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. https://www.feinsteininstitute.org/robert-s-boas-center-for-genomics-and-human-genetics/gap-registry/ 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: http://www.copdgene.org/directory 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 ClinicalTrials.gov. 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 https://github.com/immunogenomics

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.

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.

Genetic studies have revealed that autoimmune susceptibility variants are over-represented in memory CD4⁺ T cell regulatory elements^1-3. Understanding how genetic variation affects gene expression in different T cell physiological states is essential for deciphering genetic mechanisms of autoimmunity^4,5. 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.

{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

Polymorphisms in the human leukocyte antigen (HLA) genes within the major histocompatibility complex (MHC) locus strongly influence autoimmune disease risk^1–5. 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 increased^6–8; and ii) the peripheral hypothesis, where HLA risk alleles increase the affinity for pathogenic antigens^9–11. 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.

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.

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.