Leveraging a Novel Phenotype to Identify Complex Traits and Biological Pathways Associated with Resilience to Alzheimer’s Disease
Authors: Phillips Jared , Dumitrescu Logan C. , Archer Derek B. , Regelson Alexandra N. , Mukherjee Shubhabrata , Lee Michael L. , Choi Seo‐Eun , Scollard Phoebe , Trittschuh Emily H. , Kukull Walter A. , Biber Sarah A , Mez Jesse , Mahoney Emily R. , Clifton Michelle , Libby Julia B. , Walters Skylar , Bush William S. , Engelman Corinne D. , Lu Qiongshi , Fardo David W. , Widaman Keith F. , Buckley Rachel F , Mormino Elizabeth , Sanders Elizabeth , Clark Lindsay R. , Gifford Katherine A. , Vardarajan Badri N. , Cuccaro Michael L. , Pericak‐Vance Margaret A , Farrer Lindsay A. , Wang Li‐San , Schellenberg Gerard D. , Haines Jonathan L. , Jefferson Angela L. , Johnson Sterling C. , Albert Marilyn S. , Keene C Dirk , Saykin Andrew J. , Risacher Shannon L. , Larson Eric B , Sperling Reisa A , Mayeux Richard , Goate Alison M. , Renton Alan E. , Marcora Edoardo , Fulton‐Howard Brian , Patel Tulsi , Bennett David A. , Schneider Julie A. , Barnes Lisa L. , Cruchaga Carlos , Hassenstab Jason J. , Belloy Michael E. , Andrews Shea J , Resnick Susan M. , Bilgel Murat , An Yang , Beason‐Held Lori L , Walker Keenan A. , Duggan Michael R , Klinedinst Brandon S , Crane Paul K. , Hohman Timothy J. ,   The Alzheimer’s Disease Sequencing Project (ADSP)
Published: January 2025

Background

Previous models of resilience to Alzheimer’s Disease (AD) have relied on cross‐sectional designs and inclusion of measures of neuropathology. Here, we present a novel modeling approach incorporating longitudinal data and the use of APOE and higher order interaction terms to approximate neuropathological resilience, vastly increasing participant diversity and statistical power. We validate this approach and report novel genetic associations with neuropathological resilience.

Method

Leveraging 20,513 participants (Table 1), we explored the genetics of resilience metrics using mixed‐effects regressions across ancestries. We conducted GWAS on four resilience metrics: two in a “silver” model, which benefit from a larger sample size and did not include neuropathology at autopsy, and two in a “gold” model, which incorporate neuropathology at autopsy. These models were built in all diagnoses and separately in cognitively unimpaired (CU) at baseline participants. Silver‐gold meta‐analyses were conducted to maximize statistical power. Models adjusted for age, sex, APOE ε4 allele count, presence of the APOE ε2 allele and all covariate interactions with interval (years from baseline). Residual cognitive resilience was quantified from residuals in three cognitive domains (memory, executive function, and language). Post‐GWAS analyses included gene and pathway tests using MAGMA and genetic correlation with 65 traits using GNOVA.

Result

Silver and gold resilience metrics were highly correlated (0.77‐0.88) and showed positive genetic covariance (0.01‐0.13). We identified negative correlations of resilience with multiple sclerosis and neuropsychiatric traits, including Tourette syndrome and schizophrenia (all PFDR<2.6×10−2; Figure 1). Furthermore, we observed three significant pathway associations with resilience: metabolism of amino acids and derivatives in the silver all participants meta‐analysis, negative regulation of transforming growth factor beta production in the silver CU at baseline meta‐analysis, and the SARS pathway in the gold CU at baseline meta‐analysis (all PFDR<5.0×10−2). Finally, we identified a locus on chromosome 17 associated with resilience that approached genome‐wide significance (top SNP: rs757022, MAF = 0.18, b = 0.08, P = 1.1×10−7; Figure 2).

Conclusion

Using novel, longitudinal modeling approaches, we identified multiple biological pathways and complex traits associated with resilience to AD as well as genetic loci approaching genome‐wide significance. Our findings indicate that silver standard modeling approaches can complement current gold standard methods to accelerate genetic discovery.