Case-control association mapping by proxy using family history of disease

X-chromosome-wide association study for Alzheimer's disease

Due to methodological reasons, the X-chromosome has not been featured in the major genome-wide association studies on Alzheimer's Disease (AD). To address this and better characterize the genetic landscape of AD, we performed an in-depth X-Chromosome-Wide Association Study (XWAS) in 115,841 AD cases or AD proxy cases, including 52,214 clinically-diagnosed AD cases, and 613,671 controls. We considered three approaches to account for the different X-chromosome inactivation (XCI) states in females, i.e. random XCI, skewed XCI, and escape XCI. We did not detect any genome-wide significant signals (P ≤ 5 × 10-8) but identified seven X-chromosome-wide significant loci (P ≤ 1.6 × 10-6). The index variants were common for the Xp22.32, FRMPD4, DMD and Xq25 loci, and rare for the WNK3, PJA1, and DACH2 loci. Overall, this well-powered XWAS found no genetic risk factors for AD on the non-pseudoautosomal region of the X-chromosome, but it identified suggestive signals warranting further investigations.

Brain and blood transcriptome-wide association studies identify five novel genes associated with Alzheimer's disease

BackgroundGenome-wide association studies (GWAS) have identified numerous genetic variants associated with Alzheimer's disease (AD), but their functional implications remain unclear. Transcriptome-wide association studies (TWAS) offer enhanced statistical power by analyzing genetic associations at the gene level rather than at the variant level, enabling assessment of how genetically-regulated gene expression influences AD risk. However, previous AD-TWAS have been limited by small expression quantitative trait loci (eQTL) reference datasets or reliance on AD-by-proxy phenotypes.ObjectiveTo perform the most powerful AD-TWAS to date using summary statistics from the largest available brain and blood cis-eQTL meta-analyses applied to the largest clinically-adjudicated AD GWAS.MethodsWe implemented the OTTERS TWAS pipeline to predict gene expression using the largest available cis-eQTL data from cortical brain tissue (MetaBrain; N = 2683) and blood (eQTLGen; N = 31,684), and then applied these models to AD-GWAS data (Cases = 21,982; Controls = 44,944).ResultsWe identified and validated five novel gene associations in cortical brain tissue (PRKAG1, C3orf62, LYSMD4, ZNF439, SLC11A2) and six genes proximal to known AD-related GWAS loci (Blood: MYBPC3; Brain: MTCH2, CYB561, MADD, PSMA5, ANXA11). Further, using causal eQTL fine-mapping, we generated sparse models that retained the strength of the AD-TWAS association for MTCH2, MADD, ZNF439, CYB561, and MYBPC3.ConclusionsOur comprehensive AD-TWAS discovered new gene associations and provided insights into the functional relevance of previously associated variants, which enables us to further understand the genetic architecture underlying AD risk.

Association of Alzheimer's disease genetic risk with age-dependent changes in plasma amyloid-β42:40 in Veterans

BackgroundIdentifying biomarkers of Alzheimer's disease (AD) is critical for early diagnosis and AD risk assessment.ObjectiveWe examined the hypothesis that the plasma amyloid-β 42 and 40 (Aβ42:40) ratio has a curvilinear relationship with age among individuals who are at higher genetic risk for AD.MethodsThis study investigated the relationship between plasma amyloid-β 42 and 40 (Aβ42:40) ratio and age in 315 men and women Veterans, including those at genetic risk for AD. Hierarchical regression models investigated linear and nonlinear relationships between age, genetic risk, and Aβ42:40.ResultsWe observed a curvilinear relationship between age and Aβ42:40 in individuals with higher genetic risk, characterized by an increase in the Aβ42:40 during midlife followed by a decrease in older age.ConclusionsThese findings highlight distinct patterns in Aβ metabolism among genetically predisposed individuals, suggesting that early metabolic shifts may play a role in the progression of AD. Understanding these nuanced changes is essential for refining the use of Aβ42:40 ratio as a biomarker, potentially leading to more accurate risk stratification and earlier intervention strategies in AD.

Enhancing nonlinear transcriptome- and proteome-wide association studies via trait imputation with applications to Alzheimer's disease

Genome-wide association studies (GWAS) performed on large cohort and biobank datasets have identified many genetic loci associated with Alzheimer's disease (AD). However, the younger demographic of biobank participants relative to the typical age of late-onset AD has resulted in an insufficient number of AD cases, limiting the statistical power of GWAS and any downstream analyses. To mitigate this limitation, several trait imputation methods have been proposed to impute the expected future AD status of individuals who may not have yet developed the disease. This paper explores the use of imputed AD status in nonlinear transcriptome/proteome-wide association studies (TWAS/PWAS) to identify genes and proteins whose genetically regulated expression is associated with AD risk. In particular, we considered the TWAS/PWAS method DeLIVR, which utilizes deep learning to model the nonlinear effects of expression on disease. We trained transcriptome and proteome imputation models for DeLIVR on data from the Genotype-Tissue Expression (GTEx) Project and the UK Biobank (UKB), respectively, with imputed AD status in UKB participants as the outcome. Next, we performed hypothesis testing for the DeLIVR models using clinically diagnosed AD cases from the Alzheimer's Disease Sequencing Project (ADSP). Our results demonstrate that nonlinear TWAS/PWAS trained with imputed AD outcomes successfully identifies known and putative AD risk genes and proteins. Notably, we found that training with imputed outcomes can increase statistical power without inflating false positives, enabling the discovery of molecular exposures with potentially nonlinear effects on neurodegeneration.

Genetic association studies using disease liabilities from deep neural networks

The case-control study is a widely used method for investigating the genetic underpinnings of binary traits. However, long-term, prospective cohort studies often grapple with absent or evolving health-related outcomes. Here, we propose two methods, liability and meta, for conducting genome-wide association studies (GWASs) that leverage disease liabilities calculated from deep patient phenotyping. Analyzing 38 common traits in ∼300,000 UK Biobank participants, we identified an increased number of loci in comparison to the number identified by the conventional case-control approach, and there were high replication rates in larger external GWASs. Further analyses confirmed the disease specificity of the genetic architecture; the meta method demonstrated higher robustness when phenotypes were imputed with low accuracy. Additionally, polygenic risk scores based on disease liabilities more effectively predicted newly diagnosed cases in the 2022 dataset, which were controls in the earlier 2019 dataset. Our findings demonstrate that integrating high-dimensional phenotypic data into deep neural networks enhances genetic association studies while capturing disease-relevant genetic architecture.

2025 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association

BACKGROUND

The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2025 AHA Statistical Update is the product of a full year's worth of effort in 2024 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. This year's edition includes a continued focus on health equity across several key domains and enhanced global data that reflect improved methods and incorporation of ≈3000 new data sources since last year's Statistical Update.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Tau pathway-based gene analysis on PET identifies CLU and FYN in a Korean cohort

INTRODUCTION

The influence of genetic variation on tau protein aggregation, a key factor in Alzheimer's disease (AD), remains not fully understood. We aimed to identify novel genes associated with brain tau deposition using pathway-based candidate gene association analysis in a Korean cohort.

METHODS

We analyzed data for 146 older adults from the well-established Korean AD continuum cohort (Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's Disease; KBASE). Fifteen candidate genes related to both tau pathways and AD were selected. Association analyses were performed using PLINK: A tool set for whole-genome association and population-based linkage analyses (PLINK) on tau deposition measured by 18F-AV-1451 positron emission tomography (PET) scans, with additional voxel-wise analysis conducted using Statistical Parametric Mapping 12 (SPM12).

RESULTS

CLU and FYN were significantly associated with tau deposition, with the most significant single-nucleotide polymorphisms (SNPs) being rs149413552 and rs57650567, respectively. These SNPs were linked to increased tau across key brain regions and showed additive effects with apolipoprotein E (APOE) ε4.

DISCUSSION

CLU and FYN may play specific roles in tau pathophysiology, offering potential targets for biomarkers and therapies.

HIGHLIGHTS

Gene-based analysis identified CLU and FYN as associated with tau deposition on positron emission tomography (PET). CLU rs149413552 and FYN rs57650567 were associated with brain tau deposition. rs149413552 and rs57650567 were associated with structural brain atrophy. CLU rs149413552 was associated with immediate verbal memory. CLU and FYN may play specific roles in tau pathophysiology.

Matching Heterogeneous Cohorts by Projected Principal Components Reveals Two Novel Alzheimer's Disease-Associated Genes in the Hispanic Population

Alzheimer's disease (AD) is the most common form of dementia in elderly, affecting 6.9 million individuals in the United States. Some studies have suggested the prevalence of AD is greater in individuals who self-identify as Hispanic. Focused results are relevant for personalized and equitable clinical interventions. Ethnicity as a stratifying tool in genetic studies is often accompanied by genomic inflation due to heterogeneity. In this study, we report GWAS and meta-analyses conducted among NIAGADS subjects who self-identified as Hispanic and All of Us (AoU) sub-cohorts matched to that cohort, using projected genetically-derived principal components, with and without age and sex. In Hispanic NIAGADS subjects, we identified a common variant in PIEZO2 that was protective for AD with a p-value just beyond genome-wide significance (p = 5.4 * 10-8). Meta-analyses with genetically-matched AoU participants yielded three (two novel) genome-wide significant AD-associated loci based on rare lead variants: rs374043832 (RGS6/PSEN1), rs192423465 (ASPSCR1), and rs935208076 (GDAP2), which were also nominally significant in AoU sub-cohorts. We also show how genomic inflation can be mitigated in heterogeneous populations while increasing sample size and result generalizability.

Brain morphology mediating the effects of common genetic risk variants on Alzheimer's disease

Background

Late-onset Alzheimer's disease (LOAD) has been associated with alterations in the morphology of multiple brain structures, and it is likely that disease mechanisms differ between brain regions. Coupling genetic determinants of LOAD with measures of brain morphology could localize and identify primary causal neurobiological pathways.

Objective

To determine causal pathways from genetic risk variants of LOAD via brain morphology to LOAD.

Methods

Mediation and Mendelian randomization (MR) analysis were performed using common genetic variation, T1 MRI and clinical data collected by UK Biobank and Alzheimer's Disease Neuroimaging Initiative.

Results

Thickness of the entorhinal cortex and the volumes of the hippocampus, amygdala and inferior lateral ventricle mediated the effect of APOE ε4 on LOAD. MR showed that a thinner entorhinal cortex, a smaller hippocampus and amygdala, and a larger volume of the inferior lateral ventricles, increased the risk of LOAD as well as vice versa.

Conclusions

Combining neuroimaging and genetic data can give insight into the causal neuropathological pathways of LOAD.

Enhancing Gene Expression Predictions Using Deep Learning and Functional Annotations

Transcriptome-wide association studies (TWAS) aim to uncover genotype-phenotype relationships through a two-stage procedure: predicting gene expression from genotypes using an expression quantitative trait locus (eQTL) data set, then testing the predicted expression for trait associations. Accurate gene expression prediction in stage 1 is crucial, as it directly impacts the power to identify associations in stage 2. Currently, the first stage of such studies is primarily conducted using linear models like elastic net regression, which fail to capture the nonlinear relationships inherent in biological systems. Deep learning methods have the potential to model such nonlinear effects, but have yet to demonstrably outperform linear methods at this task. To address this gap, we propose a new deep learning architecture to predict gene expression from genotypic variation across individuals. Our method utilizes a learnable input scaling layer in conjunction with a convolutional encoder to capture nonlinear effects and higher-order interactions without compromising on interpretability. We further augment this approach to allow for parameter sharing across multiple networks, enabling us to utilize prior information for individual variants in the form of functional annotations. Evaluations on real-world genomic data show that our method consistently outperforms elastic net regression across a large set of heritable genes. Furthermore, our model statistically significantly improved predictive performance by leveraging functional annotations, whereas elastic net regression failed to show equivalent gains when using the same information, suggesting that our method can capture nonlinear functional information beyond the capability of linear models.

Expanding functions of the phosphatidylinositol/phosphatidate lipid transporter, PITPNC1 in physiology and in pathology

PITPNC1 was the last of the PITPs to be identified and has been characterized as a binding protein for phosphatidylinositol and phosphatidate. In mammals, PITPNC1 is expressed as two splice variants whilst in zebrafish is expressed from two separate genes. The two splice variants have different expression profiles with the long splice variant having a prominent role in the brain. Several physiological functions have been identified including neuronal and metabolic functions. PITPNC1 also plays a significant role in cancer and has been identified as a risk factor in type 2 diabetes. Here, we review our current understanding of PITPNC1 in cell physiology and pathology.

Using Family History Data to Improve the Power of Association Studies: Application to Cancer in UK Biobank

In large cohort studies the number of unaffected individuals outnumbers the number of affected individuals, and the power can be low to detect associations for outcomes with low prevalence. We consider how including recorded family history in regression models increases the power to detect associations between genetic variants and disease risk. We show theoretically and using Monte-Carlo simulations that including a family history of the disease, with a weighting of 0.5 compared with true cases, increases the power to detect associations. This is a powerful approach for detecting variants with moderate effects, but for larger effect sizes a weighting of > 0.5 can be more powerful. We illustrate this both for common variants and for exome sequencing data for over 400,000 individuals in UK Biobank to evaluate the association between the burden of protein-truncating variants in genes and risk for four cancer types.

Shared Genetic Architecture Between COVID-19 Severity and Alzheimer's Disease Across European and African Ancestries

The global outbreak of COVID-19, caused by the SARS-CoV-2 virus, has been linked to long-term neurological complications, including an increased risk of Alzheimer's disease (AD) among older adults. However, the precise genetic impact of COVID-19 on long-term AD development remains unclear. This study leveraged genome-wide association study (GWAS) data and genotype data to explore the genetic association between AD and various COVID-19 phenotypes across European ancestry (EA) and African ancestry (AA) cohorts, and the possibility of a causal effect of COVID-19 on AD. We first calculated polygenic risk scores (PRSs) of three COVID-19 phenotypes in AD cases and controls from both EA and AA populations, then determined the genetic associations between COVID-19 PRSs and AD by logistic regression analyses with or without adjusting for age, sex, and APOE genotypes. Significant positive associations were found between AD diagnosis and COVID-19 PRSs in both populations, with the strongest associations identified in the AA population. However, Mendelian randomization (MR) analyses revealed no evidence of a causal effect of COVID-19 phenotypes on AD liability. We explored this finding further through the analysis of shared genomic regions between the COVID-19 phenotypes and AD and found a region of overlap on chromosome 17 that was highly pleiotropic for traits implicating immune function, psychiatric disorders, and lung function phenotypes. These findings suggest that while COVID-19 and AD share overlapping polygenic contributions involving peripheral genes across multiple traits, they lack a direct connection involving core genes that drive the development of their respective pathologies.

Alcohol Use Disorder Polygenic Score Compared With Family History and ADH1B

Importance

Identification of individuals at high risk of alcohol use disorder (AUD) and subsequent application of prevention and intervention programs has been reported to decrease the incidence of AUD. The polygenic score (PGS), which measures an individual's genetic liability to a disease, can potentially be used to evaluate AUD risk.

Objective

To assess the estimability and generalizability of the PGS, compared with family history and ADH1B, in evaluating the risk of AUD among populations of European ancestry.

Design, Setting, and Participants

This genetic association study was conducted between October 1, 2023, and May 21, 2024. A 2-stage design was used. First, the pruning and thresholding method was used to calculate PGSs in the screening stage. Second, the estimability and generalizability of the best PGS was determined using 2 independent samples in the testing stage. Three cohorts ascertained to study AUD were used in the screening stage: the Collaborative Study on the Genetics of Alcoholism (COGA), the Study of Addiction: Genetics and Environment (SAGE), and the Australian Twin-Family Study of Alcohol Use Disorder (OZALC). The All of Us Research Program (AOU), which comprises participants with diverse backgrounds and conditions, and the Indiana Biobank (IB), consisting of Indiana University Health system patients, were used to test the best PGS. For the COGA, SAGE, and OZALC cohorts, cases with AUD were determined using Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) or Fifth Edition (DSM-5) criteria; controls did not meet any criteria or did not have any other substance use disorders. For the AOU and IB cohorts, cases with AUD were identified using International Classification of Diseases, Ninth Revision (ICD-9) or International Classification of Diseases, Tenth Revision (ICD-10) codes; controls were aged 21 years or older and did not have AUD.

Exposure

The PGS was calculated using single-nucleotide variants with concordant effects in 3 large-scale genome-wide association studies of AUD-related phenotypes.

Main Outcomes and Measures

The main outcome was AUD determined with DSM-IV or DSM-5 criteria and ICD-9 or ICD-10 codes. Generalized linear mixed models and logistic regression models were used to analyze related and unrelated samples, respectively.

Results

The COGA, SAGE, and OZALC cohorts included a total of 8799 samples (6323 cases and 2476 controls; 50.6% were men). The AOU cohort had a total of 116 064 samples (5660 cases and 110 404 controls; 60.4% were women). The IB cohort had 6373 samples (936 cases and 5437 controls; 54.9% were women). The 5% of samples with the highest PGS in the AOU and IB cohorts were approximately 2 times more likely to develop AUD (odds ratio [OR], 1.96 [95% CI, 1.78-2.16]; P = 4.10 × 10-43; and OR, 2.07 [95% CI, 1.59-2.71]; P = 9.15 × 10-8, respectively) compared with the remaining 95% of samples; these ORs were comparable to family history of AUD. For the 5% of samples with the lowest PGS in the AOU and IB cohorts, the risk of AUD development was approximately half (OR, 0.53 [95% CI, 0.45-0.62]; P = 6.98 × 10-15; and OR, 0.57 [95% CI, 0.39-0.84]; P = 4.88 × 10-3) compared with the remaining 95% of samples; these ORs were comparable to the protective effect of ADH1B. PGS had similar estimabilities in male and female individuals.

Conclusions and Relevance

In this study of AUD risk among populations of European ancestry, PGSs were calculated using concordant single-nucleotide variants and the best PGS was tested in targeted datasets. The findings suggest that the PGS may potentially be used to evaluate AUD risk. More datasets with similar AUD prevalence as in general populations are needed to further test the generalizability of PGS.

Pervasive biases in proxy genome-wide association studies based on parental history of Alzheimer's disease

Almost every recent Alzheimer's disease (AD) genome-wide association study (GWAS) has performed meta-analysis to combine studies with clinical diagnosis of AD with studies that use proxy phenotypes based on parental disease history. Here, we report major limitations in current GWAS-by-proxy (GWAX) practices due to uncorrected survival bias and nonrandom participation in parental illness surveys, which cause substantial discrepancies between AD GWAS and GWAX results. We demonstrate that the current AD GWAX provide highly misleading genetic correlations between AD risk and higher education, which subsequently affects a variety of genetic epidemiological applications involving AD and cognition. Our study sheds light on potential issues in the design and analysis of middle-aged biobank cohorts and underscores the need for caution when interpreting genetic association results based on proxy-reported parental disease history.

Genetic liability estimated from large-scale family data improves genetic prediction, risk score profiling, and gene mapping for major depression

Large biobank samples provide an opportunity to integrate broad phenotyping, familial records, and molecular genetics data to study complex traits and diseases. We introduce Pearson-Aitken Family Genetic Risk Scores (PA-FGRS), a method for estimating disease liability from patterns of diagnoses in extended, age-censored genealogical records. We then apply the method to study a paradigmatic complex disorder, major depressive disorder (MDD), using the iPSYCH2015 case-cohort study of 30,949 MDD cases, 39,655 random population controls, and more than 2 million relatives. We show that combining PA-FGRS liabilities estimated from family records with molecular genotypes of probands improves three lines of inquiry. Incorporating PA-FGRS liabilities improves classification of MDD over and above polygenic scores, identifies robust genetic contributions to clinical heterogeneity in MDD associated with comorbidity, recurrence, and severity and can improve the power of genome-wide association studies. Our method is flexible and easy to use, and our study approaches are generalizable to other datasets and other complex traits and diseases.

DINGO: increasing the power of locus discovery in maternal and fetal genome-wide association studies of perinatal traits

Perinatal traits are influenced by fetal and maternal genomes. We investigate the performance of three strategies to detect loci in maternal and fetal genome-wide association studies (GWASs) of the same quantitative trait: (i) the traditional strategy of analysing maternal and fetal GWASs separately; (ii) a two-degree-of-freedom test which combines information from maternal and fetal GWASs; and (iii) a one-degree-of-freedom test where signals from maternal and fetal GWASs are meta-analysed together conditional on estimated sample overlap. We demonstrate that the optimal strategy depends on the extent of sample overlap, correlation between phenotypes, whether loci exhibit fetal and/or maternal effects, and whether these effects are directionally concordant. We apply our methods to summary statistics from a recent GWAS meta-analysis of birth weight. Both the two-degree-of-freedom and meta-analytic approaches increase the number of genetic loci for birth weight relative to separately analysing the scans. Our best strategy identifies an additional 62 loci compared to the most recently published meta-analysis of birth weight. We conclude that whilst the two-degree-of-freedom test may be useful for the analysis of certain perinatal phenotypes, for most phenotypes, a simple meta-analytic strategy is likely to perform best, particularly in situations where maternal and fetal GWASs only partially overlap.

Connecting dementia risk loci to the CSF proteome identifies pathophysiological leads for dementia

Genome-wide association studies have successfully identified many genetic risk loci for dementia, but exact biological mechanisms through which genetic risk factors contribute to dementia remains unclear. Integrating CSF proteomic data with dementia risk loci could reveal intermediate molecular pathways connecting genetic variance to the development of dementia. We tested to what extent effects of known dementia risk loci can be observed in CSF levels of 665 proteins [proximity extension-based (PEA) immunoassays] in a deeply-phenotyped mixed memory clinic cohort [n = 502, mean age (standard deviation, SD) = 64.1 (8.7) years, 181 female (35.4%)], including patients with Alzheimer's disease (AD, n = 213), dementia with Lewy bodies (DLB, n = 50) and frontotemporal dementia (FTD, n = 93), and controls (n = 146). Validation was assessed in independent cohorts (n = 99 PEA platform, n = 198, mass reaction monitoring-targeted mass spectroscopy and multiplex assay). We performed additional analyses stratified according to diagnostic status (AD, DLB, FTD and controls separately), to explore whether associations between CSF proteins and genetic variants were specific to disease or not. We identified four AD risk loci as protein quantitative trait loci (pQTL): CR1-CR2 (rs3818361, P = 1.65 × 10-8), ZCWPW1-PILRB (rs1476679, P = 2.73 × 10-32), CTSH-CTSH (rs3784539, P = 2.88 × 10-24) and HESX1-RETN (rs186108507, P = 8.39 × 10-8), of which the first three pQTLs showed direct replication in the independent cohorts. We identified one AD-specific association between a rare genetic variant of TREM2 and CSF IL6 levels (rs75932628, P = 3.90 × 10-7). DLB risk locus GBA showed positive trans effects on seven inter-related CSF levels in DLB patients only. No pQTLs were identified for FTD loci, either for the total sample as for analyses performed within FTD only. Protein QTL variants were involved in the immune system, highlighting the importance of this system in the pathophysiology of dementia. We further identified pQTLs in stratified analyses for AD and DLB, hinting at disease-specific pQTLs in dementia. Dissecting the contribution of risk loci to neurobiological processes aids in understanding disease mechanisms underlying dementia.

Role of the X Chromosome in Alzheimer Disease Genetics

Importance

The X chromosome has remained enigmatic in Alzheimer disease (AD), yet it makes up 5% of the genome and carries a high proportion of genes expressed in the brain, making it particularly appealing as a potential source of unexplored genetic variation in AD.

Objectives

To perform the first large-scale X chromosome-wide association study (XWAS) of AD.

Design, Setting, and Participants

This was a meta-analysis of genetic association studies in case-control, family-based, population-based, and longitudinal AD-related cohorts from the US Alzheimer's Disease Genetics Consortium, the Alzheimer's Disease Sequencing Project, the UK Biobank, the Finnish health registry, and the US Million Veterans Program. Risk of AD was evaluated through case-control logistic regression analyses. Data were analyzed between January 2023 and March 2024. Genetic data available from high-density single-nucleotide variant microarrays and whole-genome sequencing and summary statistics for multitissue expression and protein quantitative trait loci available from published studies were included, enabling follow-up genetic colocalization analyses. A total of 1 629 863 eligible participants were selected from referred and volunteer samples, 477 596 of whom were excluded for analysis exclusion criteria. The number of participants who declined to participate in original studies was not available.

Main Outcome and Measures

Risk of AD, reported as odds ratios (ORs) with 95% CIs. Associations were considered at X chromosome-wide (P < 1 × 10-5) and genome-wide (P < 5 × 10-8) significance. Primary analyses are nonstratified, while secondary analyses evaluate sex-stratified effects.

Results

Analyses included 1 152 284 participants of non-Hispanic White, European ancestry (664 403 [57.7%] female and 487 881 [42.3%] male), including 138 558 individuals with AD. Six independent genetic loci passed X chromosome-wide significance, with 4 showing support for links between the genetic signal for AD and expression of nearby genes in brain and nonbrain tissues. One of these 4 loci passed conservative genome-wide significance, with its lead variant centered on an intron of SLC9A7 (OR, 1.03; 95% CI, 1.02-1.04) and colocalization analyses prioritizing both the SLC9A7 and nearby CHST7 genes. Of these 6 loci, 4 displayed evidence for escape from X chromosome inactivation with regard to AD risk.

Conclusion and Relevance

This large-scale XWAS of AD identified the novel SLC9A7 locus. SLC9A7 regulates pH homeostasis in Golgi secretory compartments and is anticipated to have downstream effects on amyloid β accumulation. Overall, this study advances our knowledge of AD genetics and may provide novel biological drug targets. The results further provide initial insights into elucidating the role of the X chromosome in sex-based differences in AD.

Whole exome sequencing analyses identified novel genes for Alzheimer's disease and related dementia

INTRODUCTION

The heritability of Alzheimer's disease (AD) is estimated to be 58%-79%. However, known genes can only partially explain the heritability.

METHODS

Here, we conducted gene-based exome-wide association study (ExWAS) of rare variants and single-variant ExWAS of common variants, utilizing data of 54,569 clinically diagnosed/proxy AD and related dementia (ADRD) and 295,421 controls from the UK Biobank.

RESULTS

Gene-based ExWAS identified 11 genes predicting a higher ADRD risk, including five novel ones, namely FRMD8, DDX1, DNMT3L, MORC1, and TGM2, along with six previously reported ones, SORL1, GRN, PSEN1, ABCA7, GBA, and ADAM10. Single-variant ExWAS identified two ADRD-associated novel genes, SLCO1C1 and NDNF. The identified genes were predominantly enriched in amyloid-β process pathways, microglia, and brain regions like hippocampus. The druggability evidence suggests that DDX1, DNMT3L, TGM2, SLCO1C1, and NDNF could be effective drug targets.

DISCUSSION

Our study contributes to the current body of evidence on the genetic etiology of ADRD.

HIGHLIGHTS

Gene-based analyses of rare variants identified five novel genes for Alzheimer's disease and related dementia (ADRD), including FRMD8, DDX1, DNMT3L, MORC1, and TGM2. Single-variant analyses of common variants identified two novel genes for ADRD, including SLCO1C1 and NDNF. The identified genes were predominantly enriched in amyloid-β process pathways, microglia, and brain regions like hippocampus. DDX1, DNMT3L, TGM2, SLCO1C1, and NDNF could be effective drug targets.

Benzimidazole-based small molecules as anticancer agents targeting telomeric G-quadruplex and inhibiting telomerase enzyme

Telomeres, crucial for chromosomal integrity, have been related to aging and cancer formation, mainly through regulating G-quadruplex structures. G-quadruplexes are structural motifs that can arise as secondary structures of nucleic acids, especially in guanine-rich DNA and RNA regions. Targeting these structures by small compounds shows promise in the selective suppression of cell growth, opening up novel possibilities for anticancer treatment. A comprehensive investigation of the many structural forms of G-quadruplex ligands is required to create ground-breaking anticancer drugs. Recent research into using specific benzimidazole molecules in stabilizing telomeric DNA into G-quadruplex structures has highlighted their ability to influence oncogene expression and demonstrate antiproliferative characteristics against cancer cells. This review describes the benzimidazole derivative, designed to enhance the stability of the G-quadruplex structure DNA to suppress the activity of telomerase enzyme, exhibiting promising potential for anticancer therapy.

Identification of 16 novel Alzheimer's disease susceptibility loci using multi-ancestry meta-analyses of clinical Alzheimer's disease and AD-by-proxy cases from four whole genome sequencing datasets

Alzheimer's disease (AD) is the most prevalent form of dementia. While many AD-associated genetic determinants have been previously identified, few studies have analyzed individuals of non-European ancestry. Here, we describe a multi-ancestry genome-wide association study of clinically-diagnosed AD and AD-by-proxy using whole genome sequencing data from NIAGADS, NIMH, UKB, and All of Us (AoU) consisting of 49,149 cases (12,074 clinically-diagnosed and 37,075 AD-by-proxy) and 383,225 controls. Nearly half of NIAGADS and AoU participants are of non-European ancestry. For clinically-diagnosed AD, we identified 14 new loci - five common (FBN2,/SCL27A6, AC090115.1, DYM, KCNG1/AL121785.1, TIAM1) and nine rare (VWA5B1, RNU6-755P/LMX1A, MOB1A, MORC1-AS1, LINC00989, PDE4D, RNU2-49P/CDO1, NEO1, and SLC35G3/AC022916.1). Meta-analysis of UKB and AoU AD-by-proxy cases yielded two new rare loci (RPL23/LASP1 and CEBPA/ AC008738.6) which were also nominally significant in NIAGADS. In summary, we provide evidence for 16 novel AD loci and advocate for more studies using WGS-based GWAS of diverse cohorts.

Genetic association studies using disease liabilities from deep neural networks

The case-control study is a widely used method for investigating the genetic underpinnings of binary traits. However, long-term, prospective cohort studies often grapple with absent or evolving health-related outcomes. Here, we propose two methods, liability and meta, for conducting genome-wide association study (GWAS) that leverage disease liabilities calculated from deep patient phenotyping. Analyzing 38 common traits in ~300,000 UK Biobank participants, we identified an increased number of loci compared to the conventional case-control approach, with high replication rates in larger external GWAS. Further analyses confirmed the disease-specificity of the genetic architecture with the meta method demonstrating higher robustness when phenotypes were imputed with low accuracy. Additionally, polygenic risk scores based on disease liabilities more effectively predicted newly diagnosed cases in the 2022 dataset, which were controls in the earlier 2019 dataset. Our findings demonstrate that integrating high-dimensional phenotypic data into deep neural networks enhances genetic association studies while capturing disease-relevant genetic architecture.

Deep5hmC: predicting genome-wide 5-hydroxymethylcytosine landscape via a multimodal deep learning model

MOTIVATION

5-Hydroxymethylcytosine (5hmC), a crucial epigenetic mark with a significant role in regulating tissue-specific gene expression, is essential for understanding the dynamic functions of the human genome. Despite its importance, predicting 5hmC modification across the genome remains a challenging task, especially when considering the complex interplay between DNA sequences and various epigenetic factors such as histone modifications and chromatin accessibility.

RESULTS

Using tissue-specific 5hmC sequencing data, we introduce Deep5hmC, a multimodal deep learning framework that integrates both the DNA sequence and epigenetic features such as histone modification and chromatin accessibility to predict genome-wide 5hmC modification. The multimodal design of Deep5hmC demonstrates remarkable improvement in predicting both qualitative and quantitative 5hmC modification compared to unimodal versions of Deep5hmC and state-of-the-art machine learning methods. This improvement is demonstrated through benchmarking on a comprehensive set of 5hmC sequencing data collected at four developmental stages during forebrain organoid development and across 17 human tissues. Compared to DeepSEA and random forest, Deep5hmC achieves close to 4% and 17% improvement of Area Under the Receiver Operating Characteristic (AUROC) across four forebrain developmental stages, and 6% and 27% across 17 human tissues for predicting binary 5hmC modification sites; and 8% and 22% improvement of Spearman correlation coefficient across four forebrain developmental stages, and 17% and 30% across 17 human tissues for predicting continuous 5hmC modification. Notably, Deep5hmC showcases its practical utility by accurately predicting gene expression and identifying differentially hydroxymethylated regions (DhMRs) in a case-control study of Alzheimer's disease (AD). Deep5hmC significantly improves our understanding of tissue-specific gene regulation and facilitates the development of new biomarkers for complex diseases.

AVAILABILITY AND IMPLEMENTATION

Deep5hmC is available via https://github.com/lichen-lab/Deep5hmC.

scaDA: A novel statistical method for differential analysis of single-cell chromatin accessibility sequencing data

Single-cell ATAC-seq sequencing data (scATAC-seq) has been widely used to investigate chromatin accessibility on the single-cell level. One important application of scATAC-seq data analysis is differential chromatin accessibility (DA) analysis. However, the data characteristics of scATAC-seq such as excessive zeros and large variability of chromatin accessibility across cells impose a unique challenge for DA analysis. Existing statistical methods focus on detecting the mean difference of the chromatin accessible regions while overlooking the distribution difference. Motivated by real data exploration that distribution difference exists among cell types, we introduce a novel composite statistical test named "scaDA", which is based on zero-inflated negative binomial model (ZINB), for performing differential distribution analysis of chromatin accessibility by jointly testing the abundance, prevalence and dispersion simultaneously. Benefiting from both dispersion shrinkage and iterative refinement of mean and prevalence parameter estimates, scaDA demonstrates its superiority to both ZINB-based likelihood ratio tests and published methods by achieving the highest power and best FDR control in a comprehensive simulation study. In addition to demonstrating the highest power in three real sc-multiome data analyses, scaDA successfully identifies differentially accessible regions in microglia from sc-multiome data for an Alzheimer's disease (AD) study that are most enriched in GO terms related to neurogenesis and the clinical phenotype of AD, and AD-associated GWAS SNPs.

Genetic associations with human longevity are enriched for oncogenic genes

Human lifespan is shaped by both genetic and environmental exposures and their interaction. To enable precision health, it is essential to understand how genetic variants contribute to earlier death or prolonged survival. In this study, we tested the association of common genetic variants and the burden of rare non-synonymous variants in a survival analysis, using age-at-death (N = 35,551, median [min, max] = 72.4 [40.9, 85.2]), and last-known-age (N = 358,282, median [min, max] = 71.9 [52.6, 88.7]), in European ancestry participants of the UK Biobank. The associations we identified seemed predominantly driven by cancer, likely due to the age range of the cohort. Common variant analysis highlighted three longevity-associated loci: APOE, ZSCAN23, and MUC5B. We identified six genes whose burden of loss-of-function variants is significantly associated with reduced lifespan: TET2, ATM, BRCA2, CKMT1B, BRCA1 and ASXL1. Additionally, in eight genes, the burden of pathogenic missense variants was associated with reduced lifespan: DNMT3A, SF3B1, CHL1, TET2, PTEN, SOX21, TP53 and SRSF2. Most of these genes have previously been linked to oncogenic-related pathways and some are linked to and are known to harbor somatic variants that predispose to clonal hematopoiesis. A direction-agnostic (SKAT-O) approach additionally identified significant associations with C1orf52, TERT, IDH2, and RLIM, highlighting a link between telomerase function and longevity as well as identifying additional oncogenic genes. Our results emphasize the importance of understanding genetic factors driving the most prevalent causes of mortality at a population level, highlighting the potential of early genetic testing to identify germline and somatic variants increasing one's susceptibility to cancer and/or early death.

Sex differences in the associations of socioeconomic factors and cognitive performance with family history of Alzheimer's disease

INTRODUCTION

While higher socioeconomic factors (SEF) and cognitive performance (CP) have been associated with reduced Alzheimer's disease (AD) risk, recent evidence highlighted that these factors may have opposite effects on family history of AD (FHAD).

METHODS

Leveraging data from the UK Biobank (N=448,100) and the All of Us Research Program (N=240,319), we applied generalized linear regression models, polygenic risk scoring (PRS), and one-sample Mendelian randomization (MR) to test the sex-specific SEF and CP associations with AD and FHAD.

RESULTS

Observational and genetically informed analyses highlighted that higher SEF and CP were associated with reduced AD and sibling-FHAD, while these factors were associated with increased parent-FHAD. We also observed that population minorities may present different patterns with respect to sibling-FHAD vs. parent-FHAD. Sex differences in FHAD associations were identified in ancestry-specific and SEF PRS and MR results.

DISCUSSION

This study contributes to understanding the sex-specific relationships linking SEF and CP to FHAD, highlighting the potential role of reporting, recall, and surviving-related dynamics.

Specification curve analysis to identify heterogeneity in risk factors for dementia: findings from the UK Biobank

BACKGROUND

In 2020, the Lancet Commission identified 12 risk factors as priorities for prevention of dementia, and other studies identified APOE e4/e4 genotype and family history of Alzheimer's disease strongly associated with dementia outcomes; however, it is unclear how robust these relationships are across dementia subtypes and analytic scenarios. Specification curve analysis (SCA) is a new tool to probe how plausible analytical scenarios influence outcomes.

METHODS

We evaluated the heterogeneity of odds ratios for 12 risk factors reported from the Lancet 2020 report and two additional strong associated non-modifiable factors (APOE e4/e4 genotype and family history of Alzheimer's disease) with dementia outcomes across 450,707 UK Biobank participants using SCA with 5357 specifications across dementia subtypes (outcomes) and analytic models (e.g., standard demographic covariates such as age or sex and/or 14 correlated risk factors).

RESULTS

SCA revealed variable dementia risks by subtype and age, with associations for TBI and APOE e4/e4 robust to model specification; in contrast, diabetes showed fluctuating links with dementia subtypes. We found that unattributed dementia participants had similar risk factor profiles to participants with defined subtypes.

CONCLUSIONS

We observed heterogeneity in the risk of dementia, and estimates of risk were influenced by the inclusion of a combination of other modifiable risk factors; non-modifiable demographic factors had a minimal role in analytic heterogeneity. Future studies should report multiple plausible analytic scenarios to test the robustness of their association. Considering these combinations of risk factors could be advantageous for the clinical development and evaluation of novel screening models for different types of dementia.

Reconstructing parent genomes using siblings and other relatives

Reconstructing the DNA of ancestors from their descendants has the potential to empower phenotypic analyses (including association and genetic nurture studies), improve pedigree reconstruction, and shed light on the ancestral population and phenotypes of ancestors. We developed HAPI-RECAP, a method that reconstructs the DNA of parents from full siblings and their relatives. This tool leverages HAPI2's output, a new phasing approach that applies to siblings (and optionally one or both parents) and reliably infers parent haplotypes but does not link the ungenotyped parents' DNA across chromosomes or between segments flanking ambiguities. By combining IBD between the reconstructed parents and the relatives, HAPI-RECAP resolves the source parent of these segments. Moreover, the method exploits crossovers the children inherited and sex-specific genetic maps to infer the reconstructed parents' sexes. We validated these methods on research participants from both 23andMe, Inc. and the San Antonio Mexican American Family Studies. Given data for one parent, HAPI2 reconstructs large fractions of the missing parent's DNA, between 77.6% and 99.97% among all families, and 90.3% on average in three- and four-child families. When reconstructing both parents, HAPI-RECAP inferred between 33.2% and 96.6% of the parents' genotypes, averaging 70.6% in four-child families. Reconstructed genotypes have average error rates < 10-3, or comparable to those from direct genotyping. HAPI-RECAP inferred the parent sexes 100% correctly given IBD-linked segments and can also reconstruct parents without any IBD. As datasets grow in size, more families will be implicitly collected; HAPI-RECAP holds promise to enable high quality parent genotype reconstruction.

The ABC's of Alzheimer risk gene ABCA7

Alzheimer's disease (AD) is a growing problem worldwide. Since ABCA7's identification as a risk gene, it has been extensively researched for its role in the disease. We review its recently characterized structure and what the mechanistic insights teach us about its function. We furthermore provide an overview of identified ABCA7 mutations, their presence in different ancestries and protein domains and how they might cause AD. For ABCA7 PTC variants and a VNTR expansion, haploinsufficiency is proposed as the most likely mode-of-action, although splice events could further influence disease risk. Overall, the need to better understand expression of canonical ABCA7 and its isoforms in disease is indicated. Finally, ABCA7's potential functions in lipid metabolism, phagocytosis, amyloid deposition, and the interplay between these three, is described. To conclude, in this review, we provide a comprehensive overview and discussion about the current knowledge on ABCA7 in AD, and what research questions remain. HIGHLIGHTS: Alzheimer's risk-increasing variants in ABCA7 can be found in up to 7% of AD patients. We review the recently characterized protein structure of ABCA7. We present latest insights in genetics, expression patterns, and functions of ABCA7.

Brain and Blood Transcriptome-Wide Association Studies Identify Five Novel Genes Associated with Alzheimer's Disease

INTRODUCTION

Transcriptome-wide Association Studies (TWAS) extend genome-wide association studies (GWAS) by integrating genetically-regulated gene expression models. We performed the most powerful AD-TWAS to date, using summary statistics from cis -eQTL meta-analyses and the largest clinically-adjudicated Alzheimer's Disease (AD) GWAS.

METHODS

We implemented the OTTERS TWAS pipeline, leveraging cis -eQTL data from cortical brain tissue (MetaBrain; N=2,683) and blood (eQTLGen; N=31,684) to predict gene expression, then applied these models to AD-GWAS data (Cases=21,982; Controls=44,944).

RESULTS

We identified and validated five novel gene associations in cortical brain tissue ( PRKAG1 , C3orf62 , LYSMD4 , ZNF439 , SLC11A2 ) and six genes proximal to known AD-related GWAS loci (Blood: MYBPC3 ; Brain: MTCH2 , CYB561 , MADD , PSMA5 , ANXA11 ). Further, using causal eQTL fine-mapping, we generated sparse models that retained the strength of the AD-TWAS association for MTCH2 , MADD , ZNF439 , CYB561 , and MYBPC3 .

DISCUSSION

Our comprehensive AD-TWAS discovered new gene associations and provided insights into the functional relevance of previously associated variants.

Interpretation of 10 years of Alzheimer's disease genetic findings in the perspective of statistical heterogeneity

Common genetic variants and susceptibility loci associated with Alzheimer's disease (AD) have been discovered through large-scale genome-wide association studies (GWAS), GWAS by proxy (GWAX) and meta-analysis of GWAS and GWAX (GWAS+GWAX). However, due to the very low repeatability of AD susceptibility loci and the low heritability of AD, these AD genetic findings have been questioned. We summarize AD genetic findings from the past 10 years and provide a new interpretation of these findings in the context of statistical heterogeneity. We discovered that only 17% of AD risk loci demonstrated reproducibility with a genome-wide significance of P < 5.00E-08 across all AD GWAS and GWAS+GWAX datasets. We highlighted that the AD GWAS+GWAX with the largest sample size failed to identify the most significant signals, the maximum number of genome-wide significant genetic variants or maximum heritability. Additionally, we identified widespread statistical heterogeneity in AD GWAS+GWAX datasets, but not in AD GWAS datasets. We consider that statistical heterogeneity may have attenuated the statistical power in AD GWAS+GWAX and may contribute to explaining the low repeatability (17%) of genome-wide significant AD susceptibility loci and the decreased AD heritability (40-2%) as the sample size increased. Importantly, evidence supports the idea that a decrease in statistical heterogeneity facilitates the identification of genome-wide significant genetic loci and contributes to an increase in AD heritability. Collectively, current AD GWAX and GWAS+GWAX findings should be meticulously assessed and warrant additional investigation, and AD GWAS+GWAX should employ multiple meta-analysis methods, such as random-effects inverse variance-weighted meta-analysis, which is designed specifically for statistical heterogeneity.

Deep5hmC: Predicting genome-wide 5-Hydroxymethylcytosine landscape via a multimodal deep learning model

5-hydroxymethylcytosine (5hmC), a critical epigenetic mark with a significant role in regulating tissue-specific gene expression, is essential for understanding the dynamic functions of the human genome. Using tissue-specific 5hmC sequencing data, we introduce Deep5hmC, a multimodal deep learning framework that integrates both the DNA sequence and the histone modification information to predict genome-wide 5hmC modification. The multimodal design of Deep5hmC demonstrates remarkable improvement in predicting both qualitative and quantitative 5hmC modification compared to unimodal versions of Deep5hmC and state-of-the-art machine learning methods. This improvement is demonstrated through benchmarking on a comprehensive set of 5hmC sequencing data collected at four time points during forebrain organoid development and across 17 human tissues. Notably, Deep5hmC showcases its practical utility by accurately predicting gene expression and identifying differentially hydroxymethylated regions in a case-control study of Alzheimer's disease.

2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association

BACKGROUND

The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2024 AHA Statistical Update is the product of a full year's worth of effort in 2023 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. The AHA strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional global data, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

scaDA: A Novel Statistical Method for Differential Analysis of Single-Cell Chromatin Accessibility Sequencing Data

Single-cell ATAC-seq sequencing data (scATAC-seq) has been widely used to investigate chromatin accessibility on the single-cell level. One important application of scATAC-seq data analysis is differential chromatin accessibility analysis. However, the data characteristics of scATAC-seq such as excessive zeros and large variability of chromatin accessibility across cells impose a unique challenge for DA analysis. Existing statistical methods focus on detecting the mean difference of the chromatin accessible regions while overlooking the distribution difference. Motivated by real data exploration that distribution difference exists among cell types, we introduce a novel composite statistical test named "scaDA", which is based on zero-inflated negative binomial model (ZINB), for performing differential distribution analysis of chromatin accessibility by jointly testing the abundance, prevalence and dispersion simultaneously. Benefiting from both dispersion shrinkage and iterative refinement of mean and prevalence parameter estimates, scaDA demonstrates its superiority to both ZINB-based likelihood ratio tests and published methods by achieving the highest power and best FDR control in a comprehensive simulation study. In addition to demonstrating the highest power in three real sc-multiome data analyses, scaDA successfully identifies differentially accessible regions in microglia from sc-multiome data for an Alzheimer's disease (AD) study, regions which are most enriched in GO terms related to neurogenesis, the clinical phenotype of AD, and SNPs identified in AD-associated GWAS.

Associations between genetically predicted plasma protein levels and Alzheimer's disease risk: a study using genetic prediction models

BACKGROUND

Specific peripheral proteins have been implicated to play an important role in the development of Alzheimer's disease (AD). However, the roles of additional novel protein biomarkers in AD etiology remains elusive. The availability of large-scale AD GWAS and plasma proteomic data provide the resources needed for the identification of causally relevant circulating proteins that may serve as risk factors for AD and potential therapeutic targets.

METHODS

We established and validated genetic prediction models for protein levels in plasma as instruments to investigate the associations between genetically predicted protein levels and AD risk. We studied 71,880 (proxy) cases and 383,378 (proxy) controls of European descent.

RESULTS

We identified 69 proteins with genetically predicted concentrations showing associations with AD risk. The drugs almitrine and ciclopirox targeting ATP1A1 were suggested to have a potential for being repositioned for AD treatment.

CONCLUSIONS

Our study provides additional insights into the underlying mechanisms of AD and potential therapeutic strategies.

The role of SPP/SPPL intramembrane proteases in membrane protein homeostasis

Signal peptide peptidase (SPP) and the four SPP-like proteases SPPL2a, SPPL2b, SPPL2c and SPPL3 constitute a family of aspartyl intramembrane proteases with homology to presenilins. The different members reside in distinct cellular localisations within the secretory pathway and the endo-lysosomal system. Despite individual cleavage characteristics, they all cleave single-span transmembrane proteins with a type II orientation exhibiting a cytosolic N-terminus. Though the identification of substrates is not complete, SPP/SPPL-mediated proteolysis appears to be rather selective. Therefore, according to our current understanding cleavage by SPP/SPPL proteases rather seems to serve a regulatory function than being a bulk proteolytic pathway. In the present review, we will summarise our state of knowledge on SPP/SPPL proteases and in particular highlight recently identified substrates and the functional and/or (patho)-physiological implications of these cleavage events. Based on this, we aim to provide an overview of the current open questions in the field. These are connected to the regulation of these proteases at the cellular level but also in context of disease and patho-physiological processes. Furthermore, the interplay with other proteostatic systems capable of degrading membrane proteins is beginning to emerge.

Phenotype integration improves power and preserves specificity in biobank-based genetic studies of major depressive disorder

Biobanks often contain several phenotypes relevant to diseases such as major depressive disorder (MDD), with partly distinct genetic architectures. Researchers face complex tradeoffs between shallow (large sample size, low specificity/sensitivity) and deep (small sample size, high specificity/sensitivity) phenotypes, and the optimal choices are often unclear. Here we propose to integrate these phenotypes to combine the benefits of each. We use phenotype imputation to integrate information across hundreds of MDD-relevant phenotypes, which significantly increases genome-wide association study (GWAS) power and polygenic risk score (PRS) prediction accuracy of the deepest available MDD phenotype in UK Biobank, LifetimeMDD. We demonstrate that imputation preserves specificity in its genetic architecture using a novel PRS-based pleiotropy metric. We further find that integration via summary statistics also enhances GWAS power and PRS predictions, but can introduce nonspecific genetic effects depending on input. Our work provides a simple and scalable approach to improve genetic studies in large biobanks by integrating shallow and deep phenotypes.

Genetic and clinical factors underlying a self-reported family history of heart disease

AIMS

To estimate how much information conveyed by self-reported family history of heart disease (FHHD) is already explained by clinical and genetic risk factors.

METHODS AND RESULTS

Cross-sectional analysis of UK Biobank participants without pre-existing coronary artery disease using a multivariable model with self-reported FHHD as the outcome. Clinical (diabetes, hypertension, smoking, apolipoprotein B-to-apolipoprotein AI ratio, waist-to-hip ratio, high sensitivity C-reactive protein, lipoprotein(a), triglycerides) and genetic risk factors (polygenic risk score for coronary artery disease [PRSCAD], heterozygous familial hypercholesterolemia [HeFH]) were exposures. Models were adjusted for age, sex, and cholesterol-lowering medication use. Multiple logistic regression models were fitted to associate FHHD with risk factors, with continuous variables treated as quintiles. Population attributable risks (PAR) were subsequently calculated from the resultant odds ratios. Among 166 714 individuals, 72 052 (43.2%) participants reported an FHHD. In a multivariable model, genetic risk factors PRSCAD (OR 1.30, CI 1.27-1.33) and HeFH (OR 1.31, 1.11-1.54) were most strongly associated with FHHD. Clinical risk factors followed: hypertension (OR 1.18, CI 1.15-1.21), lipoprotein(a) (OR 1.17, CI 1.14-1.20), apolipoprotein B-to-apolipoprotein AI ratio (OR 1.13, 95% CI 1.10-1.16), and triglycerides (OR 1.07, CI 1.04-1.10). For the PAR analyses: 21.9% (CI 18.19-25.63) of the risk of reporting an FHHD is attributed to clinical factors, 22.2% (CI% 20.44-23.88) is attributed to genetic factors, and 36.0% (CI 33.31-38.68) is attributed to genetic and clinical factors combined.

CONCLUSIONS

A combined model of clinical and genetic risk factors explains only 36% of the likelihood of FHHD, implying additional value in the family history.

Pervasive biases in proxy GWAS based on parental history of Alzheimer's disease

Almost every recent Alzheimer's disease (AD) genome-wide association study (GWAS) has performed meta-analysis to combine studies with clinical diagnosis of AD with studies that use proxy phenotypes based on parental disease history. Here, we report major limitations in current GWAS-by-proxy (GWAX) practices due to uncorrected survival bias and non-random participation of parental illness survey, which cause substantial discrepancies between AD GWAS and GWAX results. We demonstrate that current AD GWAX provide highly misleading genetic correlations between AD risk and higher education which subsequently affects a variety of genetic epidemiologic applications involving AD and cognition. Our study sheds important light on the design and analysis of mid-aged biobank cohorts and underscores the need for caution when interpreting genetic association results based on proxy-reported parental disease history.

Beyond the amyloid cascade: An update of Alzheimer's disease pathophysiology

Alzheimer's disease (AD) is a multi-etiology disease. The biological system of AD is associated with multidomain genetic, molecular, cellular, and network brain dysfunctions, interacting with central and peripheral immunity. These dysfunctions have been primarily conceptualized according to the assumption that amyloid deposition in the brain, whether from a stochastic or a genetic accident, is the upstream pathological change. However, the arborescence of AD pathological changes suggests that a single amyloid pathway might be too restrictive or inconsistent with a cascading effect. In this review, we discuss the recent human studies of late-onset AD pathophysiology in an attempt to establish a general updated view focusing on the early stages. Several factors highlight heterogenous multi-cellular pathological changes in AD, which seem to work in a self-amplifying manner with amyloid and tau pathologies. Neuroinflammation has an increasing importance as a major pathological driver, and perhaps as a convergent biological basis of aging, genetic, lifestyle and environmental risk factors.

Lymphoma in Border Collies: Genome-Wide Association and Pedigree Analysis

There has been considerable interest in studying cancer in dogs and its potential as a model system for humans. One area of research has been the search for genetic risk variants in canine lymphoma, which is amongst the most common canine cancers. Previous studies have focused on a limited number of breeds, but none have included Border Collies. The aims of this study were to identify relationships between Border Collie lymphoma cases through an extensive pedigree investigation and to utilise relationship information to conduct genome-wide association study (GWAS) analyses to identify risk regions associated with lymphoma. The expanded pedigree analysis included 83,000 Border Collies, with 71 identified lymphoma cases. The analysis identified affected close relatives, and a common ancestor was identified for 54 cases. For the genomic study, a GWAS was designed to incorporate lymphoma cases, putative "carriers", and controls. A case-control GWAS was also conducted as a comparison. Both analyses showed significant SNPs in regions on chromosomes 18 and 27. Putative top candidate genes from these regions included DLA-79, WNT10B, LMBR1L, KMT2D, and CCNT1.

Exome sequencing identifies breast cancer susceptibility genes and defines the contribution of coding variants to breast cancer risk

Linkage and candidate gene studies have identified several breast cancer susceptibility genes, but the overall contribution of coding variation to breast cancer is unclear. To evaluate the role of rare coding variants more comprehensively, we performed a meta-analysis across three large whole-exome sequencing datasets, containing 26,368 female cases and 217,673 female controls. Burden tests were performed for protein-truncating and rare missense variants in 15,616 and 18,601 genes, respectively. Associations between protein-truncating variants and breast cancer were identified for the following six genes at exome-wide significance (P < 2.5 × 10-6): the five known susceptibility genes ATM, BRCA1, BRCA2, CHEK2 and PALB2, together with MAP3K1. Associations were also observed for LZTR1, ATR and BARD1 with P < 1 × 10-4. Associations between predicted deleterious rare missense or protein-truncating variants and breast cancer were additionally identified for CDKN2A at exome-wide significance. The overall contribution of coding variants in genes beyond the previously known genes is estimated to be small.

Direct and INdirect effects analysis of Genetic lOci (DINGO): A software package to increase the power of locus discovery in GWAS meta-analyses of perinatal phenotypes and traits influenced by indirect genetic effects

Perinatal traits are influenced by genetic variants from both fetal and maternal genomes. Genome-wide association studies (GWAS) of these phenotypes have typically involved separate fetal and maternal scans, however, this approach may be inefficient as it does not utilize the information shared across the individual GWAS. In this manuscript we investigate the performance of three strategies to detect loci in maternal and fetal GWAS of the same trait: (i) the traditional strategy of analysing maternal and fetal GWAS separately; (ii) a novel two degree of freedom test which combines information from maternal and fetal GWAS; and (iii) a novel one degree of freedom test where signals from maternal and fetal GWAS are meta-analysed together conditional on the estimated sample overlap. We demonstrate through a combination of analytical formulae and data simulation that the optimal strategy depends on the extent of sample overlap/relatedness between the maternal and fetal GWAS, the correlation between own and offspring phenotypes, whether loci jointly exhibit fetal and maternal effects, and if so, whether these effects are directionally concordant. We apply our methods to summary results statistics from a recent GWAS meta-analysis of birth weight from deCODE, the UK Biobank and the Early Growth Genetics (EGG) consortium. Both the two degree of freedom (213 loci) and meta-analytic approach (226 loci) dramatically increase the number of robustly associated genetic loci for birth weight relative to separately analysing the scans (183 loci). Our best strategy identifies an additional 62 novel loci compared to the most recent published meta-analysis of birth weight and implicates both known and new biological pathways in the aetiology of the trait. We implement our methods in the online DINGO (Direct and INdirect effects analysis of Genetic lOci) software package, which allows users to perform one and/or two degree of freedom tests easily and computationally efficiently across the genome. We conclude that whilst the novel two degree of freedom test may be particularly useful for the analysis of certain perinatal phenotypes where many loci exhibit discordant maternal and fetal genetic effects, for most phenotypes, a simple meta-analytic strategy is likely to perform best, particularly in situations where maternal and fetal GWAS only partially overlap.

Current views on meningeal lymphatics and immunity in aging and Alzheimer's disease

Alzheimer's disease (AD) is an aging-related form of dementia associated with the accumulation of pathological aggregates of amyloid beta and neurofibrillary tangles in the brain. These phenomena are accompanied by exacerbated inflammation and marked neuronal loss, which altogether contribute to accelerated cognitive decline. The multifactorial nature of AD, allied to our still limited knowledge of its etiology and pathophysiology, have lessened our capacity to develop effective treatments for AD patients. Over the last few decades, genome wide association studies and biomarker development, alongside mechanistic experiments involving animal models, have identified different immune components that play key roles in the modulation of brain pathology in AD, affecting its progression and severity. As we will relay in this review, much of the recent efforts have been directed to better understanding the role of brain innate immunity, and particularly of microglia. However, and despite the lack of diversity within brain resident immune cells, the brain border tissues, especially the meninges, harbour a considerable number of different types and subtypes of adaptive and innate immune cells. Alongside microglia, which have taken the centre stage as important players in AD research, there is new and exciting evidence pointing to adaptive immune cells, namely T and B cells found in the brain and its meninges, as important modulators of neuroinflammation and neuronal (dys)function in AD. Importantly, a genuine and functional lymphatic vascular network is present around the brain in the outermost meningeal layer, the dura. The meningeal lymphatics are directly connected to the peripheral lymphatic system in different mammalian species, including humans, and play a crucial role in preserving a "healthy" immune surveillance of the CNS, by shaping immune responses, not only locally at the meninges, but also at the level of the brain tissue. In this review, we will provide a comprehensive view on our current knowledge about the meningeal lymphatic vasculature, emphasizing its described roles in modulating CNS fluid and macromolecule drainage, meningeal and brain immunity, as well as glial and neuronal function in aging and in AD.

Regional genetic correlations highlight relationships between neurodegenerative disease loci and the immune system

Neurodegenerative diseases, including Alzheimer's and Parkinson's disease, are devastating complex diseases resulting in physical and psychological burdens on patients and their families. There have been important efforts to understand their genetic basis leading to the identification of disease risk-associated loci involved in several molecular mechanisms, including immune-related pathways. Regional, in contrast to genome-wide, genetic correlations between pairs of immune and neurodegenerative traits have not been comprehensively explored, but could uncover additional immune-mediated risk-associated loci. Here, we systematically assess the role of the immune system in five neurodegenerative diseases by estimating regional genetic correlations between these diseases and immune-cell-derived single-cell expression quantitative trait loci (sc-eQTLs). We also investigate correlations between diseases and protein levels. We observe significant (FDR < 0.01) correlations between sc-eQTLs and neurodegenerative diseases across 151 unique genes, spanning both the innate and adaptive immune systems, across most diseases tested. With Parkinson's, for instance, RAB7L1 in CD4+ naïve T cells is positively correlated and KANSL1-AS1 is negatively correlated across all adaptive immune cell types. Follow-up colocalization highlight candidate causal risk genes. The outcomes of this study will improve our understanding of the immune component of neurodegeneration, which can warrant repurposing of existing immunotherapies to slow disease progression.

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.

Alzheimer's disease and related dementias among aging veterans: Examining gene-by-environment interactions with post-traumatic stress disorder and traumatic brain injury

INTRODUCTION

Post-traumatic stress disorder (PTSD) and traumatic brain injury (TBI) confer risk for Alzheimer's disease and related dementias (ADRD).

METHODS

This study from the Million Veteran Program (MVP) evaluated the impact of apolipoprotein E (APOE) ε4, PTSD, and TBI on ADRD prevalence in veteran cohorts of European ancestry (EA; n = 11,112 ADRD cases, 170,361 controls) and African ancestry (AA; n = 1443 ADRD cases, 16,191 controls). Additive-scale interactions were estimated using the relative excess risk due to interaction (RERI) statistic.

RESULTS

PTSD, TBI, and APOE ε4 showed strong main-effect associations with ADRD. RERI analysis revealed significant additive APOE ε4 interactions with PTSD and TBI in the EA cohort and TBI in the AA cohort. These additive interactions indicate that ADRD prevalence associated with PTSD and TBI increased with the number of inherited APOE ε4 alleles.

DISCUSSION

PTSD and TBI history will be an important part of interpreting the results of ADRD genetic testing and doing accurate ADRD risk assessment.