Machine learning methods applied to genotyping data capture interactions between single nucleotide variants in late onset Alzheimer's disease.

Introduction: Genome-wide association studies (GWAS) in late onset Alzheimer's disease (LOAD) provide lists of individual genetic determinants. However, GWAS do not capture the synergistic effects among multiple genetic variants and lack good specificity. Methods: We applied tree-based machine learning algorithms (MLs) to discriminate LOAD (>700 individuals) and age-matched unaffected subjects in UK Biobank with single nucleotide variants (SNVs) from Alzheimer's disease (AD) studies, obtaining specific genomic profiles with the prioritized SNVs. Results: MLs prioritized a set of SNVs located in genes PVRL2, TOMM40, APOE, and APOC1, also influencing gene expression and splicing. The genomic profiles in this region showed interaction patterns involving rs405509 and rs1160985, also present in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. rs405509 located in APOE promoter interacts with rs429358 among others, seemingly neutralizing their predisposing effect. Discussion: Our approach efficiently discriminates LOAD from controls, capturing genomic profiles defined by interactions among SNVs in a hot-spot region.

FaST-LMM for Two-Way Epistasis Tests on High-Performance Clusters.

We introduce a version of the epistasis test in FaST-LMM for clusters of multithreaded processors. This new software maintains the sensitivity of the original FaST-LMM while delivering acceleration that is close to linear on 12-16 nodes of two recent platforms, with respect to improved implementation of FaST-LMM presented in an earlier work. This efficiency is attained through several enhancements on the original single-node version of FaST-LMM, together with the development of a message passing interface (MPI)-based version that ensures a balanced distribution of the workload as well as a multigraphics processing unit (GPU) module that can exploit the presence of multiple GPUs per node.

Consent Codes: Upholding Standard Data Use Conditions.

A systematic way of recording data use conditions that are based on consent permissions as found in the datasets of the main public genome archives (NCBI dbGaP and EMBL-EBI/CRG EGA).