Progress in Polygenic Composite Scores in Alzheimer's and Other Complex Diseases.
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ABSTRACT:
SUBMITTER: Chasioti D
PROVIDER: S-EPMC6475476 | biostudies-literature | 2019 May
REPOSITORIES: biostudies-literature
Publications
Progress in Polygenic Composite Scores in Alzheimer's and Other Complex Diseases.
Chasioti Danai D Yan Jingwen J Nho Kwangsik K Saykin Andrew J AJ
Trends in genetics : TIG 20190325 5
Advances in high-throughput genotyping and next-generation sequencing (NGS) coupled with larger sample sizes brings the realization of precision medicine closer than ever. Polygenic approaches incorporating the aggregate influence of multiple genetic variants can contribute to a better understanding of the genetic architecture of many complex diseases and facilitate patient stratification. This review addresses polygenic concepts, methodological developments, hypotheses, and key issues in study ...[more]
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Project description:Polygenic scores (PSs) are becoming a useful tool to identify individuals with high genetic risk for complex diseases, and several projects are currently testing their utility for translational applications. It is also tempting to use PSs to assess whether genetic variation can explain a part of the geographic distribution of a phenotype. However, it is not well known how the population genetic properties of the training and target samples affect the geographic distribution of PSs. Here, we evaluate geographic differences, and related biases, of PSs in Finland in a geographically well-defined sample of 2,376 individuals from the National FINRISK study. First, we detect geographic differences in PSs for coronary artery disease (CAD), rheumatoid arthritis, schizophrenia, waist-hip ratio (WHR), body-mass index (BMI), and height, but not for Crohn disease or ulcerative colitis. Second, we use height as a model trait to thoroughly assess the possible population genetic biases in PSs and apply similar approaches to the other phenotypes. Most importantly, we detect suspiciously large accumulations of geographic differences for CAD, WHR, BMI, and height, suggesting bias arising from the population's genetic structure rather than from a direct genotype-phenotype association. This work demonstrates how sensitive the geographic patterns of current PSs are for small biases even within relatively homogeneous populations and provides simple tools to identify such biases. A thorough understanding of the effects of population genetic structure on PSs is essential for translational applications of PSs.
| S-EPMC6562021 | biostudies-literature
Project description:Objective:Genome-wide association studies (GWAS) have identified over 30 susceptibility loci associated with Alzheimer's disease (AD). Using AD GWAS data from the International Genomics of Alzheimer's Project (IGAP), Polygenic Risk Score (PRS) was successfully applied to predict life time risk of AD development. A recently introduced Polygenic Hazard Score (PHS) is able to quantify individuals with age-specific genetic risk for AD. The aim of this study was to quantify the age-specific genetic risk for AD with PRS and compare the results generated by PRS with those from PHS. Methods:Quantification of individual differences in age-specific genetic risk for AD identified by the PRS, was performed with Cox Regression on 9903 (2626 cases and 7277 controls) individuals from the Genetic and Environmental Risk in Alzheimer's Disease consortium (GERAD). Polygenic Hazard Scores were generated for the same individuals. The age-specific genetic risk for AD identified by the PRS was compared with that generated by the PHS. This was repeated using varying SNPs P-value thresholds for disease association. Results:Polygenic Risk Score significantly predicted the risk associated with age at AD onset when SNPs were preselected for association to AD at P ? 0.001. The strongest effect (B = 0.28, SE = 0.04, P = 2.5 × 10-12) was observed for PRS based upon genome-wide significant SNPs (P ? 5 × 10-8). The strength of association was weaker with less stringent SNP selection thresholds. Interpretation:Both PRS and PHS can be used to predict an age-specific risk for developing AD. The PHS approach uses SNP effect sizes derived with the Cox Proportional Hazard Regression model. When SNPs were selected based upon AD GWAS case/control P ? 10-3, we found no advantage of using SNP effects sizes calculated with the Cox Proportional Hazard Regression model in our study. When SNPs are selected for association with AD risk at P > 10-3, the age-specific risk prediction results are not significant for either PRS or PHS. However PHS could be more advantageous than PRS of age specific AD risk predictions when SNPs are prioritized for association with AD age at onset (i.e., powerful Cox Regression GWAS study).
| S-EPMC6414493 | biostudies-literature
Project description:Polygenic risk scores (PRSs) can boost risk prediction in late-onset Alzheimer's disease (LOAD) beyond apolipoprotein E (APOE) but have not been leveraged to identify genetic resilience factors. Here, we sought to identify resilience-conferring common genetic variants in (1) unaffected individuals having high PRSs for LOAD, and (2) unaffected APOE-ε4 carriers also having high PRSs for LOAD. We used genome-wide association study (GWAS) to contrast "resilient" unaffected individuals at the highest genetic risk for LOAD with LOAD cases at comparable risk. From GWAS results, we constructed polygenic resilience scores to aggregate the addictive contributions of risk-orthogonal common variants that promote resilience to LOAD. Replication of resilience scores was undertaken in eight independent studies. We successfully replicated two polygenic resilience scores that reduce genetic risk penetrance for LOAD. We also showed that polygenic resilience scores positively correlate with polygenic risk scores in unaffected individuals, perhaps aiding in staving off disease. Our findings align with the hypothesis that a combination of risk-independent common variants mediates resilience to LOAD by moderating genetic disease risk.
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Project description:We test 26 polygenic predictors using tens of thousands of genetic siblings from the UK Biobank (UKB), for whom we have SNP genotypes, health status, and phenotype information in late adulthood. Siblings have typically experienced similar environments during childhood, and exhibit negligible population stratification relative to each other. Therefore, the ability to predict differences in disease risk or complex trait values between siblings is a strong test of genomic prediction in humans. We compare validation results obtained using non-sibling subjects to those obtained among siblings and find that typically most of the predictive power persists in between-sibling designs. In the case of disease risk we test the extent to which higher polygenic risk score (PRS) identifies the affected sibling, and also compute Relative Risk Reduction as a function of risk score threshold. For quantitative traits we examine between-sibling differences in trait values as a function of predicted differences, and compare to performance in non-sibling pairs. Example results: Given 1 sibling with normal-range PRS score (< 84 percentile, < + 1 SD) and 1 sibling with high PRS score (top few percentiles, i.e. > + 2 SD), the predictors identify the affected sibling about 70-90% of the time across a variety of disease conditions, including Breast Cancer, Heart Attack, Diabetes, etc. 55-65% of the time the higher PRS sibling is the case. For quantitative traits such as height, the predictor correctly identifies the taller sibling roughly 80 percent of the time when the (male) height difference is 2 inches or more.
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Project description:BACKGROUND:Late-onset Alzheimer's disease (AD) is highly heritable. The effect of many common genetic variants, single nucleotide polymorphisms (SNPs), confer risk. Variants are clustered in areas of biology, notably immunity and inflammation, cholesterol metabolism, endocytosis, and ubiquitination. Polygenic scores (PRS), which weight the sum of an individual's risk alleles, have been used to draw inferences about the pathological processes underpinning AD. OBJECTIVE:This paper aims to systematically review how AD PRS are being used to study a range of outcomes and phenotypes related to neurodegeneration. METHODS:We searched the literature from July 2008-July 2018 following PRISMA guidelines. RESULTS:57 studies met criteria. The AD PRS can distinguish AD cases from controls. The ability of AD PRS to predict conversion from mild cognitive impairment (MCI) to AD was less clear. There was strong evidence of association between AD PRS and cognitive impairment. AD PRS were correlated with a number of biological phenotypes associated with AD pathology, such as neuroimaging changes and amyloid and tau measures. Pathway-specific polygenic scores were also associated with AD-related biologically relevant phenotypes. CONCLUSION:PRS can predict AD effectively and are associated with cognitive impairment. There is also evidence of association between AD PRS and other phenotypes relevant to neurodegeneration. The associations between pathway specific polygenic scores and phenotypic changes may allow us to define the biology of the disease in individuals and indicate who may benefit from specific treatments. Longitudinal cohort studies are required to test the ability of PGS to delineate pathway-specific disease activity.
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Project description:Polygenic Risk Scores (PRS) for AD offer unique possibilities for reliable identification of individuals at high and low risk of AD. However, there is little agreement in the field as to what approach should be used for genetic risk score calculations, how to model the effect of APOE, what the optimal p-value threshold (pT) for SNP selection is and how to compare scores between studies and methods. We show that the best prediction accuracy is achieved with a model with two predictors (APOE and PRS excluding APOE region) with pT<0.1 for SNP selection. Prediction accuracy in a sample across different PRS approaches is similar, but individuals' scores and their associated ranking differ. We show that standardising PRS against the population mean, as opposed to the sample mean, makes the individuals' scores comparable between studies. Our work highlights the best strategies for polygenic profiling when assessing individuals for AD risk.
| S-EPMC8302739 | biostudies-literature
Project description:Genetic 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.
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Project description:The success of genome-wide association studies (GWAS) completed in the last 15 years has reinforced a key fact: polygenic architecture makes a substantial contribution to variation of susceptibility to complex disease, including Alzheimer's disease. One straight-forward way to capture this architecture and predict which individuals in a population are most at risk is to calculate a polygenic risk score (PRS). This score aggregates the risk conferred across multiple genetic variants, ultimately representing an individual's predicted genetic susceptibility for a disease. PRS have received increasing attention after having been successfully used in complex traits. This has brought with it renewed attention on new methods which improve the accuracy of risk prediction. While these applications are initially informative, their utility is far from equitable: the majority of PRS models use samples heavily if not entirely of individuals of European descent. This basic approach opens concerns of health equity if applied inaccurately to other population groups, or health disparity if we fail to use them at all. In this review we will examine the methods of calculating PRS and some of their previous uses in disease prediction. We also advocate for, with supporting scientific evidence, inclusion of data from diverse populations in these existing and future studies of population risk via PRS.
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Project description:BackgroundAn increasing body of evidence suggests that neuroinflammation is one of the key drivers of late-onset Alzheimer's disease (LOAD) pathology. Due to the increased permeability of the blood-brain barrier (BBB) in older adults, peripheral plasma proteins can infiltrate the central nervous system (CNS) and drive neuroinflammation through interactions with neurons and glial cells. Because these inflammatory factors are heritable, a greater understanding of their genetic relationship with LOAD could identify new biomarkers that contribute to LOAD pathology or offer protection against it.MethodsWe used a genome-wide association study (GWAS) of 90 different plasma proteins (n = 17,747) to create polygenic scores (PGSs) in an independent discovery (cases = 1,852 and controls = 1,990) and replication (cases = 799 and controls = 778) cohort. Multivariate logistic regression was used to associate the plasma protein PGSs with LOAD diagnosis while controlling for age, sex, principal components 1-2, and the number of APOE-e4 alleles as covariates. After meta-analyzing the PGS-LOAD associations between the two cohorts, we then performed a two-sample Mendelian randomization (MR) analysis using the summary statistics of significant plasma protein level PGSs in the meta-analysis as an exposure, and a GWAS of clinically diagnosed LOAD (cases = 21,982, controls = 41,944) as an outcome to explore possible causal relationships between the two.ResultsWe identified four plasma protein level PGSs that were significantly associated (FDR-adjusted p < 0.05) with LOAD in a meta-analysis of the discovery and replication cohorts: CX3CL1, hepatocyte growth factor (HGF), TIE2, and matrix metalloproteinase-3 (MMP-3). When these four plasma proteins were used as exposures in MR with LOAD liability as the outcome, plasma levels of HGF were inferred to have a negative causal relationship with the disease when single-nucleotide polymorphisms (SNPs) used as instrumental variables were not restricted to cis-variants (OR/95%CI = 0.945/0.906-0.984, p = 0.005).ConclusionOur results show that plasma HGF has a negative causal relationship with LOAD liability that is driven by pleiotropic SNPs possibly involved in other pathways. These findings suggest a low transferability between PGS and MR approaches, and future research should explore ways in which LOAD and the plasma proteome may interact.
| S-EPMC11300309 | biostudies-literature
Project description:Background: APOE is the largest genetic risk factor for sporadic Alzheimer's disease (AD), but there is a substantial polygenic component as well. Polygenic risk scores (PRS) can summarize small effects across the genome but may obscure differential risk associated with different molecular processes and pathways. Variability at the genetic level may contribute to the extensive phenotypic heterogeneity of Alzheimer's disease (AD). Here, we examine polygenic risk impacting specific pathways associated with AD and examined its relationship with clinical status and AD biomarkers of amyloid, tau, and neurodegeneration (A/T/N). Methods: A total of 1,411 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) with genotyping data were included. Sets of variants identified from a pathway analysis of AD GWAS summary statistics were combined into clusters based on their assigned pathway. We constructed pathway-specific PRSs for each participant and tested their associations with diagnostic status (AD vs cognitively normal), abnormal levels of amyloid and ptau (positive vs negative), and hippocampal volume. The APOE region was excluded from all PRSs, and analyses controlled for APOE -ε4 carrier status. Results: Thirteen pathway clusters were identified relating to categories such as immune response, amyloid precursor processing, protein localization, lipid transport and binding, tyrosine kinase, and endocytosis. Eight pathway-specific PRSs were significantly associated with AD dementia diagnosis. Amyloid-positivity was associated with endocytosis and fibril formation, response misfolded protein, and regulation protein tyrosine PRSs. Ptau positivity and hippocampal volume were both related to protein localization and mitophagy PRS, and ptau positivity was additionally associated with an immune signaling PRS. A global AD PRS showed stronger associations with diagnosis and all biomarkers compared to pathway PRSs, suggesting a strong synergistic effect of all loci contributing to the global AD PRS. Conclusions: Pathway PRS may contribute to understanding separable disease processes, but do not appear to add significant power for predictive purposes. These findings demonstrate that, although genetic risk for AD is widely distributed, AD-phenotypes may be preferentially associated with risk in specific pathways. Defining genetic risk along multiple dimensions at the individual level may help clarify the etiological heterogeneity in AD.
| S-EPMC10002839 | biostudies-literature