Associated Activities

U01 AG058635

Principal investigator:
Alison Goate

The aims of U01 AG058635 Genomic approach to identification of microglial networks involved in Alzheimer’s disease are 1) To integrate existing GWAS and WGS/WES association data with transcriptomic data to identify key genes, networks and functions of MCs, which modulate AD risk. 2) Use iPSC-derived microglia to test the impact of knock-down of candidate genes and test their effects on microglial gene expression, networks and functions in vitro.

U01 AG058922

Principal investigators:
Carlos Cruchaga,
Alison Goate

Families that experience high rates of AD are expected to be highly enriched for genetic AD risk factors. Through the Familial Alzheimer Sequencing (FASe) project we are using sequence data from these families to identify risk variants. Our lab has sequencing data from families that are densely affected by late onset AD (LOAD). We have recently completed alignment of all raw data to the newer GRCh38 genome assembly. We then performed PLINK based QC, identity by descent (IBD) tests, and principal component analysis (PCA) to confirm non-Hispanic whites, which is a focus of this study. In total, 1,774 individuals from 497 families (Table 1) with 2,901,993 variants (SNPs and indels) will be included in our ongoing analysis.

Table 1. Comparison of the number of families (per household size) included in the previous and current release of the FASe study

We have stabilized a complete pipeline and Docker images to process whole exome and whole genome data from different raw files (fastq, cram or bam) that are freely available on our Github and Dockerhub NeuroGenomicsAndInformatics/dockerNGS). From these familial LOAD (fLOAD) data sets we have recently identified variants in three genes (PLD3, UNC5C, and CPAMD8) that segregate with disease status. These were verified in larger data sets, and further analyzed to determine the mechanism of effect on AD. These preliminary results support the flexibility of this approach and strongly suggest that protective and risk variants with large effect size will be found.

U01 AG052410

Principal investigators:
Margaret Pericak-Vance,
Gary Beecham,
Goldie Byrd,
Richard Mayeux,
Christiane Reitz

African Americans have been substantially underrepresented in Alzheimer’s disease (AD) genomic efforts. Through this proposal, we conducted genomic studies of AD in African Americans. Specifically, AG052410 is a family-based study in African Americans that parallels the family-based efforts in the ADSP Discovery phase enhancing and extending current ADSP efforts to a broader AD community. Specifically, we 1) Expanded our existing African American family and case/control dataset; 2.) Generalized and refined ADSP risk and protective loci in familial AA AD. 3.) Prioritize variants by admixture mapping and bioinformatics analysis and 4.) Began multi- functional implications of the known risk (ABCA7) and protective loci and identified possible additional genic targets. Our overall goal is to identify targets for therapeutic development that will either prevent or significantly delay the onset of AD.

U01 AG062943

Principal investigators:
Margaret Pericak-Vance,
Richard Mayeux

AG062943 is part ADSP-FUS and is funded by the NIA to identify, organize and collate Alzheimer’s disease and dementia data sets for Whole Genome Sequencing and genome-wide array data generation with a focus on underserved and underrepresented populations and groups. ADSP-FUS collaborates closely with NCRAD for sample processing and quality control, with UHUHS for WGS, Hussman Institute for Human Genomics (HIHG) Center for Genomic Technology (CGT) for genotyping data, the HIHG Center for Genetic Epidemiology and Statistical Genetics for Quality Control (QC) analysis and with GCAD for harmonization and QC of sequence data. In addition, ADSP-FUS harmonizes the available clinical data for consistency across and within WGS data sets. In this ADSP-FUS project, we performed WGS of an additional 4,200+ samples that will significantly increase our power to find and refine genetic effects as well as enrich the clinical phenotypic data available for AD. These data include 819 autopsy-confirmed cases and controls (355 samples from the University of Miami (UM) Brain Bank and 464 samples from Case Western Reserve University (CWRU) Autopsy Confirmed Series) as well as over 900 African individuals from the IBADAN study from Indiana University, ~1,400 individuals in an Early Onset Alzheimer Disease (EOAD) Cohort (Age at Onset (AAO) <65 years) from the Alzheimer’s Disease Genetics Consortium (ADGC), 850 individuals from the phenotypically rich Alzheimer’s Disease Neuroimaging Initiative 2 (ADNI2) and 800 participants from the Old Order Amish (AMISH) specifically ascertained to enhance the finding of protective AD loci.

U01 AG052409

Principal investigators:
Sudha Seshadri,
Myriam Fornage

Identifying novel variants and genes related to Alzheimer’s disease (AD) endophenotypes is critical to understand the underlying biology of the disease. This U01 leverages the rich collection of phenotypic and genomic data, clinical and laboratory sample resources, and investigator expertise brought together in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium. We are harmonizing phenotypic and genetic data from large, diverse samples of African, Hispanic, Asian, and European ancestry, which are extensively characterized for AD-relevant endophenotypes and genomic data. We are applying advanced analytical methods to replicate and assess the generalizability of newly discovered AD variants and genes. In addition, we are mining existing databases (e.g., Accelerated Medicine Partnerships [AMP] Program), creating novel bioinformatic tools, and performing functional follow-up studies in Drosophila to more reliably identify causal genes, variants, and pathways.

R01 AG064614

Principal investigators:
Gary Beecham,
Carlos Cruchaga,
Christiane Reitz

Genomic studies of Alzheimer’s disease (AD) have primarily focused on non-Hispanic White (NHW) participants affected by the late-onset form of the disease (LOAD; onset age: >65), or early-onset AD (EOAD; onset age <=65) cases from families showing Mendelian inheritance patterns associated with mutations in the APP, PSEN1, and PSEN2 genes. However, mutations in these three genes explain ~10% of EOAD cases. We aim to identify additional EOAD-associated variants through a large-scale whole-genome sequencing (WGS) study of unexplained EOAD (AG064614, MPIs: Beecham, University of Miami; Cruchaga, Washington University; and Reitz, Columbia University) among 4,000 samples of both EOAD and control and harmonizing these data with data from ADSP-FUS and other WGS experiments. Analyses will comprise both linkage and association-based approaches, analyses of polygenic and ancestry effects, and a thorough examination of neurocognitive, neuropsychiatric, and cardiovascular endophenotypes.