Alzheimer's disease (AD) is highly heritable and recent studies have identified over 20 diseaseassociated genomic loci. Yet these only explain a small proportion of the genetic variance, indicating that undiscovered loci remain. Here, we performed the largest genome-wide association study of clinically diagnosed AD and AD-by-proxy (71,880 cases, 383,378 controls). AD-by-proxy, based on parental diagnoses, showed strong genetic correlation with AD (rg=0.81). Meta-analysis identified 29 risk loci, implicating 215 potential causative genes. Associated genes are strongly expressed in immune-related tissues and cell types (spleen, liver and microglia). Gene-set analyses indicate biological mechanisms involved in lipid-related processes and degradation of amyloid precursor proteins. We show strong genetic correlations with multiple health-related outcomes, and Mendelian randomisation results suggest a protective effect of cognitive ability on AD risk. These results are a step forward in identifying the genetic factors that contribute to AD risk and add novel insights into the neurobiology of AD.

Leaf senescence is an essential developmental process that impacts dramatically on crop yields and involves altered regulation of thousands of genes and many metabolic and signaling pathways, resulting in major changes in the leaf. The regulation of senescence is complex, and although senescence regulatory genes have been characterized, there is little information on how these function in the global control of the process. We used microarray analysis to obtain a highresolution time-course profile of gene expression during development of a single leaf over a 3-week period to senescence. A complex experimental design approach and a combination of methods were used to extract high-quality replicated data and to identify differentially expressed genes. The multiple time points enable the use of highly informative clustering to reveal distinct time points at which signaling and metabolic pathways change. Analysis of motif enrichment, as well as comparison of transcription factor (TF) families showing altered expression over the time course, identify clear groups of TFs active at different stages of leaf development and senescence. These data enable connection of metabolic processes, signaling pathways, and specific TF activity, which will underpin the development of network models to elucidate the process of senescence.

Biomarker discovery and development for clinical research, diagnostics and therapy monitoring in clinical trials have advanced rapidly in key areas of medicine, notably oncology and cardiovascular diseases, allowing for rapid early detection and supporting the evolution of biomarker-guided precision medicine-based targeted therapies. In Alzheimer’s disease (AD), breakthroughs in biomarker identification and validation include the cerebrospinal fluid (CSF) and positron emission tomography (PET) markers of amyloid β (Aβ) and tau proteins, which are highly accurate in detecting the presence of pathophysiological and neuropathological changes of AD. However, their high cost, insufficient accessibility, or invasiveness may limit their use as viable first-line tools for detecting patterns of pathophysiology. Therefore, a multi-stage, tiered approach is needed, prioritizing development of an initial screen to exclude from these tests the high numbers of people with cognitive deficits who do not demonstrate evidence of underlying AD pathophysiology. This perspective summarizes the efforts of a working group that aimed to survey the current landscape of blood-based AD biomarkers, and outlines operational steps for an effective academic-industry co-development and path forward from identification and assay development to validation for clinical use.