The TOMM40 gene rs2075650 polymorphism contributes to Alzheimer's disease in Caucasian, and Asian populations

Intrauterine chromium exposure and cognitive developmental delay: The modifying effect of genetic predisposition

There is limited evidence on the effects of intrauterine chromium (Cr) exposure on children's cognitive developmental delay (CDD). Further, little is known about the genetic factors in modifying the association between intrauterine Cr exposure and CDD. The present study involved 2361 mother-child pairs, in which maternal plasma Cr concentrations were assessed, a polygenic risk score for the child was constructed, and the child's cognitive development was evaluated using the Bayley Scales of Infant Development. The risks of CDD conferred by intrauterine Cr exposure in children with different genetic backgrounds were evaluated by logistic regression. The additive interaction between intrauterine Cr exposure and genetic factors was evaluated by calculating the relative excess risk due to interaction (RERI), attributable proportion due to interaction (AP), and synergy index (SI). According to present study, higher intrauterine Cr exposure was significantly associated with increased CDD risk [each unit increase in ln-transformed maternal plasma Cr concentration (ln-Cr): adjusted OR (95 % CI), 1.18 (1.04-1.35); highest vs lowest quartile: adjusted OR (95 % CI), 1.57 (1.10-2.23)]. The dose-response relationship of intrauterine Cr exposure and CDD for children with high genetic risk was more prominent [each unit increased ln-Cr: adjusted OR (95 % CI), 1.36 (1.09-1.70)]. Joint effects between intrauterine Cr exposure and genetic factors were found. Specifically, for high genetic risk carriers, the association between intrauterine Cr exposure and CDD was more evident [highest vs lowest quartile: adjusted OR (95 % CI), 2.33 (1.43-3.80)]. For those children with high intrauterine Cr exposure and high genetic risk, the adjusted AP was 0.39 (95 % CI, 0.07-0.72). Conclusively, intrauterine Cr exposure was a high-risk factor for CDD in children, particularly for those with high genetic risk. Intrauterine Cr exposure and one's adverse genetic background jointly contribute to an increased risk of CDD in children.

Body Composition and Alzheimer's Disease: A Holistic Review

Alzheimer's disease (AD) represents a significant global health challenge and affects approximately 50 million people worldwide. This overview of published reviews provides a comprehensive understanding of the intricate correlations between AD and body composition, focusing particularly on obesity. We used a systematic approach to collect and analyze relevant reviews on the topic of obesity and Alzheimer's disease. A comprehensive search of electronic databases, including PubMed, MEDLINE, and Google Scholar, was conducted. We searched keywords such as "Alzheimer's disease", "body composition", "lean mass", "bone mass", and "fat mass". We considered only reviews written within the past 5 years and in English. Fifty-six relevant reviews were identified that shed light on the multiple connections between AD and body composition. The review involves several aspects, including the impact of lean mass, bone mass, and endocrinological factors related to obesity, as well as inflammation, neuroinflammation, and molecular/genetic factors. The findings highlight the complex interplay of these elements in the development of AD, underscoring the need for holistic approaches to reduce the risk of AD and to explore innovative strategies for diagnosis, prevention, and treatment.

On the effect heterogeneity of established disease susceptibility loci for Alzheimer's disease across different genetic ancestries

INTRODUCTION

Genome-wide association studies have identified numerous disease susceptibility loci (DSLs) for Alzheimer's disease (AD). However, only a limited number of studies have investigated the dependence of the genetic effect size of established DSLs on genetic ancestry.

METHODS

We utilized the whole genome sequencing data from the Alzheimer's Disease Sequencing Project (ADSP) including 35,569 participants. A total of 25,459 subjects in four distinct populations (African ancestry, non-Hispanic White, admixed Hispanic, and Asian) were analyzed.

RESULTS

We found that nine DSLs showed significant heterogeneity across populations. Single nucleotide polymorphism (SNP) rs2075650 in translocase of outer mitochondrial membrane 40 (TOMM40) showed the largest heterogeneity (Cochran's Q = 0.00, I2 = 90.08), followed by other SNPs in apolipoprotein C1 (APOC1) and apolipoprotein E (APOE). Two additional loci, signal-induced proliferation-associated 1 like 2 (SIPA1L2) and solute carrier 24 member 4 (SLC24A4), showed significant heterogeneity across populations.

DISCUSSION

We observed substantial heterogeneity for the APOE-harboring 19q13.32 region with TOMM40/APOE/APOC1 genes. The largest risk effect was seen among African Americans, while Asians showed a surprisingly small risk effect.

Cognitive impairment in long-living adults: a genome-wide association study, polygenic risk score model and molecular modeling of the APOE protein

Background

Cognitive impairment is an irreversible, aging-associated condition that robs people of their independence. The purpose of this study was to investigate possible causes of this condition and propose preventive options.

Methods

We assessed cognitive status in long-living adults aged 90+ (n = 2,559) and performed a genome wide association study using two sets of variables: Mini-Mental State Examination scores as a continuous variable (linear regression) and cognitive status as a binary variable (> 24, no cognitive impairment; <10, impairment) (logistic regression).

Results

Both variations yielded the same polymorphisms, including a well-known marker of dementia, rs429358in the APOE gene. Molecular dynamics simulations showed that this polymorphism leads to changes in the structure of alpha helices and the mobility of the lipid-binding domain in the APOE protein.

Conclusion

These changes, along with higher LDL and total cholesterol levels, could be the mechanism underlying the development of cognitive impairment in older adults. However, this polymorphism is not the only determining factor in cognitive impairment. The polygenic risk score model included 45 polymorphisms (ROC AUC 69%), further confirming the multifactorial nature of this condition. Our findings, particularly the results of PRS modeling, could contribute to the development of early detection strategies for predisposition to cognitive impairment in older adults.

The Association of Selected GWAS Reported AD Risk Loci with CSF Biomarker Levels and Cognitive Decline in Slovenian Patients

Alzheimer's disease (AD) is the most common neurodegenerative disease, with a complex genetic background. Apart from rare, familial cases, a combination of multiple risk loci contributes to the susceptibility of the disease. Genome-wide association studies (GWAS) have identified numerous AD risk loci. Changes in cerebrospinal fluid (CSF) biomarkers and imaging techniques can detect AD-related brain changes before the onset of clinical symptoms, even in the presence of preclinical mild cognitive impairment. In this study, we aimed to assess the associations between SNPs in well-established GWAS AD risk loci and CSF biomarker levels or cognitive test results in Slovenian patients with cognitive decline. The study included 82 AD patients, 28 MCI patients with pathological CSF biomarker levels and 35 MCI patients with normal CSF biomarker levels. Carriers of at least one polymorphic TOMM40 rs157581 C allele had lower Aβ42 (p = 0.033) and higher total tau (p = 0.032) and p-tau181 levels (p = 0.034). Carriers of at least one polymorphic T allele in SORCS1 rs1358030 had lower total tau (p = 0.019), while polymorphic SORCS1 rs1416406 allele was associated with lower total tau (p = 0.013) and p-tau181 (p = 0.036). In addition, carriers of at least one polymorphic T allele in BCHE rs1803274 had lower cognitive test scores (p = 0.029). The study findings may contribute to the identification of genetic markers associated with AD and MCI and provide insights into early disease diagnostics.

Neurogenomics of Alzheimer's Disease (AD): An Asian Population Review

Alzheimer's disease (AD) is on the rise worldwide. Generally, AD is considered neurodegenerative when the production and clearance of amyloid-β (Aβ) are imbalanced. Recent research on genome-wide association studies (GWAS) has been explosive; GWAS indicates a relationship between single nucleotide polymorphism (SNP) and AD. GWAS also reveals ethnic differences between Caucasians and Asians. This indicates that pathogenesis between ethnic groups is distinct. According to current scientific knowledge, AD is a disease with a complex pathogenesis that includes impaired neuronal cholesterol regulation, immunity regulation, neurotransmitters regulation, Aβ clearance, Aβ production, and vascular regulation. Here, we demonstrate the pathogenesis of AD in an Asian population and the SNP risk of AD for future AD screening before onset. According to our knowledge, this is the first review of Alzheimer's disease to demonstrate the pathogenesis of AD based on SNP in an Asian population.

A penalized linear mixed model with generalized method of moments estimators for complex phenotype prediction

MOTIVATION

Linear mixed models (LMMs) have long been the method of choice for risk prediction analysis on high-dimensional data. However, it remains computationally challenging to simultaneously model a large amount of variants that can be noise or have predictive effects of complex forms.

RESULTS

In this work, we have developed a penalized LMM with generalized method of moments (pLMMGMM) estimators for prediction analysis. pLMMGMM is built within the LMM framework, where random effects are used to model the joint predictive effects from all variants within a region. Different from existing methods that focus on linear relationships and use empirical criteria for variable screening, pLMMGMM can efficiently detect regions that harbor genetic variants with both linear and non-linear predictive effects. In addition, unlike existing LMMs that can only handle a very limited number of random effects, pLMMGMM is much less computationally demanding. It can jointly consider a large number of regions and accurately detect those that are predictive. Through theoretical investigations, we have shown that our method has the selection consistency and asymptotic normality. Through extensive simulations and the analysis of PET-imaging outcomes, we have demonstrated that pLMMGMM outperformed existing models and it can accurately detect regions that harbor risk factors with various forms of predictive effects.

AVAILABILITY AND IMPLEMENTATION

The R-package is available at https://github.com/XiaQiong/GMMLasso.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

TOMM40 genetic variants associated with healthy aging and longevity: a systematic review

Introduction

Healthy aging relies on mitochondrial functioning because this organelle provides energy and diminishes oxidative stress. Single nucleotide polymorphisms (SNPs) in TOMM40, a critical gene that produces the outer membrane protein TOM40 of mitochondria, have been associated with mitochondrial dysfunction and neurodegenerative processes. Yet it is not clear whether or how the mitochondria may impact human longevity. We conducted this review to ascertain which SNPs have been associated with markers of healthy aging.

Methods

Using the PRISMA methodology, we conducted a systematic review on PubMed and Embase databases to identify associations between TOMM40 SNPs and measures of longevity and healthy aging.

Results

Twenty-four articles were selected. The TOMM40 SNPs rs2075650 and rs10524523 were the two most commonly identified and studied SNPs associated with longevity. The outcomes associated with the TOMM40 SNPs were changes in BMI, brain integrity, cognitive functions, altered inflammatory network, vulnerability to vascular risk factors, and longevity.

Discussions

Our systematic review identified multiple TOMM40 SNPs potentially associated with healthy aging. Additional research can help to understand mechanisms in aging, including resilience, prevention of disease, and adaptation to the environment.

A penalized linear mixed model with generalized method of moments for prediction analysis on high-dimensional multi-omics data

With the advances in high-throughput biotechnologies, high-dimensional multi-layer omics data become increasingly available. They can provide both confirmatory and complementary information to disease risk and thus have offered unprecedented opportunities for risk prediction studies. However, the high-dimensionality and complex inter/intra-relationships among multi-omics data have brought tremendous analytical challenges. Here we present a computationally efficient penalized linear mixed model with generalized method of moments estimator (MpLMMGMM) for the prediction analysis on multi-omics data. Our method extends the widely used linear mixed model proposed for genomic risk predictions to model multi-omics data, where kernel functions are used to capture various types of predictive effects from different layers of omics data and penalty terms are introduced to reduce the impact of noise. Compared with existing penalized linear mixed models, the proposed method adopts the generalized method of moments estimator and it is much more computationally efficient. Through extensive simulation studies and the analysis of positron emission tomography imaging outcomes, we have demonstrated that MpLMMGMM can simultaneously consider a large number of variables and efficiently select those that are predictive from the corresponding omics layers. It can capture both linear and nonlinear predictive effects and achieves better prediction performance than competing methods.

Explainable deep transfer learning model for disease risk prediction using high-dimensional genomic data

Building an accurate disease risk prediction model is an essential step in the modern quest for precision medicine. While high-dimensional genomic data provides valuable data resources for the investigations of disease risk, their huge amount of noise and complex relationships between predictors and outcomes have brought tremendous analytical challenges. Deep learning model is the state-of-the-art methods for many prediction tasks, and it is a promising framework for the analysis of genomic data. However, deep learning models generally suffer from the curse of dimensionality and the lack of biological interpretability, both of which have greatly limited their applications. In this work, we have developed a deep neural network (DNN) based prediction modeling framework. We first proposed a group-wise feature importance score for feature selection, where genes harboring genetic variants with both linear and non-linear effects are efficiently detected. We then designed an explainable transfer-learning based DNN method, which can directly incorporate information from feature selection and accurately capture complex predictive effects. The proposed DNN-framework is biologically interpretable, as it is built based on the selected predictive genes. It is also computationally efficient and can be applied to genome-wide data. Through extensive simulations and real data analyses, we have demonstrated that our proposed method can not only efficiently detect predictive features, but also accurately predict disease risk, as compared to many existing methods.

Accurate disease risk prediction is an essential step towards precision medicine. Deep learning models have achieved the state-of-the-art performance for many prediction tasks. However, they generally suffer from the curse of dimensionality and lack of biological interpretability, both of which have greatly limited their applications to the prediction analysis of whole-genome sequencing data. We present here an explainable deep transfer learning model for the analysis of high-dimensional genomic data. Our proposed method can detect predictive genes that harbor genetic variants with both linear and non-linear effects via the proposed group-wise feature importance score. It can also efficiently and accurately model disease risk based on the detected predictive genes using the proposed transfer-learning based network architecture. Our proposed method is built at the gene level, and thus is much more biologically interpretable. It is also computationally efficiently and can be applied to whole-exome sequencing data that have millions of potential predictors. Through both simulation studies and the analysis of whole-exome data obtained from the Alzheimer’s Disease Neuroimaging Initiative, we have demonstrated that our method can efficiently detect predictive genes and it has better prediction performance than many existing methods.

Genome-wide variants and polygenic risk scores for cognitive impairment following blood or marrow transplantation

Cognitive impairment is prevalent in blood or marrow transplantation (BMT) recipients, albeit with inter-individual variability. We conducted a genome-wide association study of objective cognitive function assessed longitudinally in 239 adult BMT recipients for discovery and replicated in an independent cohort of 540 BMT survivors. Weighted genome-wide polygenic risk scores (PRS) were constructed using linkage disequilibrium pruned significant SNPs. Forty-four genome-wide significant SNPs were identified using additive (n = 3); codominant (n = 20) and genotype models (n = 21). Each additional copy of a risk allele was associated with a 0.28-point (p = 1.07 × 10-8) to a 1.82-point (p = 6.7 × 10-12) increase in a global deficit score. We replicated two SNPs (rs11634183 and rs12486041) with links to neural integrity. Patients in the top PRS quintile were at increased risk of cognitive impairment in discovery (RR = 1.95, 95%CI: 1.28-2.96, p = 0.002) and replication cohorts (OR = 1.84, 95%CI, 1.02-3.32, p = 0.043). Associations were stronger among individuals with lowest clinical risk for cognitive impairment. These findings support potential utility of PRS-based risk classification in the development of targeted interventions aimed at improving cognitive outcomes in BMT survivors.

Genetic Variants and Haplotypes of TOMM40, APOE, and APOC1 are Related to the Age of Onset of Late-onset Alzheimer Disease in a Colombian Population

BACKGROUND

The Apolipoprotein E (APOE) gene is the main risk factor for late-onset Alzheimer disease (LOAD). Genetic variants and haplotypes in regions near the APOE locus may be associated with LOAD in the Colombian population.

OBJECTIVE

We evaluated frequencies and risk of genetic variants and haplotypes in APOE, TOMM40, and APOC1 promoters, also in putative regulatory enhancer elements (TOMM40 IVS2-4 and TOMM40 IVS6), and in cis-regulatory elements (ME1 and BCR).

MATERIALS AND METHODS

Our case-control association study was carried out in 50 patients with LOAD and 50 controls. We determined frequencies and odd ratios for genetic variants and haplotypes.

RESULTS

We found a significant association between LOAD and genetic variants at the TOMM40 promoter, at TOMM40 IVS2-4 and TOMM40 IVS6 regulatory enhancer elements, and at the APOC1 promoter. Particularly, variants of Poly-T and APOC1 promoter could anticipate the age of onset of LOAD in our population. We identified three risk haplotypes in TOMM40 (ACGGAG, ACGGGG, and ATAGGC) related to LOAD's age of onset. We also found other risk or protection haplotypes at the TOMM40 and APOE promoters, at TOMM40 IVS2-4, TOMM40 IVS6 regulatory enhancer elements, and at ME1.

CONCLUSION

Genetic variants and haplotypes near the APOE locus are related to LOAD risk and accelerated onset of LOAD in the Colombian population.

APOE, TOMM40, and sex interactions on neural network connectivity

The Apolipoprotein E ε4 (APOE ε4) haplotype is the strongest genetic risk factor for late-onset Alzheimer's disease (AD). The Translocase of Outer Mitochondrial Membrane-40 (TOMM40) gene maintains cellular bioenergetics, which is disrupted in AD. TOMM40 rs2075650 ('650) G versus A carriage is consistently related to neural and cognitive outcomes, but it is unclear if and how it interacts with APOE. We examined 21 orthogonal neural networks among 8,222 middle-aged to aged participants in the UK Biobank cohort. ANOVA and multiple linear regression tested main effects and interactions with APOE and TOMM40 '650 genotypes, and if age and sex acted as moderators. APOE ε4 was associated with less strength in multiple networks, while '650 G versus A carriage was related to more language comprehension network strength. In APOE ε4 carriers, '650 G-carriage led to less network strength with increasing age, while in non-G-carriers this was only seen in women but not men. TOMM40 may shift what happens to network activity in aging APOE ε4 carriers depending on sex.

Use of deep learning genomics to discriminate Alzheimer's disease and healthy controls

Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the elderly. Because gene is an important clinical risk factor resulting in AD, genomic studies, such as genome-wide association studies (GWAS), have widely been applied into AD studies. However, main shortcomings of GWAS method were that hereditary deletions were evident in the GWAS studies, which resulted in low classification or prediction abilities by using GWAS analysis. Therefore, this paper proposed a novel deep learning genomics approach and applied it to discriminate AD patients and healthy control (HC) subjects. In this study, we selected genotype data of 988 subjects enrolled in the ADNI, including 622 AD patients and 366 HC subjects. The proposed deep learning genomics (DLG) approach was composed of three steps: quality control, SNP genotype coding, and classification. The Resnet framework was used as the DLG model in this study. In the comparative GWAS analysis, APOE ε4 status and the normalized theta-value of the significant SNP loci were seen as predictors to classify genetically using Support Vector Machine (SVM). All data were divided into one training & validation group and one test group. 5-fold cross-validation was used in 500 times. Finally, we compared the classification results between DLG model and traditional GWAS analysis. As a result, the accuracy, sensitivity, and specificity of classification for traditional GWAS analysis was 71.38%±0.63%, 63.13%±2.87% and 85.59%±6.66% in the test group; while the accuracy, sensitivity, and specificity of classification for DLG model was 92.65%±4.80%, 85.00%±16.25% and 97.10%±4.38% in the test group. Hence, the DLG model can achieve higher accuracy and sensitivity when applied to AD. More importantly, we discovered several novel genetic biomarkers of AD, including rs6311 and rs6313 in HTR2A, and rs690705 in RFC3. The roles of these novel loci in AD should be explored future.

Use of Deep-Learning Genomics to Discriminate Healthy Individuals from Those with Alzheimer's Disease or Mild Cognitive Impairment

OBJECTIVES

Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the elderly. Certain genes have been identified as important clinical risk factors for AD, and technological advances in genomic research, such as genome-wide association studies (GWAS), allow for analysis of polymorphisms and have been widely applied to studies of AD. However, shortcomings of GWAS include sensitivity to sample size and hereditary deletions, which result in low classification and predictive accuracy. Therefore, this paper proposes a novel deep-learning genomics approach and applies it to multitasking classification of AD progression, with the goal of identifying novel genetic biomarkers overlooked by traditional GWAS analysis.

METHODS

In this study, we selected genotype data from 1461 subjects enrolled in the Alzheimer's Disease Neuroimaging Initiative, including 622 AD, 473 mild cognitive impairment (MCI), and 366 healthy control (HC) subjects. The proposed deep-learning genomics (DLG) approach consists of three steps: quality control, coding of single-nucleotide polymorphisms, and classification. The ResNet framework was used for the DLG model, and the results were compared with classifications by simple convolutional neural network structure. All data were randomly assigned to one training/validation group and one test group at a ratio of 9 : 1. And fivefold cross-validation was used.

RESULTS

We compared classification results from the DLG model to those from traditional GWAS analysis among the three groups. For the AD and HC groups, the accuracy, sensitivity, and specificity of classification were, respectively, 98.78 ± 1.50%, 98.39% ± 2.50%, and 99.44% ± 1.11% using the DLG model, while 71.38% ± 0.63%, 63.13% ± 2.87%, and 85.59% ± 6.66% using traditional GWAS. Similar results were obtained from the other two intergroup classifications.

CONCLUSION

The DLG model can achieve higher accuracy and sensitivity when applied to progression of AD. More importantly, we discovered several novel genetic biomarkers of AD progression, including rs6311 and rs6313 in HTR2A, rs1354269 in NAV2, and rs690705 in RFC3. The roles of these novel loci in AD should be explored in future research.

Reassessment of Pioglitazone for Alzheimer's Disease

Alzheimer's disease is a quintessential 'unmet medical need', accounting for ∼65% of progressive cognitive impairment among the elderly, and 700,000 deaths in the United States in 2020. In 2019, the cost of caring for Alzheimer's sufferers was $244B, not including the emotional and physical toll on caregivers. In spite of this dismal reality, no treatments are available that reduce the risk of developing AD or that offer prolonged mitiagation of its most devestating symptoms. This review summarizes key aspects of the biology and genetics of Alzheimer's disease, and we describe how pioglitazone improves many of the patholophysiological determinants of AD. We also summarize the results of pre-clinical experiments, longitudinal observational studies, and clinical trials. The results of animal testing suggest that pioglitazone can be corrective as well as protective, and that its efficacy is enhanced in a time- and dose-dependent manner, but the dose-effect relations are not monotonic or sigmoid. Longitudinal cohort studies suggests that it delays the onset of dementia in individuals with pre-existing type 2 diabetes mellitus, which small scale, unblinded pilot studies seem to confirm. However, the results of placebo-controlled, blinded clinical trials have not borne this out, and we discuss possible explanations for these discrepancies.

TOMM40 and APOE variants synergistically increase the risk of Alzheimer's disease in a Chinese population

BACKGROUND

The apolipoprotein E (APOE) ε4 allele is a strong risk factor for Alzheimer's disease (AD) in Caucasian and African American populations. It suggests that other genetic factors may modulate AD pathogenesis in Chinese populations, among which the frequency of this allele is reduced but the AD prevalence is maintained. The translocase of outer mitochondrial membrane 40 (TOMM40), which is located adjacent to APOE, may play an APOE-dependent role in modulating AD pathogenesis.

AIMS

This work aimed to investigate whether TOMM40 polymorphisms modulate AD risk independently of, or in conjunction with APOE polymorphisms in Chinese populations.

METHODS

We conducted a case-control study including 834 patients with AD recruited from the Memory Clinic and 643 cognitively normal participants recruited from the community. The Taqman SNP method was used for APOE genotyping, while TOMM40 polymorphism genotyping was conducted via a polymerase chain reaction-ligase detection reaction.

RESULTS

The TOMM40 rs10119 and rs71352238 alleles were associated with AD independently of the patient APOE status. The rs10119 AA genotype and rs71352238 CC genotype were risk genotypes of AD. Individuals carrying a TOMM40 rs10119 GG/APOE ε4+ (OR, 3.73; 95% CI 1.49-9.37; P = 0.005), TOMM40 rs10119 AG/APOE ε4+ (OR, 4.16; 95% CI 3.30-5.24; P < 0.001), or TOMM40 rs10119 AA/APOE ε4+ (OR, 14.78; 95% CI 8.56-25.54; P < 0.001) genotype exhibited a significantly higher AD risk. Those carrying a TOMM40 rs71352238 TT/APOE ε4+ (OR, 3.82; 95% CI 2.32-6.29; P < 0.001), TOMM40 rs71352238 CT/APOE ε4+ (OR, 4.40; 95% CI 3.46-5.56; P < 0.001), or TOMM40 rs71352238 CC/APOE ε4+ (OR, 14.02; 95% CI 7.81-25.17; P < 0.001) genotype also exhibited a significantly increased AD risk.

DISCUSSION AND CONCLUSIONS

This study provides invaluable insights into the mechanisms underlying the prevalence of AD in Chinese populations, and supports that simultaneous TOMM40 and APOE genotyping in the clinical setting may identify individuals at high risk of developing AD.

Genome-wide association studies of brain imaging data via weighted distance correlation

MOTIVATION

Imaging genetics is mainly used to reveal the pathogenesis of neuropsychiatric risk genes and understand the relationship between human brain structure, functional and individual differences. Increasingly, the brain-wide imaging phenotypes in voxels are available to test the association with genetic markers. A challenge with analyzing such data is their high dimensionality and complex relationships.

RESULTS

To tackle this challenge, we introduce a weighed distance correlation (wdCor) that can assess the association between genetic markers and voxel-based imaging data. Importantly, the wdCor test takes the voxel-based data as a whole multivariate phenotype, which preserves the spatial continuity and might enhance the power. Besides, an adaptive permutation procedure is introduced to determine the P-values of the wdCor test and also alleviate the computational burden in GWAS. In extensive simulation studies, wdCor achieves much better performances compared to the original distance correlation. We also successfully apply wdCor to conduct a large-scale analysis on data from the Alzheimer's disease neuroimaging project (ADNI).

AVAILABILITY AND IMPLEMENTATION

Our wdCor method provides new research directions and ideas for multivariate analysis of high-dimensional data, it can also be used as a tool for scientific analysis of imaging genetics research in practical applications. The R package wdcor, and the code for reproducing all results in this article is available in Github: https://github.com/yangyuhui0129/wdcor.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

An Aging-Related Single-Nucleotide Polymorphism is Associated With Altered Clinical Outcomes and Distinct Inflammatory Profiles in Aged Blunt Trauma Patients

The contribution of individual genetic determinants of aging to the adverse clinical outcomes and altered inflammation mediator networks characteristic of aged trauma patients is unknown. The AA genotype of the aging-related single-nucleotide polymorphism (SNP) rs2075650 in TOMM40 has been associated with longevity, while the AG and GG genotypes are associated with an increased risk of Alzheimer disease. Here, we studied the effect of rs2075650 on clinical outcomes and dynamic biomarker patterns after traumatic injury. Genomic DNA was obtained from blunt trauma patients admitted to the ICU and examined for 551,839 SNPs using an Illumina microarray kit. Plasma was sampled from each patient three times within the first 24 h and daily from day 1 to 7 then assayed for 31 biomarkers using Luminex. Aged patients (65-90 years) were segregated into AA (n = 77) and AG/GG (n = 17) genotypes. Additional comparisons were made with matched groups of young patients (18-30 years), controlling for injury severity score (ISS) and sex ratio, and also segregated into AA (n = 56) and AG/GG (n = 19) genotypes. Aged patients with the AA genotype had a significantly lower requirement for ventilation and fewer days on mechanical ventilation, as well as significantly higher levels of one mediator and lower levels of two mediators. Dynamic Bayesian Network inference revealed IL-23 as a central node in each network regardless of age or genotype, with MIG and IP-10 also as key mediators in the networks of the aged patients. These findings suggest that an aging-related SNP, rs2075650, may influence clinical outcomes and inflammation networks in aged patients following blunt trauma, and thus may serve as a predictive outcome biomarker in the setting of polytrauma.

Associations of Vascular Risk Factors, APOE and TOMM40 Polymorphisms With Cognitive Function in Dementia-Free Chinese Older Adults: A Community-Based Study

Objective: The associations of vascular risk factors (VRFs), apolipoprotein E (APOE), and translocase of outer mitochondrial membrane 40 (TOMM40) with cognitive function have been investigated mostly in western societies. In the present study, we sought to examine the associations of VRFs [i.e., current smoking, current drinking, physical inactivity, obesity, total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), diabetes, and hypertension] and variants located in APOE (ε2/3/4) and TOMM40 (rs2075650) with global cognitive function in Chinese older adults, with a focus on their potential interactions. Methods: This is a cross-sectional study that included 422 permanent residents (mean age 69.2 years, 54.3% female) living in Beijing, who were free of dementia. Data were collected through interviews, clinical examinations, and laboratory tests. The two genetic polymorphisms were genotyped, and participants were dichotomized as carriers vs. non-carriers of APOE ε4 or TOMM40 G. Global cognitive function was assessed with the Mini-Mental State Examination (MMSE). Data were analyzed with multivariable linear regression models. Results: Physical inactivity and diabetes were independently associated with a lower MMSE score (all p < 0.05). When four putative VRFs (i.e., current smoking, physical inactivity, high LDL-C, and diabetes) were aggregated, an increasing number of having these factors was associated with a decreasing MMSE score in a dose-response manner (p = 0.001). TOMM40 polymorphisms, independent of the APOE ε4 allele, interacted with aggregated VRFs to influence cognitive performance, such that having one or more of these VRFs was particularly detrimental to the cognition of TOMM40 carriers. Further analyses revealed interactions of the TOMM40 polymorphism with (i) physical inactivity and (ii) diabetes, such that having either physical inactivity or diabetes in combination with carrying a TOMM40 G allele, compared to having neither, was significantly associated with a markedly lower MMSE score (all p < 0.05). Conclusion: This study provides some evidence supporting the association of vascular risk factors with poor cognitive performance among dementia-free Chinese older adults and further revealed their interactions with the TOMM40 polymorphism. The results underscore the vulnerability of global cognitive function to VRFs, which could be reinforced by carrying the TOMM40 rs2075650 G allele. These findings have potential implications for developing tailored intervention programs to maintain cognitive function.

A TOMM40/APOE allele encoding APOE-E3 predicts high likelihood of late-onset Alzheimer's disease in autopsy cases

BACKGROUND

The APOE-ε4 allele is an established risk factor for Alzheimer's disease (AD). TOMM40 located adjacent to APOE has also been implicated in AD but reports of TOMM40 associations with AD that are independent of APOE-ε4 are at variance.

METHODS

We investigated associations of AD with haplotypes defined by three TOMM40 and two APOE single nucleotide polymorphisms in 73 and 71 autopsy cases with intermediate and high likelihood of AD (defined by BRAAK stages

RESULTS

We observed eight haplotypes with a frequency >0.02. The two haplotypes encoding APOE-E4 showed strong associations with AD that did not differ between intermediate and high likelihood AD. In contrast, a TOMM40 haplotype encoding APOE-E3 was identified as risk haplotype of high- (p = .0186), but not intermediate likelihood AD (p = .7530). Furthermore, the variant allele of rs2075650 located in intron 2 of TOMM40, increased the risk of high-, but not intermediate likelihood AD on the APOE-ε3/ε3 background (p = .0230).

CONCLUSION

The striking association of TOMM40 only with high likelihood AD may explain some contrasting results for TOMM40 in clinical studies and may reflect an association with more advanced disease and/or suggest a role of TOMM40 in the pathogenesis of neurofibrillary tangles.

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Key Words

• APOE
• Alzheimer’ disease
• TOMM40
• beta-amyloid
• genetics
• haplotypes
• neurofibrillary tangles

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MESH Headings

• Aged
• Aged, 80 and over
• Alzheimer Disease/genetics
• Alzheimer Disease/pathology
• Apolipoproteins E/genetics
• Female
• Gene Frequency
• Haplotypes
• Humans
• Male
• Membrane Transport Proteins/genetics
• Mitochondrial Precursor Protein Import Complex Proteins
• Polymorphism, Single Nucleotide

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Grants

• V 344 Austrian Science Fund FWF
• Austrian Science Fund
• Kurt und Senta Herrmann Stiftung

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Affiliation(s)

Selma M. Soyal Institute of Pharmacology and ToxicologyParacelsus Medical UniversitySalzburgAustria
Markus Kwik Institute of Pharmacology and ToxicologyParacelsus Medical UniversitySalzburgAustria
Ognian Kalev Division of NeuropathologyNeuromed Campus, Kepler University HospitalLinzAustria
Stefan Lenz Division of NeuropathologyNeuromed Campus, Kepler University HospitalLinzAustria
Greta Zara Institute of Pharmacology and ToxicologyParacelsus Medical UniversitySalzburgAustria
Peter Strasser Institute of Laboratory MedicineParacelsus Medical UniversitySalzburgAustria
Wolfgang Patsch Institute of Pharmacology and ToxicologyParacelsus Medical UniversitySalzburgAustria
Serge Weis Division of NeuropathologyNeuromed Campus, Kepler University HospitalLinzAustria

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A telescope GWAS analysis strategy, based on SNPs-genes-pathways ensamble and on multivariate algorithms, to characterize late onset Alzheimer's disease

Genome–wide association studies (GWAS) have revealed a plethora of putative susceptibility genes for Alzheimer’s disease (AD), with the sole exception of APOE gene unequivocally validated in independent study. Considering that the etiology of complex diseases like AD could depend on functional multiple genes interaction network, here we proposed an alternative GWAS analysis strategy based on (i) multivariate methods and on a (ii) telescope approach, in order to guarantee the identification of correlated variables, and reveal their connections at three biological connected levels. Specifically as multivariate methods, we employed two machine learning algorithms and a genetic association test and we considered SNPs, Genes and Pathways features in the analysis of two public GWAS dataset (ADNI-1 and ADNI-2). For each dataset and for each feature we addressed two binary classifications tasks: cases vs. controls and the low vs. high risk of developing AD considering the allelic status of APOEe4. This complex strategy allowed the identification of SNPs, genes and pathways lists statistically robust and meaningful from the biological viewpoint. Among the results, we confirm the involvement of TOMM40 gene in AD and we propose GRM7 as a novel gene significantly associated with AD.

Could Alzheimer's Disease Originate in the Periphery and If So How So?

The classical amyloid cascade model for Alzheimer's disease (AD) has been challenged by several findings. Here, an alternative molecular neurobiological model is proposed. It is shown that the presence of the APOE ε4 allele, altered miRNA expression and epigenetic dysregulation in the promoter region and exon 1 of TREM2, as well as ANK1 hypermethylation and altered levels of histone post-translational methylation leading to increased transcription of TNFA, could variously explain increased levels of peripheral and central inflammation found in AD. In particular, as a result of increased activity of triggering receptor expressed on myeloid cells 2 (TREM-2), the presence of the apolipoprotein E4 (ApoE4) isoform, and changes in ANK1 expression, with subsequent changes in miR-486 leading to altered levels of protein kinase B (Akt), mechanistic (previously mammalian) target of rapamycin (mTOR) and signal transducer and activator of transcription 3 (STAT3), all of which play major roles in microglial activation, proliferation and survival, there is activation of microglia, leading to the subsequent (further) production of cytokines, chemokines, nitric oxide, prostaglandins, reactive oxygen species, inducible nitric oxide synthase and cyclooxygenase-2, and other mediators of inflammation and neurotoxicity. These changes are associated with the development of amyloid and tau pathology, mitochondrial dysfunction (including impaired activity of the electron transport chain, depleted basal mitochondrial potential and oxidative damage to key tricarboxylic acid enzymes), synaptic dysfunction, altered glycogen synthase kinase-3 (GSK-3) activity, mTOR activation, impairment of autophagy, compromised ubiquitin-proteasome system, iron dyshomeostasis, changes in APP translation, amyloid plaque formation, tau hyperphosphorylation and neurofibrillary tangle formation.

Biothiols and oxidative stress markers and polymorphisms of TOMM40 and APOC1 genes in Alzheimer's disease patients

Alzheimer’s disease (AD) is a progressive disease, with frequently observed improper biothiols turnover, homocysteine (Hcy) and glutathione (GSH). GSH protects cells from oxidative stress and may be determined by 8-oxo-2’-deoxyguanosine (8-oxo2dG) level and its repair enzyme 8-oxoguanine DNA glycosylase (OGG1). The presence of unfavorable alleles, e.g., in APOE cluster, TOMM40 or APOC1 is known to facilitate the dementia onset under oxidative stress.

The aim of the study was to analyze rs1052452, rs2075650 TOMM40 polymorphisms, rs4420638 APOC1, and their correlation with Hcy, GSH, 8-oxo2dG, OGG1 levels in plasma of AD patients and controls.

We recruited 230 individuals: 88 AD, 80 controls without (UC), 62 controls with (RC) positive family history of AD. The TOMM40 genotype was determined by HRM and capillary electrophoresis, while APOC1 by HRM. The concentrations of OGG1, 8-oxo2dG were determined by ELISA, whereas Hcy, GSH by HPLC/EC.

We showed that over 60% of AD patients had increased Hcy levels (p<0.01 vs. UC, p<0.001 vs. RC), while GSH (p<0.01 vs. UC), 8-oxo2dG (p<0.01 vs. UC, p<0.001 vs. RC) were reduced. Minor variants: rs10524523-L, rs4420638-G, rs2075650-G were significantly overrepresented in AD. For rs4420638-G, rs2075650-G variants, the association remained significant in APOE E4 non-carriers. The misbalance of analyzed biothiols, and 8-oxo2dG, OGG1 were more pronounced in carriers of major variants: rs10524523-S/VL, rs4420638-A, rs2075650-A.

We showed, for the first time, that APOC1 and TOMM40 rs2075650 polymorphisms may be independent risk factors of developing AD, whose major variants are accompanied by disruption of biothiols metabolism and inefficient removal of DNA oxidation.

State of Play in Alzheimer's Disease Genetics

Alzheimer's disease (AD), the main form of dementia in the elderly, is the most common progressive neurodegenerative disease characterized by rapidly progressive cognitive dysfunction and behavior impairment. AD exhibits a considerable heritability and great advances have been made in approaches to searching the genetic etiology of AD. In AD genetic studies, methods have developed from classic linkage-based and candidate-gene-based association studies to genome-wide association studies (GWAS) and next generation sequencing (NGS). The identification of new susceptibility genes has provided deeper insights to understand the mechanisms underlying AD. In addition to searching novel genes associated with AD in large samples, the NGS technologies can also be used to shed light on the 'black matter' discovery even in smaller samples. The shift in AD genetics between traditional studies and individual sequencing will allow biomaterials of each patient as the central unit of genetic studies. This review will cover genetic findings in AD and consequences of AD genetic findings. Firstly, we will discuss the discovery of mutations in APP, PSEN1, PSEN2, APOE, and ADAM10. Then we will summarize and evaluate the information obtained from GWAS of AD. Finally, we will outline the efforts to identify rare variants associated with AD using NGS.

Linking Alzheimer's disease and type 2 diabetes: Novel shared susceptibility genes detected by cFDR approach

BACKGROUND

Both type 2 diabetes (T2D) and Alzheimer's disease (AD) occur commonly in the aging populations and T2D has been considered as an important risk factor for AD. The heritability of both diseases is estimated to be over 50%. However, common pleiotropic single-nucleotide polymorphisms (SNPs)/loci have not been well-defined. The aim of this study is to analyze two large public accessible GWAS datasets to identify novel common genetic loci for T2D and/or AD.

METHODS AND MATERIALS

The recently developed novel conditional false discovery rate (cFDR) approach was used to analyze the summary GWAS datasets from International Genomics of Alzheimer's Project (IGAP) and Diabetes Genetics Replication And Meta-analysis (DIAGRAM) to identify novel susceptibility genes for AD and T2D.

RESULTS

We identified 78 SNPs (including 58 novel SNPs) that were associated with AD in Europeans conditional on T2D (cFDR<0.05). 66 T2D SNPs (including 40 novel SNPs) were identified by conditioning on SNPs association with AD (cFDR<0.05). A conjunction-cFDR (ccFDR) analysis detected 8 pleiotropic SNPs with a significance threshold of ccFDR<0.05 for both AD and T2D, of which 5 SNPs (rs6982393, rs4734295, rs7812465, rs10510109, rs2421016) were novel findings. Furthermore, among the 8 SNPs annotated at 6 different genes, 3 corresponding genes TP53INP1, TOMM40 and C8orf38 were related to mitochondrial dysfunction, critically involved in oxidative stress, which potentially contribute to the etiology of both AD and T2D.

CONCLUSION

Our study provided evidence for shared genetic loci between T2D and AD in European subjects by using cFDR and ccFDR analyses. These results may provide novel insight into the etiology and potential therapeutic targets of T2D and/or AD.