scholarly journals From biobanking to personalized prevention of obesity, diabetes and metabolic syndrome

2022 ◽  
Vol 20 (8) ◽  
pp. 3123
Author(s):  
A. I. Ershova ◽  
A. A. Ivanova ◽  
A. V. Kiseleva ◽  
E. A. Sotnikova ◽  
A. N. Meshkov ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Genetic Risk ◽  
Metabolic Disorders ◽  
Association Studies ◽  
International Standards ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Alcoholic Fatty Liver ◽  
High Genetic Risk ◽  
Increasing Demand

The growing prevalence of metabolic disorders creates an increasing demand for novel approaches to their prevention and therapy. Novel genetic diagnostic technologies are developed every year, which makes it possible to identify people who are at the highest genetic risk of diabetes, non-alcoholic fatty liver disease, and metabolic syndrome. Early intervention strategies can be used to prevent metabolic disorders in this group of people. Genetic risk scores (GRSs) are a powerful tool to identify people with a high genetic risk. Millions of genetic variants are analyzed in genome-wide association studies in order to combine them into GRSs. It has become possible to store and process such huge amounts of data with the help of biobanks, where biological samples are stored according to international standards. Genetic studies include more and more people every year that increases the predictive power of GRSs. It has already been demonstrated that the use of GRSs makes future preventive measures more effective. In the near future, GRSs are likely to become part of clinical guidelines so that they can be widely used to identify people at high risk for metabolic syndrome and its components.

scholarly journals Multiomic blood correlates of genetic risk identify presymptomatic disease alterations

2020 ◽  
Vol 117 (35) ◽  
pp. 21813-21820
Author(s):  
Michael Wainberg ◽  
Andrew T. Magis ◽  
John C. Earls ◽  
Jennifer C. Lovejoy ◽  
Nasa Sinnott-Armstrong ◽  
...  
Keyword(s):  
Biological Networks ◽  
Genetic Risk ◽  
Complex Traits ◽  
Clinical Laboratory ◽  
Clinical Symptoms ◽  
Association Studies ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
High Genetic Risk ◽  
Genetic And Environmental Factors

Transitions from health to disease are characterized by dysregulation of biological networks under the influence of genetic and environmental factors, often over the course of years to decades before clinical symptoms appear. Understanding these dynamics has important implications for preventive medicine. However, progress has been hindered both by the difficulty of identifying individuals who will eventually go on to develop a particular disease and by the inaccessibility of most disease-relevant tissues in living individuals. Here we developed an alternative approach using polygenic risk scores (PRSs) based on genome-wide association studies (GWAS) for 54 diseases and complex traits coupled with multiomic profiling and found that these PRSs were associated with 766 detectable alterations in proteomic, metabolomic, and standard clinical laboratory measurements (clinical labs) from blood plasma across several thousand mostly healthy individuals. We recapitulated a variety of known relationships (e.g., glutamatergic neurotransmission and inflammation with depression, IL-33 with asthma) and found associations directly suggesting therapeutic strategies (e.g., Ω-6 supplementation and IL-13 inhibition for amyotrophic lateral sclerosis) and influences on longevity (leukemia inhibitory factor, ceramides). Analytes altered in high-genetic-risk individuals showed concordant changes in disease cases, supporting the notion that PRS-associated analytes represent presymptomatic disease alterations. Our results provide insights into the molecular pathophysiology of a range of traits and suggest avenues for the prevention of health-to-disease transitions.

scholarly journals Critical Analysis of Genome-Wide Association Studies: Triple Negative Breast Cancer Quae Exempli Causa

2020 ◽  
Vol 21 (16) ◽  
pp. 5835
Author(s):  
Maria-Ancuta Jurj ◽  
Mihail Buse ◽  
Alina-Andreea Zimta ◽  
Angelo Paradiso ◽  
Schuyler S. Korban ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Genetic Risk ◽  
Triple Negative ◽  
Cancer Susceptibility ◽  
Association Studies ◽  
Breast Cancer Susceptibility ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Genome Wide

Genome-wide association studies (GWAS) are useful in assessing and analyzing either differences or variations in DNA sequences across the human genome to detect genetic risk factors of diseases prevalent within a target population under study. The ultimate goal of GWAS is to predict either disease risk or disease progression by identifying genetic risk factors. These risk factors will define the biological basis of disease susceptibility for the purposes of developing innovative, preventative, and therapeutic strategies. As single nucleotide polymorphisms (SNPs) are often used in GWAS, their relevance for triple negative breast cancer (TNBC) will be assessed in this review. Furthermore, as there are different levels and patterns of linkage disequilibrium (LD) present within different human subpopulations, a plausible strategy to evaluate known SNPs associated with incidence of breast cancer in ethnically different patient cohorts will be presented and discussed. Additionally, a description of GWAS for TNBC will be presented, involving various identified SNPs correlated with miRNA sites to determine their efficacies on either prognosis or progression of TNBC in patients. Although GWAS have identified multiple common breast cancer susceptibility variants that individually would result in minor risks, it is their combined effects that would likely result in major risks. Thus, one approach to quantify synergistic effects of such common variants is to utilize polygenic risk scores. Therefore, studies utilizing predictive risk scores (PRSs) based on known breast cancer susceptibility SNPs will be evaluated. Such PRSs are potentially useful in improving stratification for screening, particularly when combining family history, other risk factors, and risk prediction models. In conclusion, although interpretation of the results from GWAS remains a challenge, the use of SNPs associated with TNBC may elucidate and better contextualize these studies.

scholarly journals Dissociable influences of APOE ε4 and polygenic risk of AD dementia on amyloid and cognition

Neurology ◽  
2018 ◽  
Vol 90 (18) ◽  
pp. e1605-e1612 ◽  
Author(s):  
Tian Ge ◽  
Mert R. Sabuncu ◽  
Jordan W. Smoller ◽  
Reisa A. Sperling ◽  
Elizabeth C. Mormino ◽  
...  
Keyword(s):  
Cognitive Decline ◽  
Genetic Risk ◽  
Large Scale ◽  
Association Studies ◽  
Specific Effect ◽  
Hippocampal Volume ◽  
Risk Scores ◽  
Hippocampal Atrophy ◽  
Genome Wide Association Studies ◽  
Polygenic Risk

ObjectiveTo investigate the effects of genetic risk of Alzheimer disease (AD) dementia in the context of β-amyloid (Aβ) accumulation.MethodsWe analyzed data from 702 participants (221 clinically normal, 367 with mild cognitive impairment, and 114 with AD dementia) with genetic data and florbetapir PET available. A subset of 669 participants additionally had longitudinal MRI scans to assess hippocampal volume. Polygenic risk scores (PRSs) were estimated with summary statistics from previous large-scale genome-wide association studies of AD dementia. We examined relationships between APOE ε4 status and PRS with longitudinal Aβ and cognitive and hippocampal volume measurements.ResultsAPOE ε4 was strongly related to baseline Aβ, whereas only weak associations between PRS and baseline Aβ were present. APOE ε4 was additionally related to greater memory decline and hippocampal atrophy in Aβ+ participants. When APOE ε4 was controlled for, PRS was related to cognitive decline in Aβ+ participants. Finally, PRSs were associated with hippocampal atrophy in Aβ− participants and weakly associated with baseline hippocampal volume in Aβ+ participants.ConclusionsGenetic risk factors of AD dementia demonstrate effects related to Aβ, as well as synergistic interactions with Aβ. The specific effect of faster cognitive decline in Aβ+ individuals with higher genetic risk may explain the large degree of heterogeneity in cognitive trajectories among Aβ+ individuals. Consideration of genetic variants in conjunction with baseline Aβ may improve enrichment strategies for clinical trials targeting Aβ+ individuals most at risk for imminent cognitive decline.

scholarly journals The contribution of common genetic risk variants for ADHD to a general factor of childhood psychopathology

2017 ◽  
Author(s):  
Isabell Brikell ◽  
Henrik Larsson ◽  
Yi Lu ◽  
Erik Pettersson ◽  
Qi Chen ◽  
...  
Keyword(s):  
Genetic Risk ◽  
Association Studies ◽  
Neurodevelopmental Disorder ◽  
Risk Scores ◽  
General Factor ◽  
Genome Wide Association Studies ◽  
General Psychopathology ◽  
Oppositional Defiant ◽  
Risk Variants ◽  
Genetic Risk Variants

AbstractAttention-deficit/hyperactivity disorder (ADHD) is a heritable neurodevelopmental disorder, with common genetic risk variants implicated in the clinical diagnosis and symptoms of ADHD. However, given evidence of comorbidity and genetic overlap across neurodevelopmental and externalizing conditions, it remains unclear whether these genetic risk variants are ADHD-specific. The aim of this study was to evaluate the associations between ADHD genetic risks and related neurodevelopmental and externalizing conditions, and to quantify the extent to which any such associations can be attributed to a general genetic liability towards psychopathology. We derived ADHD polygenic risk scores (PRS) for 13,460 children aged 9 and 12 years from the Child and Adolescent Twin Study in Sweden, using results from an independent meta-analysis of genome-wide association studies of ADHD diagnosis and symptoms. Associations between ADHD PRS, a latent general psychopathology factor, and six latent neurodevelopmental and externalizing factors were estimated using structural equation modelling. ADHD PRS were statistically significantly associated with elevated levels of inattention, hyperactivity/impulsivity, autistic traits, learning difficulties, oppositional-defiant, and conduct problems (standardized regression coefficients=0.07-0.12). Only the association with specific hyperactivity/impulsivity remained significant after accounting for a general psychopathology factor, on which all symptoms loaded positively (standardized mean loading=0.61, range=0.32-0.91). ADHD PRS simultaneously explained 1% (p-value<0.001) of the variance in the general psychopathology factor and 0.50% (p-value<0.001) in the specific hyperactivity/impulsivity factor. Our results suggest that common genetic risk variants associated with ADHD have largely general pleiotropic effects on neurodevelopmental and externalizing traits in the general population, in addition to a specific association with hyperactivity/impulsivity symptoms.

scholarly journals Efficient implementation of penalized regression for genetic risk prediction

2018 ◽  
Author(s):  
Florian Privé ◽  
Hugues Aschard ◽  
Michael G.B. Blum
Keyword(s):  
Logistic Regression ◽  
Genetic Risk ◽  
Association Studies ◽  
Predictive Performance ◽  
Penalized Regression ◽  
Risk Scores ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Polygenic Risk ◽  
Penalized Logistic Regression

AbstractPolygenic Risk Scores (PRS) consist in combining the information across many single-nucleotide polymorphisms (SNPs) in a score reflecting the genetic risk of developing a disease. PRS might have a major impact on public health, possibly allowing for screening campaigns to identify high-genetic risk individuals for a given disease. The “Clumping+Thresholding” (C+T) approach is the most common method to derive PRS. C+T uses only univariate genome-wide association studies (GWAS) summary statistics, which makes it fast and easy to use. However, previous work showed that jointly estimating SNP effects for computing PRS has the potential to significantly improve the predictive performance of PRS as compared to C+T.In this paper, we present an efficient method to jointly estimate SNP effects, allowing for practical application of penalized logistic regression (PLR) on modern datasets including hundreds of thousands of individuals. Moreover, our implementation of PLR directly includes automatic choices for hyper-parameters. The choice of hyper-parameters for a predictive model is very important since it can dramatically impact its predictive performance. As an example, AUC values range from less than 60% to 90% in a model with 30 causal SNPs, depending on the p-value threshold in C+T.We compare the performance of PLR, C+T and a derivation of random forests using both real and simulated data. PLR consistently achieves higher predictive performance than the two other methods while being as fast as C+T. We find that improvement in predictive performance is more pronounced when there are few effects located in nearby genomic regions with correlated SNPs; for instance, AUC values increase from 83% with the best prediction of C+T to 92.5% with PLR. We confirm these results in a data analysis of a case-control study for celiac disease where PLR and the standard C+T method achieve AUC of 89% and of 82.5%.In conclusion, our study demonstrates that penalized logistic regression can achieve more discriminative polygenic risk scores, while being applicable to large-scale individual-level data thanks to the implementation we provide in the R package bigstatsr.

scholarly journals Interaction between Genetic Risk Scores for reduced pulmonary function and smoking, asthma and endotoxin

Thorax ◽  
2021 ◽  
pp. thoraxjnl-2020-215624
Author(s):  
Sinjini Sikdar ◽  
Annah B Wyss ◽  
Mi Kyeong Lee ◽  
Thanh T Hoang ◽  
Marie Richards ◽  
...  
Keyword(s):  
Pulmonary Function ◽  
Genetic Risk ◽  
Association Studies ◽  
European Ancestry ◽  
Risk Scores ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Inverse Association ◽  
Genetic Risk Scores ◽  
Genome Wide

RationaleGenome-wide association studies (GWASs) have identified numerous loci associated with lower pulmonary function. Pulmonary function is strongly related to smoking and has also been associated with asthma and dust endotoxin. At the individual SNP level, genome-wide analyses of pulmonary function have not identified appreciable evidence for gene by environment interactions. Genetic Risk Scores (GRSs) may enhance power to identify gene–environment interactions, but studies are few.MethodsWe analysed 2844 individuals of European ancestry with 1000 Genomes imputed GWAS data from a case–control study of adult asthma nested within a US agricultural cohort. Pulmonary function traits were FEV1, FVC and FEV1/FVC. Using data from a recent large meta-analysis of GWAS, we constructed a weighted GRS for each trait by combining the top (p value<5×10−9) genetic variants, after clumping based on distance (±250 kb) and linkage disequilibrium (r2=0.5). We used linear regression, adjusting for relevant covariates, to estimate associations of each trait with its GRS and to assess interactions.ResultsEach trait was highly significantly associated with its GRS (all three p values<8.9×10−8). The inverse association of the GRS with FEV1/FVC was stronger for current smokers (pinteraction=0.017) or former smokers (pinteraction=0.064) when compared with never smokers and among asthmatics compared with non-asthmatics (pinteraction=0.053). No significant interactions were observed between any GRS and house dust endotoxin.ConclusionsEvaluation of interactions using GRSs supports a greater impact of increased genetic susceptibility on reduced pulmonary function in the presence of smoking or asthma.

scholarly journals Clinical Application of Genetic Prediction in the Management of CAD

2021 ◽  
Vol 6 (01) ◽  
pp. 46-52
Author(s):  
Robert Roberts ◽  
Jacques Fair
Keyword(s):  
Risk Factors ◽  
Primary Prevention ◽  
Genetic Risk ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Cardiac Events ◽  
High Genetic Risk ◽  
Risk Variants ◽  
Single Number ◽  
Conventional Risk Factors

Sequencing of the human genome followed by the HapMap project made possible the unbiased genome-wide association studies that led to the discovery of hundreds of genetic risk variants predisposing to CAD. The total genetic risk for CAD can be expressed in a single number based on the number of variants inherited. A GRS derived from genotyping with microarrays containing these risk variants has been evaluated in over 1 million individuals. Risk stratification for CAD based on the GRS was shown to be superior to conventional risk factors. Placebo-controlled clinical trials showed individuals with high genetic risk had a 40-50% reduction in cardiac events with a favorable lifestyle, and cholesterol-lowering drugs. The risk of CAD based on conventional risk factors such as hypertension are age-dependent, occurring primarily in the sixth or seventh decade which is too late for primary prevention. The GRS is independent of age and can be determined at birth if needed. Incorporation of the GRS into clinical practice would transform the primary prevention of CAD, the number one killer.

scholarly journals The Foundational data initiative for Parkinsons disease (FOUNDIN-PD): enabling efficient translation from genetic maps to mechanism

2021 ◽  
Author(s):  
Elisangela Bressan ◽  
Xylena Reed ◽  
Vikas Bansal ◽  
Elizabeth Hutchins ◽  
Melanie M. Cobb ◽  
...  
Keyword(s):  
Genetic Risk ◽  
Association Studies ◽  
Genetic Maps ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Cell Type ◽  
Parkinsons Disease ◽  
Risk Variants ◽  
Wide Range ◽  
Cellular Context

In the FOUNdational Data INitiative for Parkinsons Disease (FOUNDIN-PD) we sought to produce a multi-layered molecular dataset in a large cohort of 95 Induced pluripotent stem cells (iPSC) lines at multiple timepoints during differentiation to dopaminergic (DA) neurons, a major affected cell type in Parkinsons Disease (PD). The lines are derived from the Parkinsons Progression Markers Initiative (PPMI) study that includes both people with PD and unaffected individuals across a wide range of polygenic risk scores (PRS) with both risk variants identified by genome-wide association studies (GWAS), and monogenic causal alleles. We generated genetic, epigenetic, regulatory, transcriptomic, proteomic, and longitudinal cellular imaging data from iPSC-derived DA neurons to understand key molecular relationships between disease associated genetic variation and proximate molecular events in a PD relevant cell-type. Analyses of all data modalities collected in FOUNDIN-PD suggest that the differentiation to DA neurons, while not fully mature, was successful and robust. Interrogation of PD genetic risk in this relevant cellular context may elucidate the functional effects of some of these risk variants alone or in combination with other variants. These data reveal that DA neurons derived from human iPSC provide a valuable cellular context and foundational atlas for modeling PD-related genetic risk. In addition to making the data and analyses for this molecular atlas readily available, we have integrated these data into the browsable FOUNDIN-PD data portal (https://www.foundinpd.org) to be used as a resource for understanding the molecular pathogenesis of PD.

Prostate cancer meta-analysis from more than 145,000 men to identify 65 novel prostate cancer susceptibility loci.

2017 ◽  
Vol 35 (6_suppl) ◽  
pp. 1-1
Author(s):  
Rosalind Eeles ◽  
Ali Amin Al Olama ◽  
Sonja Berndt ◽  
Fredrik Wiklund ◽  
David V Conti ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Relative Risk ◽  
Genetic Risk ◽  
Cancer Susceptibility ◽  
Association Studies ◽  
Genetic Risk Score ◽  
European Ancestry ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Susceptibility Loci

1 Background: Currently genome-wide association studies (GWAS) have identified over 100 prostate cancer (PrCa) susceptibility loci, capturing 33% of the PrCa familial relative risk (FRR) in Europeans. To identify further susceptibility variants, we conducted a PrCa GWAS, larger than previous studies, comprising ~49,000 cases and ~29,000 controls among individuals of European and Asian descent using the OncoArray, a platform consisting of a 260K GWAS backbone and 310K custom content selected from previous GWAS and fine-mapping studies of multiple cancers ( http://epi.grants.cancer.gov/oncoarray/ ). Methods: Genotypes from the OncoArray were used to impute genotypes from ~70M variants using the October 2014 release of the 1000 genomes project as a reference, and then combined with several previous PrCa GWAS of European ancestry: UK stage 1 (1,906 cases/1,934 controls) and stage 2 (3,888 cases/3,956 controls); CaPS 1 (498 cases/502 controls) and CaPS 2 (1,483 cases/519 controls); BPC3 (2,137 cases/3,101 controls); NCI PEGASUS (4,622 cases/2,954 controls); and iCOGS (21,209 cases/ 20,440 controls). Risk analyses for overall PrCa risk, aggressive PrCa (several definitions defined by PrCa clinical characteristics), and Gleason score were performed. Logistic and linear regression summary statistics were meta-analysed using an inverse variance fixed effect approach. Results: We identified novel loci significantly associated ( P < 5.0x10-8) with overall PrCa (N = 65). Our novel findings are comprised of several missense variants, including a SNP in the ATM gene - a key member of the DNA repair pathway. When combined multiplicatively, the 65 novel PrCa loci identified here increases the captured heritability of PrCa, explaining 38.5% of the FRR when combining novel and previously identified PrCa loci. Conclusions: In risk stratification, men in the top 1% of the genetic risk score group have a relative risk of 5.6 fold for developing PrCa compared with the median risk group. These results will improve the utility of genetic risk scores for targeted screening and prevention for prostate cancer.

scholarly journals Polygenic Risk Scores for Cardio-renal-metabolic Diseases in the Penn Medicine Biobank

2019 ◽  
Author(s):  
R.L. Kember ◽  
A. Verma ◽  
S. Verma ◽  
A. Lucas ◽  
R. Judy ◽  
...  
Keyword(s):  
Genetic Risk ◽  
Metabolic Diseases ◽  
Association Studies ◽  
African Ancestry ◽  
Treatment Strategies ◽  
European Ancestry ◽  
Risk Scores ◽  
Future Research ◽  
Genome Wide Association Studies ◽  
Polygenic Risk

AbstractCardio-renal-metabolic (CaReMe) conditions are common and the leading cause of mortality around the world. Genome-wide association studies have shown that these diseases are polygenic and share many genetic risk factors. Identifying individuals at high genetic risk will allow us to target prevention and treatment strategies. Polygenic risk scores (PRS) are aggregate weighted counts that can demonstrate an individual’s genetic liability for disease. However, current PRS are often based on European ancestry individuals, limiting the implementation of precision medicine efforts in diverse populations. In this study, we develop PRS for six diseases and traits related to cardio-renal-metabolic disease in the Penn Medicine Biobank. We investigate their performance in both European and African ancestry individuals, and identify genetic and phenotypic overlap within these conditions. We find that genetic risk is associated with the primary phenotype in both ancestries, but this does not translate into a model of predictive value in African ancestry individuals. We conclude that future research should prioritize genetic studies in diverse ancestries in order to address this disparity.

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