Predicted gene expression identifies novel tissue‐specific gene predictors of memory performance in older adults
Authors: Janve Vaibhav A , Archer Derek B , Eissman Jaclyn M. , Durant Alaina , Mukherjee Shubhabrata , Lee Michael L. , Choi Seo‐Eun , Scollard Phoebe , Trittschuh Emily H. , Mez Jesse B. , Bush William S. , Engelman Corinne D , Lu Qiongshi , Fardo David W. , Buckley Rachel F. , Mormino Elizabeth C. , Kunkle Brian W. , Naj Adam C. , Gifford Katherine A. , Cuccaro Michael L. , Cruchaga Carlos , Pericak‐Vance Margaret A. , Farrer Lindsay A. , Wang Li‐San , Schellenberg Gerald D. , Haines Jonathan L. , Jefferson Angela L. , Kukull Walter A. , Albert Marilyn S. , Keene C Dirk , Saykin Andrew J. , Sperling Reisa A. , Mayeux Richard , Thompson Paul M , Martin Eden R. , Bennett David A. A , Barnes Lisa L. , Schneider Julie A , Dumitrescu Logan C , Crane Paul K , Hohman Timothy J. ,   Alzheimer’s Disease Neuroimaging Initiative (ADNI)Alzheimer’s Disease Genetics Consortium (ADGC)Alzheimer’s Disease Sequencing Project (ADSP)
Published: December 2023

Background

Memory performance can serve as a strong endophenotype for Alzheimer’s disease (AD) that changes early and continues to decline with disease progression. Yet, the genetic architecture of memory is not well characterized in older adults. Here, we build on existing memory GWAS studies by performing predicted gene expression analysis (PrediXcan) among older (60+) individuals from four cohorts of aging and investigate specific gene‐tissue drivers of genetically regulated gene expression associated with memory performance.

Method

Tissue‐specific (49 tissues, 5455 genes) PrediXcan models were built following the method described in Gamazon et al. (Nature Genetics 2015) leveraging model weights derived in GTEx (v8 release, build 38). Baseline and longitudinal memory scores were harmonized leveraging cognitive item‐level data on 19,707 non‐Hispanic White participants from four cohort studies of aging and AD (mean age 75.6±7.7, 55% female) using confirmatory factor analyses models. PrediXcan analyses were run adjusting for age at baseline, sex, and 5 population PCs and then meta‐analyzed using a fixed effects model. Correction for multiple comparisons accounting for all gene‐tissue combinations (267,267) was completed with the false discovery rate procedure (fdr‐p<0.05). Sensitivity analyses excluded all non‐AD dementia and other comorbid conditions (N = 16,373; 57% females).

Result

As expected, several signals emerged from chromosome 19, including 48 gene‐tissue combinations near the APOE locus. Outside of APOE, we identified 20 gene‐tissue combinations from 17 known AD loci and three novel loci: higher predicted PUS7 expression in the caudate (β = ‐0.018, p = 0.034) and higher RP11‐18C3.1 expression in colon (β = ‐0.0165, fdr‐p = 0.034) related to faster cognitive decline, while higher predicted LRRC25 in the nucleus accumbens related to slower cognitive decline (β = 0.009, fdr‐p = 0.015). These signals remain comparable in sensitivity analysis.

Conclusion

We identified multiple candidates for future mechanistic analysis. LRRC25 is a particularly interesting candidate that is differentially expressed in the AD brain, regulates autophagy in myeloid cells, and is in a co‐expression network with other known AD genes in the immune pathway like MS4A4A (https://agora.adknowledgeportal.org/). Future work will test for replication of these effects and deconvolve genetically‐regulated versus measured gene expression effects in the AD brain.