scholarly journals Positioning Personal Polygenic Risk score against the population background

2019 ◽  
Author(s):  
Ganna Leonenko ◽  
Emily Baker ◽  
Karl Michael Schmidt ◽  
Valentina Escott-Price ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Risk ◽  
Genetic Disorder ◽  
Genetic Research ◽  
European Ancestry ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Public Data ◽  
European Populations

AbstractThe polygenic risk scores (PRS) approach has been widely used across different traits for estimating polygenic risk, pleiotropy and disease prediction, but mostly in European populations. The predictive ability of the PRS in non-European populations is currently limited due to the lack of genetic research performed in populations of non-European ancestry. One of the main challenges of the practical use of PRS is to place an individual’s personal score in the context of the PRS distribution in the underlying population. In this paper we present an approach for estimating the parameters of the PRS distribution in a population using summary information from public data.Unstandardized PRS are usually not directly comparable even between European studies. Our approach can be used for standardisation whilst accounting for genotyping platforms, data quality and ancestry. It can be applied to assessing polygenic disease risk for individuals from a European population for any complex genetic disorder and, assuming that most of the disease risk loci are likely to be shared between populations, to estimating the disease risk for individuals from other populations. We demonstrate the precision of our method with simulations. We show the utility of our estimates in application to Alzheimer’s disease in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study. We present population specific PRSs for different populations using 1000 Genomes data.

scholarly journals Common variants in Alzheimer’s disease and risk stratification by polygenic risk scores

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Itziar de Rojas ◽  
Sonia Moreno-Grau ◽  
Niccolo Tesi ◽  
Benjamin Grenier-Boley ◽  
Victor Andrade ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Risk Stratification ◽  
Risk Score ◽  
Disease Risk ◽  
Age At Onset ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Study Results

AbstractGenetic discoveries of Alzheimer’s disease are the drivers of our understanding, and together with polygenetic risk stratification can contribute towards planning of feasible and efficient preventive and curative clinical trials. We first perform a large genetic association study by merging all available case-control datasets and by-proxy study results (discovery n = 409,435 and validation size n = 58,190). Here, we add six variants associated with Alzheimer’s disease risk (near APP, CHRNE, PRKD3/NDUFAF7, PLCG2 and two exonic variants in the SHARPIN gene). Assessment of the polygenic risk score and stratifying by APOE reveal a 4 to 5.5 years difference in median age at onset of Alzheimer’s disease patients in APOE ɛ4 carriers. Because of this study, the underlying mechanisms of APP can be studied to refine the amyloid cascade and the polygenic risk score provides a tool to select individuals at high risk of Alzheimer’s disease.

scholarly journals Deep learning methods improve polygenic risk analysis and prediction for Alzheimer’s disease

2021 ◽  
Author(s):  
Xiaopu Zhou ◽  
Yu Chen ◽  
Fanny Ip ◽  
Yuanbing Jiang ◽  
Han Cao ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Risk Prediction ◽  
Disease Risk ◽  
Human Diseases ◽  
Polygenic Risk Score ◽  
Learning Methods ◽  
Polygenic Risk ◽  
Analytical Approaches

Abstract Recent advances in genetic sequencing have enabled comprehensive genetic analyses of human diseases, resulting in the identification of numerous genetic risk factors for heritable disorders including Alzheimer’s disease (AD). Such analyses enable AD risk prediction well before disease onset, which is critical for early interventions. However, current analytical approaches have limited ability to accurately estimate the risk effects of genetic variants owing to epistatic effects, which have been overlooked in most previous studies, resulting in unsatisfactory disease risk prediction. Herein, we modeled AD polygenic risk using deep learning methods, which outperformed existing models (i.e., weighted polygenic risk score and lasso models) for classifying disease risk. Moreover, by examining the associations between the outcomes from deep learning methods and multi-omics data obtained from our in-house Chinese AD cohorts, we identified the pathways that are potentially regulated by AD polygenic risk, including immune-associated signaling pathways. Thus, our results demonstrate the utility of deep learning methods for modeling the genetic risks of human diseases, which can facilitate both disease risk classification and the study of disease mechanisms.

P4-240: Using polygenic risk score to predict Alzheimer's disease

2015 ◽  
Vol 11 (7S_Part_19) ◽  
pp. P872-P872 ◽  
Author(s):  
Valentina Escott-Price ◽  
Rebecca Sims ◽  
Denise Harold ◽  
Maria Vronskaya ◽  
Peter Holmans ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Risk Score ◽  
Polygenic Risk Score ◽  
Polygenic Risk

scholarly journals The Genetic Research in Alzheimer Disease (GERALD) Initiative Finds rs9320913 as a Neural eQTL of lincRNA AL589740.1

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Lidia Lopez-Gutierrez ◽  
José María García-Alberca ◽  
Silvia Mendoza ◽  
Esther Gris ◽  
María Paz De la Guía ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Alzheimer Disease ◽  
Mental State ◽  
Fluid Intelligence ◽  
Educational Achievement ◽  
Disease Risk ◽  
Genetic Research ◽  
Gene Desert ◽  
Factors Affecting

Alzheimer’s disease is the most common cause of dementia worldwide, and longitudinal studies are crucial to find the factors affecting disease development. Here, we describe a novel initiative from southern Spain designed to contribute in the identification of the genetic component of the cognitive decline of Alzheimer’s disease patients. The germline variant rs9320913 is a C>A substitution mapping within a gene desert. Although it has been previously associated to a higher educational achievement and increased fluid intelligence, its role on Alzheimer’s disease risk and progression remains elusive. A total of 407 subjects were included in the study, comprising 153 Alzheimer disease patients and 254 healthy controls. We have explored the rs9320913 contribution to both Alzheimer disease risk and progression according to the Mini-Mental State Exams. We found that rs9320913 maps within a central nervous system lincRNA AL589740.1. eQTL results show that rs9320913 correlated with the brain-frontal cortex ( beta = − 0.15 , p value = 0.057) and brain-spinal cord (beta of -0.23, p value = 0.037). We did not find rs9320913 to be associated to AD risk, although AA patients seemed to exhibit a less pronounced Mini-Mental State Exam score decline.

ABCA7, a Genetic Risk Factor Associated with Alzheimer’s Disease Risk in African Americans

2022 ◽  
pp. 1-15
Author(s):  
Kaitlyn E. Stepler ◽  
Taneisha R. Gillyard ◽  
Calla B. Reed ◽  
Tyra M. Avery ◽  
Jamaine S. Davis ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
African American ◽  
Amyloid Beta ◽  
Disease Risk ◽  
African Ancestry ◽  
European Ancestry ◽  
Black Adults ◽  
American Black ◽  
The Impact

African American/Black adults are twice as likely to have Alzheimer’s disease (AD) compared to non-Hispanic White adults. Genetics partially contributes to this disparity in AD risk, among other factors, as there are several genetic variants associated with AD that are more prevalent in individuals of African or European ancestry. The phospholipid-transporting ATPase ABCA7 (ABCA7) gene has stronger associations with AD risk in individuals with African ancestry than in individuals with European ancestry. In fact, ABCA7 has been shown to have a stronger effect size than the apolipoprotein E (APOE) ɛ4 allele in African American/Black adults. ABCA7 is a transmembrane protein involved in lipid homeostasis and phagocytosis. ABCA7 dysfunction is associated with increased amyloid-beta production, reduced amyloid-beta clearance, impaired microglial response to inflammation, and endoplasmic reticulum stress. This review explores the impact of ABCA7 mutations that increase AD risk in African American/Black adults on ABCA7 structure and function and their contributions to AD pathogenesis. The combination of biochemical/biophysical and ‘omics-based studies of these variants needed to elucidate their downstream impact and molecular contributions to AD pathogenesis is highlighted.

P3-077: ALZHEIMER'S DISEASE (AD) POLYGENIC RISK SCORE (PRS) AS A PREDICTOR OF CONVERSION FROM MILD COGNITIVE IMPAIRMENT (MCI)

2006 ◽  
Vol 14 (7S_Part_20) ◽  
pp. P1094-P1094
Author(s):  
Sultan Raja Chaudhury ◽  
Tulsi Patel ◽  
Abigail Fallows ◽  
Keeley J. Brookes ◽  
Tamar Guetta-Baranes ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Risk Score ◽  
Polygenic Risk Score ◽  
Polygenic Risk

scholarly journals POLYGENIC RISK SCORE ANALYSIS OF ALZHEIMER’S DISEASE IN CASES WITHOUT APOE4 OR APOE2 ALLELES

2018 ◽  
pp. 1-4
Author(s):  
V. Escott-Price ◽  
A. Myers ◽  
M. Huentelman ◽  
M. Shoai ◽  
J. Hardy
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Risk Score ◽  
Clinical Trial Design ◽  
Polygenic Risk Score ◽  
Carrier Status ◽  
Predictive Utility ◽  
Polygenic Risk ◽  
Score Analysis ◽  
E4 Allele

The We and others have previously shown that polygenic risk score analysis (PRS) has considerable predictive utility for identifying those at high risk of developing Alzheimer’s disease (AD) with an area under the curve (AUC) of >0.8. However, by far the greatest determinant of this risk is the apolipoprotein E locus with the E4 allele alone giving an AUC of ~0.68 and the inclusion of the protective E2 allele increasing this to ~0.69 in a clinical cohort. An important question is to determine how good PRS is at predicting risk in those who do not carry the E4 allele (E3 homozygotes, E3E2 and E2E2) and in those who carry neither the E4 or E2 allele (i.e. E3 homozygotes). Previous studies have shown that PRS remains a significant predictor of AD risk in clinical cohorts after controlling for APOE ε4 carrier status. In this study we assess the accuracy of PRS prediction in a cohort of pathologically confirmed AD cases and controls. The exclusion of APOE4 carriers has surprisingly little effect on the PRS prediction accuracy (AUC ~0.83 [95% CI: 0.80-0.86]), and the accuracy remained higher than that in clinical cohorts with APOE included as a predictor. From a practical perspective this suggests that PRS analysis will have predictive utility even in E4 negative individuals and may be useful in clinical trial design.

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