A Comprehensive Machine Learning Framework for the Exact Prediction of the Age of Onset in Familial and Sporadic Alzheimer's Disease.

Machine learning (ML) algorithms are widely used to develop predictive frameworks. Accurate prediction of Alzheimer's disease (AD) age of onset (ADAOO) is crucial to investigate potential treatments, follow-up, and therapeutic interventions. Although genetic and non-genetic factors affecting ADAOO were elucidated by other research groups and ours, the comprehensive and sequential application of ML to provide an exact estimation of the actual ADAOO, instead of a high-confidence-interval ADAOO that may fall, remains to be explored. Here, we assessed the performance of ML algorithms for predicting ADAOO using two AD cohorts with early-onset familial AD and with late-onset sporadic AD, combining genetic and demographic variables. Performance of ML algorithms was assessed using the root mean squared error (RMSE), the R-squared (R2), and the mean absolute error (MAE) with a 10-fold cross-validation procedure. For predicting ADAOO in familial AD, boosting-based ML algorithms performed the best. In the sporadic cohort, boosting-based ML algorithms performed best in the training data set, while regularization methods best performed for unseen data. ML algorithms represent a feasible alternative to accurately predict ADAOO with little human intervention. Future studies may include predicting the speed of cognitive decline in our cohorts using ML.

Familial Alzheimer's Disease and Recessive Modifiers.

Alzheimer's disease (AD) is progressive brain disorder that affects ~ 50 million people worldwide and has no current effective treatment. AD age of onset (ADAOO) has shown to be critical for the identification of genes that modify the appearance of AD signs and symptoms in a specific population. We clinically characterized and whole-exome genotyped 71 individuals with AD from the Paisa genetic isolate, segregating the (PSEN1) E280A dominant fully penetrant mutation, and analyzed the potential recessive effects of ~ 50,000 common functional genomic variants to the ADAOO. Standard quality control and filtering procedures were applied, and recessive single- and multi-locus linear mixed-effects models were used. We identified genetic variants in the SLC9C1, CSN1S1, and LOXL4 acting recessively to delay ADAOO up to ~ 11, ~ 6, and ~ 9 years on average, respectively. In contrast, the CC recessive genotype in marker DHRS4L2-rs2273946 accelerates ADAOO by ~ 8 years. This study, reports new recessive variants modifying ADAOO in PSEN1 E280A mutation carriers. This set of genes are implicated in important biological processes and molecular functions commonly affected by genes associated with the etiology of AD such as APP, APOE, and CLU. Future functional studies using modern techniques such as induced pluripotent stem cells will allow a better understanding of the over expression and down regulation of these recessive modifier variants and hence the pathogenesis of AD. These results are important for prediction of AD and ultimately, substantial to develop new therapeutic strategies for individuals at risk or affected by AD.

Generation of one iPSC line (IMEDEAi006-A) from an early-onset familial Alzheimer's Disease (fAD) patient carrying the E280A mutation in the PSEN1 gene.

The mutation E280A in PSEN1 (presenilin-1) is the most common cause of early-onset familial Alzheimer's Disease (fAD). It presents autosomal dominant inheritance and frequently leads to the manifestation of the disease in relatively young individuals. Here we report the generation of one PSEN1 E280A iPSC line derived from an early-onset patient. OriP/EBNA1-based episomal plasmids containing OCT3/4, SOX2, KLF4, L-MYC, LIN28, BCL-xL and shp53 were used to reprogram oral mucosa fibroblasts. The iPSC line generated has normal karyotype, carry the E280A mutation, is free of plasmid integration, express high levels of pluripotency markers and can differentiate into all three germ layers.

Targeting Neuroplasticity, Cardiovascular, and Cognitive-Associated Genomic Variants in Familial Alzheimer's Disease.

The identification of novel genetic variants contributing to the widespread in the age of onset (AOO) of Alzheimer's disease (AD) could aid in the prognosis and/or development of new therapeutic strategies focused on early interventions. We recruited 78 individuals with AD from the Paisa genetic isolate in Antioquia, Colombia. These individuals belong to the world largest multigenerational and extended pedigree segregating AD as a consequence of a dominant fully penetrant mutation in the PSEN1 gene and exhibit an AOO ranging from the early 1930s to the late 1970s. To shed light on the genetic underpinning that could explain the large spread of the age of onset (AOO) of AD, 64 single nucleotide polymorphisms (SNP) associated with neuroanatomical, cardiovascular, and cognitive measures in AD were genotyped. Standard quality control and filtering procedures were applied, and single- and multi-locus linear mixed-effects models were used to identify AOO-associated SNPs. A full two-locus interaction model was fitted to define how identified SNPs interact to modulate AOO. We identified two key epistatic interactions between the APOE*E2 allele and SNPs ASTN2-rs7852878 and SNTG1-rs16914781 that delay AOO by up to ~ 8 years (95% CI 3.2-12.7, P = 1.83 × 10-3) and ~ 7.6 years (95% CI 3.3-11.8, P = 8.69 × 10-4), respectively, and validated our previous finding indicating that APOE*E2 delays AOO of AD in PSEN1 E280 mutation carriers. This new evidence involving APOE*E2 as an AOO delayer could be used for developing precision medicine approaches and predictive genomics models to potentially determine AOO in individuals genetically predisposed to AD.