NG00141-SALSA GWAS

The objective of the proposed research is to identify new genes involved in Alzheimer's disease (AD) by identifying alleles contributing to increased risk for or protection against the disease, providing insight as to why individuals with risk factors develop or escape from AD, and ultimately identifying potential avenues for therapeutic approaches and prevention of the disease. Our study design will use phenotypic (ex., AD diagnosis, age-at-onset, APOE genotype) land genomic data (ex., WGS, array, imputed genotypes) from NIAGADS studies to investigate genotype-phenotype associations. Strategies include association testing and haplotype- and family-based approaches, including estimates of relatedness and population genetics analyses as needed to perform the association testing (ex. control for population structure). NIAGADS data will not be used to investigate individual identity. Consent type and other Data Use Limitations (DUL) for each study will be respected in all analyses. Data from an individual with disease-specific consent will not be used in analyses outside of that restriction, including indirect uses such as imputation reference panels or variant summary statistics. When an individual’s DUL prohibits investigation of population genetics, population history or related issues, their data will be excluded from studies that address those issues. We intend to publish or otherwise broadly share any findings from this study with the scientific community. As such, genomic summary results from datasets with a “sensitive” designation will only be shared through publications to support study’s conclusions and through NIH-funded data repositories which maintain restricted access (ex. NIAGADS). Data from NIAGADS may be combined with non-NIAGADS data from the same or other studies (obtained from dbGaP or other sources), to improve the power for novel genetic discoveries, while respecting the consent of all participants. We expect that this activity creates no additional risks to participants. Data will be shared only among Internal Collaborators at the University of Washington. We do not plan to collaborate with External Collaborators at other institutions.

We propose to identify new genes involved in Alzheimer's disease (AD) by identifying alleles contributing to increased risk for or protection against the disease, providing insight as to why individuals with risk factors develop or escape from AD, and ultimately identifying potential avenues for therapeutic approaches and prevention of the disease. We will combine phenotype and genotype data using association testing and haplotype- and family-based approaches to identify associations and refine those signals with fine-mapping tools and external data.

The goal of this study is to identify new genes and mutations that cause or increase risk for Alzheimer disease (AD), as well as protective factors. Individuals and families were selected from the Knight-ADRC (Washington University) and the NIA-LOAD study. Only families with at least three first-degree affected individuals were included. Families with pathogenic variants in the known AD or FTD genes, or in which APOE4 segregated with disease were excluded. At least two cases and one control were selected per family. Cases had an age at onset (AAO) after 65 yo and controls had a larger age at last assessment than the latest AAO within the family. Whole exome (WES) and whole genome sequencing (WGS) was generated for 1,235 individuals (285 families) that together with data from our collaborators and the ADSP family-based cohort (3,449 individuals and 757 families) will provide enough statistical power to identify new genes for AD. Dr. Tanzi (Harvard Medical School) will provide WGS from 400 families from the NIMH Alzheimer disease genetics initiative study. We will perform single variant and gene-based analyses to identify genes and variants that increase risk for disease in AD families. Single variant analysis will consist of a combination of association and segregation analyses. We will run family-based gene-based methods to identify genes that show and overall enrichment of variants in AD cases. We will also look for protective and modifier variants. To do this we will identify families loaded with AD cases, that also include individuals with a high burden of known risk variants but that do not develop the disease (escapees). We will use the sequence data and the family structure to identify variants that segregate with the escapee phenotype. The most promising variants and genes will be replicated in independent datasets (ADSP case-control, ADNI, Knight-ADRC, NIA-LOAD ). We will perform single variant and gene-based analyses to replicate the initial findings, and survival analysis to replicate the protective variants. We will select the most promising variants/genes for functional studies

Family-based approaches led to the identification of disease-causing Alzheimer’s Disease (AD) variants in the genes encoding APP, PSEN1 and PSEN2. The identification of these genes led to the A?-cascade hypothesis and to the development of drugs that target this pathway. Recently, we have identified rare coding variants in TREM2, ABCA7, PLD3 and SORL1 with large effect sizes for risk for AD, confirming that rare coding variants play a role in the etiology of AD. In this proposal, we will identify rare risk and protective alleles using sequence data from families densely affected by AD. We hypothesize that these families are enriched for genetic risk factors. We already have sequence data from 695 families (2,462 individuals), that combined with the ADSP and the NIMH dataset will lead to a dataset of more than 1,042 families (4,684 individuals). Our preliminary results support the flexibility of this approach and strongly suggest that protective and risk variants with large effect size will be found, which will lead to a better understanding of the biology of the disease.

We have been developing more powerful statistical methods to detect common variant (CV)- or rare variant (RV)-complex trait associations and/or putative causal relationships for GWAS and DNA sequencing data. Here we propose applying our new methods, along with other suitable existing methods, to the existing ADSP sequencing data and other AD GWAS data provided by NIA, hence requesting approval for accessing the ADSP sequencing and other related GWAS/genetic data. We have the following two specific Aims: Aim1. Association testing under genetic heterogeneity: For complex traits, genetic heterogeneity, especially of RVs, is ubiquitous as well acknowledged in the literature, however there is barely any existing methodology to explicitly account for genetic heterogeneity in association analysis of RVs based on a single sample/cohort. We propose using secondary and other omic data, such as transcriptomic or metabolomic data, to stratify the given sample, then apply a weighted test to the resulting strata, explicitly accounting for genetic heterogeneity that causal RVs may be different (with varying effect sizes) across unknown and hidden subpopulations. Some preliminary analyses have confirmed power gains of the proposed approach over the standard analysis. Aim 2. Meta analysis of RV tests: Although it has been well appreciated that it is necessary to account for varying association effect sizes and directions in meta analysis of RVs for multi-ethnic cohorts, existing tests are not highly adaptive to varying association patterns across the cohorts and across the RVs, leading to power loss. We propose a highly adaptive test based on a family of SPU tests, which cover many existing meta-analysis tests as special cases. Our preliminary results demonstrated possibly substantial power gains.

We propose applying our newly developed statistical analysis methods, along with other suitable existing methods, to the existing ADSP sequencing data and other AD GWAS data to detect common or rare genetic variants associated with Alzheimer’s disease (AD). The novelty and power of our new methods are in two aspects: first, we consider and account for possible genetic heterogeneity with several subcategories of AD; second, we apply powerful meta-analysis methods to combine the association analyses across multiple subcategories of AD. The proposed research is feasible, promising and potentially significant to AD research. In addition, our proposed analyses of the existing large amount of ADSP sequencing data and other AD GWAS data with our developed new methods are novel, powerful and cost-effective.