@article{stonebraker_pace_gallins_dang_aksit_faino_gordon_macparland_bamshad_gibson_et al._2024, title={Genetic variation in severe cystic fibrosis liver disease is associated with novel mechanisms for disease pathogenesis}, volume={3}, ISSN={["1527-3350"]}, DOI={10.1097/HEP.0000000000000863}, abstractNote={It is not known why severe cystic fibrosis (CF) liver disease (CFLD) with portal hypertension occurs in only ~7% of people with CF (pwCF). We aimed to identify genetic modifiers for severe CFLD to improve understanding of disease mechanisms.Whole genome sequencing was available in 4,082 pwCF with pancreatic insufficiency (n=516 with severe CFLD; n=3,566 without CFLD). We tested ~15.9 million SNPs for association with severe CFLD versus no-CFLD, using pre-modulator clinical phenotypes including: 1) genetic variant (SERPINA1; Z-allele) previously associated with severe CFLD; 2) candidate SNPs (n=205) associated with non-CF liver diseases; 3) genome-wide association study (GWAS) of common/rare SNPs; 4) transcriptome-wide association (TWAS); and 5) gene-level and pathway analyses. The Z-allele was significantly associated with severe CFLD (p=1.1×10-4). No significant candidate SNPs were identified. GWAS identified genome-wide significant SNPs in 2 loci and 2 suggestive loci. These 4 loci contained genes [significant, PKD1 (p=8.05×10-10) and FNBP1 (p=4.74×10-9); suggestive, DUSP6 (p=1.51×10-7) and ANKUB1 (p=4.69×10-7)] relevant to severe CFLD pathophysiology. TWAS identified 3 genes [CXCR1 (p=1.01×10-6), AAMP (p=1.07×10-6), and TRBV24 (p=1.23×10-5)] involved in hepatic inflammation and innate immunity. Gene-ranked analyses identified pathways enriched in genes linked to multiple liver pathologies.These results identify loci/genes associated with severe CFLD that point to disease mechanisms involving hepatic fibrosis, inflammation and innate immune function, vascular pathology, intracellular signaling, actin cytoskeleton and tight junction integrity, and mechanisms of hepatic steatosis and insulin resistance. These discoveries will facilitate mechanistic studies and the development of therapeutics for severe CFLD.}, journal={HEPATOLOGY}, author={Stonebraker, Jaclyn and Pace, Rhonda and Gallins, Paul and Dang, Hong and Aksit, Melis and Faino, Anna and Gordon, William and Macparland, Sonya and Bamshad, Michael and Gibson, Ronald and et al.}, year={2024}, month={Mar} }
 @article{zhou_gallins_pace_dang_aksit_blue_buckingham_collaco_faino_gordon_et al._2023, title={Genetic Modifiers of Cystic Fibrosis Lung Disease Severity}, volume={207}, ISSN={["1535-4970"]}, url={https://doi.org/10.1164/rccm.202209-1653OC}, DOI={10.1164/rccm.202209-1653OC}, abstractNote={RATIONALE
Lung disease is the major cause of morbidity and mortality in persons with cystic fibrosis (pwCF). Variability in CF lung disease has substantial non-CFTR genetic influence. Identification of genetic modifiers has prognostic and therapeutic importance.


OBJECTIVES
Identify genetic modifier loci and genes/pathways associated with pulmonary disease severity.


METHODS
Whole genome sequencing (WGS) data on 4,248 unique pwCF with pancreatic insufficiency (PI) and lung function measures were combined with imputed genotypes from an additional 3,592 PI patients from the US, Canada, and France. This report describes association of ~15.9 million single nucleotide polymorphisms (SNPs), using the quantitative Kulich Normal Residual Mortality Adjusted (KNoRMA) lung disease phenotype in 7,840 pwCF using pre-modulator lung function data.


MEASUREMENTS AND MAIN RESULTS
Testing included common and rare SNPs, transcriptome-wide association, gene level, and pathway analyses. Pathway analyses identified novel associations with genes that have key roles in organ development, and we hypothesize these genes may relate to dysanapsis and/or variability in lung repair. Results confirmed and extended previous GWAS findings. These WGS data provide finely mapped genetic information to support mechanistic studies. No novel primary associations with common single variants or with rare variants were found. Multi-locus effects at chr5p13 (SLC9A3/CEP72) and chr11p13 (EHF/APIP) were identified. Variant effect size estimates at associated loci were consistently ordered across the cohorts, indicating possible age or birth cohort effects.


CONCLUSIONS
This pre-modulator genomic, transcriptomic, and pathway association study of 7,840 pwCF will facilitate mechanistic and post-modulator genetic studies and, development of novel therapeutics for CF lung disease.}, number={10}, journal={AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE}, author={Zhou, Yi-Hui and Gallins, Paul J. and Pace, Rhonda G. and Dang, Hong and Aksit, Melis A. and Blue, Elizabeth E. and Buckingham, Kati J. and Collaco, Joseph M. and Faino, Anna V. and Gordon, William W. and et al.}, year={2023}, month={May}, pages={1324–1333} }
 @article{ford_jang_chen_zhou_gallins_wright_chiu_rusyn_2022, title={A Population-Based Human In Vitro Approach to Quantify Inter-Individual Variability in Responses to Chemical Mixtures}, volume={10}, ISSN={["2305-6304"]}, url={https://doi.org/10.3390/toxics10080441}, DOI={10.3390/toxics10080441}, abstractNote={Human cell-based population-wide in vitro models have been proposed as a strategy to derive chemical-specific estimates of inter-individual variability; however, the utility of this approach has not yet been tested for cumulative exposures in mixtures. This study aimed to test defined mixtures and their individual components and determine whether adverse effects of the mixtures were likely to be more variable in a population than those of the individual chemicals. The in vitro model comprised 146 human lymphoblastoid cell lines from four diverse subpopulations of European and African descent. Cells were exposed, in concentration–response, to 42 chemicals from diverse classes of environmental pollutants; in addition, eight defined mixtures were prepared from these chemicals using several exposure- or hazard-based scenarios. Points of departure for cytotoxicity were derived using Bayesian concentration–response modeling and population variability was quantified in the form of a toxicodynamic variability factor (TDVF). We found that 28 chemicals and all mixtures exhibited concentration–response cytotoxicity, enabling calculation of the TDVF. The median TDVF across test substances, for both individual chemicals or defined mixtures, ranged from a default assumption (101/2) of toxicodynamic variability in human population to >10. The data also provide a proof of principle for single-variant genome-wide association mapping for toxicity of the chemicals and mixtures, although replication would be necessary due to statistical power limitations with the current sample size. This study demonstrates the feasibility of using a set of human lymphoblastoid cell lines as an in vitro model to quantify the extent of inter-individual variability in hazardous properties of both individual chemicals and mixtures. The data show that population variability of the mixtures is unlikely to exceed that of the most variable component, and that similarity in genome-wide associations among components may be used to accrue additional evidence for grouping of constituents in a mixture for cumulative assessments.}, number={8}, journal={TOXICS}, author={Ford, Lucie C. and Jang, Suji and Chen, Zunwei and Zhou, Yi-Hui and Gallins, Paul J. and Wright, Fred A. and Chiu, Weihsueh A. and Rusyn, Ivan}, year={2022}, month={Aug} }
 @article{zhou_gallins_etheridge_jima_scholl_wright_innocenti_2022, title={A resource for integrated genomic analysis of the human liver}, volume={12}, ISSN={["2045-2322"]}, url={https://doi.org/10.1038/s41598-022-18506-z}, DOI={10.1038/s41598-022-18506-z}, abstractNote={In this study, we generated whole-transcriptome RNA-Seq from n = 192 genotyped liver samples and used these data with existing data from the GTEx Project (RNA-Seq) and previous liver eQTL (microarray) studies to create an enhanced transcriptomic sequence resource in the human liver. Analyses of genotype-expression associations show pronounced enrichment of associations with genes of drug response. The associations are primarily consistent across the two RNA-Seq datasets, with some modest variation, indicating the importance of obtaining multiple datasets to produce a robust resource. We further used an empirical Bayesian model to compare eQTL patterns in liver and an additional 20 GTEx tissues, finding that MHC genes, and especially class II genes, are enriched for liver-specific eQTL patterns. To illustrate the utility of the resource to augment GWAS analysis with small sample sizes, we developed a novel meta-analysis technique to combine several liver eQTL data sources. We also illustrate its application using a transcriptome-enhanced re-analysis of a study of neutropenia in pancreatic cancer patients. The associations of genotype with liver expression, including splice variation and its genetic associations, are made available in a searchable genome browser.}, number={1}, journal={SCIENTIFIC REPORTS}, author={Zhou, Yi-Hui and Gallins, Paul J. and Etheridge, Amy S. and Jima, Dereje and Scholl, Elizabeth and Wright, Fred A. and Innocenti, Federico}, year={2022}, month={Sep} }
 @article{harlow_gandawijaya_bamford_martin_wood_most_tanaka_leonard_etheridge_innocenti_et al._2022, title={Identification and single-base gene-editing functional validation of a cis-EPO variant as a genetic predictor for EPO-increasing therapies}, volume={109}, ISSN={["1537-6605"]}, DOI={10.1016/j.ajhg.2022.08.004}, abstractNote={Hypoxia-inducible factor prolyl hydroxylase inhibitors (HIF-PHIs) are currently under clinical development for treating anemia in chronic kidney disease (CKD), but it is important to monitor their cardiovascular safety. Genetic variants can be used as predictors to help inform the potential risk of adverse effects associated with drug treatments. We therefore aimed to use human genetics to help assess the risk of adverse cardiovascular events associated with therapeutically altered EPO levels to help inform clinical trials studying the safety of HIF-PHIs. By performing a genome-wide association meta-analysis of EPO (n = 6,127), we identified a cis-EPO variant (rs1617640) lying in the EPO promoter region. We validated this variant as most likely causal in controlling EPO levels by using genetic and functional approaches, including single-base gene editing. Using this variant as a partial predictor for therapeutic modulation of EPO and large genome-wide association data in Mendelian randomization tests, we found no evidence (at p < 0.05) that genetically predicted long-term rises in endogenous EPO, equivalent to a 2.2-unit increase, increased risk of coronary artery disease (CAD, OR [95% CI] = 1.01 [0.93, 1.07]), myocardial infarction (MI, OR [95% CI] = 0.99 [0.87, 1.15]), or stroke (OR [95% CI] = 0.97 [0.87, 1.07]). We could exclude increased odds of 1.15 for cardiovascular disease for a 2.2-unit EPO increase. A combination of genetic and functional studies provides a powerful approach to investigate the potential therapeutic profile of EPO-increasing therapies for treating anemia in CKD.}, number={9}, journal={AMERICAN JOURNAL OF HUMAN GENETICS}, author={Harlow, Charli E. and Gandawijaya, Josan and Bamford, Rosemary A. and Martin, Emily-Rose and Wood, Andrew R. and Most, Peter J. and Tanaka, Toshiko and Leonard, Hampton L. and Etheridge, Amy S. and Innocenti, Federico and et al.}, year={2022}, month={Sep}, pages={1638–1652} }
 @article{sun_liu_rosen_huang_pace_dang_gallins_blue_ling_corvol_et al._2022, title={Leveraging TOPMed imputation server and constructing a cohort-specific imputation reference panel to enhance genotype imputation among cystic fibrosis patients}, volume={3}, ISSN={["2666-2477"]}, DOI={10.1016/j.xhgg.2022.100090}, abstractNote={Cystic fibrosis (CF) is a severe genetic disorder that can cause multiple comorbidities affecting the lungs, the pancreas, the luminal digestive system and beyond. In our previous genome-wide association studies (GWAS), we genotyped approximately 8,000 CF samples using a mixture of different genotyping platforms. More recently, the Cystic Fibrosis Genome Project (CFGP) performed deep (approximately 30×) whole genome sequencing (WGS) of 5,095 samples to better understand the genetic mechanisms underlying clinical heterogeneity among patients with CF. For mixtures of GWAS array and WGS data, genotype imputation has proven effective in increasing effective sample size. Therefore, we first performed imputation for the approximately 8,000 CF samples with GWAS array genotype using the Trans-Omics for Precision Medicine (TOPMed) freeze 8 reference panel. Our results demonstrate that TOPMed can provide high-quality imputation for patients with CF, boosting genomic coverage from approximately 0.3-4.2 million genotyped markers to approximately 11-43 million well-imputed markers, and significantly improving polygenic risk score (PRS) prediction accuracy. Furthermore, we built a CF-specific CFGP reference panel based on WGS data of patients with CF. We demonstrate that despite having approximately 3% the sample size of TOPMed, our CFGP reference panel can still outperform TOPMed when imputing some CF disease-causing variants, likely owing to allele and haplotype differences between patients with CF and general populations. We anticipate our imputed data for 4,656 samples without WGS data will benefit our subsequent genetic association studies, and the CFGP reference panel built from CF WGS samples will benefit other investigators studying CF.}, number={2}, journal={HUMAN GENETICS AND GENOMICS ADVANCES}, author={Sun, Quan and Liu, Weifang and Rosen, Jonathan D. and Huang, Le and Pace, Rhonda G. and Dang, Hong and Gallins, Paul J. and Blue, Elizabeth E. and Ling, Hua and Corvol, Harriet and et al.}, year={2022}, month={Apr} }
 @article{etheridge_gallins_jima_broadaway_ratain_schuetz_schadt_schroder_molony_zhou_et al._2020, title={A New Liver Expression Quantitative Trait Locus Map From 1,183 Individuals Provides Evidence for Novel Expression Quantitative Trait Loci of Drug Response, Metabolic, and Sex-Biased Phenotypes}, volume={107}, ISSN={["1532-6535"]}, DOI={10.1002/cpt.1751}, abstractNote={Expression quantitative trait locus (eQTL) studies in human liver are crucial for elucidating how genetic variation influences variability in disease risk and therapeutic outcomes and may help guide strategies to obtain maximal efficacy and safety of clinical interventions. Associations between expression microarray and genome‐wide genotype data from four human liver eQTL studies (n = 1,183) were analyzed. More than 2.3 million cis‐eQTLs for 15,668 genes were identified. When eQTLs were filtered against a list of 1,496 drug response genes, 187,829 cis‐eQTLs for 1,191 genes were identified. Additionally, 1,683 sex‐biased cis‐eQTLs were identified, as well as 49 and 73 cis‐eQTLs that colocalized with genome‐wide association study signals for blood metabolite or lipid levels, respectively. Translational relevance of these results is evidenced by linking DPYD eQTLs to differences in safety of chemotherapy, linking the sex‐biased regulation of PCSK9 expression to anti‐lipid therapy, and identifying the G‐protein coupled receptor GPR180 as a novel drug target for hypertriglyceridemia.}, number={6}, journal={CLINICAL PHARMACOLOGY & THERAPEUTICS}, author={Etheridge, Amy S. and Gallins, Paul J. and Jima, Dereje and Broadaway, K. Alaine and Ratain, Mark J. and Schuetz, Erin and Schadt, Eric and Schroder, Adrian and Molony, Cliona and Zhou, Yihui and et al.}, year={2020}, month={Jun}, pages={1383–1393} }
 @article{gallins_saghapour_zhou_2020, title={Exploring the Limits of Combined Image/'omics Analysis for Non-cancer Histological Phenotypes}, volume={11}, ISSN={["1664-8021"]}, DOI={10.3389/fgene.2020.555886}, abstractNote={The last several years have witnessed an explosion of methods and applications for combining image data with 'omics data, and for prediction of clinical phenotypes. Much of this research has focused on cancer histology, for which genetic perturbations are large, and the signal to noise ratio is high. Related research on chronic, complex diseases is limited by tissue sample availability, lower genomic signal strength, and the less extreme and tissue-specific nature of intermediate histological phenotypes. Data from the GTEx Consortium provides a unique opportunity to investigate the connections among phenotypic histological variation, imaging data, and 'omics profiling, from multiple tissue-specific phenotypes at the sub-clinical level. Investigating histological designations in multiple tissues, we survey the evidence for genomic association and prediction of histology, and use the results to test the limits of prediction accuracy using machine learning methods applied to the imaging data, genomics data, and their combination. We find that expression data has similar or superior accuracy for pathology prediction as our use of imaging data, despite the fact that pathological determination is made from the images themselves. A variety of machine learning methods have similar performance, while network embedding methods offer at best limited improvements. These observations hold across a range of tissues and predictor types. The results are supportive of the use of genomic measurements for prediction, and in using the same target tissue in which pathological phenotyping has been performed. Although this last finding is sensible, to our knowledge our study is the first to demonstrate this fact empirically. Even while prediction accuracy remains a challenge, the results show clear evidence of pathway and tissue-specific biology.}, journal={FRONTIERS IN GENETICS}, author={Gallins, Paul and Saghapour, Ehsan and Zhou, Yi-Hui}, year={2020}, month={Oct} }
 @misc{zhou_gallins_2019, title={A Review and Tutorial of Machine Learning Methods for Microbiome Host Trait Prediction}, volume={10}, ISSN={["1664-8021"]}, DOI={10.3389/fgene.2019.00579}, abstractNote={With the growing importance of microbiome research, there is increasing evidence that host variation in microbial communities is associated with overall host health. Advancement in genetic sequencing methods for microbiomes has coincided with improvements in machine learning, with important implications for disease risk prediction in humans. One aspect specific to microbiome prediction is the use of taxonomy-informed feature selection. In this review for non-experts, we explore the most commonly used machine learning methods, and evaluate their prediction accuracy as applied to microbiome host trait prediction. Methods are described at an introductory level, and R/Python code for the analyses is provided.}, journal={FRONTIERS IN GENETICS}, author={Zhou, Yi-Hui and Gallins, Paul}, year={2019}, month={Jun} }
 @article{frayling_beaumont_jones_yaghootkar_tuke_ruth_casanova_west_locke_sharp_et al._2018, title={A common allele in FGF21 associated with sugar intake is associated with body shape, lower total body-fat percentage, and higher blood pressure}, volume={23}, number={2}, journal={Cell reports}, author={Frayling, T. M. and Beaumont, R. N. and Jones, S. E. and Yaghootkar, H. and Tuke, M. A. and Ruth, K. S. and Casanova, F. and West, B. and Locke, J. and Sharp, S. and et al.}, year={2018}, pages={327–336} }
 @article{hu_gallins_zhou_2018, title={A zero-inflated beta-binomial model for microbiome data analysis}, volume={7}, ISSN={["2049-1573"]}, url={https://doi.org/10.1002/sta4.185}, DOI={10.1002/sta4.185}, abstractNote={The Microbiome is increasingly recognized as an important aspect of the health of host species, involved in many biological pathways and processes and potentially useful as health biomarkers. Taking advantage of high‐throughput sequencing technologies, modern bacterial microbiome studies are metagenomic, interrogating thousands of taxa simultaneously. Several data analysis frameworks have been proposed for microbiome sequence read count data and for determining the most significant features. However, there is still room for improvement. We introduce a zero‐inflated beta‐binomial to model the distribution of microbiome count data and to determine association with a continuous or categorical phenotype of interest. The approach can exploit the mean‐variance relationship to improve power and adjust for covariates. The proposed method is a mixture model with two components: (i) a zero model accounting for excess zeros and (ii) a count model to capture the remaining component by beta‐binomial regression, allowing for overdispersion effects. Simulation studies show that our proposed method effectively controls type I error and has higher power than competing methods to detect taxa associated with phenotype. An R package ZIBBSeqDiscovery is available on R CRAN. Copyright © 2018 John Wiley & Sons, Ltd.}, number={1}, journal={STAT}, publisher={Wiley}, author={Hu, Tao and Gallins, Paul and Zhou, Yi-Hui}, year={2018} }
 @article{polineni_dang_gallins_jones_pace_stonebraker_commander_krenicky_zhou_corvol_et al._2018, title={Airway mucosal host defense is key to genomic regulation of cystic fibrosis lung disease severity}, volume={197}, number={1}, journal={American Journal of Respiratory and Critical Care Medicine}, author={Polineni, D. and Dang, H. and Gallins, P. J. and Jones, L. C. and Pace, R. G. and Stonebraker, J. R. and Commander, L. A. and Krenicky, J. E. and Zhou, Y. H. and Corvol, H. and et al.}, year={2018}, pages={79–93} }
 @article{luizon_eckalbar_wang_jones_smith_laurance_lin_gallins_etheridge_wright_et al._2016, title={Genomic Characterization of Metformin Hepatic Response}, volume={12}, ISSN={["1553-7404"]}, DOI={10.1371/journal.pgen.1006449}, abstractNote={Metformin is used as a first-line therapy for type 2 diabetes (T2D) and prescribed for numerous other diseases. However, its mechanism of action in the liver has yet to be characterized in a systematic manner. To comprehensively identify genes and regulatory elements associated with metformin treatment, we carried out RNA-seq and ChIP-seq (H3K27ac, H3K27me3) on primary human hepatocytes from the same donor treated with vehicle control, metformin or metformin and compound C, an AMP-activated protein kinase (AMPK) inhibitor (allowing to identify AMPK-independent pathways). We identified thousands of metformin responsive AMPK-dependent and AMPK-independent differentially expressed genes and regulatory elements. We functionally validated several elements for metformin-induced promoter and enhancer activity. These include an enhancer in an ataxia telangiectasia mutated (ATM) intron that has SNPs in linkage disequilibrium with a metformin treatment response GWAS lead SNP (rs11212617) that showed increased enhancer activity for the associated haplotype. Expression quantitative trait locus (eQTL) liver analysis and CRISPR activation suggest that this enhancer could be regulating ATM, which has a known role in AMPK activation, and potentially also EXPH5 and DDX10, its neighboring genes. Using ChIP-seq and siRNA knockdown, we further show that activating transcription factor 3 (ATF3), our top metformin upregulated AMPK-dependent gene, could have an important role in gluconeogenesis repression. Our findings provide a genome-wide representation of metformin hepatic response, highlight important sequences that could be associated with interindividual variability in glycemic response to metformin and identify novel T2D treatment candidates.}, number={11}, journal={PLOS GENETICS}, author={Luizon, Marcelo R. and Eckalbar, Walter L. and Wang, Yao and Jones, Stacy L. and Smith, Robin P. and Laurance, Megan and Lin, Lawrence and Gallins, Paul J. and Etheridge, Amy S. and Wright, Fred and et al.}, year={2016}, month={Nov} }
 @inproceedings{theisen_williams_2016, title={Poster: risk-based attack surface approximation}, booktitle={Symposium and Bootcamp on the Science of Security}, author={Theisen, C. and Williams, L.}, year={2016}, pages={121–123} }
 @article{abdo_xia_brown_kosyk_huang_sakamuru_zhou_jack_gallins_xia_et al._2015, title={Population-Based in Vitro Hazard and Concentration-Response Assessment of Chemicals: The 1000 Genomes High-Throughput Screening Study}, volume={123}, ISSN={["1552-9924"]}, DOI={10.1289/ehp.1408775}, abstractNote={Background: Understanding of human variation in toxicity to environmental chemicals remains limited, so human health risk assessments still largely rely on a generic 10-fold factor (10½ each for toxicokinetics and toxicodynamics) to account for sensitive individuals or subpopulations. Objectives: We tested a hypothesis that population-wide in vitro cytotoxicity screening can rapidly inform both the magnitude of and molecular causes for interindividual toxicodynamic variability. Methods: We used 1,086 lymphoblastoid cell lines from the 1000 Genomes Project, representing nine populations from five continents, to assess variation in cytotoxic response to 179 chemicals. Analysis included assessments of population variation and heritability, and genome-wide association mapping, with attention to phenotypic relevance to human exposures. Results: For about half the tested compounds, cytotoxic response in the 1% most “sensitive” individual occurred at concentrations within a factor of 10½ (i.e., approximately 3) of that in the median individual; however, for some compounds, this factor was > 10. Genetic mapping suggested important roles for variation in membrane and transmembrane genes, with a number of chemicals showing association with SNP rs13120371 in the solute carrier SLC7A11, previously implicated in chemoresistance. Conclusions: This experimental approach fills critical gaps unaddressed by recent large-scale toxicity testing programs, providing quantitative, experimentally based estimates of human toxicodynamic variability, and also testable hypotheses about mechanisms contributing to interindividual variation. Citation: Abdo N, Xia M, Brown CC, Kosyk O, Huang R, Sakamuru S, Zhou YH, Jack JR, Gallins P, Xia K, Li Y, Chiu WA, Motsinger-Reif AA, Austin CP, Tice RR, Rusyn I, Wright FA. 2015. Population-based in vitro hazard and concentration–response assessment of chemicals: the 1000 Genomes high-throughput screening study. Environ Health Perspect 123:458–466; http://dx.doi.org/10.1289/ehp.1408775}, number={5}, journal={ENVIRONMENTAL HEALTH PERSPECTIVES}, author={Abdo, Nour and Xia, Menghang and Brown, Chad C. and Kosyk, Oksana and Huang, Ruili and Sakamuru, Srilatha and Zhou, Yi-Hui and Jack, John R. and Gallins, Paul and Xia, Kai and et al.}, year={2015}, month={May}, pages={458–466} }