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

A meta-analysis of gene expression quantitative trait loci in brain

Current catalogs of brain expression quantitative trait loci (eQTL) are incomplete and the findings do not replicate well across studies. All existing cortical eQTL studies are small and emphasize the need for a meta-analysis. We performed a meta-analysis of 424 brain samples across five studies to identify regulatory variants influencing gene expression in human cortex. We identified 3584 genes in autosomes and chromosome X with false discovery rate q<0.05 whose expression was significantly associated with DNA sequence variation. Consistent with previous eQTL studies, local regulatory variants tended to occur symmetrically around transcription start sites and the effect was more evident in studies with large sample sizes. In contrast to random SNPs, we observed that significant eQTLs were more likely to be near 5'-untranslated regions and intersect with regulatory features. Permutation-based enrichment analysis revealed that SNPs associated with schizophrenia and bipolar disorder were enriched among brain eQTLs. Genes with significant eQTL evidence were also strongly associated with diseases from OMIM (Online Mendelian Inheritance in Man) and the NHGRI (National Human Genome Research Institute) genome-wide association study catalog. Surprisingly, we found that a large proportion (28%) of ~1000 autosomal genes encoding proteins needed for mitochondrial structure or function were eQTLs (enrichment P-value=1.3 × 10(-9)), suggesting a potential role for common genetic variation influencing the robustness of energy supply in brain and a possible role in the etiology of some psychiatric disorders. These systematically generated eQTL information should be a valuable resource in determining the functional mechanisms of brain gene expression and the underlying biology of associations with psychiatric disorders.

Integrating Genome-Wide Association Study and Brain Expression Data Highlights Cell Adhesion Molecules and Purine Metabolism in Alzheimer's Disease

Alzheimer's disease (AD) is the most common neurodegenerative disease in the elderly. Recently, genome-wide association studies (GWAS) have been used to investigate AD pathogenesis. However, a large proportion of AD heritability has yet to be explained. We previously identified the cell adhesion molecule (CAM) pathway as a consistent signal in two AD GWAS. However, it is unclear whether CAM is present in the Genetic and Environmental Risk for Alzheimer's Disease Consortium (GERAD) GWAS and brain expression GWAS. Meanwhile, we think integrating AD GWAS and AD brain expression datasets may provide complementary information to identify important pathways involved in AD. Here, we conducted a systems analysis using (1) KEGG pathways, (2) large-scale AD GWAS from GERAD (n = 11,789), (3) two brain expression GWAS datasets (n = 399) from the AD cerebellum and temporal cortex, and (4) previous results from pathway analysis of AD GWAS. Our results indicate that (1) CAM is a consistent signal in five AD GWAS; (2) CAM is the most significant signal in AD; (3) we confirmed previous AD risk pathways related to immune system and diseases, and cardiovascular disease, etc.; and (4) we highlighted the purine metabolism pathway in AD for the first time. We believe that our results may advance our understanding of AD mechanisms and will be very informative for future genetic studies in AD.

Alzheimer's disease: early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2 and other loci

Here, we leverage a unique collection of 708 prospectively collected autopsied brains to assess the methylation state of the brain's DNA in relation to Alzheimer's disease (AD). We find that the level of methylation at 71 of the 415,848 interrogated CpGs is significantly associated with the burden of AD pathology, including CpGs in the ABCA7 and BIN1 regions, which harbor known AD susceptibility variants. We validate 11 of the differentially methylated regions in an independent set of 117 subjects. Further, we functionally validate these CpG associations and identify the nearby genes whose RNA expression is altered in AD: ANK1, CDH23, DIP2A, RHBDF2, RPL13, RNF34, SERPINF1 and SERPINF2. Our analyses suggest that these DNA methylation changes may have a role in the onset of AD since (1) they are seen in presymptomatic subjects and (2) six of the validated genes connect to a known AD susceptibility gene network.

Effects of long-term averaging of quantitative blood pressure traits on the detection of genetic associations

Blood pressure (BP) is a heritable, quantitative trait with intraindividual variability and susceptibility to measurement error. Genetic studies of BP generally use single-visit measurements and thus cannot remove variability occurring over months or years. We leveraged the idea that averaging BP measured across time would improve phenotypic accuracy and thereby increase statistical power to detect genetic associations. We studied systolic BP (SBP), diastolic BP (DBP), mean arterial pressure (MAP), and pulse pressure (PP) averaged over multiple years in 46,629 individuals of European ancestry. We identified 39 trait-variant associations across 19 independent loci (p < 5 × 10(-8)); five associations (in four loci) uniquely identified by our LTA analyses included those of SBP and MAP at 2p23 (rs1275988, near KCNK3), DBP at 2q11.2 (rs7599598, in FER1L5), and PP at 6p21 (rs10948071, near CRIP3) and 7p13 (rs2949837, near IGFBP3). Replication analyses conducted in cohorts with single-visit BP data showed positive replication of associations and a nominal association (p < 0.05). We estimated a 20% gain in statistical power with long-term average (LTA) as compared to single-visit BP association studies. Using LTA analysis, we identified genetic loci influencing BP. LTA might be one way of increasing the power of genetic associations for continuous traits in extant samples for other phenotypes that are measured serially over time.

Association of MAPT haplotypes with Alzheimer's disease risk and MAPT brain gene expression levels

Introduction

MAPT encodes for tau, the predominant component of neurofibrillary tangles that are neuropathological hallmarks of Alzheimer’s disease (AD). Genetic association of MAPT variants with late-onset AD (LOAD) risk has been inconsistent, although insufficient power and incomplete assessment of MAPT haplotypes may account for this.

Methods

We examined the association of MAPT haplotypes with LOAD risk in more than 20,000 subjects (n-cases = 9,814, n-controls = 11,550) from Mayo Clinic (n-cases = 2,052, n-controls = 3,406) and the Alzheimer’s Disease Genetics Consortium (ADGC, n-cases = 7,762, n-controls = 8,144). We also assessed associations with brain MAPT gene expression levels measured in the cerebellum (n = 197) and temporal cortex (n = 202) of LOAD subjects. Six single nucleotide polymorphisms (SNPs) which tag MAPT haplotypes with frequencies greater than 1% were evaluated.

Results

H2-haplotype tagging rs8070723-G allele associated with reduced risk of LOAD (odds ratio, OR = 0.90, 95% confidence interval, CI = 0.85-0.95, p = 5.2E-05) with consistent results in the Mayo (OR = 0.81, p = 7.0E-04) and ADGC (OR = 0.89, p = 1.26E-04) cohorts. rs3785883-A allele was also nominally significantly associated with LOAD risk (OR = 1.06, 95% CI = 1.01-1.13, p = 0.034). Haplotype analysis revealed significant global association with LOAD risk in the combined cohort (p = 0.033), with significant association of the H2 haplotype with reduced risk of LOAD as expected (p = 1.53E-04) and suggestive association with additional haplotypes. MAPT SNPs and haplotypes also associated with brain MAPT levels in the cerebellum and temporal cortex of AD subjects with the strongest associations observed for the H2 haplotype and reduced brain MAPT levels (β = -0.16 to -0.20, p = 1.0E-03 to 3.0E-03).

Conclusions

These results confirm the previously reported MAPT H2 associations with LOAD risk in two large series, that this haplotype has the strongest effect on brain MAPT expression amongst those tested and identify additional haplotypes with suggestive associations, which require replication in independent series. These biologically congruent results provide compelling evidence to screen the MAPT region for regulatory variants which confer LOAD risk by influencing its brain gene expression.

Overlap of expression quantitative trait loci (eQTL) in human brain and blood

BACKGROUND

Expression quantitative trait loci (eQTL) are genomic regions regulating RNA transcript expression levels. Genome-wide Association Studies (GWAS) have identified many variants, often in non-coding regions, with unknown functions and eQTL provide a possible mechanism by which these variants may influence observable phenotypes. Limited access and availability of tissues such as brain has led to the use of blood as a substitute for eQTL analyses.

METHODS

Here, we evaluate the overlap of eQTL reported in published studies conducted in blood and brain tissues to assess the utility of blood as an alternative to brain tissue in the study of neurological and psychiatric conditions. Expression QTL results from eight published brain studies were compared to blood eQTL identified in from a meta-analysis involving 5,311 individuals. We accounted for differences in SNP platforms and study design by using SNP proxies in high linkage disequilibrium with reported eQTL. The degree of overlap between studies was calculated by ascertaining if an eQTL identified in one study was also identified in the other study.

RESULTS

The percentage of eQTL overlapping for brain and blood expression after adjusting for differences in sample size ranged from 13 - 23% (mean 19.2%). Amongst pairs of brain studies eQTL overlap ranged from 0 - 35%, with higher degrees of overlap found for studies using expression data collected from the same brain region.

CONCLUSION

Our results suggest that whenever possible tissue specific to the pathophysiology of the disease being studied should be used for transcription analysis.

Alpha-synuclein deletion decreases motor impulsivity but does not affect risky decision making in a mouse Gambling Task

RATIONALE

There is evidence to support the role of alpha-synuclein in motor impulsivity, but the extrapolation of this finding to other types of impulsivity remains to be elucidated.

OBJECTIVE

This study aims to investigate the role of alpha-synuclein in choice impulsivity/risky decision-making by means of a mouse version of the Iowa Gambling Task (mIGT).

METHODS

Two strains of mice that differ in the expression of the alpha-synuclein gene, the C57BL/6JOlaHsd (HA) and C57BL/6J (CR), were tested in the mIGT. HA mice differ from their CR ancestors in possessing a chromosomal deletion resulting in the loss of two genes: snca, encoding alpha-synuclein and mmrn1, encoding multimerin-1. Mice were trained in the mIGT until a stable pattern of responding was achieved and then the acute effects of ethanol and cocaine in choice preference were investigated.

RESULTS

No differences between the strains were evident in risky decision-making in any of the experiments, but HA mice showed consistently reduced levels of premature responding in comparison with CR mice, confirming the reduced motor impulsivity found in a previous study. Ethanol did not modify the percentage of advantageous choices in either strain, while cocaine increased the risky choice behaviour by increasing the percentage of disadvantageous choices in both strains.

CONCLUSIONS

We provide further evidence for the involvement of alpha-synuclein in motor impulsivity and suggest that alpha-synuclein does not play a role in risky decision-making as evaluated in the mIGT.

Genome-wide identification of expression quantitative trait loci (eQTLs) in human heart

In recent years genome-wide association studies (GWAS) have uncovered numerous chromosomal loci associated with various electrocardiographic traits and cardiac arrhythmia predisposition. A considerable fraction of these loci lie within inter-genic regions. The underlying trait-associated variants likely reside in regulatory regions and exert their effect by modulating gene expression. Hence, the key to unraveling the molecular mechanisms underlying these cardiac traits is to interrogate variants for association with differential transcript abundance by expression quantitative trait locus (eQTL) analysis. In this study we conducted an eQTL analysis of human heart. For a total of 129 left ventricular samples that were collected from non-diseased human donor hearts, genome-wide transcript abundance and genotyping was determined using microarrays. Each of the 18,402 transcripts and 897,683 SNP genotypes that remained after pre-processing and stringent quality control were tested for eQTL effects. We identified 771 eQTLs, regulating 429 unique transcripts. Overlaying these eQTLs with cardiac GWAS loci identified novel candidates for studies aimed at elucidating the functional and transcriptional impact of these loci. Thus, this work provides for the first time a comprehensive eQTL map of human heart: a powerful and unique resource that enables systems genetics approaches for the study of cardiac traits.

Identification of candidate susceptibility genes for colorectal cancer through eQTL analysis

In this study, we aim to identify the genes responsible for colorectal cancer risk behind the loci identified in genome-wide association studies (GWAS). These genes may be candidate targets for developing new strategies for prevention or therapy. We analyzed the association of genotypes for 26 GWAS single nucleotide polymorphisms (SNPs) with the expression of genes within a 2 Mb region (cis-eQTLs). Affymetrix Human Genome U219 expression arrays were used to assess gene expression in two series of samples, one of healthy colonic mucosa (n = 47) and other of normal mucosa adjacent to colon cancer (n = 97, total 144). Paired tumor tissues (n = 97) were also analyzed but did not provide additional findings. Partial Pearson correlation (r), adjusted for sample type, was used for the analysis. We have found Bonferroni-significant cis-eQTLs in three loci: rs3802842 in 11q23.1 associated to C11orf53, COLCA1 (C11orf92) and COLCA2 (C11orf93; r = 0.60); rs7136702 in 12q13.12 associated to DIP2B (r = 0.63) and rs5934683 in Xp22.3 associated to SHROOM2 and GPR143 (r = 0.47). For loci in chromosomes 11 and 12, we have found other SNPs in linkage disequilibrium that are more strongly associated with the expression of the identified genes and are better functional candidates: rs7130173 for 11q23.1 (r = 0.66) and rs61927768 for 12q13.12 (r = 0.86). These SNPs are located in DNA regions that may harbor enhancers or transcription factor binding sites. The analysis of trans-eQTLs has identified additional genes in these loci that may have common regulatory mechanisms as shown by the analysis of protein-protein interaction networks.

Pathogenesis and treatment of CNS lupus

PURPOSE OF REVIEW

Neuropsychiatric manifestations pose diagnostic and therapeutic challenges in systemic lupus erythematosus (SLE). We review recently published studies on the epidemiology, pathogenesis, neuroimaging, and treatment of NPSLE.

RECENT FINDINGS

Generalized SLE activity or damage and antiphospholipid antibodies are identified as major risk factors for neuropsychiatric involvement. NPSLE patients have increased genetic burden and novel genomic approaches are expected to elucidate its pathogenesis. Animal data suggest that, in cases of disturbed blood-brain barrier, autoantibodies against the NR2 subunits of the N-methyl-D-aspartate receptor and 16/6 idiotype antibodies may cause diffuse neuropsychiatric manifestations through neuronal apoptosis or brain inflammation; data in humans are still circumstantial. In NPSLE, advanced neuroimaging uncovers structural and metabolic abnormalities in brain regions with normal appearance on conventional MRI. Treatment includes corticosteroids/immunosuppressants for inflammatory manifestations or generalized SLE activity, and antiplatelets/anticoagulation for manifestations related to antiphospholipid antibodies. In refractory cases, uncontrolled studies suggest a beneficial role of rituximab.

SUMMARY

We have begun to better understand how brain-reactive autoantibodies, present in a proportion of SLE patients, can cause brain injury and diffuse NPSLE. Further testing will be required to determine the clinical utility of advanced neuroimaging. Controlled trials are needed to guide therapeutic decisions.

Gene-centric meta-analysis in 87,736 individuals of European ancestry identifies multiple blood-pressure-related loci

Blood pressure (BP) is a heritable risk factor for cardiovascular disease. To investigate genetic associations with systolic BP (SBP), diastolic BP (DBP), mean arterial pressure (MAP), and pulse pressure (PP), we genotyped ~50,000 SNPs in up to 87,736 individuals of European ancestry and combined these in a meta-analysis. We replicated findings in an independent set of 68,368 individuals of European ancestry. Our analyses identified 11 previously undescribed associations in independent loci containing 31 genes including PDE1A, HLA-DQB1, CDK6, PRKAG2, VCL, H19, NUCB2, RELA, HOXC@ complex, FBN1, and NFAT5 at the Bonferroni-corrected array-wide significance threshold (p < 6 × 10(-7)) and confirmed 27 previously reported associations. Bioinformatic analysis of the 11 loci provided support for a putative role in hypertension of several genes, such as CDK6 and NUCB2. Analysis of potential pharmacological targets in databases of small molecules showed that ten of the genes are predicted to be a target for small molecules. In summary, we identified previously unknown loci associated with BP. Our findings extend our understanding of genes involved in BP regulation, which may provide new targets for therapeutic intervention or drug response stratification.

Genetics of gene expression in CNS

Transcriptome studies have revealed a surprisingly high level of variation among individuals in expression of key genes in the CNS under both normal and experimental conditions. Ten-fold variation is common, yet the specific causes and consequences of this variation are largely unknown. By combining classic gene mapping methods-family linkage studies and genomewide association-with high-throughput genomics, it is now possible to define quantitative trait loci (QTLs), single-gene variants, and even single SNPs and indels that control gene expression in different brain regions and cells. This review considers some of the major technical and conceptual challenges in analyzing variation in expression in the CNS with a focus on mRNAs, rather than noncoding RNAs or proteins. At one level of analysis, this work has been highly successful, and we finally have techniques that can be used to track down small numbers of loci that control expression in the CNS. But at a higher level of analysis, we still do not understand the genetic architecture of gene expression in brain, the consequences of expression QTLs on protein levels or on cell function, or the combined impact of expression differences on behavior and disease risk. These important gaps are likely to be bridged over the next several decades using (1) much larger sample sizes, (2) more powerful RNA sequencing and proteomic methods, and (3) novel statistical and computational models to predict genome-to-phenome relations.

RNA-Seq optimization with eQTL gold standards

BACKGROUND

RNA-Sequencing (RNA-Seq) experiments have been optimized for library preparation, mapping, and gene expression estimation. These methods, however, have revealed weaknesses in the next stages of analysis of differential expression, with results sensitive to systematic sample stratification or, in more extreme cases, to outliers. Further, a method to assess normalization and adjustment measures imposed on the data is lacking.

RESULTS

To address these issues, we utilize previously published eQTLs as a novel gold standard at the center of a framework that integrates DNA genotypes and RNA-Seq data to optimize analysis and aid in the understanding of genetic variation and gene expression. After detecting sample contamination and sequencing outliers in RNA-Seq data, a set of previously published brain eQTLs was used to determine if sample outlier removal was appropriate. Improved replication of known eQTLs supported removal of these samples in downstream analyses. eQTL replication was further employed to assess normalization methods, covariate inclusion, and gene annotation. This method was validated in an independent RNA-Seq blood data set from the GTEx project and a tissue-appropriate set of eQTLs. eQTL replication in both data sets highlights the necessity of accounting for unknown covariates in RNA-Seq data analysis.

CONCLUSION

As each RNA-Seq experiment is unique with its own experiment-specific limitations, we offer an easily-implementable method that uses the replication of known eQTLs to guide each step in one's data analysis pipeline. In the two data sets presented herein, we highlight not only the necessity of careful outlier detection but also the need to account for unknown covariates in RNA-Seq experiments.

Genetics of human gene expression

A steadily growing number of studies have identified and characterized expression quantitative trait loci (eQTLs) in human cell-lines, primary cells, and tissues. This class of variation has been shown to play a role in complex traits, including disease. Here, we discuss how eQTLs have the potential to accelerate discovery of disease genes and functional mechanisms underlying complex traits. We discuss how context-specificity of eQTLs is being characterized at an unprecedented scale and breadth, and how this both informs on the intricacy of human genome function, and has important ramifications for elucidating function of genetic variants of interest, particularly for those contributing to disease.

An inflection point in gene discovery efforts for neurodegenerative diseases: from syndromic diagnoses toward endophenotypes and the epigenome

We are at an inflection point in our study of the human genome as it relates to neurodegenerative disease. The sequencing of the human genome, and its associated cataloging of human genetic variation and technological as well as methodological development, introduced a period of rapid gene discovery over the past decade. These efforts have yielded many new insights and will continue to uncover the genetic architecture of syndromically defined neurodegenerative diseases in the coming decades. More recently, these successful study designs have been applied to the investigation of intermediate traits that relate to and inform our understanding of clinical syndromes and to exploration of the epigenome, the higher-order structure of DNA that dictates the expression of a given genetic risk factor. While still nascent, given the challenges of accumulating large numbers of subjects with detailed phenotypes and technological hurdles in characterizing the state of chromatin, these efforts represent key investments that will enable the study of the functional consequences of a genetic risk factor and, eventually, its contribution to the clinical manifestations of a given disease. As a community of investigators, we are therefore at an exciting inflection point at which gene discovery efforts are transitioning toward the functional characterization of implicated genetic variation; this transition is crucial for understanding the molecular, cellular, and systemic events that lead to a syndromic diagnosis for a neurodegenerative disease.

MetaRanker 2.0: a web server for prioritization of genetic variation data

MetaRanker 2.0 is a web server for prioritization of common and rare frequency genetic variation data. Based on heterogeneous data sets including genetic association data, protein–protein interactions, large-scale text-mining data, copy number variation data and gene expression experiments, MetaRanker 2.0 prioritizes the protein-coding part of the human genome to shortlist candidate genes for targeted follow-up studies. MetaRanker 2.0 is made freely available at www.cbs.dtu.dk/services/MetaRanker-2.0.

GWAS of cerebrospinal fluid tau levels identifies risk variants for Alzheimer's disease

Cerebrospinal fluid (CSF) tau, tau phosphorylated at threonine 181 (ptau), and Aβ₄₂ are established biomarkers for Alzheimer's disease (AD) and have been used as quantitative traits for genetic analyses. We performed the largest genome-wide association study for cerebrospinal fluid (CSF) tau/ptau levels published to date (n = 1,269), identifying three genome-wide significant loci for CSF tau and ptau: rs9877502 (p = 4.89 × 10⁻⁹ for tau) located at 3q28 between GEMC1 and OSTN, rs514716 (p = 1.07 × 10⁻⁸ and p = 3.22 × 10⁻⁹ for tau and ptau, respectively), located at 9p24.2 within GLIS3 and rs6922617 (p = 3.58 × 10⁻⁸ for CSF ptau) at 6p21.1 within the TREM gene cluster, a region recently reported to harbor rare variants that increase AD risk. In independent data sets, rs9877502 showed a strong association with risk for AD, tangle pathology, and global cognitive decline (p = 2.67 × 10⁻⁴, 0.039, 4.86 × 10⁻⁵, respectively) illustrating how this endophenotype-based approach can be used to identify new AD risk loci.

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.

Resolving the polymorphism-in-probe problem is critical for correct interpretation of expression QTL studies

Polymorphisms in the target mRNA sequence can greatly affect the binding affinity of microarray probe sequences, leading to false-positive and false-negative expression quantitative trait locus (QTL) signals with any other polymorphisms in linkage disequilibrium. We provide the most complete solution to this problem, by using the latest genome and exome sequence reference data to identify almost all common polymorphisms (frequency >1% in Europeans) in probe sequences for two commonly used microarray panels (the gene-based Illumina Human HT12 array, which uses 50-mer probes, and exon-based Affymetrix Human Exon 1.0 ST array, which uses 25-mer probes). We demonstrate the impact of this problem using cerebellum and frontal cortex tissues from 438 neuropathologically normal individuals. We find that although only a small proportion of the probes contain polymorphisms, they account for a large proportion of apparent expression QTL signals, and therefore result in many false signals being declared as real. We find that the polymorphism-in-probe problem is insufficiently controlled by previous protocols, and illustrate this using some notable false-positive and false-negative examples in MAPT and PRICKLE1 that can be found in many eQTL databases. We recommend that both new and existing eQTL data sets should be carefully checked in order to adequately address this issue.

Genetic insights in Alzheimer's disease

In the search for new genes in Alzheimer's disease, classic linkage-based and candidate-gene-based association studies have been supplanted by exome sequencing, genome-wide sequencing (for mendelian forms of Alzheimer's disease), and genome-wide association studies (for non-mendelian forms). The identification of new susceptibility genes has opened new avenues for exploration of the underlying disease mechanisms. In addition to detecting novel risk factors in large samples, next-generation sequencing approaches can deliver novel insights with even small numbers of patients. The shift in focus towards translational studies and sequencing of individual patients places each patient's biomaterials as the central unit of genetic studies. The notional shift needed to make the patient central to genetic studies will necessitate strong collaboration and input from clinical neurologists.

An exploratory study on STX6, MOBP, MAPT, and EIF2AK3 and late-onset Alzheimer's disease

Both Alzheimer's disease (AD) and progressive supranuclear palsy (PSP) are a class of neurodegenerative diseases associated with the pathologic aggregation of tau protein in the human brain. They share some clinical and pathologic characteristics. A recent genome-wide association study reported several single-nucleotide polymorphisms at the STX6, MOBP, MAPT, and EIF2AK3 in association with PSP. To explore whether these single-nucleotide polymorphisms are associated with AD risk, we conducted a case-control study to investigate the PSP-associated loci in 1592 Han Chinese subjects. Rs242557 at the MAPT locus was associated with late-onset AD (LOAD) (odds ratio [OR], 1.175; p = 0.026), which appeared to be stronger for LOAD patients with apolipoprotein E (APOE) ε4 allele (OR, 1.510), and this positive association was not changed after adjusting for age, sex, and the APOE ε4-carrier status (additive model: OR, 1.163; p = 0.036; dominant model: OR, 1.315; p = 0.010). Rs1768208 in MOBP and rs7571971 in EIF2AK3 showed association only in the APOE ε4 positive subjects, and these did not appear to be independent of APOE. As for rs1411478 in STX6, we did not explore any association with LOAD. Our exploratory analysis mainly suggests an association of MAPT with LOAD, especially in APOE ε4 carriers. Genotypes at MOBP and EIF2AK3 confer risk predominantly in APOE ε4-positive subjects, with indications of an interaction between APOE and MOBP or EIF2AK3 on AD risk.

Coordinating GWAS results with gene expression in a systems immunologic paradigm in autoimmunity

There has been considerable progress in our understanding of the genetic architecture of susceptibility to inflammatory diseases in recent years: several hundred susceptibility loci have been discovered in genome-wide association studies (GWAS) of human populations. This success has created an important challenge in identifying the functional consequences of these risk-associated variants and in elucidating how the repercussions of individual susceptibility loci integrate to yield dysregulation of immune pathways and, ultimately, syndromic clinical phenotypes. The integration of GWAS association signals with high-resolution transcriptome and other genomic data that capture the dynamics of cellular state and function in the context of individual's collection of susceptibility alleles has proven to be a successful avenue of investigation. The rapid pace of methodological development in this area has been coupled with an accumulation of experimental data that makes the elucidation of complex biological networks underlying susceptibility to these common inflammatory diseases a reasonable goal in the near future.