Choi W, Luo Y, Raychaudhuri S, Han B. HATK: HLA Analysis Toolkit. Bioinformatics 2021;37(3):416-418.
Degenhardt F, Mayr G, Wendorff M, Boucher G, Ellinghaus E, Ellinghaus D, ElAbd H, Rosati E, Hubenthal M, Juzenas S, Abedian S, Vahedi H, Thelma BK, Yang S-K, Ye BD, Cheon JH, Datta LW, Daryani NE, Ellul P, Esaki M, Fuyuno Y, McGovern DPB, Haritunians T, Hong M, Juyal G, Jung ES, Kubo M, Kugathasan S, Lenz TL, Leslie S, Malekzadeh R, Midha V, Motyer A, Ng SC, Okou DT, Raychaudhuri S, Schembri J, Schreiber S, Song K, Sood A, Takahashi A, Torres EA, Umeno J, Alizadeh BZ, Weersma RK, Wong SH, Yamazaki K, Karlsen TH, Rioux JD, Brant SR, Center MAAISR, Franke A, Consortium IIBDG. Transethnic analysis of the human leukocyte antigen region for ulcerative colitis reveals not only shared but also ethnicity-specific disease associations. Hum Mol Genet 2021;30(5):356-369.Abstract
Inflammatory bowel disease (IBD) is a chronic inflammatory disease of the gut. Genetic association studies have identified the highly variable human leukocyte antigen (HLA) region as the strongest susceptibility locus for IBD and specifically DRB1*01:03 as a determining factor for ulcerative colitis (UC). However, for most of the association signal such as delineation could not be made because of tight structures of linkage disequilibrium within the HLA. The aim of this study was therefore to further characterize the HLA signal using a transethnic approach. We performed a comprehensive fine mapping of single HLA alleles in UC in a cohort of 9272 individuals with African American, East Asian, Puerto Rican, Indian and Iranian descent and 40 691 previously analyzed Caucasians, additionally analyzing whole HLA haplotypes. We computationally characterized the binding of associated HLA alleles to human self-peptides and analyzed the physicochemical properties of the HLA proteins and predicted self-peptidomes. Highlighting alleles of the HLA-DRB1*15 group and their correlated HLA-DQ-DR haplotypes, we not only identified consistent associations (regarding effects directions/magnitudes) across different ethnicities but also identified population-specific signals (regarding differences in allele frequencies). We observed that DRB1*01:03 is mostly present in individuals of Western European descent and hardly present in non-Caucasian individuals. We found peptides predicted to bind to risk HLA alleles to be rich in positively charged amino acids. We conclude that the HLA plays an important role for UC susceptibility across different ethnicities. This research further implicates specific features of peptides that are predicted to bind risk and protective HLA proteins.
Millard N, Korsunsky I, Weinand K, Fonseka CY, Nathan A, Kang JB, Raychaudhuri S. Maximizing statistical power to detect clinically associated cell states with scPOST. Cell Rep Methods 2021;1(8):PMCID: PMC8740883.Abstract
As advances in single-cell technologies enable the unbiased assay of thousands of cells simultaneously, human disease studies are able to identify clinically associated cell states using case-control study designs. These studies require precious clinical samples and costly technologies; therefore, it is critical to employ study design principles that maximize power to detect cell state frequency shifts between conditions, such as disease versus healthy. Here, we present single-cell Power Simulation Tool (scPOST), a method that enables users to estimate power under different study designs. To approximate the specific experimental and clinical scenarios being investigated, scPOST takes prototype (public or pilot) single-cell data as input and generates large numbers of single-cell datasets in silico. We use scPOST to perform power analyses on three independent single-cell datasets that span diverse experimental conditions: a batch-corrected 21-sample rheumatoid arthritis dataset (5,265 cells) from synovial tissue, a 259-sample tuberculosis progression dataset (496,517 memory T cells) from peripheral blood mononuclear cells (PBMCs), and a 30-sample ulcerative colitis dataset (235,229 cells) from intestinal biopsies. Over thousands of simulations, we consistently observe that power to detect frequency shifts in cell states is maximized by larger numbers of independent clinical samples, reduced batch effects, and smaller variation in a cell state’s frequency across samples.
Luo Y, Kanai M, Choi W, Li X, Yamamoto K, Ogawa K, Gutierrez-Arcelus M, Gregersen PK, Stuart PE, Elder JT, Fellay J, Carrington M, Haas DW, Guo X, Palmer ND, Chen Y-DI, Rotter JI, Taylor KD, Rich SS, Correa A, Wilson JG, Kathiresan S, Cho MH, Metspalu A, Esko T, Okada Y, Han B, for Consortium NHLBIT-OPM (TOPM), McLaren PJ, Raychaudhuri S. A high-resolution HLA reference panel capturing global population diversity enables multi-ethnic fine-mapping in HIV host response. Nature Genetics 2020;53(10):1504-1516.Abstract
Defining causal variation by fine-mapping can be more effective in multi-ethnic genetic studies, particularly in regions such as the MHC with highly population-specific structure. To enable such studies, we constructed a large (N=21,546) high resolution HLA reference panel spanning five global populations based on whole-genome sequencing data. Expectedly, we observed unique long-range HLA haplotypes within each population group. Despite this, we demonstrated consistently accurate imputation at G-group resolution (94.2%, 93.7%, 97.8% and 93.7% in Admixed African (AA), East Asian (EAS), European (EUR) and Latino (LAT)). We jointly analyzed genome-wide association studies (GWAS) of HIV-1 viral load from EUR, AA and LAT populations. Our analysis pinpointed the MHC association to three amino acid positions (97, 67 and 156) marking three consecutive pockets (C, B and D) within the HLA-B peptide binding groove, explaining 12.9% of trait variance, and obviating effects of previously reported associations from population-specific HIV studies.Competing Interest StatementM.H.C. has received consulting or speaking fees from Illumina and AstraZeneca, and grant support from GSK and Bayer.Funding StatementThe study was supported by the National Institutes of Health (NIH) TB Research Unit Network, Grant U19 AI111224-01. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services. The Genotype and Phenotype (GaP) Registry at The Feinstein Institute for Medical Research provided fresh, de-identified human plasma; blood was collected from control subjects under an IRB-approved protocol (IRB# 09-081) and processed to isolate plasma. The GaP is a sub-protocol of the Tissue Donation Program (TDP) at Northwell Health and a national resource for genotype-phenotype studies. A.M. is supported by Gentransmed grant 2014-2020.4.01.15-0012.; D.W.H. is supported by NIH grants AI110527, AI077505, TR000445, AI069439, and AI110527. D.H.S. was supported by R01 HL92301, R01 HL67348, R01 NS058700, R01 AR48797, R01 DK071891, R01 AG058921, the General Clinical Research Center of the Wake Forest University School of Medicine (M01 RR07122, F32 HL085989), the American Diabetes Association, and a pilot grant from the Claude Pepper Older Americans Independence Center of Wake Forest University Health Sciences (P60 AG10484). J.T.E. and P.E.S. were supported by NIH/NIAMS R01 AR042742, R01 AR050511, and R01 AR063611. For some HIV cohort participants, DNA and data collection was supported by NIH/NIAID AIDS Clinical Trial Group (ACTG) grants UM1 AI068634, UM1 AI068636 and UM1 AI106701, and ACTG clinical research site grants A1069412, A1069423, A1069424, A1069503, AI025859, AI025868, AI027658, AI027661, AI027666, AI027675, AI032782, AI034853, AI038858, AI045008, AI046370, AI046376, AI050409, AI050410, AI050410, AI058740, AI060354, AI068636, AI069412, AI069415, AI069418, AI069419, AI069423, AI069424, AI069428, AI069432, AI069432, AI069434, AI069439, AI069447, AI069450, AI069452, AI069465, AI069467, AI069470, AI069471, AI069472, AI069474, AI069477, AI069481, AI069484, AI069494, AI069495, AI069496, AI069501, AI069501, AI069502, AI069503, AI069511, AI069513, AI069532, AI069534, AI069556, AI072626, AI073961, RR000046, RR000425, RR023561, RR024156, RR024160, RR024996, RR025008, RR025747, RR025777, RR025780, TR000004, TR000058, TR000124, TR000170, TR000439, TR000445, TR000457, TR001079, TR001082, TR001111, and TR024160. Molecular data for the Trans-Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung and Blood Institute (NHLBI). See the TOPMed Omics Support Table (Supplementary Table 16) for study specific omics support information. Core support including centralized genomic read mapping and genotype calling, along with variant quality metrics and filtering were provided by the TOPMed Informatics Research Center (3R01HL-117626-02S1; contract HHSN268201800002I). Core support including phenotype harmonization, data management, sample-identity QC, and general program coordination were provided by the TOPMed Data Coordinating Center (R01HL-120393; U01HL-120393; contract HHSN268201800001I). We gratefully acknowledge the studies and participants who provided biological samples and data for TOPMed. The COPDGene project was supported by Award Number U01 HL089897 and Award Number U01 HL089856 from the National Heart, Lung, and Blood Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Heart, Lung, and Blood Institute or the National Institutes of Health. The COPDGene project is also supported by the COPD Foundation through contributions made to an Industry Advisory Board comprised of AstraZeneca, Boehringer Ingelheim, GlaxoSmithKline, Novartis, Pfizer, Siemens and Sunovion. A full listing of COPDGene investigators can be found at: The Jackson Heart Study (JHS) is supported and conducted in collaboration with Jackson State University (HHSN268201800013I), Tougaloo College (HHSN268201800014I), the Mississippi State Department of Health (HHSN268201800015I) and the University of Mississippi Medical Center (HHSN268201800010I, HHSN268201800011I and HHSN268201800012I) contracts from the National Heart, Lung, and Blood Institute (NHLBI) and the National Institute on Minority Health and Health Disparities (NIMHD). The authors also wish to thank the staffs and participants of the JHS. MESA and the MESA SHARe project are conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with MESA investigators. Support for MESA is provided by contracts 75N92020D00001, HHSN268201500003I, N01-HC-95159, 75N92020D00005, N01-HC-95160, 75N92020D00002, N01-HC-95161, 75N92020D00003, N01-HC-95162, 75N92020D00006, N01-HC-95163, 75N92020D00004, N01-HC-95164, 75N92020D00007, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169, UL1-TR-000040, UL1-TR-001079, UL1-TR-001420. MESA Family is conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with MESA investigators. Support is provided by grants and contracts R01HL071051, R01HL071205, R01HL071250, R01HL071251, R01HL071258, R01HL071259, by the National Center for Research Resources, Grant UL1RR033176. The provision of genotyping data was supported in part by the National Center for Advancing Translational Sciences, CTSI grant UL1TR001881, and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center (DRC) grant DK063491 to the Southern California Diabetes Endocrinology Research Center. This project has been funded in whole or in part with federal funds from the Frederick National Laboratory for Cancer Research, under Contract No. HHSN261200800001E. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government. This Research was supported in part by the Intramural Research Program of the NIH, Frederick National Lab, Center for Cancer Research.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:The Genotype and Phenotype (GaP) Registry at The Feinstein Institute for Medical Research provided fresh, de-identified human plasma; blood was collected from control subjects under an IRB-approved protocol (IRB# 09-081) and processed to isolate plasma. The GaP is a sub-protocol of the Tissue Donation Program (TDP) at Northwell Health and a national resource for genotype-phenotype studies. Each study was previously approved by respective institutional review boards (IRBs), including for the generation of WGS data and association with phenotypes. All participants provided written consent.All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesThe source code is available for download at
Amariuta T, Ishigaki K, Sugishita H, Ohta T, Koido M, Dey KK, Matsuda K, Murakami Y, Price AL, Kawakami E, Terao C, Raychaudhuri S. Improving the trans-ancestry portability of polygenic risk scores by prioritizing variants in predicted cell-type-specific regulatory elements. Nature Genetics 2020;52:1346-1354.Abstract
Poor trans-ancestry portability of polygenic risk scores is a consequence of Eurocentric genetic studies and limited knowledge of shared causal variants. Leveraging regulatory annotations may improve portability by prioritizing functional over tagging variants. We constructed a resource of 707 cell-type-specific IMPACT regulatory annotations by aggregating 5,345 epigenetic datasets to predict binding patterns of 142 transcription factors across 245 cell types. We then partitioned the common SNP heritability of 111 genome-wide association study summary statistics of European (average n ≈ 189,000) and East Asian (average n ≈ 157,000) origin. IMPACT annotations captured consistent SNP heritability between populations, suggesting prioritization of shared functional variants. Variant prioritization using IMPACT resulted in increased trans-ancestry portability of polygenic risk scores from Europeans to East Asians across all 21 phenotypes analyzed (49.9% mean relative increase in R2). Our study identifies a crucial role for functional annotations such as IMPACT to improve the trans-ancestry portability of genetic data.
Asgari S, Luo Y, Belbin GM, Bartell E, Calderon R, Slowikowski K, Contreras C, Yataco R, Galea JT, Jimenez J, Coit JM, Farroñay C, Nazarian RM, O’Connor TD, Dietz HC, Hirschhorn J, Guio H, Lecca L, Kenny EE, Freeman E, Murray MB, Raychaudhuri S. A positively selected, common, missense variant in FBN1 confers a 2.2 centimeter reduction of height in the Peruvian population. Nature 2020;582(7811):234-239.Abstract
Peruvians are among the shortest people in the world. To understand the genetic basis of short stature in Peru, we examined an ethnically diverse group of Peruvians and identified a novel, population-specific, missense variant in FBN1 (E1297G) that is significantly associated with lower height in the Peruvian population. Each copy of the minor allele (frequency = 4.7%) reduces height by 2.2 cm (4.4 cm in homozygous individuals). This is the largest effect size known for a common height-associated variant. This variant shows strong evidence of positive selection within the Peruvian population and is significantly more frequent in Native American populations from coastal regions of Peru compared to populations from the Andes or the Amazon, suggesting that short stature in Peruvians is the result of adaptation to the coastal environment.One Sentence Summary A mutation found in Peruvians has the largest known effect on height for a common variant. This variant is specific to Native American ancestry.
Gutierrez-Arcelus M#, Baglaenko Y#, Arora J, Hannes S, Luo Y, Amariuta T, Teslovich N, Rao DA, Ermann J, Jonsson AH, for Consortium NHLBIT-OPM (TOPM), Navarrete C, Rich SS, Taylor KD, Rotter JI, Gregersen PK, Esko T, Brenner MB, Raychaudhuri S. Allele-specific expression changes dynamically during T cell activation in HLA and other autoimmune loci. Nature Genetics 2020;52:247-253.Abstract
Genetic studies have revealed that autoimmune susceptibility variants are over-represented in memory CD4+ T cell regulatory elements1-3. Understanding how genetic variation affects gene expression in different T cell physiological states is essential for deciphering genetic mechanisms of autoimmunity4,5. Here, we characterized the dynamics of genetic regulatory effects at eight time points during memory CD4+ T cell activation with high-depth RNA-seq in healthy individuals. We discovered widespread, dynamic allele-specific expression across the genome, where the balance of alleles changes over time. These genes were enriched fourfold within autoimmune loci. We found pervasive dynamic regulatory effects within six HLA genes. HLA-DQB1 alleles had one of three distinct transcriptional regulatory programs. Using CRISPR-Cas9 genomic editing we demonstrated that a promoter variant is causal for T cell-specific control of HLA-DQB1 expression. Our study shows that genetic variation in cis-regulatory elements affects gene expression in a manner dependent on lymphocyte activation status, contributing to the interindividual complexity of immune responses.
Wei K#, Korsunsky I#, Marshall JL, Gao A, Watts GFM, Major T, Croft AP, Watts J, Blazar P, Lange J, Thornhill T, Filer A, Raza K, Donlin LT, Accelerating Medicines Partnership-Rheumatoid arthritis/Systemic Lupus Erythematosus (AMP RA/SLE), Siebel CW, Buckley CD, Raychaudhuri S*, Brenner* MB. Notch signalling drives synovial fibroblast identity and arthritis pathology. Nature 2020;582(7811):259-264.
Cui J, Raychaudhuri S, Karlson EW, Speyer C, Malspeis S, Guan H, Sparks JA, Ni H, Liu X, Stevens E, Williams JN, Davenport EE, Knevel R, Costenbader KH. Interactions Between Genome-Wide Genetic Factors and Smoking Influencing Risk of Systemic Lupus Erythematosus [Internet]. Arthritis & Rheumatology 2020;72(11):1863-1871. Publisher's VersionAbstract
{Objective To identify interactions between genetic factors and current or recent smoking in relation to risk of developing systemic lupus erythematosus (SLE). Methods For the study, 673 patients with SLE (diagnosed according to the American College of Rheumatology 1997 updated classification criteria) were matched by age, sex, and race (first 3 genetic principal components) to 3,272 control subjects without a history of connective tissue disease. Smoking status was classified as current smoking/having recently quit smoking within 4 years before diagnosis (or matched index date for controls) versus distant past/never smoking. In total, 86 single-nucleotide polymorphisms and 10 classic HLA alleles previously associated with SLE were included in a weighted genetic risk score (wGRS), with scores dichotomized as either low or high based on the median value in control subjects (low wGRS being defined as less than or equal to the control median; high wGRS being defined as greater than the control median). Conditional logistic regression models were used to estimate both the risk of SLE and risk of anti–double-stranded DNA autoantibody–positive (dsDNA+) SLE. Additive interactions were assessed using the attributable proportion (AP) due to interaction, and multiplicative interactions were assessed using a chi-square test (with 1 degree of freedom) for the wGRS and for individual risk alleles. Separate repeated analyses were carried out among subjects of European ancestry only. Results The mean ± SD age of the SLE patients at the time of diagnosis was 36.4 ± 15.3 years. Among the 673 SLE patients included, 92.3% were female and 59.3% were dsDNA+. Ethnic distributions were as follows: 75.6% of European ancestry, 4.5% of Asian ancestry, 11.7% of African ancestry, and 8.2% classified as other ancestry. A high wGRS (odds ratio [OR] 2.0
Ishigaki K, Lagattuta K, Luo Y, James E, Buckner J, Raychaudhuri S. HLA autoimmune risk alleles restrict the hypervariable region of T cell receptors [Internet]. 2020; medRxivAbstract
Polymorphisms in the human leukocyte antigen (HLA) genes within the major histocompatibility complex (MHC) locus strongly influence autoimmune disease risk15. Two non-exclusive hypotheses exist about the pathogenic role of HLAalleles; i) the central hypothesis, where HLA risk alleles influence thymic selection so that the probability of T cell receptors (TCRs) reactive to pathogenic antigens is increased68; and ii) the peripheral hypothesis, where HLA risk alleles increase the affinity for pathogenic antigens911. The peripheral hypothesis has been the main research focus in autoimmunity, while human data on the central hypothesis are lacking. Here, we investigated the influence of HLA alleles on TCR composition at the highly diverse complementarity determining region 3 (CDR3), where TCR recognizes antigens. We demonstrated unexpectedly powerful HLA-CDR3 associations. The strongest association was found at HLA-DRB1 amino acid position 13 (n = 628 subjects, explained variance = 9.4%; P = 4.1 x 10−138). This HLA position mediates genetic risk for multiple autoimmune diseases. In structural analysis of TCR-peptide-MHC complexes, we observed that HLA-DRB1 position 13 does not interact directly with CDR3, but is proximate to antigenic peptide residues that are also close to CDR3. We identified multiple CDR3 amino acid features enriched by HLA risk alleles; for example, the risk alleles of rheumatoid arthritis, type 1 diabetes, and celiac disease all increase the hydrophobicity of CDR3 position 109 (P < 2.1 x 10−5). In the setting of celiac disease, the CDR3 features favored by HLA risk alleles are more enriched among candidate pathogenic TCRs than control TCRs (P = 2.4 × 10−6 for gliadin specific TCRs). Together, these results provide novel genetic evidence supporting the central hypothesis.
Huizinga TWJ, Holers MV, Anolik J, Brenner MB, Buckley CD, Bykerk V, Connolly SE, Deane KD, Guo J, Hodge M, Hoffmann S, Nestle F, Pitzalis C, Raychaudhuri S, Yamamoto K, Li Z, Klareskog L. Disruptive innovation in rheumatology: new networks of global public–private partnerships are needed to take advantage of scientific progress [Internet]. Annals of the Rheumatic Diseases 2020;79(5):553-555. Publisher's Version
Amariuta T, Luo Y, Knevel R, Okada Y, Raychaudhuri S. Advances in genetics toward identifying pathogenic cell states of rheumatoid arthritis [Internet]. Immunological Reviews 2020;294(1):188-204. Publisher's VersionAbstract
Rheumatoid arthritis (RA) risk has a large genetic component (~60%) that is still not fully understood. This has hampered the design of effective treatments that could promise lifelong remission. RA is a polygenic disease with 106 known genome-wide significant associated loci and thousands of small effect causal variants. Our current understanding of RA risk has suggested cell-type-specific contexts for causal variants, implicating CD4 + effector memory T cells, as well as monocytes, B cells and stromal fibroblasts. While these cellular states and categories are still mechanistically broad, future studies may identify causal cell subpopulations. These efforts are propelled by advances in single cell profiling. Identification of causal cell subpopulations may accelerate therapeutic intervention to achieve lifelong remission.
Svensson MND, Zoccheddu M, Yang S, Nygaard G, Secchi C, Doody KM, Slowikowski K, Mizoguchi F, Humby F, Hands R, Santelli E, Sacchetti C, Wakabayashi K, Wu DJ, Barback C, Ai R, Wang W, Sims GP, Mydel P, Kasama T, Boyle DL, Galimi F, Vera D, Tremblay ML, Raychaudhuri S, Brenner MB, Firestein GS, Pitzalis C, Ekwall A-KH, Stanford SM, Bottini N. Synoviocyte-targeted therapy synergizes with TNF inhibition in arthritis reversal [Internet]. Science Advances 2020;6(26):eaba4353. Publisher's VersionAbstract
Fibroblast-like synoviocytes (FLS) are joint-lining cells that promote rheumatoid arthritis (RA) pathology. Current disease-modifying antirheumatic agents (DMARDs) operate through systemic immunosuppression. FLS-targeted approaches could potentially be combined with DMARDs to improve control of RA without increasing immunosuppression. Here, we assessed the potential of immunoglobulin-like domains 1 and 2 (Ig1&2), a decoy protein that activates the receptor tyrosine phosphatase sigma (PTPRS) on FLS, for RA therapy. We report that PTPRS expression is enriched in synovial lining RA FLS and that Ig1&2 reduces migration of RA but not osteoarthritis FLS. Administration of an Fc-fusion Ig1&2 attenuated arthritis in mice without affecting innate or adaptive immunity. Furthermore, PTPRS was down-regulated in FLS by tumor necrosis factor (TNF) via a phosphatidylinositol 3-kinase–mediated pathway, and TNF inhibition enhanced PTPRS expression in arthritic joints. Combination of ineffective doses of TNF inhibitor and Fc-Ig1&2 reversed arthritis in mice, providing an example of synergy between FLS-targeted and immunosuppressive DMARD therapies.
Krebs K, Bovjin J, Zheng N, Lepamets M, Censin JC, Jurgenson T, Sarg D, Abner E, Laisk T, Luo Y, Skotte L, Geller F, Feenstra B, Wang W, Auton A, Auton A, Raychaudhuri S, Esko T, Metspalu A, Laur S, Roden DM, Wei W-Q, Holmes MV, Lindgren CM, Phillips EJ, Magi R, Milani L, Fadista J. Genome-wide Study Identifies Association between HLA-B 55:01 and Self-Reported Penicillin Allergy [Internet]. American Journal of Human Genetics 2020;107(4):612-621. Publisher's VersionAbstract
Hypersensitivity reactions to drugs are often unpredictable and can be life threatening, underscoring a need for understanding their underlying mechanisms and risk factors. The extent to which germline genetic variation influences the risk of commonly reported drug allergies such as penicillin allergy remains largely unknown. We extracted data from the electronic health records of more than 600,000 participants from the UK, Estonian, and Vanderbilt University Medical Center's BioVU biobanks to study the role of genetic variation in the occurrence of self-reported penicillin hypersensitivity reactions. We used imputed SNP to HLA typing data from these cohorts to further fine map the human leukocyte antigen (HLA) association and replicated our results in 23andMe's research cohort involving a total of 1.12 million individuals. Genome-wide meta-analysis of penicillin allergy revealed two loci, including one located in the HLA region on chromosome 6. This signal was further fine-mapped to the HLA-B55:01 allele (OR 1.41 95% CI 1.33-1.49, p value 2.04 × 10-31) and confirmed by independent replication in 23andMe's research cohort (OR 1.30 95% CI 1.25-1.34, p value 1.00 × 10-47). The lead SNP was also associated with lower lymphocyte counts and in silico follow-up suggests a potential effect on T-lymphocytes at HLA-B55:01. We also observed a significant hit in PTPN22 and the GWAS results correlated with the genetics of rheumatoid arthritis and psoriasis. We present robust evidence for the role of an allele of the major histocompatibility complex (MHC) I gene HLA-B in the occurrence of penicillin allergy.
Bonfa E, Gossec L, Isenberg DA, Li Z, Raychaudhuri S. How COVID-19 is changing rheumatology clinical practice [Internet]. Nature Reviews. Rheumatology 2020; Publisher's VersionAbstract
The emergence of COVID-19 in early 2020 led to unprecedented changes to rheumatology clinical practice worldwide, including the closure of research laboratories, the restructuring of hospitals and the rapid transition to virtual care. As governments sought to slow and contain the spread of the disease, rheumatologists were presented with the difficult task of managing risks, to their patients as well as to themselves, while learning and implementing new systems for remote health care. Consequently, the COVID-19 pandemic led to a transformation in health infrastructures and telemedicine that could become powerful tools for rheumatologists, despite having some limitations. In this Viewpoint, five experts from different regions discuss their experiences of the pandemic, including the most challenging aspects of this unexpected transition, the advantages and limitations of virtual visits, and potential opportunities going forward.
Choi W, Luo Y, Raychaudhuri S, Han B. HATK: HLA analysis toolkit [Internet]. Bioinformatics 2020; Publisher's VersionAbstract
Fine-mapping human leukocyte antigen (HLA) genes involved in disease susceptibility to individual alleles or amino acid residues has been challenging. Using information regarding HLA alleles obtained from HLA typing, HLA imputation or HLA inference, our software expands the alleles to amino acid sequences using the most recent IMGT/HLA database and prepares a dataset suitable for fine-mapping analysis. Our software also provides useful functionalities, such as various association tests, visualization tools and nomenclature conversion.
Orange DE, Yao V, Sawicka K, Fak J, Frank MO, Parveen S, Blachere NE, Hale C, Zhang F, Raychaudhuri S, Troyanskaya OG, Darnell RB. RNA Identification of PRIME Cells Predicting Rheumatoid Arthritis Flares [Internet]. New England Journal of Medicine 2020;383(3):218-228. Publisher's VersionAbstract

Background: Rheumatoid arthritis, like many inflammatory diseases, is characterized by episodes of quiescence and exacerbation (flares). The molecular events leading to flares are unknown.

Methods: We established a clinical and technical protocol for repeated home collection of blood in patients with rheumatoid arthritis to allow for longitudinal RNA sequencing (RNA-seq). Specimens were obtained from 364 time points during eight flares over a period of 4 years in our index patient, as well as from 235 time points during flares in three additional patients. We identified transcripts that were differentially expressed before flares and compared these with data from synovial single-cell RNA-seq. Flow cytometry and sorted-blood-cell RNA-seq in additional patients were used to validate the findings.

Results: Consistent changes were observed in blood transcriptional profiles 1 to 2 weeks before a rheumatoid arthritis flare. B-cell activation was followed by expansion of circulating CD45-CD31-PDPN+ preinflammatory mesenchymal, or PRIME, cells in the blood from patients with rheumatoid arthritis; these cells shared features of inflammatory synovial fibroblasts. Levels of circulating PRIME cells decreased during flares in all 4 patients, and flow cytometry and sorted-cell RNA-seq confirmed the presence of PRIME cells in 19 additional patients with rheumatoid arthritis.

Conclusions: Longitudinal genomic analysis of rheumatoid arthritis flares revealed PRIME cells in the blood during the period before a flare and suggested a model in which these cells become activated by B cells in the weeks before a flare and subsequently migrate out of the blood into the synovium. (Funded by the National Institutes of Health and others.).

Knevel R, le Cessie S, Terao CC, Slowikowski K, Cui J, Huizinga TWJ, Costenbader KH, Liao KP, Karlson EW, Raychaudhuri S. Using genetics to prioritize diagnoses for rheumatology outpatients with inflammatory arthritis [Internet]. Science Translational Medicine 2020;12(545) Full TextAbstract
Multiple slowly progressing diseases initially present with inflammatory arthritis, and it can be difficult to clinically differentiate these conditions. Knevel et al. show that genetic data could be used to triage inflammatory arthritis–causing diagnoses at a patient’s first visit, improving the likelihood of a correct initial diagnosis and potentially expediting appropriate treatment. Their genetic diagnostic tool, here optimized for rheumatic disease diagnosis, could, in principle, be calibrated for other phenotypically similar diseases with different underlying genetics.It is challenging to quickly diagnose slowly progressing diseases. To prioritize multiple related diagnoses, we developed G-PROB (Genetic Probability tool) to calculate the probability of different diseases for a patient using genetic risk scores. We tested G-PROB for inflammatory arthritis–causing diseases (rheumatoid arthritis, systemic lupus erythematosus, spondyloarthropathy, psoriatic arthritis, and gout). After validating on simulated data, we tested G-PROB in three cohorts: 1211 patients identified by International Classification of Diseases (ICD) codes within the eMERGE database, 245 patients identified through ICD codes and medical record review within the Partners Biobank, and 243 patients first presenting with unexplained inflammatory arthritis and with final diagnoses by record review within the Partners Biobank. Calibration of G-probabilities with disease status was high, with regression coefficients from 0.90 to 1.08 (1.00 is ideal). G-probabilities discriminated true diagnoses across the three cohorts with pooled areas under the curve (95% CI) of 0.69 (0.67 to 0.71), 0.81 (0.76 to 0.84), and 0.84 (0.81 to 0.86), respectively. For all patients, at least one disease could be ruled out, and in 45% of patients, a likely diagnosis was identified with a 64% positive predictive value. In 35% of cases, the clinician’s initial diagnosis was incorrect. Initial clinical diagnosis explained 39% of the variance in final disease, which improved to 51% (P < 0.0001) after adding G-probabilities. Converting genotype information before a clinical visit into an interpretable probability value for five different inflammatory arthritides could potentially be used to improve the diagnostic efficiency of rheumatic diseases in clinical practice.
Ishigaki K, Akiyama M, Kanai M, Takahashi A, Kawakami E, Sugishita H, Sakaue S, Matoba N, Low S-K, Okada Y, Terao C, Amariuta T, Gazal S, Kochi Y, Horikoshi M, Suzuki K, Ito K, Momozawa Y, Hirata M, Matsuda K, Ikeda M, Iwata N, Ikegawa S, Kou I, Tanaka T, Nakagawa H, Suzuki A, Hirota T, Tamari M, Chayama K, Miki D, Mori M, Nagayama S, Daigo Y, Miki Y, Katagiri T, Ogawa O, Obara W, Ito H, Yoshida T, Imoto I, Takahashi T, Tanikawa C, Suzuki T, Sinozaki N, Minami S, Yamaguchi H, Asai S, Takahashi Y, Yamaji K, Takahashi K, Fujioka T, Takata R, Yanai H, Masumoto A, Koretsune Y, Kutsumi H, Higashiyama M, Murayama S, Minegishi N, Suzuki K, Tanno K, Shimizu A, Yamaji T, Iwasaki M, Sawada N, Uemura H, Tanaka K, Naito M, Sasaki M, Wakai K, Tsugane S, Yamamoto M, Yamamoto K, Murakami Y, Nakamura Y, Raychaudhuri S*, Inazawa J*, Yamauchi T*, Kadowaki T*, Kubo M*, Kamatani Y*. Large-scale genome-wide association study in a Japanese population identifies novel susceptibility loci across different diseases [Internet]. Nature Genetics 2020;52(7):669-679. Publisher's VersionAbstract
The overwhelming majority of participants in current genetic studies are of European ancestry1–3, limiting our genetic understanding of complex disease in non-European populations. To address this, we aimed to elucidate polygenic disease biology in the East Asian population by conducting a genome-wide association study (GWAS) with 212,453 Japanese individuals across 42 diseases. We detected 383 independent signals in 331 loci for 30 diseases, among which 45 loci were novel (P < 5 × 10-8). Compared with known variants, novel variants have lower frequency in European populations but comparable frequency in East Asian populations, suggesting the advantage of this study in discovering these novel variants. Three novel signals were in linkage disequilibrium (r2 > 0.6) with missense variants which are monomorphic in European populations (1000 Genomes Project) including rs11235604(p.R220W of ATG16L2, a autophagy-related gene) associated with coronary artery disease. We further investigated enrichment of heritability within 2,868 annotations of genome-wide transcription factor occupancy, andidentified 378 significant enrichments across nine diseases (FDR < 0.05) (e.g. NF-κB for immune-related diseases). This large-scale GWAS in a Japanese population provides insights into the etiology of common complex diseases and highlights the importance of performing GWAS in non-European populations.
Slowikowski K#, Nguyen# HN, Noss EH, Simmons DP, Mizoguchi F, Watts GFM, Gurish MF, Brenner MB*, Raychaudhuri S*. CUX1 and IκBζ (NFKBIZ) mediate the synergistic inflammatory response to TNF and IL-17A in stromal fibroblasts. [Internet]. Proc Natl Acad Sci. 2020;117(10):5532-5541. Publisher's VersionAbstract
The role of stromal fibroblasts in chronic inflammation is unfolding. In rheumatoid arthritis, leukocyte-derived cytokines TNF and IL-17A work together, activating fibroblasts to become a dominant source of the hallmark cytokine IL-6. However, IL-17A alone has minimal effect on fibroblasts. To identify key mediators of the synergistic response to TNF and IL-17A in human synovial fibroblasts, we performed time series, dose-response, and gene-silencing transcriptomics experiments. Here we show that in combination with TNF, IL-17A selectively induces a specific set of genes mediated by factors including cut-like homeobox 1 (CUX1) and IκBζ (NFKBIZ). In the promoters of CXCL1, CXCL2, and CXCL3, we found a putative CUX1-NF-κB binding motif not found elsewhere in the genome. CUX1 and NF-κB p65 mediate transcription of these genes independent of LIFR, STAT3, STAT4, and ELF3. Transcription of NFKBIZ, encoding the atypical IκB factor IκBζ, is IL-17A dose-dependent, and IκBζ only mediates the transcriptional response to TNF and IL-17A, but not to TNF alone. In fibroblasts, IL-17A response depends on CUX1 and IκBζ to engage the NF-κB complex to produce chemoattractants for neutrophil and monocyte recruitment.