Biology subjects

Baker, E.

Publications and source records attributed to Baker, E..

23 records · Page 2Linked to original sources

bioRxiv2021

The impact of genetic risk for Alzheimers disease on the structural brain networks of young adults

We investigated the structural brain networks of 562 young adults in relation to polygenic risk for Alzheimers disease, using magnetic resonance imaging (MRI) and genotype data from the Avon Longitudinal Study of Parents and Children. Diffusion MRI data were used to perform whole-brain tractography and to generate structural brain networks for the whole-brain connectome, and for the default mode, limbic and visual subnetworks. The mean clustering coefficient, mean betweenness centrality, characteristic path length, global efficiency and mean nodal strength were calculated for these networks, for each participant. The connectivity of the rich-club, feeder and local connections was also calculated. Polygenic risk scores (PRS), estimating each participants genetic risk, were calculated at genome-wide level and for nine specific disease pathways. Correlations were calculated between the PRS and a) the graph theoretical metrics of the structural networks and b) the rich-club, feeder and local connectivity of the whole-brain networks. In the visual subnetwork, the mean nodal strength was negatively correlated with the genomewide PRS (r=-0.19, p=1.3x10-5), the mean betweenness centrality was positively correlated with the plasma lipoprotein particle assembly PRS (r=0.16, p=9.2x10-4), and the mean clustering coefficient was negatively correlated with the tau protein binding PRS (r=-0.16, p=9.2x10-4). In the default mode network, the mean nodal strength was negatively correlated with the genomewide PRS (r=-0.14, p=1.5x10-3). The rich-club and feeder connectivities were negatively correlated with the genome-wide PRS (r=-0.16, p=3.7x10-4; r=-0.15, p=8.8x10-4). Our results indicate small reductions in brain connectivity in young adults at risk of developing Alzheimers disease in later life.

neuroscience↗

bioRxiv2021

Behcet's Disease Network Analysis: A Convergent Functional Genomics Approach

Behcets disease (BD) is a multisystem inflammatory disease that affects patients along the historic silk road. Thus far, the pathogenesis of the disease has proved elusive due to the complex genetic interactions and unknown environmental or viral triggering factors of the disease. In this paper, we seek to clarify the genetic factors of the disease while also uncovering other diseases of interest that present with a similar genotype as BD. To do this, we employ a convergent functional genomics approach by leveraging the hierarchical similarity tool available in Geneweaver. Through our analysis, we were able to ascertain 7 BD consensus genes and 16 autoimmune diseases with genetic overlap with BD. The results of our study will inform further research into the pathogenesis of Behcets Disease.

bioinformatics↗

bioRxiv2020

Identifying genetic variants associated with cerebellar volume in 33,265 individuals from the UK-Biobank

There is expanding interest in researching the cerebellum given accumulating evidence of its important contributions to cognitive and emotional functions, in addition to more established sensorimotor roles. While large genome-wide association studies (GWAS) have shed light on the common allele architecture of cortical and subcortical brain structures, the cerebellum remains under investigated. We conducted a meta-GWAS of cerebellar volume in 33,265 UK-Biobank European participants. Results show cerebellar volume to be moderately heritable (h2SNP=50.6%). We identified 33 independent genome-wide associated SNPs with total cerebellar volume, with 6 of these SNPs mapped to protein-coding genes and 5 more shown to alter cerebellar gene expression. We highlight 21 unique candidate genes for follow-up analysis. Cerebellar volume showed significant genetic correlation with brainstem, pallidum and thalamus volumes, but no significant correlations with neuropsychiatric phenotypes. Our results provide important new knowledge of the genetic architecture of cerebellar volume and its relationship with other brain phenotypes.

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↗

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↗