Biology subjects

Chimusa, E. R.

Publications and source records attributed to Chimusa, E. R..

10 recordsLinked to original sources

bioRxiv2024

Modelling Microbiome Association with Host Phenotypes Using a Bayesian Dirichlet Process Model

Dysbiosis in the human gut microbiome has been shown to be intimately involved in the pathogenesis of a wide range of communicable and non-communicable diseases. As microbiome wide association study becomes the workhorse for identifying association between microbial taxa and human diseases/traits, proper modelling of microbial taxa abundances is critical. In particular, statistical frameworks need to effectively model correlation among microbial taxa as well as latent heterogeneity across samples. Here, a Bayesian method using the Dirichlet process random effects model is devised for microbiome association study. The proposed method uses a weighted combination of phylogenetic and radial basis function kernels to model taxa effects, and a non-parametrically modelled latent variable to model latent heterogeneity among samples. Using simulated and real microbiome datasets, it is shown that the method has high statistical power for association inference. SoftwareThe R codes to implement the method has been incorporated into a script phy-loDPM.R, and is available online at https://github.com/AwanyDenis/phyloDPM.

bioinformatics↗

bioRxiv2023

Network-based integrative multi-omics approach reveals biosignatures specific to COVID-19 disease phases.

BackgroundCOVID-19 disease is characterized by a spectrum of disease phases (mild, moderate, and severe). Each disease phase is marked by changes in omics profiles with corresponding changes in the expression of features (biosignatures). However, integrative analysis of multiple omics data from different experiments across studies to investigate biosignatures at various disease phases is limited. Exploring an integrative multi-omics profile analysis through a network approach could be used to determine biosignatures associated with specific disease phases and enable the examination of the relationships between the biosignatures. AimTo identify and characterize biosignatures underlying various COVID-19 disease phases in an integrative multi-omics data analysis. MethodWe leveraged the correlation network approach to integrate transcriptomics, metabolomics, proteomics, and lipidomics data. The World Health Organization (WHO) Ordinal Scale (WOS) was used as a disease severity reference to harmonize COVID-19 patient metadata across two studies with independent data. A unified COVID-19 knowledge graph was constructed by assembling a disease-specific interactome from the literature and databases. Disease-state omics-specific graphs were constructed by integrating multi-omics data with the unified COVID-19 knowledge graph. We expanded on the network layers of multiXrank, a random walk with restart on multilayer network algorithm, to explore disease state omics-specific graphs and perform enrichment analysis. ResultsNetwork analysis revealed the biosignatures involved in inducing chemokines and inflammatory responses as hubs in the severe and moderate disease phases. We observed more shared biosignatures between severe and moderate disease phases as compared to mild-moderate and mild-severe disease phases. We further identified both biosignatures that discriminate between the disease states and interactions between biosignatures that are either common between or associated with COVID-19 disease phases. Interestingly, cross-layer interactions between different omics profiles increased with disease severity. ConclusionThis study identified both biosignatures of different omics types enriched in disease-related pathways and their associated interactions that are either common between or unique to mild, moderate, and severe COVID-19. These biosignatures include molecular features that underlie the observed clinical heterogeneity of COVID-19 and emphasize the need for disease-phase-specific treatment strategies. In addition, the approach implemented here can be used for other diseases. Key findings Integrative multi-omics analysis revealed biosignatures and biosignature interactions associated with COVID-19 disease states. Disease severity increases with biosignature interactions across different multi-omics data. The harmonization approach proposed and implemented here can be applied to other diseases

bioinformatics↗

bioRxiv2020

Whole Genome Sequencing-based Characterization of Human Genome Variation and Mutation Burden in Botswana

The study of human genome variations can contribute towards understanding population diversity and the genetic aetiology of health-related traits. We sought to characterise human genomic variations of Botswana in order to assess diversity and elucidate mutation burden in the population using whole genome sequencing. Whole genome sequences of 390 unrelated individuals from Botswana were available for computational analysis. The sequences were mapped to the human reference genome GRCh38. Population joint variant calling was performed using Genome Analysis Tool Kit (GATK) and BCFTools. Variant characterisation was achieved by annotating the variants with a suite of databases in ANNOVAR and snpEFF. The genomic architecture of Botswana was delineated through principal component analysis, structure analysis and FST. We identified a total of 27.7 million unique variants. Variant prioritisation revealed 24 damaging variants with the most damaging variants being ACTRT2 rs3795263, HOXD12 rs200302685, ABCB5 rs111647033, ATP8B4 rs77004004 and ABCC12 rs113496237. We observed admixture of the Khoe-San, Niger-Congo and European ancestries in the population of Botswana, however population substructure was not observed. This exploration of whole genome sequences presents a comprehensive characterisation of human genomic variations in the population of Botswana and their potential in contributing to a deeper understanding of population diversity and health in Africa and the African diaspora.

genomics↗

bioRxiv2020

Heritability jointly Explained by Host Genotype and Microbiome:Will Improve Traits Prediction?

As we observe the 70th anniversary of the publication by Robertson that formalized the notion of heritability, geneticists remain puzzled by the problem of missing/hidden heritability, where heritability estimates from genome-wide association studies (GWAS) fall short of that from twin-based studies. Many possible explanations have been offered for this discrepancy, including existence of genetic variants poorly captured by existing arrays, dominance, epistasis, and unaccounted-for environmental factors; albeit these remain controversial. We believe a substantial part of this problem could be solved or better understood by incorporating the hosts microbiota information in the GWAS model for heritability estimation; ultimately also increasing human traits prediction for clinical utility. This is because, despite empirical observations such as (i) the intimate role of the microbiome in many complex human phenotypes, (ii) the overlap between genetic variants associated with both microbiome attributes and complex diseases, and (iii) the existence of heritable bacterial taxa, current GWAS models for heritability estimate do not take into account the contributory role of the microbiome. Furthermore, heritability estimate from twin-based studies does not discern microbiome component of the observed total phenotypic variance. Here, we summarize the concept of heritability in GWAS and microbiome-wide association studies (MWAS), focusing on its estimation, from a statistical genetics perspective. We then discuss a possible method to incorporate the microbiome in the estimation of heritability in host GWAS.

bioinformatics↗

bioRxiv2020

BiGen: Integrative Clinical and Brain-Imaging Genetics Analysis Using Structural Equation Model

The identification of genetic variants associated with complex brain diseases has evolved in the past decades. Studies in the field have taken different approaches and study designs including genome-wide association studies. Neuroimaging and connectomics have also improved our understanding of the structural connectivity of the human brain and produced reliable measurements. Combining both neuroimaging and genetic characteristics significantly contributes to understand their complex relationship in affecting behaviour and cognition. Throughout this thesis we proposed analysis pipeline to study the association between imaging and genetics of two different types of brain disease, which is, Alzheimers disease and glioblastoma. We observe the need for a unified model to study the complex interplay between genetic, environmental and clinical, neuroimaging and phenotype features. In this chapter, we developed BiGen, a mathematical model to measure the inter-correlation structure through the integration of genetic, environmental, neuroimaging and disease measurements. We utilised the structural equation model and used a path construct of latent variables to study the hidden association between genes and brain-related diseases, mediated by connectivity characteristics. We applied BiGen to simulated data and to a dataset from the Alzheimers Disease Neuroimaging Initiative.

bioinformatics↗

bioRxiv2019

Relating Global and Local Connectome Changes to Dementia and Targeted Gene Expression in Alzheimer’s Disease

Networks are present in many aspects of our lives, and networks in neuroscience have recently gained much attention leading to novel representations of brain connectivity. The integration of neuroimaging characteristics and genetics data allows a better understanding of the effects of the genetic variations on brain structural and functional connections. The current work uses whole-brain tractography in a longitudinal setting, and by measuring the brain structural connectivity changes studies the neurodegeneration of Alzheimers disease. This is accomplished by examining the effect of targeted genetic risk factors on the most common local and global brain connectivity measures. Furthermore, we examined the extent to which Clinical Dementia Rating relates to brain connections longitudinally, as well as to gene expression. Here we show that the expression of PLAU gene increases the change over time in betweenness centrality related to the fusiform gyrus. We also show that the betweenness centrality metric impact dementia-related changes in distinct brain regions. Our findings provide insights into the complex longitudinal interplay between genetics and brain characteristics and highlight the role of Alzheimers genetic risk factors in the estimation of regional brain connectivity alterations.

bioinformatics↗

bioRxiv2019

Genome-wide heritability analysis of severe malaria susceptibility and resistance reveals evidence of polygenic inheritance

ObjectiveEstimating SNP-heritability (h2g) of severe malaria/resistance and its distribution across the genome might shed new light in to the underlying biology.\n\nMethodWe investigated h2g of severe malaria susceptibility and resistance from genome-wide association study (GWAS) dataset (sample size =11, 657). We partitioned the h2g in to chromosomes, allele frequencies and annotations. We further examined none-cell type specific and cell type specific enrichments from GWAS-summary statistics.\n\nResultsWe estimated the h2g of severe malaria at 0.21 (se=0.05, p=2.7x10-5), 0.20 (se =0.05, p=7.5x10-5) and 0.17 (se =0.05, p= 7.2x10-4) in Gambian, Kenyan and Malawi populations, respectively. The h2g attributed to the GWAS significant SNPs and the well-known sickle cell (HbS) variant was approximately 0.07 and 0.03, respectively. We prepared African population reference panel and obtained comparable h2g estimate (0.21 (se = 0.02, p< 1x10-5)) from GWAS-summary statistics meta-analysed across the three populations. Partitioning analysis from raw genotype data showed significant enrichment of h2g in protein coding genic SNPs while summary statistics analysis suggests pattern of enrichment in multiple categories.\n\nConclusionWe report for the first time that the heritability of malaria susceptibility and resistance is largely ascribed by common SNPs and the causal variants are overrepresented in protein coding regions of the genome. Overall, our results suggest that malaria susceptibility and resistance is a polygenic trait. Further studies with larger sample sizes are needed to better understand the underpinning genetics of resistance and susceptibility to severe malaria.

bioinformatics↗

bioRxiv2018

Concordance Of Genetic Variation That Increases Risk For Tourette Syndrome And That Influences Its Underlying Neurocircuitry

BACKGROUNDThere have been considerable recent advances in understanding the genetic architecture of Tourette Syndrome (TS) as well as its underlying neurocircuitry. However, the mechanisms by which genetic variations that increase risk for TS - and its main symptom dimensions - influence relevant brain regions are poorly understood. Here we undertook a genome-wide investigation of the overlap between TS genetic risk and genetic influences on the volume of specific subcortical brain structures that have been implicated in TS.\n\nMETHODSWe obtained summary statistics for the most recent TS genome-wide association study (GWAS) from the TS Psychiatric Genomics Consortium Working Group (4,644 cases and 8,695 controls) and GWAS of subcortical volumes from the ENIGMA consortium (30,717 individuals). We also undertook analyses using GWAS summary statistics of key symptom factors in TS, namely social disinhibition and symmetry behaviour. SNP Effect Concordance Analysis (SECA) was used to examine genetic pleiotropy - the same SNP affecting two traits - and concordance - the agreement in SNP effect directions across these two traits. In addition, a conditional false discovery rate (FDR) analysis was performed, conditioning the TS risk variants on each of the seven subcortical and the intracranial brain volume GWAS. Linkage Disequilibrium Score Regression (LDSR) was used as validation of SECA.\n\nRESULTSSECA revealed significant pleiotropy between TS and putaminal (p=2x10-4) and caudal (p=4x10-4) volumes, independent of direction of effect, and significant concordance between TS and lower thalamic volume (p=1x10-3). LDSR lent additional support for the association between TS and thalamic volume (p=5.85x10-2). Furthermore, SECA revealed significant evidence of concordance between the social disinhibition symptom dimension and lower thalamic volume (p=1x10-3), as well as concordance between symmetry behaviour and greater putaminal volume (p=7x10-4). Conditional FDR analysis further revealed novel variants significantly associated with TS (p<8x10-7) when conditioning on intracranial (rs2708146, q=0.046; and rs72853320, q=0.035 and hippocampal (rs1922786, q=0.001 volumes respectively.\n\nCONCLUSIONThese data indicate concordance for genetic variations involved in disorder risk and subcortical brain volumes in TS. Further work with larger samples is needed to fully delineate the genetic architecture of these disorders and their underlying neurocircuitry.

genomics↗

bioRxiv2018

Genome-Wide Association Study of Brain Connectivity Changes for Alzheimer’s Disease

Variations in the human genome have been found to be an essential factor that affects susceptibility to Alzheimers disease. Genome-wide association studies (GWAS) have identified genetic loci that significantly contribute to the risk of Alzheimers. The availability of genetic data, coupled with brain imaging technologies have opened the door for further discoveries, by using data integration methodologies and new study designs. Although methods have been proposed for integrating image characteristics and genetic information for studying Alzheimers, the measurement of disease is often taken at a single time point, therefore, not allowing the disease progression to be taken into consideration. In longitudinal settings, we analyzed neuroimaging and single nucleotide polymorphism datasets obtained from the Alzheimers Disease Neuroimaging Initiative for three clinical stages of the disease, including healthy control, early mild cognitive impairment and Alzheimers disease subjects. We conducted a GWAS regressing the absolute change of global connectivity metrics on the genetic variants, and used the GWAS summary statistics to compute the gene and pathway scores. We observed significant associations between the change in structural brain connectivity defined by tractography and genes, which have previously been reported to biologically manipulate the risk and progression of certain neurodegenerative disorders, including Alzheimers disease.

bioinformatics↗

bioRxiv2017

Polygenic selection underlies evolution of human brain structure and behavioral traits

Seemingly paradoxical characteristics of psychiatric disorders, including moderate to high prevalence, reduced fecundity, and high heritability have motivated explanations for the persistence of common risk alleles for severe psychiatric phenotypes throughout human evolution. Proposed mechanisms include balancing selection, drift, and weak polygenic adaptation acting either directly, or indirectly through selection on correlated traits. While many mechanisms have been proposed, few have been empirically tested. Leveraging publicly available data of unprecedented sample size, we studied twenty-five traits (i.e., ten neuropsychiatric disorders, three personality traits, total intracranial volume, seven subcortical brain structure volume traits, and four complex traits without neuropsychiatric associations) for evidence of several different signatures of selection over a range of evolutionary time scales. Consistent with the largely polygenic architecture of neuropsychiatric traits, we found no enrichment of trait-associated single-nucleotide polymorphisms (SNPs) in regions of the genome that underwent classical selective sweeps (i.e., events which would have driven selected alleles to near fixation). However, we discovered that SNPs associated with some, but not all, behaviors and brain structure volumes are enriched in genomic regions under selection since divergence from Neanderthals ~600,000 years ago, and show further evidence for signatures of ancient and recent polygenic adaptation. Individual subcortical brain structure volumes demonstrate genome-wide evidence in support of a mosaic theory of brain evolution while total intracranial volume and height appear to share evolutionary constraints consistent with concerted evolution. We further characterized the biological processes potentially targeted by selection, through expression Quantitative Trait Locus (eQTL) and Gene Ontology (GO) enrichment analyses and found evidence for the role of regulatory functions among selected SNPs in immune and brain tissues. Taken together, our results suggest that alleles associated with neuropsychiatric, behavioral, and brain volume phenotypes have experienced both ancient and recent polygenic adaptation in human evolution, acting through neurodevelopmental and immune-mediated pathways.

evolutionary biology↗