Bridging big data: procedures for combining non-equivalent cognitive measures from the ENIGMA Consortium

Investigators in the cognitive neurosciences have turned to Big Data to address persistent replication and reliability issues by increasing sample sizes, statistical power, and representativeness of data. While there is tremendous potential to advance science through open data sharing, these efforts unveil a host of new questions about how to integrate data arising from distinct sources and instruments. We focus on the most frequently assessed area of cognition - memory testing - and demonstrate a process for reliable data harmonization across three common measures. We aggregated raw data from 53 studies from around the world which measured at least one of three distinct verbal learning tasks, totaling N = 10,505 healthy and brain-injured individuals. A mega analysis was conducted using empirical bayes harmonization to isolate and remove site effects, followed by linear models which adjusted for common covariates. After corrections, a continuous item response theory (IRT) model estimated each individual subject’s latent verbal learning ability while accounting for item difficulties. Harmonization significantly reduced inter-site variance by 37% while preserving covariate effects. The effects of age, sex, and education on scores were found to be highly consistent across memory tests. IRT methods for equating scores across AVLTs agreed with held-out data of dually-administered tests, and these tools are made available for free online. This work demonstrates that large-scale data sharing and harmonization initiatives can offer opportunities to address reproducibility and integration challenges across the behavioral sciences

Penetrance estimation of Alzheimer disease in SORL1 loss-of-function variant carriers using a family-based strategy and stratification by APOE genotypes

International audienceAbstract Background Alzheimer disease (AD) is a common complex disorder with a high genetic component. Loss-of-function (LoF) SORL1 variants are one of the strongest AD genetic risk factors. Estimating their age-related penetrance is essential before putative use for genetic counseling or preventive trials. However, relative rarity and co-occurrence with the main AD risk factor, APOE -ε4, make such estimations difficult. Methods We proposed to estimate the age-related penetrance of SORL1 -LoF variants through a survival framework by estimating the conditional instantaneous risk combining (i) a baseline for non-carriers of SORL1- LoF variants, stratified by APOE-ε4 , derived from the Rotterdam study ( N = 12,255), and (ii) an age-dependent proportional hazard effect for SORL1- LoF variants estimated from 27 extended pedigrees (including 307 relatives ≥ 40 years old, 45 of them having genotyping information) recruited from the French reference center for young Alzheimer patients. We embedded this model into an expectation-maximization algorithm to accommodate for missing genotypes. To correct for ascertainment bias, proband phenotypes were omitted. Then, we assessed if our penetrance curves were concordant with age distributions of APOE -ε4-stratified SORL1- LoF variant carriers detected among sequencing data of 13,007 cases and 10,182 controls from European and American case-control study consortia. Results SORL1- LoF variants penetrance curves reached 100% (95% confidence interval [99–100%]) by age 70 among APOE -ε4ε4 carriers only, compared with 56% [40–72%] and 37% [26–51%] in ε4 heterozygous carriers and ε4 non-carriers, respectively. These estimates were fully consistent with observed age distributions of SORL1- LoF variant carriers in case-control study data. Conclusions We conclude that SORL1- LoF variants should be interpreted in light of APOE genotypes for future clinical applications