Analysis of Whole-Exome Sequencing Data for Alzheimer Disease Stratified by APOE Genotype

Allele age estimators designed for whole genome datasets show only a modest decrease in accuracy when applied to whole exome datasets

Personalized genomics in the healthcare system is becoming increasingly accessible as the costs of sequencing decreases. With the increase in number of genomes, larger numbers of rare variants are being discovered and much work is being done to identify their functional impacts in relation to disease phenotypes. One way to characterize these variants is to estimate the time the mutation entered the population. However, allele age estimators such as Relate, Genealogical Estimator of Variant Age, and time of coalescence, were developed based on the assumption that datasets include the entire genome. We examined the performance of each of these estimators on simulated exome data under a neutral constant population size model and found that each provides usable estimates of allele age from whole-exome datasets. To test the robustness of these methods, analyses were undertaken to simulate data under a population expansion model and background selection. Relate performs the best amongst all three estimators with Pearson coefficients of 0.64 and 0.68 (neutral constant and expansion population model) with a 17 percent and 15 percent drop in accuracy between whole genome and whole exome estimations. Of the three estimators, Relate is best able to parallelize to yield quick results with little resources, however even Relate is only able to scale to thousands of samples making it unable to match the hundreds of thousands of samples being currently released. While more work is needed to expand the capabilities of current methods of estimating allele age, these methods estimate the age of mutations with a modest decrease in performance.

Genetics of Alzheimer's Disease in the African American Population

Black/African American (AA) individuals have a higher risk of Alzheimer's disease (AD) than White non-Hispanic persons of European ancestry (EUR) for reasons that may include economic disparities, cardiovascular health, quality of education, and biases in the methods used to diagnose AD. AD is also heritable, and some of the differences in risk may be due to genetics. Many AD-associated variants have been identified by candidate gene studies, genome-wide association studies (GWAS), and genome-sequencing studies. However, most of these studies have been performed using EUR cohorts. In this paper, we review the genetics of AD and AD-related traits in AA individuals. Importantly, studies of genetic risk factors in AA cohorts can elucidate the molecular mechanisms underlying AD risk in AA and other populations. In fact, such studies are essential to enable reliable precision medicine approaches in persons with considerable African ancestry. Furthermore, genetic studies of AA cohorts allow exploration of the ways the impact of genes can vary by ancestry, culture, and economic and environmental disparities. They have yielded important gains in our knowledge of AD genetics, and increasing AA individual representation within genetic studies should remain a priority for inclusive genetic study design.

Step by step: towards a better understanding of the genetic architecture of Alzheimer's disease

Alzheimer's disease (AD) is considered to have a large genetic component. Our knowledge of this component has progressed over the last 10 years, thanks notably to the advent of genome-wide association studies and the establishment of large consortia that make it possible to analyze hundreds of thousands of cases and controls. The characterization of dozens of chromosomal regions associated with the risk of developing AD and (in some loci) the causal genes responsible for the observed disease signal has confirmed the involvement of major pathophysiological pathways (such as amyloid precursor protein metabolism) and opened up new perspectives (such as the central role of microglia and inflammation). Furthermore, large-scale sequencing projects are starting to reveal the major impact of rare variants - even in genes like APOE - on the AD risk. This increasingly comprehensive knowledge is now being disseminated through translational research; in particular, the development of genetic risk/polygenic risk scores is helping to identify the subpopulations more at risk or less at risk of developing AD. Although it is difficult to assess the efforts still needed to comprehensively characterize the genetic component of AD, several lines of research can be improved or initiated. Ultimately, genetics (in combination with other biomarkers) might help to redefine the boundaries and relationships between various neurodegenerative diseases.

Regulation of K+-Dependent Na+/Ca2+-Exchangers (NCKX)

Potassium-dependent sodium-calcium exchangers (NCKX) have emerged as key determinants of calcium (Ca²⁺) signaling and homeostasis, especially in environments where ion concentrations undergo large changes, such as excitatory cells and transport epithelia. The regulation of NCKX transporters enables them to respond to the changing cellular environment thereby helping to shape the extent and kinetics of Ca²⁺ signals. This review examines the current knowledge of the different ways in which NCKX activity can be modulated. These include (i) cellular and dynamic subcellular location (ii); changes in protein expression mediated at the gene, transcript, or protein level (iii); genetic changes resulting in altered protein structure or expression (iv); regulation via changes in substrate concentration (v); and post-translational modification, partner protein interactions, and allosteric regulation. Detailed mechanistic understanding of NCKX regulation is an emerging area of research with the potential to provide important new insights into transporter function, the control of Ca²⁺ signals, and possible interventions for dysregulated Ca²⁺ homeostasis.

Omics-based biomarkers discovery for Alzheimer's disease

Alzheimer's disease (AD) is the most common neurodegenerative disorders presenting with the pathological hallmarks of amyloid plaques and tau tangles. Over the past few years, great efforts have been made to explore reliable biomarkers of AD. High-throughput omics are a technology driven by multiple levels of unbiased data to detect the complex etiology of AD, and it provides us with new opportunities to better understand the pathophysiology of AD and thereby identify potential biomarkers. Through revealing the interaction networks between different molecular levels, the ultimate goal of multi-omics is to improve the diagnosis and treatment of AD. In this review, based on the current AD pathology and the current status of AD diagnostic biomarkers, we summarize how genomics, transcriptomics, proteomics and metabolomics are all conducing to the discovery of reliable AD biomarkers that could be developed and used in clinical AD management.

Deep neural networks with controlled variable selection for the identification of putative causal genetic variants

Deep neural networks (DNNs) have been successfully utilized in many scientific problems for their high prediction accuracy, but their application to genetic studies remains challenging due to their poor interpretability. Here we consider the problem of scalable, robust variable selection in DNNs for the identification of putative causal genetic variants in genome sequencing studies. We identified a pronounced randomness in feature selection in DNNs due to its stochastic nature, which may hinder interpretability and give rise to misleading results. We propose an interpretable neural network model, stabilized using ensembling, with controlled variable selection for genetic studies. The merit of the proposed method includes: flexible modelling of the nonlinear effect of genetic variants to improve statistical power; multiple knockoffs in the input layer to rigorously control the false discovery rate; hierarchical layers to substantially reduce the number of weight parameters and activations, and improve computational efficiency; and stabilized feature selection to reduce the randomness in identified signals. We evaluate the proposed method in extensive simulation studies and apply it to the analysis of Alzheimer's disease genetics. We show that the proposed method, when compared with conventional linear and nonlinear methods, can lead to substantially more discoveries.

Genetic architecture of RNA editing regulation in Alzheimer's disease across diverse ancestral populations

Most Alzheimer's disease (AD)-associated genetic variants do not change protein coding sequence and thus likely exert their effects through regulatory mechanisms. RNA editing, the post-transcriptional modification of RNA bases, is a regulatory feature that is altered in AD patients that differs across ancestral backgrounds. Editing QTLs (edQTLs) are DNA variants that influence the level of RNA editing at a specific site. To study the relationship of DNA variants genome-wide, and particularly in AD-associated loci, with RNA editing, we performed edQTL analyses in self-reported individuals of African American (AF) or White (EU) race with corresponding global genetic ancestry averaging 82.2% African ancestry (AF) and 96.8% European global ancestry (EU) in the two groups, respectively. We used whole-genome genotyping array and RNA sequencing data from peripheral blood of 216 AD cases and 212 age-matched, cognitively intact controls. We identified 2144 edQTLs in AF and 3579 in EU, of which 1236 were found in both groups. Among these, edQTLs in linkage disequilibrium (r2 > 0.5) with AD-associated genetic variants in the SORL1, SPI1 and HLA-DRB1 loci were associated with sites that were differentially edited between AD cases and controls. While there is some shared RNA editing regulatory architecture, most edQTLs had distinct effects on the rate of RNA editing in different ancestral populations suggesting a complex architecture of RNA editing regulation. Altered RNA editing may be one possible mechanism for the functional effect of AD-associated variants and may contribute to observed differences in the genetic etiology of AD between ancestries.

A Fast and Robust Strategy to Remove Variant-Level Artifacts in Alzheimer Disease Sequencing Project Data

Background and Objectives

Exome sequencing (ES) and genome sequencing (GS) are expected to be critical to further elucidate the missing genetic heritability of Alzheimer disease (AD) risk by identifying rare coding and/or noncoding variants that contribute to AD pathogenesis. In the United States, the Alzheimer Disease Sequencing Project (ADSP) has taken a leading role in sequencing AD-related samples at scale, with the resultant data being made publicly available to researchers to generate new insights into the genetic etiology of AD. To achieve sufficient power, the ADSP has adapted a study design where subsets of larger AD cohorts are collected and sequenced across multiple centers, using a variety of sequencing platforms. This approach may lead to variable variant quality across sequencing centers and/or platforms. In this study, we sought to implement and evaluate filters that can be applied fast to robustly remove variant-level artifacts in the ADSP data.

Methods

We implemented a robust quality control procedure to handle ADSP data. We evaluated this procedure while performing exome-wide and genome-wide association analyses on AD risk using the latest ADSP whole ES (WES) and whole GS (WGS) data releases (NG00067.v5).

Results

We observed that many variants displayed large variation in allele frequencies across sequencing centers/platforms and contributed to spurious association signals with AD risk. We also observed that sequencing platform/center adjustment in association models could not fully account for these spurious signals. To address this issue, we designed and implemented variant filters that could capture and remove these center-specific/platform-specific artifactual variants.

Discussion

We derived a fast and robust approach to filter variants that represent sequencing center-related or platform-related artifacts underlying spurious associations with AD risk in ADSP WES and WGS data. This approach will be important to support future robust genetic association studies on ADSP data, as well as other studies with similar designs.

Effects of age, amyloid, sex, and APOE ε4 on the CSF proteome in normal cognition

Introduction

It is important to understand which biological processes change with aging, and how such changes are associated with increased Alzheimer's disease (AD) risk. We studied how cerebrospinal fluid (CSF) proteomics changed with age and tested if associations depended on amyloid status, sex, and apolipoprotein E Ɛ4 genotype.

Methods

We included 277 cognitively intact individuals aged 46 to 89 years from Alzheimer's Disease Neuroimaging Initiative, European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery, and Metabolic Syndrome in Men. In total, 1149 proteins were measured with liquid chromatography mass spectrometry with multiple reaction monitoring/Rules-Based Medicine, tandem mass tag mass spectrometry, and SOMAscan. We tested associations between age and protein levels in linear models and tested enrichment for Reactome pathways.

Results

Levels of 252 proteins increased with age independently of amyloid status. These proteins were associated with immune and signaling processes. Levels of 21 proteins decreased with older age exclusively in amyloid abnormal participants and these were enriched for extracellular matrix organization.

Discussion

We found amyloid-independent and -dependent CSF proteome changes with older age, perhaps representing physiological aging and early AD pathology.

FOXG1 as a Potential Therapeutic Target for Alzheimer's Disease with a Particular Focus on Cell Cycle Regulation

BACKGROUND

Several recent findings have revealed that targeting of cell cycle reentry and (or) progression may provide an opportunity for the therapeutic intervention of Alzheimer's disease (AD). FOXG1 has been shown to play important roles in pattern formation, cell proliferation, and cell specification. Thus far, the roles of FoxG1 and its involvement in AD are largely unknown.

OBJECTIVE

Our study aimed to explore the intervention effect of FOXG1 on AD pathology and its potential mechanism with a particular focus on cell cycle regulation.

METHODS

We investigated the association of Foxg1 gene variants with AD-like behavioral deficits, p21 expression, neuronal apoptosis, and amyloid-β (Aβ) aggregate formation; we further determined whether targeting FOXG1-regulated cell cycle has therapeutic potential in AD.

RESULTS

Paralleling AD-like behavioral abnormalities, neuronal apoptosis, and Aβ deposits, a significant reduction in the expression of FOXG1 was observed in APP/PS1 mice at 6 months of age. Using the APP/PS1;Foxg1fl/fl-CreAAV mouse line, we found that FOXG1 potentially antagonized cell cycle reentry by negatively regulating the levels of p21-activated kinase (PAK3). By reducing p21cip1-mediated arrest at the G2 stage and regulating cyclin A1- and cyclin B-dependent progression patterns of the cell cycle, FOXG1 blocked neuronal apoptosis and Aβ deposition.

CONCLUSION

These results indicate that FOXG1 contributes to the regulation of the neuronal cell cycle, thereby affecting brain abnormalities in AD. An elevation of the FOXG1 level, either pharmacologically or through other means, could present a therapeutic strategy for AD.

Association of mitochondrial variants and haplogroups identified by whole exome sequencing with Alzheimer's disease

INTRODUCTION

Findings regarding the association between mitochondrial DNA (mtDNA) variants and Alzheimer's disease (AD) are inconsistent.

METHODS

We developed a pipeline for accurate assembly and variant calling in mitochondrial genomes embedded within whole exome sequences (WES) from 10,831 participants from the Alzheimer's Disease Sequencing Project (ADSP). Association of AD risk was evaluated with each mtDNA variant and variants located in 1158 nuclear genes related to mitochondrial function using the SCORE test. Gene-based tests were performed using SKAT-O.

RESULTS

Analysis of 4220 mtDNA variants revealed study-wide significant association of AD with a rare MT-ND4L variant (rs28709356 C>T; minor allele frequency = 0.002; P = 7.3 × 10-5 ) as well as with MT-ND4L in a gene-based test (P = 6.71 × 10-5 ). Significant association was also observed with a MT-related nuclear gene, TAMM41, in a gene-based test (P = 2.7 × 10-5 ). The expression of TAMM41 was lower in AD cases than controls (P = .00046) or mild cognitive impairment cases (P = .03).

DISCUSSION

Significant findings in MT-ND4L and TAMM41 provide evidence for a role of mitochondria in AD.

Genomics and Functional Genomics of Alzheimer's Disease

Alzheimer's disease (AD) is a complex and multifactorial neurodegenerative disease. Due to its long clinical course and lack of an effective treatment, AD has become a major public health problem in the USA and worldwide. Due to variation in age-at-onset, AD is classified into early-onset (< 60 years) and late-onset (≥ 60 years) forms with early-onset accounting for only 5-10% of all cases. With the exception of a small number of early-onset cases that are afflicted because of high penetrant single gene mutations in APP, PSEN1, and PSEN2 genes, AD is genetically heterogeneous, especially the late-onset form having a polygenic or oligogenic risk inheritance. Since the identification of APOE as the most significant risk factor for late-onset AD in 1993, the path to the discovery of additional AD risk genes had been arduous until 2009 when the use of large genome-wide association studies opened up the discovery gateways that led the identification of ~ 95 additional risk loci from 2009 to early 2022. This article reviews the history of AD genetics followed by the potential molecular pathways and recent application of functional genomics methods to identify the causal AD gene(s) among the many genes that reside within a single locus. The ultimate goal of integrating genomics and functional genomics is to discover novel pathways underlying the AD pathobiology in order to identify drug targets for the therapeutic treatment of this heterogeneous disorder.

Region-based analysis of rare genomic variants in whole-genome sequencing datasets reveal two novel Alzheimer's disease-associated genes: DTNB and DLG2

Alzheimer's disease (AD) is a genetically complex disease for which nearly 40 loci have now been identified via genome-wide association studies (GWAS). We attempted to identify groups of rare variants (alternate allele frequency <0.01) associated with AD in a region-based, whole-genome sequencing (WGS) association study (rvGWAS) of two independent AD family datasets (NIMH/NIA; 2247 individuals; 605 families). Employing a sliding window approach across the genome, we identified several regions that achieved association p values <10^-6, using the burden test or the SKAT statistic. The genomic region around the dystobrevin beta (DTNB) gene was identified with the burden and SKAT test and replicated in case/control samples from the ADSP study reaching genome-wide significance after meta-analysis (p_meta = 4.74 × 10^-8). SKAT analysis also revealed region-based association around the Discs large homolog 2 (DLG2) gene and replicated in case/control samples from the ADSP study (p_meta = 1 × 10^-6). In conclusion, in a region-based rvGWAS of AD we identified two novel AD genes, DLG2 and DTNB, based on association with rare variants.

Epigenomic features related to microglia are associated with attenuated effect of APOE ε4 on Alzheimer's disease risk in humans

Not all apolipoprotein E (APOE) ε4 carriers who survive to advanced age develop Alzheimer's disease (AD); factors attenuating the risk of ε4 on AD may exist. Guided by the top ε4-attenuating signals from methylome-wide association analyses (N = 572, ε4+ and ε4-) of neurofibrillary tangles and neuritic plaques, we conducted a meta-analysis for pathological AD within the ε4+ subgroups (N = 235) across four independent collections of brains. Cortical RNA-seq and microglial morphology measurements were used in functional analyses. Three out of the four significant CpG dinucleotides were captured by one principal component (PC1), which interacts with ε4 on AD, and is associated with expression of innate immune genes and activated microglia. In ε4 carriers, reduction in each unit of PC1 attenuated the odds of AD by 58% (odds ratio = 2.39, 95% confidence interval = [1.64,3.46], P = 7.08 × 10^-6 ). An epigenomic factor associated with a reduced proportion of activated microglia (epigenomic factor of activated microglia, EFAM) appears to attenuate the risk of ε4 on AD.

Potential Novel Genes for Late-Onset Alzheimer's Disease in East-Asian Descent Identified by APOE-Stratified Genome-Wide Association Study

The present study reports two novel genome-wide significant loci for late-onset Alzheimer’s disease (LOAD) identified from APOE ε4 non-carrier subjects of East Asian origin. A genome-wide association study of Alzheimer’s disease was performed in 2,291 Korean seniors in the discovery phase, from the Gwangju Alzheimer’ and Related Dementias (GARD) cohort study. The study was replicated in a Japanese cohort of 1,956 subjects that suggested two novel susceptible SNPs in two genes: LRIG1 and CACNA1A. This study demonstrates that the discovery of AD-associated variants is feasible in non-European ethnic groups using samples comprising fewer subjects from the more homogeneous genetic background.

A Machine Learning Method to Identify Genetic Variants Potentially Associated With Alzheimer's Disease

There is hope that genomic information will assist prediction, treatment, and understanding of Alzheimer's disease (AD). Here, using exome data from ∼10,000 individuals, we explore machine learning neural network (NN) methods to estimate the impact of SNPs (i.e., genetic variants) on AD risk. We develop an NN-based method (netSNP) that identifies hundreds of novel potentially protective or at-risk AD-associated SNPs (along with an effect measure); the majority with frequency under 0.01. For case individuals, the number of "protective" (or "at-risk") netSNP-identified SNPs in their genome correlates positively (or inversely) with their age of AD diagnosis and inversely (or positively) with autopsy neuropathology. The effect measure increases correlations. Simulations suggest our results are not due to genetic linkage, overfitting, or bias introduced by netSNP. These findings suggest that netSNP can identify SNPs associated with AD pathophysiology that may assist with the diagnosis and mechanistic understanding of the disease.

Novel Alzheimer's disease risk variants identified based on whole-genome sequencing of APOE ε4 carriers

Alzheimer's disease (AD) is a progressive neurodegenerative disease associated with a complex genetic etiology. Besides the apolipoprotein E ε4 (APOE ε4) allele, a few dozen other genetic loci associated with AD have been identified through genome-wide association studies (GWAS) conducted mainly in individuals of European ancestry. Recently, several GWAS performed in other ethnic groups have shown the importance of replicating studies that identify previously established risk loci and searching for novel risk loci. APOE-stratified GWAS have yielded novel AD risk loci that might be masked by, or be dependent on, APOE alleles. We performed whole-genome sequencing (WGS) on DNA from blood samples of 331 AD patients and 169 elderly controls of Korean ethnicity who were APOE ε4 carriers. Based on WGS data, we designed a customized AD chip (cAD chip) for further analysis on an independent set of 543 AD patients and 894 elderly controls of the same ethnicity, regardless of their APOE ε4 allele status. Combined analysis of WGS and cAD chip data revealed that SNPs rs1890078 (P = 6.64E-07) and rs12594991 (P = 2.03E-07) in SORCS1 and CHD2 genes, respectively, are novel genetic variants among APOE ε4 carriers in the Korean population. In addition, nine possible novel variants that were rare in individuals of European ancestry but common in East Asia were identified. This study demonstrates that APOE-stratified analysis is important for understanding the genetic background of AD in different populations.

Omics Data and Their Integrative Analysis to Support Stratified Medicine in Neurodegenerative Diseases

Molecular and clinical heterogeneity is increasingly recognized as a common characteristic of neurodegenerative diseases (NDs), such as Alzheimer's disease, Parkinson's disease and amyotrophic lateral sclerosis. This heterogeneity makes difficult the development of early diagnosis and effective treatment approaches, as well as the design and testing of new drugs. As such, the stratification of patients into meaningful disease subgroups, with clinical and biological relevance, may improve disease management and the development of effective treatments. To this end, omics technologies-such as genomics, transcriptomics, proteomics and metabolomics-are contributing to offer a more comprehensive view of molecular pathways underlying the development of NDs, helping to differentiate subtypes of patients based on their specific molecular signatures. In this article, we discuss how omics technologies and their integration have provided new insights into the molecular heterogeneity underlying the most prevalent NDs, aiding to define early diagnosis and progression markers as well as therapeutic targets that can translate into stratified treatment approaches, bringing us closer to the goal of personalized medicine in neurology.

Multiomics integrative analysis identifies APOE allele-specific blood biomarkers associated to Alzheimer's disease etiopathogenesis

Alzheimer's disease (AD) is the most common form of dementia, currently affecting 35 million people worldwide. Apolipoprotein E (APOE) ε4 allele is the major risk factor for sporadic, late-onset AD (LOAD), which comprises over 95% of AD cases, increasing the risk of AD 4-12 fold. Despite this, the role of APOE in AD pathogenesis is still a mystery. Aiming for a better understanding of APOE-specific effects, the ADAPTED consortium analysed and integrated publicly available data of multiple OMICS technologies from both plasma and brain stratified by APOE haplotype (APOE2, APOE3 and APOE4). Combining genome-wide association studies (GWAS) with differential mRNA and protein expression analyses and single-nuclei transcriptomics, we identified genes and pathways contributing to AD in both APOE dependent and independent fashion. Interestingly, we characterised a set of biomarkers showing plasma and brain consistent protein profiles and opposite trends in APOE2 and APOE4 AD cases that could constitute screening tools for a disease that lacks specific blood biomarkers. Beside the identification of APOE-specific signatures, our findings advocate that this novel approach, based on the concordance across OMIC layers and tissues, is an effective strategy for overcoming the limitations of often underpowered single-OMICS studies.

Exome sequencing revealed PDE11A as a novel candidate gene for early-onset Alzheimer's disease

To identify novel risk genes and better understand the molecular pathway underlying Alzheimer's disease (AD), whole-exome sequencing was performed in 215 early-onset AD (EOAD) patients and 255 unrelated healthy controls of Han Chinese ethnicity. Subsequent validation, computational annotation and in vitro functional studies were performed to evaluate the role of candidate variants in EOAD. We identified two rare missense variants in the phosphodiesterase 11A (PDE11A) gene in individuals with EOAD. Both variants are located in evolutionarily highly conserved amino acids, are predicted to alter the protein conformation and are classified as pathogenic. Furthermore, we found significantly decreased protein levels of PDE11A in brain samples of AD patients. Expression of PDE11A variants and knockdown experiments with specific short hairpin RNA (shRNA) for PDE11A both resulted in an increase of AD-associated Tau hyperphosphorylation at multiple epitopes in vitro. PDE11A variants or PDE11A shRNA also caused increased cyclic adenosine monophosphate (cAMP) levels, protein kinase A (PKA) activation and cAMP response element-binding protein phosphorylation. In addition, pretreatment with a PKA inhibitor (H89) suppressed PDE11A variant-induced Tau phosphorylation formation. This study offers insight into the involvement of Tau phosphorylation via the cAMP/PKA pathway in EOAD pathogenesis and provides a potential new target for intervention.

Reliability of genomic variants across different next-generation sequencing platforms and bioinformatic processing pipelines

Background

Next Generation Sequencing (NGS) is the fundament of various studies, providing insights into questions from biology and medicine. Nevertheless, integrating data from different experimental backgrounds can introduce strong biases. In order to methodically investigate the magnitude of systematic errors in single nucleotide variant calls, we performed a cross-sectional observational study on a genomic cohort of 99 subjects each sequenced via (i) Illumina HiSeq X, (ii) Illumina HiSeq, and (iii) Complete Genomics and processed with the respective bioinformatic pipeline. We also repeated variant calling for the Illumina cohorts with GATK, which allowed us to investigate the effect of the bioinformatics analysis strategy separately from the sequencing platform’s impact.

Results

The number of detected variants/variant classes per individual was highly dependent on the experimental setup. We observed a statistically significant overrepresentation of variants uniquely called by a single setup, indicating potential systematic biases. Insertion/deletion polymorphisms (indels) were associated with decreased concordance compared to single nucleotide polymorphisms (SNPs). The discrepancies in indel absolute numbers were particularly prominent in introns, Alu elements, simple repeats, and regions with medium GC content. Notably, reprocessing sequencing data following the best practice recommendations of GATK considerably improved concordance between the respective setups.

Conclusion

We provide empirical evidence of systematic heterogeneity in variant calls between alternative experimental and data analysis setups. Furthermore, our results demonstrate the benefit of reprocessing genomic data with harmonized pipelines when integrating data from different studies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-020-07362-8.

Large‐scale sequencing studies expand the known genetic architecture of Alzheimer's disease

Introduction

Genes implicated by genome‐wide association studies and family‐based studies of Alzheimer's disease (AD) are largely discordant. We hypothesized that genes identified by sequencing studies like the Alzheimer's Disease Sequencing Project (ADSP) may bridge this gap and highlight shared biological mechanisms.

Methods

We performed structured literature review of genes prioritized by ADSP studies, genes underlying familial dementias, and genes nominated by genome‐wide association studies. Gene set enrichment analyses of each list identified enriched pathways.

Results

The genes prioritized by the ADSP, familial dementia studies, and genome‐wide association studies minimally overlapped. Each gene set identified dozens of enriched pathways, several of which were shared (e.g., regulation of amyloid beta clearance).

Discussion

Alternative study designs provide unique insights into AD genetics. Shared pathways enriched by different genes highlight their relevance to AD pathogenesis, while the patterns of pathway enrichment unique to each gene set provide additional targets for functional studies.

APOE and Alzheimer's disease: advances in genetics, pathophysiology, and therapeutic approaches

The APOE ε4 allele remains the strongest genetic risk factor for sporadic Alzheimer's disease and the APOE ε2 allele the strongest genetic protective factor after multiple large scale genome-wide association studies and genome-wide association meta-analyses. However, no therapies directed at APOE are currently available. Although initial studies causally linked APOE with amyloid-β peptide aggregation and clearance, over the past 5 years our understanding of APOE pathogenesis has expanded beyond amyloid-β peptide-centric mechanisms to tau neurofibrillary degeneration, microglia and astrocyte responses, and blood-brain barrier disruption. Because all these pathological processes can potentially contribute to cognitive impairment, it is important to use this new knowledge to develop therapies directed at APOE. Several therapeutic approaches have been successful in mouse models expressing human APOE alleles, including increasing or reducing APOE levels, enhancing its lipidation, blocking the interactions between APOE and amyloid-β peptide, and genetically switching APOE4 to APOE3 or APOE2 isoforms, but translation to human clinical trials has proven challenging.

EPIGENOMIC FEATURES RELATED TO MICROGLIA ARE ASSOCIATED WITH ATTENUATED EFFECT OF APOE ε4 ON ALZHEIMER'S DISEASE RISK IN HUMANS

Not all APOE ε4 carriers who survive to advanced age develop Alzheimer's disease (AD); factors attenuating the risk of ε4 on AD may exist. Guided by the top ε4-attenuating signals from methylome-wide association analyses (N=572, ε4+ and ε4-) of neurofibrillary tangles and neuritic plaques, we conducted a meta-analysis for pathological AD within the ε4+ subgroups (N=235) across four independent collections of brains. Cortical RNA-seq and microglial morphology measurements were used in functional analyses. Three out of the four significant CpG dinucleotides were captured by one principle component (PC1), which interacts with ε4 on AD, and is associated with expression of innate immune genes and activated microglia. In ε4 carriers, reduction in each unit of PC1 attenuated the odds of AD by 58% (OR=2.39, 95%CI=[1.64,3.46], P=7.08x10^-6). An epigenomic factor associated with a reduced proportion of activated microglia (microglial epigenomic factor 1) appears to attenuate the risk of ε4 on AD.

AD risk score for the early phases of disease based on unsupervised machine learning

INTRODUCTION

Identifying cognitively normal individuals at high risk for progression to symptomatic Alzheimer's disease (AD) is critical for early intervention.

METHODS

An AD risk score was derived using unsupervised machine learning. The score was developed using data from 226 cognitively normal individuals and included cerebrospinal fluid, magnetic resonance imaging, and cognitive measures, and validated in an independent cohort.

RESULTS

Higher baseline AD progression risk scores (hazard ratio = 2.70, P < 0.001) were associated with greater risks of progression to clinical symptoms of mild cognitive impairment (MCI). Baseline scores had an area under the curve of 0.83 (95% confidence interval: 0.75 to 0.91) for identifying subjects who progressed to MCI/dementia within 5 years. The validation procedure, using data from the Alzheimer's Disease Neuroimaging Initiative, demonstrated accuracy of prediction across the AD spectrum.

DISCUSSION

The derived risk score provides high predictive accuracy for identifying which individuals with normal cognition are likely to show clinical decline due to AD within 5 years.