scholarly journals Genome‐wide pleiotropy analysis identifies novel blood pressure variants and improves its polygenic risk scores

2022 ◽  
Author(s):  
Xiaofeng Zhu ◽  
Luke Zhu ◽  
Heming Wang ◽  
Richard S. Cooper ◽  
Aravinda Chakravarti
Keyword(s):  
Blood Pressure ◽  
Risk Scores ◽  
Polygenic Risk ◽  
Genome Wide

scholarly journals Associations of maternal blood pressure-raising polygenic risk scores with fetal weight

2021 ◽  
Author(s):  
Tsegaselassie Workalemahu ◽  
Mohammad L. Rahman ◽  
Marion Ouidir ◽  
Jing Wu ◽  
Cuilin Zhang ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Maternal Blood ◽  
Fetal Weight ◽  
Risk Scores ◽  
Polygenic Risk ◽  
Maternal Blood Pressure

Abstract P256: Genetic Determinants Of Blood Pressure Associate With Apparent Treatment Resistant Hypertension

Hypertension ◽  
2021 ◽  
Vol 78 (Suppl_1) ◽  
Author(s):  
Joseph H Breeyear ◽  
Megan M Shuey ◽  
Todd L Edwards ◽  
Jacklyn Hellwege
Keyword(s):  
Blood Pressure ◽  
Risk Scores ◽  
Uncontrolled Hypertension ◽  
Polygenic Risk Score ◽  
Genetic Determinants ◽  
Treatment Resistant ◽  
Polygenic Risk ◽  
Blood Pressures ◽  
The Uk ◽  
Hispanic White

Hypertension is estimated to affect more than 49.6% of US adults 20 years and older. Of those individuals with hypertension, more than ten million are classified as apparent treatment resistant hypertensive (aTRH). The attributable risk of uncontrolled hypertension was estimated to be 49% for cardiovascular disease and 62% for stroke. We developed a polygenic risk score (PRS) for systolic (SBP) and diastolic (DBP) blood pressure to examine the association between the genetic determinants of blood pressure and aTRH with the goal of identifying high risk individuals. The meta-analyzed transethnic results of Giri et al., Biobank Japan, and Liang et al. were used to generate a PRS with PRS-CS followed by p -value thresholding, and validation in the UK Biobank (n max =341,930). Associations were modeled with logistic regression adjusted for age, age-squared, BMI, sex, and ten principal components of ancestry in BioVU’s transethnic population (n max =37,978), as well as non-Hispanic Black (n max =5,026) and non-Hispanic White (n max =28,545) subsets. The SBP PRS was significantly associated with an increased aTRH risk in the non-Hispanic White subset (1.08 (1.04 - 1.12), p = 0.00037) and transethnic (1.08 (1.04 - 1.13), p = 0.00020) populations, but not the non-Hispanic Black subset. The DBP PRS was not associated with aTRH in any population. Our findings present evidence that individuals with a higher genetic predisposition towards hypertension are at higher risk of aTRH. By integrating polygenic risk scores and clinical covariates in prediction of aTRH, individuals’ therapeutic regimens may be tailored to help maintain stable blood pressures, therefore reducing their risk of comorbidities.

scholarly journals Age-related late-onset disease heritability patterns and implications for genome-wide association studies

2018 ◽  
Author(s):  
Roman Teo Oliynyk
Keyword(s):  
Old Age ◽  
Cumulative Incidence ◽  
Late Onset ◽  
Association Studies ◽  
Genome Wide Association ◽  
Risk Scores ◽  
Cerebral Stroke ◽  
Genome Wide Association Studies ◽  
Polygenic Risk ◽  
Genome Wide

AbstractBackgroundGenome-wide association studies and other computational biology techniques are gradually discovering the causal gene variants that contribute to late-onset human diseases. After more than a decade of genome-wide association study efforts, these can account for only a fraction of the heritability implied by familial studies, the so-called “missing heritability” problem.MethodsComputer simulations of polygenic late-onset diseases in an aging population have quantified the risk allele frequency decrease at older ages caused by individuals with higher polygenic risk scores becoming ill proportionately earlier. This effect is most prominent for diseases characterized by high cumulative incidence and high heritability, examples of which include Alzheimer’s disease, coronary artery disease, cerebral stroke, and type 2 diabetes.ResultsThe incidence rate for late-onset diseases grows exponentially for decades after early onset ages, guaranteeing that the cohorts used for genome-wide association studies overrepresent older individuals with lower polygenic risk scores, whose disease cases are disproportionately due to environmental causes such as old age itself. This mechanism explains the decline in clinical predictive power with age and the lower discovery power of familial studies of heritability and genome-wide association studies. It also explains the relatively constant-with-age heritability found for late-onset diseases of lower prevalence, exemplified by cancers.ConclusionsFor late-onset polygenic diseases showing high cumulative incidence together with high initial heritability, rather than using relatively old age-matched cohorts, study cohorts combining the youngest possible cases with the oldest possible controls may significantly improve the discovery power of genome-wide association studies.

scholarly journals Effect of polygenic risk scores on depression in childhood trauma

2014 ◽  
Vol 205 (2) ◽  
pp. 113-119 ◽  
Author(s):  
Wouter J. Peyrot ◽  
Yuri Milaneschi ◽  
Abdel Abdellaoui ◽  
Patrick F. Sullivan ◽  
Jouke J. Hottenga ◽  
...  
Keyword(s):  
Childhood Trauma ◽  
Meta Analysis ◽  
Genetic Effect ◽  
Risk Scores ◽  
Environment Interaction ◽  
Polygenic Risk ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Genome Wide ◽  
Direct Genetic Effect

BackgroundResearch on gene×environment interaction in major depressive disorder (MDD) has thus far primarily focused on candidate genes, although genetic effects are known to be polygenic.AimsTo test whether the effect of polygenic risk scores on MDD is moderated by childhood trauma.MethodThe study sample consisted of 1645 participants with a DSM-IV diagnosis of MDD and 340 screened controls from The Netherlands. Chronic or remitted episodes (severe MDD) were present in 956 participants. The occurrence of childhood trauma was assessed with the Childhood Trauma Interview and the polygenic risk scores were based on genome-wide meta-analysis results from the Psychiatric Genomics Consortium.ResultsThe polygenic risk scores and childhood trauma independently affected MDD risk, and evidence was found for interaction as departure from both multiplicativity and additivity, indicating that the effect of polygenic risk scores on depression is increased in the presence of childhood trauma. The interaction effects were similar in predicting all MDD risk and severe MDD risk, and explained a proportion of variation in MDD risk comparable to the polygenic risk scores themselves.ConclusionsThe interaction effect found between polygenic risk scores and childhood trauma implies that (1) studies on direct genetic effect on MDD gain power by focusing on individuals exposed to childhood trauma, and that (2) individuals with both high polygenic risk scores and exposure to childhood trauma are particularly at risk for developing MDD.

scholarly journals Perspective: The Clinical Use of Polygenic Risk Scores: Race, Ethnicity, and Health Disparities

2019 ◽  
Vol 29 (3) ◽  
pp. 513-516 ◽  
Author(s):  
Megan C. Roberts ◽  
Muin J. Khoury ◽  
George A. Mensah
Keyword(s):  
Precision Medicine ◽  
Association Studies ◽  
Clinical Care ◽  
Genomic Research ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Polygenic Risk ◽  
Adverse Health Outcomes ◽  
Genome Wide ◽  
Disease Risks

Polygenic risk scores (PRS) are an emerging precision medicine tool based on multiple gene variants that, taken alone, have weak associations with disease risks, but collec­tively may enhance disease predictive value in the population. However, the benefit of PRS may not be equal among non-European populations, as they are under-represented in genome-wide association studies (GWAS) that serve as the basis for PRS develop­ment. In this perspective, we discuss a path forward, which includes: 1) inclusion of underrepresented populations in PRS research; 2) global efforts to build capacity for genomic research; 3) equitable imple­mentation of these tools in clinical practice; and 4) traditional public health approaches to reduce risk of adverse health outcomes as an important component to precision health. As precision medicine is imple­mented in clinical care, researchers must ensure that advances from PRS research will benefit all.Ethn Dis.2019;29(3):513-516; doi:10.18865/ed.29.3.513.

Performance of polygenic risk scores for cancer prediction in an academic biobank.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 1528-1528
Author(s):  
Heena Desai ◽  
Anh Le ◽  
Ryan Hausler ◽  
Shefali Verma ◽  
Anurag Verma ◽  
...  
Keyword(s):  
Risk Score ◽  
Genetic Variants ◽  
Association Studies ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Genome Wide Association Studies ◽  
Polygenic Risk ◽  
European Americans ◽  
Genome Wide ◽  
Common Genetic Variants

1528 Background: The discovery of rare genetic variants associated with cancer have a tremendous impact on reducing cancer morbidity and mortality when identified; however, rare variants are found in less than 5% of cancer patients. Genome wide association studies (GWAS) have identified hundreds of common genetic variants significantly associated with a number of cancers, but the clinical utility of individual variants or a polygenic risk score (PRS) derived from multiple variants is still unclear. Methods: We tested the ability of polygenic risk score (PRS) models developed from genome-wide significant variants to differentiate cases versus controls in the Penn Medicine Biobank. Cases for 15 different cancers and cancer-free controls were identified using electronic health record billing codes for 11,524 European American and 5,994 African American individuals from the Penn Medicine Biobank. Results: The discriminatory ability of the 15 PRS models to distinguish their respective cancer cases versus controls ranged from 0.68-0.79 in European Americans and 0.74-0.93 in African Americans. Seven of the 15 cancer PRS trended towards an association with their cancer at a p<0.05 (Table), and PRS for prostate, thyroid and melanoma were significantly associated with their cancers at a bonferroni corrected p<0.003 with OR 1.3-1.6 in European Americans. Conclusions: Our data demonstrate that common variants with significant associations from GWAS studies can distinguish cancer cases versus controls for some cancers in an unselected biobank population. Given the small effects, future studies are needed to determine how best to incorporate PRS with other risk factors in the precision prediction of cancer risk. [Table: see text]

scholarly journals Alzheimer’s disease genetic risk and sleep phenotypes: association with more slow-waves and daytime sleepiness

2020 ◽  
Author(s):  
Vincenzo Muto ◽  
Ekaterina Koshmanova ◽  
Pouya Ghaemmaghami ◽  
Mathieu Jaspar ◽  
Christelle Meyer ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Young Adults ◽  
Genetic Variants ◽  
Daytime Sleepiness ◽  
Sleep Disturbances ◽  
Cognitive Disorders ◽  
Risk Scores ◽  
Polygenic Risk ◽  
Genome Wide

AbstractSleep disturbances and genetic variants have been identified as risk factors for Alzheimer’s disease. Whether genome-wide polygenic risk scores (PRS) for AD associate with sleep phenotypes in young adults, decades before typical AD symptom onset, is currently not known. We extensively phenotyped sleep under different sleep conditions and compute whole-genome Polygenic Risk Scores (PRS) for AD in a carefully selected homogenous sample of healthy 363 young men (22.1 y ± 2.7) devoid of sleep and cognitive disorders. AD PRS was associated with more slow wave energy, i.e. the cumulated power in the 0.5-4 Hz EEG band, a marker of sleep need, during habitual sleep and following sleep loss. Furthermore higher AD PRS was correlated with higher habitual daytime sleepiness. These results imply that sleep features may be associated with AD liability in young adults, when current AD biomarkers are typically negative, and reinforce the idea that sleep may be an efficient intervention target for AD.

scholarly journals Constructing germline research cohorts from the discarded reads of clinical tumor sequences

2021 ◽  
Author(s):  
Alexander Gusev ◽  
Stefan M Groha ◽  
Kodi Taraszka ◽  
Yevgeniy R Semenov ◽  
Noah Zaitlen
Keyword(s):  
Snp Array ◽  
Genetic Ancestry ◽  
Risk Scores ◽  
Hla Alleles ◽  
Polygenic Risk ◽  
Genome Wide ◽  
Tumor Sequencing ◽  
Limited Scope ◽  
Somatic Copy Number Alterations ◽  
Panel Sequencing

Background: Hundreds of thousands of cancer patients have had targeted (panel) tumor sequencing to identify clinically meaningful mutations. In addition to improving patient outcomes, this activity has led to significant discoveries in basic and translational domains. However, the targeted nature of clinical tumor sequencing has a limited scope, especially for germline genetics. In this work, we assess the utility of discarded, off-target reads from tumor-only panel sequencing for recovery of genome-wide germline genotypes through imputation. Methods: We develop a framework for inference of germline variants from tumor panel sequencing, including imputation, quality control, inference of genetic ancestry, germline polygenic risk scores, and HLA alleles. We benchmark our framework on 833 individuals with tumor sequencing and matched germline SNP array data. We then apply our approach to a prospectively collected panel sequencing cohort of 25,889 tumors. Results: We demonstrate high to moderate accuracy of each inferred feature relative to direct germline SNP array genotyping: individual common variants were imputed with a mean accuracy (correlation) of 0.86; genetic ancestry was inferred with a correlation of >0.98; polygenic risk scores were inferred with a correlation of >0.90; and individual HLA alleles were inferred with correlation of >0.89. We demonstrate a minimal influence on accuracy of somatic copy number alterations and other tumor features. We showcase the feasibility and utility of our framework by analyzing 25,889 tumors and identifying relationships between genetic ancestry, polygenic risk, and tumor characteristics that could not be studied with conventional data. Conclusions: We conclude that targeted tumor sequencing can be leveraged to build rich germline research cohorts from existing data, and make our analysis pipeline publicly available to facilitate this effort.

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