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Approximately 30% of older adults exhibit the neuropathological features of Alzheimer's disease without signs of cognitive impairment. Yet, little is known about the genetic factors that allow these potentially resilient individuals to remain cognitively unimpaired in the face of substantial neuropathology. We performed a large, genome-wide association study (GWAS) of two previously validated metrics of cognitive resilience quantified using a latent variable modelling approach and representing better-than-predicted cognitive performance for a given level of neuropathology. Data were harmonized across 5108 participants from a clinical trial of Alzheimer's disease and three longitudinal cohort studies of cognitive ageing. All analyses were run across all participants and repeated restricting the sample to individuals with unimpaired cognition to identify variants at the earliest stages of disease. As expected, all resilience metrics were genetically correlated with cognitive performance and education attainment traits (P-values < 2.5 × 10-20), and we observed novel correlations with neuropsychiatric conditions (P-values < 7.9 × 10-4). Notably, neither resilience metric was genetically correlated with clinical Alzheimer's disease (P-values > 0.42) nor associated with APOE (P-values > 0.13). In single variant analyses, we observed a genome-wide significant locus among participants with unimpaired cognition on chromosome 18 upstream of ATP8B1 (index single nucleotide polymorphism rs2571244, minor allele frequency = 0.08, P = 2.3 × 10-8). The top variant at this locus (rs2571244) was significantly associated with methylation in prefrontal cortex tissue at multiple CpG sites, including one just upstream of ATPB81 (cg19596477; P = 2 × 10-13). Overall, this comprehensive genetic analysis of resilience implicates a putative role of vascular risk, metabolism, and mental health in protection from the cognitive consequences of neuropathology, while also providing evidence for a novel resilience gene along the bile acid metabolism pathway. Furthermore, the genetic architecture of resilience appears to be distinct from that of clinical Alzheimer's disease, suggesting that a shift in focus to molecular contributors to resilience may identify novel pathways for therapeutic targets.

Alzheimer's disease (AD) is a complex genetic disorder with no effective treatments. More than 20 common markers have been identified, which are associated with AD. Recently, several rare variants have been identified in Amyloid Precursor Protein (APP), Triggering Receptor Expressed On Myeloid Cells 2 (TREM2) and Unc-5 Netrin Receptor C (UNC5C) that affect risk for AD. Despite the many successes, the genetic architecture of AD remains unsolved. We used Genome-wide Complex Trait Analysis to (1) estimate phenotypic variance explained by genetics; (2) calculate genetic variance explained by known AD single nucleotide polymorphisms (SNPs); and (3) identify the genomic locations of variation that explain the remaining unexplained genetic variance. In total, 53.24% of phenotypic variance is explained by genetics, but known AD SNPs only explain 30.62% of the genetic variance. Of the unexplained genetic variance, approximately 41% is explained by unknown SNPs in regions adjacent to known AD SNPs, and the remaining unexplained genetic variance outside these regions.

Late onset Alzheimer's disease (LOAD) is a genetically complex and clinically heterogeneous disease. Recent large-scale genome wide association studies (GWAS) have identified more than twenty loci that modify risk for AD. Despite the identification of these loci, little progress has been made in identifying the functional variants that explain the association with AD risk. Thus, we sought to determine whether the novel LOAD GWAS single nucleotide polymorphisms (SNPs) alter expression of LOAD GWAS genes and whether expression of these genes is altered in AD brains. The majority of LOAD GWAS SNPs occur in gene dense regions under large linkage disequilibrium (LD) blocks, making it unclear which gene(s) are modified by the SNP. Thus, we tested for brain expression quantitative trait loci (eQTLs) between LOAD GWAS SNPs and SNPs in high LD with the LOAD GWAS SNPs in all of the genes within the GWAS loci. We found a significant eQTL between rs1476679 and PILRB and GATS, which occurs within the ZCWPW1 locus. PILRB and GATS expression levels, within the ZCWPW1 locus, were also associated with AD status. Rs7120548 was associated with MTCH2 expression, which occurs within the CELF1 locus. Additionally, expression of several genes within the CELF1 locus, including MTCH2, were highly correlated with one another and were associated with AD status. We further demonstrate that PILRB, as well as other genes within the GWAS loci, are most highly expressed in microglia. These findings together with the function of PILRB as a DAP12 receptor supports the critical role of microglia and neuroinflammation in AD risk.

ObjectiveRecent genome-wide association studies (GWAS) of late-onset Alzheimer disease (LOAD) identified 9 novel risk loci. Discovery of functional variants within genes at these loci is required to confirm their role in Alzheimer disease (AD). Single nucleotide polymorphisms that influence gene expression (eSNPs) constitute an important class of functional variants. We therefore investigated the influence of the novel LOAD risk loci on human brain gene expression.MethodsWe measured gene expression levels in the cerebellum and temporal cortex of autopsied AD subjects and those with other brain pathologies (∼400 total subjects). To determine whether any of the novel LOAD risk variants are eSNPs, we tested their cis-association with expression of 6 nearby LOAD candidate genes detectable in human brain (ABCA7, BIN1, CLU, MS4A4A, MS4A6A, PICALM) and an additional 13 genes ±100 kb of these SNPs. To identify additional eSNPs that influence brain gene expression levels of the novel candidate LOAD genes, we identified SNPs ±100 kb of their location and tested for cis-associations.ResultsCLU rs11136000 (p = 7.81 × 10(-4)) and MS4A4A rs2304933/rs2304935 (p = 1.48 × 10(-4)-1.86 × 10(-4)) significantly influence temporal cortex expression levels of these genes. The LOAD-protective CLU and risky MS4A4A locus alleles associate with higher brain levels of these genes. There are other cis-variants that significantly influence brain expression of CLU and ABCA7 (p = 4.01 × 10(-5)-9.09 × 10(-9)), some of which also associate with AD risk (p = 2.64 × 10(-2)-6.25 × 10(-5)).ConclusionsCLU and MS4A4A eSNPs may at least partly explain the LOAD risk association at these loci. CLU and ABCA7 may harbor additional strong eSNPs. These results have implications in the search for functional variants at the novel LOAD risk loci.