Genome-wide association interaction analysis for 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.

Probing the biological consequences of a previously undescribed de novo mutation of ZMYND11 in a schizophrenia patient by CRISPR genome editing and induced pluripotent stem cell based in vitro disease-modeling

BACKGROUND

Schizophrenia (SCZ) is a severe neuropsychiatric disorder of complex, poorly understood etiology, associated with both genetic and environmental factors. De novo mutations (DNMs) represent a new source of genetic variation in SCZ, however, in most cases their biological significance remains unclear. We sought to investigate molecular disease pathways connected to DNMs in SCZ by combining human induced pluripotent stem cell (hiPSC) based disease modeling and CRISPR-based genome editing.

METHODS

We selected a SCZ case-parent trio with the case individual carrying a potentially disease causing 1495C > T nonsense DNM in the zinc finger MYND domain-containing protein 11 (ZMYND11), a gene implicated in biological processes relevant for SCZ. In the patient-derived hiPSC line the mutation was corrected using CRISPR, while monoallelic or biallelic frameshift mutations were introduced into a control hiPSC line. Isogenic cell lines were differentiated into hippocampal neuronal progenitor cells (NPCs) and functionally active dentate gyrus granule cells (DGGCs). Immunofluorescence microscopy and RNA sequencing were used to test for morphological and transcriptomic differences at NPC and DGCC stages. Functionality of neurons was investigated using calcium-imaging and multi-electrode array measurements.

RESULTS

Morphology in the mutant hippocampal NPCs and neurons was preserved, however, we detected significant transcriptomic and functional alterations. RNA sequencing showed massive upregulation of neuronal differentiation genes, and downregulation of cell adhesion genes. Decreased reactivity to glutamate was demonstrated by calcium-imaging.

CONCLUSIONS

Our findings lend support to the involvement of glutamatergic dysregulation in the pathogenesis of SCZ. This approach represents a powerful model system for precision psychiatry and pharmacological research.

Genetic Factors Contribute to the Phenotypic Variability in GJB2-Related Hearing Impairment

Recessive variants in GJB2 are the most important genetic cause of sensorineural hearing impairment (SNHI) worldwide. Phenotypes vary significantly in GJB2-related SNHI, even in patients with identical variants. For instance, patients homozygous for the GJB2 p.V37I variant, which is highly prevalent in the Asian populations, usually present with mild-to-moderate SNHI; yet severe-to-profound SNHI is occasionally observed in approximately 10% of p.V37I homozygotes. To investigate the genomic underpinnings of the phenotypic variability, we performed next-generation sequencing of GJB2 and other deafness genes in 63 p.V37I homozygotes with extreme phenotypic severities. Additional pathogenic variants of other deafness genes were identified in five of the 35 patients with severe-to-profound SNHI. Furthermore, case-control association analyses were conducted for 30 unrelated p.V37I homozygotes with severe-to-profound SNHI against 28 p.V37I homozygotes with mild-to-moderate SNHI, and 120 population controls from the Taiwan Biobank. The severe-to-profound group exhibited a higher frequency of the crystallin lambda 1 (CRYL1) variant (rs14236), located upstream of GJB2, than the mild-to-moderate and Taiwan Biobank groups. Our results demonstrated that pathogenic variants in other deafness genes and a possible modifier, the CRYL1 rs14236 variant, may contribute to phenotypic variability in GJB2-realted SNHI, highlighting the importance of comprehensive genomic surveys to delineate the genotype-phenotype correlations.

Novel Alzheimer's disease genes and epistasis identified using machine learning GWAS platform

Alzheimer's disease (AD) is a complex genetic disease, and variants identified through genome-wide association studies (GWAS) explain only part of its heritability. Epistasis has been proposed as a major contributor to this 'missing heritability', however, many current methods are limited to only modelling additive effects. We use VariantSpark, a machine learning approach to GWAS, and BitEpi, a tool for epistasis detection, to identify AD associated variants and interactions across two independent cohorts, ADNI and UK Biobank. By incorporating significant epistatic interactions, we captured 10.41% more phenotypic variance than logistic regression (LR). We validate the well-established AD loci, APOE, and identify two novel genome-wide significant AD associated loci in both cohorts, SH3BP4 and SASH1, which are also in significant epistatic interactions with APOE. We show that the SH3BP4 SNP has a modulating effect on the known pathogenic APOE SNP, demonstrating a possible protective mechanism against AD. SASH1 is involved in a triplet interaction with pathogenic APOE SNP and ACOT11, where the SASH1 SNP lowered the pathogenic interaction effect between ACOT11 and APOE. Finally, we demonstrate that VariantSpark detects disease associations with 80% fewer controls than LR, unlocking discoveries in well annotated but smaller cohorts.

Leveraging the genetic correlation between traits improves the detection of epistasis in genome-wide association studies

Epistasis, commonly defined as the interaction between genetic loci, is known to play an important role in the phenotypic variation of complex traits. As a result, many statistical methods have been developed to identify genetic variants that are involved in epistasis, and nearly all of these approaches carry out this task by focusing on analyzing one trait at a time. Previous studies have shown that jointly modeling multiple phenotypes can often dramatically increase statistical power for association mapping. In this study, we present the "multivariate MArginal ePIstasis Test" (mvMAPIT)-a multioutcome generalization of a recently proposed epistatic detection method which seeks to detect marginal epistasis or the combined pairwise interaction effects between a given variant and all other variants. By searching for marginal epistatic effects, one can identify genetic variants that are involved in epistasis without the need to identify the exact partners with which the variants interact-thus, potentially alleviating much of the statistical and computational burden associated with conventional explicit search-based methods. Our proposed mvMAPIT builds upon this strategy by taking advantage of correlation structure between traits to improve the identification of variants involved in epistasis. We formulate mvMAPIT as a multivariate linear mixed model and develop a multitrait variance component estimation algorithm for efficient parameter inference and P-value computation. Together with reasonable model approximations, our proposed approach is scalable to moderately sized genome-wide association studies. With simulations, we illustrate the benefits of mvMAPIT over univariate (or single-trait) epistatic mapping strategies. We also apply mvMAPIT framework to protein sequence data from two broadly neutralizing anti-influenza antibodies and approximately 2,000 heterogeneous stock of mice from the Wellcome Trust Centre for Human Genetics. The mvMAPIT R package can be downloaded at https://github.com/lcrawlab/mvMAPIT.

Genome-wide association study of abdominal MRI-measured visceral fat: The multiethnic cohort adiposity phenotype study

Few studies have explored the genetic underpinnings of intra-abdominal visceral fat deposition, which varies substantially by sex and race/ethnicity. Among 1,787 participants in the Multiethnic Cohort (MEC)-Adiposity Phenotype Study (MEC-APS), we conducted a genome-wide association study (GWAS) of the percent visceral adiposity tissue (VAT) area out of the overall abdominal area, averaged across L1-L5 (%VAT), measured by abdominal magnetic resonance imaging (MRI). A genome-wide significant signal was found on chromosome 2q14.3 in the sex-combined GWAS (lead variant rs79837492: Beta per effect allele = -4.76; P = 2.62 × 10-8) and in the male-only GWAS (lead variant rs2968545: (Beta = -6.50; P = 1.09 × 10-9), and one suggestive variant was found at 13q12.11 in the female-only GWAS (rs79926925: Beta = 6.95; P = 8.15 × 10-8). The negatively associated variants were most common in European Americans (T allele of rs79837492; 5%) and African Americans (C allele of rs2968545; 5%) and not observed in Japanese Americans, whereas the positively associated variant was most common in Japanese Americans (C allele of rs79926925, 5%), which was all consistent with the racial/ethnic %VAT differences. In a validation step among UK Biobank participants (N = 23,699 of mainly British and Irish ancestry) with MRI-based VAT volume, both rs79837492 (Beta = -0.026, P = 0.019) and rs2968545 (Beta = -0.028, P = 0.010) were significantly associated in men only (n = 11,524). In the MEC-APS, the association between rs79926925 and plasma sex hormone binding globulin levels reached statistical significance in females, but not in males, with adjustment for total adiposity (Beta = -0.24; P = 0.028), on the log scale. Rs79837492 and rs2968545 are located in intron 5 of CNTNAP5, and rs79926925, in an intergenic region between GJB6 and CRYL1. These novel findings differing by sex and racial/ethnic group warrant replication in additional diverse studies with direct visceral fat measurements.

Downregulation of Crystallin Lambda 1 is a New Independent Prognostic Marker in Clear Cell Renal Cell Carcinoma

Background

Clear cell renal cell carcinoma (ccRCC), the most prevalent kidney cancer subtype, has a high mortality rate. Crystallin lambda 1 (CRYL1) encodes an enzyme that catalyzes the dehydrogenation of L-gulonate into dehydro-L-gulonate in uronate cycle. CRYL1 dysregulation has been linked to the progression of several cancers. This research aimed to evaluate the prognostic significance of CRYL1 expression in ccRCC prognosis.

Methods

Clinical data and gene expression profiles on ccRCC were retrieved from the University of California Santa Cruz Xena platform. Differences (variations) in the expression profiles of CRYL1 in ccRCC and healthy tissues were found using RNA-sequencing data, and these findings were validated using qPCR with real-world samples. CRYL1 expression levels were also linked to clinicopathological characteristics, survival, and immune microenvironments. The potential pathway via which CRYL1 expression levels impact the prognosis of patients with ccRCC was investigated using gene set enrichment analysis (GSEA).

Results

In ccRCC tissues, CRYL1 expression levels were lower compared to healthy renal tissues in TCGA cohort (n = 535, P < 0.001), which was validated in another real-world cohort (n = 14, P < 0.001). Lower CRYL1 expression levels were linked to unfavorable clinicopathological characteristics and prognoses (P < 0.001). According to multivariate Cox regression analysis (P < 0.001), CRYL1 expression levels in patients with ccRCC could serve as an independent prognostic indicator. Furthermore, a strong link between CRYL1 expression levels and immune microenvironment was observed (P < 0.001). Finally, GSEA revealed that CRYL1 expression levels (P < 0.001) were associated with fatty acid metabolism, G2M checkpoint delays, and epithelial-mesenchymal transitions in ccRCC.

Conclusion

Our study found that lower levels of CRYL1 expression were linked to unfavorable clinicopathological characteristics and worse prognoses, and CRYL1 could serve as a new target for the treatment of ccRCC, which is useful for personalized medicine.

Bench Research Informed by GWAS Results

Scientifically interesting as well as practically important phenotypes often belong to the realm of complex traits. To the extent that these traits are hereditary, they are usually 'highly polygenic'. The study of such traits presents a challenge for researchers, as the complex genetic architecture of such traits makes it nearly impossible to utilise many of the usual methods of reverse genetics, which often focus on specific genes. In recent years, thousands of genome-wide association studies (GWAS) were undertaken to explore the relationships between complex traits and a large number of genetic factors, most of which are characterised by tiny effects. In this review, we aim to familiarise 'wet biologists' with approaches for the interpretation of GWAS results, to clarify some issues that may seem counterintuitive and to assess the possibility of using GWAS results in experiments on various complex traits.

Pseudogene ACTBP2 increases blood-brain barrier permeability by promoting KHDRBS2 transcription through recruitment of KMT2D/WDR5 in Aβ1-42 microenvironment

The blood–brain barrier (BBB) has a vital role in maintaining the homeostasis of the central nervous system (CNS). Changes in the structure and function of BBB can accelerate Alzheimer’s disease (AD) development. β-Amyloid (Aβ) deposition is the major pathological event of AD. We elucidated the function and possible molecular mechanisms of the effect of pseudogene ACTBP2 on the permeability of BBB in Aβ_1–42 microenvironment. BBB model treated with Aβ_1–42 for 48 h were used to simulate Aβ-mediated BBB dysfunction in AD. We proved that pseudogene ACTBP2, RNA-binding protein KHDRBS2, and transcription factor HEY2 are highly expressed in ECs that were obtained in a BBB model in vitro in Aβ_1–42 microenvironment. In Aβ_1–42-incubated ECs, ACTBP2 recruits methyltransferases KMT2D and WDR5, binds to KHDRBS2 promoter, and promotes KHDRBS2 transcription. The interaction of KHDRBS2 with the 3′UTR of HEY2 mRNA increases the stability of HEY2 and promotes its expression. HEY2 increases BBB permeability in Aβ_1–42 microenvironment by transcriptionally inhibiting the expression of ZO-1, occludin, and claudin-5. We confirmed that knocking down of Khdrbs2 or Hey2 increased the expression levels of ZO-1, occludin, and claudin-5 in APP/PS1 mice brain microvessels. ACTBP2/KHDRBS2/HEY2 axis has a crucial role in the regulation of BBB permeability in Aβ_1–42 microenvironment, which may provide a novel target for the therapy of AD.

Genetic Variability in Molecular Pathways Implicated in Alzheimer's Disease: A Comprehensive Review

Alzheimer's disease (AD) is a complex neurodegenerative disease, affecting a significant part of the population. The majority of AD cases occur in the elderly with a typical age of onset of the disease above 65 years. AD presents a major burden for the healthcare system and since population is rapidly aging, the burden of the disease will increase in the future. However, no effective drug treatment for a full-blown disease has been developed to date. The genetic background of AD is extensively studied; numerous genome-wide association studies (GWAS) identified significant genes associated with increased risk of AD development. This review summarizes more than 100 risk loci. Many of them may serve as biomarkers of AD progression, even in the preclinical stage of the disease. Furthermore, we used GWAS data to identify key pathways of AD pathogenesis: cellular processes, metabolic processes, biological regulation, localization, transport, regulation of cellular processes, and neurological system processes. Gene clustering into molecular pathways can provide background for identification of novel molecular targets and may support the development of tailored and personalized treatment of AD.

Genome-wide epistasis analysis for Alzheimer's disease and implications for genetic risk prediction

BACKGROUND

Single-nucleotide polymorphisms (SNPs) identified by genome-wide association studies only explain part of the heritability of Alzheimer's disease (AD). Epistasis has been considered as one of the main causes of "missing heritability" in AD.

METHODS

We performed genome-wide epistasis screening (N = 10,389) for the clinical diagnosis of AD using three popularly adopted methods. Subsequent analyses were performed to eliminate spurious associations caused by possible confounding factors. Then, candidate genetic interactions were examined for their co-expression in the brains of AD patients and analyzed for their association with intermediate AD phenotypes. Moreover, a new approach was developed to compile the epistasis risk factors into an epistasis risk score (ERS) based on multifactor dimensional reduction. Two independent datasets were used to evaluate the feasibility of ERSs in AD risk prediction.

RESULTS

We identified 2 candidate genetic interactions with PFDR <  0.05 (RAMP3-SEMA3A and NSMCE1-DGKE/C17orf67) and another 5 genetic interactions with PFDR <  0.1. Co-expression between the identified interactions supported the existence of possible biological interactions underlying the observed statistical significance. Further association of candidate interactions with intermediate phenotypes helps explain the mechanisms of neuropathological alterations involved in AD. Importantly, we found that ERSs can identify high-risk individuals showing earlier onset of AD. Combined risk scores of SNPs and SNP-SNP interactions showed slightly but steadily increased AUC in predicting the clinical status of AD.

CONCLUSIONS

In summary, we performed a genome-wide epistasis analysis to identify novel genetic interactions potentially implicated in AD. We found that ERS can serve as an indicator of the genetic risk of AD.

The Role of the ATP-Binding Cassette A1 (ABCA1) in Human Disease

Cholesterol homeostasis is essential in normal physiology of all cells. One of several proteins involved in cholesterol homeostasis is the ATP-binding cassette transporter A1 (ABCA1), a transmembrane protein widely expressed in many tissues. One of its main functions is the efflux of intracellular free cholesterol and phospholipids across the plasma membrane to combine with apolipoproteins, mainly apolipoprotein A-I (Apo A-I), forming nascent high-density lipoprotein-cholesterol (HDL-C) particles, the first step of reverse cholesterol transport (RCT). In addition, ABCA1 regulates cholesterol and phospholipid content in the plasma membrane affecting lipid rafts, microparticle (MP) formation and cell signaling. Thus, it is not surprising that impaired ABCA1 function and altered cholesterol homeostasis may affect many different organs and is involved in the pathophysiology of a broad array of diseases. This review describes evidence obtained from animal models, human studies and genetic variation explaining how ABCA1 is involved in dyslipidemia, coronary heart disease (CHD), type 2 diabetes (T2D), thrombosis, neurological disorders, age-related macular degeneration (AMD), glaucoma, viral infections and in cancer progression.

The Application of Artificial Intelligence in the Genetic Study of Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disease in which genetic factors contribute approximately 70% of etiological effects. Studies have found many significant genetic and environmental factors, but the pathogenesis of AD is still unclear. With the application of microarray and next-generation sequencing technologies, research using genetic data has shown explosive growth. In addition to conventional statistical methods for the processing of these data, artificial intelligence (AI) technology shows obvious advantages in analyzing such complex projects. This article first briefly reviews the application of AI technology in medicine and the current status of genetic research in AD. Then, a comprehensive review is focused on the application of AI in the genetic research of AD, including the diagnosis and prognosis of AD based on genetic data, the analysis of genetic variation, gene expression profile, gene-gene interaction in AD, and genetic analysis of AD based on a knowledge base. Although many studies have yielded some meaningful results, they are still in a preliminary stage. The main shortcomings include the limitations of the databases, failing to take advantage of AI to conduct a systematic biology analysis of multilevel databases, and lack of a theoretical framework for the analysis results. Finally, we outlook the direction of future development. It is crucial to develop high quality, comprehensive, large sample size, data sharing resources; a multi-level system biology AI analysis strategy is one of the development directions, and computational creativity may play a role in theory model building, verification, and designing new intervention protocols for AD.

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.

A novel estimator of between-study variance in random-effects models

BACKGROUND

With the rapid development of high-throughput sequencing technologies, many datasets on the same biological subject are generated. A meta-analysis is an approach that combines results from different studies on the same topic. The random-effects model in a meta-analysis enables the modeling of differences between studies by incorporating the between-study variance.

RESULTS

This paper proposes a moments estimator of the between-study variance that represents the across-study variation. A new random-effects method (DSLD2), which involves two-step estimation starting with the DSL estimate and the [Formula: see text] in the second step, is presented. The DSLD2 method is compared with 6 other meta-analysis methods based on effect sizes across 8 aspects under three hypothesis settings. The results show that DSLD2 is a suitable method for identifying differentially expressed genes under the first hypothesis. The DSLD2 method is also applied to Alzheimer's microarray datasets. The differentially expressed genes detected by the DSLD2 method are significantly enriched in neurological diseases.

CONCLUSIONS

The results from both simulationes and an application show that DSLD2 is a suitable method for detecting differentially expressed genes under the first hypothesis.

Genetic risk for coronary heart disease alters the influence of Alzheimer's genetic risk on mild cognitive impairment

Understanding genetic influences on Alzheimer's disease (AD) may improve early identification. AD polygenic risk scores (AD-PRSs) are associated with increased odds of AD and mild cognitive impairment (MCI). Additional sources of genetic risk may also contribute to disease outcomes. Coronary artery disease (CAD) is a risk factor for AD, interacts with AD pathology, and is also heritable. We showed that incidence-based and prevalence-based CAD-PRSs moderate the association between the AD-PRS and MCI, but in opposing directions. Higher incidence-based CAD-PRSs interacted with the AD-PRS to further increase MCI risk. Conversely, the AD-PRS was predictive of MCI when prevalence-based CAD-PRSs were low. The latter finding is likely due to prevalent CAD cases being biased toward longer postevent survival times, perhaps selecting for protective loci that offset AD risk. These results demonstrate (1) the importance of examining multiple PRSs and their interactions; (2) how genetic risk for one disease can modify the impact of genetic risk for another; and (3) the importance of considering ascertainment procedures of GWAS used for genetic risk prediction.

Alzheimer's disease-associated (hydroxy)methylomic changes in the brain and blood

BACKGROUND

Late-onset Alzheimer's disease (AD) is a complex multifactorial affliction, the pathogenesis of which is thought to involve gene-environment interactions that might be captured in the epigenome. The present study investigated epigenome-wide patterns of DNA methylation (5-methylcytosine, 5mC) and hydroxymethylation (5-hydroxymethylcytosine, 5hmC), as well as the abundance of unmodified cytosine (UC), in relation to AD.

RESULTS

We identified epigenetic differences in AD patients (n = 45) as compared to age-matched controls (n = 35) in the middle temporal gyrus, pertaining to genomic regions close to or overlapping with genes such as OXT (- 3.76% 5mC, pŠidák = 1.07E-06), CHRNB1 (+ 1.46% 5hmC, pŠidák = 4.01E-04), RHBDF2 (- 3.45% UC, pŠidák = 4.85E-06), and C3 (- 1.20% UC, pŠidák = 1.57E-03). In parallel, in an independent cohort, we compared the blood methylome of converters to AD dementia (n = 54) and non-converters (n = 42), at a preclinical stage. DNA methylation in the same region of the OXT promoter as found in the brain was found to be associated with subsequent conversion to AD dementia in the blood of elderly, non-demented individuals (+ 3.43% 5mC, pŠidák = 7.14E-04).

CONCLUSIONS

The implication of genome-wide significant differential methylation of OXT, encoding oxytocin, in two independent cohorts indicates it is a promising target for future studies on early biomarkers and novel therapeutic strategies in AD.

Quantitative Trait Module-Based Genetic Analysis of Alzheimer's Disease

The pathological features of Alzheimer's Disease (AD) first appear in the medial temporal lobe and then in other brain structures with the development of the disease. In this work, we investigated the association between genetic loci and subcortical structure volumes of AD on 393 samples in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Brain subcortical structures were clustered into modules using Pearson's correlation coefficient of volumes across all samples. Module volumes were used as quantitative traits to identify not only the main effect loci but also the interactive effect loci for each module. Thirty-five subcortical structures were clustered into five modules, each corresponding to a particular brain structure/area, including the limbic system (module I), the corpus callosum (module II), thalamus-cerebellum-brainstem-pallidum (module III), the basal ganglia neostriatum (module IV), and the ventricular system (module V). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment results indicate that the gene annotations of the five modules were distinct, with few overlaps between different modules. We identified several main effect loci and interactive effect loci for each module. All these loci are related to the function of module structures and basic biological processes such as material transport and signal transduction.

Extensive transcriptomic study emphasizes importance of vesicular transport in C9orf72 expansion carriers

The majority of the clinico-pathological variability observed in patients harboring a repeat expansion in the C9orf72-SMCR8 complex subunit (C9orf72) remains unexplained. This expansion, which represents the most common genetic cause of frontotemporal lobar degeneration (FTLD) and motor neuron disease (MND), results in a loss of C9orf72 expression and the generation of RNA foci and dipeptide repeat (DPR) proteins. The C9orf72 protein itself plays a role in vesicular transport, serving as a guanine nucleotide exchange factor that regulates GTPases. To further elucidate the mechanisms underlying C9orf72-related diseases and to identify potential disease modifiers, we performed an extensive RNA sequencing study. We included individuals for whom frontal cortex tissue was available: FTLD and FTLD/MND patients with (n = 34) or without (n = 44) an expanded C9orf72 repeat as well as control subjects (n = 24). In total, 6706 genes were differentially expressed between these groups (false discovery rate [FDR] < 0.05). The top gene was C9orf72 (FDR = 1.41E-14), which was roughly two-fold lower in C9orf72 expansion carriers than in (disease) controls. Co-expression analysis revealed groups of correlated genes (modules) that were enriched for processes such as protein folding, RNA splicing, synaptic signaling, metabolism, and Golgi vesicle transport. Within our cohort of C9orf72 expansion carriers, machine learning uncovered interesting candidates associated with clinico-pathological features, including age at onset (vascular endothelial growth factor A [VEGFA]), C9orf72 expansion size (cyclin dependent kinase like 1 [CDKL1]), DPR protein levels (eukaryotic elongation factor 2 kinase [EEF2K]), and survival after onset (small G protein signaling modulator 3 [SGSM3]). Given the fact that we detected a module involved in vesicular transport in addition to a GTPase activator (SGSM3) as a potential modifier, our findings seem to suggest that the presence of a C9orf72 repeat expansion might hamper vesicular transport and that genes affecting this process may modify the phenotype of C9orf72-linked diseases.

The new genetic landscape of Alzheimer's disease: from amyloid cascade to genetically driven synaptic failure hypothesis?

A strong genetic predisposition (60–80% of attributable risk) is present in Alzheimer’s disease (AD). In view of this major genetic component, identification of the genetic risk factors has been a major objective in the AD field with the ultimate aim to better understand the pathological processes. In this review, we present how the genetic risk factors are involved in APP metabolism, β-amyloid peptide production, degradation, aggregation and toxicity, innate immunity, and Tau toxicity. In addition, on the basis of the new genetic landscape, resulting from the recent high-throughput genomic approaches and emerging neurobiological information, we propose an over-arching model in which the focal adhesion pathway and the related cell signalling are key elements in AD pathogenesis. The core of the focal adhesion pathway links the physiological functions of amyloid precursor protein and Tau with the pathophysiological processes they are involved in. This model includes several entry points, fitting with the different origins for the disease, and supports the notion that dysregulation of synaptic plasticity is a central node in AD. Notably, our interpretation of the latest data from genome wide association studies complements other hypotheses already developed in the AD field, i.e., amyloid cascade, cellular phase or propagation hypotheses. Genetically driven synaptic failure hypothesis will need to be further tested experimentally within the general AD framework.

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.

Genome-wide analyses as part of the international FTLD-TDP whole-genome sequencing consortium reveals novel disease risk factors and increases support for immune dysfunction in FTLD

Frontotemporal lobar degeneration with neuronal inclusions of the TAR DNA-binding protein 43 (FTLD-TDP) represents the most common pathological subtype of FTLD. We established the international FTLD-TDP whole-genome sequencing consortium to thoroughly characterize the known genetic causes of FTLD-TDP and identify novel genetic risk factors. Through the study of 1131 unrelated Caucasian patients, we estimated that C9orf72 repeat expansions and GRN loss-of-function mutations account for 25.5% and 13.9% of FTLD-TDP patients, respectively. Mutations in TBK1 (1.5%) and other known FTLD genes (1.4%) were rare, and the disease in 57.7% of FTLD-TDP patients was unexplained by the known FTLD genes. To unravel the contribution of common genetic factors to the FTLD-TDP etiology in these patients, we conducted a two-stage association study comprising the analysis of whole-genome sequencing data from 517 FTLD-TDP patients and 838 controls, followed by targeted genotyping of the most associated genomic loci in 119 additional FTLD-TDP patients and 1653 controls. We identified three genome-wide significant FTLD-TDP risk loci: one new locus at chromosome 7q36 within the DPP6 gene led by rs118113626 (p value = 4.82e - 08, OR = 2.12), and two known loci: UNC13A, led by rs1297319 (p value = 1.27e - 08, OR = 1.50) and HLA-DQA2 led by rs17219281 (p value = 3.22e - 08, OR = 1.98). While HLA represents a locus previously implicated in clinical FTLD and related neurodegenerative disorders, the association signal in our study is independent from previously reported associations. Through inspection of our whole-genome sequence data for genes with an excess of rare loss-of-function variants in FTLD-TDP patients (n ≥ 3) as compared to controls (n = 0), we further discovered a possible role for genes functioning within the TBK1-related immune pathway (e.g., DHX58, TRIM21, IRF7) in the genetic etiology of FTLD-TDP. Together, our study based on the largest cohort of unrelated FTLD-TDP patients assembled to date provides a comprehensive view of the genetic landscape of FTLD-TDP, nominates novel FTLD-TDP risk loci, and strongly implicates the immune pathway in FTLD-TDP pathogenesis.

How to increase our belief in discovered statistical interactions via large-scale association studies?

The understanding that differences in biological epistasis may impact disease risk, diagnosis, or disease management stands in wide contrast to the unavailability of widely accepted large-scale epistasis analysis protocols. Several choices in the analysis workflow will impact false-positive and false-negative rates. One of these choices relates to the exploitation of particular modelling or testing strategies. The strengths and limitations of these need to be well understood, as well as the contexts in which these hold. This will contribute to determining the potentially complementary value of epistasis detection workflows and is expected to increase replication success with biological relevance. In this contribution, we take a recently introduced regression-based epistasis detection tool as a leading example to review the key elements that need to be considered to fully appreciate the value of analytical epistasis detection performance assessments. We point out unresolved hurdles and give our perspectives towards overcoming these.

Male-specific epistasis between WWC1 and TLN2 genes is associated with Alzheimer's disease

Systematic epistasis analyses in multifactorial disorders are an important step to better characterize complex genetic risk structures. We conducted a hypothesis-free sex-stratified genome-wide screening for epistasis contributing to Alzheimer's disease (AD) susceptibility. We identified a statistical epistasis signal between the single nucleotide polymorphisms rs3733980 and rs7175766 that was associated with AD in males (genome-wide significant p_{Bonferroni-corrected}=0.0165). This signal pointed toward the genes WW and C2 domain containing 1, aka KIBRA; 5q34 and TLN2 (talin 2; 15q22.2). Gene-based meta-analysis in 3 independent consortium data sets confirmed the identified interaction: the most significant (p_{meta-Bonferroni-corrected}=9.02*10^-3) was for the single nucleotide polymorphism pair rs1477307 and rs4077746. In functional studies, WW and C2 domain containing 1, aka KIBRA and TLN2 coexpressed in the temporal cortex brain tissue of AD subjects (β=0.17, 95% CI 0.04 to 0.30, p=0.01); modulated Tau toxicity in Drosophila eye experiments; colocalized in brain tissue cells, N2a neuroblastoma, and HeLa cell lines; and coimmunoprecipitated both in brain tissue and HEK293 cells. Our finding points toward new AD-related pathways and provides clues toward novel medical targets for the cure of AD.

The search for gene-gene interactions in genome-wide association studies: challenges in abundance of methods, practical considerations, and biological interpretation

One of the primary goals in this era of precision medicine is to understand the biology of human diseases and their treatment, such that each individual patient receives the best possible treatment for their disease based on their genetic and environmental exposures. One way to work towards achieving this goal is to identify the environmental exposures and genetic variants that are relevant to each disease in question, as well as the complex interplay between genes and environment. Genome-wide association studies (GWAS) have allowed for a greater understanding of the genetic component of many complex traits. However, these genetic effects are largely small and thus, our ability to use these GWAS finding for precision medicine is limited. As more and more GWAS have been performed, rather than focusing only on common single nucleotide polymorphisms (SNPs) and additive genetic models, many researchers have begun to explore alternative heritable components of complex traits including rare variants, structural variants, epigenetics, and genetic interactions. While genetic interactions are a plausible reality that could explain some of the heritabliy that has not yet been identified, especially when one considers the identification of genetic interactions in model organisms as well as our understanding of biological complexity, still there are significant challenges and considerations in identifying these genetic interactions. Broadly, these can be summarized in three categories: abundance of methods, practical considerations, and biological interpretation. In this review, we will discuss these important elements in the search for genetic interactions along with some potential solutions. While genetic interactions are theoretically understood to be important for complex human disease, the body of evidence is still building to support this component of the underlying genetic architecture of complex human traits. Our hope is that more sophisticated modeling approaches and more robust computational techniques will enable the community to identify these important genetic interactions and improve our ability to implement precision medicine in the future.

Genetics of neurodegenerative diseases: an overview

Genetic factors are central to the etiology of neurodegeneration, both as monogenic causes of heritable disease and as modifiers of susceptibility to complex, sporadic disorders. Over the last two decades, the identification of disease genes and risk loci has led to some of the greatest advances in medicine and invaluable insights into pathogenic mechanisms and disease pathways. Large-scale research efforts, novel study designs, and advances in methodology are rapidly expanding our understanding of the genome and the genetic architecture of neurodegenerative disease. Here, we review major developments in the field to date, highlighting overarching historic trends and general insights. Monogenic neurodegenerative diseases are discussed from the perspectives of both rare Mendelian forms of common disorders, such as Alzheimer disease and Parkinson disease, and heterogeneous heritable conditions, including ataxias and spastic paraplegias. Next, we summarize the experiences from investigations of complex neurodegenerative disorders, including genomewide association studies. In the final section, we reflect upon the limitations of current findings and outline important future directions. Genetics plays an essential role in translational research, ultimately aiming to develop novel disease-modifying therapies for neurodegenerative disorders. We anticipate that individual genetic profiling will also be increasingly relevant in a clinical context, with implications for patient care in line with the proposed ideal of personalized medicine.

Importance of tau in cognitive decline as revealed by developmental exposure to lead

Previous reports by us have determined that developmental exposure to the heavy metal lead (Pb) resulted in cognitive impairment in aging wildtype mice, and a latent induction in biomarkers associated with both the tau and amyloid pathways. However, the relationship between these two pathways and their correlation to cognitive performance needs to be scrutinized. Here, we investigated the impact of developmental Pb (0.2%) exposure on the amyloid and tau pathways in a transgenic mouse model lacking the tau gene. Cognitive function, and levels of intermediates in the amyloid and tau pathways following postnatal Pb exposure were assessed on young adult and mature transgenic mice. No significant difference in behavioral performance, amyloid precursor protein (APP), or amyloid beta (Aβ) levels was observed in transgenic mice exposed to Pb. Regulators of the tau pathway were impacted by the absence of tau, but no additional change was imparted by Pb exposure. These results revealed that developmental Pb exposure does not cause cognitive decline or change the expression of the amyloid pathway in the absence of tau. The essentiality of tau to mediate cognitive decline by environmental perturbations needs further investigation.

Inference of Causative Genes for Alzheimer's Disease Due to Dosage Imbalance

Copy number variations (CNVs) have recently drawn attention as an important genetic factor for diseases, especially common neuropsychiatric disorders including Alzheimer's disease (AD). Because most of the pathogenic CNV regions overlap with multiple genes, it has been challenging to identify the true disease-causing genes amongst them. Notably, a recent study reported that CNV regions containing ohnologs, which are dosage-sensitive genes, are likely to be deleterious. Utilizing the unique feature of ohnologs could be useful for identifying causative genes with pathogenic CNVs, however its effectiveness is still unclear. Although it has been reported that AD is strongly affected by CNVs, most of AD-causing genes with pathogenic CNVs have not been identified yet. Here, we show that dosage-sensitive ohnologs within CNV regions reported in patients with AD are related to the nervous system and are highly expressed in the brain, similar to other known susceptible genes for AD. We found that CNV regions in patients with AD contained dosage-sensitive genes, which are ohnologs not overlapping with control CNV regions, frequently. Furthermore, these dosage-sensitive genes in pathogenic CNV regions had a strong enrichment in the nervous system for mouse knockout phenotype and high expression in the brain similar to the known susceptible genes for AD. Our results demonstrated that selecting dosage-sensitive ohnologs out of multiple genes with pathogenic CNVs is effective in identifying the causative genes for AD. This methodology can be applied to other diseases caused by dosage imbalance and might help to establish the medical diagnosis by analysis of CNVs.

Conserved brain myelination networks are altered in Alzheimer's and other neurodegenerative diseases

INTRODUCTION

Comparative transcriptome analyses in Alzheimer's disease (AD) and other neurodegenerative proteinopathies can uncover both shared and distinct disease pathways.

METHODS

We analyzed 940 brain transcriptomes including patients with AD, progressive supranuclear palsy (PSP; a primary tauopathy), and control subjects.

RESULTS

We identified transcriptional coexpression networks implicated in myelination, which were lower in PSP temporal cortex (TCX) compared with AD. Some of these associations were retained even after adjustments for brain cell population changes. These TCX myelination network structures were preserved in cerebellum but they were not differentially expressed in cerebellum between AD and PSP. Myelination networks were downregulated in both AD and PSP, when compared with control TCX samples.

DISCUSSION

Downregulation of myelination networks may underlie both PSP and AD pathophysiology, but may be more pronounced in PSP. These data also highlight conservation of transcriptional networks across brain regions and the influence of cell type changes on these networks.

Lipid and Alzheimer's disease genes associated with healthy aging and longevity in healthy oldest-old

Several studies have found that long-lived individuals do not appear to carry lower numbers of common disease-associated variants than ordinary people; it has been hypothesized that they may instead carry protective variants. An intriguing type of protective variant is buffering variants that protect against variants that have deleterious effects. We genotyped 18 variants in 15 genes related to longevity or healthy aging that had been previously reported as having a gene-gene interaction or buffering effect. We compared a group of 446 healthy oldest-old ‘Super-Seniors’ (individuals 85 or older who have never been diagnosed with cancer, cardiovascular disease, dementia, diabetes or major pulmonary disease) to 421 random population-based midlife controls. Cases and controls were of European ancestry. Association tests of individual SNPs showed that Super-Seniors were less likely than controls to carry an APOEε4 allele or a haptoglobin HP2 allele. Interactions between APOE/FOXO3, APOE/CRYL1, and LPA/CRYL1 did not remain significant after multiple testing correction. In a network analysis of the candidate genes, lipid and cholesterol metabolism was a common theme. APOE, HP, and CRYL1 have all been associated with Alzheimer’s Disease, the pathology of which involves lipid and cholesterol pathways. Age-related changes in lipid and cholesterol maintenance, particularly in the brain, may be central to healthy aging and longevity.

Genetics of Alzheimer's Disease: the Importance of Polygenic and Epistatic Components

PURPOSE OF REVIEW

We aimed to summarize the recent advances in genetic findings of Alzheimer's disease (AD), focusing on traditional single-marker and gene approaches and non-traditional ones, i.e., polygenic and epistatic components.

RECENT FINDINGS

Genetic studies have progressed over the last few decades from linkage to genome-wide association studies (GWAS), and most recently studies utilizing high-throughput sequencing. So far, GWASs have identified several common variants characterized by small effect sizes (besides APOE-ε4). Sequencing has facilitated the study of rare variants with larger effects. Nevertheless, missing heritability for AD remains extensive; a possible explanation might lie in the existence of polygenic and epistatic components. We review findings achieved by single-marker approaches, but also polygenic and epistatic associations. The latter two are critical, yet-underexplored mechanisms. Genes involved in complex diseases are likely regulated by mechanisms and pathways involving many other genes, an aspect potentially missed by traditional approaches.

Discovering Pair-wise Synergies in Microarray Data

Informative gene selection can have important implications for the improvement of cancer diagnosis and the identification of new drug targets. Individual-gene-ranking methods ignore interactions between genes. Furthermore, popular pair-wise gene evaluation methods, e.g. TSP and TSG, are helpless for discovering pair-wise interactions. Several efforts to discover pair-wise synergy have been made based on the information approach, such as EMBP and FeatKNN. However, the methods which are employed to estimate mutual information, e.g. binarization, histogram-based and KNN estimators, depend on known data or domain characteristics. Recently, Reshef et al. proposed a novel maximal information coefficient (MIC) measure to capture a wide range of associations between two variables that has the property of generality. An extension from MIC(X; Y) to MIC(X₁; X₂; Y) is therefore desired. We developed an approximation algorithm for estimating MIC(X₁; X₂; Y) where Y is a discrete variable. MIC(X₁; X₂; Y) is employed to detect pair-wise synergy in simulation and cancer microarray data. The results indicate that MIC(X₁; X₂; Y) also has the property of generality. It can discover synergic genes that are undetectable by reference feature selection methods such as MIC(X; Y) and TSG. Synergic genes can distinguish different phenotypes. Finally, the biological relevance of these synergic genes is validated with GO annotation and OUgene database.

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.

Meta-Analysis of Differential Connectivity in Gene Co-Expression Networks in Multiple Sclerosis

Differential gene expression analyses to investigate multiple sclerosis (MS) molecular pathogenesis cannot detect genes harboring genetic and/or epigenetic modifications that change the gene functions without affecting their expression. Differential co-expression network approaches may capture changes in functional interactions resulting from these alterations. We re-analyzed 595 mRNA arrays from publicly available datasets by studying changes in gene co-expression networks in MS and in response to interferon (IFN)-β treatment. Interestingly, MS networks show a reduced connectivity relative to the healthy condition, and the treatment activates the transcription of genes and increases their connectivity in MS patients. Importantly, the analysis of changes in gene connectivity in MS patients provides new evidence of association for genes already implicated in MS by single-nucleotide polymorphism studies and that do not show differential expression. This is the case of amiloride-sensitive cation channel 1 neuronal (ACCN1) that shows a reduced number of interacting partners in MS networks, and it is known for its role in synaptic transmission and central nervous system (CNS) development. Furthermore, our study confirms a deregulation of the vitamin D system: among the transcription factors that potentially regulate the deregulated genes, we find TCF3 and SP1 that are both involved in vitamin D3-induced p27Kip1 expression. Unveiling differential network properties allows us to gain systems-level insights into disease mechanisms and may suggest putative targets for the treatment.

Developmental lead exposure and lifespan alterations in epigenetic regulators and their correspondence to biomarkers of Alzheimer's disease

INTRODUCTION

Early life lead (Pb) exposure results in a latent increase in Alzheimer's disease (AD)-related proteins, and cognitive deficits late in life in both rodents and primates. This study was conducted to investigate if these late life changes were accompanied by epigenetic alterations.

METHODS

Western blot analysis and RT-PCR were used to measure Deoxyribonucleic acid methylation regulators (DNMT1, DNMT3a, MeCP2, MAT2A) and histone proteins (H3K9Ac, H3K4me2, H3K27me3).

RESULTS

Cerebral levels of DNMT1 and MeCP2 were significantly reduced in mice exposed to Pb early in life, whereas the expression of DNMT3a was not altered. Levels of MAT2a were increased in the Pb-exposed mice across the lifespan. H3K9Ac and H3K4me2, involved in gene activation, were decreased, whereas the repressive mark H3K27me3 was elevated.

DISCUSSION

Epigenetic modifiers are affected by the developmental exposure to Pb and may play a role in mediating the latent increases in AD-related proteins in the brain.

ABCA1-Mediated Cholesterol Efflux Capacity to Cerebrospinal Fluid Is Reduced in Patients With Mild Cognitive Impairment and Alzheimer's Disease

Background

Animal and human studies indicate that ABCA1‐mediated cholesterol transport is important in Alzheimer's disease (AD). We hypothesized that the efficiency of cerebrospinal fluid (CSF) to facilitate ABCA1‐mediated cholesterol efflux would be reduced in participants with mild cognitive impairment (MCI) or AD compared with cognitively healthy participants.

Methods and Results

CSF was collected from a cross‐sectional study of cognitively healthy participants (n=47) and participants with MCI (n=35) or probable AD (n=26).The capacity of CSF to mediate cholesterol transport was assessed using a BHK cell line that can be induced to express the ABCA1 transporter. ABCA1‐mediated cholesterol efflux capacity was 30% less in participants with MCI or AD compared with cognitively healthy participants (P<0.001 for both). Cholesterol efflux capacity correlated with CSF cholesterol content (r=0.37, P<0.001). CSF phosphatidylcholine decreased in participants with MCI and AD compared with cognitively healthy participants (9% less in MCI and 27% less in AD compared with cognitively healthy participants, P=0.01) and correlated with CSF efflux capacity (r=0.3, P=0.001). CSF sphingomyelin also correlated with the efflux capacity (r=0.24, P=0.02). Concentrations of CSF apoA‐I and apoE did not significantly correlate with measures of efflux capacity.

Conclusions

In people with MCI and AD, the capacity of CSF to facilitate ABCA1‐mediated cholesterol efflux is impaired. This lesser cholesterol efflux in MCI supports a pathophysiological role for ABCA1‐mediated cholesterol transport in early neurodegeneration.

An Overview of Genome-Wide Association Studies in Alzheimer's Disease

Genome-wide association studies (GWASs) have revealed a plethora of putative susceptibility genes for Alzheimer's disease (AD). With the sole exception of the APOE gene, these AD susceptibility genes have not been unequivocally validated in independent studies. No single novel functional risk genetic variant has been identified. In this review, we evaluate recent GWASs of AD, and discuss their significance, limitations, and challenges in the investigation of the genetic spectrum of AD.

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.

Genetics and underlying pathology of dementia

As the population steadily ages, dementia, in all its forms, remains a great societal challenge. Yet, our knowledge of their etiology remains rather limited. To this end, genetic studies can give us insight into the underlying mechanisms that lead to the development of dementia, potentially facilitating treatments in the future. In this review we cover the most recent genetic risk factors associated with the onset of the four most common dementia types today, including Alzheimer's disease (AD), Vascular Dementia (VaD), Frontotemporal Lobar Degeneration (FTLD) and Lewy Body Dementia (LBD). Moreover, we discuss the overlap in major underlying pathologies of dementia derived from their genetic associations. While all four dementia types appear to involve genes associated with tau-pathology and neuroinflammation only LBD, AD and VaD appear to involve amyloid genes while LBD and FTLD share alpha synuclein genes. Together these findings suggest that some of the dementias may exist along a spectrum and demonstrates the necessity to conduct large-scale studies pinpointing the etiology of the dementias and potential gene and environment interactions that may influence their development.

Missing heritability of common diseases and treatments outside the protein-coding exome

Genetic factors strongly influence risk of common human diseases and treatment outcomes but the causative variants remain largely unknown; this gap has been called the 'missing heritability'. We propose several hypotheses that in combination have the potential to narrow the gap. First, given a multi-stage path from wellness to disease, we propose that common variants under positive evolutionary selection represent normal variation and gate the transition between wellness and an 'off-well' state, revealing adaptations to changing environmental conditions. In contrast, genome-wide association studies (GWAS) focus on deleterious variants conveying disease risk, accelerating the path from off-well to illness and finally specific diseases, while common 'normal' variants remain hidden in the noise. Second, epistasis (dynamic gene-gene interactions) likely assumes a central role in adaptations and evolution; yet, GWAS analyses currently are poorly designed to reveal epistasis. As gene regulation is germane to adaptation, we propose that epistasis among common normal regulatory variants, or between common variants and less frequent deleterious variants, can have strong protective or deleterious phenotypic effects. These gene-gene interactions can be highly sensitive to environmental stimuli and could account for large differences in drug response between individuals. Residing largely outside the protein-coding exome, common regulatory variants affect either transcription of coding and non-coding RNAs (regulatory SNPs, or rSNPs) or RNA functions and processing (structural RNA SNPs, or srSNPs). Third, with the vast majority of causative variants yet to be discovered, GWAS rely on surrogate markers, a confounding factor aggravated by the presence of more than one causative variant per gene and by epistasis. We propose that the confluence of these factors may be responsible to large extent for the observed heritability gap.