STATISTICAL ANALYSES TO IDENTIFY GENETIC VARIANTS ASSOCIATED WITH NEUROPATHOLOGICAL AND MRI-BASED ENDOPHENOTYPES OF ALZHEIMER’S DISEASE

Aged individuals often accumulate multiple brain pathologies that contribute individually and synergistically to cognitive decline and dementia. Alzheimer’s disease (AD) is the most commonly diagnosed form of dementia, constituting over 50% of dementia diagnoses, and poses an enormous burden on human health and well-being. In the United States alone, over six million individuals are living with AD, and the disease imposes financial costs of treatment and care of over $300 billion annually. The most common form of AD, late-onset AD (LOAD), presents in individuals aged 65 years and older and is highly heritable, with twin and family studies estimating its heritability at ~60%. Genome-wide association studies (GWAS), in which millions of genetic variants are individually assessed for association with a trait through generalized linear regression models, have been an enormously successful approach for studying the genetic risk of LOAD. Recent GWAS of AD have included upwards of 700,000-1,200,000 participants and have identified more than 70 individual genetic risk loci for LOAD. However, the simple clinical or proxy definitions of LOAD used in these studies do not reflect the complexity of the underlying disease. In addition to the pathognomonic LOAD neuropathologies of amyloid-beta plaques and tau neurofibrillary tangles, the majority of dementia patients also have TDP-43, alpha-synuclein, cerebrovascular pathology, or some combination thereof. Individual neuropathologies likely have both independent and shared genetic risk factors, and studying precisely defined neuropathologic phenotypes through GWAS can act as a complementary approach to large case-control based studies; however, neuropathologic endophenotypes have been relatively neglected in GWAS relative to clinical phenotypes. Brain volumes are another class of endophenotypes associated with a variety of important life and health outcomes, including LOAD. GWAS have identified genetic loci associated with brain MRI volumes measured via magnetic resonance imaging (MRI), although these studies have either exclusively studied genetic variants with minor allele frequencies (MAF) ≥1% or have primarily focused on participants of European ancestry. Investigating the relationship between rare genetic variants (MAF In the first study, we performed a genome-wide association study of brain arteriolosclerosis. We then performed a gene-based analysis to prioritize genes and Bayesian colocalization analyses to identify functional effects of risk loci. In the second study, we expanded our investigation, performing GWAS and functional analyses on 11 neuropathologic endophenotypes. We then performed targeted analyses to confirm association between DNA methylation and RNA expression with neuropathologic endophenotypes. In the third study, we investigated the genetic factors of four brain volume endophenotypes (intracranial volume, total brain volume, hippocampal volume, and lateral ventricular volume) using whole-genome sequence data in a cohort of participants with diverse ancestry

Analysis of Genes (\u3ci\u3eTMEM106B\u3c/i\u3e, \u3ci\u3eGRN\u3c/i\u3e, \u3ci\u3eABCC9\u3c/i\u3e, \u3ci\u3eKCNMB2\u3c/i\u3e, and \u3ci\u3eAPOE\u3c/i\u3e) Implicated in Risk for LATE-NC and Hippocampal Sclerosis Provides Pathogenetic Insights: A Retrospective Genetic Association Study

Limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC) is the most prevalent subtype of TDP-43 proteinopathy, affecting up to 1/3rd of aged persons. LATE-NC often co-occurs with hippocampal sclerosis (HS) pathology. It is currently unknown why some individuals with LATE-NC develop HS while others do not, but genetics may play a role. Previous studies found associations between LATE-NC phenotypes and specific genes: TMEM106B, GRN, ABCC9, KCNMB2, and APOE. Data from research participants with genomic and autopsy measures from the National Alzheimer’s Coordinating Center (NACC; n = 631 subjects included) and the Religious Orders Study and Memory and the Rush Aging Project (ROSMAP; n = 780 included) were analyzed in the current study. Our goals were to reevaluate disease-associated genetic variants using newly collected data and to query whether the specific genotype/phenotype associations could provide new insights into disease-driving pathways. Research subjects included in prior LATE/HS genome-wide association studies (GWAS) were excluded. Single nucleotide variants (SNVs) within 10 kb of TMEM106B, GRN, ABCC9, KCNMB2, and APOE were tested for association with HS and LATE-NC, and separately for Alzheimer’s pathologies, i.e. amyloid plaques and neurofibrillary tangles. Significantly associated SNVs were identified. When results were meta-analyzed, TMEM106B, GRN, and APOE had significant gene-based associations with both LATE and HS, whereas ABCC9 had significant associations with HS only. In a sensitivity analysis limited to LATE-NC + cases, ABCC9 variants were again associated with HS. By contrast, the associations of TMEM106B, GRN, and APOE with HS were attenuated when adjusting for TDP-43 proteinopathy, indicating that these genes may be associated primarily with TDP-43 proteinopathy. None of these genes except APOE appeared to be associated with Alzheimer’s-type pathology. In summary, using data not included in prior studies of LATE or HS genomics, we replicated several previously reported gene-based associations and found novel evidence that specific risk alleles can differentially affect LATE-NC and HS

Whole genome sequence association analysis of Brain MRI measures

BackgroundGenome-wide association studies (GWAS) of brain volumes have identified common genetic variants with modest effect sizes that lie mainly in non-coding regions. We sought to identify low frequency and rare variants influencing brain volumes by performing association analyses using whole-genome sequence data from the Trans-Omics for Precision Medicine (TOPMed) Program.MethodWe analyzed up to 7,607 participants (57% women; 62% European ancestry, 21% African-Americans, 15% Hispanic/Latino, 2% Chinese-American), mean age of 60.5 (16.2), from eight TOPMed population- or family-based studies (FHS, GENESTAR, CHS, GENOA, ARIC, CARDIA, MESA, and SAFS). We excluded participants with dementia, stroke, presence of large brain infarcts, tumor or low-quality scans. We tested the association of hippocampal (HV), total brain (TBV), lateral ventricular (LVV) and intracranial (ICV) volumes to individual genetic variants with minor allele counts ≥ 15 using mixed-effect linear regression models adjusted for age, age2, sex, study and the first 10 principal components. Models for HV, TBV and log(LVV) were adjusted for ICV. We accounted for relatedness using an empirical kinship matrix and trait variance variability by using a random effect for study.ResultWe detected one novel region with low frequency variants associated with HV (13q14, P = 5.8×10−9). The top 13q14 variant for HV (rs115674829) minor allele frequency (MAF) was 2% in our pooled sample but was more common in the pan-African 1000 Genomes population (MAF = 14%). This variant lies in LINC00598 at 237kb from FOXO1, a member of the forkhead family of transcription factors that has been linked to Alzheimer’s Disease. Additionally, we detected new suggestive associations (P≤10−7) for TBV (16p11) and LVV (1q25, 2q22, 3q13, 5q14, and 10q23), including common variants. Finally, we confirmed the association of common variants in GWAS loci for all traits.ConclusionOur whole genome sequence analyses revealed intriguing new loci of low-frequency and common variants, and replicated loci previously associated with brain volumes. Future work will include ancestry-specific and conditional analyses, as well as gene-based and scan tests.Supported by: AG058589, AG052409, AG054076, NO1-HC-25195, HHSN268201500001I and 75N92019D00031, R01HL131136, P30 AG066546, and K99AG066849.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/175473/1/alz064947.pd

Genetic associations with dementia‐related proteinopathy: Application of item response theory

IntroductionAlthough dementia-related proteinopathy has a strong negative impact on public health, and is highly heritable, understanding of the related genetic architecture is incomplete.MethodsWe applied multidimensional generalized partial credit modeling (GPCM) to test genetic associations with dementia-related proteinopathies. Data were analyzed to identify candidate single nucleotide variants for the following proteinopathies: Aβ, tau, α-synuclein, and TDP-43.ResultsFinal included data comprised 966 participants with neuropathologic and WGS data. Three continuous latent outcomes were constructed, corresponding to TDP-43-, Aβ/Tau-, and α-synuclein-related neuropathology endophenotype scores. This approach helped validate known genotype/phenotype associations: for example, TMEM106B and GRN were risk alleles for TDP-43 pathology; and GBA for α-synuclein/Lewy bodies. Novel suggestive proteinopathy-linked alleles were also discovered, including several (SDHAF1, TMEM68, and ARHGEF28) with colocalization analyses and/or high degrees of biologic credibility.DiscussionA novel methodology using GPCM enabled insights into gene candidates for driving misfolded proteinopathies.HighlightsLatent factor scores for proteinopathies were estimated using a generalized partial credit model. The three latent continuous scores corresponded well with proteinopathy severity. Novel genes associated with proteinopathies were identified. Several genes had high degrees of biologic credibility for dementia risk factors