Biology subjects

Han, S. K.

Publications and source records attributed to Han, S. K..

4 recordsLinked to original sources

bioRxiv2022

Mapping genomic regulation of kidney disease and traits through high-resolution and interpretable eQTLs

Expression quantitative trait locus (eQTL) studies illuminate genomic variants that regulate specific genes and contribute to fine-mapped loci discovered via genome-wide association studies (GWAS). Efforts to maximize their accuracy are ongoing. Using 240 glomerular (GLOM) and 311 tubulointerstitial (TUBE) micro-dissected samples from human kidney biopsies, we discovered 5,371 GLOM and 9,787 TUBE eQTLs by incorporating kidney single-nucleus open chromatin data and transcription start site distance as an "integrative prior" for Bayesian statistical fine mapping. The use of an integrative prior resulted in higher resolution eQTLs illustrated by (1) smaller numbers of variants in credible sets with greater confidence, (2) increased enrichment of partitioned heritability for GWAS of two kidney traits, (3) an increased number of variants colocalized with the GWAS loci, and (4) enrichment of computationally predicted functional regulatory variants. A subset of variants and genes were validated experimentally in vitro and using a Drosophila nephrocyte model. More broadly, this study demonstrates that tissue-specific eQTL maps informed by single-nucleus open chromatin data have enhanced utility for diverse downstream analyses.

genomics↗

bioRxiv2022

Tissue-specific and tissue-agnostic effects of genome sequence variation modulating blood pressure

Genome-wide association studies (GWAS) have mapped thousands of variants for numerous polygenic traits and diseases. However, with some exceptions, mechanistic understanding of which precise variants affect which genes in which tissues to modulate trait variation is still lacking. To this end, we introduce a novel class of genomic analyses applicable to any complex trait using GWAS together with gene expression and chromatin accessibility data from multiple tissues. Here we identify the transcription factors (TFs) and regulatory variants within active enhancers regulating specific genes in individual tissues to explain trait heritability of blood pressure (BP), a classical polygenic trait. We show that kidney-, adrenal-, heart-, and arterial-specific regulatory variants contribute to 2.5%, 5.3%, 7.7%, and 11.8% of variant heritability, respectively. Collectively, [~]500,000 predicted regulatory variants across these four tissues explain 33.4% of variant heritability. We demonstrate that these variants are enriched in enhancers binding specific TFs in each tissue. Our findings suggest that gene regulatory networks perturbed by common regulatory variants in a tissue relevant to a phenotype are the primary source of interindividual variation of BP. These studies provide an approach to scan each human tissue for its physiological contribution to a trait.

genetics↗

bioRxiv2022

Quality assessment and refinement of chromatin accessibility data using a sequence-based predictive model

Chromatin accessibility assays are central to the genome-wide identification of gene regulatory elements associated with transcriptional regulation. However, the data have highly variable quality arising from several biological and technical factors. To surmount this problem, we use the predictability of open-chromatin peaks from DNA sequence-based machine-learning models to evaluate and refine chromatin accessibility data. Our framework, gapped k-mer SVM quality check (gkmQC), provides the quality metrics for a sample based on the prediction accuracy of the trained models. We tested 886 samples with DNase-seq from the ENCODE/Roadmap projects to demonstrate that gkmQC can effectively identify high-quality samples underperforming owing to marginal read depths. Peaks identified in high-quality samples by gkmQC are more accurately aligned at functional regulatory elements, show greater enrichment of regulatory elements harboring functional variants from genome-wide association studies (GWAS), and explain greater heritability of phenotypes from their relevant tissues. Moreover, gkmQC can optimize the peak-calling threshold to identify additional peaks, especially for single-cell chromatin accessibility data as well as bulk data. Here we provide a standalone open-source toolkit (https://github.com/Dongwon-Lee/gkmQC) for such analyses and share improved regulatory maps using gkmQC. These resources will contribute to the functional interpretation of disease-associated regulatory genetic variation.

genomics↗

bioRxiv2019

Link clustering explains non-central essential genes in protein interaction networks

Essential genes (EGs) often form central nodes in protein-protein interaction (PPI) networks. However, many reports have shown that numerous EGs are non-central, suggesting that another principle governs gene essentiality. We propose link clustering as a distinct indicator of the essentiality for non-central nodes. Specifically, in various human and yeast PPI networks, we found that 29 to 47% of EGs were better characterized by link clustering than by centrality. Such non-central EGs with clustered links have significant impacts on communities at lower hierarchical levels, suggesting that their essentiality derives from functional dependency among relevant local neighbors, rather than their implication on global connectivity. Moreover, these non-central EGs exhibited several distinct characteristics: they tend to be younger and fast-evolving, and likely change their essentiality across different human cell lines and between human and mouse than central-EGs. Author summaryThe centrality in network structure was thought to be the cause of gene essentiality, as it conveys nodes importance in given networks. However, many essential genes are also found to be non-central, which leads us to ask whether a principle other than centrality may govern gene essentiality. Here we demonstrate that link clustering conveying functional dependency between nodes is the other principle characterizing gene essentiality. The link clustering explains numerous essential genes that are non-central, which are distinct from central ones regarding their molecular function and evolution. Our results clearly establish a two-dimensional principle governing gene essentiality.

systems biology↗