Research in our laboratory focuses on dementia (Alzheimer’s disease & frontotemporal dementia) and addiction (alcohol dependence). In each of these projects our goal is to understand the molecular basis of disease in order to identify novel targets for therapeutic development. We use genetic and genomic approaches to identify susceptibility alleles, this work includes genome wide association studies and whole genome/exome sequencing in families multiply affected by disease and in case control cohorts. Since 2013 and the discovery of TREM2 as an AD risk factor (Guerreiro et al., 2013) we have focused on the role of myeloid cells in the genetics of AD. This has recently resulted in a paper (Huang et al., 2017) linking a network of AD risk genes to another myeloid expressed gene, SPI1. We have also pioneered the use of endophenotypes to uncover both risk and protective alleles in both our Alzheimer’s disease and our alcoholism studies. Our functional analyses are aimed at validating these and other genes identified in the genetic studies. We are using induced pluripotent stem cells from individuals with known genetic causes of disease in order to model the effects of these mutations in disease relevant cell types and performing in vivo analyses in collaboration with Drs. Edoardo Marcora and Anne Schaefer.
Genetic Studies of Alzheimer's Disease and Related Dementias
Identification and Characterization of AD Risk Networks using Multi-Dimensional “omics” Data
Genome-wide association, whole genome/exome sequencing and gene network studies have already enabled researchers to identify twenty loci influencing Alzheimer’s disease (AD) risk and another half dozen genes carrying specific rare variants that influence disease risk. With the new whole-genome sequence (WGS) and whole-exome sequence (WES) data from 10,000+ AD cases and controls from the ADSP, combined with mRNA expression data from 3,500+ individuals from AMP, it is now possible to develop a more comprehensive picture of the genetic architecture of AD and associated risk. Our goal is to identify and validate therapeutic targets for AD by identifying genes that functionally drive or protect from AD and interrogating their respective gene networks for therapeutic targets. Genetic and pathway-based analyses have strongly implicated a small number of networks including immune response, phagocytosis, lipid metabolism and endocytosis. We will integrate data from genetic studies and gene expression/regulation studies to identify risk and resilience genes to pinpoint key networks that functionally drive AD development and progression. We will take two complementary approaches to identify risk and resilience AD genes: (1) we will use a family-based approach to identify both risk and protective alleles using publicly available data and our own WGS/WES data from both NIALOAD and Utah families; and (2) we will use publicly available high-dimensional molecular data from AD cases and controls to construct global interaction and causal networks. We will then focus our analysis of ADSP case control sequence data on the most compelling networks, thereby reducing our search space and increasing power. To identify therapeutic targets, we will use network analysis to test known drugs that target networks identified in our sequence analysis of both family-based and case control data. We will then validate our findings by performing in vitro experiments based our in silico observations and determine the functional consequences of risk/resilience alleles identified from the AD sequence data. Together, the findings from this study will pinpoint key networks that functionally drive AD and will provide critical insight into therapeutic intervention.
Understanding the Mechanism of SPl1 Dependent Alzheimer Disease Risk
The primary goal of this project is to understand how variation within the SPI1 locus influences Alzheimer’s Disease (AD) risk. Recently, genetic and molecular evidence has implicated myeloid cells in the etiology of AD, including our finding that AD risk alleles are enriched for cis-eQTL effects in monocytes but not CD4+ T-lymphocytes. Our studies have identified SPI1 as a novel risk factor for Alzheimer’s disease, and that there are four non-coding variants that constitute the most likely functional variants, either individually or in combination. We have shown computationally and experimentally that SPI1, which codes for the transcription factor PU.1, regulates expression of many other AD risk factor genes including CD33, MS4A4A, MS4A6A, TYROBP, APOE, CLU. Indeed, genes regulated by PU.1 are highly enriched for AD risk loci suggesting that this network is a potential therapeutic target for AD prevention drugs. We are pursuing cell culture and animal models to determine the impact of variation in expression of SPI1 on global gene expression in microglia, function of microglia and AD pathology in mouse models.
Genomic Approach to Identification of Microglial Networks Involved in Alzheimer’s Disease Risk
The goal of this project is to use genetic and genomic approaches to identify functional networks enriched for Alzheimer’s disease (AD) risk and protective loci, and to use this information to determine how cellular function and physiology is impacted by these genetic factors in disease-relevant cell types and animal models. Initial analyses of both genome-wide association data and whole genome sequencing data provide substantial evidence to support the hypothesis that many AD risk genes operate within one or more inter-connected networks that modulate myeloid cell function. As part of this application we plan to extend this work to more fully characterize the affected network(s) by analyzing the largest GWAS and WGS datasets to date and integrating this data with macrophage and monocyte gene expression and epigenomic data. Further we will validate our findings through experimental approaches in iPSC-derived microglia/macrophages and an animal model.
Identification of Novel Alzheimer’s Disease Genes Using Next Generation Sequencing
Genetic studies of AD had identified mutations in the amyloid-beta precursor protein (APP) presenilin (PSEN1) and presenilin 2 (PSEN2) genes cause Mendelian forms of AD. These mutations have been found in a small number of people, but the identification of such mutations and genes provided a better understanding of the biology of AD. More recent studies using genome-wide association studies (GWAS) approaches in late onset AD have identified more than thirty loci that influence risk for AD. For some of these loci genetic variants in specific genes have been identified, but for the majority of these loci the disease gene remains to be identified. The goal of this project is to use genetic and genomic approaches to identify functional networks enriched for AD risk and protective loci, and to use this information to determine how myeloid cellular function is impacted by these genetic factors in disease-relevant cell types. The ultimate goal is to use this information to identify drugs that target these networks and to test the drugs in appropriate cellular models. During the last three years we have revealed substantial evidence to support the hypothesis that many AD risk genes operate within an inter-connected network that modulates myeloid cell function. We plan to extend this work to more fully characterize the affected network(s) using bioinformatics and to validate our findings through experimental approaches in BV2 cells and human induced pluripotent stem cells (hiPSC)-derived microglia.
Understanding the Mechanism of MS4A-Dependent AD Risk
A major obstacle for the translation of Late onset AD (LOAD) GWAS data into an actionable (from a drug discovery perspective) understanding of disease etiology is not only the increased complexity of the genetic architecture of LOAD compared to autosomal-dominant AD, but also the fact that (with a few exceptions) the causal genes are much more difficult to identify unequivocally. Indeed, most genetic variants associated with LOAD don’t occur within genes but instead fall into non-coding regions of the genome that modulate the expression of one or more genes in specific cell types. By applying novel computational and statistical methods for integrated analyses of human genetics and genomics datasets, we have identified strong candidate genes, biochemical pathways and cellular processes. In particular, we have discovered that variants in most of the LOAD GWAS loci function to modulate the expression of genes that play important roles in cells of the myeloid lineage, such as monocytes and macrophages (including microglia), the professional phagocytes of the innate immune system. Several of these candidate genes (together with well-validated LOAD genes like APOE, TREM2 and ABCA7) cluster around the phagocytic clearance of lipid-rich cellular debris (hereafter referred to as efferocytosis), a core and evolutionarily conserved function of myeloid cells that is essential for the maintenance of tissue homeostasis, immune tolerance, and the resolution of inflammation. A recent publication indicated that members of the MS4A family are lipid sensors in the olfactory system. This observation led us to proposed that the MS4A genes linked to AD could be lipid sensors for microglia, potentially unifying them with the efferocytosis hypothesis. The goal of this research is to identify candidate the causal gene(s) in the MS4A cluster (an AD-associated locus) and to investigate their role in AD pathogenesis for the development of therapeutic agents.
Dominantly Inherited Alzheimer Network – Genetics Core
The Dominantly Inherited Alzheimer Network (DIAN) established an international, multicenter registry of individuals (gene carriers and noncarriers; asymptomatic and symptomatic) who are at risk of carrying a known causative mutation for AD in the amyloid precursor protein (APP), presenilin 1 (PSEN1), or presenilin 2 (PSEN2) genes. Individuals are evaluated upon enrollment in DIAN and longitudinally thereafter with standard instruments including the Uniform Data Set of the Alzheimer’s Disease Centers and protocols developed by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) for structural, functional, amyloid imaging , biological fluids (blood; CSF), and histopathological examination of cerebral tissue in individuals who come to autopsy. We have expanded our network of centers and begun longitudinal characterization of a large series of autosomal dominant AD (ADAD) kindreds with known disease-causing mutations and will continue longitudinal follow up of these kindreds to identify the earliest detectable changes associated with development of disease and to characterize the temporal series of events that occurs up to and including the diagnosis of symptomatic AD. The goal of the Genetics Core of the DIAN initiative is to provide genetic information and useful biological and genomic materials to the research community for the study of AD. We have already collected genomic samples from 531 individuals and generated fibroblasts from 99 individuals. We anticipate collection of an additional 125 new individuals, including participants from NIH and self-funded sites. We will expand the fibroblast and induced pluripotent stem cell collection. The Core will maintain and curate a list of pathogenic mutations and confirm that new DIAN families carry an ADAD mutation. The Core will also generate GWAS and APOE genotype data on all individuals and obtain biological materials (fibroblasts, induced pluripotent stem cells, white blood cells) to perform cell-based functional studies. All data will be placed in the DIAN database. We will support all projects in DIAN and perform analyses with other Cores to identify novel factors modulating age at onset in these families.
The Familial Alzheimer Sequencing (FASe) Project
Family-based approaches led to the identification of disease-causing Alzheimer’s Disease (AD) variants in the genes encoding amyloid-beta precursor protein (APP), presenilin 1 (PSEN1) and presenilin 2 (PSEN2). Subsequently, the identification of these genes led to the Aβ-cascade hypothesis and recently to the development of drugs that target that pathway. In this proposal, we will identify rare risk and protective alleles. In a recent study, we identified a rare coding variant in TREM2 with large effect size for risk for AD, confirming that rare coding variants play a role in the etiology of AD. We will use sequence data from families densely affected by AD, because we hypothesize that these families are enriched for genetic risk factors. We already have access to sequence data from 695 families (2,462 individuals), that combined with the ADSP data will lead to a very large family-based dataset: more than 805 families and 4,512 participants. Our preliminary results support the flexibility of this approach and strongly suggest that protective and risk variants with large effect size will be found. The identification of those variants and genes will lead to a better understanding of the biology of the disease.
The National Institute on Aging (NIA) Late Onset of Alzheimer’s Disease (LOAD) Family-Based Study (FBS)
To date, a total of 1,454 multiplex late onset AD (LOAD) families have been recruited with 8,543 family members clinically assessed and DNA sampled. We have also recruited 1,030 controls. Genome-wide SNP arrays have been generated on 5,428 individuals, exome chip genotyping on 1,278 individuals, whole exome sequencing in 1,484 and whole genome in 928 family members and controls. The NIA-LOAD FBS provides an excellent opportunity to improve our understanding of the clinical and biological impact of genetic variation in the elderly. Phenotypic information is continually updated in these families by regular cognitive evaluations and autopsy at the time of death to confirm the diagnosis of LOAD. We have begun to recruit additional family members with a particular emphasis on the offspring generation. We have been able to bank brain tissue from family members creating one of the largest collections of brain tissues for familial LOAD. We will now expand biological sampling to include RNA and peripheral blood mononuclear cells in selected families. As additional genes and variants are identified, the members of the NIA LOAD Family Study will again play a central role as we explore: What is the impact of these risk and protective variants on disease risk? Are the genetic variants highly penetrant? What is the risk of developing LOAD in offspring? Can the presence of variants be used for stratification of patients into specific subtypes for clinical trials? Can the family data be used to identify novel biomarkers of disease risk, age at onset onset or progression? The NIA-LOAD FBS dataset is uniquely poised to address these clinical and biological questions because of its large size, rigorous ascertainment criteria, standardized clinical assessment and lack of restriction to specific mutations. This is by far the largest collection of LOAD families available in the world and virtually every major genetic study of Alzheimer’s disease has included patients and controls from the NIA-LOAD FBS dataset.
Investigating the MAPT H1 Haplotype Genetic Susceptibility for PSP and FTD
Stem Cell Tau Consortium
Addiction: Collaborative Study on the Genetics of Alcoholism (COGA)
Collaborative Study on the Genetics of Alcoholism
The Collaborative Study on the Genetics of Alcoholism (COGA) is a tightly integrated and interdisciplinary project, whose overarching goals are to understand the contributions and interactions of genetic, neurobiological, and environmental factors on risk and resilience over the developmental course of AUD, including relapse and recovery. COGA is a family-based study of large, ethnically diverse families, some densely affected by AUD, and family members have been characterized in clinical, behavioral, neuropsychological, neurophysiological, and socio-environmental domains, yielding a rich phenotypic dataset paired with a large repository of biospecimens and genome wide SNP data (GWAS) in 12,145 family members. The breadth and depth of longitudinal assessments in COGA families allow genomic analyses to be conducted within a developmental context, allowing inferences regarding genetic susceptibility and environmental malleability, which may contribute to avenues for prevention and intervention. To understand the genetics of AUD and its interplay with environment, we propose three inter-related and inter-dependent projects (Genomics, Brain Function, Lifespan) supported by three essential cores (NIAAA-COGA Sharing Repository, Data Management, and Administrative).
The overarching specific aims of the Genomics Project for the next five years of COGA are:
- To characterize loci, genes and biological pathways underlying alcohol use and AUD for further functional prioritization
- To determine the impact of new and existing genetic and genomic findings on diverse COGA phenotypes to understand how polygenic risk relates to the development and persistence of AUD and its sequelae
- To elucidate the effects of genes/loci and alcohol on genomic and cellular neuronal signatures that contribute to alcohol-related phenotypes, using human iPSC-derived cells
Huang et al. A common haplotype lowers PU.1 expression in myeloid cells and delays onset of Alzheimer's disease
TCW et al. An Efficient Platform for Astrocyte Differentiation from Human Induced Pluripotent Stem Cells.
Meet the Team
Francesca Garretti, Ph.D.
Jessica Schulman, MS
Associate Computational Scientist
Merck’s Director of Neuroscience Matthew Kennedy shares why these early results have him feeling hopeful. He is joined by Alison Goate, the director of the Ronald M. Loeb Center for Alzheimer’s Disease at Mount Sinai. Read More
As the director of the Ronald M. Loeb Center on Alzheimer’s Disease at Mount Sinai, Dr. Alison Goate specializes in researching the disease’s basic mechanisms to improve our treatment options. November is National Alzheimer’s Disease Awareness Month. Read More
Alzheimer's Association recognizes four scientists with Lifetime Achievement Awards at AAIC 2015 — July 21, 2015
The Alzheimer’s Association recognizes four leading scientists for their contributions to advancing Alzheimer’s disease and dementia research. The awards were presented during the opening session at the Alzheimer’s Association International Conference® 2015 (AAIC® 2015) in Washington, D.C. Read More
Scientists have identified early genetic markers that can potentially predict who is at an increased risk for developing Alzheimer’s, Medical Daily reported.Currently, in order to determine if someone will develop Alzheimer’s disease, doctors use tests that analyze the amount of Tau protein buildup in the central nervous system. The more Tau in an individual’s system, the more likely he or she will progress towards dementia. Read More