scholarly journals Effect of Body Weight on Age at Onset in Huntington Disease

2021 ◽  
Vol 7 (4) ◽  
pp. e603
Author(s):  
Jorien M.M. van der Burg ◽  
Patrick Weydt ◽  
Georg Bernhard Landwehrmeyer ◽  
N. Ahmad Aziz
Keyword(s):  
Weight Loss ◽  
Body Weight ◽  
Huntington Disease ◽  
Association Studies ◽  
Age At Onset ◽  
Disease Onset ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Clinical Progression ◽  
Genetically Determined

ObjectiveWeight loss is associated with clinical progression in Huntington disease (HD), but whether body weight causally affects disease onset or progression is unknown. Therefore, we aimed to assess whether genetically determined variations in body weight are causally related to age at onset in HD.MethodsUsing data from different recent genome-wide association studies, we performed a 2-sample mendelian randomization (MR) analysis to assess whether genetic markers of body mass index (BMI) are causally related to residual age at onset in HD, i.e., the difference between observed and expected age at onset based on mutation size. Our study had a statistical power of 90% to detect a causal effect of ≥3.8 months per BMI unit change at a type I error rate of 0.05.ResultsInverse-variance weighted MR estimates showed that a higher genetically determined BMI was not causally related to residual age at onset in HD (β = −0.44 years per unit increase in BMI, confidence interval: −1.33 to 0.46, p = 0.34). All other complementary (nonparametric) MR regression methods yielded similar results.ConclusionsAlthough maintaining a healthy and stable body weight remains important in patients with HD, promoting weight gain with the aim of delaying disease onset or slowing down disease progression should be discouraged. Our findings point toward the existence of underlying pathologic processes that dictate both the rate of clinical progression and weight loss in HD, which need further elucidation as targeting these pathways, rather than body weight per se, could be of therapeutic value.

Anti-obesity effects of GIPR antagonists alone and in combination with GLP-1R agonists in preclinical models

2018 ◽  
Vol 10 (472) ◽  
pp. eaat3392 ◽  
Author(s):  
Elizabeth A. Killion ◽  
Jinghong Wang ◽  
Junming Yie ◽  
Stone D.-H. Shi ◽  
Darren Bates ◽  
...  
Keyword(s):  
Weight Loss ◽  
Body Weight ◽  
Body Weight Gain ◽  
Association Studies ◽  
Conditional Knockout ◽  
Genome Wide Association Studies ◽  
Knockout Mouse Model ◽  
Conditional Knockout Mouse ◽  
Glucagon Like Peptide 1 ◽  
Treatment Of Obesity

Glucose-dependent insulinotropic polypeptide (GIP) receptor (GIPR) has been identified in multiple genome-wide association studies (GWAS) as a contributor to obesity, and GIPR knockout mice are protected against diet-induced obesity (DIO). On the basis of this genetic evidence, we developed anti-GIPR antagonistic antibodies as a potential therapeutic strategy for the treatment of obesity and observed that a mouse anti-murine GIPR antibody (muGIPR-Ab) protected against body weight gain, improved multiple metabolic parameters, and was associated with reduced food intake and resting respiratory exchange ratio (RER) in DIO mice. We replicated these results in obese nonhuman primates (NHPs) using an anti-human GIPR antibody (hGIPR-Ab) and found that weight loss was more pronounced than in mice. In addition, we observed enhanced weight loss in DIO mice and NHPs when anti-GIPR antibodies were codosed with glucagon-like peptide-1 receptor (GLP-1R) agonists. Mechanistic and crystallographic studies demonstrated that hGIPR-Ab displaced GIP and bound to GIPR using the same conserved hydrophobic residues as GIP. Further, using a conditional knockout mouse model, we excluded the role of GIPR in pancreatic β-cells in the regulation of body weight and response to GIPR antagonism. In conclusion, these data provide preclinical validation of a therapeutic approach to treat obesity with anti-GIPR antibodies.

scholarly journals Genetic Evidence That Carbohydrate-Stimulated Insulin Secretion Leads to Obesity

2018 ◽  
Vol 64 (1) ◽  
pp. 192-200 ◽  
Author(s):  
Christina M Astley ◽  
Jennifer N Todd ◽  
Rany M Salem ◽  
Sailaja Vedantam ◽  
Cara B Ebbeling ◽  
...  
Keyword(s):  
Body Weight ◽  
Insulin Secretion ◽  
Causal Relationship ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Sensitivity Analyses ◽  
European Ancestry ◽  
Oral Glucose ◽  
Genome Wide Association Studies ◽  
Genetically Determined

Abstract BACKGROUND A fundamental precept of the carbohydrate–insulin model of obesity is that insulin secretion drives weight gain. However, fasting hyperinsulinemia can also be driven by obesity-induced insulin resistance. We used genetic variation to isolate and estimate the potentially causal effect of insulin secretion on body weight. METHODS Genetic instruments of variation of insulin secretion [assessed as insulin concentration 30 min after oral glucose (insulin-30)] were used to estimate the causal relationship between increased insulin secretion and body mass index (BMI), using bidirectional Mendelian randomization analysis of genome-wide association studies. Data sources included summary results from the largest published metaanalyses of predominantly European ancestry for insulin secretion (n = 26037) and BMI (n = 322154), as well as individual-level data from the UK Biobank (n = 138541). Data from the Cardiology and Metabolic Patient Cohort study at Massachusetts General Hospital (n = 1675) were used to validate genetic associations with insulin secretion and to test the observational association of insulin secretion and BMI. RESULTS Higher genetically determined insulin-30 was strongly associated with higher BMI (β = 0.098, P = 2.2 × 10−21), consistent with a causal role in obesity. Similar positive associations were noted in sensitivity analyses using other genetic variants as instrumental variables. By contrast, higher genetically determined BMI was not associated with insulin-30. CONCLUSIONS Mendelian randomization analyses provide evidence for a causal relationship of glucose-stimulated insulin secretion on body weight, consistent with the carbohydrate–insulin model of obesity.

scholarly journals Polymorphisms Associated with Age at Onset in Patients with Moderate-to-Severe Plaque Psoriasis

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Rocío Prieto-Pérez ◽  
Guillermo Solano-López ◽  
Teresa Cabaleiro ◽  
Manuel Román ◽  
Dolores Ochoa ◽  
...  
Keyword(s):  
Plaque Psoriasis ◽  
Age Of Onset ◽  
Late Onset ◽  
Association Studies ◽  
Age At Onset ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Healthy Controls ◽  
Type Ii ◽  
Severe Plaque Psoriasis

Psoriasis is a chronic skin disease in which genetics play a major role. Although many genome-wide association studies have been performed in psoriasis, knowledge of the age at onset remains limited. Therefore, we analyzed 173 single-nucleotide polymorphisms in genes associated with psoriasis and other autoimmune diseases in patients with moderate-to-severe plaque psoriasis type I (early-onset, <40 years) or type II (late-onset, ≥40 years) and healthy controls. Moreover, we performed a comparison between patients with type I psoriasis and patients with type II psoriasis. Our comparison of a stratified population with type I psoriasisn=155and healthy controlsN=197is the first to reveal a relationship between theCLMN,FBXL19,CCL4L,C17orf51,TYK2,IL13,SLC22A4,CDKAL1, andHLA-B/MICAgenes. When we compared type I psoriasis with type II psoriasisN=36, we found a significant association between age at onset and the genesPSORS6,TNF-α,FCGR2A,TNFR1,CD226,HLA-C,TNFAIP3, andCCHCR1. Moreover, we replicated the association between rs12191877 (HLA-C) and type I psoriasis and between type I and type II psoriasis. Our findings highlight the role of genetics in age of onset of psoriasis.

scholarly journals A comprehensive evaluation of methods for Mendelian randomization using realistic simulations and an analysis of 38 biomarkers for risk of type 2 diabetes

2021 ◽  
Author(s):  
Guanghao Qi ◽  
Nilanjan Chatterjee
Keyword(s):  
Type 2 Diabetes ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Real Data ◽  
Causal Effects ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Simulation Studies ◽  
Sample Sizes

Abstract Background Previous studies have often evaluated methods for Mendelian randomization (MR) analysis based on simulations that do not adequately reflect the data-generating mechanisms in genome-wide association studies (GWAS) and there are often discrepancies in the performance of MR methods in simulations and real data sets. Methods We use a simulation framework that generates data on full GWAS for two traits under a realistic model for effect-size distribution coherent with the heritability, co-heritability and polygenicity typically observed for complex traits. We further use recent data generated from GWAS of 38 biomarkers in the UK Biobank and performed down sampling to investigate trends in estimates of causal effects of these biomarkers on the risk of type 2 diabetes (T2D). Results Simulation studies show that weighted mode and MRMix are the only two methods that maintain the correct type I error rate in a diverse set of scenarios. Between the two methods, MRMix tends to be more powerful for larger GWAS whereas the opposite is true for smaller sample sizes. Among the other methods, random-effect IVW (inverse-variance weighted method), MR-Robust and MR-RAPS (robust adjust profile score) tend to perform best in maintaining a low mean-squared error when the InSIDE assumption is satisfied, but can produce large bias when InSIDE is violated. In real-data analysis, some biomarkers showed major heterogeneity in estimates of their causal effects on the risk of T2D across the different methods and estimates from many methods trended in one direction with increasing sample size with patterns similar to those observed in simulation studies. Conclusion The relative performance of different MR methods depends heavily on the sample sizes of the underlying GWAS, the proportion of valid instruments and the validity of the InSIDE assumption. Down-sampling analysis can be used in large GWAS for the possible detection of bias in the MR methods.

scholarly journals Gene4PD: A Comprehensive Genetic Database of Parkinson’s Disease

2021 ◽  
Vol 15 ◽  
Author(s):  
Bin Li ◽  
Guihu Zhao ◽  
Qiao Zhou ◽  
Yali Xie ◽  
Zheng Wang ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Neurodegenerative Disorder ◽  
Late Onset ◽  
Association Studies ◽  
Age At Onset ◽  
Genetic Data ◽  
Genome Wide Association Studies ◽  
Onset Age ◽  
Loss Of Function

Parkinson’s disease (PD) is a complex neurodegenerative disorder with a strong genetic component. A growing number of variants and genes have been reported to be associated with PD; however, there is no database that integrate different type of genetic data, and support analyzing of PD-associated genes (PAGs). By systematic review and curation of multiple lines of public studies, we integrate multiple layers of genetic data (rare variants and copy-number variants identified from patients with PD, associated variants identified from genome-wide association studies, differentially expressed genes, and differential DNA methylation genes) and age at onset in PD. We integrated five layers of genetic data (8302 terms) with different levels of evidences from more than 3,000 studies and prioritized 124 PAGs with strong or suggestive evidences. These PAGs were identified to be significantly interacted with each other and formed an interconnected functional network enriched in several functional pathways involved in PD, suggesting these genes may contribute to the pathogenesis of PD. Furthermore, we identified 10 genes were associated with a juvenile-onset (age ≤ 30 years), 11 genes were associated with an early-onset (age of 30–50 years), whereas another 10 genes were associated with a late-onset (age &gt; 50 years). Notably, the AAOs of patients with loss of function variants in five genes were significantly lower than that of patients with deleterious missense variants, while patients with VPS13C (P = 0.01) was opposite. Finally, we developed an online database named Gene4PD (http://genemed.tech/gene4pd) which integrated published genetic data in PD, the PAGs, and 63 popular genomic data sources, as well as an online pipeline for prioritize risk variants in PD. In conclusion, Gene4PD provides researchers and clinicians comprehensive genetic knowledge and analytic platform for PD, and would also improve the understanding of pathogenesis in PD.

scholarly journals 297 GWAS for complex models accounting for populations structure with GBLUP and ssGBLUP

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 32-32
Author(s):  
Juan P Steibel ◽  
Ignacio Aguilar
Keyword(s):  
Hypothesis Testing ◽  
Large Scale ◽  
Mixed Model ◽  
Prediction Models ◽  
Association Studies ◽  
Least Square ◽  
Type I ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies ◽  
Formal Hypothesis Testing

Abstract Genomic Best Linear Unbiased Prediction (GBLUP) is the method of choice for incorporating genomic information into the genetic evaluation of livestock species. Furthermore, single step GBLUP (ssGBLUP) is adopted by many breeders’ associations and private entities managing large scale breeding programs. While prediction of breeding values remains the primary use of genomic markers in animal breeding, a secondary interest focuses on performing genome-wide association studies (GWAS). The goal of GWAS is to uncover genomic regions that harbor variants that explain a large proportion of the phenotypic variance, and thus become candidates for discovering and studying causative variants. Several methods have been proposed and successfully applied for embedding GWAS into genomic prediction models. Most methods commonly avoid formal hypothesis testing and resort to estimation of SNP effects, relying on visual inspection of graphical outputs to determine candidate regions. However, with the advent of high throughput phenomics and transcriptomics, a more formal testing approach with automatic discovery thresholds is more appealing. In this work we present the methodological details of a method for performing formal hypothesis testing for GWAS in GBLUP models. First, we present the method and its equivalencies and differences with other GWAS methods. Moreover, we demonstrate through simulation analyses that the proposed method controls type I error rate at the nominal level. Second, we demonstrate two possible computational implementations based on mixed model equations for ssGBLUP and based on the generalized least square equations (GLS). We show that ssGBLUP can deal with datasets with extremely large number of animals and markers and with multiple traits. GLS implementations are well suited for dealing with smaller number of animals with tens of thousands of phenotypes. Third, we show several useful extensions, such as: testing multiple markers at once, testing pleiotropic effects and testing association of social genetic effects.

scholarly journals Association of GWAS-Based Candidate Genes with HDL-Cholesterol Levels before and after Bariatric Surgery in the Swedish Obese Subjects Study

2011 ◽  
Vol 96 (6) ◽  
pp. E953-E957 ◽  
Author(s):  
Mark A. Sarzynski ◽  
Peter Jacobson ◽  
Tuomo Rankinen ◽  
Björn Carlsson ◽  
Lars Sjöström ◽  
...  
Keyword(s):  
Bariatric Surgery ◽  
Weight Loss ◽  
Candidate Genes ◽  
Association Studies ◽  
Statistical Significance ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Cross Sectional ◽  
Time Points ◽  
Obese Subjects

Context and Objective: The magnitude of weight loss-induced high-density lipoprotein cholesterol (HDL-C) changes may depend on genetic factors. We examined the associations of eight candidate genes, identified by genome-wide association studies, with HDL-C at baseline and 10 yr after bariatric surgery in the Swedish Obese Subjects study. Methods: Single-nucleotide polymorphisms (SNP) (n = 60) in the following gene loci were genotyped: ABCA1, APOA5, CETP, GALNT2, LIPC, LIPG, LPL, and MMAB/MVK. Cross-sectional associations were tested before (n = 1771) and 2 yr (n = 1583) and 10 yr (n = 1196) after surgery. Changes in HDL-C were tested between baseline and yr 2 (n = 1518) and yr 2 and 10 (n = 1149). A multiple testing corrected threshold of P = 0.00125 was used for statistical significance. Results: In adjusted multivariate models, CETP SNP rs3764261 explained from 3.2–4.2% (P &lt; 10−14) of the variation in HDL-C at all three time points, whereas CETP SNP rs9939224 contributed an additional 0.6 and 0.9% at baseline and yr 2, respectively. LIPC SNP rs1077834 showed consistent associations across all time points (R2 = 0.4–1.1%; 3.8 × 10−6 &lt; P &lt; 3 × 10−3), whereas LPL SNP rs6993414 contributed approximately 0.5% (5 × 10−4 &lt; P &lt; 0.0012) at yr 2 and 10. In aggregate, four SNP in three genes explained 4.2, 6.8, and 5.6% of the HDL-C variance at baseline, yr 2, and yr 10, respectively. None of the SNP was significantly associated with weight loss-related changes in HDL-C. Conclusions: SNP in the CETP, LIPC, and LPL loci contribute significantly to plasma HDL-C levels in obese individuals, and the associations persist even after considerable weight loss due to bariatric surgery. However, they are not associated with surgery-induced changes in HDL-C levels.

Genetics of connective tissue diseases

2013 ◽  
Author(s):  
Myles Lewis ◽  
Tim Vyse
Keyword(s):  
Systemic Sclerosis ◽  
Connective Tissue ◽  
Large Scale ◽  
Apoptotic Cell ◽  
Association Studies ◽  
Group Analysis ◽  
Connective Tissue Diseases ◽  
Susceptibility Genes ◽  
Type I ◽  
Genome Wide Association Studies

The advent of genome-wide association studies (GWAS) has been an exciting breakthrough in our understanding of the genetic aetiology of autoimmune diseases. Substantial overlap has been found in susceptibility genes across multiple diseases, from connective tissue diseases and rheumatoid arthritis (RA) to inflammatory bowel disease, coeliac disease, and psoriasis. Major technological advances now permit genotyping of millions of single nucleotide polymorphisms (SNPs). Group analysis of SNPs by haplotypes, aided by completion of the Hapmap project, has improved our ability to pinpoint causal genetic variants. International collaboration to pool large-scale cohorts of patients has enabled GWAS in systemic lupus erythematosus (SLE), systemic sclerosis and Behçet's disease, with studies in progress for ANCA-associated vasculitis. These 'hypothesis-free' studies have revealed many novel disease-associated genes. In both SLE and systemic sclerosis, identified genes map to known pathways including antigen presentation (MHC, TNFSF4), autoreactivity of B and T lymphocytes (BLK, BANK1), type I interferon production (STAT4, IRF5) and the NFκ‎B pathway (TNIP1). In SLE alone, additional genes appear to be involved in dysregulated apoptotic cell clearance (ITGAM, TREX1, C1q, C4) and recognition of immune complexes (FCGR2A, FCGR3B). Future developments include whole-genome sequencing to identify rare variants, and efforts to understand functional consequences of susceptibility genes. Putative environmental triggers for connective tissue diseases include infectious agents, especially Epstein-Barr virus; cigarette smoking; occupational exposure to toxins including silica; and low vitamin D, due to its immunomodulatory effects. Despite numerous studies looking at toxin exposure and connective tissue diseases, conclusive evidence is lacking, due to either rarity of exposure or rarity of disease.

scholarly journals A new efficient method to detect genetic interactions for lung cancer GWAS

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Jennifer Luyapan ◽  
Xuemei Ji ◽  
Siting Li ◽  
Xiangjun Xiao ◽  
Dakai Zhu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Dimensionality Reduction ◽  
Multifactor Dimensionality Reduction ◽  
Disease Onset ◽  
Genetic Interactions ◽  
Survival Outcomes ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Data Set ◽  
Genome Wide

Abstract Background Genome-wide association studies (GWAS) have proven successful in predicting genetic risk of disease using single-locus models; however, identifying single nucleotide polymorphism (SNP) interactions at the genome-wide scale is limited due to computational and statistical challenges. We addressed the computational burden encountered when detecting SNP interactions for survival analysis, such as age of disease-onset. To confront this problem, we developed a novel algorithm, called the Efficient Survival Multifactor Dimensionality Reduction (ES-MDR) method, which used Martingale Residuals as the outcome parameter to estimate survival outcomes, and implemented the Quantitative Multifactor Dimensionality Reduction method to identify significant interactions associated with age of disease-onset. Methods To demonstrate efficacy, we evaluated this method on two simulation data sets to estimate the type I error rate and power. Simulations showed that ES-MDR identified interactions using less computational workload and allowed for adjustment of covariates. We applied ES-MDR on the OncoArray-TRICL Consortium data with 14,935 cases and 12,787 controls for lung cancer (SNPs = 108,254) to search over all two-way interactions to identify genetic interactions associated with lung cancer age-of-onset. We tested the best model in an independent data set from the OncoArray-TRICL data. Results Our experiment on the OncoArray-TRICL data identified many one-way and two-way models with a single-base deletion in the noncoding region of BRCA1 (HR 1.24, P = 3.15 × 10–15), as the top marker to predict age of lung cancer onset. Conclusions From the results of our extensive simulations and analysis of a large GWAS study, we demonstrated that our method is an efficient algorithm that identified genetic interactions to include in our models to predict survival outcomes.

scholarly journals Integrated genomics analysis highlights important SNPs and genes implicated in moderate-to-severe asthma based on GWAS and eQTL datasets

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Zhouzhou Dong ◽  
Yunlong Ma ◽  
Hua Zhou ◽  
Linhui Shi ◽  
Gongjie Ye ◽  
...  
Keyword(s):  
Bayesian Analysis ◽  
Severe Asthma ◽  
Antigen Processing ◽  
Association Studies ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Bioinformatics Analyses

Abstract Background Severe asthma is a chronic disease contributing to disproportionate disease morbidity and mortality. From the year of 2007, many genome-wide association studies (GWAS) have documented a large number of asthma-associated genetic variants and related genes. Nevertheless, the molecular mechanism of these identified variants involved in asthma or severe asthma risk remains largely unknown. Methods In the current study, we systematically integrated 3 independent expression quantitative trait loci (eQTL) data (N = 1977) and a large-scale GWAS summary data of moderate-to-severe asthma (N = 30,810) by using the Sherlock Bayesian analysis to identify whether expression-related variants contribute risk to severe asthma. Furthermore, we performed various bioinformatics analyses, including pathway enrichment analysis, PPI network enrichment analysis, in silico permutation analysis, DEG analysis and co-expression analysis, to prioritize important genes associated with severe asthma. Results In the discovery stage, we identified 1129 significant genes associated with moderate-to-severe asthma by using the Sherlock Bayesian analysis. Two hundred twenty-eight genes were prominently replicated by using MAGMA gene-based analysis. These 228 replicated genes were enriched in 17 biological pathways including antigen processing and presentation (Corrected P = 4.30 × 10− 6), type I diabetes mellitus (Corrected P = 7.09 × 10− 5), and asthma (Corrected P = 1.72 × 10− 3). With the use of a series of bioinformatics analyses, we highlighted 11 important genes such as GNGT2, TLR6, and TTC19 as authentic risk genes associated with moderate-to-severe/severe asthma. With respect to GNGT2, there were 3 eSNPs of rs17637472 (PeQTL = 2.98 × 10− 8 and PGWAS = 3.40 × 10− 8), rs11265180 (PeQTL = 6.0 × 10− 6 and PGWAS = 1.99 × 10− 3), and rs1867087 (PeQTL = 1.0 × 10− 4 and PGWAS = 1.84 × 10− 5) identified. In addition, GNGT2 is significantly expressed in severe asthma compared with mild-moderate asthma (P = 0.045), and Gngt2 shows significantly distinct expression patterns between vehicle and various glucocorticoids (Anova P = 1.55 × 10− 6). Conclusions Our current study provides multiple lines of evidence to support that these 11 identified genes as important candidates implicated in the pathogenesis of severe asthma.

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