Gene expression endophenotypes: a novel approach for gene discovery in Alzheimer's disease

Exploring the impact of circular RNA on ALS progression: A systematic review

Amyotrophic lateral sclerosis is a neurodegenerative disease that damages motor neurons and causes gradual muscular weakening and paralysis. Although studies have linked a number of genetic and environmental factors to ALS, the specific causes and mechanisms of the disease are still unclear. The pivotal role of circular RNA in the pathogenesis of ALS is a newly emerging area of research. The term "circular RNA" describes a particular class of RNA molecule that, in contrast to most RNA molecules, has a closed-loop structure. According to recent research, circular RNA might be essential for the development and progression of ALS. It has been discovered that these circular RNAs support important cellular functions related to ALS, including protein turnover, mitochondrial function, RNA processing, and cellular transport. Gaining knowledge about the precise roles and processes of circular RNA in the development of ALS could assist in understanding the pathophysiology of the disease and possibly pave the way for the development of targeted therapies. However, the understanding of circular RNA in ALS is still limited, and more research is needed to fully elucidate its role. In order to gain a comprehensive understanding of the role of circRNAs in ALS, it is imperative to delve into the various mechanisms through which circRNAs may contribute to the development and progression of the disease. Examining the current status of circRNA research in ALS and offering insights into their potential as therapeutic targets and diagnostic markers are the primary objectives of this review.

Analysis of ROQUIN, Tristetraprolin (TTP), and BDNF/miR-16/TTP regulatory axis in late onset Alzheimer’s disease

Alzheimer’s disease (AD) is a heterogeneous degenerative disorder of the brain that is on the rise worldwide. One of the critical processes that might be disturbed in AD is gene expression regulation. Tristetraprolin (TTP) and RC3H1 gene (ROQUIN) are two RNA-binding proteins (RBPs) that target AU-rich elements (AREs) and constitutive decay elements (CDEs), respectively. TTP and ROQUIN, members of the CCCH zinc-finger protein family, have been demonstrated to fine-tune numerous inflammatory factors. In addition, miR-16 has distinct characteristics and may influence the target mRNA through the ARE site. Interestingly, BDNF mRNA has ARE sites in the 3’ untranslated region (UTR) and can be targeted by regulatory factors, such as TTP and miR-16 on MRE sequences, forming BDNF/miR-16/TTP regulatory axis. A number of two microarray datasets were downloaded, including information on mRNAs (GSE106241) and miRNAs (GSE157239) from individuals with AD and corresponding controls. R software was used to identify BDNF, TTP, ROQUIN, and miR-16 expression levels in temporal cortex (TC) tissue datasets. Q-PCR was also used to evaluate the expression of these regulatory factors and the expression of BDNF in the blood of 50 patients with AD and 50 controls. Bioinformatic evaluation showed that TTP and miR-16 overexpression might act as post-transcriptional regulatory factors to control BDNF expression in AD in TC samples. Instead, this expression pattern was not found in peripheral blood samples from patients with AD compared to normal controls. ROQUIN expression was increased in the peripheral blood of patients with AD. Hsa-miR-16-5p levels did not show significant differences in peripheral blood samples. Finally, it was shown that TTP and BDNF, based on evaluating the receiver operating characteristic (ROC), effectively identify patients with AD from healthy controls. This study could provide a new perspective on the molecular regulatory processes associated with AD pathogenic mechanisms linked to the BDNF growth factor, although further research is needed on the possible roles of these factors in AD.

Manifestations of Alzheimer's disease genetic risk in the blood are evident in a multiomic analysis in healthy adults aged 18 to 90

Genetics play an important role in late-onset Alzheimer's Disease (AD) etiology and dozens of genetic variants have been implicated in AD risk through large-scale GWAS meta-analyses. However, the precise mechanistic effects of most of these variants have yet to be determined. Deeply phenotyped cohort data can reveal physiological changes associated with genetic risk for AD across an age spectrum that may provide clues to the biology of the disease. We utilized over 2000 high-quality quantitative measurements obtained from blood of 2831 cognitively normal adult clients of a consumer-based scientific wellness company, each with CLIA-certified whole-genome sequencing data. Measurements included: clinical laboratory blood tests, targeted chip-based proteomics, and metabolomics. We performed a phenome-wide association study utilizing this diverse blood marker data and 25 known AD genetic variants and an AD-specific polygenic risk score (PGRS), adjusting for sex, age, vendor (for clinical labs), and the first four genetic principal components; sex-SNP interactions were also assessed. We observed statistically significant SNP-analyte associations for five genetic variants after correction for multiple testing (for SNPs in or near NYAP1, ABCA7, INPP5D, and APOE), with effects detectable from early adulthood. The ABCA7 SNP and the APOE2 and APOE4 encoding alleles were associated with lipid variability, as seen in previous studies; in addition, six novel proteins were associated with the e2 allele. The most statistically significant finding was between the NYAP1 variant and PILRA and PILRB protein levels, supporting previous functional genomic studies in the identification of a putative causal variant within the PILRA gene. We did not observe associations between the PGRS and any analyte. Sex modified the effects of four genetic variants, with multiple interrelated immune-modulating effects associated with the PICALM variant. In post-hoc analysis, sex-stratified GWAS results from an independent AD case-control meta-analysis supported sex-specific disease effects of the PICALM variant, highlighting the importance of sex as a biological variable. Known AD genetic variation influenced lipid metabolism and immune response systems in a population of non-AD individuals, with associations observed from early adulthood onward. Further research is needed to determine whether and how these effects are implicated in early-stage biological pathways to AD. These analyses aim to complement ongoing work on the functional interpretation of AD-associated genetic variants.

Harnessing endophenotypes and network medicine for Alzheimer's drug repurposing

Following two decades of more than 400 clinical trials centered on the "one drug, one target, one disease" paradigm, there is still no effective disease-modifying therapy for Alzheimer's disease (AD). The inherent complexity of AD may challenge this reductionist strategy. Recent observations and advances in network medicine further indicate that AD likely shares common underlying mechanisms and intermediate pathophenotypes, or endophenotypes, with other diseases. In this review, we consider AD pathobiology, disease comorbidity, pleiotropy, and therapeutic development, and construct relevant endophenotype networks to guide future therapeutic development. Specifically, we discuss six main endophenotype hypotheses in AD: amyloidosis, tauopathy, neuroinflammation, mitochondrial dysfunction, vascular dysfunction, and lysosomal dysfunction. We further consider how this endophenotype network framework can provide advances in computational and experimental strategies for drug-repurposing and identification of new candidate therapeutic strategies for patients suffering from or at risk for AD. We highlight new opportunities for endophenotype-informed, drug discovery in AD, by exploiting multi-omics data. Integration of genomics, transcriptomics, radiomics, pharmacogenomics, and interactomics (protein-protein interactions) are essential for successful drug discovery. We describe experimental technologies for AD drug discovery including human induced pluripotent stem cells, transgenic mouse/rat models, and population-based retrospective case-control studies that may be integrated with multi-omics in a network medicine methodology. In summary, endophenotype-based network medicine methodologies will promote AD therapeutic development that will optimize the usefulness of available data and support deep phenotyping of the patient heterogeneity for personalized medicine in AD.

Role of Forkhead Transcription Factors of the O Class (FoxO) in Development and Progression of Alzheimer's Disease

In the Central Nervous System (CNS), a specific loss of focal neurons leads to mental and neurological disorders like dementia, Alzheimer's Disease (AD), Huntington's disease, Parkinson's disease, etc. AD is a neurological degenerative disorder, which is progressive and irreversible in nature and is the widely recognized reason for dementia in the geriatric populace. It affects 10% of people above the age of 65 and is the fourth driving reason for death in the United States. Numerous evidence suggests that the neuronal compartment is not the only genesis of AD, but transcription factors also hold significant importance in the occurrence and advancement of the disease. It is the need of the time to find the novel molecular targets and new techniques for treating or slowing down the progression of neurological disorders, especially AD. In this article, we summarised a conceivable association between transcriptional factors and their defensive measures against neurodegeneration and AD. The mammalian forkhead transcription factors of the class O (FoxO) illustrate one of the potential objectives for the development of new methodologies against AD and other neurocognitive disorders. The presence of FoxO is easily noticeable in the "cognitive centers" of the brain, specifically in the amygdala, hippocampus, and the nucleus accumbens. FoxO proteins are the prominent and necessary factors in memory formation and cognitive functions. FoxO also assumes a pertinent role in the protection of multiple cells in the brain by controlling the involving mechanism of autophagy and apoptosis and also modulates the process of phosphorylation of the targeted protein, thus FoxO must be a putative target in the mitigation of AD. This review features the role of FoxO as an important biomarker and potential new targets for the treatment of AD.

A multiomics approach to heterogeneity in Alzheimer's disease: focused review and roadmap

Aetiological and clinical heterogeneity is increasingly recognized as a common characteristic of Alzheimer's disease and related dementias. This heterogeneity complicates diagnosis, treatment, and the design and testing of new drugs. An important line of research is discovery of multimodal biomarkers that will facilitate the targeting of subpopulations with homogeneous pathophysiological signatures. High-throughput 'omics' are unbiased data-driven techniques that probe the complex aetiology of Alzheimer's disease from multiple levels (e.g. network, cellular, and molecular) and thereby account for pathophysiological heterogeneity in clinical populations. This review focuses on data reduction analyses that identify complementary disease-relevant perturbations for three omics techniques: neuroimaging-based subtypes, metabolomics-derived metabolite panels, and genomics-related polygenic risk scores. Neuroimaging can track accrued neurodegeneration and other sources of network impairments, metabolomics provides a global small-molecule snapshot that is sensitive to ongoing pathological processes, and genomics characterizes relatively invariant genetic risk factors representing key pathways associated with Alzheimer's disease. Following this focused review, we present a roadmap for assembling these multiomics measurements into a diagnostic tool highly predictive of individual clinical trajectories, to further the goal of personalized medicine in Alzheimer's disease.

eQTLs Weighted Genetic Correlation Analysis Detected Brain Region Differences in Genetic Correlations for Complex Psychiatric Disorders

BACKGROUND

Psychiatric disorders are usually caused by the dysfunction of various brain regions. Incorporating the genetic information of brain regions into correlation analysis can provide novel clues for pathogenetic and therapeutic studies of psychiatric disorders.

METHODS

The latest genome-wide association study (GWAS) summary data of schizophrenia (SCZ), bipolar disorder (BIP), autism spectrum disorder (AUT), major depression disorder (MDD), and attention-deficit/hyperactivity disorder (ADHD) were obtained from the Psychiatric GWAS Consortium (PGC). The expression quantitative trait loci (eQTLs) datasets of 10 brain regions were driven from the genotype-tissue expression (GTEx) database. The PGC GWAS summaries were first weighted by the GTEx eQTLs summaries for each brain region. Linkage disequilibrium score regression was applied to the weighted GWAS summary data to detect genetic correlation for each pair of 5 disorders.

RESULTS

Without considering brain region difference, significant genetic correlations were observed for BIP vs SCZ (P = 1.68 × 10-63), MDD vs SCZ (P = 5.08 × 10-45), ADHD vs MDD (P = 1.93 × 10-44), BIP vs MDD (P = 6.39 × 10-9), AUT vs SCZ (P = .0002), and ADHD vs SCZ (P = .0002). Utilizing brain region related eQTLs weighted LD score regression, different strengths of genetic correlations were observed within various brain regions for BIP vs SCZ, MDD vs SCZ, ADHD vs MDD, and SCZ vs ADHD. For example, the most significant genetic correlations were observed at anterior cingulate cortex (P = 1.13 × 10-34) for BIP vs SCZ.

CONCLUSIONS

This study provides new clues for elucidating the mechanism of genetic correlations among various psychiatric disorders.

Association of Circulating YKL-40 Levels and CHI3L1 Variants with the Risk of Spinal Deformity Progression in Adolescent Idiopathic Scoliosis

The cellular and molecular mechanisms underlying spinal deformity progression in adolescent idiopathic scoliosis (AIS) remain poorly understood. In this study, 804 French-Canadian patients and 278 age- and sex-matched controls were enrolled and genotyped for 12 single nucleotide polymorphisms (SNPs) in the chitinase 3-like 1 (CHI3L1) gene or its promoter. The plasma YKL-40 levels were determined by ELISA. We showed that elevation of circulating YKL-40 levels was correlated with a reduction of spinal deformity progression risk. We further identified significant associations of multiple CHI3L1 SNPs and their haplotypes with plasma YKL-40 levels and scoliosis severity as a function of their classification in a specific endophenotype. In the endophenotype FG3 group, we found that patients harboring the haplotype G-G-A-G-G-A (rs880633|rs1538372|rs4950881|rs10399805|rs6691378|rs946261), which presented in 48% of the cases, showed a positive correlation with the plasma YKL-40 levels (P = 7.6 × 10-6 and coefficient = 36). Conversely, the haplotype A-A-G-G-G-G, which presented in 15% of the analyzed subjects, showed a strong negative association with the plasma YKL-40 levels (P = 2 × 10-9 and coefficient = -9.56). We found that this haplotype showed the strongest association with AIS patients in endophenotype FG2 (P = 9.9 × 10-6 and coefficient = -13.53), who more often develop severe scoliosis compared to those classified in the other two endophenotypes. Of note, it showed stronger association in females (P = 1.6 × 10-7 and coefficient = -10.08) than males (P = 0.0021 and coefficient = -9.01). At the functional level, we showed that YKL-40 treatments rescued Gi-coupled receptor signalling dysfunction occurring in primary AIS osteoblasts. Collectively, our findings reveal a novel role for YKL-40 in AIS pathogenesis and a new molecular mechanism interfering with spinal deformity progression.

Consensus Genome-Wide Expression Quantitative Trait Loci and Their Relationship with Human Complex Trait Disease

Most of the risk loci identified from genome-wide association (GWA) studies do not provide direct information on the biological basis of a disease or on the underlying mechanisms. Recent expression quantitative trait locus (eQTL) association studies have provided information on genetic factors associated with gene expression variation. These eQTLs might contribute to phenotype diversity and disease susceptibility, but interpretation is handicapped by low reproducibility of the expression results. To address this issue, we have generated a set of consensus eQTLs by integrating publicly available data for specific human populations and cell types. Overall, we find over 4000 genes that are involved in high-confidence eQTL relationships. To elucidate the role that eQTLs play in human common diseases, we matched the high-confidence eQTLs to a set of 335 disease risk loci identified from the Wellcome Trust Case Control Consortium GWA study and follow-up studies for 7 human complex trait diseases-bipolar disorder (BD), coronary artery disease (CAD), Crohn's disease (CD), hypertension (HT), rheumatoid arthritis (RA), type 1 diabetes (T1D), and type 2 diabetes (T2D). The results show that the data are consistent with ∼50% of these disease loci arising from an underlying expression change mechanism.

A Genome-wide Expression Association Analysis Identifies Genes and Pathways Associated with Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease with strong genetic components. To identity novel risk variants for ALS, utilizing the latest genome-wide association studies (GWAS) and eQTL study data, we conducted a genome-wide expression association analysis by summary data-based Mendelian randomization (SMR) method. Summary data were derived from a large-scale GWAS of ALS, involving 12577 cases and 23475 controls. The eQTL annotation dataset included 923,021 cis-eQTL for 14,329 genes and 4732 trans-eQTL for 2612 genes. Genome-wide single gene expression association analysis was conducted by SMR software. To identify ALS-associated biological pathways, the SMR analysis results were further subjected to gene set enrichment analysis (GSEA). SMR single gene analysis identified one significant and four suggestive genes associated with ALS, including C9ORF72 (P value = 7.08 × 10^-6), NT5C3L (P value = 1.33 × 10^-5), GGNBP2 (P value = 1.81 × 10^-5), ZNHIT3(P value = 2.94 × 10^-5), and KIAA1600(P value = 9.97 × 10^-5). GSEA identified 7 significant biological pathways, such as PEROXISOME (empirical P value = 0.006), GLYCOLYSIS_GLUCONEOGENESIS (empirical P value = 0.043), and ARACHIDONIC_ACID_ METABOLISM (empirical P value = 0.040). Our study provides novel clues for the genetic mechanism studies of ALS.

Genetic regulation of gene expression in the epileptic human hippocampus

Epilepsy is a serious and common neurological disorder. Expression quantitative loci (eQTL) analysis is a vital aid for the identification and interpretation of disease-risk loci. Many eQTLs operate in a tissue- and condition-specific manner. We have performed the first genome-wide cis-eQTL analysis of human hippocampal tissue to include not only normal (n = 22) but also epileptic (n = 22) samples. We demonstrate that disease-associated variants from an epilepsy GWAS meta-analysis and a febrile seizures (FS) GWAS are significantly more enriched with epilepsy-eQTLs than with normal hippocampal eQTLs from two larger independent published studies. In contrast, GWAS meta-analyses of two other brain diseases associated with hippocampal pathology (Alzheimer's disease and schizophrenia) are more enriched with normal hippocampal eQTLs than with epilepsy-eQTLs. These observations suggest that an eQTL analysis that includes disease-affected brain tissue is advantageous for detecting additional risk SNPs for the afflicting and closely related disorders, but not for distinct diseases affecting the same brain regions. We also show that epilepsy eQTLs are enriched within epilepsy-causing genes: an epilepsy cis-gene is significantly more likely to be a causal gene for a Mendelian epilepsy syndrome than to be a causal gene for another Mendelian disorder. Epilepsy cis-genes, compared to normal hippocampal cis-genes, are more enriched within epilepsy-causing genes. Hence, we utilize the epilepsy eQTL data for the functional interpretation of epilepsy disease-risk variants and, thereby, highlight novel potential causal genes for sporadic epilepsy. In conclusion, an epilepsy-eQTL analysis is superior to normal hippocampal tissue eQTL analyses for identifying the variants and genes underlying epilepsy.

Can Genetic Analysis of Putative Blood Alzheimer's Disease Biomarkers Lead to Identification of Susceptibility Loci?

Although 24 Alzheimer’s disease (AD) risk loci have been reliably identified, a large portion of the predicted heritability for AD remains unexplained. It is expected that additional loci of small effect will be identified with an increased sample size. However, the cost of a significant increase in Case-Control sample size is prohibitive. The current study tests whether exploring the genetic basis of endophenotypes, in this case based on putative blood biomarkers for AD, can accelerate the identification of susceptibility loci using modest sample sizes. Each endophenotype was used as the outcome variable in an independent GWAS. Endophenotypes were based on circulating concentrations of proteins that contributed significantly to a published blood-based predictive algorithm for AD. Endophenotypes included Monocyte Chemoattractant Protein 1 (MCP1), Vascular Cell Adhesion Molecule 1 (VCAM1), Pancreatic Polypeptide (PP), Beta2 Microglobulin (B2M), Factor VII (F7), Adiponectin (ADN) and Tenascin C (TN-C). Across the seven endophenotypes, 47 SNPs were associated with outcome with a p-value ≤1x10^-7. Each signal was further characterized with respect to known genetic loci associated with AD. Signals for several endophenotypes were observed in the vicinity of CR1, MS4A6A/MS4A4E, PICALM, CLU, and PTK2B. The strongest signal was observed in association with Factor VII levels and was located within the F7 gene. Additional signals were observed in MAP3K13, ZNF320, ATP9B and TREM1. Conditional regression analyses suggested that the SNPs contributed to variation in protein concentration independent of AD status. The identification of two putatively novel AD loci (in the Factor VII and ATP9B genes), which have not been located in previous studies despite massive sample sizes, highlights the benefits of an endophenotypic approach for resolving the genetic basis for complex diseases. The coincidence of several of the endophenotypic signals with known AD loci may point to novel genetic interactions and should be further investigated.

Expression Quantitative Trait Loci Information Improves Predictive Modeling of Disease Relevance of Non-Coding Genetic Variation

Disease-associated loci identified through genome-wide association studies (GWAS) frequently localize to non-coding sequence. We and others have demonstrated strong enrichment of such single nucleotide polymorphisms (SNPs) for expression quantitative trait loci (eQTLs), supporting an important role for regulatory genetic variation in complex disease pathogenesis. Herein we describe our initial efforts to develop a predictive model of disease-associated variants leveraging eQTL information. We first catalogued cis-acting eQTLs (SNPs within 100kb of target gene transcripts) by meta-analyzing four studies of three blood-derived tissues (n = 586). At a false discovery rate < 5%, we mapped eQTLs for 6,535 genes; these were enriched for disease-associated genes (P < 10⁻⁰⁴), particularly those related to immune diseases and metabolic traits. Based on eQTL information and other variant annotations (distance from target gene transcript, minor allele frequency, and chromatin state), we created multivariate logistic regression models to predict SNP membership in reported GWAS. The complete model revealed independent contributions of specific annotations as strong predictors, including evidence for an eQTL (odds ratio (OR) = 1.2–2.0, P < 10⁻¹¹) and the chromatin states of active promoters, different classes of strong or weak enhancers, or transcriptionally active regions (OR = 1.5–2.3, P < 10⁻¹¹). This complete prediction model including eQTL association information ultimately allowed for better discrimination of SNPs with higher probabilities of GWAS membership (6.3–10.0%, compared to 3.5% for a random SNP) than the other two models excluding eQTL information. This eQTL-based prediction model of disease relevance can help systematically prioritize non-coding GWAS SNPs for further functional characterization.

A depressive endophenotype of poorer cognition among cognitively healthy community-dwelling adults: results from the Western Australia memory study

OBJECTIVE

The objective was to evaluate in a cognitively normal population the utility of an endophenotype of the depression-cognition link previously shown to be related to cognitive functioning in mild cognitive impairment and Alzheimer's disease.

METHODS

The data of 460 cognitively normal adults aged 32-92 years (M = 63.5, standard deviation = 9.24) from the Western Australian Memory Study with the Cross-national comparisons of the Cambridge Cognitive Examination-revised (CAMCOG-R) scores and 30-item Geriatric Depression Scale (GDS) scores were analyzed to determine the relationship between the five-item depressive endophenotype (DepE) scale drawn from the GDS and level of performance on a measure of cognitive functioning.

RESULTS

For the entire sample, there was a nonsignificant trend toward a negative relationship between DepE and CAMCOG-R scores. When analyzed for those 65 years and older, there was a significant negative relationship between the two measures (p = 0.001) with DepE scores significantly increasing the risk for performing more poorly on the CAMCOG-R (odds ratio = 1.53). Analysis of data for those 70 years and older showed that DepE was the only predictor significantly related to poorer CAMCOG-R performance (p = 0.001). For the 70 years and older group, DepE scores significantly increased the risk of poorer CAMCOG-R scores (odds ratio = 2.23). Analysis of the entire sample on the basis of ApoEε4 carrier status revealed that DepE scores were significantly negatively related only to ApoEε4 noncarrier regardless of age.

CONCLUSIONS

Elevated DepE scores are associated with poor neuropsychological performance among cognitively normal older adults. Use of the DepE may allow for the identification of a subset of older adults where depression is a primary factor in cognitive decline and who may benefit from antidepressant therapies.

Arguments for the sake of endophenotypes: examining common misconceptions about the use of endophenotypes in psychiatric genetics

Endophenotypes are measurable biomarkers that are correlated with an illness, at least in part, because of shared underlying genetic influences. Endophenotypes may improve our power to detect genes influencing risk of illness by being genetically simpler, closer to the level of gene action, and with larger genetic effect sizes or by providing added statistical power through their ability to quantitatively rank people within diagnostic categories. Furthermore, they also provide insight into the mechanisms underlying illness and will be valuable in developing biologically-based nosologies, through efforts such as RDoC, that seek to explain both the heterogeneity within current diagnostic categories and the overlapping clinical features between them. While neuroimaging, electrophysiological, and cognitive measures are currently most used in psychiatric genetic studies, researchers currently are attempting to identify candidate endophenotypes that are less genetically complex and potentially closer to the level of gene action, such as transcriptomic and proteomic phenotypes. Sifting through tens of thousands of such measures requires automated, high-throughput ways of assessing, and ranking potential endophenotypes, such as the Endophenotype Ranking Value. However, despite the potential utility of endophenotypes for gene characterization and discovery, there is considerable resistance to endophenotypic approaches in psychiatry. In this review, we address and clarify some of the common issues associated with the usage of endophenotypes in the psychiatric genetics community.

Transcriptional regulation and its misregulation in Alzheimer's disease

Alzheimer’s disease (AD) is a devastating neurodegenerative disorder characterized by loss of memory and cognitive function. A key neuropathological event in AD is the accumulation of amyloid-β (Aβ) peptide. The production and clearance of Aβ in the brain are regulated by a large group of genes. The expression levels of these genes must be fine-tuned in the brain to keep Aβ at a balanced amount under physiological condition. Misregulation of AD genes has been found to either increase AD risk or accelerate the disease progression. In recent years, important progress has been made in uncovering the regulatory elements and transcriptional factors that guide the expression of these genes. In this review, we describe the mechanisms of transcriptional regulation for the known AD genes and the misregualtion that leads to AD susceptibility.

Genetic architecture of resilience of executive functioning

The genetic basis of resilience, defined as better cognitive functioning than predicted based on neuroimaging or neuropathology, is not well understood. Our objective was to identify genetic variation associated with executive functioning resilience. We computed residuals from regression models of executive functioning, adjusting for age, sex, education, Hachinski score, and MRI findings (lacunes, cortical thickness, volumes of white matter hyperintensities and hippocampus). We estimated heritability and analyzed these residuals in models for each SNP. We further evaluated our most promising SNP result by evaluating cis-associations with brain levels of nearby (±100 kb) genes from a companion data set, and comparing expression levels in cortex and cerebellum from decedents with AD with those from other non-AD diseases. Complete data were available for 750 ADNI participants of European descent. Executive functioning resilience was highly heritable (H² = 0.76; S.E. = 0.44). rs3748348 on chromosome 14 in the region of RNASE13 was associated with executive functioning resilience (p-value = 4.31 × 10⁻⁷). rs3748348 is in strong linkage disequilibrium (D' of 1.00 and 0.96) with SNPs that map to TPPP2, a member of the α-synuclein family of proteins. We identified nominally significant associations between rs3748348 and expression levels of three genes (FLJ10357, RNASE2, and NDRG2). The strongest association was for FLJ10357 in cortex, which also had the most significant difference in expression between AD and non-AD brains, with greater expression in cortex of decedents with AD (p-value = 7 × 10⁻⁷). Further research is warranted to determine whether this signal can be replicated and whether other loci may be associated with cognitive resilience.

A depressive endophenotype of mild cognitive impairment and Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is a devastating public health problem that affects over 5.4 million Americans. Depression increases the risk of Mild Cognitive Impairment (MCI) and AD. By understanding the influence of depression on cognition, the potential exists to identify subgroups of depressed elders at greater risk for cognitive decline and AD. The current study sought to: 1) clinically identify a sub group of geriatric patients who suffer from depression related cognitive impairment; 2) cross validate this depressive endophenotype of MCI/AD in an independent cohort.

METHODS AND FINDINGS

Data was analyzed from 519 participants of Project FRONTIER. Depression was assessed with the GDS30 and cognition was assessed using the EXIT 25 and RBANS. Five GDS items were used to create the Depressive endophenotype of MCI and AD (DepE). DepE was significantly negatively related to RBANS index scores of Immediate Memory (B=-2.22, SE=.37, p<0.001), visuospatial skills (B=-1.11, SE=0.26, p<0.001), Language (B=-1.03, SE=0.21, p<0.001), Attention (B=-2.56, SE=0.49, p<0.001), and Delayed Memory (B=-1.54, SE = 037, p<0.001), and higher DepE scores were related to poorer executive functioning (EXIT25; B=0.65, SE=0.19, p=0.001). DepE scores significantly increased risk for MCI diagnosis (odds ratio [OR] = 2.04; 95% CI=1.54-2.69). Data from 235 participants in the TARCC (Texas Alzheimer's Research & Care Consortium) were analyzed for cross-validation of findings in an independent cohort. The DepE was significantly related to poorer scores on all measures, and a significantly predicted of cognitive change over 12- and 24-months.

CONCLUSION

The current findings suggest that a depressive endophenotype of MCI and AD exists and can be clinically identified using the GDS-30. Higher scores increased risk for MCI and was cross-validated by predicting AD in the TARCC. A key purpose for the search for distinct subgroups of individuals at risk for AD and MCI is to identify novel treatment and preventative opportunities.

Evaluation of memory endophenotypes for association with CLU, CR1, and PICALM variants in black and white subjects

BACKGROUND

Genetic variants at the CLU, CR1, and PICALM loci associate with risk for late-onset Alzheimer's disease (LOAD) in genomewide association studies. In this study, our aim was to determine whether the LOAD risk variants at these three loci influence memory endophenotypes in black and white subjects.

METHODS

We pursued an association study between single nucleotide polymorphism genotypes at the CLU, CR1, and PICALM loci and memory endophenotypes. We assessed black subjects (AA series: 44 with LOAD and 224 control subjects) recruited at Mayo Clinic Florida and whites recruited at Mayo Clinic Minnesota (RS series: 372 with LOAD and 1690 control subjects) and Florida (JS series: 60 with LOAD and 529 control subjects). Single nucleotide polymorphisms at the LOAD risk loci CLU (rs11136000), CR1 (rs6656401, rs3818361), and PICALM (rs3851179) were genotyped and tested for association with Logical Memory immediate recall, Logical Memory delayed recall, Logical Memory percent retention, Visual Reproduction immediate recall, Visual Reproduction delayed recall, and Visual Reproduction percent retention scores from the Wechsler Memory Scale-Revised using multivariable linear regression analysis, adjusting for age at exam, sex, education, and apolipoprotein E ε4 dosage.

RESULTS

We identified nominally significant or suggestive associations between the LOAD-risky CR1 variants and worse Logical Memory immediate recall scores in blacks (P = .068-.046, β = -2.7 to -1.2). The LOAD-protective CLU variant is associated with better logical memory endophenotypes in white subjects (P = .099-.027, β = 0.31-0.93). The CR1 associations persisted when the control subjects from the AA series were assessed separately. The CLU associations appeared to be driven by one of the white series (RS) and were also observed when the control subset from RS was analyzed.

CONCLUSION

These results suggest for the first time that LOAD risk variants at CR1 may influence memory endophenotypes in blacks. In addition, the CLU LOAD-protective variant may confer enhanced memory in whites. Although these results would not remain significant after stringent corrections for multiple testing, they need to be considered in the context of the LOAD associations with which they have biological consistency. They also provide estimates for effect sizes on memory endophenotypes that could guide future studies. The detection of memory effects for these variants in clinically normal subjects, implies that these LOAD risk loci might modify memory prior to clinical diagnosis of AD.

Alternative Approaches in Gene Discovery and Characterization in Alzheimer's Disease

Uncovering the genetic risk and protective factors for complex diseases is of fundamental importance for advancing therapeutic and biomarker discoveries. This endeavor is particularly challenging for neuropsychiatric diseases where diagnoses predominantly rely on the clinical presentation, which may be heterogeneous, possibly due to the heterogeneity of the underlying genetic susceptibility factors and environmental exposures. Although genome-wide association studies of various neuropsychiatric diseases have recently identified susceptibility loci, there likely remain additional genetic risk factors that underlie the liability to these conditions. Furthermore, identification and characterization of the causal risk variant(s) in each of these novel susceptibility loci constitute a formidable task, particularly in the absence of any prior knowledge about their function or mechanism of action. Biologically relevant, quantitative phenotypes, i.e., endophenotypes, provide a powerful alternative to the more traditional, binary disease phenotypes in the discovery and characterization of susceptibility genes for neuropsychiatric conditions. In this review, we focus on Alzheimer's disease (AD) as a model neuropsychiatric disease and provide a synopsis of the recent literature on the use of endophenotypes in AD genetics. We highlight gene expression, neuropathology and cognitive endophenotypes in AD, with examples demonstrating the utility of these alternative approaches in the discovery of novel susceptibility genes and pathways. In addition, we discuss how these avenues generate testable hypothesis about the pathophysiology of genetic factors that have far-reaching implications for therapies.

Allele-specific behavior of molecular networks: understanding small-molecule drug response in yeast

The study of systems genetics is changing the way the genetic and molecular basis of phenotypic variation, such as disease susceptibility and drug response, is being analyzed. Moreover, systems genetics aids in the translation of insights from systems biology into genetics. The use of systems genetics enables greater attention to be focused on the potential impact of genetic perturbations on the molecular states of networks that in turn affects complex traits. In this study, we developed models to detect allele-specific perturbations on interactions, in which a genetic locus with alternative alleles exerted a differing influence on an interaction. We utilized the models to investigate the dynamic behavior of an integrated molecular network undergoing genetic perturbations in yeast. Our results revealed the complexity of regulatory relationships between genetic loci and networks, in which different genetic loci perturb specific network modules. In addition, significant within-module functional coherence was found. We then used the network perturbation model to elucidate the underlying molecular mechanisms of individual differences in response to 100 diverse small molecule drugs. As a result, we identified sub-networks in the integrated network that responded to variations in DNA associated with response to diverse compounds and were significantly enriched for known drug targets. Literature mining results provided strong independent evidence for the effectiveness of these genetic perturbing networks in the elucidation of small-molecule responses in yeast.

Non-coding RNA and pseudogenes in neurodegenerative diseases: "The (un)Usual Suspects"

Neurodegenerative disorders and cancer are severe diseases threatening human health. The glaring differences between neurons and cancer cells mask the processes involved in their pathogenesis. Defects in cell cycle, DNA repair, and cell differentiation can determine unlimited proliferation in cancer, or conversely, compromise neuronal plasticity, leading to cell death and neurodegeneration. Alteration in regulatory networks affecting gene expression contribute to human diseases onset, including neurodegenerative disorders, and deregulation of non-coding RNAs – particularly microRNAs (miRNAs) – is supposed to have a significant impact. Recently, competitive endogenous RNAs (ceRNAs) – acting as sponges – have been identified in cancer, indicating a new and intricate regulatory network. Given that neurodegenerative disorders and cancer share altered genes and pathways, and considering the emerging role of miRNAs in neurogenesis, we hypothesize ceRNAs may be implicated in neurodegenerative diseases. Here we propose, and computationally predict, such regulatory mechanism may be shared between the diseases. It is predictable that similar regulation occurs in other complex diseases, and further investigation is needed.

Evaluation of Parkinson disease risk variants as expression-QTLs

The recent Parkinson Disease GWAS Consortium meta-analysis and replication study reports association at several previously confirmed risk loci SNCA, MAPT, GAK/DGKQ, and HLA and identified a novel risk locus at RIT2. To further explore functional consequences of these associations, we investigated modification of gene expression in prefrontal cortex brain samples of pathologically confirmed PD cases (N = 26) and controls (N = 24) by 67 associated SNPs in these 5 loci. Association between the eSNPs and expression was evaluated using a 2-degrees of freedom test of both association and difference in association between cases and controls, adjusted for relevant covariates. SNPs at each of the 5 loci were tested for cis-acting effects on all probes within 250 kb of each locus. Trans-effects of the SNPs on the 39,122 probes passing all QC on the microarray were also examined. From the analysis of cis-acting SNP effects, several SNPs in the MAPT region show significant association to multiple nearby probes, including two strongly correlated probes targeting the gene LOC644246 and the duplicated genes LRRC37A and LRRC37A2, and a third uncorrelated probe targeting the gene DCAKD. Significant cis-associations were also observed between SNPs and two probes targeting genes in the HLA region on chromosome 6. Expanding the association study to examine trans effects revealed an additional 23 SNP-probe associations reaching statistical significance (p<2.8 × 10(-8)) including SNPs from the SNCA, MAPT and RIT2 regions. These findings provide additional context for the interpretation of PD associated SNPs identified in recent GWAS as well as potential insight into the mechanisms underlying the observed SNP associations.

Novel late-onset Alzheimer disease loci variants associate with brain gene expression

OBJECTIVE

Recent genome-wide association studies (GWAS) of late-onset Alzheimer disease (LOAD) identified 9 novel risk loci. Discovery of functional variants within genes at these loci is required to confirm their role in Alzheimer disease (AD). Single nucleotide polymorphisms that influence gene expression (eSNPs) constitute an important class of functional variants. We therefore investigated the influence of the novel LOAD risk loci on human brain gene expression.

METHODS

We measured gene expression levels in the cerebellum and temporal cortex of autopsied AD subjects and those with other brain pathologies (∼400 total subjects). To determine whether any of the novel LOAD risk variants are eSNPs, we tested their cis-association with expression of 6 nearby LOAD candidate genes detectable in human brain (ABCA7, BIN1, CLU, MS4A4A, MS4A6A, PICALM) and an additional 13 genes ±100 kb of these SNPs. To identify additional eSNPs that influence brain gene expression levels of the novel candidate LOAD genes, we identified SNPs ±100 kb of their location and tested for cis-associations.

RESULTS

CLU rs11136000 (p = 7.81 × 10(-4)) and MS4A4A rs2304933/rs2304935 (p = 1.48 × 10(-4)-1.86 × 10(-4)) significantly influence temporal cortex expression levels of these genes. The LOAD-protective CLU and risky MS4A4A locus alleles associate with higher brain levels of these genes. There are other cis-variants that significantly influence brain expression of CLU and ABCA7 (p = 4.01 × 10(-5)-9.09 × 10(-9)), some of which also associate with AD risk (p = 2.64 × 10(-2)-6.25 × 10(-5)).

CONCLUSIONS

CLU and MS4A4A eSNPs may at least partly explain the LOAD risk association at these loci. CLU and ABCA7 may harbor additional strong eSNPs. These results have implications in the search for functional variants at the novel LOAD risk loci.

Brain expression genome-wide association study (eGWAS) identifies human disease-associated variants

Genetic variants that modify brain gene expression may also influence risk for human diseases. We measured expression levels of 24,526 transcripts in brain samples from the cerebellum and temporal cortex of autopsied subjects with Alzheimer's disease (AD, cerebellar n = 197, temporal cortex n = 202) and with other brain pathologies (non–AD, cerebellar n = 177, temporal cortex n = 197). We conducted an expression genome-wide association study (eGWAS) using 213,528 cisSNPs within ±100 kb of the tested transcripts. We identified 2,980 cerebellar cisSNP/transcript level associations (2,596 unique cisSNPs) significant in both ADs and non–ADs (q<0.05, p = 7.70×10⁻⁵–1.67×10⁻⁸²). Of these, 2,089 were also significant in the temporal cortex (p = 1.85×10⁻⁵–1.70×10⁻¹⁴¹). The top cerebellar cisSNPs had 2.4-fold enrichment for human disease-associated variants (p<10⁻⁶). We identified novel cisSNP/transcript associations for human disease-associated variants, including progressive supranuclear palsy SLCO1A2/rs11568563, Parkinson's disease (PD) MMRN1/rs6532197, Paget's disease OPTN/rs1561570; and we confirmed others, including PD MAPT/rs242557, systemic lupus erythematosus and ulcerative colitis IRF5/rs4728142, and type 1 diabetes mellitus RPS26/rs1701704. In our eGWAS, there was 2.9–3.3 fold enrichment (p<10⁻⁶) of significant cisSNPs with suggestive AD–risk association (p<10⁻³) in the Alzheimer's Disease Genetics Consortium GWAS. These results demonstrate the significant contributions of genetic factors to human brain gene expression, which are reliably detected across different brain regions and pathologies. The significant enrichment of brain cisSNPs among disease-associated variants advocates gene expression changes as a mechanism for many central nervous system (CNS) and non–CNS diseases. Combined assessment of expression and disease GWAS may provide complementary information in discovery of human disease variants with functional implications. Our findings have implications for the design and interpretation of eGWAS in general and the use of brain expression quantitative trait loci in the study of human disease genetics.

Genetic variants that regulate gene expression levels can also influence human disease risk. Discovery of genomic loci that alter brain gene expression levels (brain expression quantitative trait loci = eQTLs) can be instrumental in the identification of genetic risk underlying both central nervous system (CNS) and non–CNS diseases. To systematically assess the role of brain eQTLs in human disease and to evaluate the influence of brain region and pathology in eQTL mapping, we performed an expression genome-wide association study (eGWAS) in 773 brain samples from the cerebellum and temporal cortex of ∼200 autopsied subjects with Alzheimer's disease (AD) and ∼200 with other brain pathologies (non–AD). We identified ∼3,000 significant associations between cisSNPs near ∼700 genes and their cerebellar transcript levels, which replicate in ADs and non–ADs. More than 2,000 of these associations were reproducible in the temporal cortex. The top cisSNPs are enriched for both CNS and non–CNS disease-associated variants. We identified novel and confirmed previous cisSNP/transcript associations for many disease loci, suggesting gene expression regulation as their mechanism of action. These findings demonstrate the reproducibility of the eQTL approach across different brain regions and pathologies, and advocate the combined use of gene expression and disease GWAS for identification and functional characterization of human disease-associated variants.