Biology subjects

Leonenko, G.

Publications and source records attributed to Leonenko, G..

7 recordsLinked to original sources

bioRxiv2026

Proteomic Signatures of Neuropathological Alterations in Alzheimer's Disease: Insights from the Bio-Hermes Study

Large-scale plasma proteomics can capture molecular changes across the Alzheimers disease (AD) continuum and provide insight into biological mechanisms associated with AD pathology. We analysed the Bio-Hermes cohort (n = 961), with participants enrolled across 17 sites in the United States from April 2021 to November 2022. Participants were stratified by clinical status and amyloid PET scan-based Core1 biomarker status (CN Core1-, CN Core1+, MCI Core1+, and AD dementia Core1+). We performed differential abundance analyses across biologically defined contrasts, clustered proteins into co-expression networks, and evaluated protein panels to distinguish participants with biologically defined AD from amyloid-negative cognitively normal controls. We also used Mendelian randomization (MR) to assess genetic evidence for potential causal relationships with AD risk. The biologically defined contrast, Core1+ vs. CN Core1-, identified 69 differentially abundant proteins. Across AD stages, eight core proteins were consistently dysregulated from preclinical through prodromal and dementia phases, and three additional proteins emerged at MCI Core1+ and remained altered in AD dementia Core1+. We identified 29 co-expression modules, six of which varied significantly across the AD continuum. Among differential abundance proteins, ACHE ranked highest for distinguishing biologically defined AD from CN Core1-. Stage-specific protein panels improved the discriminatory performance for MCI Core1+ (AUC = 0.850) and AD dementia Core1+ (AUC = 0.856). MR provided genetic evidence consistent with an association between plasma ACHE abundance and AD risk. Plasma proteomics delineated a stage-spanning core signature across the AD continuum. These findings nominate co-expression modules and candidate proteins for further validation in early detection and AD screening.

neuroscience↗

bioRxiv2022

What does heritability of Alzheimer's disease represent?

INTRODUCTIONBoth Alzheimers disease (AD) and ageing have a strong genetic component. In each case, many associated variants have been discovered, but how much missing heritability remains to be discovered is debated. Variability in the estimation of SNP-based heritability could explain the differences in reported heritability. METHODSWe compute heritability in five large independent cohorts (N=7,396, 1,566, 803, 12,528 and 3,963) to determine whether a consensus for the AD heritability estimate can be reached. These cohorts vary by sample size, age of cases and controls and phenotype definition. We compute heritability a) for all SNPs, b) excluding APOE region, c) excluding both APOE and genome-wide association study hit regions, and d) SNPs overlapping a microglia gene-set. RESULTSSNP-based heritability of Alzheimers disease is between 38 and 66% when age and genetic disease architecture are correctly accounted for. The heritability estimates decrease by 12% [SD=8%] on average when the APOE region is excluded and an additional 1% [SD=3%] when genome-wide significant regions were removed. A microglia gene-set explains 69-84% of our estimates of SNP-based heritability using only 3% of total SNPs in all cohorts. CONCLUSIONThe heritability of neurodegenerative disorders cannot be represented as a single number, because it is dependent on the ages of cases and controls. Genome-wide association studies pick up a large proportion of total AD heritability when age and genetic architecture are correctly accounted for. Around 13% of SNP-based heritability can be explained by known genetic loci and the remaining heritability likely resides around microglial related genes. Author SummaryEstimates of heritability in Alzheimers disease, the proportion of phenotypic variance explained by genetics, are very varied across different studies, therefore, the amount of missing heritability not yet captured by current genome-wide association studies is debated. We investigate this in five independent cohorts, provide estimates based on these cohorts and detail necessary suggestions to accurately calculate heritability in age-related disorders. We also confirm the importance of microglia relevant genetic markers in Alzheimers disease. This manuscript provides suggestions for other researchers computing heritability in late-onset disorders and the microglia gene-set used in this study will be published alongside this manuscript and made available to other researchers. The correct assessment of disease heritability will aid in better understanding the amount of missing heritability in Alzheimers disease.

genetics↗

bioRxiv2020

Monocyte-specific changes in gene expression implicate LACTB2 and PLIN2 in Alzheimer's disease.

More than 50 genetic loci have been identified as being associated with Alzheimers 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.

neuroscience↗

bioRxiv2019

Positioning Personal Polygenic Risk score against the population background.

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 individuals 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.\n\nUnstandardized 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 Alzheimers disease in the Alzheimers Disease Neuroimaging Initiative (ADNI) study. We present population specific PRSs for different populations using 1000 Genomes data.

bioinformatics↗

bioRxiv2019

Differences in Genetic Liability for Insomnia and Hypersomnia in Bipolar Disorder Subtypes

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).\n\nMethodsIn 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.\n\nResultsWithin 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 x 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 x 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 x 10-5) compared to controls, but did not distinguish subtypes from each other.\n\nConclusionsBipolar 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.

genomics↗

bioRxiv2018

Gene-Based Analysis in HRC Imputed Genome Wide Association Data Identifies Three Novel Genes For Alzheimer’s Disease

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 Alzheimers 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.\n\nThree 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 Alzheimers 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.

genetics↗

bioRxiv2017

Psychosis and the level of mood incongruence in Bipolar Disorder are related to genetic liability for Schizophrenia

ImportanceBipolar 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/mechanisms.\n\nObjectivesTo investigate the relationship between common-variant liability for schizophrenia, indexed by polygenic risk scores (PRS) and psychotic presentations of BD, using clinical descriptions which consider both occurrence and level of mood-incongruent psychotic features.\n\nDesignCase-control design: using multinomial logistic regression, to estimate differential associations of PRS across categories of cases and controls.\n\nSettings & Participants4399 BDcases, mean [sd] age-at-interview 46[12] years, of which 2966 were woman (67%) from the BD Research Network (BDRN) were included in the final analyses, with data for 4976 schizophrenia cases and 9012 controls from the Type-1 diabetes genetics consortium and Generation Scotland included for comparison.\n\nExposureStandardised PRS, calculated using alleles with an association p-value threshold < 0.05 in the second Psychiatric Genomics Consortium genome-wide association study of schizophrenia, adjusted for the first 10 population principal components and genotyping-platform.\n\nMain outcome measureMultinomial logit models estimated PRS associations with BD stratified by (1) Research Diagnostic Criteria (RDC) BD subtypes (2) Lifetime occurrence of psychosis.(3) Lifetime mood-incongruent psychotic features and (4) ordinal logistic regression examined PRS associations across levels of mood-incongruence. Ratings were derived from the Schedule for Clinical Assessment in Neuropsychiatry interview (SCAN) and the Bipolar Affective Disorder Dimension Scale (BADDS).\n\nResultsAcross clinical phenotypes, there was an exposure-response gradient with the strongest PRS association for schizophrenia (RR=1.94, (95% C.1.1.86, 2.01)), then schizoaffective BD (RR=1.37, (95% C.I. 1.22, 1.54)), BD I (RR= 1.30, (95% C.I. 1.24, 1.36)) and BD II (RR=1.04, (95% C.1. 0.97, 1.11)). Within BD cases, there was an effect gradient, indexed by the nature of psychosis, with prominent mood-incongruent psychotic features having the strongest association (RR=1.46, (95% C.1.1.36, 1.57)), followed by mood-congruent psychosis (RR= 1.24, (95% C.1. 1.17, 1.33)) and lastly, BD cases with no history of psychosis (RR= 1.09, (95% C.1. 1.04, 1.15)).\n\nConclusionWe show for the first time a polygenic-risk gradient, across schizophrenia and bipolar disorder, indexed by the occurrence and level of mood-incongruent psychotic symptoms.

genetics↗