Search

A neuroimaging-based biomarker termed the brain age is thought to reflect variability in the brain's ageing process and predict longevity. Using Insight 46, a unique narrow-age birth cohort, we aimed to examine potential drivers and correlates of brain age.Participants, born in a single week in 1946 in mainland Britain, have had 24 prospective waves of data collection to date, including MRI and amyloid PET imaging at approximately 70 years old. Using MRI data from a previously defined selection of this cohort, we derived brain-predicted age from an established machine-learning model (trained on 2001 healthy adults aged 18-90 years); subtracting this from chronological age (at time of assessment) gave the brain-predicted age difference (brain-PAD). We tested associations with data from early life, midlife, and late life, as well as rates of MRI-derived brain atrophy.Between May 28, 2015, and Jan 10, 2018, 502 individuals were assessed as part of Insight 46. We included 456 participants (225 female), with a mean chronological age of 70·7 years (SD 0·7; range 69·2 to 71·9). The mean brain-predicted age was 67·9 years (8·2, 46·3 to 94·3). Female sex was associated with a 5·4-year (95% CI 4·1 to 6·8) younger brain-PAD than male sex. An increase in brain-PAD was associated with increased cardiovascular risk at age 36 years (β=2·3 [95% CI 1·5 to 3·0]) and 69 years (β=2·6 [1·9 to 3·3]); increased cerebrovascular disease burden (1·9 [1·3 to 2·6]); lower cognitive performance (-1·3 [-2·4 to -0·2]); and increased serum neurofilament light concentration (1·2 [0·6 to 1·9]). Higher brain-PAD was associated with future hippocampal atrophy over the subsequent 2 years (0·003 mL/year [0·000 to 0·006] per 5-year increment in brain-PAD). Early-life factors did not relate to brain-PAD. Combining 12 metrics in a hierarchical partitioning model explained 33% of the variance in brain-PAD.Brain-PAD was associated with cardiovascular risk, and imaging and biochemical markers of neurodegeneration. These findings support brain-PAD as an integrative summary metric of brain health, reflecting multiple contributions to pathological brain ageing, and which might have prognostic utility.Alzheimer's Research UK, Medical Research Council Dementia Platforms UK, Selfridges Group Foundation, Wolfson Foundation, Wellcome Trust, Brain Research UK, Alzheimer's Association.

Plasma biomarkers for Alzheimer’s disease-related pathologies have undergone rapid developments during the past few years, and there are now well-validated blood tests for amyloid and tau pathology, as well as neurodegeneration and astrocytic activation. To define Alzheimer’s disease with biomarkers rather than clinical assessment, we assessed prediction of research-diagnosed disease status using these biomarkers and tested genetic variants associated with the biomarkers that may reflect more accurately the risk of biochemically defined Alzheimer’s disease instead of the risk of dementia. In a cohort of Alzheimer’s disease cases [n = 1439, mean age 68 years (standard deviation = 8.2)] and screened controls [n = 508, mean age 82 years (standard deviation = 6.8)], we measured plasma concentrations of the 40 and 42 amino acid-long amyloid-β (Aβ) fragments (Aβ40 and Aβ42, respectively), tau phosphorylated at amino acid 181 (P-tau181), neurofilament light (NfL) and glial fibrillary acidic protein (GFAP) using state-of-the-art Single molecule array (Simoa) technology. We tested the relationships between the biomarkers and Alzheimer’s disease genetic risk, age at onset and disease duration. We also conducted a genome-wide association study for association of disease risk genes with these biomarkers. The prediction accuracy of Alzheimer’s disease clinical diagnosis by the combination of all biomarkers, APOE and polygenic risk score reached area under receiver operating characteristic curve (AUC) = 0.81, with the most significant contributors being ε4, Aβ40 or Aβ42, GFAP and NfL. All biomarkers were significantly associated with age in cases and controls (P < 4.3 × 10−5). Concentrations of the Aβ-related biomarkers in plasma were significantly lower in cases compared with controls, whereas other biomarker levels were significantly higher in cases. In the case-control genome-wide analyses, APOE-ε4 was associated with all biomarkers (P = 0.011−4.78 × 10−8), except NfL. No novel genome-wide significant single nucleotide polymorphisms were found in the case-control design; however, in a case-only analysis, we found two independent genome-wide significant associations between the Aβ42/Aβ40 ratio and WWOX and COPG2 genes. Disease prediction modelling by the combination of all biomarkers indicates that the variance attributed to P-tau181 is mostly captured by APOE-ε4, whereas Aβ40, Aβ42, GFAP and NfL biomarkers explain additional variation over and above APOE. We identified novel plausible genome wide-significant genes associated with Aβ42/Aβ40 ratio in a sample which is 50 times smaller than current genome-wide association studies in Alzheimer’s disease.

Background Metformin, a medication for type 2 diabetes, has been linked to many non-diabetes health benefits including increasing healthy lifespan. Previous work has only examined the benefits of metformin over periods of less than ten years, which may not be long enough to capture the true effect of this medication on longevity. Methods We searched medical records for Wales, UK, using the Secure Anonymised Information Linkage dataset for type 2 diabetes patients treated with metformin (N = 129,140) and sulphonylurea (N = 68,563). Non-diabetic controls were matched on sex, age, smoking, and history of cancer and cardiovascular disease. Survival analysis was performed to examine survival time after first treatment, using a range of simulated study periods. Findings Using the full twenty-year period, we found that type 2 diabetes patients treated with metformin had shorter survival time than matched controls, as did sulphonylurea patients. Metformin patients had better survival than sulphonylurea patients, controlling for age. Within the first three years, metformin therapy showed a benefit over matched controls, but this reversed after five years of treatment. Interpretation While metformin does appear to confer benefits to longevity in the short term, these initial benefits are outweighed by the effects of type 2 diabetes when patients are observed over a period of up to twenty years. Longer study periods are therefore recommended for studying longevity and healthy lifespan. Evidence before this study Work examining the non-diabetes outcomes of metformin therapy has suggested that there metformin has a beneficial effect on longevity and healthy lifespan. Both clinical trials and observational studies broadly support this hypothesis, but tend to be limited in the length of time over which they can study patients or participants. Added value of this study By using medical records we are able to study individuals with Type 2 diabetes over a period of two decades. We are also able to account for the effects of cancer, cardiovascular disease, hypertension, deprivation, and smoking on longevity and survival time following treatment. Implications of all the available evidence We confirm that there is an initial benefit to longevity of metformin therapy, but this benefit does not outweigh the negative effect on longevity of diabetes. Therefore, we suggest that longer study periods are required for inference to be made about longevity in future research.

Polygenic risk scores (PRS) have been widely adopted as a tool for measuring common variant liability and they have been shown to predict lifetime risk of Alzheimer’s disease (AD) development. However, the relationship between PRS and AD pathogenesis is largely unknown. To this end, we performed a differential gene-expression and associated disrupted biological pathway analyses of AD PRS vs. case/controls in human brain-derived cohort sample (cerebellum/temporal cortex; MayoRNAseq). The results highlighted already implicated mechanisms: immune and stress response, lipids, fatty acids and cholesterol metabolisms, endosome and cellular/neuronal death, being disrupted biological pathways in both case/controls and PRS, as well as previously less well characterised processes such as cellular structures, mitochondrial respiration and secretion. Despite heterogeneity in terms of differentially expressed genes in case/controls vs. PRS, there was a consensus of commonly disrupted biological mechanisms. Glia and microglia-related terms were also significantly disrupted, albeit not being the top disrupted Gene Ontology terms. GWAS implicated genes were significantly and in their majority, up-regulated in response to different PRS among the temporal cortex samples, suggesting potential common regulatory mechanisms. Tissue specificity in terms of disrupted biological pathways in temporal cortex vs. cerebellum was observed in relation to PRS, but limited tissue specificity when the datasets were analysed as case/controls. The largely common biological mechanisms between a case/control classification and in association with PRS suggests that PRS stratification can be used for studies where suitable case/control samples are not available or the selection of individuals with high and low PRS in clinical trials.

The APOE-ε4 allele is known to predispose to amyloid deposition and consequently is strongly associated with Alzheimer's disease (AD) risk. There is debate as to whether the APOE gene accounts for all genetic variation of the APOE locus. Another question which remains is whether APOE-ε4 carriers have other genetic factors influencing the progression of amyloid positive individuals to AD. We conducted a genome-wide association study in a sample of 5,390 APOE-ε4 homozygous (ε4ε4) individuals (288 cases and 5,102 controls) aged 65 or over in the UK Biobank. We found no significant associations of SNPs in the APOE locus with AD in the sample of ε4ε4 individuals. However, we identified a novel genome-wide significant locus associated to AD, mapping to DAB1 (rs112437613, OR=2.28, CI=1.73-3.01, p=5.4 × 10−9). This identification of DAB1 led us to investigate other components of the DAB1-RELN pathway for association. Analysis of the DAB1-RELN pathway indicated that the pathway itself was associated with AD, therefore suggesting an epistatic interaction between the APOE locus and the DAB1-RELN pathway.

Increasing evidence supports a role for deficient Wnt signaling in Alzheimer’s disease (AD). Studies reveal that the secreted Wnt antagonist Dickkopf-3 (DKK3) colocalizes to amyloid plaques in AD patients. Here, we investigate the contribution of DKK3 to synapse integrity in healthy and AD brains. Our findings show that DKK3 expression is upregulated in the brains of AD subjects and that DKK3 protein levels increase at early stages in the disease. In hAPP- J20 and hAPPNL-G-F/NL-G-FAD models, extracellular DKK3 levels are increased and DKK3 accumulates at dystrophic neuronal processes around plaques. Functionally, DKK3 triggers the loss of excitatory synapses through blockade of the Wnt/GSK3β signaling with a concomitant increase in inhibitory synapses signaling via activation of the Wnt/JNK pathway. In contrast, DKK3 knockdown restores synapse number and memory in hAPP-J20 mice. Collectively, our findings identify DKK3 as a novel driver of synaptic defects and memory impairment in AD.

Alzheimer's disease (AD) is a devastating neurodegenerative condition with significant genetic heritability. Several genes have been implicated in the onset of AD with the apolipoprotein E (APOE) gene being the strongest single genetic risk loci. Evidence suggests that the effect of APOE alters with age during disease progression. Here, we aim to investigate the impact of APOE and other variants outside the APOE region on AD risk in younger and older participants. Using data from both the Alzheimer's Disease Neuroimaging Initiative and the UK Biobank, we computed the polygenic risk score of each individual informed by the latest genetic study from the International Genomics of Alzheimer's Project. Our analysis showed that the effect of APOE on the disease risk is greater in younger participants and reduces as participants' age increases. Our findings indicate the increased impact of polygenic risk score as participants' age increases. Therefore, AD in older individuals can potentially be triggered by the cumulative effect of genes which are outside the APOE region., Highlights • Polygenic risk score analysis was used in ADNI and the UK Biobank data sets. • APOE's effect on Alzheimer's disease risk was greater in the younger group (age

For more than 150 years, controversy over the status of post-partum psychosis has hindered research and caused considerable confusion for clinicians and women, with potentially negative consequences. We aimed to explore the hypothesis that genetic vulnerability differs between women with first-onset post-partum psychosis and those with bipolar disorder more generally.In this case-control study on first-onset post-partum psychosis and bipolar disorder in the UK, we included 203 women with first-onset post-partum psychosis (defined as a manic, mixed, or psychotic depression episode within 6 weeks of delivery without a psychiatric history) and 1225 parous women with a history of bipolar disorder. Information on women with bipolar disorder was obtained from the Bipolar Disorder Research Network database, and participants were recruited through screening community mental health teams across the UK and via the media and patient support organisations. All were assessed using a semistructured face-to-face psychiatric interview and psychiatric case note review. 2809 women from the general population were recruited via the national UK Blood Services and the 1958 Birth Cohort (UK National Child Development Study) as controls and matched to cases according to genetic ancestry. All self-reported their ethnicity as White and were recruited from across the UK. Polygenic risk scores (PRSs) were generated from discovery genome-wide association studies of schizophrenia, bipolar disorder, and major depression. Logistic regression was used to model the effect of each PRS on diagnosis, and the RRs and ORs presented were adjusted for ten principal components of genetic variation to account for population stratification.203 women with first-onset post-partum psychosis (median age at interview: 46 years [IQR 37-55]) and 1225 women with bipolar disorder (49 years [41-58]) were recruited between September, 1991, and May, 2013, as well as 2809 controls. Women with first-onset post-partum psychosis had similar bipolar disorder and schizophrenia PRSs to women with bipolar disorder, which were significantly higher than those of controls. When compared with controls, women with first-onset post-partum psychosis had an adjusted relative risk ratio (RR) for bipolar disorder PRSs of 1·71 (95% CI 1·56-1·86, p0·0001) and for schizophrenia PRSs of 1·82 (1·66-1·97, p0·0001). The effect sizes were similar when comparing women with bipolar disorder to controls (adjusted RR 1·77 [1·69-1·84], p0·0001 for bipolar disorder PRSs; 2·00 (1·92-2·08), p0·0001 for schizophrenia PRSs). Although women with bipolar disorder also had higher major depression PRSs than did controls (1·24 [1·17-1·31], p0·0001), women with first-onset post-partum psychosis did not differ from controls in their polygenic liability to major depression (0·97 (0·82-1·11), p=0·63).Our study supports the recognition of first-onset post-partum psychosis as a separate nosological entity within the bipolar disorder spectrum both in research and clinical settings.Wellcome Trust and Medical Research Council.

Polygenic Risk Scores (PRS) for AD offer unique possibilities for reliable identification of individuals at high and low risk of AD. However, there is little agreement in the field as to what approach should be used for genetic risk score calculations, how to model the effect of APOE, what the optimal p-value threshold (pT) for SNP selection is and how to compare scores between studies and methods. We show that the best prediction accuracy is achieved with a model with two predictors (APOE and PRS excluding APOE region) with pT, While polygenic risk scores have been shown to be correlated with disease risk, there is little agreement on how the score should be calculated. Here the authors investigate risk scores for Alzheimer’s disease, finding that the most effective approach includes an APOE score and a polygenic score excluding APOE.

Genetic case-control association studies are often based on clinically ascertained cases and population or convenience controls. It is known that some of the controls will contain cases, as they are usually not screened for the disease of interest. However, even clinically assessed cases and controls can be misassigned. For Alzheimer's disease (AD), it is important to know the accuracy of the clinical assignment. The predictive accuracy of AD risk by polygenic risk score analysis has been reported in both clinical and pathologically confirmed cohorts. The genetic risk prediction can provide additional insights to inform classification of subjects to case and control sets at a preclinical stage. In this study, we take a mathematical approach and aim to assess the importance of a genetic component for the assignment of subjects to AD-positive and -negative groups, and provide an estimate of misassignment rates (MARs) in AD case/control cohorts accounting for genetic prediction modeling results. The derived formulae provide a tool to estimate MARs in any sample. This approach can also provide an estimate of the maximal and minimal MARs and therefore could be useful for statistical power estimation at the study design stage. We illustrate this approach in 2 independent clinical cohorts and estimate misdiagnosis rate up to 36% in controls unscreened for the APOE genotype, and up to 29% when E3 homozygous subjects are used as controls in clinical studies.

There is little agreement regarding the approach and optimal p-value threshold of SNPs to calculate genetic risk scores for Alzheimer’s disease (AD). This reflects a fundamental underlying debate on the polygenic versus oligogenic disease architecture. We re-investigated the assumptions underlying the choice of specific p-value thresholds defining genetic loci used to determine polygenic risk scores (PRS). We find the optimal p-value threshold for SNP selection is 0.1, which supports the polygenic architecture of AD. We found that previous studies supporting an oligogenic model of AD did not take account of the reduction of APOE-ε4 allele frequency in older individuals, which skewed the results towards lower p-value thresholds and eclipsed the contribution of genes associated to AD with higher p-values. The polygenic approach to AD is also effective to identify individuals at high or low AD risk, when only APOE-ε3 homozygous individuals are considered. We also introduce the standardisation of PRS against a population data which ensures comparability of the PRS between studies. In conclusion, our work demonstrates that AD is fundamentally a polygenic disease and that stratifying populations for AD risk best takes the full PRS score into account.

More than 50 genetic loci have been identified as being associated with Alzheimer’s disease (AD) from genome-wide association studies (GWAS) and many of these are involved in immune pathways and lipid metabolism. Therefore, we performed a transcriptome-wide association study (TWAS) of immune-relevant cells, to study the mis-regulation of genes implicated in AD. We used expression and genetic data from naive and induced CD14+ monocytes and two GWAS of AD to study genetically controlled gene expression in monocytes at different stages of differentiation and compared the results with those from TWAS of brain and blood. We identified nine genes with statistically independent TWAS signals, seven are known AD risk genes from GWAS: BIN1, PTK2B, SPI1, MS4A4A, MS4A6E, APOE and PVR and two, LACTB2 and PLIN2/ADRP, are novel candidate genes for AD. Three genes, SPI1, PLIN2 and LACTB2, are TWAS significant specifically in monocytes. LACTB2 is a mitochondrial endoribonuclease and PLIN2/ADRP associates with intracellular neutral lipid storage droplets (LSDs) which have been shown to play a role in the regulation of the immune response. Notably, LACTB2 and PLIN2 were not detected from GWAS alone.

Introduction Insomnia, hypersomnia and evening chronotype are common in bipolar disorder (BD) but research examining the role of genetics is mixed. Stratifying by bipolar subtypes could elucidate this relationship and inform sleep and BD research. Aim: to determine whether genetic liability to insomnia, hypersomnia and chronotype differ between bipolar subtypes (type 1, BD-I or type 2, BD-II). Method Case-control study of 4672 participants with BD (67% female, 3404 BD-I, 1268 BD-II) enrolled in the Bipolar Disorder Research Network and 5714 controls (49% female) recruited from the 1958 British Birth Cohort and UK Blood Service. All participants were of European ancestry. BD subtypes were determined by semi-structured psychiatric interview and case notes. Genetic liability to sleep traits was assessed using genetic risk scores (GRS), which were derived using alleles from genome-wide association studies of insomnia, sleep duration, daytime sleepiness and chronotype. Analyses used multinomial regression to determine whether GRS for insomnia, hypersomnia (daytime sleepiness or sleep duration) and chronotype were associated with BD-I or BD-II when compared to controls. Results Insomnia GRS were associated with increased risk of BD-II (RR=1.14, 95% CI=1.07–1.21, P=8.26 × 10–5) but not BD-I (RR=0.98, 95% CI=0.94–1.03, P=0.409) relative to controls. Sleep duration GRS were associated with increased relative risk of BD-I (RR=1.10, 95% CI=1.06–1.15, P=1.13 × 10–5), but not BD-II (RR=0.99, 95% CI=0.93–1.06, P=0.818). Daytime sleepiness and chronotype GRS did not distinguish bipolar subtypes. Discussion Bipolar subtypes differ in genetic liability to insomnia and sleep duration, providing further evidence that bipolar subtypes should be considered separately in research on sleep in BD. The distinct findings for sleep duration and daytime sleepiness support existing literature suggesting that these are distinct subtypes of hypersomnia.

The polygenic risk scores (PRS) approach has been widely used across different traits for estimating polygenic risk, pleiotropy and disease prediction, but mostly in European populations. The predictive ability of the PRS in non-European populations is currently limited due to the lack of genetic research performed in populations of non-European ancestry. One of the main challenges of the practical use of PRS is to place an individual’s personal score in the context of the PRS distribution in the underlying population. In this paper we present an approach for estimating the parameters of the PRS distribution in a population using summary information from public data.Unstandardized PRS are usually not directly comparable even between European studies. Our approach can be used for standardisation whilst accounting for genotyping platforms, data quality and ancestry. It can be applied to assessing polygenic disease risk for individuals from a European population for any complex genetic disorder and, assuming that most of the disease risk loci are likely to be shared between populations, to estimating the disease risk for individuals from other populations. We demonstrate the precision of our method with simulations. We show the utility of our estimates in application to Alzheimer’s disease in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study. We present population specific PRSs for different populations using 1000 Genomes data.

A novel POLARIS gene-based analysis approach was employed to compute gene-based polygenic risk score (PRS) for all individuals in the latest HRC imputed GERAD (N cases=3,332 and N controls=9,832) data using the International Genomics of Alzheimer’s Project summary statistics (N cases=13,676 and N controls=27,322, excluding GERAD subjects) to identify the SNPs and weight their risk alleles for the PRS score. SNPs were assigned to known, protein coding genes using GENCODE (v19). SNPs are assigned using both 1) no window around the gene and 2) a window of 35kb upstream and 10kb downstream to include transcriptional regulatory elements. The overall association of a gene is determined using a logistic regression model, adjusting for population covariates. Three novel gene-wide significant genes were determined from the POLARIS gene-based analysis using a gene window; PPARGC1A, RORA and ZNF423 . The ZNF423 gene resides in an Alzheimer’s disease (AD)-specific protein network which also includes other AD-related genes. The PPARGC1A gene has been linked to energy metabolism and the generation of amyloid beta plaques and the RORA has strong links with genes which are differentially expressed in the hippocampus. We also demonstrate no enrichment for genes in either loss of function intolerant or conserved noncoding sequence regions.

BackgroundInsomnia and hypersomnia are common in bipolar disorder (BD) but it is unclear whether genetic factors influence this association. Stratifying by bipolar subtypes could elucidate the nature of this association and inform research on sleep and BD. We therefore aimed to determine whether polygenic risk scores (PRS) for insomnia, daytime sleepiness and sleep duration differ according to bipolar subtypes (bipolar I disorder, BD-I or bipolar II disorder, BD-II).MethodsIn this case-control study, we used multinomial regression to determine whether PRS for insomnia, daytime sleepiness, and sleep duration were associated with risk of BD-I or BD-II when compared to each other and to controls. Cases (n=4672) were recruited within the United Kingdom from the Bipolar Disorder Research Network. Controls (n=5714) were recruited from the 1958 British Birth Cohort and UK Blood Service. All participants were of European ancestry. Clinical subtypes of BD were determined by semi-structured psychiatric interview (the Schedules for Clinical Assessment in Neuropsychiatry) and case notes.ResultsWithin cases, 3404 and 1268 met DSM-IV criteria for BD-I and BD-II, respectively. Insomnia PRS was associated with increased risk of BD-II (RR = 1.14, 95% CI = 1.07-1.21,P= 8.26 × 10−5) but not BD-I (RR = 0.98, 95% CI = 0.94-1.03,P= .409) relative to controls. In contrast, sleep duration PRS was associated with increased relative risk of BD-I (RR = 1.10, 95% CI = 1.06-1.15,P= 1.13 × 10−5), but not BD-II (RR = 0.99, 95% CI = 0.93-1.06,P= .818). Daytime sleepiness PRS was associated with an increased risk of BD-I (RR = 1.07, 95% CI = 1.02-1.11,P= .005) and BD-II (RR = 1.14, 95% CI = 1.07-1.22,P= 3.22 × 10−5) compared to controls, but did not distinguish subtypes from each other.ConclusionsBipolar subtypes differ in genetic liability to insomnia and sleep duration. Our findings provide further evidence that the distinction between BD-I and BD-II has biological and genetic validity. This distinction will be critical in the selection of participants for future research on the role of sleep disturbance in BD.

A novel POLARIS gene-based analysis approach was employed to compute gene-based polygenic risk score (PRS) for all individuals in the latest HRC imputed GERAD (N cases=3,332 and N controls=9,832) data using the International Genomics of Alzheimer’s Project summary statistics (N cases=13,676 and N controls=27,322, excluding GERAD subjects) to identify the SNPs and weight their risk alleles for the PRS score. SNPs were assigned to known, protein coding genes using GENCODE (v19). SNPs are assigned using both 1) no window around the gene and 2) a window of 35kb upstream and 10kb downstream to include transcriptional regulatory elements. The overall association of a gene is determined using a logistic regression model, adjusting for population covariates.Three novel gene-wide significant genes were determined from the POLARIS gene-based analysis using a gene window; PPARGC1A, RORA and ZNF423. The ZNF423 gene resides in an Alzheimer’s disease (AD)-specific protein network which also includes other AD-related genes. The PPARGC1A gene has been linked to energy metabolism and the generation of amyloid beta plaques and the RORA has strong links with genes which are differentially expressed in the hippocampus. We also demonstrate no enrichment for genes in either loss of function intolerant or conserved noncoding sequence regions.

Importance Bipolar disorder (BD) overlaps schizophrenia in its clinical presentation and genetic liability. Alternative approaches to patient stratification beyond current diagnostic categories are needed to understand the underlying disease processes and mechanisms. Objective To investigate the association between common-variant liability for schizophrenia, indexed by polygenic risk scores (PRSs), and psychotic presentations of BD. Design, Setting, and Participants This case-control study in the United Kingdom used multinomial logistic regression to estimate differential PRS associations across categories of cases and controls. Participants included in the final analyses were 4436 cases of BD from the Bipolar Disorder Research Network. These cases were compared with the genotypic data for 4976 cases of schizophrenia and 9012 controls from the Type 1 Diabetes Genetics Consortium study and the Generation Scotland study. Data were collected between January 1, 2000, and December 31, 2013. Data analysis was conducted from March 1, 2016, to February 28, 2017. Exposures Standardized PRSs, calculated using alleles with an association threshold of P < .05 in the second Psychiatric Genomics Consortium genome-wide association study of schizophrenia, were adjusted for the first 10 population principal components and genotyping platforms. Main Outcomes and Measures Multinomial logit models estimated PRS associations with BD stratified by Research Diagnostic Criteria subtypes of BD, by lifetime occurrence of psychosis, and by lifetime mood-incongruent psychotic features. Ordinal logistic regression examined PRS associations across levels of mood incongruence. Ratings were derived from the Schedules for Clinical Assessment in Neuropsychiatry interview and the Bipolar Affective Disorder Dimension Scale. Results Of the 4436 cases of BD, 2966 (67%) were female patients, and the mean (SD) age at interview was 46 [12] years. Across clinical phenotypes, there was an exposure-response gradient, with the strongest PRS association for schizophrenia (risk ratio [RR] = 1.94; 95% CI, 1.86-2.01), followed by schizoaffective BD (RR = 1.37; 95% CI, 1.22-1.54), bipolar I disorder subtype (RR = 1.30; 95% CI, 1.24-1.36), and bipolar II disorder subtype (RR = 1.04; 95% CI, 0.97-1.11). Within BD cases, there was an effect gradient, indexed by the nature of psychosis. Prominent mood-incongruent psychotic features had the strongest association (RR = 1.46; 95% CI, 1.36-1.57), followed by mood-congruent psychosis (RR = 1.24; 95% CI, 1.17-1.33) and BD with no history of psychosis (RR = 1.09; 95% CI, 1.04-1.15). Conclusions and Relevance For the first time to date, a study shows a polygenic-risk gradient across schizophrenia and BD, indexed by the occurrence and level of mood-incongruent psychotic symptoms.

Background The aetiology of Bipolar Disorder (BD) and Schizophrenia (SZ) involves a complex gene-environment interplay and this involves common risk alleles that are shared across the disorders. Bipolar disorder is heterogeneous in presentation and we and others have postulated that the particular clinical presentation is in part determined by the particular risk alleles carried. To investigate this, we have employed sparse Canonical Correlation Analysis (sCCA), to seek patterns of correlation between psychotic symptoms and schizophrenia risk alleles in a large (N=3903) deeply phenotyped sample of subjects with BD. Methods Canonical Correlation Analysis (CCA) seeks linear relationships between two sets of multi-dimensional variables. In the psychiatric genetics field, this approach can be adapted to enable the analysis of both phenotype and genetic variables in one framework. This has the advantage of minimizing the number of statistical tests required. A recent extension of CCA to sparse canonical correlation analysis (sCCA) allows application of the general approach to high dimensional genomic data (Witten et al., 2009). sCCA has advantages compared to CCA in terms of precision of parameter estimates and statistical power. We employed sCCA to analyse 30 psychotic symptoms assessed with the OPerational CRITeria checklist (OPCRIT) and 82 independent genome-wide significant SNPs that were identified by the SZ PGC2 study (The Psychiatric Genomics Consortium, 2014) for which we had data in our BD sample. We applied the sCCA as a purely data driven approach to individual OPCRIT psychotic symptoms and also to groups of OPCRIT psychotic symptoms (“positive”, “negative”, “disorganised”) defined by a previously published factor analysis study of schizophrenia. Results sCCA analysis of the individual OPCRIT items with the set of significantly associated with SZ 82 sCCA analysis based on individual psychotic symptoms revealed a significant association (p=0.045) with the largest weights attributed to delusions of influence, bizarre behaviour and grandiose delusions and an indel SNP on chromosome 3 (3:180594593, build 37). sCCA analysis on the same set of SNPs and OPCRIT symptom groups confirmed association with the same SNP and the “disorganised” group of OPCRIT symptoms (p=0.01). The results also show significant association when we use less stringent p-value thresholds in SZ. In this case the most heavily weighted SNP remains 3:180594593, but other SNPs also contribute to the analysis. Discussion Our results suggest the CCA canonical correlation approach might be a useful tool to explore phenotype-genotype relationships. It can be applied to generate data-driven hypotheses with the potential to provide further insights into the complex architecture of psychiatric disorders. To the best of our knowledge, this is the first study to apply this approach in analysing phenotypes and genotypes together. sCCA offers the potential to be extended to include a larger number of fine grained systematic descriptors of BD, and for testing for correlation with genetic profiles from other co-morbid disorders.

This paper is concerned with the numerical solution of high-dimensional Fokker-\ud Planck equations related to multi-dimensional diffusion with polynomial coefficients\ud or Pearson diffusions. Classification of multi-dimensional Pearson diffusion follows\ud from the classification of one-dimensional Pearson diffusion. There are six important\ud classes of Pearson diffusion - three of them possess an infinite system of moments\ud (Gaussian, Gamma, Beta) while the other three possess a finite number of moments\ud (inverted Gamma, Student and Fisher-Snedecor). Numerical approximations to the\ud solution of the Fokker-Planck equation are generated using the spectral method.\ud The use of an adaptive reduced basis technique facilitates a significant reduction in\ud the number of degrees of freedom required in the approximation through the determination\ud of an optimal basis using the singular value decomposition (SVD). The\ud basis functions are constructed dynamically so that the numerical approximation\ud is optimal in the current finite-dimensional subspace of the solution space. This is\ud achieved through basis enrichment and projection stages. Numerical results with different\ud boundary conditions are presented to demonstrate the accuracy and efficiency\ud of the numerical scheme.

The etiologies of bipolar disorder (BD) and schizophrenia include a large number of common risk alleles, many of which are shared across the disorders. BD is clinically heterogeneous and it has been postulated that the pattern of symptoms is in part determined by the particular risk alleles carried, and in particular, that risk alleles also confer liability to schizophrenia influence psychotic symptoms in those with BD. To investigate links between psychotic symptoms in BD and schizophrenia risk alleles we employed a data-driven approach in a genotyped and deeply phenotyped sample of subjects with BD. We used sparse canonical correlation analysis (sCCA) (Witten, Tibshirani,Hastie, ) to analyze 30 psychotic symptoms, assessed with the OPerational CRITeria checklist, and 82 independent genome-wide significant single nucleotide polymorphisms (SNPs) identified by the Schizophrenia Working group of the Psychiatric Genomics Consortium for which we had data in our BD sample (3,903 subjects). As a secondary analysis, we applied sCCA to larger groups of SNPs, and also to groups of symptoms defined according to a published factor analyses of schizophrenia. sCCA analysis based on individual psychotic symptoms revealed a significant association (p = .033), with the largest weights attributed to a variant on chromosome 3 (rs11411529), chr3:180594593, build 37) and delusions of influence, bizarre behavior and grandiose delusions. sCCA analysis using the same set of SNPs supported association with the same SNP and the group of symptoms defined "factor 3" (p = .012). A significant association was also observed to the "factor 3" phenotype group when we included a greater number of SNPs that were less stringently associated with schizophrenia; although other SNPs contributed to the significant multivariate association result, the greatest weight remained assigned to rs11411529. Our results suggest that the canonical correlation is a useful tool to explore phenotype-genotype relationships. To the best of our knowledge, this is the first study to apply this approach to complex, polygenic psychiatric traits. The sparse canonical correlation approach offers the potential to include a larger number of fine-grained systematic descriptors, and to include genetic markers associated with other disorders that are genetically correlated with BD.

Background\ud Schizophrenia (SZ) and Bipolar disorder (BD) show evidence for partial overlap in phenotypic and genetic influences based on family, twins, adoption and Psychiatric Genetic Consortium (PGC) studies. They have lifetime prevalence of about 1% and 2.4%, and heritability estimates of 60-80% and 40-70%, respectively. In the last decade BD has been investigated using dimensional structuring of psychoses based on symptomatic-functional checklists that provides reliable approach to phenotypic assessment.\ud \ud Recent research suggests moving towards developing Phenotype-based Genetic Association Studies. In this approach, patients will only be put into groups consisting of others with symptoms similar to their own. Canonical Correlation Analysis (CCA) is statistical technique designed to identify relationships (usually hidden) between two sets of variables. We use CCA to combine genotypic and phenotypic variables and measure correlation between those sets. This analysis estimates canonical correlation between psychotic symptoms measured using validated item check list (OPCRIT), and genome-wide significant (GWS) loci from PGC2 schizophrenia.\ud \ud Methods\ud For our analysis we used phenotype and genetic data for 5,507 BD cases. Imputation of genetic data was performed with 1000Genomes (Phase 3, 2014) then quality control was applied (INFO>0.8, HWE>1e-6, MAF>0.01). Additional quality control was performed on phenotypic symptom coverage. CCA was employed as implemented in R, using package “CCA” with GWS loci from PGC2 SZ and OPCRIT items. SNPs were standardised and adjusted for 10 population covariates calculated from imputed data using principal component approach prior to CCA.\ud \ud Results\ud Canonical correlation analysis was run on 4422 cases on 89 available GWS PGC2 SZ SNPs or their proxies (with r2>0.6). 60 phenotypic variables were taken from OPCRIT measurements including mood disturbance, biological indices, atypical depression, substance use, psychosis and social functioning. We found no significant canonical correlations indicating absence of hidden sub-clusters at individual symptom level of BD associated with SZ GWS loci.\ud \ud Discussion\ud Our analysis was focused to find correlation from bipolar phenotype by using OPCRIT questionnaire and GWS SZ loci from PGC2. We have shown that there were no significant canonical correlation coefficients suggesting that there is no direct association between SZ associated genetic loci and BP at individual symptom level.\ud \ud CCA is canonical correlation analysis is one of potential of data-driven approaches to identify hidden genotype-phenotype relationships. It provides opportunities to generate and test different hypotheses and understand more about complex architecture of psychiatric disorders. In the next stage we plan to extend our analysis to more fine grained systematic descriptors of BD and test for correlation with genetic profiles from a number of co-morbid disorders, as well as the full range of phenotypic and genetic data that are available.

Risk of schizophrenia is conferred by alleles occurring across the full spectrum of frequencies from common SNPs of weak effect through to ultra rare alleles, some of which may be moderately to highly penetrant. Previous studies have suggested that some of the risk of schizophrenia is attributable to uncommon alleles represented on Illumina exome arrays. Here, we present the largest study of exomic variation in schizophrenia to date, using samples from the United Kingdom and Sweden (10,011 schizophrenia cases and 13,791 controls). Single variants, genes, and gene sets were analyzed for association with schizophrenia. No single variant or gene reached genome‐wide significance. Among candidate gene sets, we found significant enrichment for rare alleles (minor allele frequency [MAF] 4). We also show risk alleles within this frequency range exist, but confer smaller effects and should be identified by larger studies.

Inversion of radiative transfer models (RTM) using a lookup-table (LUT) approach against satellite reflectance data can lead to concurrent retrievals of biophysical parameters such as leaf chlorophyll content (Chl) and leaf area index (LAI), but optimization strategies are not consolidated yet. ESA's upcoming satellites Sentinel-2 (S2) and Sentinel-3 (S3) aim to ensure continuity of old generation satellite sensors by providing superspectral images of high spatial and temporal resolution. This unprecedented data availability leads to an urgent need for developing robust, accurate, and operational retrieval methods. For three simulated Sentinel settings (S2-10 m: 4 bands, S2-20 m: 8 bands and S3-OLCI: 19 bands) various optimization strategies in LUT-based RTM inversion have been evaluated, being the role of i) added noise, ii) multiple best solutions, iii) combined parameters (Chl ×LAI), and iv) applied cost functions. By inverting the PROSAIL model and using data from the ESA-led field campaign SPARC (Barrax, Spain), it was demonstrated that introducing noise and opting for multiple best solutions in the inversion considerably improved retrievals. However, the widely used RMSE was not the best performing cost function. Three families of alternative cost functions were applied here: information measures, minimum contrast, and M-estimates. We found that so-called “Power divergence measure”, “Trigonometric”, and spectral measure with “Contrast function K(x) = -log(x) + x”, yielded more accurate results, although this also depended on the biophysical parameter. Particularly, when simultaneous retrieval of multiple biophysical parameters is the objective then “Contrast function K(x) = -log(x) + x” provided most consistent optimized estimates of leaf Chl, LAI and canopy Chl across the different Sentinel configurations (relative RMSE: 24-29 %).

Leaf area index (LAI) is one of the key parameters for the calculation of the energy budget, photosynthesis, and the interception of precipitation in land-surface models at local to global scales. Estimation of LAI from satellite data is a challenging and difficult problem. Studies over the past decades have focused predominantly on the improvement of forward modeling of the radiative transfer problem and on the application of more realistic numerical inversion schemes. Little or no attention has been paid to alternatives for the least squares method as a statistical distance measure or cost function, used to minimize the distance between observations and model predictions. The least-squares method has properties that assume noise with a Gaussian distribution and zero mean, an assumption often violated when LAI is estimated from satellite reflectance data. Here, we test the use of alternative statistical distance measures or cost functions to estimate LAI. We combine a look-up table (LUT)-inversion method based on the FLIGHT radiative transfer model and test how well it estimates LAI from MODIS reflectance data for a large set of alternative cost functions. We consider three classes of statistical distance measures or cost functions: information divergence measures, M-estimates, and minimum contrast methods. We estimate LAI from the Moderate Resolution Imaging Spectrometer (MODIS) surface reflectance product (MOD09GA) for 11 VALERI and BigFoot sites around the globe. These sites consist of a wide range of tree-cover types that include conifer, broadleaf and mixed (conifer, broadleaf, grassland) forest sites. We develop LUTs with FLIGHT for conifer and broadleaf forests and we show that improvements can be obtained for the estimation of LAI by choosing a cost function appropriate for a particular problem. Results show error reductions of 20% compared with the MODIS LAI retrieval (MOD15A2).

Background Bipolar disorder (BD) includes a broad range of phenotypic features – most research examining its genetic underpinning focuses on the overall syndrome rather than its subphenotypic components. BD is a highly heritable disorder and while its genetic architecture is not fully elucidated there is increasingly strong support for a very polygenic component with partial polygenic overlap with schizophrenia (SZ). Here we present preliminary results from an analysis examining the association of SZ derived polygenic risk scores (PRS) across different BD subtypes (Bipolar Type 1(BPI), Bipolar Type 2 (BPII) and Schizoaffective disorder (SAD), and subphenotypes defined by the presence of psychosis and its associated level of mood incongruence. Methods We used a UK sample of 3101 BP cases, collected using a consistently administrated interview protocol with OPCRIT confirmed lifetime diagnosis of Research Diagnostic Criteria (RDC - BPI, BPII or SAD). PRS were generated using 1000 genomes (phase3, 2014) imputed genetic data (INFO score>0.8, HWE>1e-6, MAF>0.01) trained on the results from Psychiatric Genetic Consortium (PGC2) SZ GWAS, pruned r2 Results PRS with the SZ association p-value cut-off of 0.5, have shown significant association across the BD subtypes with relative risk ratios RRR = 1.28 (95%CI: 1.18-1.39, p Discussion Preliminary analyses show a genetic - phenotypic association, between polygenic features of schizophrenia and BP characterised by the presence of psychosis, suggesting the phenotypic overlap between SZ and BD may in part be driven by polygenic overlap. This finding is further supported by the association of PRS and level of mood incongruence in the content of psychotic symptoms. These results suggest the PRS effect is not solely mediated though the psychosis subphenotype, at least in the BPI group. Our analyses show promise for the strategy of examining polygenic/phenotypic associations based on fine grained clinical characterisations

The start-up of plane Couette flow of a finitely extensible nonlinear elastic (FENE) fluid is considered. A numerical method for solving this problem based on a decoupled micro{macro approach is described. The polymeric stress is determined in the microscopic part of the calculation by computing the solution of a Fokker-Planck equation for the configuration probability density function. The solution of this equation often suffers from the so-called problem of "curse of dimensionality." An efficient solution procedure is described based on the use of an adaptive reduced basis function technique in conjunction with high-order spectral approximations. A substantial reduction in the number of degrees of freedom to achieve a required level of accuracy is obtained compared with standard tensor product representation of the basis. Some sample numerical results are presented to highlight properties of the model and the numerical scheme.

The numerical solution of the one-dimensional Fokker-Planck equation for describing the evolution of the configuration probability density function associated with kinetic theory models in polymer dynamics is presented. The finitely extensible non-linear elastic (FENE) model is considered and the spectral element discretisation is applied using an adaptive reduced basis technique. This technique facilitates a significant reduction in the number of degrees of freedom required in the approximation through the determination of an optimal basis using the singular value decomposition (SVD). The basis functions are constructed dynamically so that the numerical approximation is optimal in the current finite-dimensional subspace of the solution space. This is achieved through basis enrichment and projection. The reduced basis method is extended to the high-dimensional Fokker-Planck equation, using the d-dumbbell FENE model, by consideration of a high-dimensional singular value decomposition. Some numerical results are presented to demonstrate the efficiency of the numerical scheme.

This paper studies the transientsolution of the Markovian queue, namely M/Ek/1model. Anew approach is introduced for finding exact solution for this queueing system. The transient probabilities are expressed recurrently.

This paper considers the Erlang queueing system M/Ek/1, where customers arrive at random at mean rate λ and the service times have an Erlang distribution with parameter k and mean service rate μ. It is difficult to obtain a transient solution in explicit form to the queue equations because of their complex structure. We propose a simple method of computing Wq(t)—the mean waiting time of a customer arriving in the queue at time t, based on an exponential function approximation.