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 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 Genomics and Functional Genomics of Alzheimer’s Disease

2021 ◽  
Author(s):  
M. Ilyas Kamboh
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Genomics ◽  
Early Onset ◽  
Late Onset ◽  
Association Studies ◽  
Age At Onset ◽  
Significant Risk ◽  
Major Public Health Problem ◽  
Genome Wide Association Studies

AbstractAlzheimer’s disease (AD) is a complex and multifactorial neurodegenerative disease. Due to its long clinical course and lack of an effective treatment, AD has become a major public health problem in the USA and worldwide. Due to variation in age-at-onset, AD is classified into early-onset (< 60 years) and late-onset (≥ 60 years) forms with early-onset accounting for only 5–10% of all cases. With the exception of a small number of early-onset cases that are afflicted because of high penetrant single gene mutations in APP, PSEN1, and PSEN2 genes, AD is genetically heterogeneous, especially the late-onset form having a polygenic or oligogenic risk inheritance. Since the identification of APOE as the most significant risk factor for late-onset AD in 1993, the path to the discovery of additional AD risk genes had been arduous until 2009 when the use of large genome-wide association studies opened up the discovery gateways that led the identification of ~ 95 additional risk loci from 2009 to early 2022. This article reviews the history of AD genetics followed by the potential molecular pathways and recent application of functional genomics methods to identify the causal AD gene(s) among the many genes that reside within a single locus. The ultimate goal of integrating genomics and functional genomics is to discover novel pathways underlying the AD pathobiology in order to identify drug targets for the therapeutic treatment of this heterogeneous disorder.

scholarly journals Polygenic risk scores across the extended psychosis spectrum

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lukasz Smigielski ◽  
Sergi Papiol ◽  
Anastasia Theodoridou ◽  
Karsten Heekeren ◽  
Miriam Gerstenberg ◽  
...  
Keyword(s):  
At Risk ◽  
Association Studies ◽  
Bayesian Regression ◽  
Mitigation Measures ◽  
Risk Scores ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Type Ii ◽  
Polygenic Risk ◽  
Common Genetic Variants

AbstractAs early detection of symptoms in the subclinical to clinical psychosis spectrum may improve health outcomes, knowing the probabilistic susceptibility of developing a disorder could guide mitigation measures and clinical intervention. In this context, polygenic risk scores (PRSs) quantifying the additive effects of multiple common genetic variants hold the potential to predict complex diseases and index severity gradients. PRSs for schizophrenia (SZ) and bipolar disorder (BD) were computed using Bayesian regression and continuous shrinkage priors based on the latest SZ and BD genome-wide association studies (Psychiatric Genomics Consortium, third release). Eight well-phenotyped groups (n = 1580; 56% males) were assessed: control (n = 305), lower (n = 117) and higher (n = 113) schizotypy (both groups of healthy individuals), at-risk for psychosis (n = 120), BD type-I (n = 359), BD type-II (n = 96), schizoaffective disorder (n = 86), and SZ groups (n = 384). PRS differences were investigated for binary traits and the quantitative Positive and Negative Syndrome Scale. Both BD-PRS and SZ-PRS significantly differentiated controls from at-risk and clinical groups (Nagelkerke’s pseudo-R2: 1.3–7.7%), except for BD type-II for SZ-PRS. Out of 28 pairwise comparisons for SZ-PRS and BD-PRS, 9 and 12, respectively, reached the Bonferroni-corrected significance. BD-PRS differed between control and at-risk groups, but not between at-risk and BD type-I groups. There was no difference between controls and schizotypy. SZ-PRSs, but not BD-PRSs, were positively associated with transdiagnostic symptomology. Overall, PRSs support the continuum model across the psychosis spectrum at the genomic level with possible irregularities for schizotypy. The at-risk state demands heightened clinical attention and research addressing symptom course specifiers. Continued efforts are needed to refine the diagnostic and prognostic accuracy of PRSs in mental healthcare.

scholarly journals The effect of sample size on polygenic hazard models for prostate cancer

2019 ◽  
Author(s):  
Roshan A. Karunamuni ◽  
Minh-Phuong Huynh-Le ◽  
Chun C. Fan ◽  
Rosalind A. Eeles ◽  
Douglas F. Easton ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Sample Size ◽  
Genetic Variants ◽  
Model Building ◽  
Age Of Onset ◽  
Association Studies ◽  
Age At Onset ◽  
Genome Wide Association Studies ◽  
Polygenic Scores ◽  
Testing Set

AbstractWe aimed to determine the effect of sample size on performance of polygenic hazard score (PHS) models in predicting the age at onset of prostate cancer. Age and genotypes were obtained for 40,861 men from the PRACTICAL consortium. The dataset included 201,590 SNPs per subject, and was split into training (34,444 samples) and testing (6,417 samples) sets. Two PHS model-building strategies were investigated. Established-SNP model considered 65 SNPs that had been associated with prostate cancer in the literature. A stepwise SNP selection was used to develop Discovery-SNP models. The performance of each PHS model was calculated for random sizes of the training set (1 to 30 thousand). The performance of a representative Established-SNP model was estimated for random sizes of the testing set (0.5 to 6 thousand). Mean HR98/50 (hazard ratio of top 2% to the average in the test set) of the Established-SNP model increased from 1.73[95%CI: 1.69-1.77] to 2.41[2.40-2.43] when the number of training samples was increased from 1 to 30 thousand. The corresponding HR98/50 of the Discovery-SNP model increased from 1.05[0.93-1.18] to 2.19[2.16-2.23]. HR98/50 of a representative Established-SNP model using testing set sample sizes of 0.6 and 6 thousand observations were 1.78[1.70-1.85] and 1.73[1.71-1.76], respectively. We estimate that a study population of 20 to 30 thousand men is required to develop Discovery-SNP PHS models for prostate cancer. The required sample size could be reduced to 10 thousand samples, if a set of SNPs associated with the disease has already been established.Author summaryPolygenic hazard scores represent a recent advancement in polygenic prediction to model the age of onset of various diseases, such as Alzheimer’s disease or prostate cancer. These scores accumulate small effect sizes from several tens of genetic variants and can be used to establish an individual’s risk of experiencing an event relative to a control population across time. The largest barrier to the development of polygenic hazard scores is the large number of study subjects needed to develop the underlying models. We sought to understand the effect of varying the total number of samples on the performance of a polygenic hazard score in the context of prostate cancer. We found that the performance of the score did not appreciably change beyond 20 to 30 thousand observations when developing the model from scratch. However, when the discovery of the genetic variants can be borrowed from those already identified in the literature to be associated with the disease, the required number of samples is reduced to 10 thousand with no appreciable detriment in performance. We hope that these results can guide the design of future studies of polygenic scores in other diseases and demonstrate the importance of genome-wide association studies.

scholarly journals Patterns of convergence and divergence between bipolar disorder type I and type II: evidence from integrative genomic analyses

2021 ◽  
Author(s):  
Yunqi Huang ◽  
Yunjia Liu ◽  
Yulu Wu ◽  
Yiguo Tang ◽  
Siyi Liu ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Association Studies ◽  
Brain Regions ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Type Ii ◽  
Association Analyses ◽  
Convergence And Divergence ◽  
The Difference ◽  
Genetic Structures

Genome-wide association studies (GWAS) analyses have revealed genetic evidence of bipolar disorder (BD), but little is known about genetic structure of BD subtypes. We aimed to investigate genetic overlap and distinction of bipolar type I (BDI) & type II (BDII) by conducting integrative post-GWAS analyses. This study utilized single nucleotide polymorphism (SNP)-level approaches to uncover correlated and distinct genetic loci. Transcriptome-wide association analyses (TWAS) were then approached to pinpoint functional genes expressed in specific brain tissues and blood. Next, we performed cross-phenotype analysis including exploring the potential causal associations between BDI & II and drug responses and comparing the difference of genetic structures among four different psychiatric traits. Our results find SNP-level evidence revealed three genomic loci, SLC25A17, ZNF184 and RPL10AP3 shared by BDI & II, while one locus (i.e., MAD1L1) and significant gene sets involved in calcium channel activity, neural and synapsed signals that distinguished two subtypes. TWAS data implicated different genes effecting BDI & II through expression in specific brain regions (e.g., nucleus accumbens for BDI). Cross-phenotype analyses indicated that BDI & II have different drug response, but share continuous genetic structures with schizophrenia (SCZ) and major depression disorder (MDD), which help fill the gaps left by the dichotomy of mental disorder. These combined evidences illustrate genetic convergence and divergence between BDI & II and provide an underlying biological and trans-diagnostic insight into major psychiatric disorders.

Examination of type I/type II alcoholism typology in a Greek hospital treatment population

2004 ◽  
Vol 19 (4) ◽  
pp. 214-218 ◽  
Author(s):  
Lefteris Lykouras ◽  
George Moussas ◽  
Alexander Botsis
Keyword(s):  
Alcohol Dependence ◽  
Early Onset ◽  
Suicide Risk ◽  
Age Of Onset ◽  
Late Onset ◽  
Hospital Treatment ◽  
Type I ◽  
Type Ii ◽  
Treatment Unit ◽  
Greek Hospital

AbstractObjectiveThe study aims at testing the validity of two types of classification of male alcoholism in a Greek hospital treatment sample.MethodThe study population was drawn from male patients with alcohol dependence admitted to the Alcohol Treatment Unit of the Psychiatric Hospital of Attica. Seventy-three patients comprised the study sample after exclusion of subjects with alcohol dependence suffering from a comorbid serious medical condition, schizophrenic disorder, bipolar disorder, drug dependence or abuse, organic mental disorder or inability to read. The alcoholics were grouped in type I and II adopting the criterion of age-of-onset used by von Knorring et al. (1985). Impulsivity, suicide risk and violence risk were measured by means of the impulse control scale (ICS), the suicide risk scale (SRS) and the past feelings and acts of violence scale (PFAVS).ResultsFifty patients with alcohol dependence were defined as late-onset and 23 as early-onset. Compared to late-onset patients, early-onset individuals with alcohol dependence had more familial alcoholism (P = 0.032); they were in a higher rate unmarried (P = 0.001), had no stable job before entry in the Unit (P = 0.007) and scored higher on ICS (P = 0.046) and SRS (P = 0.024).ConclusionThe present study confirms type I/type II dichotomy of male alcoholism and also shows that the age-of-onset is a valid classification criterion.

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.

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 The clinical utility of gene testing for Alzheimer’s disease

2011 ◽  
Vol 3 (1) ◽  
pp. 1 ◽  
Author(s):  
Emily R. Atkins ◽  
Peter K. Panegyres
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Onset ◽  
Late Onset ◽  
Association Studies ◽  
Susceptibility Gene ◽  
Genome Wide Association Studies ◽  
Apoe Ε4 ◽  
Gene Testing ◽  
A Genome

Alzheimer’s disease (AD) is the largest cause of dementia, affecting 35.6 million people in 2010. Amyloid precursor protein, presenilin 1 and presenilin 2 mutations are known to cause familial early-onset AD, whereas apolipoprotein E (APOE) ε4 is a susceptibility gene for late-onset AD. The genes for phosphatidylinositol- binding clathrin assembly protein, clusterin and complement receptor 1 have recently been described by genome-wide association studies as potential risk factors for lateonset AD. Also, a genome association study using single neucleotide polymorphisms has identified an association of neuronal sortilin related receptor and late-onset AD. Gene testing, and also predictive gene testing, may be of benefit in suspected familial early-onset AD however it adds little to the diagnosis of lateonset AD and does not alter the treatment. We do not recommend APOE ε4 genotyping.

Early and Late Onset Bipolar Disorders in Older Adults

2017 ◽  
Vol 41 (S1) ◽  
pp. S211-S211
Author(s):  
N. Smaoui ◽  
L. Zouari ◽  
N. Charfi ◽  
M. Maâlej-Bouali ◽  
N. Zouari ◽  
...  
Keyword(s):  
Older Adults ◽  
Bipolar Disorder ◽  
Early Onset ◽  
Age Of Onset ◽  
Late Onset ◽  
Age At Onset ◽  
Bipolar Disorders ◽  
Medical Diagnoses ◽  
The Mean ◽  
Bipolar Patients

IntroductionAge of onset of illness may be useful in explaining the heterogeneity among older bipolar patients.ObjectiveTo examine the relationship of age of onset with clinical, demographic and behavioral variables, in older patients with bipolar disorder.MethodsThis was a cross-sectional, descriptive and analytical study, including 24 patients suffering from bipolar disorders, aged 65 years or more and followed-up in outpatient psychiatry unit at Hedi Chaker university hospital in Sfax in Tunisia. We used a standardized questionnaire including socio-demographic, behavioral and clinical data. Age of onset was split at age 40 years into early-onset (< 40 years; n = 12) and late-onset (≥ 40 years; n = 12) groups.ResultsThe mean age for the entire sample was 68.95 years. The mean age of onset was 39.95 years. The majority (60%) of patients were diagnosed with bipolar I. Few meaningful differences emerged between early-onset and late-onset groups, except that tobacco use was significantly higher in the late-onset group (66.6% vs. 16.6%; P = 0.027). No significant differences between the early-onset and late-onset groups were seen on demographic variables, family history and number of medical diagnoses or presence of psychotic features.ConclusionOur study found few meaningful behavioral differences between early versus late age at onset in older adults with bipolar disorder.Disclosure of interestThe authors have not supplied their declaration of competing interest.

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