Alzheimer’s disease (AD) pathology appears several years before clinical symptoms, so identifying ways to detect individuals in the preclinical stage is imperative. The cerebrospinal fluid (CSF) Tau/Aβ₄₂ ratio is currently the best known predictor of AD status and cognitive decline, and the ratio of CSF levels of chitinase-3-like 1 protein (CHI3L1, YKL-40) and amyloid beta (Aβ₄₂) were reported as predictive, but individual variability and group overlap inhibits their utility for individual diagnosis making it necessary to find ways to improve sensitivity of these biomarkers.

We used linear regression to identify genetic loci associated with CSF YKL-40 levels in 379 individuals (80 cognitively impaired and 299 cognitively normal) from the Charles F and Joanne Knight Alzheimer’s Disease Research Center. We tested correlations between YKL-40 and CSF Tau/Aβ₄₂ ratio, Aβ₄₂, tau, and phosphorylated tau (ptau₁₈₁). We used studentized residuals from a linear regression model of the log-transformed, standardized protein levels and the additive reference allele counts from the most significant locus to adjust YKL-40 values and tested the differences in correlations with CSF Tau/Aβ₄₂ ratio, Aβ₄₂, tau, and ptau₁₈₁.

We found that genetic variants on the CH13L1 locus were significantly associated with CSF YKL-40 levels, but not AD risk, age at onset, or disease progression. The most significant variant is a reported expression quantitative trait locus for CHI3L1, the gene which encodes YKL-40, and explained 12.74 % of the variance in CSF YKL-40 in our study. YKL-40 was positively correlated with ptau₁₈₁ (r = 0.521) and the strength of the correlation significantly increased with the addition of genetic information (r = 0.573, p = 0.006).

CSF YKL-40 levels are likely a biomarker for AD, but we found no evidence that they are an AD endophenotype. YKL-40 levels are highly regulated by genetic variation, and by including genetic information the strength of the correlation between YKL-40 and ptau₁₈₁ levels is significantly improved. Our results suggest that studies of potential biomarkers may benefit from including genetic information.