Browsing by Subject "Polygenic risk scores (PRS)"

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    Alzheimer’s Disease Polygenic Risk in the LEADS Cohort
    (Wiley, 2025-01-03) Nudelman, Kelly N.; Pentchev, Julian V.; Jackson, Trever; Eloyan, Ani; Dage, Jeffrey L.; Foroud, Tatiana M.; Hammers, Dustin B.; Carrillo, Maria C.; Dickerson, Bradford C.; Rabinovici, Gil D.; Apostolova, Liana G.; LEADS Consortium; Medical and Molecular Genetics, School of Medicine
    Background: Currently, it is unclear to what extent late‐onset Alzheimer’s disease (AD) risk variants contribute to early‐onset AD (EOAD). One method to clarify the contribution of late‐onset AD genetic risk to EOAD is to investigate the association of AD polygenic risk scores (PRS) with EOAD. We hypothesize that in the Longitudinal Early‐Onset Alzheimer’s Disease Study (LEADS), EOAD participants will have greater PRS than early‐onset amyloid‐negative cognitively‐impaired participants (EOnonAD) and controls, and investigate the association of AD PRS with age of disease onset (AoO) and cognitive performance. Methods: GWAS data was generated for LEADS participants, including those with EOAD, EOnonAD, and controls, with the Illumina Global Screening Array. A PRS was calculated using the 31 SNPs and weights published previously by Desikan et al. (2017) for LEADS participants with imputed GWAS data (N = 369). Logistic regression models including age, sex, PRS, and genetic ancestry principal components were tested to identify predictors of EOAD (N = 210) vs. EOnonAD (N = 69) and controls (N = 89). ANCOVA models were used to assess group differences in PRS scores. Kaplan‐Meier regression was used to assess differences in EOAD AoO for tertile‐binned PRS groups. Within EOAD, pre‐calculated cognitive domain scores for speed and attention, working memory, episodic memory, language, and visuospatial performance were assessed for correlation with PRS. Results: The AD PRS was a predictor of EOAD (p = 0.014), with the model explaining 10.5% of variance (X2 = 40.971, p<0.001). EOAD participants had higher PRS scores (mean = 0.0012, standard deviation (SD) = 0.015) compared to EOnonAD and controls (mean = ‐0.0018, SD = 0.015) (F = 6.602, p = 0.011). Survival analysis indicated no significant differences in EOAD AoO between PRS groups (X2 = 3.396, p = 0.183). In the EOAD group, PRS was associated with cognitive scores for speed and attention (r = 0.204, p = 0.007), language (r = 0.230, p = 0.002), and visuospatial performance (r = 0.166, p = 0.037). Conclusions: In the LEADS cohort, AD PRS is a predictor for EOAD, and is associated with cognitive performance, but does not predict EOAD AoO. This suggests that while late onset AD‐associated genetic variants contribute to disease risk and processes, they do not account for a large portion of disease risk, and do not explain differences in disease AoO in the LEADS cohort.
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    Using polygenic risk scores and APOE e4 to evaluate the risk for Alzheimer’s disease in European ancestry populations
    (Wiley, 2025-01-03) Lai, Dongbing; Zhang, Michael; Foroud, Tatiana M.; Medical and Molecular Genetics, School of Medicine
    Background: APOE e4 has been used to evaluate the risk for Alzheimer’s diseases (AD) but there exist other AD risk genes, and their effects can be collectively measured by polygenic risk scores (PRS). In this study, we sought to use both PRS (APOE excluded) and APOE e4 to evaluate the AD risk. Method: The discovery dataset was meta‐analysis of three large‐scale European ancestry AD GWAS (Kunkle et al, 2019, the UK Biobank, and the FinnGen consortium). SNPs within 500Kb from transcript starting and ending sites of APOE were excluded. PRS‐CS was used to calculate PRS. Target datasets were European ancestry samples from NIA Alzheimer’s disease centers (ADC, 2,413 cases and 3,423 controls) and All of Us research program (AOU, 1,177 cases and 60,607 controls). Participants having age at onset (cases) or age at the last interview (controls) <60 were excluded. The prevalence of AD were higher in ADC (41.35%) and lower in AOU (1.91%) than those in general populations; therefore, we combined ADC and AOU samples to approximate the PRS distribution in general populations. Then we dichotomized PRS as high (highest 10%) and other (the remaining 90%) based on the PRS distribution of combined sample. Cox proportional hazard model was used to test the effects of dichotomized PRS and e4 genotypes (0, 1, and 2 copies of e4 alleles) by adjusting for sex. Additionally, we performed sex stratified analyses in ADC only as numbers of high PRS and e4/e4 carriers in AOU in either sex were <5. Result: Results are summarized in Table 1. We used those having other PRS and no e4 as the reference group. In both ADC and AOU, high PRS or e4 were significantly associated with the AD risk (PRS hazard ratios (HRs): 1.31‐1.74, P‐values≤0.01; e4 HRs: 1.39‐8.68, P‐values≤2.80E‐06) but having both substantially increased the AD risk (HRs: 2.07‐14.26, P‐values≤6.80E‐05). In ADC, both high PRS and e4 had larger effects in females than in males. Conclusion: The effects of PRS were modest and cannot be used alone to evaluate the AD risk; however, PRS can potentially be used with APOE e4 to evaluate the AD risk.