O1‐03‐03: Identification of Novel Candidate Genes for Early‐Onset Alzheimer's Disease Through Integrated Whole‐Exome Sequencing and Exome Chip Array Association Analysis

Neuropharmacological potentials of β-carboline alkaloids for neuropsychiatric disorders

Neuropsychiatric disorders are diseases of the central nervous system (CNS) which are characterised by complex pathomechanisms that including homeostatic failure, malfunction, atrophy, pathology remodelling and reactivity anomaly of the neuronal system where treatment options remain challenging. β-Carboline (βC) alkaloids are scaffolds of structurally diverse tricyclic pyrido[3,4-b]indole alkaloid with vast occurrence in nature. Their unique structural features which favour interactions with enzymes and protein receptor targets account for their potent neuropharmacological properties. However, our current understanding of their biological mechanisms for these beneficial effects, especially for neuropsychiatric disorders is sparse. Therefore, we present a comprehensive review of the scientific progress in the last two decades on the prospective pharmacology and physiology of the βC alkaloids in the treatment of some neuropsychiatric conditions such as depression, anxiety, Alzheimer's disease, Parkinson's disease, brain tumour, essential tremor, epilepsy and seizure, licking behaviour, dystonia, agnosia, spasm, positive ingestive response as demonstrated in non-clinical models. The current evidence supports that βC alkaloids offer potential therapeutic agents against most of these disorders and amenable for further drug design.

Multikernel linear mixed model with adaptive lasso for complex phenotype prediction

Linear mixed models (LMMs) and their extensions have been widely used for high-dimensional genomic data analyses. While LMMs hold great promise for risk prediction research, the high dimensionality of the data and different effect sizes of genomic regions bring great analytical and computational challenges. In this work, we present a multikernel linear mixed model with adaptive lasso (KLMM-AL) to predict phenotypes using high-dimensional genomic data. We develop two algorithms for estimating parameters from our model and also establish the asymptotic properties of LMM with adaptive lasso when only one dependent observation is available. The proposed KLMM-AL can account for heterogeneous effect sizes from different genomic regions, capture both additive and nonadditive genetic effects, and adaptively and efficiently select predictive genomic regions and their corresponding effects. Through simulation studies, we demonstrate that KLMM-AL outperforms most of existing methods. Moreover, KLMM-AL achieves high sensitivity and specificity of selecting predictive genomic regions. KLMM-AL is further illustrated by an application to the sequencing dataset obtained from the Alzheimer's disease neuroimaging initiative.