Allele Counts Research Articles

Variants near X‐Chromosome Genes NLGN4X and PTCHD1 are Significantly Associated with Alzheimer’s Disease Risk

AbstractBackgroundGenome‐wide association studies (GWAS) for AD have identified numerous associated loci but have been focused only on the autosomes. We sought to identify novel AD‐associated genes on X‐Chromosome (X‐Chr).MethodWe evaluated the association of AD with X‐Chr variants in GWAS datasets assembled by the Alzheimer Disease Genetics Consortium including 26,322 non‐Hispanic White (NHW) individuals using logistic regression models with covariates for age, sex and principal components of ancestry. The number of independent SNPs in X‐Chr was calculated by linkage disequilibrium pruning yielding a study‐wide significance threshold of P<7.58×10−7. Genes near SNPs surpassing this threshold were further evaluated by expression quantitative trait locus (eQTL) in the BRAINEAC database and differential gene expression analyses using RNA‐seq data obtained from autopsied brains contained (GEO: GSE44772 and GSE33000). We also tested association of AD with missense or splicing variants with minor allele count (MAC)≥10 in the same genes using GENESIS and whole exome sequencing (WES) data obtained by the Alzheimer Disease Sequencing Project (ADSP) for 11,172 NHWs.ResultWe identified novel associations with variants near NLGN4X (rs34056759; minor allele frequency [MAF] = 0.15, OR = 1.15, P = 5.14×10−7) and PTCHD1 (rs5970663; MAF = 0.30, OR = 1.13, P = 8.26×10−7). The rs5970663 risk allele C was significantly associated with increased expression of PTCHD1 in the medulla region, and PTCHD1 expression was significantly higher in AD cases than in controls in the prefrontal cortex (P<2.1×10−8) and visual cortex (P<2.3×10−4). Rs34056759 was not associated with NLGN4X expression, but NLGN4X expression was significantly lower in AD cases compared to controls in the prefrontal cortex (P<8.7×10−7). A rare PTCHD1 variant (rs201933353; MAF = 0.001) was nominally associated with AD among 6,318 women (OR = 3.72, P = 0.06).ConclusionWe identified novel associations of AD with X‐Chr variants near NLGN4X and PTCHD1, loci that were previously associated with autism spectrum disorder. We plan to replicate, fine‐map, and annotate the candidate loci identified in this study using large whole genome sequencing ADSP datasets.

Effect of SNPs in TMPRSS2 to severe COVID-19 in patients with prostate cancer.

e17043 Background: The cellular transmembrane protease serine 2 (TMPRSS2) cleaves the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to let viral cell entry. Patients (pts) with prostate carcinoma (PCa) are expected to be at higher risk for coronavirus disease 2019 (COVID-19). However, TMPRSS2 transcription is significantly down-regulated by luteinizing hormone releasing hormone agonists (LHRH-a) and enzalutamide used to treat different PCa stages and this could alter the ability of SARS-CoV-2 to enter target cells. This study aims to evaluate if Single Nucleotide Polymorphisms (SNPs) in the TMPRSS2 gene are associated to a different risk for developing severe COVID-19. Methods: This is the translational part of a large retrospective-prospective real world study designed to estimate whether anti-androgen therapy for PCa is associated to a lower severity of COVID-19. Blood samples were collected from volunteer pts affected with PCa treated at our Institution who developed COVID-19. To be included, pts should receive at least LHRH-a for locally advanced o metastatic hormone sensitive or resistant disease. Genomic DNA of PCa pts was genotyped with the Axiom Human Genotyping SARS-CoV-2 array. Raw genotyping data were analyzed using the Axiom Analysis Suite and genotypes of 219 TMPRSS2 variants were extracted. Identified SNPs were used to compare reference and alternative allele counts between PCa pts and a control cohort of healthy European subjects (n = 2848; DOI:10.1056/NEJMoa2020283) collected in the Milan area (Italy) with chi squared test. Uni- and multivariate logistic regressions were performed to investigate associations between such identified SNPs and severe COVID-19, defined as any between the need for hospitalization or access to an intensive care unit or oxygen therapy. Results: 45 pts were enrolled from November 2020 to May 2022. Median age at COVID-19 detection was 77 years (range 51-94). 10/45 (22%) pts had severe COVID-19, but none died. At COVID-19 diagnosis, 4/45 pts (9%) were receiving chemotherapy for PCa, 13/45 (29%) new generation hormonal agents, 24/45 (53%) LHRH-a, the remaining 4/45 (9%) other therapies. Compared to the control cohort, we identified 52 TMPRSS2 variants associated with the susceptibility to SARS-CoV-2 infection in the cohort of PCa pts (P-value < 0.05). Among them, 3 SNPs were associated with severity in univariable logistic regression models: 2 resulted to be protective (rs9974589, p 0.01, OR 0.26; rs28442123, p 0.04, OR 0.30), and one was associated with increased risk of severe COVID-19 (rs2070788, p 0.01, OR 3.8). A multivariate logistic model (p = 0.0004, AUC = 0.88) was performed considering rs11908828 (p = 0,0109, OR = 32.25), rs28442123 (p = 0.03, OR = 0.11) and rs2070788 (p = 0.02, OR = 6.33). Conclusions: These preliminary findings point to a possible role of TMPRSS2 SNPs in modulating the severity of COVID-19 in PCa pts. Validation in wider series is needed.

Understanding and modeling human traits and diseases: Insights from the comparative genomics resources of Zoonomia

Understanding and modeling human traits and diseases: Insights from the comparative genomics resources of Zoonomia

Saddlepoint approximations to score test statistics in logistic regression for analyzing genome-wide association studies.

We investigate saddlepoint approximations of tail probabilities of the score test statistic in logistic regression for genome-wide association studies. The inaccuracy in the normal approximation of the score test statistic increases with increasing imbalance in the response and with decreasing minor allele counts. Applying saddlepoint approximation methods greatly improve the accuracy, even far out in the tails of the distribution. By using exact results for a simple logistic regression model, as well as simulations for models with nuisance parameters, we compare double saddlepoint methods for computing two-sided -values and mid- -values. These methods are also compared to a recent single saddlepoint procedure. We investigate the methods further on data from UK Biobank with skin and soft tissue infections as phenotype, using both common and rare variants.

Prevalence estimation of ATTRv in China based on genetic databases.

Introduction: Amyloid transthyretin (ATTR) is divided into either hereditary (ATTRv) or sporadic (ATTRwt) and ATTRv is a rare hereditary disease transmitted as an autosomal dominant manner. Its global prevalence is traditionally estimated as 5,000 to 10,000 persons. However, it may be underestimated and the exact prevalence of ATTRv in China mainland remains unknown. Methods: The Genome Aggregation database (gnomAD) database (containing 125,748 exomes) and two genomic sequencing databases--China Metabolic Analytics Project (ChinaMAP) (containing 10588 individuals) and Amcarelab gene database (containing 45392 exomes), were integrated to estimate the prevalence of ATTRv in the world and mainland Chinese populations. Pathogenic variants allele frequency and the prevalence of ATTRv was calculated. Results: Six variants, counting 470 alleles, were defined as pathogenic variants in gnomAD. The prevalence of ATTRv in the world population was 57.4/100,000. Two variants (2 allele counts) and 15 variants (34 individuals) were defined as pathogenic variants in the ChinaMAP database and the Amcarelab exome database, respectively. Thus, the estimated prevalence interval of ATTRv in mainland China was 18.9/100,000-74,9/100,000. Conclusion: The present study demonstrated that the previous prevalence was greatly underestimated using traditional methods. Therefore, raising awareness of the disease is essential for recognizing ATTRv in its early stage.

Abstract 3124: Identification of sub-clonal loss of heterozygosity (LOH) in glioblastomas analyzed with spatial transcriptomics

Abstract We propose a method to leverage spatial transcriptomics and bioinformatic analysis to identify sub-clonal loss of heterozygosity that could otherwise be missed by standard bulk sequencing in glioblastoma tumors. Glioblastomas are highly invasive and aggressive brain tumors with a low five-year survival rate of less than 10%. Current treatment strategies are not sufficient for long term disease outcome, and innovative approaches to analyze glioblastoma are imperative for future improved options. Many factors contribute to difficulty of treatment, though one critical consideration is the diffuse nature and known intratumoral heterogeneity of this cancer. It is important to determine LOH heterogeneity, as this genomic alteration is associated with copy number variation and specific classifications of gliomas. Loss of heterozygosity is an irreversible deletion of DNA that is a common alteration in cancer. Application of spatial transcriptomics can increase our understanding of genomic complexity across a tissue, and unsupervised graph clustering can computationally separate spatial regions with similar gene expression patterns. Our method investigates spatial clusters individually at known heterozygous SNP sites to identify evidence for LOH with Bayesian inference and a hidden Markov model. Our analysis centers on the balance of reference (A) and non-reference (B) spatial transcriptomics read counts at SNPs of interest. B allele frequency is the number of reads aligned to the alternative allele divided by the total number of reads counted at the SNP. At a normal two-copy state at a heterozygous site, B allele frequency is ideally 50 percent. LOH can be identified as this value deviates toward 0 or 100, and copy number alterations impact this frequency. Our method utilizes Bayes factor values, or likelihood ratios between two models, using allele counts to provide support for either LOH or a heterozygous event. For our analysis, spatial transcriptomics BAMs are processed with a graph clustering algorithm and split into cluster-specific BAMs. We calculate allele count coverage for each cluster at pre-selected heterozygous SNP positions identified through germline exome sequencing. Bayes factor values are then calculated at each SNP, resulting in evidence for LOH or a heterozygous event. Chromosomal regions are segmented by a hidden Markov approach. Cumulative metrics for each segment are evaluated to determine a “state” label of heterozygous, LOH, or undefined. We tested our method on three WHO grade 4 IDH wild-type EGFRvIII positive fresh frozen glioblastoma tissue samples. Sub-clonal heterogeneity was identified in two samples and confirmed by bulk exome sequencing, bulk RNA sequencing, and analysis of initial clinical reports. Citation Format: Michelle G. Webb, Frances Chow, Carmel McCullough, Bohan Zhang, Kyle Hurth, John Carpten, Gabriel Zada, David W. Craig. Identification of sub-clonal loss of heterozygosity (LOH) in glioblastomas analyzed with spatial transcriptomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3124.

Genomic insights into the NPGS intermediate wheatgrass germplasm collection

AbstractThe National Plant Germplasm System (NPGS) is a vital resource for genetic diversity, yet utilization of this resource requires a thorough understanding of the germplasm and genetic diversity. Intermediate wheatgrass (IWG, Thinopyrum intermedium) is a perennial grass species that has been improved for forage production through breeding utilizing the NPGS collection and has also been targeted for domestication as a perennial grain crop. To better characterize the IWG collection, we combined previously published forage data with new agronomic and genomic data. A total of 331 NPGS accessions were genomically profiled with genotyping‐by‐sequencing (GBS) and a genome‐wide association study (GWAS) was used to evaluate trait architecture. Along with the GWAS, in silico bulk samples were profiled by recoding GBS data to conduct association mapping through allele counting with extreme‐phenotype (XP)‐GWAS. Genomic analysis revealed two subpopulations, which were defined as European and Asian groups, and are differentiated around the Black Sea region. Phenotypic observations for forage and agronomic traits differed between the two groups (p < 0.05), even though greater than 70% of the genetic variance was partitioned within individual accessions. Finally, XP‐GWAS revealed 303 marker–trait associations for five agronomic and four forage traits. These results suggest that genetic diversity within the NPGS collection should lead to genetic gains for both forage and grain breeding as well as opportunities for breeding programs to enhance genetic diversity. More broadly, the methods we applied could be applicable to low‐resourced species, leveraging existing, and new data, to strengthen genetic characterization and breeding efficiency.

Do female bluethroats without extra-pair offspring have more MHC-compatible social mates?

Genes of the major histocompatibility complex (MHC) are crucial for adaptive immunity in jawed vertebrates, and theory predicts that there should be mate choice for optimizing MHC constitution in the offspring. In a previous study, we demonstrated a non-random female choice of extra-pair males in the bluethroat (Luscinia svecica), yielding offspring that was closer to an intermediate MHC class II (MHCII) allele count than their within-pair halfsiblings. The present study tests whether social pairs with only within-pair young (WPY) in their brood, in the same study population, had a combined MHC-constitution closer to a presumed intermediate optimum, than social pairs with extra-pair young (EPY), with a corresponding pattern in their offspring. As expected, we found that WPY from pure WPY-broods were more MHC-optimal than WPY from mixed broods, but only in broods of young (second year) males. Correspondingly, there was a tendency for social pairs with only WPY in their brood to be more MHC-compatible than social pairs with EPY in their brood, when the male was young. Older bluethroat males have considerably larger testes than young males, and their higher sperm competitiveness could help them secure paternity in their own brood, also when they are not MHC-compatible. In other words, in the sexual conflict over paternity, females may be more likely to realise their preference for a MHC-compatible mate when paired to a young male. As a possible fitness indicator, immune responsiveness to an injected antigen (PHA) was elevated for offspring closer to “the golden mean” in MHCII allele count.Significance statementThis study contributes to our understanding of MHC-based mate choice in extra-pair mating systems, by showing that female bluethroats (Luscinia svecica) with an MHCII-compatible social mate tend to have no extra-pair young in their brood, but only when the social male is young. This elucidates a possible sexual conflict, in which older social males are able to override female preferences and prevent other males from gaining paternity in their brood through higher sperm production. Studying systems in which extra-pair paternity occurs offers an insight into the genetic benefits of mate choice, as extra-pair males, in contrast to social males, generally contribute only sperm. Further, the strict and thorough genotyping scheme applied in this study enabled us to demonstrate a preference for “the golden mean” in MHC-diversity in a species with one of the highest MHC class II-diversity known to date.

Performance Characteristics of Oncomine Focus Assay for Theranostic Analysis of Solid Tumors, A (21-Months) Real-Life Study.

Next generation sequencing analysis is crucial for therapeutic decision in various solid tumor contexts. The sequencing method must remain accurate and robust throughout the instrument lifespan allowing the biological validation of patients' results. This study aims to evaluate the long-term sequencing performances of the Oncomine Focus assay kit allowing theranostic DNA and RNA variants detection on the Ion S5XL instrument. We evaluated the sequencing performances of 73 consecutive chips over a 21-month period and detailed the sequencing data obtained from both quality controls and clinical samples. The metrics describing sequencing quality remained stable throughout the study. We showed that an average of 11 × 106 (±0.3 × 106) reads were obtained using a 520 chip leading to an average of 6.0 × 105 (±2.6 × 105) mapped reads per sample. Of 400 consecutive samples, 95.8 ± 16% of amplicons reached the depth threshold of 500X. Slight modifications of the bioinformatics workflow improved DNA analytical sensitivity and allowed the systematic detection of expected SNV, indel, CNV, and RNA alterations in quality controls samples. The low inter-run variability of DNA and RNA-even at low variant allelic fraction, amplification factor, or reads counts-indicated that our method was adapted to clinical practice. The analysis of 429 clinical DNA samples indicated that the modified bioinformatics workflow allowed detection of 353 DNA variants and 88 gene amplifications. RNA analysis of 55 clinical samples revealed 7 alterations. This is the first study showing the long-term robustness of the Oncomine Focus assay in clinical routine practice.

Neuropathology-based APOE genetic risk score better quantifies Alzheimer's risk.

Apolipoprotein E (APOE) ε4-carrier status or ε4 allele count are included in analyses to account for the APOE genetic effect on Alzheimer's disease (AD); however, this does not account for protective effects of APOE ε2 or heterogeneous effect of ε2, ε3, and ε4 haplotypes. We leveraged results from an autopsy-confirmed AD study to generate a weighted risk score for APOE (APOE-npscore). We regressed cerebrospinal fluid (CSF) amyloid and tau biomarkers on APOE variables from the Wisconsin Registry for Alzheimer's Prevention (WRAP), Wisconsin Alzheimer's Disease Research Center (WADRC), and Alzheimer's Disease Neuroimaging Initiative (ADNI). The APOE-npscore explained more variance and provided a better model fit for all three CSF measures than APOE ε4-carrier status and ε4 allele count. These findings were replicated in ADNI and observed in subsets of cognitively unimpaired (CU) participants. The APOE-npscore reflects the genetic effect on neuropathology and provides an improved method to account for APOE in AD-related analyses.

Evaluation and Analysis of Absence of Homozygosity (AOH) Using Chromosome Analysis by Medium Coverage Whole Genome Sequencing (CMA-seq) in Prenatal Diagnosis.

Absence of homozygosity (AOH) is a genetic characteristic known to cause human diseases mainly through autosomal recessive or imprinting mechanisms. The importance and necessity of accurate AOH detection has become more clinically significant in recent years. However, it remains a challenging task for sequencing-based methods thus far. In this study, we developed and optimized a new bioinformatic algorithm based on the assessment of minimum sequencing coverage, optimal bin size, the Z-score threshold of four types of allele count and the frequency for accurate genotyping using 28 AOH negative samples, and redefined the AOH detection cutoff value. We showed the performance of chromosome analysis by five-fold coverage whole genome sequencing (CMA-seq) for AOH identification in 27 typical prenatal/postnatal AOH positive samples, which were previously confirmed by chromosomal microarray analysis with single nucleotide polymorphism array (CMA/SNP array). The blinded study indicated that for all three forms of AOH, including whole genomic AOH, single chromosomal AOH and segmental AOH, and all kinds of sample types, including chorionic villus sampling, amniotic fluid, cord blood, peripheral blood and abortive tissue, CMA-seq showed equivalent detection power to that of routine CMA/SNP arrays (750K). The subtle difference between the two methods is that CMA-seq is prone to detect small inconsecutive AOHs, while CMA/SNP array reports it as a whole. Based on our newly developed bioinformatic algorithm, it is feasible to detect clinically significant AOH using CMA-seq in prenatal diagnosis.

Fast and accurate joint inference of coancestry parameters for populations and/or individuals.

We introduce a fast, new algorithm for inferring from allele count data the FST parameters describing genetic distances among a set of populations and/or unrelated diploid individuals, and a tree with branch lengths corresponding to FST values. The tree can reflect historical processes of splitting and divergence, but seeks to represent the actual genetic variance as accurately as possible with a tree structure. We generalise two major approaches to defining FST, via correlations and mismatch probabilities of sampled allele pairs, which measure shared and non-shared components of genetic variance. A diploid individual can be treated as a population of two gametes, which allows inference of coancestry coefficients for individuals as well as for populations, or a combination of the two. A simulation study illustrates that our fast method-of-moments estimation of FST values, simultaneously for multiple populations/individuals, gains statistical efficiency over pairwise approaches when the population structure is close to tree-like. We apply our approach to genome-wide genotypes from the 26 worldwide human populations of the 1000 Genomes Project. We first analyse at the population level, then a subset of individuals and in a final analysis we pool individuals from the more homogeneous populations. This flexible analysis approach gives advantages over traditional approaches to population structure/coancestry, including visual and quantitative assessments of long-standing questions about the relative magnitudes of within- and between-population genetic differences.

MEGA-ANALYSIS OF A CONSORTIUM OF FOUR CENTENARIAN STUDIES IDENTIFIES NOVEL EXTREME LONGEVITY VARIANTS

Abstract The meta-analysis of extreme longevity (EL) conducted in 2017 using a consortium of four longevity studies—the New England Centenarian Study (NECS), the Long Life Family Study (LLFS), the Southern Italian Centenarian Study (SICS), and the Longevity Gene Project (LGP)—confirmed previous associations with APOE alleles and identified additional candidate genes. The current study builds upon this work. Using an aggregated set of the four studies (mega-analysis), we sought to identify additional longevity variants. The current study includes 234 additional cases with a total of 2304 cases (median age=103, age range=[96, 119]), defined as living past the age at which less than 1% individuals from the 1900 - 1920 birth year cohorts survived and approximately 6,000 controls. Approximately 10 million genotyped and imputed SNPs that passed imputation quality score threshold of 0.7 and that had a minor allele count of 3 or greater were analyzed. We ran a mixed effects logistic model for EL adjusting for sex, top principal components, indicator for living in Denmark, indicator for living in Italy, and the genetic relationship matrix. The analysis identified 21 novel loci that are genome-wide significant, both common and rare, and these loci were not previously published. The genome-wide significant loci include genes such as ADGRL2 (p=6.46-15), HLA-DPA1 (p=1.06-8), GRK5 (p=2.01-8), TECTB (p=3.38-8), KCNQ1 (p=7.53-10), TEAD2 (p=9.53-10), and several intergenic regions. We are currently in the process of seeking replication of the top results in independent cohorts.

Diabetes and hypertension are associated with elevated beta‐amyloid burden: evidence from the prospective population‐based Rotterdam Study

AbstractBackgroundHigher vascular risk increases the likelihood of developing dementia. Better understanding the association between vascular risk and Alzheimer’s disease (AD) pathology at the pre‐dementia stage is critical for developing effective strategies to delay cognitive decline. In this work, we estimated the impact of vascular risk on the presence and severity of in‐vivo measured brain beta‐amyloid (Aβ) plaques in participants from the population‐based Rotterdam Study.MethodsVascular risk factors (hypertension, hypercholesterolemia, diabetes, obesity, physical inactivity, and smoking) were assessed twelve and seven years prior to 18F‐florbetaben positron emission tomography (PET) in 506 dementia‐free participants. Vascualr risk factors were associated with binary amyloid PET status or continuous PET readouts (SUVr values) using logistic and linear regression models respectively, adjusted for age, sex, education, APOE4 risk allele count, and time between vascular risk and PET assessment.ResultsParticipants’ mean age at time of amyloid PET was 68 years (range: 60‐90), 262 (51.8%) were women and 158 (31.2%) carried at least one APOE4 risk allele (Figure 1). The adjusted prevalence estimates of an amyloid‐positive PET status markedly increased with age (12% in 60‐69y vs. 45.5% in 80‐89y age‐groups) and APOE4 allele count (8.3% in non‐carriers vs. 34.8 to 58.8% in carriers of one or two risk allele(s); Figure 2). A diagnosis of diabetes seven years prior to PET assessment was associated with a higher risk of a positive amyloid status (OR[95%CI]=4.07[1.66–9.71], P=0.002; Figure 3) and higher SUVr values, indicating more severe Aβ pathology (standardized beta=0.478[0.205–0.750], P=0.001; Figure 4). We found evidence for an association between hypertension and higher SUVr values in APOE4 carriers, but not in non‐carriers (interaction: standardized beta=0.425[0.092–0.758], P=0.013; Figure 5). Hypercholesterolemia, obesity, physical inactivity and smoking were not related to amyloid PET measures.ConclusionThe current findings suggest a contribution of diabetes and hypertension to the pathophysiology of AD. Since both conditions respond well to lifestyle modification and drug treatment, further research should be conducted to examine the preventative effect of early risk management on the development of AD neuropathology.

Diabetes and hypertension are associated with elevated beta‐amyloid burden: evidence from the population‐based Rotterdam Study

AbstractBackgroundHigher vascular risk increases the likelihood of developing dementia. Better understanding the association between vascular risk and Alzheimer’s disease (AD) pathology at the pre‐dementia stage is critical for developing effective strategies to delay cognitive decline. In this work, we estimated the impact of vascular risk on the presence and severity of in‐vivo measured brain beta‐amyloid (Aβ) plaques in participants from the population‐based Rotterdam Study.MethodVascular risk factors (hypertension, hypercholesterolemia, diabetes, obesity, physical inactivity, and smoking) were assessed twelve and seven years prior to 18F‐florbetaben positron emission tomography (PET) in 506 dementia‐free participants. Vascualr risk factors were associated with binary amyloid PET status or continuous PET readouts (SUVr values) using logistic and linear regression models respectively, adjusted for age, sex, education, APOE4 risk allele count, and time between vascular risk and PET assessmentResultParticipants’ mean age at time of amyloid PET was 68 years (range: 60‐90), 262 (51.8%) were women and 158 (31.2%) carried at least one APOE4 risk allele (Figure 1). The adjusted prevalence estimates of an amyloid‐positive PET status markedly increased with age (12% in 60‐69y vs. 45.5% in 80‐89y age‐groups) and APOE4 allele count (8.3% in non‐carriers vs. 34.8 to 58.8% in carriers of one or two risk allele(s); Figure 2). A diagnosis of diabetes seven years prior to PET assessment was associated with a higher risk of a positive amyloid status (OR[95%CI] = 4.07[1.66‐9.71], P = 0.002; Figure 3) and higher SUVr values, indicating more severe Aβ pathology (standardized beta = 0.478[0.205–0.750], P = 0.001; Figure 4). We found evidence for an association between hypertension and higher SUVr values in APOE4 carriers, but not in non‐carriers (interaction: standardized beta = 0.425[0.092–0.758], P = 0.013; Figure 5). Hypercholesterolemia, obesity, physical inactivity and smoking were not related to amyloid PET measures.ConclusionThe current findings suggest a contribution of diabetes and hypertension to the pathophysiology of AD. Since both conditions respond well to lifestyle modification and drug treatment, further research should be conducted to examine the preventative effect of early risk management on the development of AD neuropathology.

Transcriptomic Analysis of Whole Blood in Admixed Latinx Alzheimer Disease Cohorts

AbstractBackgroundIdentifying the genes and biological pathways involved in Alzheimer disease (AD) is critical in the effort to develop effective therapies. Significant work has identified genetic variants conferring risk and protection for AD in individuals of diverse ancestries, but identification of downstream functional effects including modulation of gene regulation is lacking, particularly in individuals of diverse ancestries. Therefore, to explore transcriptional changes between clinically diagnosed AD and cognitively intact age‐matched controls, herein we analyzed RNA sequencing data from peripheral blood collected from individuals of admixed genetic backgrounds.MethodTotal RNA was extracted from peripheral whole blood stored in PAXGene tubes from 47 Cubans (22 AD and 25 controls), 85 Peruvians (41 AD and 44 controls), and 168 Puerto Ricans (88 AD and 80 controls). PolyA selected mRNA was sequenced to more than 40 million paired end read per sample on the Illumina NovaSeq 6000. The bioinformatic pipeline included mapping to the human reference genome (GRCh38), gene quantifications against the GENCODE v35 annotation set, and differential expression was calculated using DESeq2 with sex, age at blood draw, and count of APOEe4 alleles as covariates. Functional categorization was performed by gene set enrichment of gene ontology and KEGG pathways.ResultAcross the cohorts, a total of 358 protein‐coding genes (FDR ≤ 0.05, Fold change ≥ 1.25) were differentially expressed with 238 down‐regulated and 120 up‐regulated in AD relative to controls. Despite the few genes overlapping between ethnicities, pathway analysis revealed common pathways including up‐regulation of genes involved in inflammation and RNA processing and down‐regulation of genes involved in cellular detoxification and lipid transport, among others. Interestingly, while few specific genes overlap in differential expression overlap with a published set of genes from non‐Hispanic Europeans and African Americans (Griswold et al, 2018), the pathways identified are similar.ConclusionOur analysis reveals a signature of gene expression that implicates increased inflammation and decreased cellular detoxification based on gene expression analysis in admixed Latinx AD. Convergence of pathways across these and African American and European cohorts supports the idea of distinct genes but similar underlying pathological processes contributing to AD across individuals of diverse ancestries.

Functional Annotations‐Informed Whole Genome Sequence Analysis Identifies Novel Rare Variants for AD in the Alzheimer’s Disease Sequencing Project

AbstractBackgroundIncorporating functional annotations improves power to identify rare variants (RV) in the analysis of whole genome sequencing (WGS) association studies. We incorporated Alzheimer’s Disease (AD)‐specific annotations based on partitioning heritability into the variant‐Set Test for Association using Annotation information (STAAR‐O) framework to identify RVs associated with AD in the diverse sample of the Alzheimer’s Disease Sequencing Project (ADSP).MethodWe performed WGS association analyses in a sample of 4,074 individuals (1,668 AD cases; 2,406 controls) sequenced as part of the ADSP Discovery Extension Phase. We first estimated heritability of all single nucleotide variants in functional annotation categories using the GCTA tool embedded in FunSPU. For each annotation category, a global weight was derived based on the partitioned AD heritability. Among weighted functional scores, we selected the top 12 to leverage the most informative functional annotations for incorporation into the STAAR‐O test.2 We performed agnostic region‐based association analysis using sliding windows, defined as 2kb in length with a skip length of 1kb. Association tests were performed with RV (minor allele frequency <1%), adjusting for sex, sequencing center, platform, study, 4 principal components, and a genetic relatedness matrix. Replication of significant associations was carried out in an independent sample of 10,083 individuals (5,717 AD cases, 4,366 controls) sequenced as part of the ADSP Follow‐Up Study and using the same analytical models.ResultOut of 2.65 million 2‐kb overlapping windows with a total minor allele count >10, two non‐consecutive windows on chromosome 17 were associated with AD (P<5×10−8) (Figures). Both windows associations were replicated in the independent sample (P = 0.003 and 0.016). The top variant of one significant window was rs534148850 (MAF = 0.0005, P = 5.55×10−9) located downstream of PLEKHM1P1, a pseudogene, and MIR4315‐2, a microRNA with predicted targets enriched in apoptosis pathways. The top variant of the other window was rs532055552 (MAF = 0.005, P = 6.20×10−9) located downstream of CEP112, a coiled domain‐containing protein involved in the regulation of gamma‐aminobutyric acid A receptor surface expression.ConclusionIncorporating AD‐relevant functional annotations to a powerful RV association framework, we discovered and replicated two novel genetic regions harboring RV associated with AD.

APOE4 copy number‐dependent Complement Pathway Dysregulation

AbstractBackgroundComplement has been implicated in the pathophysiology of AD based on human genetic studies, human post‐mortem pathology, and mouse genetic studies. However, the relationship between complement activation and other AD risk factors (such as the APOE4 allele) remains unclear.MethodWe used pathway analysis of the ADNI targeted proteomics dataset to identify protein clusters highly correlated with each other, including a complement pathway cluster. We then developed a hybrid proteomics platform, which allows for both discovery proteomics (via LC‐MS/MS) and precise quantitation of complement protein‐derived peptides via Stable Isotope Labelled (SIL) internal control peptides for the complement proteins. We then tested this hybrid proteomics platform on over 130 CSF samples from cognitively normal older adults (ages 60‐80), together with assays for CSF amyloid beta, tau and p‐tau‐181p. Spearman correlation tests were used to evaluate correlations between protein pathway expression and demographic variables, such as APOE4 allele number.ResultPathway analysis of the ADNI targeted proteomics dataset identified a cluster of complement proteins whose levels were highly correlated with each other in the CSF; this cluster included peptides from C1QB, C2, C3, C4A, C5, C6, C8B and CRP and several additional proteins. Across ∼300 individuals (including cognitively normal controls, MCI and AD patients), expression of this complement protein cluster was inversely correlated with APOE4 allele count and gender (with lower expression in females), and a positive correlation with age (P<0.05 for each). To further study complement protein levels in human CSF across the AD spectrum, we developed a hybrid proteomics platform for precise quantitation of complement proteins and for unbiased discovery proteomics. In 130 CSF samples from older adults, we were able to detect ∼9,000 different peptides from 884 different proteins, and to precisely quantitate all proteins in the classical, alternative and mannose binding lectin complement pathways using the SIL peptide internal controls. Here we report data on APOE4 copy number dependent changes in quantified CSF complement protein levels, and on APOE4 dependent changes across the human CSF proteome.ConclusionAPOE4 is associated with a reduction in CSF complement protein levels, which may reflect increased complement pathway activation and ensuing degradation of these proteins.

Longitudinal GWAS Identifies Novel Genetic Variants and Complex Traits Associated with Resilience to Alzheimer’s Disease

AbstractBackgroundWe completed a large genetic analysis of resilience to cognitive decline in Alzheimer’s Disease (AD) and discovered novel variants, genes, and complex traits associated with better‐than or worse‐than‐expected cognitive performance given an individual’s age, sex, and APOE genotype.MethodLeveraging 15,933 non‐Hispanic white participants across four longitudinal cohort studies of aging and AD (Figure 1), our group determined the effects of genetic variants on resilience metrics using mixed‐effects regressions. Models adjusted for age, sex, APOE ε4 allele count, presence of the APOE ε2 allele and all covariate interactions with interval (years from baseline). The outcomes of interest were residual cognitive resilience, quantified from residuals in three cognitive domains (memory, executive function, and language), and combined resilience, summarized as the covariance of educational attainment with residual cognitive resilience. Post‐GWAS analyses included gene tests using MAGMA and estimates of genetic correlation with 65 complex traits using GNOVA.ResultWe observed genome‐wide significant associations at multiple established AD loci, including BIN1 and CR1 (Figure 2). We observed a novel association with combined resilience on chromosome 13 (top SNP: rs11838654, MAF = 0.06, P = 4.7×10−8; Figure 3) and a novel signal on chromosome 1 approaching significance (top SNP: rs2817183, MAF = 0.41, P = 5.1×10−8). Interestingly, rs11838654 is an eQTL for four genes in hippocampus (WBP4, COG6, MRPS31, and NHLRC3I; Braineac database). We also observed an association with residual cognitive resilience on chromosome 5 that approached genome‐wide significance (top SNP: rs4482935, MAF = 0.25, P = 5.5×10−8; Figure 2). Gene‐level tests identified associations of CD2AP (P.fdr = 0.027) and ZNF146 (P.fdr = 0.049) with residual cognitive resilience and combined resilience, respectively. Additionally, we identified negative genetic correlations of combined resilience with ischemic stroke and coronary artery disease (all P.fdr<2.5×10−2; Figure 4).ConclusionCompared to models of resilience that regress out the effects of AD neuropathology on cognition, the present models benefit from larger sample size at the cost of molecular precision. Although the genetic architecture of resilience from these less precise models more closely resembles that of clinical AD, we uncovered novel genetic drivers of resilience through this approach. Such findings will require future replication but suggest a trajectory‐based definition of resilience holds substantial promise for discovery.

Whole genome sequence association analysis of Brain MRI measures

AbstractBackgroundGenome‐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.