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The transfer of mitochondrial DNA into the nuclear genomes of eukaryotes (Numts) has been linked to lifespan in nonhuman species and recently demonstrated to occur in rare instances from one human generation to the next. Here, we investigated numtogenesis dynamics in humans in 2 ways. First, we quantified Numts in 1,187 postmortem brain and blood samples from different individuals. Compared to circulating immune cells (n = 389), postmitotic brain tissue (n = 798) contained more Numts, consistent with their potential somatic accumulation. Within brain samples, we observed a 5.5-fold enrichment of somatic Numt insertions in the dorsolateral prefrontal cortex (DLPFC) compared to cerebellum samples, suggesting that brain Numts arose spontaneously during development or across the lifespan. Moreover, an increase in the number of brain Numts was linked to earlier mortality. The brains of individuals with no cognitive impairment (NCI) who died at younger ages carried approximately 2 more Numts per decade of life lost than those who lived longer. Second, we tested the dynamic transfer of Numts using a repeated-measures whole-genome sequencing design in a human fibroblast model that recapitulates several molecular hallmarks of aging. These longitudinal experiments revealed a gradual accumulation of 1 Numt every ~13 days. Numtogenesis was independent of large-scale genomic instability and unlikely driven by cell clonality. Targeted pharmacological perturbations including chronic glucocorticoid signaling or impairing mitochondrial oxidative phosphorylation (OxPhos) only modestly increased the rate of numtogenesis, whereas patient-derived SURF1-mutant cells exhibiting mtDNA instability accumulated Numts 4.7-fold faster than healthy donors. Combined, our data document spontaneous numtogenesis in human cells and demonstrate an association between brain cortical somatic Numts and human lifespan. These findings open the possibility that mito-nuclear horizontal gene transfer among human postmitotic tissues produces functionally relevant human Numts over timescales shorter than previously assumed.

The Major Histocompatibility Complex (MHC) makes the largest genetic contribution to multiple sclerosis (MS) susceptibility, with 32 independent effects across the region explaining 20% of the heritability in European populations. Variation is high across populations with allele frequency differences and population-specific risk alleles identified. We sought to identify MHC-specific MS susceptibility variants and assess the effect of ancestral risk modification within 2652 Latinx and Hispanic individuals as well as 2435 Black and African American individuals. We have identified several novel susceptibility alleles which are rare in European populations including HLA-B*53:01, and we have utilized the differing linkage disequilibrium patterns inherent to these populations to identify an independent role for HLA-DRB1*15:01 and HLA-DQB1*06:02 on MS risk. We found a decrease in Native American ancestry in MS cases vs controls across the MHC, peaking near the previously identified MICB locus with a decrease of ~5.5% in Hispanics and ~0.4% in African Americans. We have identified several susceptibility variants, including within the MICB gene region, which show global ancestry risk modification and indicate ancestral differences which may be due in part to correlated environmental factors. We have also identified several susceptibility variants for which MS risk is modified by local ancestry and indicate true ancestral genetic differences; including HLA-DQB1*06:02 for which MS risk for European allele carriers is almost two times the risk for African allele carriers. These results validate the importance of investigating MS susceptibility at an ancestral level and offer insight into the epidemiology of MS phenotypic diversity.

The majority of genetic variants detected in genome wide association studies (GWAS) exert their effects on phenotypes through gene regulation. Motivated by this observation, we propose a multi-omic integration method that models the cascading effects of genetic variants from epigenome to transcriptome and eventually to the phenome in identifying target genes influenced by risk alleles. This cascading epigenomic analysis for GWAS, which we refer to as CEWAS, comprises two types of models: one for linking cis genetic effects to epigenomic variation and another for linking cis epigenomic variation to gene expression. Applying these models in cascade to GWAS summary statistics generates gene level statistics that reflect genetically-driven epigenomic effects. We show on sixteen brain-related GWAS that CEWAS provides higher gene detection rate than related methods, and finds disease relevant genes and gene sets that point toward less explored biological processes. CEWAS thus presents a novel means for exploring the regulatory landscape of GWAS variants in uncovering disease mechanisms.

Inbreeding depression has been observed in many different species, but in humans a systematic analysis has been difficult so far. Here, analysing more than 1.3 million individuals, the authors show that a genomic inbreeding coefficient (FROH) is associated with disadvantageous outcomes in 32 out of 100 traits tested.

Transcriptome-wide association studies (TWAS) have been widely used to integrate transcriptomic and genetic data to study complex human diseases. Within a test dataset lacking transcriptomic data, traditional two-stage TWAS methods first impute gene expression by creating a weighted sum that aggregates SNPs with their corresponding cis-eQTL effects on reference transcriptome. Traditional TWAS methods then employ a linear regression model to assess the association between imputed gene expression and test phenotype, thereby assuming the effect of a cis-eQTL SNP on test phenotype is a linear function of the eQTL's estimated effect on reference transcriptome. To increase TWAS robustness to this assumption, we propose a novel Variance-Component TWAS procedure (VC-TWAS) that assumes the effects of cis-eQTL SNPs on phenotype are random (with variance proportional to corresponding reference cis-eQTL effects) rather than fixed. VC-TWAS is applicable to both continuous and dichotomous phenotypes, as well as individual-level and summary-level GWAS data. Using simulated data, we show VC-TWAS is more powerful than traditional TWAS methods based on a two-stage Burden test, especially when eQTL genetic effects on test phenotype are no longer a linear function of their eQTL genetic effects on reference transcriptome. We further applied VC-TWAS to both individual-level (N = ~3.4K) and summary-level (N = ~54K) GWAS data to study Alzheimer's dementia (AD). With the individual-level data, we detected 13 significant risk genes including 6 known GWAS risk genes such as TOMM40 that were missed by traditional TWAS methods. With the summary-level data, we detected 57 significant risk genes considering only cis-SNPs and 71 significant genes considering both cis- and trans- SNPs, which also validated our findings with the individual-level GWAS data. Our VC-TWAS method is implemented in the TIGAR tool for public use.

Discovering drugs that efficiently treat brain diseases has been challenging. Genetic variants that modulate the expression of potential drug targets can be utilized to assess the efficacy of therapeutic interventions. We therefore employed Mendelian Randomization (MR) on gene expression measured in brain tissue to identify drug targets involved in neurological and psychiatric diseases. We conducted a two-sample MR using cis-acting brain-derived expression quantitative trait loci (eQTLs) from the Accelerating Medicines Partnership for Alzheimer's Disease consortium (AMP-AD) and the CommonMind Consortium (CMC) meta-analysis study (n = 1,286) as genetic instruments to predict the effects of 7,137 genes on 12 neurological and psychiatric disorders. We conducted Bayesian colocalization analysis on the top MR findings (using P

Complexity of cell-type composition has created much skepticism surrounding the interpretation of bulk tissue transcriptomic studies. Recent studies have shown that deconvolution algorithms can be applied to computationally estimate cell-type proportions from gene expression data of bulk blood samples, but their performance when applied to brain tissue is unclear. Here, we have generated an immunohistochemistry (IHC) dataset for five major cell-types from brain tissue of 70 individuals, who also have bulk cortical gene expression data. With the IHC data as the benchmark, this resource enables quantitative assessment of deconvolution algorithms for brain tissue. We apply existing deconvolution algorithms to brain tissue by using marker sets derived from human brain single cell and cell-sorted RNA-seq data. We show that these algorithms can indeed produce informative estimates of constituent cell-type proportions. In fact, neuronal subpopulations can also be estimated from bulk brain tissue samples. Further, we show that including the cell-type proportion estimates as confounding factors is important for reducing false associations between Alzheimer's disease phenotypes and gene expression. Lastly, we demonstrate that using more accurate marker sets can substantially improve statistical power in detecting cell-type specific expression quantitative trait loci (eQTLs).

Biomedical research studies have generated large multi-omic datasets to study complex diseases like Alzheimer's disease (AD). An important aim of these studies is the identification of candidate genes that demonstrate congruent disease-related alterations across the different data types measured by the study. We developed a new method to detect such candidate genes in large multi-omic case-control studies that measure multiple data types in the same set of samples. The method is based on a gene-centric integrative coefficient quantifying to what degree consistent differences are observed in the different data types. For statistical inference, a Bayesian hierarchical model is used to study the distribution of the integrative coefficient. The model employs a conditional autoregressive prior to integrate a functional gene network and to share information between genes known to be functionally related. We applied the method to an AD dataset consisting of histone acetylation, DNA methylation, and RNA transcription data from human cortical tissue samples of 233 subjects, and we detected 816 genes with consistent differences between persons with AD and controls. The findings were validated in protein data and in RNA transcription data from two independent AD studies. Finally, we found three subnetworks of jointly dysregulated genes within the functional gene network which capture three distinct biological processes: myeloid cell differentiation, protein phosphorylation and synaptic signaling. Further investigation of the myeloid network indicated an upregulation of this network in early stages of AD prior to accumulation of hyperphosphorylated tau and suggested that increased CSF1 transcription in astrocytes may contribute to microglial activation in AD. Thus, we developed a method that integrates multiple data types and external knowledge of gene function to detect candidate genes, applied the method to an AD dataset, and identified several disease-related genes and processes demonstrating the usefulness of the integrative approach.

Alzheimer's disease and related disorders (ADRD) may manifest cognitive and non-cognitive phenotypes including motor impairment, suggesting a shared underlying biology. We tested the hypothesis that five cortical proteins identified from a gene network that drives AD and cognitive phenotypes are also related to motor function in the same individuals. We examined 1208 brains of older adults with motor and cognitive assessments prior to death. Cortical proteins were quantified with SRM proteomics and we collected indices of AD and other related pathologies. A higher level of IGFBP5 was associated with poorer motor function proximate to death but AK4, HSPB2, ITPK1 and PLXNB1 were unrelated to motor function. The association of IGFBP5 with motor function was unrelated to the presence of indices of brain pathologies. In contrast, the addition of a term for cognition attenuated the association of IGFBP5 with motor function by about 90% and they were no longer related. These data lend support for the idea that unidentified cortical proteins like IGFBP5, which may not manifest a known pathologic footprint, may contribute to motor and cognitive function in older adults.

BackgroundThere are few data concerning the association between season and cognition and its neurobiological correlates in older persons-effects with important translational and therapeutic implications for the diagnosis and treatment of Alzheimer disease (AD). We aimed to measure these effects.Methods and findingsWe analyzed data from 3,353 participants from 3 observational community-based cohort studies of older persons (the Rush Memory and Aging Project [MAP], the Religious Orders Study [ROS], and the Minority Aging Research Study [MARS]) and 2 observational memory-clinic-based cohort studies (Centre de Neurologie Cognitive [CNC] study at Lariboisière Hospital and the Sunnybrook Dementia Study [SDS]). We performed neuropsychological testing and, in subsets of participants, evaluated cerebrospinal fluid AD biomarkers, standardized structured autopsy measures, and/or prefrontal cortex gene expression by RNA sequencing. We examined the association between season and these variables using nested multiple linear and logistic regression models. There was a robust association between season and cognition that was replicated in multiple cohorts (amplitude = 0.14 SD [a measure of the magnitude of seasonal variation relative to overall variability; 95% CI 0.07-0.23], p = 0.007, in the combined MAP, ROS, and MARS cohorts; amplitude = 0.50 SD [95% CI 0.07-0.66], p = 0.017, in the SDS cohort). Average composite global cognitive function was higher in the summer and fall compared to winter and spring, with the difference equivalent in cognitive effect to 4.8 years' difference in age (95% CI 2.1-8.4, p = 0.002). Further, the odds of meeting criteria for mild cognitive impairment or dementia were higher in the winter and spring (odds ratio 1.31 [95% CI 1.10-1.57], p = 0.003). These results were robust against multiple potential confounders including depressive symptoms, sleep, physical activity, and thyroid status and persisted in cases with AD pathology. Moreover, season had a marked effect on cerebrospinal fluid Aβ 42 level (amplitude 0.30 SD [95% CI 0.10-0.64], p = 0.003), which peaked in the summer, and on the brain expression of 4 cognition-associated modules of co-expressed genes (m6: amplitude = 0.44 SD [95% CI 0.21-0.65], p = 0.0021; m13: amplitude = 0.46 SD [95% CI 0.27-0.76], p = 0.0009; m109: amplitude = 0.43 SD [95% CI 0.24-0.67], p = 0.0021; and m122: amplitude 0.46 SD [95% CI 0.20-0.71], p = 0.0012), which were in phase or anti-phase to the rhythms of cognition and which were in turn associated with binding sites for several seasonally rhythmic transcription factors including BCL11A, CTCF, EGR1, MEF2C, and THAP1. Limitations include the evaluation of each participant or sample once per annual cycle, reliance on self-report for measurement of environmental and behavioral factors, and potentially limited generalizability to individuals in equatorial regions or in the southern hemisphere.ConclusionsSeason has a clinically significant association with cognition and its neurobiological correlates in older adults with and without AD pathology. There may be value in increasing dementia-related clinical resources in the winter and early spring, when symptoms are likely to be most pronounced. Moreover, the persistence of robust seasonal plasticity in cognition and its neurobiological correlates, even in the context of concomitant AD pathology, suggests that targeting environmental or behavioral drivers of seasonal cognitive plasticity, or the key transcription factors and genes identified in this study as potentially mediating these effects, may allow us to substantially improve cognition in adults with and without AD.

PURPOSE:Human females have a unique duration of post-reproductive longevity, during which sex-specific mechanisms ma influence later-life mechanisms of neuronal resilience and vulnerability. The maintenance of energy metabolism, through the oxidative phosphorylation (OXPHOS) apparatus, is essential for brain health. Given the known association between reproductive period (years from menarche to menopause) and cognitive aging, we examined the hypothesis that cumulative estrogen exposure across the lifetime may be associated with differential methylation of genes in the OXPHOS pathway. METHODS:Using DNA methylation patterns in the post-mortem dorsolateral prefrontal cortex (DLPFC) of 426 women prospectively followed until death in the Religious Orders Study and Rush Memory and Aging Project, we examined the relationship between reproductive period (subtracting age at menarche from age at menopause) and DNA methylation of a published set of autosomal OXPHOS genes previously implicated in stroke susceptibility. We then performed an unsupervised analysis of methylation levels across the Hallmark pathways from the Molecular Signatures Database. RESULTS:We observed a strong association between reproductive period and DNA methylation status across OXPHOS CpGs. We replicated this association between reproductive period and DNA methylation in a much larger set of OXPHOS genes in our unsupervised analysis. Here, reproductive period also showed associations with methylation in genes related to E2F, MYC and MTORC1 signaling, fatty acid metabolism and DNA repair. CONCLUSION:This study provides evidence from both a supervised and unsupervised analyses, that lifetime cumulative endogenous steroid exposures may play a role in maintenance of post-menopausal cellular balance, including in brain tissue.

IntroductionThe molecular underpinnings of the dissociation of cognitive performance and neuropathological burden are poorly understood, and there are currently no known genetic or epigenetic determinants of the dissociation.Methods and findings"Residual cognition" was quantified by regressing out the effects of cerebral pathologies and demographic characteristics on global cognitive performance proximate to death. To identify genes influencing residual cognition, we leveraged neuropathological, genetic, epigenetic, and transcriptional data available for deceased participants of the Religious Orders Study (n = 492) and the Rush Memory and Aging Project (n = 487). Given that our sample size was underpowered to detect genome-wide significance, we applied a multistep approach to identify genes influencing residual cognition, based on our prior observation that independent genetic and epigenetic risk factors can converge on the same locus. In the first step (n = 979), we performed a genome-wide association study with a predefined suggestive p < 10-5, and nine independent loci met this threshold in eight distinct chromosomal regions. Three of the six genes within 100 kb of the lead SNP are expressed in the dorsolateral prefrontal cortex (DLPFC): UNC5C, ENC1, and TMEM106B. In the second step, in the subset of participants with DLPFC DNA methylation data (n = 648), we found that residual cognition was related to differential DNA methylation of UNC5C and ENC1 (false discovery rate < 0.05). In the third step, in the subset of participants with DLPFC RNA sequencing data (n = 469), brain transcription levels of UNC5C and ENC1 were evaluated for their association with residual cognition: RNA levels of both UNC5C (estimated effect = -0.40, 95% CI -0.69 to -0.10, p = 0.0089) and ENC1 (estimated effect = 0.0064, 95% CI 0.0033 to 0.0096, p = 5.7 × 10-5) were associated with residual cognition. In secondary analyses, we explored the mechanism of these associations and found that ENC1 may be related to the previously documented effect of depression on cognitive decline, while UNC5C may alter the composition of presynaptic terminals. Of note, the TMEM106B allele identified in the first step as being associated with better residual cognition is in strong linkage disequilibrium with rs1990622A (r2 = 0.66), a previously identified protective allele for TDP-43 proteinopathy. Limitations include the small sample size for the genetic analysis, which was underpowered to detect genome-wide significance, the evaluation being limited to a single cortical region for epigenetic and transcriptomic data, and the use of categorical measures for certain non-amyloid-plaque, non-neurofibrillary-tangle neuropathologies.ConclusionsThrough a multistep analysis of cognitive, neuropathological, genomic, epigenomic, and transcriptomic data, we identified ENC1 and UNC5C as genes with convergent genetic, epigenetic, and transcriptomic evidence supporting a potential role in the dissociation of cognition and neuropathology in an aging population, and we expanded our understanding of the TMEM106B haplotype that is protective against TDP-43 proteinopathy.

Patterns of linkage between the ε4 allele of Apolipoprotein E (APOE) and '523 poly-T alleles in the adjacent gene, TOMM40, differ between Caucasian and African Americans. The extent to which this difference affects the risk of Alzheimer's disease (AD) is unclear. We compared the APOE ε4-TOMM40 '523 haplotypes between older Caucasian and African Americans, and examined their relationship with AD dementia. Data came from three community based cohort studies of diverse participants. APOE genotypes were determined by polymorphisms of rs429358 and rs7412. TOMM40 '523 genotypes were defined by the poly-T repeat length of rs10524523 (short ['523-S]: poly-T ≤ 19, long ['523-L]: 20 ≤ poly-T ≤ 29, and very long ['523-VL]: poly-T ≥ 30). Cox proportional hazards models examined the effect of haplotype variation on the risk of incident AD dementia. A total of 1,848 Caucasian and 540 African American individuals were included in the study. In Caucasians, nearly none (0.8%) of the non-ε4 carriers and almost all (94.2%) of the ε4 carriers had '523-L. The classification was highly concordant. Each ε4 allele doubled the risk for AD dementia and the dose effect was evident. Almost identical effect size and effect pattern were observed for TOMM40 '523-L. In African Americans, nearly none (1.1%) of the non-ε4 carriers had '523-L, but only 47.8% of the ε4 carriers had '523-L. The concordance was weaker compared with Caucasians. The effect patterns on incident AD dementia differed distinctively between ε4 and '523-L carriers. Further, both genotypic and allelic data support that among African Americans the ε4-'523-L haplotype had stronger effect on risk of AD dementia than other ε4-'523 haplotypes.

Nicotinamide mononucleotide adenylyl transferase 2 (NMNAT2) is neuroprotective in numerous preclinical models of neurodegeneration. Here, we show that brain nmnat2 mRNA levels correlate positively with global cognitive function and negatively with AD pathology. In AD brains, NMNAT2 mRNA and protein levels are reduced. NMNAT2 shifts its solubility and colocalizes with aggregated Tau in AD brains, similar to chaperones, which aid in the clearance or refolding of misfolded proteins. Investigating the mechanism of this observation, we discover a novel chaperone function of NMNAT2, independent from its enzymatic activity. NMNAT2 complexes with heat shock protein 90 (HSP90) to refold aggregated protein substrates. NMNAT2's refoldase activity requires a unique C-terminal ATP site, activated in the presence of HSP90. Furthermore, deleting NMNAT2 function increases the vulnerability of cortical neurons to proteotoxic stress and excitotoxicity. Interestingly, NMNAT2 acts as a chaperone to reduce proteotoxic stress, while its enzymatic activity protects neurons from excitotoxicity. Taken together, our data indicate that NMNAT2 exerts its chaperone or enzymatic function in a context-dependent manner to maintain neuronal health.

[This corrects the article DOI: 10.1371/journal.pgen.1005378.].

Recently, we have shown that the Parkinson's disease (PD) susceptibility locus MAPT (microtubule associated protein tau) is associated with parkinsonism in older adults without a clinical diagnosis of PD. In this study, we investigated the relationship between parkinsonian signs and MAPT transcripts by assessing the effect of MAPT haplotypes on alternative splicing and expression levels of the most common isoforms in two prospective clinicopathologic studies of aging.using regression analysis, controlling for age, sex, study and neuropathology, we evaluated 976 subjects with clinical, genotyping and brain pathology data for haplotype analysis. For transcript analysis, we obtained MAPT gene and isoform-level expression from the dorsolateral prefrontal cortex for 505 of these subjects.The MAPT H2 haplotype was associated with lower total MAPT expression (p = 1.2x10-14) and global parkinsonism at both study entry (p = 0.001) and proximate to death (p = 0.050). Specifically, haplotype H2 was primarily associated with bradykinesia in both assessments (p

Genome-wide association studies (GWAS) have identified more than 100 genetic variants contributing to BMI, a measure of body size, or waist-to-hip ratio (adjusted for BMI, WHRadjBMI), a measure of body shape. Body size and shape change as people grow older and these changes differ substantially between men and women. To systematically screen for age- and/or sex-specific effects of genetic variants on BMI and WHRadjBMI, we performed meta-analyses of 114 studies (up to 320,485 individuals of European descent) with genome-wide chip and/or Metabochip data by the Genetic Investigation of Anthropometric Traits (GIANT) Consortium. Each study tested the association of up to ~2.8M SNPs with BMI and WHRadjBMI in four strata (men ≤50y, men >50y, women ≤50y, women >50y) and summary statistics were combined in stratum-specific meta-analyses. We then screened for variants that showed age-specific effects (G x AGE), sex-specific effects (G x SEX) or age-specific effects that differed between men and women (G x AGE x SEX). For BMI, we identified 15 loci (11 previously established for main effects, four novel) that showed significant (FDR

Circadian rhythms modulate the biology of many human tissues, including brain tissues, and are driven by a near 24-hour transcriptional feedback loop. These rhythms are paralleled by 24-hour rhythms of large portions of the transcriptome. The role of dynamic DNA methylation in influencing these rhythms is uncertain. While recent work in Neurospora suggests that dynamic site-specific circadian rhythms of DNA methylation may play a role in modulating the fungal molecular clock, such rhythms and their relationship to RNA expression have not, to our knowledge, been elucidated in mammalian tissues, including human brain tissues. We hypothesized that 24-hour rhythms of DNA methylation exist in the human brain, and play a role in driving 24-hour rhythms of RNA expression. We analyzed DNA methylation levels in post-mortem human dorsolateral prefrontal cortex samples from 738 subjects. We assessed for 24-hour rhythmicity of 420,132 DNA methylation sites throughout the genome by considering methylation levels as a function of clock time of death and parameterizing these data using cosine functions. We determined global statistical significance by permutation. We then related rhythms of DNA methylation with rhythms of RNA expression determined by RNA sequencing. We found evidence of significant 24-hour rhythmicity of DNA methylation. Regions near transcription start sites were enriched for high-amplitude rhythmic DNA methylation sites, which were in turn time locked to 24-hour rhythms of RNA expression of nearby genes, with the nadir of methylation preceding peak transcript expression by 1-3 hours. Weak ante-mortem rest-activity rhythms were associated with lower amplitude DNA methylation rhythms as were older age and the presence of Alzheimer's disease. These findings support the hypothesis that 24-hour rhythms of DNA methylation, particularly near transcription start sites, may play a role in driving 24-hour rhythms of gene expression in the human dorsolateral prefrontal cortex, and may be affected by age and Alzheimer's disease.

Alzheimer's disease (AD) and related dementias are a major public health challenge and present a therapeutic imperative for which we need additional insight into molecular pathogenesis. We performed a genome-wide association study and analysis of known genetic risk loci for AD dementia using neuropathologic data from 4,914 brain autopsies. Neuropathologic data were used to define clinico-pathologic AD dementia or controls, assess core neuropathologic features of AD (neuritic plaques, NPs; neurofibrillary tangles, NFTs), and evaluate commonly co-morbid neuropathologic changes: cerebral amyloid angiopathy (CAA), Lewy body disease (LBD), hippocampal sclerosis of the elderly (HS), and vascular brain injury (VBI). Genome-wide significance was observed for clinico-pathologic AD dementia, NPs, NFTs, CAA, and LBD with a number of variants in and around the apolipoprotein E gene (APOE). GalNAc transferase 7 (GALNT7), ATP-Binding Cassette, Sub-Family G (WHITE), Member 1 (ABCG1), and an intergenic region on chromosome 9 were associated with NP score; and Potassium Large Conductance Calcium-Activated Channel, Subfamily M, Beta Member 2 (KCNMB2) was strongly associated with HS. Twelve of the 21 non-APOE genetic risk loci for clinically-defined AD dementia were confirmed in our clinico-pathologic sample: CR1, BIN1, CLU, MS4A6A, PICALM, ABCA7, CD33, PTK2B, SORL1, MEF2C, ZCWPW1, and CASS4 with 9 of these 12 loci showing larger odds ratio in the clinico-pathologic sample. Correlation of effect sizes for risk of AD dementia with effect size for NFTs or NPs showed positive correlation, while those for risk of VBI showed a moderate negative correlation. The other co-morbid neuropathologic features showed only nominal association with the known AD loci. Our results discovered new genetic associations with specific neuropathologic features and aligned known genetic risk for AD dementia with specific neuropathologic changes in the largest brain autopsy study of AD and related dementias.

Genome-wide association studies (GWAS) and subsequent dense-genotyping of associated loci identified over a hundred single-nucleotide polymorphism (SNP) variants associated with the risk of rheumatoid arthritis (RA), type 1 diabetes (T1D), and celiac disease (CeD). Immunological and genetic studies suggest a role for CD4-positive effector memory T (CD+ TEM) cells in the pathogenesis of these diseases. To elucidate mechanisms of autoimmune disease alleles, we investigated molecular phenotypes in CD4+ effector memory T cells potentially affected by these variants. In a cohort of genotyped healthy individuals, we isolated high purity CD4+ TEM cells from peripheral blood, then assayed relative abundance, proliferation upon T cell receptor (TCR) stimulation, and the transcription of 215 genes within disease loci before and after stimulation. We identified 46 genes regulated by cis-acting expression quantitative trait loci (eQTL), the majority of which we detected in stimulated cells. Eleven of the 46 genes with eQTLs were previously undetected in peripheral blood mononuclear cells. Of 96 risk alleles of RA, T1D, and/or CeD in densely genotyped loci, eleven overlapped cis-eQTLs, of which five alleles completely explained the respective signals. A non-coding variant, rs389862A, increased proliferative response (p=4.75 × 10-8). In addition, baseline expression of seventeen genes in resting cells reliably predicted proliferative response after TCR stimulation. Strikingly, however, there was no evidence that risk alleles modulated CD4+ TEM abundance or proliferation. Our study underscores the power of examining molecular phenotypes in relevant cells and conditions for understanding pathogenic mechanisms of disease variants.

BACKGROUND:Alzheimer's disease is a common debilitating dementia with known heritability, for which 20 late onset susceptibility loci have been identified, but more remain to be discovered. This study sought to identify new susceptibility genes, using an alternative gene-wide analytical approach which tests for patterns of association within genes, in the powerful genome-wide association dataset of the International Genomics of Alzheimer's Project Consortium, comprising over 7 m genotypes from 25,580 Alzheimer's cases and 48,466 controls. PRINCIPAL FINDINGS:In addition to earlier reported genes, we detected genome-wide significant loci on chromosomes 8 (TP53INP1, p = 1.4×10-6) and 14 (IGHV1-67 p = 7.9×10-8) which indexed novel susceptibility loci. SIGNIFICANCE:The additional genes identified in this study, have an array of functions previously implicated in Alzheimer's disease, including aspects of energy metabolism, protein degradation and the immune system and add further weight to these pathways as potential therapeutic targets in Alzheimer's disease.

Integrating genetic data from families with highly penetrant forms of disease together with genetic data from outbred populations represents a promising strategy to uncover the complete frequency spectrum of risk alleles for complex traits such as rheumatoid arthritis (RA). Here, we demonstrate that rare, low-frequency and common alleles at one gene locus, phospholipase B1 (PLB1), might contribute to risk of RA in a 4-generation consanguineous pedigree (Middle Eastern ancestry) and also in unrelated individuals from the general population (European ancestry). Through identity-by-descent (IBD) mapping and whole-exome sequencing, we identified a non-synonymous c.2263G>C (p.G755R) mutation at the PLB1 gene on 2q23, which significantly co-segregated with RA in family members with a dominant mode of inheritance (P = 0.009). We further evaluated PLB1 variants and risk of RA using a GWAS meta-analysis of 8,875 RA cases and 29,367 controls of European ancestry. We identified significant contributions of two independent non-coding variants near PLB1 with risk of RA (rs116018341 [MAF = 0.042] and rs116541814 [MAF = 0.021], combined P = 3.2 × 10(-6)). Finally, we performed deep exon sequencing of PLB1 in 1,088 RA cases and 1,088 controls (European ancestry), and identified suggestive dispersion of rare protein-coding variant frequencies between cases and controls (P = 0.049 for C-alpha test and P = 0.055 for SKAT). Together, these data suggest that PLB1 is a candidate risk gene for RA. Future studies to characterize the full spectrum of genetic risk in the PLB1 genetic locus are warranted.

The major histocompatibility complex (MHC) region is strongly associated with multiple sclerosis (MS) susceptibility. HLA-DRB1*15:01 has the strongest effect, and several other alleles have been reported at different levels of validation. Using SNP data from genome-wide studies, we imputed and tested classical alleles and amino acid polymorphisms in 8 classical human leukocyte antigen (HLA) genes in 5,091 cases and 9,595 controls. We identified 11 statistically independent effects overall: 6 HLA-DRB1 and one DPB1 alleles in class II, one HLA-A and two B alleles in class I, and one signal in a region spanning from MICB to LST1. This genomic segment does not contain any HLA class I or II genes and provides robust evidence for the involvement of a non-HLA risk allele within the MHC. Interestingly, this region contains the TNF gene, the cognate ligand of the well-validated TNFRSF1A MS susceptibility gene. The classical HLA effects can be explained to some extent by polymorphic amino acid positions in the peptide-binding grooves. This study dissects the independent effects in the MHC, a critical region for MS susceptibility that harbors multiple risk alleles.

Although genetic and non-genetic studies in mouse and human implicate the CD40 pathway in rheumatoid arthritis (RA), there are no approved drugs that inhibit CD40 signaling for clinical care in RA or any other disease. Here, we sought to understand the biological consequences of a CD40 risk variant in RA discovered by a previous genome-wide association study (GWAS) and to perform a high-throughput drug screen for modulators of CD40 signaling based on human genetic findings. First, we fine-map the CD40 risk locus in 7,222 seropositive RA patients and 15,870 controls, together with deep sequencing of CD40 coding exons in 500 RA cases and 650 controls, to identify a single SNP that explains the entire signal of association (rs4810485, P = 1.4×10(-9)). Second, we demonstrate that subjects homozygous for the RA risk allele have ∼33% more CD40 on the surface of primary human CD19+ B lymphocytes than subjects homozygous for the non-risk allele (P = 10(-9)), a finding corroborated by expression quantitative trait loci (eQTL) analysis in peripheral blood mononuclear cells from 1,469 healthy control individuals. Third, we use retroviral shRNA infection to perturb the amount of CD40 on the surface of a human B lymphocyte cell line (BL2) and observe a direct correlation between amount of CD40 protein and phosphorylation of RelA (p65), a subunit of the NF-κB transcription factor. Finally, we develop a high-throughput NF-κB luciferase reporter assay in BL2 cells activated with trimerized CD40 ligand (tCD40L) and conduct an HTS of 1,982 chemical compounds and FDA-approved drugs. After a series of counter-screens and testing in primary human CD19+ B cells, we identify 2 novel chemical inhibitors not previously implicated in inflammation or CD40-mediated NF-κB signaling. Our study demonstrates proof-of-concept that human genetics can be used to guide the development of phenotype-based, high-throughput small-molecule screens to identify potential novel therapies in complex traits such as RA.

Anti-tumor necrosis factor alpha (anti-TNF) biologic therapy is a widely used treatment for rheumatoid arthritis (RA). It is unknown why some RA patients fail to respond adequately to anti-TNF therapy, which limits the development of clinical biomarkers to predict response or new drugs to target refractory cases. To understand the biological basis of response to anti-TNF therapy, we conducted a genome-wide association study (GWAS) meta-analysis of more than 2 million common variants in 2,706 RA patients from 13 different collections. Patients were treated with one of three anti-TNF medications: etanercept (n = 733), infliximab (n = 894), or adalimumab (n = 1,071). We identified a SNP (rs6427528) at the 1q23 locus that was associated with change in disease activity score (ΔDAS) in the etanercept subset of patients (P = 8 × 10(-8)), but not in the infliximab or adalimumab subsets (P>0.05). The SNP is predicted to disrupt transcription factor binding site motifs in the 3' UTR of an immune-related gene, CD84, and the allele associated with better response to etanercept was associated with higher CD84 gene expression in peripheral blood mononuclear cells (P = 1 × 10(-11) in 228 non-RA patients and P = 0.004 in 132 RA patients). Consistent with the genetic findings, higher CD84 gene expression correlated with lower cross-sectional DAS (P = 0.02, n = 210) and showed a non-significant trend for better ΔDAS in a subset of RA patients with gene expression data (n = 31, etanercept-treated). A small, multi-ethnic replication showed a non-significant trend towards an association among etanercept-treated RA patients of Portuguese ancestry (n = 139, P = 0.4), but no association among patients of Japanese ancestry (n = 151, P = 0.8). Our study demonstrates that an allele associated with response to etanercept therapy is also associated with CD84 gene expression, and further that CD84 expression correlates with disease activity. These findings support a model in which CD84 genotypes and/or expression may serve as a useful biomarker for response to etanercept treatment in RA patients of European ancestry.

To optimally leverage the scalability and unique features of the electronic health records (EHR) for research that would ultimately improve patient care, we need to accurately identify patients and extract clinically meaningful measures. Using multiple sclerosis (MS) as a proof of principle, we showcased how to leverage routinely collected EHR data to identify patients with a complex neurological disorder and derive an important surrogate measure of disease severity heretofore only available in research settings.In a cross-sectional observational study, 5,495 MS patients were identified from the EHR systems of two major referral hospitals using an algorithm that includes codified and narrative information extracted using natural language processing. In the subset of patients who receive neurological care at a MS Center where disease measures have been collected, we used routinely collected EHR data to extract two aggregate indicators of MS severity of clinical relevance multiple sclerosis severity score (MSSS) and brain parenchymal fraction (BPF, a measure of whole brain volume).The EHR algorithm that identifies MS patients has an area under the curve of 0.958, 83% sensitivity, 92% positive predictive value, and 89% negative predictive value when a 95% specificity threshold is used. The correlation between EHR-derived and true MSSS has a mean R(2) = 0.38±0.05, and that between EHR-derived and true BPF has a mean R(2) = 0.22±0.08. To illustrate its clinical relevance, derived MSSS captures the expected difference in disease severity between relapsing-remitting and progressive MS patients after adjusting for sex, age of symptom onset and disease duration (p = 1.56×10(-12)).Incorporation of sophisticated codified and narrative EHR data accurately identifies MS patients and provides estimation of a well-accepted indicator of MS severity that is widely used in research settings but not part of the routine medical records. Similar approaches could be applied to other complex neurological disorders.

To assess the potential of an online platform, PatientsLikeMe.com (PLM), for research in multiple sclerosis (MS). An investigation of the role of body mass index (BMI) on MS disease course was conducted to illustrate the utility of the platform.First, we compared the demographic characteristics of subjects from PLM and from a regional MS center. Second, we validated PLM's patient-reported outcome measure (MS Rating Scale, MSRS) against standard physician-rated tools. Finally, we analyzed the relation of BMI to the MSRS measure.Compared with 4,039 MS Center patients, the 10,255 PLM members were younger, more educated, and less often male and white. Disease course was more often relapsing remitting, with younger symptom onset and shorter disease duration. Differences were significant because of large sample sizes but small in absolute terms. MSRS scores for 121 MS Center patients revealed acceptable agreement between patient-derived and physician-derived composite scores (weighted kappa = 0.46). The Walking domain showed the highest weighted kappa (0.73) and correlation (rs = 0.86) between patient and physician scores. Additionally, there were good correlations between the patient-reported MSRS composite and walking scores and physician-derived measures: Expanded Disability Status Scale (composite rs = 0.61, walking rs = 0.74), Timed 25 Foot Walk (composite rs = 0.70, walking rs = 0.69), and Ambulation Index (composite rs = 0.81, walking rs = 0.84). Finally, using PLM data, we found a modest correlation between BMI and cross-sectional MSRS (rho = 0.17) and no association between BMI and disease course.The PLM population is comparable to a clinic population, and its patient-reported MSRS is correlated with existing clinical instruments. Thus, this online platform may provide a venue for MS investigations with unique strengths (frequent data collection, large sample sizes). To illustrate its applicability, we assessed the role of BMI in MS disease course but did not find a clinically meaningful role for BMI in this setting.

Genome-wide association (GWA) studies have identified numerous, replicable, genetic associations between common single nucleotide polymorphisms (SNPs) and risk of common autoimmune and inflammatory (immune-mediated) diseases, some of which are shared between two diseases. Along with epidemiological and clinical evidence, this suggests that some genetic risk factors may be shared across diseases-as is the case with alleles in the Major Histocompatibility Locus. In this work we evaluate the extent of this sharing for 107 immune disease-risk SNPs in seven diseases: celiac disease, Crohn's disease, multiple sclerosis, psoriasis, rheumatoid arthritis, systemic lupus erythematosus, and type 1 diabetes. We have developed a novel statistic for Cross Phenotype Meta-Analysis (CPMA) which detects association of a SNP to multiple, but not necessarily all, phenotypes. With it, we find evidence that 47/107 (44%) immune-mediated disease risk SNPs are associated to multiple-but not all-immune-mediated diseases (SNP-wise P(CPMA)

Postoperative ventricular dysfunction (VnD) occurs in 9-20% of coronary artery bypass graft (CABG) surgical patients and is associated with increased postoperative morbidity and mortality. Understanding genetic causes of postoperative VnD should enhance patient risk stratification and improve treatment and prevention strategies. We aimed to determine if genetic variants associate with occurrence of in-hospital VnD after CABG surgery.A genome-wide association study identified single nucleotide polymorphisms (SNPs) associated with postoperative VnD in male subjects of European ancestry undergoing isolated primary CABG surgery with cardiopulmonary bypass. VnD was defined as the need for ≥2 inotropes or mechanical ventricular support after CABG surgery. Validated SNPs were assessed further in two replication CABG cohorts and meta-analysis was performed.Over 100 SNPs were associated with VnD (P2.1) of developing in-hospital VnD after CABG surgery. However, three genetic loci identified by meta-analysis were more modestly associated with development of postoperative VnD. Studies of larger cohorts to assess these loci as well as to define other genetic mechanisms and related biology that link genetic variants to postoperative ventricular dysfunction are warranted.

Brain atrophy and cognitive dysfunction are neurodegenerative features of Multiple Sclerosis (MS). We used a candidate gene approach to address whether genetic variants implicated in susceptibility to late onset Alzheimer's Disease (AD) influence brain volume and cognition in MS patients.MS subjects were genotyped for five single nucleotide polymorphisms (snps) associated with susceptibility to AD: PICALM, CR1, CLU, PCK1, and ZNF224. We assessed brain volume using Brain Parenchymal Fraction (BPF) measurements obtained from Magnetic Resonance Imaging (MRI) data and cognitive function using the Symbol Digit Modalities Test (SDMT). Genotypes were correlated with cross-sectional BPF and SDMT scores using linear regression after adjusting for sex, age at symptom onset, and disease duration. 722 MS patients with a mean (±SD) age at enrollment of 41 (±10) years were followed for 44 (±28) months. The AD risk-associated allele of a non-synonymous SNP in the PCK1 locus (rs8192708G) is associated with a smaller average brain volume (P=0.0047) at the baseline MRI, but it does not impact our baseline estimate of cognition. PCK1 is additionally associated with higher baseline T2-hyperintense lesion volume (P=0.0088). Finally, we provide technical validation of our observation in a subset of 641 subjects that have more than one MRI study, demonstrating the same association between PCK1 and smaller average brain volume (P=0.0089) at the last MRI visit.Our study provides suggestive evidence for greater brain atrophy in MS patients bearing the PCK1 allele associated with AD-susceptibility, yielding new insights into potentially shared neurodegenerative process between MS and late onset AD.

Recent genetic studies have identified a growing number of loci with suggestive evidence of association with susceptibility to Alzheimer's disease (AD). However, little is known of the role of these candidate genes in influencing intermediate phenotypes associated with a diagnosis of AD, including cognitive decline or AD neuropathologic burden.Thirty-two single nucleotide polymorphisms (SNPs) previously implicated in AD susceptibility were genotyped in 414 subjects with both annual clinical evaluation and completed brain autopsies from the Religious Orders Study and the Rush Memory and Aging Project. Regression analyses evaluated the relation of SNP genotypes to continuous measures of AD neuropathology and cognitive function proximate to death. A SNP in the zinc finger protein 224 gene (ZNF224, rs3746319) was associated with both global AD neuropathology (p = 0.009) and global cognition (p = 0.002); whereas, a SNP at the phosphoenolpyruvate carboxykinase locus (PCK1, rs8192708) was selectively associated with global cognition (p = 3.57 x 10(-4)). The association of ZNF224 with cognitive impairment was mediated by neurofibrillary tangles, whereas PCK1 largely influenced cognition independent of AD pathology, as well as Lewy bodies and infarcts.The findings support the association of several loci with AD, and suggest how intermediate phenotypes can enhance analysis of susceptibility loci in this complex genetic disorder.

Multiple sclerosis (MS) and type 1 diabetes (T1D) are organ-specific autoimmune disorders with significant heritability, part of which is conferred by shared alleles. For decades, the Human Leukocyte Antigen (HLA) complex was the only known susceptibility locus for both T1D and MS, but loci outside the HLA complex harboring risk alleles have been discovered and fully replicated. A genome-wide association scan for MS risk genes and candidate gene association studies have previously described the IL2RA gene region as a shared autoimmune locus. In order to investigate whether autoimmunity risk at IL2RA was due to distinct or shared alleles, we performed a genetic association study of three IL2RA variants in a DNA collection of up to 9,407 healthy controls, 2,420 MS, and 6,425 T1D subjects as well as 1,303 MS parent/child trios. Here, we report "allelic heterogeneity" at the IL2RA region between MS and T1D. We observe an allele associated with susceptibility to one disease and risk to the other, an allele that confers susceptibility to both diseases, and an allele that may only confer susceptibility to T1D. In addition, we tested the levels of soluble interleukin-2 receptor (sIL-2RA) in the serum from up to 69 healthy control subjects, 285 MS, and 1,317 T1D subjects. We demonstrate that multiple variants independently correlate with sIL-2RA levels.

Lymphoblastoid cell lines (LCLs), originally collected as renewable sources of DNA, are now being used as a model system to study genotype-phenotype relationships in human cells, including searches for QTLs influencing levels of individual mRNAs and responses to drugs and radiation. In the course of attempting to map genes for drug response using 269 LCLs from the International HapMap Project, we evaluated the extent to which biological noise and non-genetic confounders contribute to trait variability in LCLs. While drug responses could be technically well measured on a given day, we observed significant day-to-day variability and substantial correlation to non-genetic confounders, such as baseline growth rates and metabolic state in culture. After correcting for these confounders, we were unable to detect any QTLs with genome-wide significance for drug response. A much higher proportion of variance in mRNA levels may be attributed to non-genetic factors (intra-individual variance--i.e., biological noise, levels of the EBV virus used to transform the cells, ATP levels) than to detectable eQTLs. Finally, in an attempt to improve power, we focused analysis on those genes that had both detectable eQTLs and correlation to drug response; we were unable to detect evidence that eQTL SNPs are convincingly associated with drug response in the model. While LCLs are a promising model for pharmacogenetic experiments, biological noise and in vitro artifacts may reduce power and have the potential to create spurious association due to confounding.

The major histocompatibility complex (MHC) is one of the most extensively studied regions in the human genome because of the association of variants at this locus with autoimmune, infectious, and inflammatory diseases. However, identification of causal variants within the MHC for the majority of these diseases has remained difficult due to the great variability and extensive linkage disequilibrium (LD) that exists among alleles throughout this locus, coupled with inadequate study design whereby only a limited subset of about 20 from a total of approximately 250 genes have been studied in small cohorts of predominantly European origin. We have performed a review and pooled analysis of the past 30 years of research on the role of the MHC in six genetically complex disease traits - multiple sclerosis (MS), type 1 diabetes (T1D), systemic lupus erythematosus (SLE), ulcerative colitis (UC), Crohn's disease (CD), and rheumatoid arthritis (RA) - in order to consolidate and evaluate the current literature regarding MHC genetics in these common autoimmune and inflammatory diseases. We corroborate established MHC disease associations and identify predisposing variants that previously have not been appreciated. Furthermore, we find a number of interesting commonalities and differences across diseases that implicate both general and disease-specific pathogenetic mechanisms in autoimmunity.