scholarly journals Genetic Studies of Metabolomics Change After a Liquid Meal Illuminate Novel Pathways for Glucose and Lipid Metabolism

2021 ◽  
Author(s):  
Ruifang Li-Gao ◽  
David A. Hughes ◽  
Jan B. van Klinken ◽  
Renée de Mutsert ◽  
Frits R. Rosendaal ◽  
...  
Keyword(s):  
Lipid Metabolism ◽  
Association Studies ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Insulinogenic Index ◽  
Genetic Studies ◽  
Liquid Meal ◽  
Glucose And Lipid Metabolism ◽  
Matsuda Index ◽  
The Uk

Humans spend the greater part of the day in a postprandial state. However, the genetic basis of postprandial blood measures is relatively uncharted territory. We set out to examine the genetics of variation in concentrations of postprandial metabolites (t=150 min) in response to a liquid mixed meal through genome-wide association studies (GWAS) performed in the Netherlands Epidemiology of Obesity study (N=5,705). The metabolite response GWAS identified an association between glucose change and rs10830963:G in the melatonin receptor 1B (beta (SE): -0.23 (0.03), P-value: 2.15×10<sup>-19</sup>). In addition, <i>ANKRD55</i> locus led by rs458741:C showed strong associations to extremely large VLDL particle (XXLVLDL) response (with XXLVLDLC: beta (SE): 0.17 (0.03) P-value: 5.76×10<sup>-10 </sup>and with XXLVLDLCE: beta (SE): 0.17 (0.03), P-value: 9.74×10<sup>-10</sup>), which also revealed strong associations to body composition and diabetes in the UK Biobank (p-values<5×10<sup>-8</sup>). Furthermore, the associations between XXLVLDL response and insulinogenic index, HOMA-β, ISI matsuda index and HbA1c in the NEO study further implied the role of chylomicron synthesis in diabetes (with FDR corrected q-value<0.05). To conclude, genetic studies of metabolomics change after a liquid meal illuminate novel pathways for glucose and lipid metabolism. Further studies are warranted to corroborate biological pathways of <i>ANKRD55</i> locus underlying diabetes.

scholarly journals Genetic Studies of Metabolomics Change After a Liquid Meal Illuminate Novel Pathways for Glucose and Lipid Metabolism

2021 ◽  
Author(s):  
Ruifang Li-Gao ◽  
David A. Hughes ◽  
Jan B. van Klinken ◽  
Renée de Mutsert ◽  
Frits R. Rosendaal ◽  
...  
Keyword(s):  
Lipid Metabolism ◽  
Association Studies ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Insulinogenic Index ◽  
Genetic Studies ◽  
Liquid Meal ◽  
Glucose And Lipid Metabolism ◽  
Matsuda Index ◽  
The Uk

Humans spend the greater part of the day in a postprandial state. However, the genetic basis of postprandial blood measures is relatively uncharted territory. We set out to examine the genetics of variation in concentrations of postprandial metabolites (t=150 min) in response to a liquid mixed meal through genome-wide association studies (GWAS) performed in the Netherlands Epidemiology of Obesity study (N=5,705). The metabolite response GWAS identified an association between glucose change and rs10830963:G in the melatonin receptor 1B (beta (SE): -0.23 (0.03), P-value: 2.15×10<sup>-19</sup>). In addition, <i>ANKRD55</i> locus led by rs458741:C showed strong associations to extremely large VLDL particle (XXLVLDL) response (with XXLVLDLC: beta (SE): 0.17 (0.03) P-value: 5.76×10<sup>-10 </sup>and with XXLVLDLCE: beta (SE): 0.17 (0.03), P-value: 9.74×10<sup>-10</sup>), which also revealed strong associations to body composition and diabetes in the UK Biobank (p-values<5×10<sup>-8</sup>). Furthermore, the associations between XXLVLDL response and insulinogenic index, HOMA-β, ISI matsuda index and HbA1c in the NEO study further implied the role of chylomicron synthesis in diabetes (with FDR corrected q-value<0.05). To conclude, genetic studies of metabolomics change after a liquid meal illuminate novel pathways for glucose and lipid metabolism. Further studies are warranted to corroborate biological pathways of <i>ANKRD55</i> locus underlying diabetes.

scholarly journals Reproducibility in the UK Biobank of Genome-Wide Significant Signals Discovered in Earlier Genome-wide Association Studies

2020 ◽  
Author(s):  
Jack W. O’Sullivan ◽  
John P. A. Ioannidis
Keyword(s):  
Effect Size ◽  
Association Studies ◽  
Genome Wide Association ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Single Nucleotide ◽  
Genome Wide ◽  
The Uk ◽  
Open Question

AbstractWith the establishment of large biobanks, discovery of single nucleotide polymorphism (SNPs) that are associated with various phenotypes has been accelerated. An open question is whether SNPs identified with genome-wide significance in earlier genome-wide association studies (GWAS) are replicated also in later GWAS conducted in biobanks. To address this question, the authors examined a publicly available GWAS database and identified two, independent GWAS on the same phenotype (an earlier, “discovery” GWAS and a later, replication GWAS done in the UK biobank). The analysis evaluated 136,318,924 SNPs (of which 6,289 had reached p<5e-8 in the discovery GWAS) from 4,397,962 participants across nine phenotypes. The overall replication rate was 85.0% and it was lower for binary than for quantitative phenotypes (58.1% versus 94.8% respectively). There was a18.0% decrease in SNP effect size for binary phenotypes, but a 12.0% increase for quantitative phenotypes. Using the discovery SNP effect size, phenotype trait (binary or quantitative), and discovery p-value, we built and validated a model that predicted SNP replication with area under the Receiver Operator Curve = 0.90. While non-replication may often reflect lack of power rather than genuine false-positive findings, these results provide insights about which discovered associations are likely to be seen again across subsequent GWAS.

scholarly journals A meta-analysis of the genome-wide association studies on two genetically correlated phenotypes (self-reported headache and self-reported migraine) identifies four new risk loci for headaches (N=397,385)

2021 ◽  
Author(s):  
Weihua Meng ◽  
Parminder Reel ◽  
Charvi Nangia ◽  
Aravind Rajendrakumar ◽  
Harry Hebert ◽  
...  
Keyword(s):  
Association Studies ◽  
Meta Analysis ◽  
The Self ◽  
Genome Wide Association ◽  
P Value ◽  
Clinical Settings ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Genetic Mechanisms ◽  
The Uk

Headache is one of the commonest complaints that doctors need to address in clinical settings. The genetic mechanisms of different types of headache are not well understood. In this study, we performed a meta-analysis of genome-wide association studies (GWAS) on the self-reported headache phenotype from the UK Biobank cohort and the self-reported migraine phenotype from the 23andMe resource using the metaUSAT for genetically correlated phenotypes (N=397,385). We identified 38 loci for headaches, of which 34 loci have been reported before and 4 loci were newly identified. The LRP1-STAT6-SDR9C7 region in chromosome 12 was the most significantly associated locus with a leading P value of 1.24 x 10-62 of rs11172113. The ONECUT2 gene locus in chromosome 18 was the strongest signal among the 4 new loci with a P value of 1.29 x 10-9 of rs673939. Our study demonstrated that the genetically correlated phenotypes of self-reported headache and self-reported migraine can be meta-analysed together in theory and in practice to boost study power to identify more new variants for headaches. This study has paved way for a large GWAS meta-analysis study involving cohorts of different, though genetically correlated headache phenotypes.

scholarly journals Genetic Studies of Metabolomics Change After a Liquid Meal Illuminate Novel Pathways for Glucose and Lipid Metabolism

Diabetes ◽  
2021 ◽  
pp. db210397
Author(s):  
Ruifang Li-Gao ◽  
David A. Hughes ◽  
Jan B. van Klinken ◽  
Renée de Mutsert ◽  
Frits R. Rosendaal ◽  
...  
Keyword(s):  
Lipid Metabolism ◽  
Genetic Studies ◽  
Liquid Meal ◽  
Glucose And Lipid Metabolism

scholarly journals Reproducibility in the UK Biobank of Genome-Wide Significant Signals Discovered in Earlier Genome-Wide Association Studies

2021 ◽  
Author(s):  
Jack W. O’Sullivan ◽  
John P . A. Ioannidis
Keyword(s):  
Effect Size ◽  
Association Studies ◽  
Significant Snps ◽  
Genome Wide Association ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Genome Wide ◽  
The Uk ◽  
Open Question

Abstract With the establishment of large biobanks, discovery of single nucleotide polymorphism (SNPs) associated with various phenotypes has accelerated. An open question is whether genome-wide significant SNPs identified in earlier genome-wide association studies (GWAS) are replicated in later GWAS conducted in biobanks. To address this, we examined a publicly available GWAS database and identified two, independent GWAS on the same phenotype (an earlier, “discovery” GWAS and a later, “replication” GWAS done in UK biobank). The analysis evaluated 136,318,924 SNPs (of which 6,289 reached p<5e-8 in the discovery GWAS) from 4,397,962 participants across nine phenotypes. The overall replication rate was 85.0%; although lower for binary than quantitative phenotypes (58.1% versus 94.8% respectively). There was a 18.0% decrease in SNP effect size for binary phenotypes, but a 12.0% increase for quantitative phenotypes. Using the discovery SNP effect size, phenotype trait (binary or quantitative), and discovery p-value, we built and validated a model that predicted SNP replication with area under the Receiver Operator Curve = 0.90. While non-replication may reflect lack of power rather than genuine false-positives, these results provide insights about which discovered associations are likely to be replicated across subsequent GWAS.

PSVIII-10 Genome-wide CNV analysis unravels a deletion associated with parasite resistance in Florida native sheep

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 243-244
Author(s):  
Brittany N Diehl ◽  
Andres A Pech-Cervantes ◽  
Thomas H Terrill ◽  
Ibukun M Ogunade ◽  
Owen Rae ◽  
...  
Keyword(s):  
Copy Number ◽  
Association Studies ◽  
Chromosome 21 ◽  
Trend Test ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Parasite Resistance ◽  
Association Testing ◽  
Genome Wide ◽  
Native Sheep

Abstract Florida Native sheep is an indigenous breed from Florida and expresses superior parasite resistance. Previous candidate and genome wide association studies with Florida Native sheep have identified single nucleotide polymorphisms with additive and non-additive effects associated with parasite resistance. However, the role of other potential DNA variants, such as copy number variants (CNVs), controlling this complex trait have not been evaluated. The objective of the present study was to investigate the importance of CNVs on resistance to natural Haemonchus contortus infections in Florida Native sheep. A total of 200 sheep were evaluated in the present study. Phenotypic records included fecal egg count (FEC, eggs/gram), FAMACHA score, and packed cell volume (PCV, %). Sheep were genotyped using the GGP Ovine 50K SNP chip. The copy number analysis was used to identify CNVs using the univariate method. A total of 170 animals with CNVs and phenotypic data were used for the association testing. Association tests were carried out using single linear regression and Principal Component Analysis (PCA) correction to identify CNVs associated with FEC, FAMACHA, and PCV. To confirm our results, a second association testing using the correlation-trend test with PCA correction was performed. Significant CNVs were detected when their adjusted p-value was &lt; 0.05 after FDR correction. A deletion CNV in chromosome 21 was associated with FEC. This DNA variant was located in intron 2 of RAB3IL gene and overlapped a QTL associated with changes in eosinophil number. Our study demonstrated for the first time that CNVs could be potentially involved with parasite resistance in this heritage sheep breed.

scholarly journals Assessing the contribution of rare-to-common protein-coding variants to circulating metabolic biomarker levels via 412,394 UK Biobank exome sequences

2021 ◽  
Author(s):  
Abhishek Nag ◽  
Lawrence Middleton ◽  
Ryan S Dhindsa ◽  
Dimitrios Vitsios ◽  
Eleanor M Wigmore ◽  
...  
Keyword(s):  
Gene Networks ◽  
Rare Variants ◽  
Association Studies ◽  
Low Frequency ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Protein Coding ◽  
The Uk ◽  
Metabolic Biomarkers ◽  
Coding Variants

Genome-wide association studies have established the contribution of common and low frequency variants to metabolic biomarkers in the UK Biobank (UKB); however, the role of rare variants remains to be assessed systematically. We evaluated rare coding variants for 198 metabolic biomarkers, including metabolites assayed by Nightingale Health, using exome sequencing in participants from four genetically diverse ancestries in the UKB (N=412,394). Gene-level collapsing analysis, that evaluated a range of genetic architectures, identified a total of 1,303 significant relationships between genes and metabolic biomarkers (p<1x10-8), encompassing 207 distinct genes. These include associations between rare non-synonymous variants in GIGYF1 and glucose and lipid biomarkers, SYT7 and creatinine, and others, which may provide insights into novel disease biology. Comparing to a previous microarray-based genotyping study in the same cohort, we observed that 40% of gene-biomarker relationships identified in the collapsing analysis were novel. Finally, we applied Gene-SCOUT, a novel tool that utilises the gene-biomarker association statistics from the collapsing analysis to identify genes having similar biomarker fingerprints and thus expand our understanding of gene networks.

scholarly journals Boosting GWAS using biological networks: A study on susceptibility to familial breast cancer

2021 ◽  
Vol 17 (3) ◽  
pp. e1008819
Author(s):  
Héctor Climente-González ◽  
Christine Lonjou ◽  
Fabienne Lesueur ◽  
Dominique Stoppa-Lyonnet ◽  
Nadine Andrieu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Progression ◽  
Biological Networks ◽  
Familial Breast Cancer ◽  
Association Studies ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Functional Relationships ◽  
Network Methods ◽  
The Impact

Genome-wide association studies (GWAS) explore the genetic causes of complex diseases. However, classical approaches ignore the biological context of the genetic variants and genes under study. To address this shortcoming, one can use biological networks, which model functional relationships, to search for functionally related susceptibility loci. Many such network methods exist, each arising from different mathematical frameworks, pre-processing steps, and assumptions about the network properties of the susceptibility mechanism. Unsurprisingly, this results in disparate solutions. To explore how to exploit these heterogeneous approaches, we selected six network methods and applied them to GENESIS, a nationwide French study on familial breast cancer. First, we verified that network methods recovered more interpretable results than a standard GWAS. We addressed the heterogeneity of their solutions by studying their overlap, computing what we called the consensus. The key gene in this consensus solution was COPS5, a gene related to multiple cancer hallmarks. Another issue we observed was that network methods were unstable, selecting very different genes on different subsamples of GENESIS. Therefore, we proposed a stable consensus solution formed by the 68 genes most consistently selected across multiple subsamples. This solution was also enriched in genes known to be associated with breast cancer susceptibility (BLM, CASP8, CASP10, DNAJC1, FGFR2, MRPS30, and SLC4A7, P-value = 3 × 10−4). The most connected gene was CUL3, a regulator of several genes linked to cancer progression. Lastly, we evaluated the biases of each method and the impact of their parameters on the outcome. In general, network methods preferred highly connected genes, even after random rewirings that stripped the connections of any biological meaning. In conclusion, we present the advantages of network-guided GWAS, characterize their shortcomings, and provide strategies to address them. To compute the consensus networks, implementations of all six methods are available at https://github.com/hclimente/gwas-tools.

scholarly journals An atlas of polygenic risk score associations to highlight putative causal relationships across the human phenome

2018 ◽  
Author(s):  
Tom G. Richardson ◽  
Sean Harrison ◽  
Gibran Hemani ◽  
George Davey Smith
Keyword(s):  
Web Application ◽  
Large Scale ◽  
Complex Disease ◽  
Association Studies ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Genome Wide Association Studies ◽  
Genetic Liability ◽  
Polygenic Risk ◽  
The Uk

AbstractThe age of large-scale genome-wide association studies (GWAS) has provided us with an unprecedented opportunity to evaluate the genetic liability of complex disease using polygenic risk scores (PRS). In this study, we have analysed 162 PRS (P<5×l0 05) derived from GWAS and 551 heritable traits from the UK Biobank study (N=334,398). Findings can be investigated using a web application (http://mrcieu.mrsoftware.org/PRS_atlas/), which we envisage will help uncover both known and novel mechanisms which contribute towards disease susceptibility.To demonstrate this, we have investigated the results from a phenome-wide evaluation of schizophrenia genetic liability. Amongst findings were inverse associations with measures of cognitive function which extensive follow-up analyses using Mendelian randomization (MR) provided evidence of a causal relationship. We have also investigated the effect of multiple risk factors on disease using mediation and multivariable MR frameworks. Our atlas provides a resource for future endeavours seeking to unravel the causal determinants of complex disease.

Sign in / Sign up

Export Citation Format

Share Document