Common polygenic variation enhances risk prediction for Alzheimer’s disease

Abstract

The identification of subjects at high risk for Alzheimer's disease is important for prognosis and early intervention. We investigated the polygenic architecture of Alzheimer's disease and the accuracy of Alzheimer's disease prediction models, including and excluding the polygenic component in the model. This study used genotype data from the powerful dataset comprising 17 008 cases and 37 154 controls obtained from the International Genomics of Alzheimer's Project (IGAP). Polygenic score analysis tested whether the alleles identified to associate with disease in one sample set were significantly enriched in the cases relative to the controls in an independent sample. The disease prediction accuracy was investigated in a subset of the IGAP data, a sample of 3049 cases and 1554 controls (for whom APOE genotype data were available) by means of sensitivity, specificity, area under the receiver operating characteristic curve (AUC) and positive and negative predictive values. We observed significant evidence for a polygenic component enriched in Alzheimer's disease (P = 4.9 × 10(-26)). This enrichment remained significant after APOE and other genome-wide associated regions were excluded (P = 3.4 × 10(-19)). The best prediction accuracy AUC = 78.2% (95% confidence interval 77-80%) was achieved by a logistic regression model with APOE, the polygenic score, sex and age as predictors. In conclusion, Alzheimer's disease has a significant polygenic component, which has predictive utility for Alzheimer's disease risk and could be a valuable research tool complementing experimental designs, including preventative clinical trials, stem cell selection and high/low risk clinical studies. In modelling a range of sample disease prevalences, we found that polygenic scores almost doubles case prediction from chance with increased prediction at polygenic extremes.

No Genetic Overlap Between Circulating Iron Levels and Alzheimer’s Disease

“…The discovery GWAS meta-analysis datasets used in the study contain large sample sizes (in total 54,162 for AD and 23,986 for serum iron status) and show both AD and serum iron measures to have a strong polygenic components [27,31]. For serum iron measures using replication cohorts, the lead SNPs at the 11 significant loci explained 3.4, 7.2, 6.7, and 0.9% of the phenotypic variance for iron, transferrin, saturation, and (log-transformed) ferritin, respectively [30].…”

“…Previously an AD polygenic score analysis has shown that disease prediction accuracy is greatest including SNPs with p value <0.5. Including the full polygenic score significantly improved prediction over use of APOE alone where including both APOE and PRS gave AUC = 78.2% [31]. Examples of the AD PRS based on the IGAP discovery analysis demonstrating genetic overlap with other traits include neuroimaging measures of subcortical brain volumes, plasma C-reactive protein, and lipids [32,33].…”

Polygenic risk of Alzheimer disease is associated with early- and late-life processes

“…6 However, the effect sizes of these variants are small (odds ratios , 1.22), and recent work suggests that numerous additional loci distributed throughout the genome explain a much larger portion of the variance than the select few that surpass GWAS-level significance thresholds. [7][8][9] For instance, GWAS-confirmed loci account for 2% of the variance in discriminating patients with AD dementia and controls, beyond the 6% accounted for by APOE, whereas examination across the remaining 2 million common genetic variants explains an additional 25% of the variance. 9 Thus, aggregation across a large number of loci is likely a more sensitive method to establish underlying genetic risk to AD dementia than solely examining loci surpassing stringent GWAS-level significance thresholds.…”

POLARIS: Polygenic LD‐adjusted risk score approach for set‐based analysis of GWAS data

“…These include sample stratification, risk prediction, and the detection of relationships between different subphenotypes (see, e.g., Allardyce et al., 2017; Escott‐Price et al., 2015, and Foley et al., 2017, respectively). The PRS method can also be adapted to partition the polygenic risk based on meaningful SNP sets, such as genes or biological pathways, and to determine whether a set of SNPs, weighted with their individual genetic risk effects, is associated at the whole‐genome or set‐specific levels.…”

“…In their most widely used form, PRS's have been applied to genome‐wide SNP data where they can capture a useful fraction of genetic liability to polygenic traits. PRS's can also be used as genome‐wide predictors of affected status (Escott‐Price et al., 2015; Purcell et al., 2009; Ripke et al., 2014). We reasoned that the basic principles of polygenic score analysis can also be applied to individual genes, or to gene‐set analyses.…”