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.

Fig. 1. Distribution of polygenic risk score (PRS), including APOE SNPs (N = 167) amongst late-onset Alzheimer’s disease (LOAD) cases, converters and non-converters from mild-cognitive impairment (MCI), and controls.

The range of scores for individuals within each group are described in the figure (grey circles) with the average PRS for each group indicated by the black circle. Significant differences were observed with one-way ANOVA across all four groups (p < 0.0001), with post hoc Tukey indicating significance between pairwise comparisons indicated with ****(p < 0.0001)

Fig. 2. Proportion of late-onset Alzheimer’s disease (LOAD) cases and cognitively healthy controls in each decile using the best predictive model.

Individual probabilities were generated using PRSice-2. The polygenic risk scores (PRS) including APOE and covariates for gender and age at death were used to distribute individuals into deciles. The dark bars represent the proportion of LOAD cases which fall into each decile and the light bars represent the proportion of controls. The number of LOAD cases and controls who fall within each decile are indicted in each block

Fig. 3. Proportion of mild cognitive impairment (MCI) non-converters and those who converted to late-onset Alzheimer’s disease.

Individual probabilities for the best predictive model were generated from polygenic risk scores (PRS) including APOE and covariates for gender and age at recruitment to distribute individuals into deciles. The dark bars represent the proportion of converting MCIs in each decile and the light bars represent the proportion of non-converting MCIs. The number of converters and non-converters who fall within each decile are listed