Epistasis in sporadic Alzheimer's disease

Revealing third-order interactions through the integration of machine learning and entropy methods in genomic studies

BACKGROUND

Non-linear relationships at the genotype level are essential in understanding the genetic interactions of complex disease traits. Genome-wide association Studies (GWAS) have revealed statistical association of the SNPs in many complex diseases. As GWAS results could not thoroughly reveal the genetic background of these disorders, Genome-Wide Interaction Studies have started to gain importance. In recent years, various statistical approaches, such as entropy-based methods, have been suggested for revealing these non-additive interactions between variants. This study presents a novel prioritization workflow integrating two-step Random Forest (RF) modeling and entropy analysis after PLINK filtering. PLINK-RF-RF workflow is followed by an entropy-based 3-way interaction information (3WII) method to capture the hidden patterns resulting from non-linear relationships between genotypes in Late-Onset Alzheimer Disease to discover early and differential diagnosis markers.

RESULTS

Three models from different datasets are developed by integrating PLINK-RF-RF analysis and entropy-based three-way interaction information (3WII) calculation method, which enables the detection of the third-order interactions, which are not primarily considered in epistatic interaction studies. A reduced SNP set is selected for all three datasets by 3WII analysis by PLINK filtering and prioritization of SNP with RF-RF modeling, promising as a model minimization approach. Among SNPs revealed by 3WII, 4 SNPs out of 19 from GenADA, 1 SNP out of 27 from ADNI, and 4 SNPs out of 106 from NCRAD are mapped to genes directly associated with Alzheimer Disease. Additionally, several SNPs are associated with other neurological disorders. Also, the genes the variants mapped to in all datasets are significantly enriched in calcium ion binding, extracellular matrix, external encapsulating structure, and RUNX1 regulates estrogen receptor-mediated transcription pathways. Therefore, these functional pathways are proposed for further examination for a possible LOAD association. Besides, all 3WII variants are proposed as candidate biomarkers for the genotyping-based LOAD diagnosis.

CONCLUSION

The entropy approach performed in this study reveals the complex genetic interactions that significantly contribute to LOAD risk. We benefited from the entropy-based 3WII as a model minimization step and determined the significant 3-way interactions between the prioritized SNPs by PLINK-RF-RF. This framework is a promising approach for disease association studies, which can also be modified by integrating other machine learning and entropy-based interaction methods.

Deep learning-based polygenic risk analysis for Alzheimer's disease prediction

BACKGROUND

The polygenic nature of Alzheimer's disease (AD) suggests that multiple variants jointly contribute to disease susceptibility. As an individual's genetic variants are constant throughout life, evaluating the combined effects of multiple disease-associated genetic risks enables reliable AD risk prediction. Because of the complexity of genomic data, current statistical analyses cannot comprehensively capture the polygenic risk of AD, resulting in unsatisfactory disease risk prediction. However, deep learning methods, which capture nonlinearity within high-dimensional genomic data, may enable more accurate disease risk prediction and improve our understanding of AD etiology. Accordingly, we developed deep learning neural network models for modeling AD polygenic risk.

METHODS

We constructed neural network models to model AD polygenic risk and compared them with the widely used weighted polygenic risk score and lasso models. We conducted robust linear regression analysis to investigate the relationship between the AD polygenic risk derived from deep learning methods and AD endophenotypes (i.e., plasma biomarkers and individual cognitive performance). We stratified individuals by applying unsupervised clustering to the outputs from the hidden layers of the neural network model.

RESULTS

The deep learning models outperform other statistical models for modeling AD risk. Moreover, the polygenic risk derived from the deep learning models enables the identification of disease-associated biological pathways and the stratification of individuals according to distinct pathological mechanisms.

CONCLUSION

Our results suggest that deep learning methods are effective for modeling the genetic risks of AD and other diseases, classifying disease risks, and uncovering disease mechanisms.

Network approaches for omics studies of neurodegenerative diseases

The recent methodological advances in multi-omics approaches, including genomic, transcriptomic, metabolomic, lipidomic, and proteomic, have revolutionized the research field by generating “big data” which greatly enhanced our understanding of the molecular complexity of the brain and disease states. Network approaches have been routinely applied to single-omics data to provide critical insight into disease biology. Furthermore, multi-omics integration has emerged as both a vital need and a new direction to connect the different layers of information underlying disease mechanisms. In this review article, we summarize popular network analytic approaches for single-omics data and multi-omics integration and discuss how these approaches have been utilized in studying neurodegenerative diseases.

Alzheimer's Disease Risk Variant rs3865444 in the CD33 Gene: A Possible Role in Susceptibility to Multiple Sclerosis

Polymorphisms in genes encoding receptors that modulate the activity of microglia and macrophages are attractive candidates for participation in genetic susceptibility to multiple sclerosis (MS). The aims of the study were to (1) investigate the association between Alzheimer’s disease-linked variant rs3865444:C>A in the CD33 gene and MS risk, (2) assess the effect of the strongest MS risk allele HLA-DRB1*15:01 on this association, and (3) analyze the correlation of rs3865444 with selected clinical phenotypes, i.e., age of onset and disease severity. CD33 rs3865444 was genotyped in a cohort of 579 patients and 1145 controls and its association with MS risk and clinical phenotypes was analyzed by logistic and linear regression analysis, respectively. Statistical evaluation revealed that rs3865444 reduces the risk of MS in the HLA-DRB1*15:01-positive subpopulation but not in the cohort negative for HLA-DRB1*15:01. A significant antagonistic epistasis between rs3865444 A and HLA-DRB1*15:01 alleles in the context of MS risk was detected by the interaction synergy factor analysis. Comparison of allele and genotype distribution between relapsing-remitting MS, secondary progressive MS, and control groups revealed that rs3865444 C to A substitution may also be associated with a decreased risk of transition of MS to its secondary progressive form, irrespective of the HLA-DRB1*15:01 carrier status. On the other hand, no correlation could be found between rs3865444 and the age of disease onset or MS severity score. Future studies are required to shed more light on the role of CD33 in MS pathogenesis.

Association between H2 haplotype of microtubule associated protein tau gene (deletion / insertion) with Alzheimer Disease in Tunisian patients

It is widely recognized that Alzheimer's disease (AD) is the main cause of dementia in the elderly. AD is typically characterized by the extraneuronal plaque made up essentially of the amyloid β peptide and intraneuronal tangles of hyperphosphorylated microtubule-associated Tau protein. This study investigates the possible interaction between AD and the deletion/insertion polymorphism in intron 9 of the Tau gene haplotype and APOE state in a Tunisian AD cases population (n = 85) and control (n = 91). The H2/H2 genotype was higher in the AD group as compared to the controls (22.4% vs. 7.8%). The frequency of H2 allele is higher in the patients group, and the difference of allele frequency is statistically significant between the two groups (χ2 = 12.220, p < 0.05). H2 allele is correlated with the female gender within the patient group (χ2 = 7.649, p = 0.006) Tau H2 haplotype can be identified as a risk factor of AD in the studied Tunisian population and was associated to female gender. There is no significant correlation between the frequency of Tau gene ins/del polymorphism and cognitive profile distribution in the patient group (p > 0.05).

Advancing human disease research with fish evolutionary mutant models

Model organism research is essential to understand disease mechanisms. However, laboratory-induced genetic models can lack genetic variation and often fail to mimic the spectrum of disease severity. Evolutionary mutant models (EMMs) are species with evolved phenotypes that mimic human disease. EMMs complement traditional laboratory models by providing unique avenues to study gene-by-environment interactions, modular mutations in noncoding regions, and their evolved compensations. EMMs have improved our understanding of complex diseases, including cancer, diabetes, and aging, and illuminated mechanisms in many organs. Rapid advancements of sequencing and genome-editing technologies have catapulted the utility of EMMs, particularly in fish. Fish are the most diverse group of vertebrates, exhibiting a kaleidoscope of specialized phenotypes, many that would be pathogenic in humans but are adaptive in the species' specialized habitat. Importantly, evolved compensations can suggest avenues for novel disease therapies. This review summarizes current research using fish EMMs to advance our understanding of human disease.

Importance-Penalized Joint Graphical Lasso (IPJGL): differential network inference via GGMs

MOTIVATION

Differential network inference is a fundamental and challenging problem to reveal gene interactions and regulation relationships under different conditions. Many algorithms have been developed for this problem; however, they do not consider the differences between the importance of genes, which may not fit the real-world situation. Different genes have different mutation probabilities, and the vital genes associated with basic life activities have less fault tolerance to mutation. Equally treating all genes may bias the results of differential network inference. Thus, it is necessary to consider the importance of genes in the models of differential network inference.

RESULTS

Based on the Gaussian graphical model with adaptive gene importance regularization, we develop a novel Importance-Penalized Joint Graphical Lasso method (IPJGL) for differential network inference. The presented method is validated by the simulation experiments as well as the real datasets. Furthermore, to precisely evaluate the results of differential network inference, we propose a new metric named APC2 for the differential levels of gene pairs. We apply IPJGL to analyze the TCGA colorectal and breast cancer datasets and find some candidate cancer genes with significant survival analysis results, including SOST for colorectal cancer and RBBP8 for breast cancer. We also conduct further analysis based on the interactions in the Reactome database and confirm the utility of our method.

AVAILABILITY AND IMPLEMENTATION

R source code of Importance-Penalized Joint Graphical Lasso is freely available at https://github.com/Wu-Lab/IPJGL.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Interplay between stress-related genes may influence Alzheimer's disease development: The results of genetic interaction analyses of human data

Emerging evidence from experimental and clinical research suggests that stress-related genes may play key roles in AD development. The fact that genome-wide association studies were not able to detect a contribution of such genes to AD indicates the possibility that these genes may influence AD non-linearly, through interactions of their products. In this paper, we selected two stress-related genes (GCN2/EIF2AK4 and APP) based on recent findings from experimental studies which suggest that the interplay between these genes might influence AD in humans. To test this hypothesis, we evaluated the effects of interactions between SNPs in these two genes on AD occurrence, using the Health and Retirement Study data on white indidividuals. We found several interacting SNP-pairs whose associations with AD remained statistically significant after correction for multiple testing. These findings emphasize the importance of nonlinear mechanisms of polygenic AD regulation that cannot be detected in traditional association studies. To estimate collective effects of multiple interacting SNP-pairs on AD, we constructed a new composite index, called Interaction Polygenic Risk Score, and showed that its association with AD is highly statistically significant. These results open a new avenue in the analyses of mechanisms of complex multigenic AD regulation.

Novel methods for epistasis detection in genome-wide association studies

More and more genome-wide association studies are being designed to uncover the full genetic basis of common diseases. Nonetheless, the resulting loci are often insufficient to fully recover the observed heritability. Epistasis, or gene-gene interaction, is one of many hypotheses put forward to explain this missing heritability. In the present work, we propose epiGWAS, a new approach for epistasis detection that identifies interactions between a target SNP and the rest of the genome. This contrasts with the classical strategy of epistasis detection through exhaustive pairwise SNP testing. We draw inspiration from causal inference in randomized clinical trials, which allows us to take into account linkage disequilibrium. EpiGWAS encompasses several methods, which we compare to state-of-the-art techniques for epistasis detection on simulated and real data. The promising results demonstrate empirically the benefits of EpiGWAS to identify pairwise interactions.

Modeling epistasis in mice and yeast using the proportion of two or more distinct genetic backgrounds: Evidence for "polygenic epistasis"

Background

The majority of quantitative genetic models used to map complex traits assume that alleles have similar effects across all individuals. Significant evidence suggests, however, that epistatic interactions modulate the impact of many alleles. Nevertheless, identifying epistatic interactions remains computationally and statistically challenging. In this work, we address some of these challenges by developing a statistical test for polygenic epistasis that determines whether the effect of an allele is altered by the global genetic ancestry proportion from distinct progenitors.

Results

We applied our method to data from mice and yeast. For the mice, we observed 49 significant genotype-by-ancestry interaction associations across 14 phenotypes as well as over 1,400 Bonferroni-corrected genotype-by-ancestry interaction associations for mouse gene expression data. For the yeast, we observed 92 significant genotype-by-ancestry interactions across 38 phenotypes. Given this evidence of epistasis, we test for and observe evidence of rapid selection pressure on ancestry specific polymorphisms within one of the cohorts, consistent with epistatic selection.

Conclusions

Unlike our prior work in human populations, we observe widespread evidence of ancestry-modified SNP effects, perhaps reflecting the greater divergence present in crosses using mice and yeast.

Many statistical tests which link genetic markers in the genome to differences in traits rely on the assumption that the same polymorphism will have identical effects in different individuals. However, there is substantial evidence indicating that this is not the case. Epistasis is the phenomenon in which multiple polymorphisms interact with one another to amplify or negate each other’s effects on a trait. We hypothesized that individual SNP effects could be changed in a polygenic manner, such that the proportion of as genetic ancestry, rather than specific markers, might be used to capture epistatic interactions. Motivated by this possibility, we develop a new statistical test that allowed us to examine the genome to identify polymorphisms which have different effects depending on the ancestral makeup of each individual. We use our test in two different populations of inbred mice and a yeast panel and demonstrate that these sorts of variable effect polymorphisms exist in 14 different physical traits in mice and 38 phenotypes in yeast as well as in murine gene expression. We use the term “polygenic epistasis” to distinguish these interactions from the more conventional two- or multi-locus interactions.

Genome-wide interaction analysis of pathological hallmarks in Alzheimer's disease

Genome-wide association studies have identified many loci associated with Alzheimer's dementia. However, these variants only explain part of the heritability of Alzheimer's disease (AD). As genetic epistasis can be a major contributor to the "missing heritability" of AD, we conducted genome-wide epistasis screening for AD pathologies in 2 independent cohorts. First, we performed a genome-wide epistasis study of AD-related brain pathologies (N_max = 1318) in ROS/MAP. Candidate interactions were validated using cerebrospinal fluid biomarkers of AD in ADNI (N_max = 1128). Further functional analysis tested the association of candidate interactions with neuroimaging phenotypes. For tau and amyloid-β pathology, we identified 2803 and 464 candidate SNP-SNP interactions, respectively. Associations of candidate SNP-SNP interactions with brain volume and white matter changes from neuroimages provides additional insights into their molecular functions. Transcriptional analysis supported possible gene-gene interactions identified by statistical screening through their co-expression in the brain. In summary, we outlined an exhaustive epistasis analysis to identify novel genetic interactions with potential roles in AD pathologies. We further delved into the functional relevance of candidate interactions by association with neuroimaging phenotypes and analysis of co-expression between corresponding gene pairs.

Combinatorial and statistical prediction of gene expression from haplotype sequence

MOTIVATION

Genome-wide association studies (GWAS) have discovered thousands of significant genetic effects on disease phenotypes. By considering gene expression as the intermediary between genotype and disease phenotype, expression quantitative trait loci studies have interpreted many of these variants by their regulatory effects on gene expression. However, there remains a considerable gap between genotype-to-gene expression association and genotype-to-gene expression prediction. Accurate prediction of gene expression enables gene-based association studies to be performed post hoc for existing GWAS, reduces multiple testing burden, and can prioritize genes for subsequent experimental investigation.

RESULTS

In this work, we develop gene expression prediction methods that relax the independence and additivity assumptions between genetic markers. First, we consider gene expression prediction from a regression perspective and develop the HAPLEXR algorithm which combines haplotype clusterings with allelic dosages. Second, we introduce the new gene expression classification problem, which focuses on identifying expression groups rather than continuous measurements; we formalize the selection of an appropriate number of expression groups using the principle of maximum entropy. Third, we develop the HAPLEXD algorithm that models haplotype sharing with a modified suffix tree data structure and computes expression groups by spectral clustering. In both models, we penalize model complexity by prioritizing genetic clusters that indicate significant effects on expression. We compare HAPLEXR and HAPLEXD with three state-of-the-art expression prediction methods and two novel logistic regression approaches across five GTEx v8 tissues. HAPLEXD exhibits significantly higher classification accuracy overall; HAPLEXR shows higher prediction accuracy on approximately half of the genes tested and the largest number of best predicted genes (r2>0.1) among all methods. We show that variant and haplotype features selected by HAPLEXR are smaller in size than competing methods (and thus more interpretable) and are significantly enriched in functional annotations related to gene regulation. These results demonstrate the importance of explicitly modeling non-dosage dependent and intragenic epistatic effects when predicting expression.

AVAILABILITY AND IMPLEMENTATION

Source code and binaries are freely available at https://github.com/rapturous/HAPLEX.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Association of CD33 rs3865444:C˃A polymorphism with a reduced risk of late-onset Alzheimer's disease in Slovaks is limited to subjects carrying the APOE ε4 allele

CD33 rs3865444:C>A single nucleotide polymorphism (SNP) has been previously associated with the risk of late-onset Alzheimer's disease (LOAD); however, the results have been inconsistent across different populations. CD33 is a transmembrane receptor that plays an important role in AD pathogenesis by inhibiting amyloid β42 uptake by microglial cells. In this study, we aimed to validate the association between rs3865444 and LOAD risk in the Slovak population and to evaluate whether it was affected by the carrier status of the major LOAD risk allele apolipoprotein (APOE) ε4. CD33 rs3865444 and APOE variants were genotyped in 206 LOAD patients and 487 control subjects using the polymerase chain reaction-restriction fragment length polymorphism method and direct sequencing, respectively. Logistic regression analysis revealed a significant association of rs3865444 A allele with a reduced LOAD risk that was only present in APOE ε4 allele carriers (AA + CA versus CC: p = .0085; OR = 0.45; 95% CI = 0.25-0.82). On the other hand, no such association was found in subjects without the APOE ε4 (p = .75; OR = 0.93; 95% CI = 0.61-1.42). Moreover, regression analysis detected a significant interaction between CD33 rs3865444 A and APOE ε4 alleles (p = .021 for APOE ε4 allele dosage and p = .051 for APOE ε4 carriage status), with synergy factor (SF) value of 0.49 indicating an antagonistic effect between the two alleles in LOAD risk. In conclusion, our results suggest that CD33 rs3865444:C˃A substitution may reduce the risk of LOAD in Slovaks by antagonizing the effect conferred by the major susceptibility allele APOE ε4.

Lesch-Nyhan disease: I. Construction of expression vectors for hypoxanthine-guanine phosphoribosyltransferase (HGprt) enzyme and amyloid precursor protein (APP)

Lesch-Nyhan disease (LND) is a rare X-linked inherited neurogenetic disorder of purine metabolism in which the enzyme, hypoxanthine-guanine phosphoribosyltransferase (HGprt) is defective. Despite having been characterized over 50 years ago, it remains unclear precisely how deficits in HGprt enzyme activity can lead to the neurological syndrome, especially the self-injury of LND. Several studies have proposed different hypotheses regarding the etiology of this disease, and several treatments have been tried in patients. However, up to now, there is no satisfactory explanation of the disease and for many LND patients, efficacious treatment for persistent self-injurious behavior remains unreachable. A role for epistasis between mutated hypoxanthine phosphoribosyltransferase 1 (HPRT1) and amyloid precursor protein (APP) genes has been recently suggested. This finding may provide new directions not only for investigating the role of APP in neuropathology associated with HGprt-deficiency in LND but also for the research in neurodevelopmental and neurodegenerative disorders in which the APP gene is involved in the pathogenesis of diseases and may pave the way for new strategies applicable to rational antisense drugs design. It is therefore necessary to study the HGprt enzyme and APP using expression vectors for exploring their impacts on LND as well as other human diseases, especially the ones related to APP such as Alzheimer's disease in which the physiologic function and the structure of the entire APP remain largely unclear until now. For such a purpose, we report here the construction of expression vectors as the first step (Part I) of our investigation.

The combined risk effect among BIN1, CLU, and APOE genes in Alzheimer's disease

Genome-wide associations studies (GWAS) are detecting new variants associated with late-onset of Alzheimer’s disease (LOAD), a multifactorial neurodegenerative disorder. The variants rs744373 BIN1, rs11136000 CLU and rs3764650 ABCA7 uncovered by GWAS led to different AD pathways, such as metabolism, trafficking and endocytosis of lipids and inflammation. However, most of the association studies did not replicate these variants with significance. This could be due to a small power effect evident when these variants are tested independently with LOAD. Therefore, we aimed to investigate whether the combination of different variants would additively modify the risk of association with LOAD that is observed in GWAS. We performed an association study testing pairwise variants in metabolism, trafficking and endocytosis of lipid (rs429358 and rs7412 APOE, rs744373 BIN1, rs3764650 ABCA7 and rs11136000 CLU) pathways with LOAD in samples from southeastern Brazil. Our data suggest a risk effect for LOAD between APOE with CLU and APOE with BIN1 genes.

Failure to detect synergy between variants in transferrin and hemochromatosis and Alzheimer's disease in large cohort

Alzheimer's disease (AD) is the most common cause of dementia and, despite decades of effort, there is no effective treatment. In the last decade, many association studies have identified genetic markers that are associated with AD status. Two of these studies suggest that an epistatic interaction between variants rs1049296 in the transferrin (TF) gene and rs1800562 in the homeostatic iron regulator (HFE) gene, commonly known as hemochromatosis, is in genetic association with AD. TF and HFE are involved in the transport and regulation of iron in the brain, and disrupting these processes exacerbates AD pathology through increased neurodegeneration and oxidative stress. However, by using a significantly larger data set from the Alzheimer's Disease Genetics Consortium, we fail to detect an association between TF rs1049296 or HFE rs1800562 with AD risk (TF rs1049296 p = 0.38 and HFE rs1800562 p = 0.40). In addition, logistic regression with an interaction term and a synergy factor analysis both failed to detect epistasis between TF rs1049296 and HFE rs1800562 (SF = 0.94; p = 0.48) in AD cases. Each of these analyses had sufficient statistical power (power > 0.99), suggesting that previously reported associations may be the result of more complex epistatic interactions, genetic heterogeneity, or false-positive associations because of limited sample sizes.

Interaction between Apolipoprotein E and Butyrylcholinesterase Genes on Risk of Alzheimer's Disease in a Prospective Cohort Study

BACKGROUND

An epistatic interaction between the ɛ4 allele of apolipoprotein E (APOEɛ4) gene and the K-variant of butyrylcholinesterase (BCHE-K) genes has been previously reported to increase risk of Alzheimer's disease (AD). However, these observations were largely from case-control studies with small sample sizes.

OBJECTIVE

To examine the interaction between APOEɛ4 and BCHE-K on: 1) the risk of incident AD and 2) rates of change in brain volumes and cognitive performance during the preclinical stages of AD in a prospective cohort study.

METHODS

The study sample for survival analysis included 691 Caucasian participants (age at baseline, 58.4±9.9 years; follow-up time,16.9±9.7 years) from the Baltimore Longitudinal Study of Aging. The neuroimaging sample included 302 participants with 1,388 magnetic resonance imaging (MRI) scans. Cognitive performance was assessed in 703 participants over 4,908 visits.

RESULTS

A total of 122 diagnoses (79 AD, 43 mild cognitive impairment [MCI]) were identified. Participants with both APOEɛ4 and BCHE-K variants had a 3.7-fold greater risk of AD (Hazard ratio [HR] 95% CI=1.99-6.89, p < 0.001) compared to non-carriers of both genes (APOE ɛ4 x BCHE-K interaction p = 0.025). There was no APOE ɛ4-BCHE-K interaction effect on rate of cognitive decline and brain atrophy.

CONCLUSION

The APOE and BCHE genes interact to influence risk of incident AD/MCI but not rates of brain atrophy and decline in cognitive performance before onset of cognitive impairment. This may suggest the epistatic interaction between APOE ɛ4 and BCHE-K on AD risk is disease stage-dependent.

Deep mixed model for marginal epistasis detection and population stratification correction in genome-wide association studies

BACKGROUND

Genome-wide Association Studies (GWAS) have contributed to unraveling associations between genetic variants in the human genome and complex traits for more than a decade. While many works have been invented as follow-ups to detect interactions between SNPs, epistasis are still yet to be modeled and discovered more thoroughly.

RESULTS

In this paper, following the previous study of detecting marginal epistasis signals, and motivated by the universal approximation power of deep learning, we propose a neural network method that can potentially model arbitrary interactions between SNPs in genetic association studies as an extension to the mixed models in correcting confounding factors. Our method, namely Deep Mixed Model, consists of two components: 1) a confounding factor correction component, which is a large-kernel convolution neural network that focuses on calibrating the residual phenotypes by removing factors such as population stratification, and 2) a fixed-effect estimation component, which mainly consists of an Long-short Term Memory (LSTM) model that estimates the association effect size of SNPs with the residual phenotype.

CONCLUSIONS

After validating the performance of our method using simulation experiments, we further apply it to Alzheimer's disease data sets. Our results help gain some explorative understandings of the genetic architecture of Alzheimer's disease.

β-Amyloid precursor protein (APP) and the human diseases

Several pathophysiological functions of the human β-amyloid precursor protein (APP) have been recently proposed in different human diseases such as neurodevelopmental and neurodegenerative disorders including rare diseases such as autism, fragile X syndrome, amyotrophic lateral sclerosis, multiple sclerosis, Lesch-Nyhan disease; common and complex disorders such as Alzheimer's disease; metabolic disorders such as diabetes; and also cancer. APP as well as all of its proteolytic fragments including the amyloid-β (Aβ) peptide, are part of normal physiology. The targeting of the components of APP proteolytic processing as a pharmacologic strategy will not be without consequences. Recent research results highlight the impact of alternative splicing (AS) process on human disease, and may provide new directions for the research on the impact of the human APP on human diseases. The identification of molecules capable of correcting and/or inhibiting pathological splicing events is therefore an important issue for future therapeutic approaches. To this end, the defective APP-mRNA isoform responsible for the disease in cells and tissues appears as an ideal target for epigenetic therapeutic intervention and antisense drugs are potential treatment.

MatrixEpistasis: ultrafast, exhaustive epistasis scan for quantitative traits with covariate adjustment

Motivation

For many traits, causal loci uncovered by genetic mapping studies explain only a minority of the heritable contribution to trait variation. Multiple explanations for this 'missing heritability' have been proposed. Single nucleotide polymorphism (SNP)-SNP interaction (epistasis), as one of the compelling models, has been widely studied. However, the genome-wide scan of epistasis, especially for quantitative traits, poses huge computational challenges. Moreover, covariate adjustment is largely ignored in epistasis analysis due to the massive extra computational undertaking.

Results

In the current study, we found striking differences among epistasis models using both simulation data and real biological data, suggesting that not only can covariate adjustment remove confounding bias, it can also improve power. Furthermore, we derived mathematical formulas, which enable the exhaustive epistasis scan together with full covariate adjustment to be expressed in terms of large matrix operation, therefore substantially improving the computational efficiency (∼104× faster than existing methods). We call the new method MatrixEpistasis. With MatrixEpistasis, we re-analyze a large real yeast dataset comprising 11 623 SNPs, 1008 segregants and 46 quantitative traits with covariates fully adjusted and detect thousands of novel putative epistasis with P-values < 1.48e-10.

Availability and implementation

The method is implemented in R and available at https://github.com/fanglab/MatrixEpistasis.

Supplementary information

Supplementary data are available at Bioinformatics online.

New algorithms for detecting multi-effect and multi-way epistatic interactions

Motivation

Epistasis, which is the phenomenon of genetic interactions, plays a central role in many scientific discoveries. However, due to the combinatorial nature of the problem, it is extremely challenging to decipher the exact combinations of genes that trigger the epistatic effects. Many existing methods only focus on two-way interactions. Some of the most effective methods used machine learning techniques, but many were designed for special case-and-control studies or suffer from overfitting. We propose three new algorithms for multi-effect and multi-way epistases detection, with one guaranteeing global optimality and the other two being local optimization oriented heuristics.

Results

The computational performance of the proposed heuristic algorithm was compared with several state-of-the-art methods using a yeast dataset. Results suggested that searching for the global optimal solution could be extremely time consuming, but the proposed heuristic algorithm was much more effective and efficient than others at finding a close-to-optimal solution. Moreover, it was able to provide biological insight on the exact configurations of epistases, besides achieving a higher prediction accuracy than the state-of-the-art methods.

Availability and implementation

Data source was publicly available and details are provided in the text.

A multiplex analysis of sepsis mediators during human septic shock: a preliminary study on myocardial depression and organ failures

BACKGROUND

The mechanisms of organ failure during sepsis are not fully understood. The hypothesis of circulating factors has been suggested to explain septic myocardial dysfunction. We explored the biological coherence of a large panel of sepsis mediators and their clinical relevance in septic myocardial dysfunction and organ failures during human septic shock.

METHODS

Plasma concentrations of 24 mediators were assessed on the first day of septic shock using a multi-analyte cytokine kit. Septic myocardial dysfunction and organ failures were assessed using left ventricle ejection fraction (LVEF) and the Sequential Organ Failure Assessment score, respectively.

RESULTS

Seventy-four patients with septic shock (and without immunosuppression or chronic heart failure) were prospectively included. Twenty-four patients (32%) had septic myocardial dysfunction (as defined by LVEF < 45%) and 30 (41%) died in ICU. Hierarchical clustering identified three main clusters of sepsis mediators, which were clinically meaningful. One cluster involved inflammatory cytokines of innate immunity, most of which were associated with septic myocardial dysfunction, organ failures and death; inflammatory cytokines associated with septic myocardial dysfunction had an additive effect. Another cluster involving adaptive immunity and repair (with IL-17/IFN pathway and VEGF) correlated tightly with a surrogate of early sepsis resolution (lactate clearance) and ICU survival.

CONCLUSIONS

In this preliminary study, we identified a cluster of cytokines involved in innate inflammatory response associated with septic myocardial dysfunction and organ failures, whereas the IL-17/IFN pathway was associated with a faster sepsis resolution and a better survival.

Epistasis detectably alters correlations between genomic sites in a narrow parameter window

Different genomic sites evolve inter-dependently due to the combined action of epistasis, defined as a non-multiplicative contribution of alleles at different loci to genome fitness, and the physical linkage of different loci in genome. Both epistasis and linkage, partially compensated by recombination, cause correlations between allele frequencies at the loci (linkage disequilibrium, LD). The interaction and competition between epistasis and linkage are not fully understood, nor is their relative sensitivity to recombination. Modeling an adapting population in the presence of random mutation, natural selection, pairwise epistasis, and random genetic drift, we compare the contributions of epistasis and linkage. For this end, we use a panel of haplotype-based measures of LD and their various combinations calculated for epistatic and non-epistatic pairs separately. We compute the optimal percentages of detected and false positive pairs in a one-time sample of a population of moderate size. We demonstrate that true interacting pairs can be told apart in a sufficiently short genome within a narrow window of time and parameters. Outside of this parameter region, unless the population is extremely large, shared ancestry of individual sequences generates pervasive stochastic LD for non-interacting pairs masking true epistatic associations. In the presence of sufficiently strong recombination, linkage effects decrease faster than those of epistasis, and the detection of epistasis improves. We demonstrate that the epistasis component of locus association can be isolated, at a single time point, by averaging haplotype frequencies over multiple independent populations. These results demonstrate the existence of fundamental restrictions on the protocols for detecting true interactions in DNA sequence sets.

An assessment of true and false positive detection rates of stepwise epistatic model selection as a function of sample size and number of markers

Association studies have been successful at identifying genomic regions associated with important traits, but routinely employ models that only consider the additive contribution of an individual marker. Because quantitative trait variability typically arises from multiple additive and non-additive sources, utilization of statistical approaches that include main and two-way interaction marker effects of several loci in one model could lead to unprecedented characterization of these sources. Here we examine the ability of one such approach, called the Stepwise Procedure for constructing an Additive and Epistatic Multi-Locus model (SPAEML), to detect additive and epistatic signals simulated using maize and human marker data. Our results revealed that SPAEML was capable of detecting quantitative trait nucleotides (QTNs) at sample sizes as low as n = 300 and consistently specifying signals as additive and epistatic for larger sizes. Sample size and minor allele frequency had a major influence on SPAEML's ability to distinguish between additive and epistatic signals, while the number of markers tested did not. We conclude that SPAEML is a useful approach for providing further elucidation of the additive and epistatic sources contributing to trait variability when applied to a small subset of genome-wide markers located within specific genomic regions identified using a priori analyses.

Evolutionary footprint of epistasis

Variation of an inherited trait across a population cannot be explained by additive contributions of relevant genes, due to epigenetic effects and biochemical interactions (epistasis). Detecting epistasis in genomic data still represents a significant challenge that requires a better understanding of epistasis from the mechanistic point of view. Using a standard Wright-Fisher model of bi-allelic asexual population, we study how compensatory epistasis affects the process of adaptation. The main result is a universal relationship between four haplotype frequencies of a single site pair in a genome, which depends only on the epistasis strength of the pair defined regarding Darwinian fitness. We demonstrate the existence, at any time point, of a quasi-equilibrium between epistasis and disorder (entropy) caused by random genetic drift and mutation. We verify the accuracy of these analytic results by Monte-Carlo simulation over a broad range of parameters, including the topology of the interacting network. Thus, epistasis assists the evolutionary transit through evolutionary hurdles leaving marks at the level of haplotype disequilibrium. The method allows determining selection coefficient for each site and the epistasis strength of each pair from a sequence set. The resulting ability to detect clusters of deleterious mutation close to full compensation is essential for biomedical applications. These findings help to understand the role of epistasis in multiple compensatory mutations in viral resistance to antivirals and immune response.

Epistatic interaction of apolipoprotein E and lipolysis-stimulated lipoprotein receptor genetic variants is associated with Alzheimer's disease

The ε4 allele of the apolipoprotein E (APOE) gene common polymorphism is the strongest genetic risk factor for Alzheimer's disease (AD). Human APOE gene is located on chromosome 19q13.1, a region linked to AD that also includes the LSR gene, which encodes the lipolysis-stimulated lipoprotein receptor (LSR). As an APOE receptor, LSR is involved in the regulation of lipid homeostasis in both periphery and brain. This study aimed to determine the potential interactions between 2 LSR genetic variants, rs34259399 and rs916147, and the APOE common polymorphism in 142 AD subjects (mean age: 73.16 ± 8.50 years) and 63 controls (mean age: 70.41 ± 8.49 years). A significant epistatic interaction was observed between APOE and both LSR variants, rs34259399 (beta = -0.95; p = 2 × 10^-5) and rs916147 (beta = -0.83; p = 6.8 × 10^-3). Interestingly, the interaction of LSR polymorphisms with APOE non-ε4 alleles increased AD risk. This indicates the existence of complex molecular interactions between these 2 neighboring genes involved in the pathogenesis of AD, which merits further investigation.

Alzheimer's disease susceptibility genes modify the risk of Parkinson disease and Parkinson's disease-associated cognitive impairment

The pathogenic mechanism underlying Parkinson's disease (PD) and PD- Cognitive impairment (CI) remains elusive. Its potential link to the risk factors in Alzheimer's disease (AD) is unclear. In this study, we analyzed 16 CE-associated single nucleotide polymorphisms (SNPs) in twelve genes in a Chinese cohort of 450 PD cases and 449 controls. Among our 298 cases clinically evaluated for CI, 113 cases did not show CI signs (PD-NC), 86 cases had mildly cognitive impairment (PD-MCI) and 99 cases had dementia (PD-D). We found that the APOE ε4 allele is associated with a higher risk for PD-D. Gene-gene interaction analysis revealed that three significant gene-gene interactions, including BDNF and CLU, APOE and CR1, and DYRK1A and CD2AP increase the risk for PD. Because these SNPs are known genetic risk factors for AD, their contribution to PD and PD-D shown in this study suggests that PD/PD-D and AD may share convergent pathways in their pathogenesis through gene-gene interactions.

Reversal of ApoE4-Driven Brain Pathology by Vascular Endothelial Growth Factor Treatment

Apolipoprotein E4 (ApoE4), the most prevalent genetic risk factor for Alzheimer's disease (AD), is associated with increased neurodegeneration and vascular impairments. Vascular endothelial growth factor (VEGF), originally described as a key angiogenic factor, has recently been shown to play a crucial role in the nervous system. The objective of this research is to examine the role of VEGF in mediating the apoE4-driven pathologies. We show that hippocampal VEGF levels are lower in apoE4 targeted replacement mice compared to the corresponding apoE3 mice. This effect was accompanied by a specific decrease in both VEGF receptor-2 and HIF1-α. We next set to examine whether upregulation of VEGF can reverse apoE4-driven pathologies, namely the accumulation of hyperphosphorylated tau (AT8) and Aβ42, and reduced levels of the pre-synaptic marker, VGluT1, and of the ApoE receptor, ApoER2. This was first performed utilizing intra-hippocampal injection of VEGF-expressing-lentivirus (LV-VEGF). This revealed that LV-VEGF treatment reversed the apoE4-driven cognitive deficits and synaptic pathologies. The levels of Aβ42 and AT8, however, were increased in apoE3 mice, masking any potential effects of this treatment on the apoE4 mice. Follow-up experiments utilizing VEGF-expressing adeno-associated-virus (AAV-VEGF), which expresses VEGF specifically under the GFAP astrocytic promoter, prevented this effects on apoE3 mice, and reversed the apoE4-related increase in Aβ42 and AT8. Taken together, these results suggest that apoE4-driven pathologies are mediated by a VEGF-dependent pathway, resulting in cognitive impairments and brain pathology. These animal model findings suggest that the VEGF system is a promising target for the treatment of apoE4 carriers in AD.

Special Issue: Alzheimer's disease

More than 45 million people worldwide have Alzheimer's disease (AD), a deterioration of memory and other cognitive domains that leads to death within 3 to 9 years after diagnosis. The principal risk factor for AD is age. As the aging population increases, the prevalence will approach 131 million cases worldwide in 2050. AD is therefore a global problem creating a rapidly growing epidemic and becoming a major threat to healthcare in our societies. It has been more than 20 years since it was first proposed that the neurodegeneration in AD may be caused by deposition of amyloid-β (Aβ) peptides in plaques in brain tissue. According to the amyloid hypothesis, accumulation of Aβ peptides, resulting from a chronic imbalance between Aβ production and Aβ clearance in the brain, is the primary influence driving AD pathogenesis. Current available medications appear to be able to produce moderate symptomatic benefits but not to stop disease progression. The search for biomarkers as well as novel therapeutic approaches for AD has been a major focus of research. Recent findings, however, show that neuronal-injury biomarkers are independent of Aβ suggesting epigenetic modifications, gene-gene and/or gene-environment interactions in the disease etiology, and calling for reconsideration of the pathological cascade and assessment of alternative therapeutic strategies. In addition, recent research results regarding the expression of the β-amyloid precursor protein (APP) gene resulting in the presence of various APP-mRNA isoforms and their quantification, especially for identifying the most abundant one that may decisive for the normal status or disease risk, have been reported. As such, a more complete understanding of AD pathogenesis will likely require greater insights into the physiological function of the β-amyloid precursor protein (APP).

Neurotrophin Signaling and Stem Cells-Implications for Neurodegenerative Diseases and Stem Cell Therapy

Neurotrophins (NTs) are members of a neuronal growth factor protein family whose action is mediated by the tropomyosin receptor kinase (TRK) receptor family receptors and the p75 NT receptor (p75NTR), a member of the tumor necrosis factor (TNF) receptor family. Although NTs were first discovered in neurons, recent studies have suggested that NTs and their receptors are expressed in various types of stem cells mediating pivotal signaling events in stem cell biology. The concept of stem cell therapy has already attracted much attention as a potential strategy for the treatment of neurodegenerative diseases (NDs). Strikingly, NTs, proNTs, and their receptors are gaining interest as key regulators of stem cells differentiation, survival, self-renewal, plasticity, and migration. In this review, we elaborate the recent progress in understanding of NTs and their action on various stem cells. First, we provide current knowledge of NTs, proNTs, and their receptor isoforms and signaling pathways. Subsequently, we describe recent advances in the understanding of NT activities in various stem cells and their role in NDs, particularly Alzheimer's disease (AD) and Parkinson's disease (PD). Finally, we compile the implications of NTs and stem cells from a clinical perspective and discuss the challenges with regard to transplantation therapy for treatment of AD and PD.

Time-Dependent Gene Network Modelling by Sequential Monte Carlo

Most existing methods used for gene regulatory network modeling are dedicated to inference of steady state networks, which are prevalent over all time instants. However, gene interactions evolve over time. Information about the gene interactions in different stages of the life cycle of a cell or an organism is of high importance for biology. In the statistical graphical models literature, one can find a number of methods for studying steady-state network structures while the study of time varying networks is rather recent. A sequential Monte Carlo method, namely particle filtering (PF), provides a powerful tool for dynamic time series analysis. In this work, the PF technique is proposed for dynamic network inference and its potentials in time varying gene expression data tracking are demonstrated. The data used for validation are synthetic time series data available from the DREAM4 challenge, generated from known network topologies and obtained from transcriptional regulatory networks of S. cerevisiae. We model the gene interactions over the course of time with multivariate linear regressions where the parameters of the regressive process are changing over time.

Pathway Analysis Incorporating Protein-Protein Interaction Networks Identified Candidate Pathways for the Seven Common Diseases

Pathway analysis has become popular as a secondary analysis strategy for genome-wide association studies (GWAS). Most of the current pathway analysis methods aggregate signals from the main effects of single nucleotide polymorphisms (SNPs) in genes within a pathway without considering the effects of gene-gene interactions. However, gene-gene interactions can also have critical effects on complex diseases. Protein-protein interaction (PPI) networks have been used to define gene pairs for the gene-gene interaction tests. Incorporating the PPI information to define gene pairs for interaction tests within pathways can increase the power for pathway-based association tests. We propose a pathway association test, which aggregates the interaction signals in PPI networks within a pathway, for GWAS with case-control samples. Gene size is properly considered in the test so that genes do not contribute more to the test statistic simply due to their size. Simulation studies were performed to verify that the method is a valid test and can have more power than other pathway association tests in the presence of gene-gene interactions within a pathway under different scenarios. We applied the test to the Wellcome Trust Case Control Consortium GWAS datasets for seven common diseases. The most significant pathway is the chaperones modulate interferon signaling pathway for Crohn’s disease (p-value = 0.0003). The pathway modulates interferon gamma, which induces the JAK/STAT pathway that is involved in Crohn’s disease. Several other pathways that have functional implications for the seven diseases were also identified. The proposed test based on gene-gene interaction signals in PPI networks can be used as a complementary tool to the current existing pathway analysis methods focusing on main effects of genes. An efficient software implementing the method is freely available at http://puppi.sourceforge.net.

Phenotypic characterization of a pair of molecules in tissues confer to classical Mendelian or non Mendelian ratios

Studies have reported a wide range of inflammatory responses in the nerve, skin and plasma of leprosy patients. The expression levels of each biomolecule was individualistic, however could be categorized as high and low based on their statistical mean level. Here we report for the first time, expression of a set of biomolecules relating with each other in a defined proportion. The hypothesis of this paper is that the segregation of high and low combinations of a set of biomolecules follows either classical Mendelian dihybrid ratio or epistatic ratios. This hypothesis was tested for 17 molecules in three tissues; nerve, skin and plasma and were confirmed to interact in 9:7, 9:3:4, 12:3:1, 13:3, 15:1 epistatic proportions. These findings suggest that there could be a significant role of networking of molecules in defined epistatic proportions and could be important in pathophysiology of peripheral nerve.

Mutation in the Human HPRT1 Gene and the Lesch-Nyhan Syndrome

Lesch-Nyhan syndrome (LNS) is a rare X-linked inherited neurogenetic disorder of purine metabolism in which the enzyme, hypoxanthine-guanine phosphoribosyltransferase (HGprt) is defective. The authors report a novel mutation which led to HGprt-related neurological dysfunction (HND) in two brothers from the same family with a missense mutation in exon 6 of the coding region of the HPRT1 gene: c.437T>C, p.L146S. Molecular diagnosis discloses the genetic heterogeneity of the HPRT1 gene responsible for HGprt deficiency. It allows fast, accurate carrier detection and genetic counseling.

Genetic variants in Alzheimer disease - molecular and brain network approaches

Genetic studies in late-onset Alzheimer disease (LOAD) are aimed at identifying core disease mechanisms and providing potential biomarkers and drug candidates to improve clinical care of AD. However, owing to the complexity of LOAD, including pathological heterogeneity and disease polygenicity, extraction of actionable guidance from LOAD genetics has been challenging. Past attempts to summarize the effects of LOAD-associated genetic variants have used pathway analysis and collections of small-scale experiments to hypothesize functional convergence across several variants. In this Review, we discuss how the study of molecular, cellular and brain networks provides additional information on the effects of LOAD-associated genetic variants. We then discuss emerging combinations of these omic data sets into multiscale models, which provide a more comprehensive representation of the effects of LOAD-associated genetic variants at multiple biophysical scales. Furthermore, we highlight the clinical potential of mechanistically coupling genetic variants and disease phenotypes with multiscale brain models.

Presenilin E318G variant and Alzheimer's disease risk: the Cache County study

Background

Alzheimer's disease is the leading cause of dementia in the elderly and the third most common cause of death in the United States. A vast number of genes regulate Alzheimer’s disease, including Presenilin 1 (PSEN1). Multiple studies have attempted to locate novel variants in the PSEN1 gene that affect Alzheimer's disease status. A recent study suggested that one of these variants, PSEN1 E318G (rs17125721), significantly affects Alzheimer's disease status in a large case–control dataset, particularly in connection with the APOEε4 allele.

Methods

Our study looks at the same variant in the Cache County Study on Memory and Aging, a large population-based dataset. We tested for association between E318G genotype and Alzheimer’s disease status by running a series of Fisher’s exact tests. We also performed logistic regression to test for an additive effect of E318G genotype on Alzheimer’s disease status and for the existence of an interaction between E318G and APOEε4.

Results

In our Fisher’s exact test, it appeared that APOEε4 carriers with an E318G allele have slightly higher risk for AD than those without the allele (3.3 vs. 3.8); however, the 95 % confidence intervals of those estimates overlapped completely, indicating non-significance. Our logistic regression model found a positive but non-significant main effect for E318G (p = 0.895). The interaction term between E318G and APOEε4 was also non-significant (p = 0.689).

Conclusions

Our findings do not provide significant support for E318G as a risk factor for AD in APOEε4 carriers. Our calculations indicated that the overall sample used in the logistic regression models was adequately powered to detect the sort of effect sizes observed previously. However, the power analyses of our Fisher’s exact tests indicate that our partitioned data was underpowered, particularly in regards to the low number of E318G carriers, both AD cases and controls, in the Cache county dataset. Thus, the differences in types of datasets used may help to explain the difference in effect magnitudes seen. Analyses in additional case–control datasets will be required to understand fully the effect of E318G on Alzheimer's disease status.

Genetics of Vascular Dementia: Systematic Review and Meta-Analysis

BACKGROUND

Vascular dementia (VaD) is the second most common type of dementia. So far, little is known about the contribution of genetic polymorphisms to the risk of VaD. Many candidate genetic polymorphisms have been examined in a large number of studies. However, due to the conflicting results, the genetics of VaD is still behind the shadow.

OBJECTIVE

We conducted a comprehensive meta-analysis on associations between genetic polymorphisms of any gene and VaD to investigate the genetics of VaD.

METHOD

We sought the published studies of associations between any genetic polymorphism and VaD and critically appraised them. We assessed the effects of genetic models by calculating pooled odds ratios (ORs), investigating the origin of heterogeneity by subgroup analysis, and testing the robustness by random effect model and sensitivity analysis.

RESULTS

69 studies with 4,462 cases and 11,583 controls were included. We identified APOE ɛ2/ɛ3/ɛ4 and additional four genetic polymorphisms including MTHFR C677T, PON1 L55M, TGF-β1 +29C/T, and TNF-α -850C/T associated with VaD. Tested by random effect model and sensitivity analysis, the pooled results show nice robustness.

CONCLUSIONS

Our comprehensive meta-analysis highlighted the genetic contribution to sporadic VaD. Because of the small amount of data on associations between genetic polymorphisms, except for APOE, and VaD, more studies are needed to test the existing genetic polymorphisms and detect other related genetic variants.

A Mutation in DAOA Modifies the Age of Onset in PSEN1 E280A Alzheimer's Disease

We previously reported age of onset (AOO) modifier genes in the world's largest pedigree segregating early-onset Alzheimer's disease (AD), caused by the p.Glu280Ala (E280A) mutation in the PSEN1 gene. Here we report the results of a targeted analysis of functional exonic variants in those AOO modifier genes in sixty individuals with PSEN1 E280A AD who were whole-exome genotyped for ~250,000 variants. Standard quality control, filtering, and annotation for functional variants were applied, and common functional variants located in those previously reported as AOO modifier loci were selected. Multiloci linear mixed-effects models were used to test the association between these variants and AOO. An exonic missense mutation in the G72 (DAOA) gene (rs2391191, P = 1.94 × 10⁻⁴, P_FDR = 9.34 × 10⁻³) was found to modify AOO in PSEN1 E280A AD. Nominal associations of missense mutations in the CLUAP1 (rs9790, P = 7.63 × 10⁻³, P_FDR = 0.1832) and EXOC2 (rs17136239, P = 0.0325, P_FDR = 0.391) genes were also found. Previous studies have linked polymorphisms in the DAOA gene with the occurrence of neuropsychiatric symptoms such as depression, apathy, aggression, delusions, hallucinations, and psychosis in AD. Our findings strongly suggest that this new conspicuous functional AOO modifier within the G72 (DAOA) gene could be pivotal for understanding the genetic basis of AD.

Interaction between variants in CLU and MS4A4E modulates Alzheimer's disease risk

INTRODUCTION

Ebbert et al. reported gene-gene interactions between rs11136000-rs670139 (CLU-MS4A4E) and rs3865444-rs670139 (CD33-MS4A4E). We evaluate these interactions in the largest data set for an epistasis study.

METHODS

We tested interactions using 3837 cases and 4145 controls from Alzheimer's Disease Genetics Consortium using meta-analyses and permutation analyses. We repeated meta-analyses stratified by apolipoprotein E (APOE) ε4 status, estimated combined odds ratio (OR) and population attributable fraction (cPAF), and explored causal variants.

RESULTS

Results support the CLU-MS4A4E interaction and a dominant effect. An association between CLU-MS4A4E and APOE ε4 negative status exists. The estimated synergy factor, OR, and cPAF for rs11136000-rs670139 are 2.23, 2.45, and 8.0, respectively. We identified potential causal variants.

DISCUSSION

We replicated the CLU-MS4A4E interaction in a large case-control series and observed APOE ε4 and possible dominant effect. The CLU-MS4A4E OR is higher than any Alzheimer's disease locus except APOE ε4, APP, and TREM2. We estimated an 8% decrease in Alzheimer's disease incidence without CLU-MS4A4E risk alleles and identified potential causal variants.

Epigenetic Regulation in Amyloid Precursor Protein with Genomic Rearrangements and the Lesch-Nyhan Syndrome

Recently, epigenetic regulation of alternative APP pre-mRNA splicing in the Lesch-Nyhan syndrome (LNS) has been studied (see Ref. 7) and showed for the first time, the presence of several APP-mRNA isoforms encoding divers APP protein isoforms ranging from 120 to 770 amino acids (with or without mutations and/or deletions). Here, by continuing on this work, I identified, for the first time new APP-mRNA isoforms with a deletion followed by an insertion (INDELS) in LNS and LNVs patients: c.19_2295delinsG166TT…GAGTCC…CTTAGTC…TCT489,p.Leu7Valfs*2;c.19_2295 delinsG169TT…GAGACC…CTTGGTC…TCT492,p.Leu7Valfs*2;and c.16_2313delinsG84CC…CAT616,p.Leu7Hisfs*45. A role of genomic rearrangements of APP gene via the Fork Stalling and Template Switching (FoSTeS) mechanism leading to INDELS was suggested. Epistasis between mutated HPRT1 and APP genes could be one of the factors of epigenetic modifications responsible for genomic rearrangements of APP gene. My findings accounted for epigenetic mechanism in the regulation of alternative APP pre-mRNA splicing as well as for epigenetic control of genomic rearrangements of APP gene may provide therefore new directions not only for investigating the role of APP in neuropathology associated with HGprt-deficiency in LNS and LNVs patients but also for the research in neurodevelopmental and neurodegenerative disorders by which APP gene involved in the pathogenesis of the diseases such as autism, fragile X syndrome (FXS), and Alzheimer's disease (AD) with its diversity and complexity, especially for sporadic form of AD (SAD). An accurate quantification of various APP-mRNA isoforms in brain tissues for detection of initial pathological changes or pathology development is needed and antisense drugs are the potential treatments.

Confronting the missing epistasis problem: on the reproducibility of gene-gene interactions

Epistasis (gene-gene interaction) is thought to play an integral role in the genetic basis of complex traits, and a significant amount of research has been invested into identifying this phenomenon in human disease. However, the overall success of empirical studies of epistasis in humans is unclear, as such studies are rarely systematically evaluated. Here, we have selected asthma as an example of a well-studied, complex human disease, and provide a critical analysis and replication attempt of nearly all prior reports of epistasis for this disease. Of 191 previously reported interactions, we find that 39.8% were not originally identified using an explicit test for interaction and thus may not have been true epistatic effects to begin with. Moreover, directions of effect were not described for 46.1% of the interactions, which prevents their rigorous replication. In the original studies, attempts at replication were made for 15.2% of the interactions, and 7.3% were actually replicated. In the current study, we were able to evaluate 85.9% of the interactions using a large asthma dataset from the GABRIEL Consortium. None of these interactions could be replicated based on strict criteria. However, we found nominally significant (p < 0.05) evidence in support of 23.8% of the evaluated interactions. Although many reports of epistasis are not robustly supported in the published literature, our results suggest that at least some of these reports may have been true-positive examples of epistasis. In general, improvements in empirical studies of epistasis are called for, in order to better understand the importance of this phenomenon in human disease.

Bridging the gap between statistical and biological epistasis in Alzheimer's disease

Alzheimer's disease affects millions of people worldwide and incidence is expected to rise as the population ages, but no effective therapies exist despite decades of research and more than 20 known disease markers. Research has shown that Alzheimer's disease's missing heritability remains extensive with an estimated 25% of phenotypic variance unexplained by known variants. The missing heritability may be explained by missing variants or by epistasis. Researchers often focus on individual loci rather than epistatic interactions, which is likely an oversimplification of the underlying biology since most phenotypes are affected by multiple genes. Focusing research efforts on epistasis will be critical to resolving Alzheimer's disease etiology, and a major key to identifying and properly interpreting key epistatic interactions will be bridging the gap between statistical and biological epistasis. This review covers the current state of epistasis research in Alzheimer's disease and how researchers can bridge the gap between statistical and biological epistasis to help resolve Alzheimer's disease etiology.

Additional mechanisms conferring genetic susceptibility to Alzheimer's disease

Familial Alzheimer’s disease (AD), mostly associated with early onset, is caused by mutations in three genes (APP, PSEN1, and PSEN2) involved in the production of the amyloid β peptide. In contrast, the molecular mechanisms that trigger the most common late onset sporadic AD remain largely unknown. With the implementation of an increasing number of case-control studies and the upcoming of large-scale genome-wide association studies there is a mounting list of genetic risk factors associated with common genetic variants that have been associated with sporadic AD. Besides apolipoprotein E, that presents a strong association with the disease (OR∼4), the rest of these genes have moderate or low degrees of association, with OR ranging from 0.88 to 1.23. Taking together, these genes may account only for a fraction of the attributable AD risk and therefore, rare variants and epistastic gene interactions should be taken into account in order to get the full picture of the genetic risks associated with AD. Here, we review recent whole-exome studies looking for rare variants, somatic brain mutations with a strong association to the disease, and several studies dealing with epistasis as additional mechanisms conferring genetic susceptibility to AD. Altogether, recent evidence underlines the importance of defining molecular and genetic pathways, and networks rather than the contribution of specific genes.

Identifying Highly Penetrant Disease Causal Mutations Using Next Generation Sequencing: Guide to Whole Process

Recent technological advances have created challenges for geneticists and a need to adapt to a wide range of new bioinformatics tools and an expanding wealth of publicly available data (e.g., mutation databases, and software). This wide range of methods and a diversity of file formats used in sequence analysis is a significant issue, with a considerable amount of time spent before anyone can even attempt to analyse the genetic basis of human disorders. Another point to consider that is although many possess “just enough” knowledge to analyse their data, they do not make full use of the tools and databases that are available and also do not fully understand how their data was created. The primary aim of this review is to document some of the key approaches and provide an analysis schema to make the analysis process more efficient and reliable in the context of discovering highly penetrant causal mutations/genes. This review will also compare the methods used to identify highly penetrant variants when data is obtained from consanguineous individuals as opposed to nonconsanguineous; and when Mendelian disorders are analysed as opposed to common-complex disorders.

The human β-amyloid precursor protein: biomolecular and epigenetic aspects

Beta-amyloid precursor protein (APP) is a membrane-spanning protein with a large extracellular domain and a much smaller intracellular domain. APP plays a central role in Alzheimer’s disease (AD) pathogenesis: APP processing generates β-amyloid (Aβ) peptides, which are deposited as amyloid plaques in the brains of AD individuals; point mutations and duplications of APP are causal for a subset of early-onset familial AD (FAD) (onset age <65 years old). However, these mutations in FAD represent a very small percentage of cases (∼1%). Approximately 99% of AD cases are nonfamilial and late-onset, i.e., sporadic AD (SAD) (onset age >65 years old), and the pathophysiology of this disorder is not yet fully understood. APP is an extremely complex molecule that may be functionally important in its full-length configuration, as well as the source of numerous fragments with varying effects on neural function, yet the normal function of APP remains largely unknown. This article provides an overview of our current understanding of APP, including its structure, expression patterns, proteolytic processing and putative functions. Importantly, and for the first time, my recent data concerning its epigenetic regulation, especially in alternative APP pre-mRNA splicing and in the control of genomic rearrangements of the APP gene, are also reported. These findings may provide new directions for investigating the role of APP in neuropathology associated with a deficiency in the enzyme hypoxanthine-guanine phosphoribosyltransferase (HGprt) found in patients with Lesch-Nyhan syndrome (LNS) and its attenuated variants (LNVs). Also, these findings may be of significance for research in neurodevelopmental and neurodegenerative disorders in which the APP gene is involved in the pathogenesis of diseases such as autism, fragile X syndrome (FXS) and AD, with its diversity and complexity, SAD in particular. Accurate quantification of various APP-mRNA isoforms in brain tissues is needed, and antisense drugs are potential treatments.

Epistasis in the risk of human neuropsychiatric disease

Neuropsychiatric disease represents the ideal class of disease to assess the role of epistasis, as more genes are expressed in the brain than in any other tissue. In this chapter, two well-studied neuropsychiatric diseases are examined, Alzheimer's disease (AD) and schizophrenia, which have been shown to have multiple and, often, replicated interactions that associate with clinical endpoints or related phenotypes. In each case, a single gene is represented in a plurality of epistatic interactions, apolipoprotein E (APOE) for AD and catechol-O-methyltransferase for schizophrenia. Interestingly, of the two, only APOE has clear-cut and consistent evidence for a marginal association. Unraveling the underlying reasons is important in understanding both genetic etiology and architecture as well as how to use genetics to provide better personalized treatments.