorts and case-control studies from existing collections was performed when the genome-wide association study (GWAS) 'revolution' began, back in 2005. Although global representation has increased in the intervening 15 years, further expansion and inclusion of diverse populations in genetic and genomic studies is still needed. In this review, I discuss the progress made in incorporating multi-ancestry participants in genetic analyses of type 2 diabetes and related glycaemic traits, and associated opportunities and challenges. I also discuss how increased representation of global diversity in genetic and genomic studies is required to fulfil the promise of precision medicine for all.

| S-EPMC8563561 | biostudies-literature

Genetic complexity traits

Project description:The hidden complexity of Mendelian traits across yeast natural populations

| PRJEB11500 | ENA

Local Genetic Correlation Gives Insights into the Shared Genetic Architecture of Complex Traits.

Project description:Although genetic correlations between complex traits provide valuable insights into epidemiological and etiological studies, a precise quantification of which genomic regions disproportionately contribute to the genome-wide correlation is currently lacking. Here, we introduce ?-HESS, a technique to quantify the correlation between pairs of traits due to genetic variation at a small region in the genome. Our approach requires GWAS summary data only and makes no distributional assumption on the causal variant effect sizes while accounting for linkage disequilibrium (LD) and overlapping GWAS samples. We analyzed large-scale GWAS summary data across 36 quantitative traits, and identified 25 genomic regions that contribute significantly to the genetic correlation among these traits. Notably, we find 6 genomic regions that contribute to the genetic correlation of 10 pairs of traits that show negligible genome-wide correlation, further showcasing the power of local genetic correlation analyses. Finally, we report the distribution of local genetic correlations across the genome for 55 pairs of traits that show putative causal relationships.

| S-EPMC5673668 | biostudies-literature

Molecular insights into the aetiology of female reproductive ageing.

Project description:As age at pubertal onset declines and age at first pregnancy increases, the mechanisms that regulate female reproductive lifespan become increasingly relevant to population health. The timing of menarche and menopause can have profound effects not only on fertility but also on the risk of diseases such as type 2 diabetes mellitus, cardiovascular disease and breast cancer. Genetic studies have identified dozens of highly penetrant rare mutations associated with reproductive disorders, and also ?175 common genetic variants associated with the timing of puberty or menopause. These findings, alongside other functional studies, have highlighted a diverse range of mechanisms involved in reproductive ageing, implicating core biological processes such as cell cycle regulation and energy homeostasis. The aim of this article is to review the contribution of such genetic findings to our understanding of the molecular regulation of reproductive timing, as well as the biological basis of the epidemiological links between reproductive ageing and disease risk.

| S-EPMC6309261 | biostudies-literature

Negative electroretinograms: genetic and acquired causes, diagnostic approaches and physiological insights.

Project description:The dark-adapted human electroretinogram (ERG) response to a standard bright flash includes a negative-going a-wave followed by a positive-going b-wave that crosses the baseline. An electronegative waveform (or negative ERG) results when the b-wave is selectively reduced such that the ERG fails to cross the baseline following the a-wave. In the context of a normally sized a-wave, it indicates a site of retinal dysfunction occurring after phototransduction (commonly at the photoreceptor to bipolar cell synapse). This is an important finding. In genetic disease, the pattern of ERG abnormality can point to variants in a small group of genes (frequently those associated with congenital stationary night blindness and X-linked retinoschisis, but negative ERGs can also be seen in other conditions including syndromic disease). In acquired disease, there are numerous causes, but specific features may point to melanoma-associated retinopathy (MAR). In some cases, the visual symptoms precede the diagnosis of the melanoma and so the ERG findings can initiate investigations facilitating early detection and treatment. Negative ERGs can occur in other paraneoplastic conditions, and in a range of other diseases. This review will outline the physiological basis for the negative ERG, report prevalences in the literature from different cohorts, discuss the range of causes, displaying examples of a number of ERG phenotypes, highlight features of a clinical approach to patients, and briefly discuss further insights relating to current flows shaping the a-wave trough and from single-cell transcriptome analysis.

| S-EPMC8377097 | biostudies-literature

Treatment-Resistant Schizophrenia: Insights From Genetic Studies and Machine Learning Approaches.

Project description:Schizophrenia (SCZ) is a severe psychiatric disorder affecting approximately 23 million people worldwide. It is considered the eighth leading cause of disability according to the World Health Organization and is associated with a significant reduction in life expectancy. Antipsychotics represent the first-choice treatment in SCZ, but approximately 30% of patients fail to respond to acute treatment. These patients are generally defined as treatment-resistant and are eligible for clozapine treatment. Treatment-resistant patients show a more severe course of the disease, but it has been suggested that treatment-resistant schizophrenia (TRS) may constitute a distinct phenotype that is more than just a more severe form of SCZ. TRS is heritable, and genetics has been shown to play an important role in modulating response to antipsychotics. Important efforts have been put into place in order to better understand the genetic architecture of TRS, with the main goal of identifying reliable predictive markers that might improve the management and quality of life of TRS patients. However, the number of candidate gene and genome-wide association studies specifically focused on TRS is limited, and to date, findings do not allow the disentanglement of its polygenic nature. More recent studies implemented polygenic risk score, gene-based and machine learning methods to explore the genetics of TRS, reporting promising findings. In this review, we present an overview on the genetics of TRS, particularly focusing our discussion on studies implementing polygenic approaches.

| S-EPMC6548883 | biostudies-literature

Generalized multifactor dimensionality reduction approaches to identification of genetic interactions underlying ordinal traits.

Project description:The manifestation of complex traits is influenced by gene-gene and gene-environment interactions, and the identification of multifactor interactions is an important but challenging undertaking for genetic studies. Many complex phenotypes such as disease severity are measured on an ordinal scale with more than two categories. A proportional odds model can improve statistical power for these outcomes, when compared to a logit model either collapsing the categories into two mutually exclusive groups or limiting the analysis to pairs of categories. In this study, we propose a proportional odds model-based generalized multifactor dimensionality reduction (GMDR) method for detection of interactions underlying polytomous ordinal phenotypes. Computer simulations demonstrated that this new GMDR method has a higher power and more accurate predictive ability than the GMDR methods based on a logit model and a multinomial logit model. We applied this new method to the genetic analysis of low-density lipoprotein (LDL) cholesterol, a causal risk factor for coronary artery disease, in the Multi-Ethnic Study of Atherosclerosis, and identified a significant joint action of the CELSR2, SERPINA12, HPGD, and APOB genes. This finding provides new information to advance the limited knowledge about genetic regulation and gene interactions in metabolic pathways of LDL cholesterol. In conclusion, the proportional odds model-based GMDR is a useful tool that can boost statistical power and prediction accuracy in studying multifactor interactions underlying ordinal traits.

| S-EPMC8495755 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data