Biology subjects

Kang, H. M.

Publications and source records attributed to Kang, H. M..

32 records · Page 2Linked to original sources

bioRxiv2020

Type 2 and interferon inflammation strongly regulate SARS-CoV-2 related gene expression in the airway epithelium

Coronavirus disease 2019 (COVID-19) outcomes vary from asymptomatic infection to death. This disparity may reflect different airway levels of the SARS-CoV-2 receptor, ACE2, and the spike protein activator, TMPRSS2. Here we explore the role of genetics and co-expression networks in regulating these genes in the airway, through the analysis of nasal airway transcriptome data from 695 children. We identify expression quantitative trait loci (eQTL) for both ACE2 and TMPRSS2, that vary in frequency across world populations. Importantly, we find TMPRSS2 is part of a mucus secretory network, highly upregulated by T2 inflammation through the action of interleukin-13, and that interferon response to respiratory viruses highly upregulates ACE2 expression. Finally, we define airway responses to coronavirus infections in children, finding that these infections upregulate IL6 while also stimulating a more pronounced cytotoxic immune response relative to other respiratory viruses. Our results reveal mechanisms likely influencing SARS-CoV-2 infectivity and COVID-19 clinical outcomes.

genomics↗

bioRxiv2019

Heterogeneous transcriptome response to DNA damage at single cell resolution.

Cancer cells often heterogeneously respond to genotoxic chemotherapy, leading to fractional killing and chemoresistance1, 2, which remain as the major obstacles in cancer treatment. It is widely believed that DNA damage induces a uniform response in regulating transcription and that cell fate is passively determined by a threshold mechanism evaluating the level of transcriptional responses3. On the contrary to this assumption, here we show that a surprisingly high level of heterogeneity exists in individual cell transcriptome responses to DNA damage, and that these transcriptome variations dictate the cell fate after DNA damage. Many DNA damage response genes, including tumor suppressor p53 targets, were exclusively expressed in only a subset of cells having specific cell fate, producing unique stress responses tailored for the fate that the cells are committed to. For instance, CDKN1A, the best known p53 target inhibiting cell cycle, was specifically expressed in a subset of cells undergoing cell cycle checkpoint, while other pro-apoptotic p53 targets were expressed only in cells undergoing apoptosis. A small group of cells exhibited neither checkpoint nor apoptotic responses, but produced a unique transcriptional program that conferred strong chemoresistance to the cells. The heterogeneous transcriptome response to DNA damage was also observed at the protein level in flow cytometry. Our results demonstrate that cell fate heterogeneity after DNA damage is mediated by distinct transcriptional programs generating fate-specific gene expression landscapes. This finding provides an important insight into understanding heterogeneous chemotherapy responses of cancer cells.

cell biology↗

bioRxiv2019

Integrating Comprehensive Functional Annotations to Boost Power and Accuracy in Gene-Based Association Analysis

Gene-based association tests aggregate genotypes across multiple variants for each gene, providing an interpretable gene-level analysis framework for genome-wide association studies (GWAS). Early gene-based test applications often focused on rare coding variants; a more recent wave of gene-based methods, e.g. TWAS, use eQTLs to interrogate regulatory associations. Regulatory variants are expected to be particularly valuable for gene-based analysis, since most GWAS associations to date are non-coding. However, identifying causal genes from regulatory associations remains challenging and contentious. Here, we present a statistical framework and computational tool to integrate heterogeneous annotations with GWAS summary statistics for gene-based analysis, applied with comprehensive coding and tissue-specific regulatory annotations. We compare power and accuracy identifying causal genes across single-annotation, omnibus, and annotation-agnostic gene-based tests in simulation studies and an analysis of 128 traits from the UK Biobank, and find that incorporating heterogeneous annotations in gene-based association analysis increases power and performance identifying causal genes.

genetics↗

bioRxiv2018

Genome-wide association study of diabetic kidney disease highlights biology involved in renal basement membrane collagen

Diabetic kidney disease (DKD) is a heritable but poorly understood complication of diabetes. To identify genetic variants predisposing to DKD, we performed genome-wide association analyses in 19,406 individuals with type 1 diabetes (T1D) using a spectrum of DKD definitions basedon albuminuria and renal function. We identified 16 genome-wide significant loci. The variant with the strongest association (rs55703767) is a common missense mutation in the collagen type IV alpha 3 chain (COL4A3) gene, which encodes a major structural component of the glomerular basement membrane (GBM) implicated in heritable nephropathies. The rs55703767 minor allele (Asp326Tyr) is protective against several definitions of DKD, including albuminuria and end-stage renal disease. Three other loci are in or near genes with known or suggestive involvement in DKD (BMP7) or renal biology (COLEC11 and DDR1). The 16 DKD-associated loci provide novel insights into the pathogenesis of DKD, identifying potential biological targets for prevention and treatment.

genetics↗

bioRxiv2018

Ancestry-agnostic estimation of DNA sample contamination from sequence reads

Detecting and estimating DNA sample contamination are important steps to ensure high quality genotype calls and reliable downstream analysis. Existing methods rely on population allele frequency information for accurate estimation of contamination rates. Correctly specifying population allele frequencies for each individual in early stage of sequence analysis is impractical or even impossible for large-scale sequencing centers that simultaneously process samples from multiple studies across diverse populations. On the other hand, incorrectly specified allele frequencies may result in substantial bias in estimated contamination rates. For example, we observed that existing methods often fail to identify 10% contaminated samples at a typical 3% contamination exclusion threshold when genetic ancestry is misspecified. Such an incomplete screening of contaminated samples substantially inflates the estimated rate of genotyping errors even in deeply sequenced genomes and exomes.\n\nWe propose a robust statistical method that accurately estimates DNA contamination and is agnostic to genetic ancestry of the intended or contaminating sample. Our method integrates the estimation of genetic ancestry and DNA contamination in a unified likelihood framework by leveraging individual-specific allele-frequencies projected from reference genotypes onto principal component coordinates. We demonstrate this method robustly and accurately estimates contamination rates across different populations and contamination rates. We further demonstrate that in the presence contamination, quantitative estimates of genetic ancestry (e.g. principal component coordinates) can be substantially biased if contamination is ignored, and that our proposed method corrects for this bias. Our method is publicly available at http://github.com/Griffan/verifyBamID

bioinformatics↗

bioRxiv2018

Population-scale study of eRNA transcription reveals bipartite functional enhancer architecture

Enhancer RNAs (eRNA) are non-coding RNAs transcribed bidirectionally from active regulatory sequences. Their expression levels correlate with the activating potentials of the enhancers, but due to their instability, eRNAs have proven difficult to quantify in large scale. To overcome this, we use capped-nascent-RNA sequencing to efficiently capture the bidirectional initiation of eRNAs. We apply this in large scale to the human lymphoblastoid cell lines from the Yoruban population, and detected nearly 75,000 eRNA transcription sites with high sensitivity and specificity. We identify genetic variants significantly associated with overall eRNA initiation levels, as well as the transcription directionality between the two divergent eRNA pairs, namely the transcription initiation and directional initiation quantitative trait loci (tiQTLs and diQTLs) respectively. High-resolution analyses of these two types of eRNA QTLs reveal distinct positions of enrichment not only at the central transcription factor (TF) binding regions but also at the flanking eRNA initiation regions, both of which are equivalently associated with mRNA expression QTLs. These two regions - the central TF binding footprint and the eRNA initiation cores - define the bipartite architecture and the function of enhancers, and may provide further insights into interpreting the significance of non-coding regulatory variants.

genomics↗

bioRxiv2018

Sex-specific and pleiotropic effects underlying kidney function identified from GWAS meta-analysis

Chronic Kidney Disease (CKD) is a growing health burden currently affecting 10-15% of adults worldwide. Estimated glomerular filtration rate (eGFR) as a marker of kidney function is commonly used to diagnose CKD. Previous genome-wide association study (GWAS) meta-analyses of CKD and eGFR or related phenotypes have identified a number of variants associated with kidney function, but these only explain a fraction of the variability in kidney phenotypes attributed to genetic components. To extend these studies, we analyzed data from the Nord-Trondelag Health Study (HUNT), which is more densely imputed than previous studies, and performed a GWAS meta-analysis of eGFR with publicly available summary statistics, more than doubling the sample size of previous meta-analyses. We identified 147 loci (53 novel loci) associated with eGFR, including genes involved in transcriptional regulation, kidney development, cellular signaling, metabolism, and solute transport. Moreover, genes at these loci show enriched expression in urogenital tissues and highlight gene sets known to play a role in kidney function. In addition, sex-stratified analysis identified three regions (prioritized genes: PPM1J, MCL1, and SLC47A1) with more significant effects in women than men. Using genetic risk scores constructed from these eGFR meta-analysis results, we show that associated variants are generally predictive of CKD but improve detection only modestly compared with other known clinical risk factors. Collectively, these results yield additional insight into the genetic factors underlying kidney function and progression to CKD.

genomics↗

bioRxiv2018

emeraLD: Rapid Linkage Disequilibrium Estimation with Massive Data Sets

SummaryEstimating linkage disequilibrium (LD) is essential for a wide range of summary statistics-based association methods for genome-wide association studies (GWAS). Large genetic data sets, e.g. the TOPMed WGS project and UK Biobank, enable more accurate and comprehensive LD estimates, but increase the computational burden of LD estimation. Here, we describe emeraLD (Efficient Methods for Estimation and Random Access of LD), a computational tool that leverages sparsity and haplotype structure to estimate LD orders of magnitude faster than existing tools.\n\nAvailability and ImplementationemeraLD is implemented in C++, and is open source under GPLv3. Source code, documentation, an R interface, and utilities for analysis of summary statistics are freely available at http://github.com/statgen/emeraLD\n\nContactcorbinq@umich.edu\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2018

Functional equivalence of genome sequencing analysis pipelines enables harmonized variant calling across human genetics projects

Hundreds of thousands of human whole genome sequencing (WGS) datasets will be generated over the next few years to interrogate a broad range of traits, across diverse populations. These data are more valuable in aggregate: joint analysis of genomes from many sources increases sample size and statistical power for trait mapping, and will enable studies of genome biology, population genetics and genome function at unprecedented scale. A central challenge for joint analysis is that different WGS data processing and analysis pipelines cause substantial batch effects in combined datasets, necessitating computationally expensive reprocessing and harmonization prior to variant calling. This approach is no longer tenable given the scale of current studies and data volumes. Here, in a collaboration across multiple genome centers and NIH programs, we define WGS data processing standards that allow different groups to produce \"functionally equivalent\" (FE) results suitable for joint variant calling with minimal batch effects. Our approach promotes broad harmonization of upstream data processing steps, while allowing for diverse variant callers. Importantly, it allows each group to continue innovating on data processing pipelines, as long as results remain compatible. We present initial FE pipelines developed at five genome centers and show that they yield similar variant calling results - including single nucleotide (SNV), insertion/deletion (indel) and structural variation (SV) - and produce significantly less variability than sequencing replicates. Residual inter-pipeline variability is concentrated at low quality sites and repetitive genomic regions prone to stochastic effects. This work alleviates a key technical bottleneck for genome aggregation and helps lay the foundation for broad data sharing and community-wide \"big-data\" human genetics studies.

bioinformatics↗

bioRxiv2018

Genome-wide association study of 1 million people identifies 111 loci for atrial fibrillation

To understand the genetic variation underlying atrial fibrillation (AF), the most common cardiac arrhythmia, we performed a genome-wide association study (GWAS) of > 1 million people, including 60,620 AF cases and 970,216 controls. We identified 163 independent risk variants at 111 loci and prioritized 165 candidate genes likely to be involved in AF. Many of the identified risk variants fall near genes where more deleterious mutations have been reported to cause serious heart defects in humans or mice (MYH6, NKX2-5, PITX2, TBC1D32, TBX5),1,2 or near genes important for striated muscle function and integrity (e.g. MYH7, PKP2, SSPN, SGCA). Experiments in rabbits with heart failure and left atrial dilation identified a heterogeneous distributed molecular switch from MYH6 to MYH7 in the left atrium, which resulted in contractile and functional heterogeneity and may predispose to initiation and maintenance of atrial arrhythmia.

genetics↗

bioRxiv2017

Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies

In genome-wide association studies (GWAS) for thousands of phenotypes in large biobanks, most binary traits have substantially fewer cases than controls. Both of the widely used approaches, linear mixed model and the recently proposed logistic mixed model, perform poorly - producing large type I error rates - in the analysis of phenotypes with unbalanced case-control ratios. Here we propose a scalable and accurate generalized mixed model association test that uses the saddlepoint approximation (SPA) to calibrate the distribution of score test statistics. This method, SAIGE, provides accurate p-values even when case-control ratios are extremely unbalanced. It utilizes state-of-art optimization strategies to reduce computational time and memory cost of generalized mixed model. The computation cost linearly depends on sample size, and hence can be applicable to GWAS for thousands of phenotypes by large biobanks. Through the analysis of UK Biobank data of 408,961 white British European-ancestry samples for >1400 binary phenotypes, we show that SAIGE can efficiently analyze large sample data, controlling for unbalanced case-control ratios and sample relatedness.

genomics↗

bioRxiv2017

Multiplexing droplet-based single cell RNA-sequencing using natural genetic barcodes

Droplet-based single-cell RNA-sequencing (dscRNA-seq) has enabled rapid, massively parallel profiling of transcriptomes from tens of thousands of cells. Multiplexing samples for single cell capture and library preparation in dscRNA-seq would enable cost-effective designs of differential expression and genetic studies while avoiding technical batch effects, but its implementation remains challenging. Here, we introduce an in-silico algorithm demuxlet that harnesses natural genetic variation to discover the sample identity of each cell and identify droplets containing two cells. These capabilities enable multiplexed dscRNA-seq experiments where cells from unrelated individuals are pooled and captured at higher throughput than standard workflows. To demonstrate the performance of demuxlet, we sequenced 3 pools of peripheral blood mononuclear cells (PBMCs) from 8 lupus patients. Given genotyping data for each individual, demuxlet correctly recovered the sample identity of > 99% of singlets, and identified doublets at rates consistent with previous estimates. In PBMCs, we demonstrate the utility of multiplexed dscRNA-seq in two applications: characterizing cell type specificity and inter-individual variability of cytokine response from 8 lupus patients and mapping genetic variants associated with cell type specific gene expression from 23 donors. Demuxlet is fast, accurate, scalable and could be extended to other single cell datasets that incorporate natural or synthetic DNA barcodes.

bioinformatics↗

bioRxiv2017

Extremely rare variants reveal patterns of germline mutation rate heterogeneity in humans

A detailed understanding of the genome-wide variability of single-nucleotide germline mutation rates is essential to studying human genome evolution. Here we use [~]36 million singleton variants from 3,560 whole-genome sequences to infer fine-scale patterns of mutation rate heterogeneity. Mutability is jointly affected by adjacent nucleotide context and diverse genomic features of the surrounding region, including histone modifications, replication timing, and recombination rate, sometimes suggesting specific mutagenic mechanisms. Remarkably, GC content, DNase hypersensitivity, CpG islands, and H3K36 trimethylation are associated with both increased and decreased mutation rates depending on nucleotide context. We validate these estimated effects in an independent dataset of [~]46,000 de novo mutations, and confirm our estimates are more accurate than previously published estimates based on ancestrally older variants without considering genomic features. Our results thus provide the most refined portrait to date of the factors contributing to genome-wide variability of the human germline mutation rate.

genomics↗

bioRxiv2017

Imputation aware tag SNP selection to improve power for multi-ethnic association studies

The emergence of very large cohorts in genomic research has facilitated a focus on genotype-imputation strategies to power rare variant association. Consequently, a new generation of genotyping arrays are being developed designed with tag single nucleotide polymorphisms (SNPs) to improve rare variant imputation. Selection of these tag SNPs poses several challenges as rare variants tend to be continentally-or even population-specific and reflect fine-scale linkage disequilibrium (LD) structure impacted by recent demographic events. To explore the landscape of tag-able variation and guide design considerations for large-cohort and biobank arrays, we developed a novel pipeline to select tag SNPs using the 26 population reference panel from Phase of the 1000 Genomes Project. We evaluate our approach using leave-one-out internal validation via standard imputation methods that allows the direct comparison of tag SNP performance by estimating the correlation of the imputed and real genotypes for each iteration of potential array sites. We show how this approach allows for an assessment of array design and performance that can take advantage of the development of deeper and more diverse sequenced reference panels. We quantify the impact of demography on tag SNP performance across populations and provide population-specific guidelines for tag SNP selection. We also examine array design strategies that target single populations versus multi-ethnic cohorts, and demonstrate a boost in performance for the latter can be obtained by prioritizing tag SNPs that contribute information across multiple populations simultaneously. Finally, we demonstrate the utility of improved array design to provide meaningful improvements in power, particularly in trans-ethnic studies. The unified framework presented will enable investigators to make informed decisions for the design of new arrays, and help empower the next phase of rare variant association for global health.

genomics↗