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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. Cardiovascular (CV)- and lifestyle-associated risk factors (RFs) are increasingly recognized as important for Alzheimer’s disease (AD) pathogenesis. Beyond the ε4 allele of apolipoprotein E (APOE), comparatively little is known about whether CV-associated genes also increase risk for AD. Using large genome-wide association studies and validated tools to quantify genetic overlap, we systematically identified single nucleotide polymorphisms (SNPs) jointly associated with AD and one or more CV-associated RFs, namely body mass index (BMI), type 2 diabetes (T2D), coronary artery disease (CAD), waist hip ratio (WHR), total cholesterol (TC), triglycerides (TG), low-density (LDL) and high-density lipoprotein (HDL). In fold enrichment plots, we observed robust genetic enrichment in AD as a function of plasma lipids (TG, TC, LDL, and HDL); we found minimal AD genetic enrichment conditional on BMI, T2D, CAD, and WHR. Beyond APOE, at conjunction FDR < 0.05 we identified 90 SNPs on 19 different chromosomes that were jointly associated with AD and CV-associated outcomes. In meta-analyses across three independent cohorts, we found four novel loci within MBLAC1 (chromosome 7, meta-p = 1.44 × 10−9), MINK1 (chromosome 17, meta-p = 1.98 × 10−7) and two chromosome 11 SNPs within the MTCH2/SPI1 region (closest gene = DDB2, meta-p = 7.01 × 10−7 and closest gene = MYBPC3, meta-p = 5.62 × 10−8). In a large ‘AD-by-proxy’ cohort from the UK Biobank, we replicated three of the four novel AD/CV pleiotropic SNPs, namely variants within MINK1, MBLAC1, and DDB2. Expression of MBLAC1, SPI1, MINK1 and DDB2 was differentially altered within postmortem AD brains. Beyond APOE, we show that the polygenic component of AD is enriched for lipid-associated RFs. We pinpoint a subset of cardiovascular-associated genes that strongly increase the risk for AD. Our collective findings support a disease model in which cardiovascular biology is integral to the development of clinical AD in a subset of individuals.

Summary Neurons that signal the angular velocity of head movements (AHV cells) are important for processing visual and spatial information. However, it has been challenging to isolate the sensory modality that drives them and to map their cortical distribution. To address this, we develop a method that enables rotating awake, head-fixed mice under a two-photon microscope in a visual environment. Starting in layer 2/3 of the retrosplenial cortex, a key area for vision and navigation, we find that 10% of neurons report angular head velocity (AHV). Their tuning properties depend on vestibular input with a smaller contribution of vision at lower speeds. Mapping the spatial extent, we find AHV cells in all cortical areas that we explored, including motor, somatosensory, visual, and posterior parietal cortex. Notably, the vestibular and visual contributions to AHV are area dependent. Thus, many cortical circuits have access to AHV, enabling a diverse integration with sensorimotor and cognitive information., Graphical abstract, Highlights • Neuronal tuning to angular head velocity (AHV) is widespread in L2/3 of mouse cortex • In most areas, AHV can be decoded with an accuracy close to psychophysical limits • AHV tuning is driven by vestibular and visual input in an area-dependent manner, Head motion signals are critical for spatial navigation and visual perception. Hennestad et al. develop a technique for rotating mice under a two-photon microscope and find neurons tuned to head rotation speed in many cortical areas. This tuning depends on vestibular and visual input in an area-dependent manner.

Alzheimer’s disease (AD) is highly heritable and recent studies have identified over 20 disease-associated genomic loci. Yet these only explain a small proportion of the genetic variance, indicating that undiscovered loci remain. Here, we performed a large genome-wide association study of clinically diagnosed AD and AD-by-proxy (71,880 cases, 383,378 controls). AD-by-proxy, based on parental diagnoses, showed strong genetic correlation with AD (rg = 0.81). Meta-analysis identified 29 risk loci, implicating 215 potential causative genes. Associated genes are strongly expressed in immune-related tissues and cell types (spleen, liver, and microglia). Gene-set analyses indicate biological mechanisms involved in lipid-related processes and degradation of amyloid precursor proteins. We show strong genetic correlations with multiple health-related outcomes, and Mendelian randomization results suggest a protective effect of cognitive ability on AD risk. These results are a step forward in identifying the genetic factors that contribute to AD risk and add novel insights into the neurobiology of AD. © 2019. This is the authors' accepted and refereed manuscript to the chapter. The final authenticated version is available online at: http://dx.doi.org/10.1038/s41588-018-0311-9

“The authors would like to thank the Norwegian Dementia Genetics Network (DemGene), the European DLB Consortium (E-DLB), Dementia Genetics Spanish Consortium (DEGESCO) and Genomic Research at Fundacio ACE (GR@ACE) Consortium. Fundacio ACE would like to thank patients and controls who participated in this project. We are indebted to private donors for their support of Fundacio ACE research programs. Fundacio ACE collaborates with the Centro de Investigacion Biomedica en Red sobre Enfermedades Neurodegenerativas (CIBERNED, Spain) and is one of the participating centers of the Dementia Genetics Spanish Consortium (DEGESCO). The study was founded by the Research Council of Norway, Norwegian Regional Health Authorities and Norwegian Health Association and 237250/EU/JPND (APGeM). The Cohort 2 results were generated with the assistance of financial support of grants PI13/02434 and PI16/01861. Accion Estrategica en Salud, integrated in the Spanish National R + D + I Plan and financed by ISCIII (Instituto de Salud Carlos III)-Subdireccion General de Evaluacion and the Fondo Europeo de Desarrollo Regional (FEDER- “Una manera de Hacer Europa”). GR@ACE project has been funded by Fundacion bancaria “La Caixa”, Grifols SA, Fundacio ACE. The funding sources did not in any way affect the results or presentation of the results.” should read: “The authors would like to thank the Norwegian Dementia Genetics Network (DemGene), the European DLB Consortium (E-DLB), Dementia Genetics Spanish Consortium (DEGESCO) and Genomic Research at Fundacio ACE (GR@ACE) Consortium. Fundacio ACE would like to thank patients and controls who participated in this project. We are indebted to private donors for their support of Fundacio ACE research programs. Fundacio ACE collaborates with the Centro de Investigacion Biomedica en Red sobre Enfermedades Neurodegenerativas (CIBERNED, Spain) and is one of the participating centers of the Dementia Genetics Spanish Consortium (DEGESCO). The study was founded by the Research Council of Norway, Norwegian Regional Health Authorities and Norwegian Health Association and 237250/EU/JPND (APGeM). The Cohort 2 results were generated with the assistance of financial support of grants PI13/02434 and PI16/01861. Accion Estrategica en Salud, integrated in the Spanish National R + D + I Plan and financed by ISCIII (Instituto de Salud Carlos III)-Subdireccion General de Evaluacion and the Fondo Europeo de Desarrollo Regional (FEDER- “Una manera de Hacer Europa”). GR@ACE project has been funded by Fundacion bancaria “La Caixa”, Grifols SA, Fundacio ACE. The funding sources did not in any way affect the results or presentation of the results. This paper represents independent research partly funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. The views expressed are those of the author and not necessarily those of the NIHR or the Department of Health and Social Care”.

Dementia with Lewy Bodies (DLB) is a common neurodegenerative disorder with poor prognosis and mainly unknown pathophysiology. Heritability estimates exceed 30% but few genetic risk variants have been identified. Here we investigated common genetic variants associated with DLB in a large European multisite sample. We performed a genome wide association study in Norwegian and European cohorts of 720 DLB cases and 6490 controls and included 19 top-associated single-nucleotide polymorphisms in an additional cohort of 108 DLB cases and 75545 controls from Iceland. Overall the study included 828 DLB cases and 82035 controls. Variants in the ASH1L/GBA (Chr1q22) and APOE ε4 (Chr19) loci were associated with DLB surpassing the genome-wide significance threshold (p

Background: A common polymorphism of the butyrylcholinesterase gene, the K-variant (BCHE-K) is associated with reduced butyrylcholinesterase (BuChE) activity. Insufficient studies exist regarding the frequency and role of BCHE-K in dementias. Objective: To determine the association of BCHE-K and APOE ɛ4 with diagnosis and rate of cognitive decline in dementia with Lewy bodies (DLB) and Alzheimer’s disease (AD) patients. Methods: Genomic DNA from 368 subjects (108 AD, 174 DLB, and 86 controls) from two routine clinical cohort studies in Norway; DemVest and TrønderBrain, were genotyped for BCHE-K and APOE ɛ4. The mild dementia DemVest subjects received annual Mini-Mental State Examination assessments for five years. Results: BCHE-K frequency was lower in DLB (33.9% ; p < 0.01) than in control subjects (51.2%), and was numerically lower in AD as well (38.9% ; p = 0.11). More rapid cognitive decline was associated with the APOE ɛ4 genotype, but not with the BCHE-K genotype. In an exploratory analysis of patients who completed all five follow-up visits, there was greater cognitive decline in BCHE-K carriers in the presence of the APOE ɛ4 allele than in the absence of these polymorphisms. Conclusion: BCHE-K is associated with a reduced risk for AD and DLB whereas APOE ɛ4 is associated with more rapid cognitive decline. The greater cognitive decline in individuals with both APOE ɛ4 and BCHE-K alleles require prospective confirmation in well-controlled trials.

Alzheimer’s disease (AD) is highly heritable and recent studies have identified over 20 disease-associated genomic loci. Yet these only explain a small proportion of the genetic variance, indicating that undiscovered loci remain. Here, we performed a large genome-wide association study of clinically diagnosed AD and AD-by-proxy (71,880 cases, 383,378 controls). AD-by-proxy, based on parental diagnoses, showed strong genetic correlation with AD (r(g)=0.81). Meta-analysis identified 29 risk loci, implicating 215 potential causative genes. Associated genes are strongly expressed in immune-related tissues and cell types (spleen, liver and microglia). Gene-set analyses indicate biological mechanisms involved in lipid-related processes and degradation of amyloid precursor proteins. We show strong genetic correlations with multiple health-related outcomes, and Mendelian randomisation results suggest a protective effect of cognitive ability on AD risk. These results are a step forward in identifying the genetic factors that contribute to AD risk and add novel insights into the neurobiology of AD.

Higher cognitive functions are regarded as one of the main distinctive traits of humans. Evidence for the cognitive evolution of human beings is mainly based on fossil records of an expanding cranium and an increasing complexity of material culture artefacts. However, the molecular genetic factors involved in the evolution are still relatively unexplored. Here, we investigated whether genomic regions that underwent positive selection in humans after divergence from Neanderthals are enriched for genetic association with phenotypes related to cognitive functions. We used genome wide association data from a study of college completion (N = 111,114), one of educational attainment (N = 293,623) and two different studies of general cognitive ability (N = 269,867 and 53,949). We found nominally significant polygenic enrichment of associations with college completion (p = 0.025), educational attainment (p = 0.043) and general cognitive ability (p = 0.015 and 0.025, respectively), suggesting that variants influencing these phenotypes are more prevalent in evolutionarily salient regions. The enrichment remained significant after controlling for other known genetic enrichment factors, and for affiliation to genes highly expressed in the brain. These findings support the notion that phenotypes related to higher order cognitive skills typical of humans have a recent genetic component that originated after the separation of the human and Neanderthal lineages.

Late onset Alzheimer’s disease (AD) is the most common form of dementia with more than 35 million people affected worldwide, and no curative treatment available. AD is highly heritable and recent genome-wide meta-analyses have identified over 20 genomic loci associated with AD, yet only explaining a small proportion of the genetic variance indicating that undiscovered loci exist. Here, we performed the largest genome-wide association study of clinically diagnosed AD and AD-by-proxy (71,880 AD cases, 383,378 controls). AD-by-proxy status is based on parental AD diagnosis, and showed strong genetic correlation with AD (rg=0.81). Genetic meta analysis identified 29 risk loci, of which 9 are novel, and implicating 215 potential causative genes. Independent replication further supports these novel loci in AD. Associated genes are strongly expressed in immune-related tissues and cell types (spleen, liver and microglia). Furthermore, gene-set analyses indicate the genetic contribution of biological mechanisms involved in lipid-related processes and degradation of amyloid precursor proteins. We show strong genetic correlations with multiple health-related outcomes, and Mendelian randomisation results suggest a protective effect of cognitive ability on AD risk. These results are a step forward in identifying more of the genetic factors that contribute to AD risk and add novel insights into the neurobiology of AD to guide new drug development.

Background Attention-Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopmental condition that affects about 5% of children and adolescents worldwide. Despite its high heritability little is known about underlying genetic factors. Among other things ADHD is tightly associated with educational failure. However, potential genetic overlap between ADHD and educational attainment has not been examined in detail so far. Exploiting epidemiological similarity between ADHD and educational attainment we aimed to improve discovery of ADHD-associated genetic factors and investigated genetic overlap between these phenotypes. Methods We used ADHD data from the PGC (2064 trios, 896 cases, 2455 controls) and educational attainment data from the SSGAC (N=328917). To investigate polygenic overlap between ADHD and educational attainment we constructed fold-enrichment plots and conditional QQ plots in both directions: conditioning ADHD on educational attainment and vice versa. To explore the nature of the polygenic overlap and test a hypothesis that investigated traits correlate genetically we calculated correlations between z-scores of ADHD and educational attainment variants for nested strata of variants, representing subsets of SNPs with increasing significance of p-values in one of the traits. Additionally we supported this hypothesis by estimating genetic correlation between ADHD and educational attainment using LD score regression. We applied condFDR/conjFDR method to identify specific loci associated with ADHD and loci associated with both ADHD and educational attainment simultaneously. Consistency of effect directions for top association signals detected in our condFDR/conjFDR analyses was tested in the independent GWAS of ADHD symptoms from EAGLE consortium (N=17666). Results Using condFDR/conjFDR method we identified five novel loci associated with ADHD, three of these being shared between ADHD and educational attainment. Leading variants for four of five identified regions are located in introns of protein coding genes: KDM4A, MEF2C, PINK1, RUNX1T1, while the remaining one is an intergenic SNP on chromosome 2 at 2p24. Four of five loci have opposite directions of effect in ADHD and educational attainment and consistent directions of effect in the independent GWAS of ADHD symptoms from the EAGLE consortium. A hypothesis of polygenic overlap between ADHD and educational attainment was supported by significant genetic correlation (rg=-0.403, p=7.90E-8), consistent pleiotropic enrichment in conditional QQ plots, >10-fold mutual enrichment of SNPs associated with both traits and growing negative correlation of association z-scores for the nested SNP strata with increasing significance in both phenotypes. Discussion We found five novel loci associated with ADHD and provided evidence for a shared genetic basis between ADHD and educational attainment, implicating three genetic loci in this overlap. Four of five identified loci showed consistent effects in the independent data set of ADHD symptoms, and inverse correlation with educational attainment. The latter is in line with prior epidemiological and genetic studies. We belive that altogether these findings provide new insights into the relationship between ADHD and educational attainment, suggesting shared molecular genetic mechanisms. Further research is required to clarify the biological effects of the identified genetic variants and how these may influence educational attainment and ADHD pathogenesis.

© The Author 2017. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. Schizophrenia (SCZ) is associated with differences in subcortical brain volumes and intracranial volume (ICV). However, little is known about the underlying etiology of these brain alterations. Here, we explored whether brain structure volumes and SCZ share genetic risk factors. Using conditional false discovery rate (FDR) analysis, we integrated genome-wide association study (GWAS) data on SCZ (n = 82315) and GWAS data on 7 subcortical brain volumes and ICV (n = 11840). By conditioning the FDR on overlapping associations, this statistical approach increases power to discover genetic loci. To assess the credibility of our approach, we stuDied the identified loci in larger GWAS samples on ICV (n = 26577) and hippocampal volume (n = 26814). We observed polygenic overlap between SCZ and volumes of hippocampus, putamen, and ICV. Based on conjunctional FDR < 0.05, we identified 2 loci shared between SCZ and ICV implicating genes FOXO3 (rs10457180) and ITIH4 (rs4687658), 2 loci shared between SCZ and hippocampal volume implicating SLC4A10 (rs4664442) and SPATS2L (rs1653290), and 2 loci shared between SCZ and volume of putamen implicating DCC (rs4632195) and DLG2 (rs11233632). The loci shared between SCZ and hippocampal volume or ICV had not reached significance in the primary GWAS on brain phenotypes. Proving our point of increased power, 2 loci did reach genome-wide significance with ICV (rs10457180) and hippocampal volume (rs4664442) in the larger GWAS. Three of the 6 identified loci are novel for SCZ. Altogether, the findings provide new insights into the relationship between SCZ and brain structure volumes, suggesting that their genetic architectures are not independent.

A large fraction of genetic risk factors for Alzheimer’s Disease (AD) is still not identified, limiting the understanding of AD pathology and study of therapeutic targets. We conducted a genome-wide association study (GWAS) of AD cases and controls of European descent from the multi-center DemGene network across Norway and two independent European cohorts. In a two-stage process, we first performed a meta-analysis using GWAS results from 2,893 AD cases and 6,858 cognitively normal controls from Norway and 25,580 cases and 48,466 controls from the International Genomics of Alzheimer’s Project (IGAP), denoted the discovery sample. Second, we selected the top hits (p

The genome-wide association study of the Psychiatric Genomics Consortium identified over one hundred schizophrenia susceptibility loci. The number of non-coding variants discovered suggests that gene regulation could mediate the effect of these variants on disease. Expression quantitative trait loci (eQTLs) contribute to variation in levels of mRNA. Given the co-occurrence of schizophrenia and several traits not involving the central nervous system (CNS), we investigated the enrichment of schizophrenia associations among eQTLs for four non-CNS tissues: adipose tissue, epidermal tissue, lymphoblastoid cells and blood. Significant enrichment was seen in eQTLs of all tissues: adipose (β = 0.18, p = 8.8 × 10-06), epidermal (β = 0.12, p = 3.1 × 10-04), lymphoblastoid (β = 0.19, p = 6.2 × 10-08) and blood (β = 0.19, p = 6.4 × 10-06). For comparison, we looked for enrichment of association with traits of known relevance to one or more of these tissues (body mass index, height, rheumatoid arthritis, systolic blood pressure and type-II diabetes) and found that schizophrenia enrichment was of similar scale to that observed when studying diseases in the context of a more likely causal tissue. To further investigate tissue specificity, we looked for differential enrichment of eQTLs with relevant Roadmap affiliation (enhancers and promoters) and varying distance from the transcription start site. Neither factor significantly contributed to the enrichment, suggesting that this is equally distributed in tissue-specific and cross-tissue regulatory elements. Our analyses suggest that functional correlates of schizophrenia risk are prevalent in non-CNS tissues. This could be because of pleiotropy or the effectiveness of variants affecting expression in different contexts. This suggests the utility of large, single-tissue eQTL experiments to increase eQTL discovery power in the study of schizophrenia, in addition to smaller, multiple-tissue approaches. Our results conform to the notion that schizophrenia is a systemic disorder involving many tissues.

Schizophrenia (SCZ) is a severe mental disorder associated with differences in subcortical brain volumes and intracranial volume (ICV). However, the molecular mechanisms underlying these brain abnormalities are poorly understood. Subcortical brain structures regulate a variety of higher-order functions including emotion, movement, cognition, and reward-seeking behavior. Moreover, the majority of dopamine D2-receptors targeted by antipsychotic drugs are located in the basal ganglia, and treatment with antipsychotics may alter the volumes of the basal ganglia and the hippocampus. Hence, elucidating whether SCZ and brain structure volumes share causative factors may provide important insights into SCZ pathogenesis and its relationship to brain structure formation. Both SCZ and brain structure volumes have high heritability estimates (60-80% and 70–90%, respectively), and brain volume alterations are also present in unaffected relatives of patients with SCZ, suggesting that genetic risk underlying SCZ may also influence brain structure volumes. Despite this, a recent study reported no evidence of shared genetic influences between subcortical brain volumes and SCZ after using a battery of (frequentist) statistical tools to analyze genome-wide association study (GWAS) data of these phenotypes (Franke et al, 2016). Here, we used an alternative approach to address the question of genetic overlap, by applying the conditional false discovery rate (cFDR) approach. This statistical framework increases power for discovery by leveraging overlapping signal across GWAS. Importantly, cFDR analysis allows for identifying overlapping loci regardless of their direction of allelic effects. Applying the cFDR methodology, we have previously demonstrated shared genetic variants between SCZ, Alzheimer’s disease, immune-related diseases and related phenotypes, and substantially increased the number of identified risk loci. Here, we analyzed summary data (p-values and z-scores) from GWAS on seven subcortical brain volumes and ICV (n=11,840) from the ENIGMA Consortium (Enhancing NeuroImaging Genetics through Meta-Analysis) and GWAS on SCZ (n=82,315) from PGC (Psychiatric Genomics Consortium). To validate our approach and assess the replicability of the identified variants, we studied them in larger GWAS samples on ICV and hippocampal volume (n≥26,577). We observed polygenic overlap between SCZ and volumes of hippocampus, putamen, and ICV. Based on conjunctional cFDR

Schizophrenia is associated with differences in personality traits, and recent studies suggest that personality traits and schizophrenia share a genetic basis. Here we aimed to identify specific genetic loci shared between schizophrenia and the Big Five personality traits using a Bayesian statistical framework. Using summary statistics from genome-wide association studies (GWAS) on personality traits in the 23andMe cohort (n = 59,225) and schizophrenia in the Psychiatric Genomics Consortium cohort (n = 82,315), we evaluated overlap in common genetic variants. The Big Five personality traits neuroticism, extraversion, openness, agreeableness and conscientiousness were measured using a web implementation of the Big Five Inventory. Applying the conditional false discovery rate approach, we increased discovery of genetic loci and identified two loci shared between neuroticism and schizophrenia and six loci shared between openness and schizophrenia. The study provides new insights into the relationship between personality traits and schizophrenia by highlighting genetic loci involved in their common genetic etiology.

Background Identifying individuals at risk for developing Alzheimer disease (AD) is of utmost importance. Although genetic studies have identified AD-associated SNPs in APOE and other genes, genetic information has not been integrated into an epidemiological framework for risk prediction. Methods and findings Using genotype data from 17,008 AD cases and 37,154 controls from the International Genomics of Alzheimer’s Project (IGAP Stage 1), we identified AD-associated SNPs (at p < 10−5). We then integrated these AD-associated SNPs into a Cox proportional hazard model using genotype data from a subset of 6,409 AD patients and 9,386 older controls from Phase 1 of the Alzheimer’s Disease Genetics Consortium (ADGC), providing a polygenic hazard score (PHS) for each participant. By combining population-based incidence rates and the genotype-derived PHS for each individual, we derived estimates of instantaneous risk for developing AD, based on genotype and age, and tested replication in multiple independent cohorts (ADGC Phase 2, National Institute on Aging Alzheimer’s Disease Center [NIA ADC], and Alzheimer’s Disease Neuroimaging Initiative [ADNI], total n = 20,680). Within the ADGC Phase 1 cohort, individuals in the highest PHS quartile developed AD at a considerably lower age and had the highest yearly AD incidence rate. Among APOE ε3/3 individuals, the PHS modified expected age of AD onset by more than 10 y between the lowest and highest deciles (hazard ratio 3.34, 95% CI 2.62–4.24, p = 1.0 × 10−22). In independent cohorts, the PHS strongly predicted empirical age of AD onset (ADGC Phase 2, r = 0.90, p = 1.1 × 10−26) and longitudinal progression from normal aging to AD (NIA ADC, Cochran–Armitage trend test, p = 1.5 × 10−10), and was associated with neuropathology (NIA ADC, Braak stage of neurofibrillary tangles, p = 3.9 × 10−6, and Consortium to Establish a Registry for Alzheimer’s Disease score for neuritic plaques, p = 6.8 × 10−6) and in vivo markers of AD neurodegeneration (ADNI, volume loss within the entorhinal cortex, p = 6.3 × 10−6, and hippocampus, p = 7.9 × 10−5). Additional prospective validation of these results in non-US, non-white, and prospective community-based cohorts is necessary before clinical use. Conclusions We have developed a PHS for quantifying individual differences in age-specific genetic risk for AD. Within the cohorts studied here, polygenic architecture plays an important role in modifying AD risk beyond APOE. With thorough validation, quantification of inherited genetic variation may prove useful for stratifying AD risk and as an enrichment strategy in therapeutic trials., Rahul Desikan and colleagues use genetic and epidemiological data from several large cohorts to derive a score for predicting the age-specific risk for developing Alzheimer's disease., Author summary Why was this study done? Across the United States, late-onset Alzheimer’s disease (AD) is the most common form of dementia. There is a strong need for in vivo markers for AD risk stratification and cohort enrichment in therapeutic trials. Although numerous studies have identified several genetic risk factors, including the ε4 allele of apolipoprotein E (APOE), genetic variants have not been integrated with genetic epidemiology for quantifying age of AD onset. What did the researchers do and find? Using genotype data from over 70,000 AD patients and normal elderly controls, we evaluated the feasibility of combining AD-associated SNPs and APOE status into a continuous measure—a polygenic hazard score (PHS)—for predicting the age-specific risk for developing AD. Using a survival model framework, we integrated single nucleotide polymorphisms associated with increased risk for AD into a PHS for each participant. By combining population-based incidence rates and the genotype-derived PHS for each individual, we derived estimates of instantaneous risk for developing AD, based on genotype and age, and tested replication in two independent cohorts. Individuals in the highest PHS quartile developed AD at a considerably lower age and had the highest yearly AD incidence rate. In independent cohorts, we found that the PHS strongly predicted empirical age of AD onset and longitudinal progression from normal aging to AD, and associated strongly with neuropathology and in vivo markers of AD neurodegeneration. Additional prospective validation of these results on non-US, non-white, and prospective community-based cohorts is necessary before clinical use. What do these findings mean? Genetic variants can be integrated within an epidemiology framework to derive a polygenic score that can quantify individual differences in age-specific genetic risk for AD, beyond APOE. Quantification of inherited genetic variation may prove useful for AD risk stratification and for therapeutic trials.

© 2017 Macmillan Publishers Limited, part of Springer Nature. The most recent genome-wide association studies (GWAS) of schizophrenia (SCZ) identified hundreds of risk variants potentially implicated in the disease. Further, novel statistical methodology designed for polygenic architecture revealed more potential risk variants. This can provide a link between individual genetic factors and the mechanistic underpinnings of SCZ. Intriguingly, a large number of genes coding for ionotropic and metabotropic receptors for various neurotransmitters - glutamate, 3-aminobutyric acid (GABA), dopamine, serotonin, acetylcholine and opioids - and numerous ion channels were associated with SCZ. Here, we review these findings from the standpoint of classical neurobiological knowledge of neuronal synaptic transmission and regulation of electrical excitability. We show that a substantial proportion of the identified genes are involved in intracellular cascades known to integrate slow' (G-protein-coupled receptors) and fast' (ionotropic receptors) neurotransmission converging on the protein DARPP-32. Inspection of the Human Brain Transcriptome Project database confirms that that these genes are indeed expressed in the brain, with the expression profile following specific developmental trajectories, underscoring their relevance to brain organization and function. These findings extend the existing pathophysiology hypothesis by suggesting a unifying role of dysregulation in neuronal excitability and synaptic integration in SCZ. This emergent model supports the concept of SCZ as an associative' disorder - a breakdown in the communication across different slow and fast neurotransmitter systems through intracellular signaling pathways - and may unify a number of currently competing hypotheses of SCZ pathophysiology.

Background: Epidemiological findings suggest a relationship between multiple sclerosis (MS) and cardiovascular disease (CVD) risk factors, although the nature of this relationship is not well understood. Objective: We used genome-wide association study (GWAS) data to identify shared genetic factors (pleiotropy) between MS and CVD risk factors. Methods: Using summary statistics from a large, recent GWAS (total n > 250,000 individuals), we investigated overlap in single nucleotide polymorphisms (SNPs) associated with MS and a number of CVD risk factors including triglycerides (TG), low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, body mass index, waist-to-hip ratio, type 2 diabetes, systolic blood pressure, and C-reactive protein level. Results and conclusion: Using conditional enrichment plots, we found 30-fold enrichment of MS SNPs for different levels of association with LDL and TG SNPs, with a corresponding reduction in conditional false discovery rate (FDR). We identified 133 pleiotropic loci outside the extended major histocompatibility complex with conditional FDR

ImportanceIdentifying individuals at risk for developing Alzheimer’s disease (AD) is of utmost importance. Although genetic studies have identified APOE and other AD associated single nucleotide polymorphisms (SNPs), genetic information has not been integrated into an epidemiological framework for personalized risk prediction.ObjectiveTo develop, replicate and validate a novel polygenic hazard score for predicting age-specific risk for AD.SettingMulti-center, multi-cohort genetic and clinical data.ParticipantsWe assessed genetic data from 17,008 AD patients and 37,154 controls from the International Genetics of Alzheimer’s Project (IGAP), and 6,409 AD patients and 9,386 older controls from Phase 1 Alzheimer’s Disease Genetics Consortium (ADGC). As independent replication and validation cohorts, we also evaluated genetic, neuroimaging, neuropathologic, CSF and clinical data from ADGC Phase 2, National Institute of Aging Alzheimer’s Disease Center (NIA ADC) and Alzheimer’s Disease Neuroimaging Initiative (ADNI) (total n = 20,680)Main Outcome(s) and Measure(s)Use the IGAP cohort to first identify AD associated SNPs (at p < 10-5). Next, integrate these AD associated SNPs into a Cox proportional hazards model using ADGC phase 1 genetic data, providing a polygenic hazard score (PHS) for each participant. Combine population based incidence rates, and genotype-derived PHS for each individual to derive estimates of instantaneous risk for developing AD, based on genotype and age. Finally, assess replication and validation of PHS in independent cohorts.ResultsIndividuals in the highest PHS quantiles developed AD at a considerably lower age and had the highest yearly AD incidence rate. Among APOE ε3/3 individuals, PHS modified expected age of AD onset by more than 10 years between the lowest and highest deciles. In independent cohorts, PHS strongly predicted empirical age of AD onset (p = 1.1 x 10-26), longitudinal progression from normal aging to AD (p = 1.54 x 10-10) and associated with markers of AD neurodegeneration.ConclusionsWe developed, replicated and validated a clinically usable PHS for quantifying individual differences in age-specific risk of AD. Beyond APOE, polygenic architecture plays an important role in modifying AD risk. Precise quantification of AD genetic risk will be useful for early diagnosis and therapeutic strategies.

Copyright 2016 American Medical Association. All rights reserved. IMPORTANCE Late-onset Alzheimer disease (AD), the most common form of dementia, places a large burden on families and society. Although epidemiological and clinical evidence suggests a relationship between inflammation and AD, their relationship is not well understood and could have implications for treatment and prevention strategies. OBJECTIVE To determine whether a subset of genes involved with increased risk of inflammation are also associated with increased risk for AD. DESIGN, SETTING, AND PARTICIPANTS In a genetic epidemiology study conducted in July 2015, we systematically investigated genetic overlap between AD (International Genomics of Alzheimer's Project stage 1) and Crohn disease, ulcerative colitis, rheumatoid arthritis, type 1 diabetes, celiac disease, and psoriasis using summary data from genome-wide association studies at multiple academic clinical research centers. P values and odds ratios from genome-wide association studies of more than 100 000 individuals were from previous comparisons of patients vs respective control cohorts. Diagnosis for each disorder was previously established for the parent study using consensus criteria. MAIN OUTCOMES AND MEASURES The primary outcomewas the pleiotropic (conjunction) false discovery rate P value. Follow-up for candidate variants included neuritic plaque and neurofibrillary tangle pathology; longitudinal Alzheimer's Disease Assessment Scale cognitive subscale scores as a measure of cognitive dysfunction (Alzheimer's Disease Neuroimaging Initiative); and gene expression in AD vs control brains (Gene Expression Omnibus data). RESULTS Eight single-nucleotide polymorphisms (false discovery rate P < .05) were associated with both AD and immune-mediated diseases. Of these, rs2516049 (closest gene HLA-DRB5; conjunction false discovery rate P = .04 for AD and psoriasis, 5.37 × 10-5for AD, and 6.03 × 10-15for psoriasis) and rs12570088 (closest gene IPMK; conjunction false discovery rate P = .009 for AD and Crohn disease, P = 5.73 × 10-6for AD, and 6.57 × 10-5for Crohn disease) demonstrated the same direction of allelic effect between AD and the immune-mediated diseases. Both rs2516049 and rs12570088 were significantly associated with neurofibrillary tangle pathology (P = .01352 and .03151, respectively); rs2516049 additionally correlated with longitudinal decline on Alzheimer's Disease Assessment Scale cognitive subscale scores (β [SE], 0.405 [0.190]; P = .03). Regarding gene expression, HLA-DRA and IPMK transcript expression was significantly altered in AD brains compared with control brains (HLA-DRA: β [SE], 0.155 [0.024]; P = 1.97 × 10-10; IPMK: β [SE], -0.096 [0.013]; P = 7.57 × 10-13). CONCLUSIONS AND RELEVANCE Our findings demonstrate genetic overlap between AD and immune-mediated diseases and suggest that immune system processes influence AD pathogenesis and progression.

Most of the genetic architecture of schizophrenia (SCZ) has not yet been identified. Here, we apply a novel statistical algorithm called Covariate-Modulated Mixture Modeling (CM3), which incorporates auxiliary information (heterozygosity, total linkage disequilibrium, genomic annotations, pleiotropy) for each single nucleotide polymorphism (SNP) to enable more accurate estimation of replication probabilities, conditional on the observed test statistic (“z-score”) of the SNP. We use a multiple logistic regression on z-scores to combine information from auxiliary information to derive a “relative enrichment score” for each SNP. For each stratum of these relative enrichment scores, we obtain nonparametric estimates of posterior expected test statistics and replication probabilities as a function of discovery z-scores, using a resampling-based approach that repeatedly and randomly partitions meta-analysis sub-studies into training and replication samples. We fit a scale mixture of two Gaussians model to each stratum, obtaining parameter estimates that minimize the sum of squared differences of the scale-mixture model with the stratified nonparametric estimates. We apply this approach to the recent genome-wide association study (GWAS) of SCZ (n = 82,315), obtaining a good fit between the model-based and observed effect sizes and replication probabilities. We observed that SNPs with low enrichment scores replicate with a lower probability than SNPs with high enrichment scores even when both they are genome-wide significant (p < 5x10-8). There were 693 and 219 independent loci with model-based replication rates ≥80% and ≥90%, respectively. Compared to analyses not incorporating relative enrichment scores, CM3 increased out-of-sample yield for SNPs that replicate at a given rate. This demonstrates that replication probabilities can be more accurately estimated using prior enrichment information with CM3., Author Summary Genome-wide association studies (GWAS) have thus far identified only a small fraction of the heritability of common complex disorders, such as schizophrenia. Here, we demonstrate that by using auxiliary information we can improve estimates of replication probabilities from GWAS summary statistics. The proposed Covariate-Modulated Mixture Model (CM3) incorporates auxiliary information to construct an “enrichment score” for each single nucleotide polymorphism (SNP). We show that a scale mixture of two Gaussians provides a good fit to the observed effect size distribution stratified by the predicted enrichment score when applied the method to a recent genome-wide association study (GWAS) of SCZ (n = 82,315). Compared to estimates performed not using auxiliary information, the CM3 more accurately models the observed replication rates by stratifying on covariate-modulated enrichment scores. We observed that SNPs with low enrichment scores replicate with a lower probability compared to SNPs with high enrichment scores, even when both are genome-wide significant (p < 5x10-8). At model-based replication rates ≥80% and ≥90% there were 693 and 219 independent loci, respectively. Increased out-of-sample yield for SNPs ranked according to CM3 demonstrate the utility of incorporating auxiliary information via CM3.

Characterizing the distribution of effects from genome-wide genotyping data is crucial for understanding important aspects of the genetic architecture of complex traits, such as number or proportion of non-null loci, average proportion of phenotypic variance explained per non-null effect, power for discovery, and polygenic risk prediction. To this end, previous work has used effect-size models based on various distributions, including the normal and normal mixture distributions, among others. In this paper we propose a scale mixture of two normals model for effect size distributions of genome-wide association study (GWAS) test statistics. Test statistics corresponding to null associations are modeled as random draws from a normal distribution with zero mean; test statistics corresponding to non-null associations are also modeled as normal with zero mean, but with larger variance. The model is fit via minimizing discrepancies between the parametric mixture model and resampling-based nonparametric estimates of replication effect sizes and variances. We describe in detail the implications of this model for estimation of the non-null proportion, the probability of replication in de novo samples, the local false discovery rate, and power for discovery of a specified proportion of phenotypic variance explained from additive effects of loci surpassing a given significance threshold. We also examine the crucial issue of the impact of linkage disequilibrium (LD) on effect sizes and parameter estimates, both analytically and in simulations. We apply this approach to meta-analysis test statistics from two large GWAS, one for Crohn’s disease (CD) and the other for schizophrenia (SZ). A scale mixture of two normals distribution provides an excellent fit to the SZ nonparametric replication effect size estimates. While capturing the general behavior of the data, this mixture model underestimates the tails of the CD effect size distribution. We discuss the implications of pervasive small but replicating effects in CD and SZ on genomic control and power. Finally, we conclude that, despite having very similar estimates of variance explained by genotyped SNPs, CD and SZ have a broadly dissimilar genetic architecture, due to differing mean effect size and proportion of non-null loci., Author Summary We describe in detail the implications of a particular mixture model (a scale mixture of two normals) for effect size distributions from genome-wide genotyping data. Parameters from this model can be used for estimation of the non-null proportion, the probability of replication in de novo samples, the local false discovery rate, power for detecting non-null loci, and proportion of variance explained from additive effects. Here, we fit this model by minimizing discrepancies with nonparametric estimates from a resampling-based algorithm. We examine the effects of linkage disequilibrium (LD) on effect sizes and parameter estimates, both analytically and in simulations. We validate this approach using meta-analysis test statistics (“z-scores”) from two large GWAS, one for Crohn’s disease and the other for schizophrenia. We demonstrate that for these studies a scale mixture of two normal distributions generally fits empirical replication effect sizes well, providing an excellent fit for the schizophrenia effect sizes but underestimating the tails of the distribution for Crohn’s disease.

Background In the era of Big Data, genome-wide association studies (GWASs) have been the main instrument to better our understanding of the genetics behind human traits. The schizophrenia GWAS by the psychiatric genomics consortium (PGC) identified many loci plausibly involved in the etiology of the disorder [Ripke et al., 2014]. However, the picture appears to be far from complete [Lichtenstein, 2009; Polderman, 2015; Gejman, 2010; Lee, 2012]. Judging by the number of non-coding variants identified by GWASs of schizophrenia and other complex traits, many causal variants are likely to affect the regulation of the genes involved rather than severely disrupt their function. Expression quantitative trait loci (eQTLs) are genomic loci that contribute to variation in levels of mRNA. It is known [Bacanu, 2014] that many eQTLs are associated with schizophrenia, as well as other phenotypes [Nicolae, 2010]. Methods Given known pleiotropy between schizophrenia and other traits, we looked for association enrichment of eQTLs in tissues not obviously relevant to psychiatric disorders (blood, adipose, LCL, and skin [Buil, 2015]). In order to consolidate any evidence of tissue-specificity, we further looked for differential enrichment in different ENCODE functional annotation categories. We used summary p-value statistics from schizophrenia as well as body mass index, Crohn's disease, height, systolic blood pressure and type II diabetes GWASs. We matched the eQTLs to SNP sets with similar minor allele frequencies (MAF) and distances from their respective transcription start sites (TSS), and no evidence of association with expression levels of any genes. We further subdivided eQTLs into proximal and distal eQTLs, according to their position relative to the TSS or to their affiliation to promoters, and we annotated them according to their ENCODE designation. We visualized the enrichment in fold enrichment plots, screened it using Fisher tests and assessed its extent by regressing the squared association z-score against SNP affiliations and LD-score. Results Significant enrichment was seen in adipose (beta=0.24, p=2.2E-06), LCL (beta=0.27, p=4.0E-08) and skin (beta=0.23, p=2.7E-06) tissues. The enrichment was as widespread as that observed in another highly polygenic trait such as height, and more widespread than in traits such as body mass index (adipose and skin) or blood pressure (adipose). The schizophrenia association extent does not appear to depend on the eQTLs' distance from the TSS nor from their explicit affiliation to specific functional elements, as defined by ENCODE, suggesting that risk alleles for schizophrenia could frequently act in many different tissue contexts. Discussion Our results are in line with the notion that schizophrenia is a disorder involving many tissues and known pleiotropy with cardiovascular and immune diseases back our findings. The statistical power of the underlying GWAS does play a role in the detection of association enrichment of the sort we sought. Polygenicity, however, is also a major factor, as exemplified by the results for height, body mass index and blood pressure. In summary, we find that looking for functional correlates of schizophrenia risk loci in non-obvious tissues is productive, and this could be because of either pleiotropy or increased effectiveness of variants that work in many different environmental contexts. This suggests the utility of large, single tissue eQTL experiments in the study of schizophrenia in addition to smaller, multiple tissue approaches.

Various alternatives have been developed to improve the winner-takes-all (WTA) mechanism in vector quantization, including the neural gas (NG). However, the behavior of these algorithms including their learning dynamics, robustness with respect to initialization, asymptotic results. etc. has only partially been studied in a rigorous mathematical analysis. The theory of on-line learning allows for an exact mathematical description of the training dynamics in model situations. We demonstrate using a system of three competing prototypes trained from a mixture of Gaussian clusters that the NG can improve convergence speed and achieves robustness to initial conditions. However, depending on the structure of the data, the NG does not always obtain the best asymptotic quantization error. (c) 2008 Elsevier B.V. All rights reserved.

Background Why schizophrenia has accompanied humans throughout our history despite its negative effect on fitness remains an evolutionary enigma. It is proposed that schizophrenia is a by-product of the complex evolution of the human brain and a compromise for humans' language, creative thinking, and cognitive abilities. Methods We analyzed recent large genome-wide association studies of schizophrenia and a range of other human phenotypes (anthropometric measures, cardiovascular disease risk factors, immune-mediated diseases) using a statistical framework that draws on polygenic architecture and ancillary information on genetic variants. We used information from the evolutionary proxy measure called the Neanderthal selective sweep (NSS) score. Results Gene loci associated with schizophrenia are significantly ( p = 7.30 × 10 −9 ) more prevalent in genomic regions that are likely to have undergone recent positive selection in humans (i.e., with a low NSS score). Variants in brain-related genes with a low NSS score confer significantly higher susceptibility than variants in other brain-related genes. The enrichment is strongest for schizophrenia, but we cannot rule out enrichment for other phenotypes. The false discovery rate conditional on the evolutionary proxy points to 27 candidate schizophrenia susceptibility loci, 12 of which are associated with schizophrenia and other psychiatric disorders or linked to brain development. Conclusions Our results suggest that there is a polygenic overlap between schizophrenia and NSS score, a marker of human evolution, which is in line with the hypothesis that the persistence of schizophrenia is related to the evolutionary process of becoming human.

Background— Epidemiological findings suggest a relationship between Alzheimer disease (AD), inflammation, and dyslipidemia, although the nature of this relationship is not well understood. We investigated whether this phenotypic association arises from a shared genetic basis. Methods and Results— Using summary statistics ( P values and odds ratios) from genome-wide association studies of >200 000 individuals, we investigated overlap in single-nucleotide polymorphisms associated with clinically diagnosed AD and C-reactive protein (CRP), triglycerides, and high- and low-density lipoprotein levels. We found up to 50-fold enrichment of AD single-nucleotide polymorphisms for different levels of association with C-reactive protein, low-density lipoprotein, high-density lipoprotein, and triglyceride single-nucleotide polymorphisms using a false discovery rate threshold HS3ST1 ; odds ratio=1.07; 95% confidence interval=1.05–1.11; P =2.86×10 −8 ) and chromosome 10 (rs7920721; closest gene, ECHDC3 ; odds ratio=1.07; 95% confidence interval=1.04–1.11; P =3.38×10 −8 ). We also found that gene expression of HS3ST1 and ECHDC3 was altered in AD brains compared with control brains. Conclusions— We demonstrate genetic overlap between AD, C-reactive protein, and plasma lipids. By conditioning on the genetic association with the cardiovascular phenotypes, we identify novel AD susceptibility loci, including 2 genome-wide significant variants conferring increased risk for AD.

Genome-wide association studies (GWAS) employing large sample sizes and sophisticated statistical methods have recently yielded detailed information on the set of genes affected in various psychiatric disorders. This is especially important for polygenic, highly heritable disorders such as schizophrenia (SCZ) [1]. The success lately witnessed in gene discovery brings up the next big challenge for psychiatric genetics - translation of the genetic associations into biological insights [2]. In this study, we propose a computational approach for investigating the effects of a collection of excitability-related genes on various neuronal characteristics. As a proof of principle, we apply our approach for studying effects of SCZ-linked genes on firing behavior of a layer V pyramidal cell (L5PC). A total of 108 genetic loci were recently confirmed to be associated with the risk of SCZ [3]. These loci span a wide set of protein-coding genes. The disorder is associated with genes affecting transmembrane currents of all major cationic species, Na+, K+, and Ca2+. In addition, some of the SCZ-linked genes are involved in regulation of the Ca2+ concentration in the intracellular medium, which is another great contributor to neuron excitability. All above aspects of electrophysiology are included in a recent multi-compartmental model of L5PC [4], which accurately describes the perisomatic firing behavior and its interplay with the generation of an apical Ca2+ spike. In this work, we rely on data from functional genomics studies that describe the effects of variants of certain ion channel or calcium transporter-encoding genes on the channel activation/inactivation properties or intracellular Ca2+ dynamics. We carry out our study by linking these effects to a change in the corresponding neuron model parameters, and observing the implication that these variants have on the information integration in an L5PC. It should be noted that information does not generally exist for the effect of single nucleotide polymorphism (SNP) variants identified through GWASs on the biophysical parameters required for the computational models. We instead use information obtained from in vitro studies of more extreme genetic variations, including loss of function mutations. A central assumption of this approach is that the effects of SNP variants can be represented as scaled-down versions of those of the more extreme variants, and that the emergence of the full psychiatric disease phenotype results from the combined effect of a large number of subtle SNP effects. Our results show a multitude of alterations of the firing behavior and integration of apical stimuli in neurons equipped with the considered gene variants. An especially interesting observation is that the variants affect the suppression of a second apical stimulus, which might have a connection with the deficit in prepulse inhibition, a condition often observed in SCZ patients. Although the analyses presented here are specific to L5PCs and SCZ-related genes, our "biophysical psychiatry" framework may be directly applicable to other cell types and other polygenic diseases, such as bipolar disorder and autism, given an identification of risk genes related to neuronal excitability. Furthermore, our approach could be directly applied to biophysically detailed models of neuronal networks and extended to consider synaptic ion channel-encoding genes that are relevant in SCZ [5].

© 2015 Macmillan Publishers Limited We investigated the genetic overlap between Alzheimer’s disease (AD) and Parkinson’s disease (PD). Using summary statistics (P-values) from large recent genome-wide association studies (GWAS) (total n=89 904 individuals), we sought to identify single nucleotide polymorphisms (SNPs) associating with both AD and PD. We found and replicated association of both AD and PD with the A allele of rs393152 within the extended MAPT region on chromosome 17 (meta analysis P-value across five independent AD cohorts=1.65 × 10-7). In independent datasets, we found a dose-dependent effect of the A allele of rs393152 on intra-cerebral MAPT transcript levels and volume loss within the entorhinal cortex and hippocampus. Our findings identify the tau-associated MAPT locus as a site of genetic overlap between AD and PD, and extending prior work, we show that the MAPT region increases risk of Alzheimer’s neurodegeneration.Molecular Psychiatry advance online publication, 17 February 2015; doi:10.1038/mp.2015.6.

© 2017 American Medical Association. All rights reserved. IMPORTANCE: Schizophrenia is associated with widespread cognitive impairments. Although cognitive deficits are one of the factors most strongly associated with functional outcome in schizophrenia, current treatment strategies largely fail to ameliorate these impairments. To develop more efficient treatment strategies in patients with schizophrenia, a better understanding of the pathogenesis of these cognitive deficits is needed. Accumulating evidence indicates that genetic risk of schizophrenia may contribute to cognitive dysfunction. OBJECTIVE: To identify genomic regions jointly influencing schizophrenia and the cognitive domains of reaction time and verbal-numerical reasoning, as well as general cognitive function, a phenotype that captures the shared variation in performance across cognitive domains. DESIGN, SETTING, AND PARTICIPANTS: Combining data from genome-wide association studies from multiple phenotypes using conditional false discovery rate analysis provides increased power to discover genetic variants and could elucidate shared molecular genetic mechanisms. Data from the following genome-wide association studies, published from July 24, 2014, to January 17, 2017, were combined: schizophrenia in the Psychiatric Genomics Consortium cohort (n = 79 757 [cases, 34 486; controls, 45 271]); verbal-numerical reasoning (n = 36 035) and reaction time (n = 111 483) in the UK Biobank cohort; and general cognitive function in CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) (n = 53 949) and COGENT (Cognitive Genomics Consortium) (n = 27 888). MAIN OUTCOMES AND MEASURES: Genetic loci identified by conditional false discovery rate analysis. Brain messenger RNA expression and brain expression quantitative trait locus functionality were determined. RESULTS: Among the participants in the genome-wide association studies, 21 loci jointly influencing schizophrenia and cognitive traits were identified: 2 loci shared between schizophrenia and verbal-numerical reasoning, 6 loci shared between schizophrenia and reaction time, and 14 loci shared between schizophrenia and general cognitive function. One locus was shared between schizophrenia and 2 cognitive traits and represented the strongest shared signal detected (nearest gene TCF20; chromosome 22q13.2), and was shared between schizophrenia (z score, 5.01; P = 5.53 × 10−7), general cognitive function (z score, –4.43; P = 9.42 × 10−6), and verbal-numerical reasoning (z score, –5.43; P = 5.64 × 10−8). For 18 loci, schizophrenia risk alleles were associated with poorer cognitive performance. The implicated genes are expressed in the developmental and adult human brain. Replicable expression quantitative trait locus functionality was identified for 4 loci in the adult human brain. CONCLUSIONS AND RELEVANCE: The discovered loci improve the understanding of the common genetic basis underlying schizophrenia and cognitive function, suggesting novel molecular genetic mechanisms.

© 2017 Srinivasan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Schizophrenia is suggested to be a by-product of the evolution in humans, a compromise for our language, creative thinking and cognitive abilities, and thus, essentially, a human disorder. The time of its origin during the course of human evolution remains unclear. Here we investigate several markers of early human evolution and their relationship to the genetic risk of schizophrenia. We tested the schizophrenia evolutionary hypothesis by analyzing genome-wide association studies of schizophrenia and other human phenotypes in a statistical framework suited for polygenic architectures. We analyzed evolutionary proxy measures: human accelerated regions, segmental duplications, and ohnologs, representing various time periods of human evolution for overlap with the human genomic loci associated with schizophrenia. Polygenic enrichment plots suggest a higher prevalence of schizophrenia associations in human accelerated regions, segmental duplications and ohnologs. However, the enrichment is mostly accounted for by linkage disequilibrium, especially with functional elements like introns and untranslated regions. Our results did not provide clear evidence that markers of early human evolution are more likely associated with schizophrenia. While SNPs associated with schizophrenia are enriched in HAR, Ohno and SD regions, the enrichment seems to be mediated by affiliation to known genomic enrichment categories. Taken together with previous results, these findings suggest that schizophrenia risk may have mainly developed more recently in human evolution.

Background In Genome-wide association studies (GWASs), biologically determined subsets of the single nucleotide polymorphisms (SNPs) can exhibit an increased proportion of extreme statistics compared to the whole set of SNPs. The excess of extreme statistics—which we refer to as "enrichment"—provides a means to improve gene discovery. This approach has proven valuable for polygenic traits like schizophrenia, for which pleiotropy [O. Andreassen et al. PLoS Genet. 9.4, e1003455 (2013)] and annotation categories in the genome [A. Schork et al. PLoS Genet. 9.4, e1003449 (2013)], successfully have been used as selection criteria. The degree of enrichment is, however, challenging to quantify; current enrichment claims are mainly based on visual inspection of QQ plots or fold enrichment plots. Methods We outline here a Bayesian method to quantify the degree of enrichment, based on a mixture model where each SNP is either null or non-null. What sets this apart from previous work, is that we incorporate the z-score covariance matrix for the multivariate (global) null distribution into the model, thereby accounting for the linkage disequilibrium dependence. By assuming that the z-scores with low absolute value are dominated by the null distribution, we can express the posterior distribution for the proportion of non-null SNPs in terms of the multivariate null distribution only, avoiding more specific assumptions about the unknown non-null distribution. We calculate this posterior distribution for the selected subsets of SNPs and its compliment, and quantify the level of enrichment by comparing the two. Results The method is tested on synthetic data, and shows promising results. We further compare the method to other approaches, like permutation tests. Finally, we show how this method can be applied to map out enrichment of SNP subsets for different psychiatric phenotypes. Discussion The proposed method seems to be a promising tool for quantifying enrichment in the context of GWAS summary statistics.

Epidemiologically related traits may share genetic risk factors, and pleiotropic analysis could identify individual loci associated with these traits. Because of their shared epidemiological associations, we conducted pleiotropic analysis of genome-wide association studies of lung cancer (12 160 lung cancer case patients and 16 838 control subjects) and cardiovascular disease risk factors (blood lipids from 188 577 subjects, type 2 diabetes from 148 821 subjects, body mass index from 123 865 subjects, and smoking phenotypes from 74 053 subjects). We found that 6p22.1 (rs6904596, ZNF184) was associated with both lung cancer (P = 5.50x10-6) and blood triglycerides (P = 1.39x10-5). We replicated the association in 6097 lung cancer case patients and 204 657 control subjects (P = 2.40 × 10-4) and in 71 113 subjects with triglycerides data (P = .01). rs6904596 reached genome-wide significance in lung cancer meta-analysis (odds ratio = 1.15, 95% confidence interval = 1.10 to 1.21,Pcombined = 5.20x10-9). The large sample size provided by the lipid GWAS data and the shared genetic risk factors between the two traits contributed to the uncovering of a hitherto unidentified genetic locus for lung cancer. This work was supported by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, DHHS, Bethesda, MD. Transdisciplinary Research for Cancer of Lung (TRICL): National Institute of Health U19 CA148127-01 (PI: Amos), Canadian Cancer Society Research Institute (No. 020214, PI: Hung). The Environment and Genetics in Lung Cancer Etiology (EAGLE), Prostate, Lung, Colon, Ovary Screening Trial (PLCO), and Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) studies were supported by the Intramural Research Program of the National Institutes of Health, National Cancer Institute (NCI), Division of Cancer Epidemiology and Genetics. ATBC was also supported by US Public Health Service contracts (N01-CN-45165, N01-RC-45035, and N01-RC-37004) from the NCI. PLCO was also supported by individual contracts from the NCI to the University of Colorado Denver (NO1-CN-25514), Georgetown University (NO1-CN-25522), the Pacific Health Research Institute (NO1-CN-25515), the Henry Ford Health System (NO1-CN-25512), the University of Minnesota (NO1-CN-25513), Washington University (NO1-CN-25516), the University of Pittsburgh (NO1-CN-25511), the University of Utah (NO1-CN-25524), the Marshfield Clinic Research Foundation (NO1-CN-25518), the University of Alabama at Birmingham (NO1-CN-75022), Westat, Inc. (NO1-CN-25476), and the University of California, Los Angeles (NO1-CN-25404). The Cancer Prevention Study-II (CPS-II) Nutrition Cohort was supported by the American Cancer Society. Funding for the Lung Cancer and Smoking study was provided by the National Institutes of Health (NIH), Genes, Environment and Health Initiative (GEI) Z01 CP 010200, NIH U01 HG004446, and NIH GEI U01 HG 004438. For the lung study, the GENEVA Coordinating Center provided assistance with genotype cleaning and general study coordination, and the Johns Hopkins University Center for Inherited Disease Research conducted genotyping. Harvard Lung Study: The Harvard Lung Cancer Study is supported by the National Institutes of Health (R01 CA092824, P50 CA090578, and R01 CA074386). Lung eQTL study: The lung eQTL study at Laval University was supported by the Chaire de pneumologie de la Fondation JD Bégin de l’Université Laval, the Fondation de l’Institut universitaire de cardiologie et de pneumologie de Québec, the Respiratory Health Network of the FRQS, the Canadian Institutes of Health Research (MOP - 123369), and the Cancer Research Society and Read for the Cure. Y. Bossé is the recipient of a Junior 2 Research Scholar award from the Fonds de recherche Québec – Santé (FRQS). Lung meQTL study: The meQTL study based on the environment and Genetics in Lung Cancer Etiology (EAGLE) study was supported by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Department of Health and Human Services. Epigenetics: This study was supported by NIH grant 1 R01 HL114094 to IAL-O and ZB, NIH grants 1 P30 H101258 and R37HL062569-13 to ZB. ZB is supported by the Ralph Edgington Chair in Medicine. CNM was supported in part by the department of Surgery, University of Southern California. Generation of epigenetic data was supported in part by a Norris Comprehensive Cancer Center core grant, award number P30CA014089, from the National Cancer Institute.

A variety of modifications have been employed to learning vector quantization (LVQ) algorithms using either crisp or soft windows for selection of data. Although these schemes have been shown in practice to improve performance, a theoretical study on the influence of windows has so far been limited. Here we rigorously analyze the influence of windows in a controlled environment of gaussian mixtures in high dimensions. Concepts from statistical physics and the theory of online learning allow an exact description of the training dynamics, yielding typical learning curves, convergence properties, and achievable generalization abilities. We compare the performance and demonstrate the advantages of various algorithms, including LVQ 2.1, generalized LVQ (GLVQ), Learning from Mistakes (LFM) and Robust Soft LVQ (RSLVQ). We find that the selection of the window parameter highly influences the learning curves but not, surprisingly, the asymptotic performances of LVQ 2.1 and RSLVQ. Although the prototypes of LVQ 2.1 exhibit divergent behavior, the resulting decision boundary coincides with the optimal decision boundary, thus yielding optimal generalization ability.