Prediction of Alzheimer's Disease-Associated Genes by Integration of GWAS Summary Data and Expression Data

Computational and functional prioritization identifies genes that rescue behavior and reduce tau protein in fly and human cell models of Alzheimer disease

Genome-wide association studies (GWASs) in Alzheimer disease (AD) have uncovered over 70 loci significantly associated with AD risk, but identifying the true causal gene(s) at these loci requires systematic functional validation that is rarely performed due to limitations of time and cost. Here, we integrate transcriptome-wide association study (TWAS) with colocalization analysis, fine-mapping, and additional annotation of AD GWAS variants to identify 123 genes at known and suggestive AD risk loci. A comparison with human AD brain transcriptome data confirmed that many of these candidate genes are dysregulated in human AD and correlate with neuropathology. We then tested all available orthologs in two well-established Drosophila AD models that express either wild-type tau or secreted β-amyloid (β42). Experimental perturbation of the 60 available candidates pinpointed 46 that modulated neuronal dysfunction in one or both fly models. The effects of 18 of these genes were concordant with the TWAS prediction, such that the direction of misexpression predicted to increase AD risk in humans exacerbated behavioral impairments in the AD fly models. Reversing the aberrant down- or upregulation of 11 of these genes (MTCH2, ELL, TAP2, HDC, DMWD, MYCL, SLC4A9, ABCA7, CSTF1, PTK2B, and CD2AP) proved neuroprotective in vivo. We further studied MTCH2 and found that it regulates steady-state tau protein levels in the Drosophila brain and reduces tau accumulation in human neural progenitor cells. This systematic, integrative approach effectively prioritizes genes at GWAS loci and reveals promising AD-relevant candidates for further investigation as risk factors or targets for therapeutic intervention.

Multi-omics analysis reveals aspirin is associated with reduced risk of Alzheimer's disease

The urgent need for safe and effective therapies for Alzheimer's disease (AD) has spurred a growing interest in repurposing existing drugs to treat or prevent AD. In this study, we combined multi-omics and clinical data to investigate possible repurposing opportunities for AD. We performed transcriptome-wide association studies (TWAS) to construct gene expression signatures of AD from publicly available GWAS summary statistics, using both transcriptome prediction models for 49 tissues from the Genotype-Tissue Expression (GTEx) project and microglia-specific models trained on eQTL data from the Microglia Genomic Atlas (MiGA). We then identified compounds capable of reversing the AD-associated changes in gene expression observed in these signatures by querying the Connectivity Map (CMap) drug perturbation database. Out of >2,000 small-molecule compounds in CMap, aspirin emerged as the most promising AD repurposing candidate. To investigate the longitudinal effects of aspirin use on AD, we collected drug exposure and AD coded diagnoses from three independent sources of real-world data: electronic health records (EHRs) from Vanderbilt University Medical Center (VUMC) and the National Institutes of Health All of Us Research Program, along with national healthcare claims from the MarketScan Research Databases. In meta-analysis of EHR data from VUMC and All of Us , we found that aspirin use before age 65 was associated with decreased risk of incident AD (hazard ratio=0.76, 95% confidence interval [CI]: 0.64-0.89, P =0.001). Consistent with the findings utilizing EHR data, analysis of claims data from MarketScan revealed significantly lower odds of aspirin exposure among AD cases compared to matched controls (odds ratio=0.32, 95% CI: 0.28-0.38, P <0.001). Our results demonstrate the value of integrating genetic and clinical data for drug repurposing studies and highlight aspirin as a promising repurposing candidate for AD, warranting further investigation in clinical trials.

Navigating the blurred boundary: Neuropathologic changes versus clinical symptoms in Alzheimer's disease, and its consequences for research in genetics

During decades scientists tried to unveil the genetic architecture of Alzheimer's disease (AD), recurring to increasingly larger sample numbers for genome-wide association studies (GWAS) in hope for higher statistical gains. Here, a retrospective look on the most prominent GWAS was performed, focusing on the quality of the diagnosis associated with the used data and databases. Different methods for AD diagnosis (or absence) carry different levels of accuracy and certainty applied to both subsets of cases and controls. Furthermore, the different phenotypes included in these databases were explored, as several incorporate other ageing comorbidities and might be encompassing many confounding agents as well. Age of the samples' donors and origin populations were also investigated as these could be biasing factors in posterior analyses. A tendency for looser diagnostic methods in more recent GWAS was observed, where greater datasets of individuals are analyzed, which may have been hampering the discovery of associated genetic variants. Specifically for AD, a diagnostic method conveying a clinical outcome may be distinct from the disease neuropathological assessment, since the first has a practical perspective that not necessarily needs a confirmation. Due to its properties and complex diagnosis, this work highlights the importance of the neuropathological confirmation of AD (or its absence) in the subjects considered for research purposes to avoid reaching statistically weak and/or misleading conclusions that may trigger further studies with powerless groundwork.

Alzheimer's disease and the immune system: A comprehensive overview with a focus on B cells, humoral immunity, and immunotherapy

Alzheimer's disease (AD) is a complex neurodegenerative disorder and the major cause of dementia. Amyloid-β (Aβ) and tau aggregation, mitochondrial dysfunction, and microglial dysregulation are key contributors to AD pathogenesis. Impairments in the blood-brain barrier have unveiled the contribution of the immune system, particularly B cells, in AD pathology. B cells, a crucial component of adaptive immunity, exhibit diverse functions, including antigen presentation and antibody production. While their role in neuroinflammatory disorders has been well-documented, their specific function in AD lacks adequate data. This review examines the dual role of the B cells and humoral immunity in modulating brain inflammation in AD and explores recent advancements in passive and active immunotherapeutic strategies targeting AD pathobiology. We summarize preclinical and clinical studies investigating B cell frequency, altered antibody levels, and their implications in neuroinflammation and immunotherapy. Notably, B cells demonstrate protective and pathological roles in AD, influencing neurodegeneration through antibody-mediated clearance of toxic aggregates and inflammatory activation inflammation. Passive immunotherapies targeting Aβ have shown potential in reducing amyloid plaques, while active immunotherapies are emerging as promising strategies, requiring further validation. Understanding the interplay between B cells, humoral immunity, microglia, and mitochondrial dysfunction is critical to unraveling AD pathogenesis. Their dual nature in disease progression underscores the need for precise therapeutic interventions to optimize immunotherapy outcomes and mitigate neuroinflammation effectively.

Multiome-wide Association Studies: Novel Approaches for Understanding Diseases

The rapid development of multiome (transcriptome, proteome, cistrome, imaging, and regulome)-wide association study methods have opened new avenues for biologists to understand the susceptibility genes underlying complex diseases. Thorough comparisons of these methods are essential for selecting the most appropriate tool for a given research objective. This review provides a detailed categorization and summary of the statistical models, use cases, and advantages of recent multiome-wide association studies. In addition, to illustrate gene-disease association studies based on transcriptome-wide association study (TWAS), we collected 478 disease entries across 22 categories from 235 manually reviewed publications. Our analysis reveals that mental disorders are the most frequently studied diseases by TWAS, indicating its potential to deepen our understanding of the genetic architecture of complex diseases. In summary, this review underscores the importance of multiome-wide association studies in elucidating complex diseases and highlights the significance of selecting the appropriate method for each study.

Omnibus proteome-wide association study identifies 43 risk genes for Alzheimer disease dementia

Transcriptome-wide association study (TWAS) tools have been applied to conduct proteome-wide association studies (PWASs) by integrating proteomics data with genome-wide association study (GWAS) summary data. The genetic effects of PWAS-identified significant genes are potentially mediated through genetically regulated protein abundance, thus informing the underlying disease mechanisms better than GWAS loci. However, existing TWAS/PWAS tools are limited by considering only one statistical model. We propose an omnibus PWAS pipeline to account for multiple statistical models and demonstrate improved performance by simulation and application studies of Alzheimer disease (AD) dementia. We employ the Aggregated Cauchy Association Test to derive omnibus PWAS (PWAS-O) p values from PWAS p values obtained by three existing tools assuming complementary statistical models-TIGAR, PrediXcan, and FUSION. Our simulation studies demonstrated improved power, with well-calibrated type I error, for PWAS-O over all three individual tools. We applied PWAS-O to studying AD dementia with reference proteomic data profiled from dorsolateral prefrontal cortex of postmortem brains from individuals of European ancestry. We identified 43 risk genes, including 5 not identified by previous studies, which are interconnected through a protein-protein interaction network that includes the well-known AD risk genes TOMM40, APOC1, and APOC2. We also validated causal genetic effects mediated through the proteome for 27 (63%) PWAS-O risk genes, providing insights into the underlying biological mechanisms of AD dementia and highlighting promising targets for therapeutic development. PWAS-O can be easily applied to studying other complex diseases.

Proteome-wide association studies using summary pQTL data of three tissues identified 30 risk genes of Alzheimer's disease dementia

Background

Proteome-wide association study (PWAS) integrating proteomic data with genome-wide association study (GWAS) summary data is a powerful tool for studying Alzheimer's disease (AD) dementia. Existing PWAS analyses of AD often rely on the availability of individual-level proteomic and genetic data of a reference panel. Leveraging summary protein quantitative trait loci (pQTL) reference data of multiple AD-relevant tissues is expected to improve PWAS findings of AD dementia.

Methods

We conducted PWAS of AD dementia by integrating publicly available summary pQTL data of brain, cerebrospinal fluid (CSF), and plasma tissues, with the latest GWAS summary data of AD dementia. For each target protein per tissue, we employed our recently published OTTERS tool to obtain omnibus PWAS p-value, to test whether the genetically regulated protein abundance in the corresponding tissue is associated with AD dementia. Protein-protein interactions and enriched pathways of identified significant PWAS risk genes were analyzed by STRING. The potential causal effects of these PWAS risk genes were assessed by probabilistic Mendelian randomization analyses.

Results

We identified 30 unique significant PWAS risk genes for AD dementia, including 11 for brain, 9 for CSF, and 16 for plasma tissues. Four of these were shared by at least two tissues, and gene MAPK3 was found in all three tissues. We found that 11 of these PWAS risk genes were associated with AD or AD pathological hall marks as shown in GWAS Catalog; 18 of these were detected by transcriptome-wide association studies (TWAS); and 25 of these, including 8 out of 9 novel genes, were interconnected within a protein-protein interaction network involving the well-known AD risk gene APOE. Especially, these PWAS risk genes were enriched in immune response, glial cell proliferation, and high-density lipoprotein particle clearance pathways. Mediated causal effects were validated for 13 PWAS risk genes (43.3%).

Conclusions

Our findings provide novel insights into the genetic mechanisms of AD dementia in brain, CSF, and plasma tissues, and targets for developing therapeutic interventions. We also demonstrated the effectiveness of integrating summary pQTL and GWAS data for mapping risk genes of complex human diseases.

Gaining new insights into the etiology of ulcerative colitis through a cross-tissue transcriptome-wide association study

Background

Genome-wide association studies (GWASs) have identified 38 loci associated with ulcerative colitis (UC) susceptibility, but the risk genes and their biological mechanisms remained to be comprehensively elucidated.

Methods

Multi-marker analysis of genomic annotation (MAGMA) software was used to annotate genes on GWAS summary statistics of UC from FinnGen database. Genetic analysis was performed to identify risk genes. Cross-tissue transcriptome-wide association study (TWAS) using the unified test for molecular signatures (UTMOST) was performed to compare GWAS summary statistics with gene expression matrix (from Genotype-Tissue Expression Project) for data integration. Subsequently, we used FUSION software to select key genes from the individual tissues. Additionally, conditional and joint analysis was conducted to improve our understanding on UC. Fine-mapping of causal gene sets (FOCUS) software was employed to accurately locate risk genes. The results of the four genetic analyses (MAGMA, UTMOST, FUSION and FOCUS) were combined to obtain a set of UC risk genes. Finally, Mendelian randomization (MR) analysis and Bayesian colocalization analysis were conducted to determine the causal relationship between the risk genes and UC. To test the robustness of our findings, the same approaches were taken to verify the GWAS data of UC on IEU.

Results

Multiple correction tests screened PIM3 as a risk gene for UC. The results of Bayesian colocalization analysis showed that the posterior probability of hypothesis 4 was 0.997 and 0.954 in the validation dataset. MR was conducted using the inverse variance weighting method and two single nucleotide polymorphisms (SNPs, rs28645887 and rs62231924) were included in the analysis (p < 0.001, 95%CI: 1.45-1.89). In the validation dataset, MR result was p < 0.001, 95%CI: 1.19-1.72, indicating a clear causal relationship between PIM3 and UC.

Conclusion

Our study validated PIM3 as a key risk gene for UC and its expression level may be related to the risk of UC, providing a novel reference for further improving the current understanding on the genetic structure of UC.

A review and analysis of key biomarkers in Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects over 50 million elderly individuals worldwide. Although the pathogenesis of AD is not fully understood, based on current research, researchers are able to identify potential biomarker genes and proteins that may serve as effective targets against AD. This article aims to present a comprehensive overview of recent advances in AD biomarker identification, with highlights on the use of various algorithms, the exploration of relevant biological processes, and the investigation of shared biomarkers with co-occurring diseases. Additionally, this article includes a statistical analysis of key genes reported in the research literature, and identifies the intersection with AD-related gene sets from databases such as AlzGen, GeneCard, and DisGeNet. For these gene sets, besides enrichment analysis, protein-protein interaction (PPI) networks utilized to identify central genes among the overlapping genes. Enrichment analysis, protein interaction network analysis, and tissue-specific connectedness analysis based on GTEx database performed on multiple groups of overlapping genes. Our work has laid the foundation for a better understanding of the molecular mechanisms of AD and more accurate identification of key AD markers.

Identification of candidate DNA methylation biomarkers related to Alzheimer's disease risk by integrating genome and blood methylome data

Alzheimer disease (AD) is a common neurodegenerative disease with a late onset. It is critical to identify novel blood-based DNA methylation biomarkers to better understand the extent of the molecular pathways affected in AD. Two sets of blood DNA methylation genetic prediction models developed using different reference panels and modelling strategies were leveraged to evaluate associations of genetically predicted DNA methylation levels with AD risk in 111,326 (46,828 proxy) cases and 677,663 controls. A total of 1,168 cytosine-phosphate-guanine (CpG) sites showed a significant association with AD risk at a false discovery rate (FDR) < 0.05. Methylation levels of 196 CpG sites were correlated with expression levels of 130 adjacent genes in blood. Overall, 52 CpG sites of 32 genes showed consistent association directions for the methylation-gene expression-AD risk, including nine genes (CNIH4, THUMPD3, SERPINB9, MTUS1, CISD1, FRAT2, CCDC88B, FES, and SSH2) firstly reported as AD risk genes. Nine of 32 genes were enriched in dementia and AD disease categories (P values ranged from 1.85 × 10-4 to 7.46 × 10-6), and 19 genes in a neurological disease network (score = 54) were also observed. Our findings improve the understanding of genetics and etiology for AD.

The role of genetics and gender specific differences in neurodegenerative disorders: Insights from molecular and immune landscape

Neurodegenerative disorders (NDDs) are the most common neurological disorders with high prevalence and have significant socioeconomic implications. Understanding the underlying cellular and molecular mechanisms associated with the immune system can be effective in disease etiology, leading to more effective therapeutic approaches for both females and males. The central nervous system (CNS) actively participates in immune responses, both within and outside the CNS. Immune system activation is a common feature in NDDs. Gender-specific factors play a significant role in the prevalence, progression, and manifestation of NDDs. Neuroinflammation, in both inflammatory neurological and neurodegenerative conditions, is defined by the triggering of microglia and astrocyte cell activation. This results in the secretion of pro-inflammatory cytokines and chemokines. Numerous studies have documented the role of neuroinflammation in neurological diseases, highlighting the involvement of immune signaling pathways in disease development. Converging evidence support immune system involvement during neurodegeneration in NDDs. In this review, we summarize emerging evidence that reveals gender-dependent differences in immune responses related to NDDs. Also, we highlight sex differences in immune responses and discuss how these sex-specific influences can increase the risk of NDDs. Understanding the role of gender-specific factors can aid in developing targeted therapeutic strategies and improving patient outcomes. Ultimately, the better understanding of these mechanisms contributed to sex-dependent immune response in NDDs, can be critically usful in targeting of immune signaling cascades in such disorders. In this regard, sex-related immune responses in NDDs may be promising and effective targets in therapeutic strategies.

De novo CLPTM1 variants with reduced GABAA R current response in patients with epilepsy

OBJECTIVE

To investigate the clinical features and potential pathogenesis mechanism of de novo CLPTM1 variants associated with epilepsy.

METHODS

Identify de novo genetic variants associated with epilepsy by reanalyzing trio-based whole-exome sequencing data. We analyzed the clinical characteristics of patients with these variants and performed functional in vitro studies in cells expressing mutant complementary DNA for these variants using whole-cell voltage-clamp current recordings and outside-out patch-clamp recordings from transiently transfected human embryonic kidney (HEK) cells.

RESULTS

Two de novo missense variants related to epilepsy were identified in the CLPTM1 gene. Functional studies indicated that CLPTM1-p.R454H and CLPTM1-p.R568Q variants reduced the γ-aminobutyric acid A receptor (GABAA R) current response amplitude recorded under voltage clamp compared to the wild-type receptors. These variants also reduced the charge transfer and altered the time course of desensitization and deactivation following rapid removal of GABA. The surface expression of the GABAA R γ2 subunit from the CLPTM1-p.R568Q group was significantly reduced compared to CLPTM1-WT.

SIGNIFICANCE

This is the first report of functionally relevant variants within the CLPTM1 gene. Patch-clamp recordings showed that these de novo CLPTM1 variants reduce GABAA R currents and charge transfer, which should promote excitation and hypersynchronous activity. This study may provide insights into the molecular mechanisms of the CLPTM1 variants underlying the patients' phenotypes, as well as for exploring potential therapeutic targets for epilepsy.

Integrative Transcriptomic Analyses of Hippocampal-Entorhinal System Subfields Identify Key Regulators in Alzheimer's Disease

The hippocampal-entorhinal system supports cognitive function and is selectively vulnerable to Alzheimer's disease (AD). Little is known about global transcriptomic changes in the hippocampal-entorhinal subfields during AD. Herein, large-scale transcriptomic analysis is performed in five hippocampal-entorhinal subfields of postmortem brain tissues (262 unique samples). Differentially expressed genes are assessed across subfields and disease states, and integrated genotype data from an AD genome-wide association study. An integrative gene network analysis of bulk and single-nucleus RNA sequencing (snRNA-Seq) data identifies genes with causative roles in AD progression. Using a system-biology approach, pathology-specific expression patterns for cell types are demonstrated, notably upregulation of the A1-reactive astrocyte signature in the entorhinal cortex (EC) during AD. SnRNA-Seq data show that PSAP signaling is involved in alterations of cell- communications in the EC during AD. Further experiments validate the key role of PSAP in inducing astrogliosis and an A1-like reactive astrocyte phenotype. In summary, this study reveals subfield-, cell type-, and AD pathology-specific changes and demonstrates PSAP as a potential therapeutic target in AD.

A splicing transcriptome-wide association study identifies novel altered splicing for Alzheimer's disease susceptibility

Alzheimer's disease (AD) is a common neurodegenerative disease in aging individuals. Alternative splicing is reported to be relevant to AD development while their roles in etiology of AD remain largely elusive. We performed a comprehensive splicing transcriptome-wide association study (spTWAS) using intronic excision expression genetic prediction models of 12 brain tissues developed through three modelling strategies, to identify candidate susceptibility splicing introns for AD risk. A total of 111,326 (46,828 proxy) cases and 677,663 controls of European ancestry were studied. We identified 343 associations of 233 splicing introns (143 genes) with AD risk after Bonferroni correction (0.05/136,884 = 3.65 × 10^-7). Fine-mapping analyses supported 155 likely causal associations corresponding to 83 splicing introns of 55 genes. Eighteen causal splicing introns of 15 novel genes (EIF2D, WDR33, SAP130, BYSL, EPHB6, MRPL43, VEGFB, PPP1R13B, TLN2, CLUHP3, LRRC37A4P, CRHR1, LINC02210, ZNF45-AS1, and XPNPEP3) were identified for the first time to be related to AD susceptibility. Our study identified novel genes and splicing introns associated with AD risk, which can improve our understanding of the etiology of AD.

OTTERS: a powerful TWAS framework leveraging summary-level reference data

Most existing TWAS tools require individual-level eQTL reference data and thus are not applicable to summary-level reference eQTL datasets. The development of TWAS methods that can harness summary-level reference data is valuable to enable TWAS in broader settings and enhance power due to increased reference sample size. Thus, we develop a TWAS framework called OTTERS (Omnibus Transcriptome Test using Expression Reference Summary data) that adapts multiple polygenic risk score (PRS) methods to estimate eQTL weights from summary-level eQTL reference data and conducts an omnibus TWAS. We show that OTTERS is a practical and powerful TWAS tool by both simulations and application studies.

Alzheimer's Disease: An Updated Overview of Its Genetics

Alzheimer's disease (AD) is the most common neurodegenerative disease in the world. It is classified as familial and sporadic. The dominant familial or autosomal presentation represents 1-5% of the total number of cases. It is categorized as early onset (EOAD; <65 years of age) and presents genetic mutations in presenilin 1 (PSEN1), presenilin 2 (PSEN2), or the Amyloid precursor protein (APP). Sporadic AD represents 95% of the cases and is categorized as late-onset (LOAD), occurring in patients older than 65 years of age. Several risk factors have been identified in sporadic AD; aging is the main one. Nonetheless, multiple genes have been associated with the different neuropathological events involved in LOAD, such as the pathological processing of Amyloid beta (Aβ) peptide and Tau protein, as well as synaptic and mitochondrial dysfunctions, neurovascular alterations, oxidative stress, and neuroinflammation, among others. Interestingly, using genome-wide association study (GWAS) technology, many polymorphisms associated with LOAD have been identified. This review aims to analyze the new genetic findings that are closely related to the pathophysiology of AD. Likewise, it analyzes the multiple mutations identified to date through GWAS that are associated with a high or low risk of developing this neurodegeneration. Understanding genetic variability will allow for the identification of early biomarkers and opportune therapeutic targets for AD.

Lipid Dys-Homeostasis Contributes to APOE4-Associated AD Pathology

The association of the APOE4 (vs. APOE3) isoform with an increased risk of Alzheimer's disease (AD) is unequivocal, but the underlying mechanisms remain incompletely elucidated. A prevailing hypothesis incriminates the impaired ability of APOE4 to clear neurotoxic amyloid-β peptides (Aβ) from the brain as the main mechanism linking the apolipoprotein isoform to disease etiology. The APOE protein mediates lipid transport both within the brain and from the brain to the periphery, suggesting that lipids may be potential co-factors in APOE4-associated physiopathology. The present study reveals several changes in the pathways of lipid homeostasis in the brains of mice expressing the human APOE4 vs. APOE3 isoform. Carriers of APOE4 had altered cholesterol turnover, an imbalance in the ratio of specific classes of phospholipids, lower levels of phosphatidylethanolamines bearing polyunsaturated fatty acids and an overall elevation in levels of monounsaturated fatty acids. These modifications in lipid homeostasis were related to increased production of Aβ peptides as well as augmented levels of tau and phosphorylated tau in primary neuronal cultures. This suite of APOE4-associated anomalies in lipid homeostasis and neurotoxic protein levels may be related to the accrued risk for AD in APOE4 carriers and provides novel insights into potential strategies for therapeutic intervention.

Identification of genetic loci shared between Alzheimer's disease and hypertension

Alzheimer's disease (AD) and high blood pressure (BP) are prevalent age-related diseases with significant unexplained heritability. A thorough analysis of genetic pleiotropy between AD and BP will lay a foundation for the study of the associated molecular mechanisms, leading to a better understanding of the development of each phenotype. We used the conditional false discovery rate (cFDR) method to identify novel genetic loci associated with both AD and BP. The cFDR approach improves the effective sample size for association testing by combining GWAS summary statistics for correlated phenotypes. We identified 50 pleiotropic SNPs for AD and BP, 7 of which are novel and have not previously been reported to be associated with either AD or BP. The novel SNPs located at STK3 are particularly noteworthy, as this gene may influence AD risk via the Hippo signaling network, which regulates cell death. Bayesian colocalization analysis demonstrated that although AD and BP are associated, they do not appear to share the same causal variants. We further performed two sample Mendelian randomization analysis, but could not detect a causal effect of BP on AD. Despite the inability to establish a causal link between AD and BP, our findings report some potential novel pleiotropic loci that may influence disease susceptibility. In summary, we identified 7 SNPs that annotate to 4 novel genes which have not previously been reported to be associated with AD nor with BP and discuss the possible role of one of these genes, STK3 in the Hippo signaling network.

TWAS Atlas: a curated knowledgebase of transcriptome-wide association studies

Transcriptome-wide association studies (TWASs), as a practical and prevalent approach for detecting the associations between genetically regulated genes and traits, are now leading to a better understanding of the complex mechanisms of genetic variants in regulating various diseases and traits. Despite the ever-increasing TWAS outputs, there is still a lack of databases curating massive public TWAS information and knowledge. To fill this gap, here we present TWAS Atlas (https://ngdc.cncb.ac.cn/twas/), an integrated knowledgebase of TWAS findings manually curated from extensive literature. In the current implementation, TWAS Atlas collects 401,266 high-quality human gene-trait associations from 200 publications, covering 22,247 genes and 257 traits across 135 tissue types. In particular, an interactive knowledge graph of the collected gene-trait associations is constructed together with single nucleotide polymorphism (SNP)-gene associations to build up comprehensive regulatory networks at multi-omics levels. In addition, TWAS Atlas, as a user-friendly web interface, efficiently enables users to browse, search and download all association information, relevant research metadata and annotation information of interest. Taken together, TWAS Atlas is of great value for promoting the utility and availability of TWAS results in explaining the complex genetic basis as well as providing new insights for human health and disease research.

Prediction of Alzheimer’s Disease by a Novel Image-Based Representation of Gene Expression

Early intervention can delay the progress of Alzheimer’s Disease (AD), but currently, there are no effective prediction tools. The goal of this study is to generate a reliable artificial intelligence (AI) model capable of detecting the high risk of AD, based on gene expression arrays from blood samples. To that end, a novel image-formation method is proposed to transform single-dimension gene expressions into a discriminative 2-dimensional (2D) image to use convolutional neural networks (CNNs) for classification. Three publicly available datasets were pooled, and a total of 11,618 common genes’ expression values were obtained. The genes were then categorized for their discriminating power using the Fisher distance (AD vs. control (CTL)) and mapped to a 2D image by linear discriminant analysis (LDA). Then, a six-layer CNN model with 292,493 parameters were used for classification. An accuracy of 0.842 and an area under curve (AUC) of 0.875 were achieved for the AD vs. CTL classification. The proposed method obtained higher accuracy and AUC compared with other reported methods. The conversion to 2D in CNN offers a unique advantage for improving accuracy and can be easily transferred to the clinic to drastically improve AD (or any disease) early detection.

Identification of Potential Driver Genes and Pathways Based on Transcriptomics Data in Alzheimer's Disease

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. To identify AD-related genes from transcriptomics and help to develop new drugs to treat AD. In this study, firstly, we obtained differentially expressed genes (DEG)-enriched coexpression networks between AD and normal samples in multiple transcriptomics datasets by weighted gene co-expression network analysis (WGCNA). Then, a convergent genomic approach (CFG) integrating multiple AD-related evidence was used to prioritize potential genes from DEG-enriched modules. Subsequently, we identified candidate genes in the potential genes list. Lastly, we combined deepDTnet and SAveRUNNER to predict interaction among candidate genes, drug and AD. Experiments on five datasets show that the CFG score of GJA1 is the highest among all potential driver genes of AD. Moreover, we found GJA1 interacts with AD from target-drugs-diseases network prediction. Therefore, candidate gene GJA1 is the most likely to be target of AD. In summary, identification of AD-related genes contributes to the understanding of AD pathophysiology and the development of new drugs.

Neuronal ROS-induced glial lipid droplet formation is altered by loss of Alzheimer's disease-associated genes

A growing list of Alzheimer's disease (AD) genetic risk factors is being identified, but the contribution of each variant to disease mechanism remains largely unknown. We have previously shown that elevated levels of reactive oxygen species (ROS) induces lipid synthesis in neurons leading to the sequestration of peroxidated lipids in glial lipid droplets (LD), delaying neurotoxicity. This neuron-to-glia lipid transport is APOD/E-dependent. To identify proteins that modulate these neuroprotective effects, we tested the role of AD risk genes in ROS-induced LD formation and demonstrate that several genes impact neuroprotective LD formation, including homologs of human ABCA1, ABCA7, VLDLR, VPS26, VPS35, AP2A, PICALM, and CD2AP Our data also show that ROS enhances Aβ42 phenotypes in flies and mice. Finally, a peptide agonist of ABCA1 restores glial LD formation in a humanized APOE4 fly model, highlighting a potentially therapeutic avenue to prevent ROS-induced neurotoxicity. This study places many AD genetic risk factors in a ROS-induced neuron-to-glia lipid transfer pathway with a critical role in protecting against neurotoxicity.

A transcriptome-wide association study identifies novel blood-based gene biomarker candidates for Alzheimer's disease risk

Alzheimer's disease (ad) adversely affects the health, quality of life and independence of patients. There is a critical need to identify novel blood gene biomarkers for ad risk assessment. We performed a transcriptome-wide association study to identify biomarker candidates for ad risk. We leveraged two sets of gene expression prediction models of blood developed using different reference panels and modeling strategies. By applying the prediction models to a meta-GWAS including 71 880 (proxy) cases and 383 378 (proxy) controls, we identified significant associations of genetically determined expression of 108 genes in blood with ad risk. Of these, 15 genes were differentially expressed between ad patients and controls with concordant directions in measured expression data. With evidence from the analyses based on both genetic instruments and directly measured expression levels, this study identifies 15 genes with strong support as biomarkers in blood for ad risk, which may enhance ad risk assessment and mechanism-focused studies.

Deep post-GWAS analysis identifies potential risk genes and risk variants for Alzheimer's disease, providing new insights into its disease mechanisms

Alzheimer's disease (AD) is a genetically complex, multifactorial neurodegenerative disease. It affects more than 45 million people worldwide and currently remains untreatable. Although genome-wide association studies (GWAS) have identified many AD-associated common variants, only about 25 genes are currently known to affect the risk of developing AD, despite its highly polygenic nature. Moreover, the risk variants underlying GWAS AD-association signals remain unknown. Here, we describe a deep post-GWAS analysis of AD-associated variants, using an integrated computational framework for predicting both disease genes and their risk variants. We identified 342 putative AD risk genes in 203 risk regions spanning 502 AD-associated common variants. 246 AD risk genes have not been identified as AD risk genes by previous GWAS collected in GWAS catalogs, and 115 of 342 AD risk genes are outside the risk regions, likely under the regulation of transcriptional regulatory elements contained therein. Even more significantly, for 109 AD risk genes, we predicted 150 risk variants, of both coding and regulatory (in promoters or enhancers) types, and 85 (57%) of them are supported by functional annotation. In-depth functional analyses showed that AD risk genes were overrepresented in AD-related pathways or GO terms-e.g., the complement and coagulation cascade and phosphorylation and activation of immune response-and their expression was relatively enriched in microglia, endothelia, and pericytes of the human brain. We found nine AD risk genes-e.g., IL1RAP, PMAIP1, LAMTOR4-as predictors for the prognosis of AD survival and genes such as ARL6IP5 with altered network connectivity between AD patients and normal individuals involved in AD progression. Our findings open new strategies for developing therapeutics targeting AD risk genes or risk variants to influence AD pathogenesis.

A transcriptome-wide association study of Alzheimer's disease using prediction models of relevant tissues identifies novel candidate susceptibility genes

BACKGROUND

Genome-wide association studies (GWAS) have identified over 56 susceptibility loci associated with Alzheimer's disease (AD), but the genes responsible for these associations remain largely unknown.

METHODS

We performed a large transcriptome-wide association study (TWAS) leveraging modified UTMOST (Unified Test for MOlecular SignaTures) prediction models of ten brain tissues that are potentially related to AD to discover novel AD genetic loci and putative target genes in 71,880 (proxy) cases and 383,378 (proxy) controls of European ancestry.

RESULTS

We identified 53 genes with predicted expression associations with AD risk at Bonferroni correction threshold (P value < 3.38 × 10^-6). Based on fine-mapping analyses, 21 genes at nine loci showed strong support for being causal.

CONCLUSIONS

Our study provides new insights into the etiology and underlying genetic architecture of AD.

Brain-Specific Gene Expression and Quantitative Traits Association Analysis for Mild Cognitive Impairment

Transcriptome–wide association studies (TWAS) have identified several genes that are associated with qualitative traits. In this work, we performed TWAS using quantitative traits and predicted gene expressions in six brain subcortical structures in 286 mild cognitive impairment (MCI) samples from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. The six brain subcortical structures were in the limbic region, basal ganglia region, and cerebellum region. We identified 9, 15, and 6 genes that were stably correlated longitudinally with quantitative traits in these three regions, of which 3, 8, and 6 genes have not been reported in previous Alzheimer’s disease (AD) or MCI studies. These genes are potential drug targets for the treatment of early–stage AD. Single–Nucleotide Polymorphism (SNP) analysis results indicated that cis–expression Quantitative Trait Loci (cis–eQTL) SNPs with gene expression predictive abilities may affect the expression of their corresponding genes by specific binding to transcription factors or by modulating promoter and enhancer activities. Further, baseline structure volumes and cis–eQTL SNPs from correlated genes in each region were used to predict the conversion risk of MCI patients. Our results showed that limbic volumes and cis–eQTL SNPs of correlated genes in the limbic region have effective predictive abilities.

Multi-tissue neocortical transcriptome-wide association study implicates 8 genes across 6 genomic loci in Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is an incurable neurodegenerative disease currently affecting 1.75% of the US population, with projected growth to 3.46% by 2050. Identifying common genetic variants driving differences in transcript expression that confer AD risk is necessary to elucidate AD mechanism and develop therapeutic interventions. We modify the FUSION transcriptome-wide association study (TWAS) pipeline to ingest gene expression values from multiple neocortical regions.

METHODS

A combined dataset of 2003 genotypes clustered to 1000 Genomes individuals from Utah with Northern and Western European ancestry (CEU) was used to construct a training set of 790 genotypes paired to 888 RNASeq profiles from temporal cortex (TCX = 248), prefrontal cortex (FP = 50), inferior frontal gyrus (IFG = 41), superior temporal gyrus (STG = 34), parahippocampal cortex (PHG = 34), and dorsolateral prefrontal cortex (DLPFC = 461). Following within-tissue normalization and covariate adjustment, predictive weights to impute expression components based on a gene's surrounding cis-variants were trained. The FUSION pipeline was modified to support input of pre-scaled expression values and support cross validation with a repeated measure design arising from the presence of multiple transcriptome samples from the same individual across different tissues.

RESULTS

Cis-variant architecture alone was informative to train weights and impute expression for 6780 (49.67%) autosomal genes, the majority of which significantly correlated with gene expression; FDR < 5%: N = 6775 (99.92%), Bonferroni: N = 6716 (99.06%). Validation of weights in 515 matched genotype to RNASeq profiles from the CommonMind Consortium (CMC) was (72.14%) in DLPFC profiles. Association of imputed expression components from all 2003 genotype profiles yielded 8 genes significantly associated with AD (FDR < 0.05): APOC1, EED, CD2AP, CEACAM19, CLPTM1, MTCH2, TREM2, and KNOP1.

CONCLUSIONS

We provide evidence of cis-genetic variation conferring AD risk through 8 genes across six distinct genomic loci. Moreover, we provide expression weights for 6780 genes as a valuable resource to the community, which can be abstracted across the neocortex and a wide range of neuronal phenotypes.

Hippocampal transcriptome-wide association study and neurobiological pathway analysis for Alzheimer's disease

Genome-wide association studies (GWASs) have identified multiple susceptibility loci for Alzheimer’s disease (AD), which is characterized by early and progressive damage to the hippocampus. However, the association of hippocampal gene expression with AD and the underlying neurobiological pathways remain largely unknown. Based on the genomic and transcriptomic data of 111 hippocampal samples and the summary data of two large-scale meta-analyses of GWASs, a transcriptome-wide association study (TWAS) was performed to identify genes with significant associations between hippocampal expression and AD. We identified 54 significantly associated genes using an AD-GWAS meta-analysis of 455,258 individuals; 36 of the genes were confirmed in another AD-GWAS meta-analysis of 63,926 individuals. Fine-mapping models further prioritized 24 AD-related genes whose effects on AD were mediated by hippocampal expression, including APOE and two novel genes (PTPN9 and PCDHA4). These genes are functionally related to amyloid-beta formation, phosphorylation/dephosphorylation, neuronal apoptosis, neurogenesis and telomerase-related processes. By integrating the predicted hippocampal expression and neuroimaging data, we found that the hippocampal expression of QPCTL and ERCC2 showed significant difference between AD patients and cognitively normal elderly individuals as well as correlated with hippocampal volume. Mediation analysis further demonstrated that hippocampal volume mediated the effect of hippocampal gene expression (QPCTL and ERCC2) on AD. This study identifies two novel genes associated with AD by integrating hippocampal gene expression and genome-wide association data and reveals candidate hippocampus-mediated neurobiological pathways from gene expression to AD.

The hippocampus is a potential neuroimaging endophenotype for Alzheimer’s disease (AD). This study identifies genes whose expression in hippocampal tissue is associated with AD and establishes the pathways from hippocampal gene expression to hippocampal volume to AD. We demonstrate that 24 genes are associated with AD in hippocampal tissue, and these genes are enriched for AD-related biological processes of amyloid-beta formation, phosphorylation/dephosphorylation, neuronal apoptosis, neurogenesis and telomerase-related processes. We further show that hippocampal volume mediates the effects of the hippocampal gene expression of QPCTL and ERCC2 on AD. These findings improve our understanding of the genetic and neural mechanisms of AD.

Common Peripheral Immunity Mechanisms in Multiple Sclerosis and Alzheimer's Disease

Neurodegenerative diseases are closely related to inflammatory and autoimmune events, suggesting that the dysregulation of the immune system is a key pathological factor. Both multiple sclerosis (MS) and Alzheimer's disease (AD) are characterized by infiltrating immune cells, activated microglia, astrocyte proliferation, and neuronal damage. Moreover, MS and AD share a common pro-inflammatory signature, characterized by peripheral leukocyte activation and transmigration to the central nervous system (CNS). MS and AD are both characterized by the accumulation of activated neutrophils in the blood, leading to progressive impairment of the blood–brain barrier. Having migrated to the CNS during the early phases of MS and AD, neutrophils promote local inflammation that contributes to pathogenesis and clinical progression. The role of circulating T cells in MS is well-established, whereas the contribution of adaptive immunity to AD pathogenesis and progression is a more recent discovery. Even so, blocking the transmigration of T cells to the CNS can benefit both MS and AD patients, suggesting that common adaptive immunity mechanisms play a detrimental role in each disease. There is also growing evidence that regulatory T cells are beneficial during the initial stages of MS and AD, supporting the link between the modulatory immune compartments and these neurodegenerative disorders. The number of resting regulatory T cells declines in both diseases, indicating a common pathogenic mechanism involving the dysregulation of these cells, although their precise role in the control of neuroinflammation remains unclear. The modulation of leukocyte functions can benefit MS patients, so more insight into the role of peripheral immune cells may reveal new targets for pharmacological intervention in other neuroinflammatory and neurodegenerative diseases, including AD.

Alzheimer Disease Pathogenesis: The Role of Autoimmunity

BACKGROUND

In addition to deposits of amyloid β (Aβ) plaques and neurofibrillary tangles, growing evidence demonstrates that complex and multifaceted biological processes can arise during Alzheimer disease (AD) pathogenesis. The recent failures of clinical trials based on the amyloid hypothesis and the presence of Aβ plaques in cognitively healthy elderly persons without AD point toward a need to explore novel pathobiological mechanisms of AD.

CONTENT

In the search for alternative AD mechanisms, numerous genome-wide association studies and mechanistic discoveries suggest a potential immunologic component of the disease. However, new experimental tools are needed to uncover these immunogenic components. The current methods, such as ELISAs or protein microarrays, have limitations of low throughput and/or sensitivity and specificity. In this article, we briefly discuss evidence of potential autoimmune contributions to AD pathobiology, describe the current methods for identifying autoantibodies in patient fluids, and outline our own efforts to develop new techniques for novel autoantibody biomarker discovery.

SUMMARY

Uncovering the putative autoimmune components of AD may be crucial in paving the way to new concepts for pathogenesis, diagnosis, and therapy.

IMPACT STATEMENT

In addition to deposits of amyloid β plaques and neurofibrillary tangles, growing evidence demonstrates that complex and multifaceted biological processes can arise during Alzheimer disease (AD) pathogenesis. Numerous research directions, including genome-wide association, clinical correlation, and mechanistic studies, have pointed to a potential autoimmunologic contribution to AD pathology. We present research suggesting the association between autoimmunity and AD and demonstrate the need for new laboratory techniques to further characterize potential brain antigen-specific autoantibodies. Uncovering the putative autoimmune components of AD may be crucial in paving the way to new concepts for pathogenesis, diagnosis, and therapy.

Considerations for integrative multi-omic approaches to explore Alzheimer's disease mechanisms

The past decade has seen the maturation of multiple different forms of high-dimensional molecular profiling to the point that these methods could be deployed in initially hundreds and more recently thousands of human samples. In the field of Alzheimer's disease (AD), these profiles have been applied to the target organ: the aging brain. In a growing number of cases, the same samples were profiled with multiple different approaches, yielding genetic, transcriptomic, epigenomic and proteomic data. Here, we review lessons learned so far as we move beyond quantitative trait locus (QTL) analyses which map the effect of genetic variation on molecular features to integrate multiple levels of "omic" data in an effort to identify the molecular drivers of AD. One thing is clear: no single layer of molecular or "omic" data is sufficient to capture the variance of AD or aging-related cognitive decline. Nonetheless, reproducible findings are emerging from current efforts, and there is evidence of convergence using different approaches. Thus, we are on the cusp of an acceleration of truly integrative studies as the availability of large numbers of well-characterized brain samples profiled in three or more dimensions enables the testing, comparison and refinement of analytic methods with which to dissect the molecular architecture of the aging brain.

Predicting Clinical Dementia Rating Using Blood RNA Levels

The Clinical Dementia Rating (CDR) is commonly used to assess cognitive decline in Alzheimer's disease patients and is included in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. We divided 741 ADNI participants with blood microarray data into three groups based on their most recent CDR assessment: cognitive normal (CDR = 0), mild cognitive impairment (CDR = 0.5), and probable Alzheimer's disease (CDR ≥ 1.0). We then used machine learning to predict cognitive status using only blood RNA levels. Only one probe for chloride intracellular channel 1 (CLIC1) was significant after correction. However, by combining individually nonsignificant probes with p-values less than 0.1, we averaged 87.87% (s = 1.02) predictive accuracy for classifying the three groups, compared to a 55.46% baseline for this study due to unequal group sizes. The best model had an overall precision of 0.902, recall of 0.895, and a receiver operating characteristic (ROC) curve area of 0.904. Although we identified one significant probe in CLIC1, CLIC1 levels alone were not sufficient to predict dementia status and cannot be used alone in a clinical setting. Additional analyses combining individually suggestive, but nonsignificant, blood RNA levels were significantly predictive and may improve diagnostic accuracy for Alzheimer's disease. Therefore, we propose that patient features that do not individually predict cognitive status might still contribute to overall cognitive decline through interactions that can be elucidated through machine learning.

Involvement of Lipids in Alzheimer's Disease Pathology and Potential Therapies

Lipids constitute the bulk of the dry mass of the brain and have been associated with healthy function as well as the most common pathological conditions of the brain. Demographic factors, genetics, and lifestyles are the major factors that influence lipid metabolism and are also the key components of lipid disruption in Alzheimer's disease (AD). Additionally, the most common genetic risk factor of AD, APOE ϵ4 genotype, is involved in lipid transport and metabolism. We propose that lipids are at the center of Alzheimer's disease pathology based on their involvement in the blood-brain barrier function, amyloid precursor protein (APP) processing, myelination, membrane remodeling, receptor signaling, inflammation, oxidation, and energy balance. Under healthy conditions, lipid homeostasis bestows a balanced cellular environment that enables the proper functioning of brain cells. However, under pathological conditions, dyshomeostasis of brain lipid composition can result in disturbed BBB, abnormal processing of APP, dysfunction in endocytosis/exocytosis/autophagocytosis, altered myelination, disturbed signaling, unbalanced energy metabolism, and enhanced inflammation. These lipid disturbances may contribute to abnormalities in brain function that are the hallmark of AD. The wide variance of lipid disturbances associated with brain function suggest that AD pathology may present as a complex interaction between several metabolic pathways that are augmented by risk factors such as age, genetics, and lifestyles. Herewith, we examine factors that influence brain lipid composition, review the association of lipids with all known facets of AD pathology, and offer pointers for potential therapies that target lipid pathways.

The MUC6/AP2A2 Locus and Its Relevance to Alzheimer's Disease: A Review

We recently reported evidence of Alzheimer's disease (AD)-linked genetic variation within the mucin 6 (MUC6) gene on chromosome 11p, nearby the adaptor-related protein complex 2 subunit alpha 2 (AP2A2) gene. This locus has interesting features related to human genomics and clinical research. MUC6 gene variants have been reported to potentially influence viral-including herpesvirus-immunity and the gut microbiome. Within the MUC6 gene is a unique variable number of tandem repeat (VNTR) region. We discovered an association between MUC6 VNTR repeat expansion and AD pathologic severity, particularly tau proteinopathy. Here, we review the relevant literature. The AD-linked VNTR polymorphism may also influence AP2A2 gene expression. AP2A2 encodes a polypeptide component of the adaptor protein complex, AP-2, which is involved in clathrin-coated vesicle function and was previously implicated in AD pathogenesis. To provide background information, we describe some key knowledge gaps in AD genetics research. The "missing/hidden heritability problem" of AD is highlighted. Extensive portions of the human genome, including the MUC6 VNTR, have not been thoroughly evaluated due to limitations of existing high-throughput sequencing technology. We present and discuss additional data, along with cautionary considerations, relevant to the hypothesis that MUC6 repeat expansion influences AD pathogenesis.

Two-stage Bayesian GWAS of 9576 individuals identifies SNP regions that are targeted by miRNAs inversely expressed in Alzheimer's and cancer

INTRODUCTION

We compared genetic variants between Alzheimer's disease (AD) and two age-related cancers-breast and prostate -to identify single-nucleotide polymorphisms (SNPs) that are associated with inverse comorbidity of AD and cancer.

METHODS

Bayesian multinomial regression was used to compare sex-stratified cases (AD and cancer) against controls in a two-stage study. A ±500 KB region around each replicated hit was imputed and analyzed after merging individuals from the two stages. The microRNAs (miRNAs) that target the genes involving these SNPs were analyzed for miRNA family enrichment.

RESULTS

We identified 137 variants with inverse odds ratios for AD and cancer located on chromosomes 19, 4, and 5. The mapped miRNAs within the network were enriched for miR-17 and miR-515 families.

DISCUSSION

The identified SNPs were rs4298154 (intergenic), within TOMM40/APOE/APOC1, MARK4, CLPTM1, and near the VDAC1/FSTL4 locus. The miRNAs identified in our network have been previously reported to have inverse expression in AD and cancer.

Identifying progressive imaging genetic patterns via multi-task sparse canonical correlation analysis: a longitudinal study of the ADNI cohort

MOTIVATION

Identifying the genetic basis of the brain structure, function and disorder by using the imaging quantitative traits (QTs) as endophenotypes is an important task in brain science. Brain QTs often change over time while the disorder progresses and thus understanding how the genetic factors play roles on the progressive brain QT changes is of great importance and meaning. Most existing imaging genetics methods only analyze the baseline neuroimaging data, and thus those longitudinal imaging data across multiple time points containing important disease progression information are omitted.

RESULTS

We propose a novel temporal imaging genetic model which performs the multi-task sparse canonical correlation analysis (T-MTSCCA). Our model uses longitudinal neuroimaging data to uncover that how single nucleotide polymorphisms (SNPs) play roles on affecting brain QTs over the time. Incorporating the relationship of the longitudinal imaging data and that within SNPs, T-MTSCCA could identify a trajectory of progressive imaging genetic patterns over the time. We propose an efficient algorithm to solve the problem and show its convergence. We evaluate T-MTSCCA on 408 subjects from the Alzheimer's Disease Neuroimaging Initiative database with longitudinal magnetic resonance imaging data and genetic data available. The experimental results show that T-MTSCCA performs either better than or equally to the state-of-the-art methods. In particular, T-MTSCCA could identify higher canonical correlation coefficients and capture clearer canonical weight patterns. This suggests that T-MTSCCA identifies time-consistent and time-dependent SNPs and imaging QTs, which further help understand the genetic basis of the brain QT changes over the time during the disease progression.

AVAILABILITY AND IMPLEMENTATION

The software and simulation data are publicly available at https://github.com/dulei323/TMTSCCA.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.