Alzheimer’s disease polygenic risk score as a predictor of conversion from mild-cognitive impairment

Abstract

Mild-cognitive impairment (MCI) occurs in up to one-fifth of individuals over the age of 65, with approximately a third of MCI individuals converting to dementia in later life. There is a growing necessity for early identification for those at risk of dementia as pathological processes begin decades before onset of symptoms. A cohort of 122 individuals diagnosed with MCI and followed up for a 36-month period for conversion to late-onset Alzheimer’s disease (LOAD) were genotyped on the NeuroChip array along with pathologically confirmed cases of LOAD and cognitively normal controls. Polygenic risk scores (PRS) for each individual were generated using PRSice-2, derived from summary statistics produced from the International Genomics of Alzheimer’s Disease Project (IGAP) genome-wide association study. Predictability models for LOAD were developed incorporating the PRS with APOE SNPs (rs7412 and rs429358), age and gender. This model was subsequently applied to the MCI cohort to determine whether it could be used to predict conversion from MCI to LOAD. The PRS model for LOAD using area under the precision-recall curve (AUPRC) calculated a predictability for LOAD of 82.5%. When applied to the MCI cohort predictability for conversion from MCI to LOAD was 61.0%. Increases in average PRS scores across diagnosis group were observed with one-way ANOVA suggesting significant differences in PRS between the groups ( p < 0.0001). This analysis suggests that the PRS model for LOAD can be used to identify individuals with MCI at risk of conversion to LOAD.

Immune response and endocytosis pathways are associated with the resilience against Alzheimer’s disease

“…Annotation: ***p-value of association < 5 × 10 −6 ; **p-value of association < 5 × 10 −4 ; *p-value of association < 5 × 10 −2 . studies showed that PRS of AD not only associated with increased risk of AD, but also with neuropathological hallmarks of AD, lifetime risk and the age at onset in both APOE ε4 carriers and non-carriers 28,29,[51][52][53][54][55] . We now add that, using our unique cohort of cognitively healthy centenarians, the PRS for AD also associates with resilience against AD at extremely old ages.…”

Improving predictive models for Alzheimer’s disease using GWAS data by incorporating misclassified samples modeling

“…In this research, we aim to explore other methods to generate polygenic models to predict Alzheimer's disease. Although it is known that age and gender are important predictors of AD (for example [19]), we are focused on predicting AD from GWAS data only to show a potential computational strategy. In this context, using GWAS only, we improved about 5% on AUC by using specific information from misclassified samples either in the train or test sets.…”

“…There are many approaches to generate multivariate predictive models from GWAS data [17]. The polygenic risk score (PRS) is the most popular approach and has been applied to AD several times [17][18][19][20][21][22]. PRS is a sum of univariate estimated coefficients; thus, it is easily interpreted.…”

“…PRS is a sum of univariate estimated coefficients; thus, it is easily interpreted. For AD, the predictive classification power, in terms of the area under the receiving operative curve (AUC), has been estimated from 0.68 to 0.83 [18][19][20]. The results depend on data included, such as APOE status, mild cognitive impairment (a hypothetical pre-stage for AD), the AD type (LOAD or EOAD), and, more importantly, the clinical information used such as age, gender, and other data.…”

Polygenic risk and pleiotropy in neurodegenerative diseases

“…Therefore, the PRS accounts for the small effects of a large number of SNPs which still contribute to disease risk, successfully capturing the polygenicity of a disease. PRS has been widely used for identifying and/or predicting an individual's disease risk ( Escott-Price et al, 2015a ; Escott-Price et al, 2015b ; Ibanez et al, 2017 ) and for studying genetic overlap between disorders traits ( Chaudhury et al, 2019 ; Creese et al, 2019 ; McLaughlin et al, 2017 ).…”