Biology subjects

Won, H.-H.

Publications and source records attributed to Won, H.-H..

6 recordsLinked to original sources

bioRxiv2024

Single-cell analysis of human diversity in circulating immune cells

Lack of diversity and proportionate representation in genomics datasets and databases contributes to inequity in healthcare outcomes globally1,2. The relationships of human diversity with biological and biomedical phenotypes are pervasive3, yet remain understudied, particularly in a single-cell genomics context. Here we present the Asian Immune Diversity Atlas (AIDA), a multi-national single-cell RNA-sequencing (scRNA-seq) healthy reference atlas of human immune cells. AIDA comprises 1,265,624 circulating immune cells from 619 healthy donors and 6 controls, spanning 7 population groups across 5 countries. AIDA is one of the largest healthy blood datasets in terms of number of cells, and also the most diverse in terms of number of population groups. Though population groups are frequently compared at the continental level, we identified a pervasive impact of sub-continental diversity on cellular and molecular properties of immune cells. These included cell populations and genes implicated in disease risk and pathogenesis as well as those relevant for diagnostics. We detected single-cell signatures of human diversity not apparent at the level of cell types, as well as modulation of the effects of age and sex by self-reported ethnicity. We discovered functional genetic variants influencing cell type-specific gene expression, including context-dependent effects, which were under-represented in analyses of non-Asian population groups, and which helped contextualise disease-associated variants. We validated our findings using multiple independent datasets and cohorts. AIDA provides fundamental insights into the relationships of human diversity with immune cell phenotypes, enables analyses of multi-ancestry disease datasets, and facilitates the development of precision medicine efforts in Asia and beyond.

genomics↗

bioRxiv2022

Genetic architecture of creativity and extensive genetic overlap with psychiatric disorders revealed from genome-wide association analyses of 241,736 individuals

Creativity is heritable and exhibits familial aggregation with psychiatric disorders, but its genomic basis and genetic relationship with psychiatric disorders remain largely unknown. Here, we conducted a genome-wide association study (GWAS) using an expanded, machine learning-based definition of creativity in individuals of European ancestry from the UK Biobank (n = 241,736) and identified 25 creativity-associated loci. Extensive genetic overlap with psychiatric disorders, including schizophrenia, major depression, bipolar I disorder, attention deficit/hyperactivity disorder, and anorexia nervosa, was demonstrated by the genetic correlation, polygenic risk score, and MiXeR analyses. The condFDR and conjFDR analyses identified additional loci for creativity and psychiatric disorders, as well as shared genetic loci between creativity and psychiatric disorders. This GWAS showed significant correlations with GWASs using traditional definitions of creativity and GWASs adjusted for educational attainment. Our findings contribute to the understanding of the genetic architecture of creativity and reveal its polygenic relationships with psychiatric disorders.

genomics↗

bioRxiv2021

netCRS: Network-based comorbidity risk score for prediction of myocardial infarction using biobank-scaled PheWAS data

The polygenic risk score (PRS) can help to identify individuals genetic susceptibility for various diseases by combining patient genetic profiles and identified single-nucleotide polymorphisms (SNPs) from genome-wide association studies. Although multiple diseases will usually afflict patients at once or in succession, conventional PRSs fail to consider genetic relationships across multiple diseases. Even multi-trait PRSs, which take into account genetic effects for more than one disease at a time, fail to consider a sufficient number of phenotypes to accurately reflect the state of disease comorbidity in a patient, or are biased in terms of the traits that are selected. Thus, we developed novel network-based comorbidity risk scores to quantify associations among multiple phenotypes from phenome-wide association studies (PheWAS). We first constructed a disease-SNP heterogeneous multi-layered network (DS-Net), which consists of a disease network (disease-layer) and SNP network (SNP-layer). The disease-layer describes the population-level interactome from PheWAS data. The SNP-layer was constructed according to linkage disequilibrium. Both layers were attached to transform the information from a population-level interactome to individual-level inferences. Then, graph-based semi-supervised learning was applied to predict possible comorbidity scores on disease-layer for each subject. The SNP-layer serves as receiving individual genotyping data in the scoring process, and the disease-layer serves as the propagated output for an individuals multiple disease comorbidity scores. The possible comorbidity scores were combined by logistic regression, and it is denoted as netCRS. The DS-Net was constructed from UK Biobank PheWAS data, and the individual genetic profiles were collected from the Penn Medicine Biobank. As a proof-of-concept study, myocardial infarction (MI) was selected to compare netCRS with the PRS with pruning and thresholding (PRS-PT). The combined model (netCRS + PRS-PT + covariates) achieved an AUC improvement of 6.26% compared to the (PRS-PT + covariates) model. In terms of risk stratification, the combined model was able to capture the risk of MI up to approximately eight-fold higher than that of the low-risk group. The netCRS and PRS-PT complement each other in predicting high-risk groups of patients with MI. We expect that using these risk prediction models will allow for the development of prevention strategies and reduction of MI morbidity and mortality.

bioinformatics↗

bioRxiv2021

A tissue-level phenome-wide network map of colocalized genes and phenotypes in the UK Biobank

BackgroundPhenome-wide association studies conducted in electronic health record (EHR)-linked biobanks have uncovered a large number of genomic loci associated with traits and diseases. However, interpretation of the complex relationships of associated genes and phenotypes is challenging. ResultsWe constructed a tissue-level phenome-wide network map of colocalized genes and phenotypes. First, we generated colocalized expression quantitative trait loci from 48 tissues of the Genotype-Tissue Expression project and from publicly available genome-wide association study summary statistics from the UK Biobank. We identified 9,151 colocalized genes for 1,411 phenotypes across 48 tissues. Then, we constructed a bipartite network using the colocalized signals to establish links between genes and phenotypes in each tissue. The majority of links are observed in a single tissue whereas only a few are present in all tissues. Finally, we applied the biLouvain clustering algorithm in each tissue-specific bipartite network to identify co-clusters of non-overlapping genes and phenotypes. The majority of co-clusters contains a small number of genes and phenotypes, and 88.6% of co-clusters are found in only one tissue. To demonstrate functionality of the phenome-wide map, we tested if these co-clusters were enriched with known biological and functional gene classes and observed several significant enrichments. Furthermore, we observed that tissue-specific co-clusters are enriched with reported drug side effects for the corresponding drug target genes in clinical trial data. ConclusionsThe phenome-wide map provides links between genes, phenotypes and tissues across a wide spectrum of biological classes and can yield biological and clinical discoveries. The phenome-wide map is publicly available at https://rstudio-connect.hpc.mssm.edu/biPheMap/.

genomics↗

bioRxiv2019

Heterozygous ATP-binding Cassette Transporter G5 Gene Deficiency and Risk of Coronary Artery Disease

BackgroundFamilial sitosterolemia is a rare, recessive Mendelian disorder characterized by hyperabsorption and decreased biliary excretion of dietary sterols. Affected individuals typically have complete genetic deficiency - homozygous loss-of-function (LoF) variants -- in the ATP-binding cassette transporter G5 (ABCG5) or G8 (ABCG8) genes, and have substantially elevated plasma sitosterol and low-density lipoprotein cholesterol (LDL-C) levels. The impact of partial genetic deficiency of ABCG5 or ABCG8, as occurs in heterozygous carriers of LoF variants, on LDL-C and risk of coronary artery disease (CAD) has remained uncertain.\n\nMethodsWe first recruited nine sitosterolemia families, identified causative LoF variants in ABCG5 or ABCG8, and evaluated the associations of these ABCG5 or ABCG8 LoF variants with plasma phytosterols and lipid levels. We next assessed for LoF variants in ABCG5 or ABCG8 in CAD cases (n=29,361) versus controls (n=357,326). We tested the association of rare LoF variants in ABCG5 or ABCG8 with blood lipids and risk for CAD. Rare LoF variants were defined as protein-truncating variants with minor allele frequency less than 0.1% in ABCG5 or ABCG8.\n\nResultsIn sitosterolemia families, seven pedigrees harbored causative LoF variants in ABCG5 and two pedigrees in ABCG8. Homozygous LoF variants in either ABCG5 or ABCG8 led to marked elevations in sitosterol and LDL-C. Of those sitosterolemia families, heterozygous carriers of ABCG5 LoF variants exhibited increased sitosterol and LDL-C levels compared to non-carriers. Within the large-scale CAD case-control cohorts, prevalence of rare LoF variants in ABCG5 and in ABCG8 were approximately 0.1% each. ABCG5 heterozygous LoF variant carriers had significantly elevated LDL-C levels (24.7 mg/dL; 95% confidence interval [CI] 14 to 35; P=1.1x10-6), and were at two-fold increased risk of CAD (odds ratio 2.06, 95% CI 1.27 to 3.35; P=0.004). By contrast, ABCG8 heterozygous LoF carrier status was not associated with increased LDL-C or risk of CAD.\n\nConclusionsAlthough familial sitosterolemia is traditionally considered as a recessive disorder, we observed that heterozygous carriers of a LoF variant in ABCG5 had significantly increased sitosterol and LDL-C levels and a two-fold increase in risk of CAD.

genetics↗

bioRxiv2019

Causal relationship of cerebrospinal fluid biomarkers with the risk of Alzheimer’s disease: A two-sample Mendelian randomization study

Whether the epidemiological association of amyloid beta (A{beta}) and tau pathology with Alzheimers disease (AD) is causal remains unclear. The recent failures to demonstrate the efficacy of several amyloid beta-modifying drugs may indicate the possibility that the observed association is not causal. These failures also led to efforts to develop tau-directed treatments whose efficacy is still tentative. Herein, we conducted a two-sample Mendelian randomization analysis to determine whether the relationship between the cerebrospinal fluid (CSF) biomarkers for amyloid and tau pathology and the risk of AD is causal. We used the summary statistics of a genome-wide association study (GWAS) for CSF biomarkers (A{beta}1-42, phosphorylated tau 181 [p-tau], and total tau [t-tau]) in 3,146 individuals and for late-onset AD (LOAD) in 21,982 LOAD cases and 41,944 cognitively normal controls. We tested the association between the change in the genetically predicted CSF biomarkers and LOAD risk. We found a modest decrease in the LOAD risk per one standard deviation (SD) increase in the genetically predicted CSF A{beta} (odds ratio [OR], 0.63 for AD; 95% confidence interval [CI], 0.38-0.87; P = 0.02). In contrast, we observed a significant increase in the LOAD risk per one SD increase in the genetically predicted CSF p-tau (OR, 2.37; 95% CI, 1.46-3.28; P = 1.09x10-5). However, no causal association was observed of the CSF t-tau with the LOAD risk (OR, 1.15; 95% CI, 0.85-1.45; P = 0.29). Our findings need to be validated in future studies with more genetic variants identified in larger GWASs for CSF biomarkers.

genomics↗