Despite major progress in the treatment of autoimmune diseases in the past two decades, most therapies do not cure disease and can be associated with increased risk of infection through broad suppression of the immune system. However, advances in understanding the causes of autoimmune disease and clinical data from novel therapeutic modalities such as chimeric antigen receptor T cell therapies provide evidence that it may be possible to re-establish immune homeostasis and, potentially, prolong remission or even cure autoimmune diseases. Here, we propose a ‘sequential immunotherapy’ framework for immune system modulation to help achieve this ambitious goal. This framework encompasses three steps: controlling inflammation; resetting the immune system through elimination of pathogenic immune memory cells; and promoting and maintaining immune homeostasis via immune regulatory agents and tissue repair. We discuss existing drugs and those in development for each of the three steps. We also highlight the importance of causal human biology in identifying and prioritizing novel immunotherapeutic strategies as well as informing their application in specific patient subsets, enabling precision medicine approaches that have the potential to transform clinical care. Advances in understanding of the cause of autoimmune diseases and clinical data from novel therapeutic modalities such as chimeric antigen receptor T cells are providing evidence that it may be possible to re-establish immune homeostasis and, potentially, prolong remission or even cure autoimmune diseases. This article proposes a three-step ‘sequential immunotherapy’ framework for immune system modulation to help achieve this ambitious goal, and discusses existing drugs and those in development for each of the three steps.

Population isolates such as those in Finland benefit genetic research because deleterious alleles are often concentrated on a small number of low-frequency variants (0.1% ≤ minor allele frequency < 5%). These variants survived the founding bottleneck rather than being distributed over a large number of ultrarare variants. Although this effect is well established in Mendelian genetics, its value in common disease genetics is less explored 1 , 2 . FinnGen aims to study the genome and national health register data of 500,000 Finnish individuals. Given the relatively high median age of participants (63 years) and the substantial fraction of hospital-based recruitment, FinnGen is enriched for disease end points. Here we analyse data from 224,737 participants from FinnGen and study 15 diseases that have previously been investigated in large genome-wide association studies (GWASs). We also include meta-analyses of biobank data from Estonia and the United Kingdom. We identified 30 new associations, primarily low-frequency variants, enriched in the Finnish population. A GWAS of 1,932 diseases also identified 2,733 genome-wide significant associations (893 phenome-wide significant (PWS), P < 2.6 × 10 –11 ) at 2,496 (771 PWS) independent loci with 807 (247 PWS) end points. Among these, fine-mapping implicated 148 (73 PWS) coding variants associated with 83 (42 PWS) end points. Moreover, 91 (47 PWS) had an allele frequency of <5% in non-Finnish European individuals, of which 62 (32 PWS) were enriched by more than twofold in Finland. These findings demonstrate the power of bottlenecked populations to find entry points into the biology of common diseases through low-frequency, high impact variants.

ABSTRACT Population isolates such as Finland provide benefits in genetic studies because the allelic spectrum of damaging alleles in any gene is often concentrated on a small number of low-frequency variants (0.1% ≤ minor allele frequency < 5%), which survived the founding bottleneck, as opposed to being distributed over a much larger number of ultra--rare variants. While this advantage is well-- established in Mendelian genetics, its value in common disease genetics has been less explored. FinnGen aims to study the genome and national health register data of 500,000 Finns, already reaching 224,737 genotyped and phenotyped participants. Given the relatively high median age of participants (63 years) and dominance of hospital-based recruitment, FinnGen is enriched for many disease endpoints often underrepresented in population-based studies (e.g., rarer immune-mediated diseases and late onset degenerative and ophthalmologic endpoints). We report here a genome-wide association study (GWAS) of 1,932 clinical endpoints defined from nationwide health registries. We identify genome--wide significant associations at 2,491 independent loci. Among these, finemapping implicates 148 putatively causal coding variants associated with 202 endpoints, 104 with low allele frequency (AF<10%) of which 62 were over two-fold enriched in Finland. We studied a benchmark set of 15 diseases that had previously been investigated in large genome-wide association studies. FinnGen discovery analyses were meta-analysed in Estonian and UK biobanks. We identify 30 novel associations, primarily low-frequency variants strongly enriched, in or specific to, the Finnish population and Uralic language family neighbors in Estonia and Russia. These findings demonstrate the power of bottlenecked populations to find unique entry points into the biology of common diseases through low-frequency, high impact variants. Such high impact variants have a potential to contribute to medical translation including drug discovery.

Caspar I. van der Made, MD; Annet Simons, PhD; Janneke Schuurs-Hoeijmakers, MD, PhD; Guus van den Heuvel, MD; Tuomo Mantere, PhD; Simone Kersten, MSc; Rosanne C. van Deuren, MSc; Marloes Steehouwer, BSc; Simon V. van Reijmersdal, BSc; Martin Jaeger, PhD; Tom Hofste, BSc; Galuh Astuti, PhD; Jordi Corominas Galbany, PhD; Vyne van der Schoot, MD, PhD; Hans van der Hoeven, MD, PhD; Wanda Hagmolen of ten Have, MD, PhD; Eva Klijn, MD, PhD; Catrien van den Meer, MD; Jeroen Fiddelaers, MD; Quirijn de Mast, MD, PhD; Chantal P. Bleeker-Rovers, MD, PhD; Leo A. B. Joosten, PhD; Helger G. Yntema, PhD; Christian Gilissen, PhD; Marcel Nelen, PhD; Jos W. M. van der Meer, MD, PhD; Han G. Brunner, MD, PhD; Mihai G. Netea, MD, PhD; Frank L. van de Veerdonk, MD, PhD; Alexander Hoischen, PhD

Phenotypes are the foundation for clinical and genetic studies of disease risk and outcomes. The growth of biobanks linked to electronic medical record (EMR) data has both facilitated and increased the demand for efficient, accurate, and robust approaches for phenotyping millions of patients. Challenges to phenotyping with EMR data include variation in the accuracy of codes, as well as the high level of manual input required to identify features for the algorithm and to obtain gold standard labels. To address these challenges, we developed PheCAP, a high-throughput semi-supervised phenotyping pipeline. PheCAP begins with data from the EMR, including structured data and information extracted from the narrative notes using natural language processing (NLP). The standardized steps integrate automated procedures, which reduce the level of manual input, and machine learning approaches for algorithm training. PheCAP itself can be executed in 1-2 d if all data are available; however, the timing is largely dependent on the chart review stage, which typically requires at least 2 weeks. The final products of PheCAP include a phenotype algorithm, the probability of the phenotype for all patients, and a phenotype classification (yes or no).

[This corrects the article DOI: 10.1371/journal.pone.0208240.].

Although human genetics can help identify new drug targets, the best way to prioritize genes as therapeutic targets is uncertain. A new study describes a framework to prioritize potential targets by integrating genome-wide association data with genomic features, disease ontologies and network connectivity.

Although plasma proteins have important roles in biological processes and are the direct targets of many drugs, the genetic factors that control inter-individual variation in plasma protein levels are not well understood. Here we characterize the genetic architecture of the human plasma proteome in healthy blood donors from the INTERVAL study. We identify 1,927 genetic associations with 1,478 proteins, a fourfold increase on existing knowledge, including trans associations for 1,104 proteins. To understand the consequences of perturbations in plasma protein levels, we apply an integrated approach that links genetic variation with biological pathway, disease, and drug databases. We show that protein quantitative trait loci overlap with gene expression quantitative trait loci, as well as with disease-associated loci, and find evidence that protein biomarkers have causal roles in disease using Mendelian randomization analysis. By linking genetic factors to diseases via specific proteins, our analyses highlight potential therapeutic targets, opportunities for matching existing drugs with new disease indications, and potential safety concerns for drugs under development.

Phenome-wide association studies (PheWAS) have been proposed as a possible aid in drug development through elucidating mechanisms of action, identifying alternative indications, or predicting adverse drug events (ADEs). Here, we select 25 single nucleotide polymorphisms (SNPs) linked through genome-wide association studies (GWAS) to 19 candidate drug targets for common disease indications. We interrogate these SNPs by PheWAS in four large cohorts with extensive health information (23andMe, UK Biobank, FINRISK, CHOP) for association with 1683 binary endpoints in up to 697,815 individuals and conduct meta-analyses for 145 mapped disease endpoints. Our analyses replicate 75% of known GWAS associations (P< 0.05) and identify nine study-wide significant novel associations (of 71 with FDR < 0.1). We describe associations that may predict ADEs, e.g., acne, high cholesterol, gout, and gallstones with rs738409 (p.I148M) in PNPLA3 and asthma with rs1990760 (p.T946A) in IFIH1. Our results demonstrate PheWAS as a powerful addition to the toolkit for drug discovery.

Objectives There is limited information on the epidemiology and treatment patterns of rheumatoid arthritis (RA) across the Arab region. We aim in this study to describe the demographic characteristics, clinical profile, and treatment patterns of patients of Arab ancestry with RA. Methods This is a cross sectional study of 895 patients with established rheumatoid arthritis enrolled from five sites (Jordan, Lebanon, Qatar, Kingdom of Saudi Arabia (KSA), and United Arab Emirates). Demographic characteristics, clinical profile, and treatment patterns are compared between the five countries. Results The majority of our patients are women, have an average disease duration of 10 years, are married and non-smokers, with completed secondary education. We report a high (>80%) ever-use of methotrexate (MTX) and steroids among our RA population, while the ever-use of disease modifying anti-rheumatic drugs (DMARDs) and TNF-inhibitors average around 67% and 33%, respectively. There are variations in RA treatment use between the five country sites. Highest utilization of steroids is identified in Jordan and KSA (p-value < 0.001), while the highest ever-use of TNF-inhibitors is reported in KSA (p-value < 0.001). Conclusion Disparities in usage of RA treatments among Arab patients are noted across the five countries. National gross domestic product (GDP), as well as some other unique features in each country likely affect these. Developing treatment guidelines specific to this region could contribute in delivering standardized therapies to RA patients.

Proteins are the primary functional units of biology and the direct targets of most drugs, yet there is limited knowledge of the genetic factors determining inter-individual variation in protein levels. Here we reveal the genetic architecture of the human plasma proteome, testing 10.6 million DNA variants against levels of 2,994 proteins in 3,301 individuals. We identify 1,927 genetic associations with 1,478 proteins, a 4-fold increase on existing knowledge, including trans associations for 1,104 proteins. To understand consequences of perturbations in plasma protein levels, we introduce an approach that links naturally occurring genetic variation with biological, disease, and drug databases. We provide insights into pathogenesis by uncovering the molecular effects of disease-associated variants. We identify causal roles for protein biomarkers in disease through Mendelian randomization analysis. Our results reveal new drug targets, opportunities for matching existing drugs with new disease indications, and potential safety concerns for drugs under development.

Recent metabolomics studies of Rheumatoid Arthritis (RA) reported few metabolites that were associated with the disease, either due to small cohort sizes or limited coverage of metabolic pathways. Our objective is to identify metabolites associated with RA and its cofounders using a new untargeted metabolomics platform. Moreover, to investigate the pathomechanism of RA by identifying correlations between RA-associated metabolites. 132 RA patients and 104 controls were analyzed for 927 metabolites. Metabolites were tested for association with RA using linear regression. OPLS-DA was used to discriminate RA patients from controls. Gaussian Graphical Models (GGMs) were used to identify correlated metabolites. 32 metabolites are identified as significantly (Bonferroni) associated with RA, including the previously reported metabolites as DHEAS, cortisol and androstenedione and extending that to a larger set of metabolites in the steroid pathway. RA classification using metabolic profiles shows a sensitivity of 91% and specificity of 88%. Steroid levels show variation among the RA patients according to the corticosteroid treatment; lowest in those taking the treatment at the time of the study, higher in those who never took the treatment, and highest in those who took it in the past. Finally, the GGM reflects metabolite relations from the steroidogenesis pathway.

OBJECTIVE:Genetic factors underlying susceptibility to rheumatoid arthritis (RA) in Arab populations are largely unknown. This genome-wide association study (GWAS) was undertaken to explore the generalizability of previously reported RA loci to Arab subjects and to discover new Arab-specific genetic loci.METHODS:The Genetics of Rheumatoid Arthritis in Some Arab States Study was designed to examine the genetics and clinical features of RA patients from Jordan, the Kingdom of Saudi Arabia, Lebanon, Qatar, and the United Arab Emirates. In total, >7 million single-nucleotide polymorphisms (SNPs) were tested for association with RA overall and with seropositive or seronegative RA in 511 RA cases and 352 healthy controls. In addition, replication of 15 signals was attempted in 283 RA cases and 221 healthy controls. A genetic risk score of 68 known RA SNPs was also examined in this study population.RESULTS:Three loci (HLA region, intergenic 5q13, and 17p13 at SMTNL2/GGT6) reached genome-wide significance in the analyses of association with RA and with seropositive RA, and for all 3 loci, evidence of independent replication was demonstrated. Consistent with the findings in European and East Asian populations, the association of RA with HLA-DRB1 amino acid position 11 conferred the strongest effect (P = 4.8 × 10-16 ), and a weighted genetic risk score of previously associated RA loci was found to be associated with RA (P = 3.41 × 10-5 ) and with seropositive RA (P = 1.48 × 10-6 ) in this population. In addition, 2 novel associations specific to Arab populations were found at the 5q13 and 17p13 loci.CONCLUSION:This first RA GWAS in Arab populations confirms that established HLA-region and known RA risk alleles contribute strongly to the risk and severity of disease in some Arab groups, suggesting that the genetic architecture of RA is similar across ethnic groups. Moreover, this study identified 2 novel RA risk loci in Arabs, offering further population-specific insights into the pathophysiology of RA.

ObjectiveIn many rheumatoid arthritis (RA) patients, disease is controlled with anti–tumor necrosis factor (anti‐TNF) biologic therapies. However, in a significant number of patients, the disease fails to respond to anti‐TNF therapy. We undertook the present study to examine the hypothesis that rare and low‐frequency genetic variants might influence response to anti‐TNF treatment.MethodsWe sequenced the coding region of 750 genes in 1,094 RA patients of European ancestry who were treated with anti‐TNF. After quality control, 690 genes were included in the analysis. We applied single‐variant association and gene‐based association tests to identify variants associated with anti‐TNF treatment response. In addition, given the key mechanistic role of TNF, we performed gene set analyses of 27 TNF pathway genes.ResultsWe identified 14,420 functional variants, of which 6,934 were predicted as nonsynonymous 2,136 of which were further predicted to be “damaging.” Despite the fact that the study was well powered, no single variant or gene showed study‐wide significant association with change in the outcome measures disease activity or European League Against Rheumatism response. Intriguingly, we observed 3 genes, of 27 with nominal signals of association (P < 0.05), that were involved in the TNF signaling pathway. However, when we performed a rigorous gene set enrichment analysis based on association P value ranking, we observed no evidence of enrichment of association at genes involved in the TNF pathway (Penrichment = 0.15, based on phenotype permutations).ConclusionOur findings suggest that rare and low‐frequency protein‐coding variants in TNF signaling pathway genes or other genes do not contribute substantially to anti‐TNF treatment response in patients with RA.

Individuals who lack a functional copy of a gene — gene knockouts — can reveal the gene's role. Most knockout research has used model organisms, but now a comprehensive catalogue of human knockouts is in sight. See Letter p.235 Understanding the function of every human gene is one of the major goals in biomedicine and analysing phenotypes in individuals with loss of function mutations in a particular gene is one way to gain such insight. Sekar Kathiresan and colleagues systematically examined predicted loss-of-function mutations from sequencing data, making preliminary efforts to investigate their functional relevance through phenotypic association. They sequenced the exomes of 10,503 individuals living in Pakistan and participating in the PROMIS study. They identified 49,138 rare predicted loss-of-function mutations that are estimated to knock out 1,317 genes, each in at least one participant, and tested for association with a panel of 201 traits measured in blood samples. The authors demonstrate the usefulness of a reverse-genetics approach, which involves recruiting participants by genotype, and discuss a framework for a human knockout project.

Objectives A recent study identified 16 genetic variants associated with N-glycosylation of human IgG. Several of the genomic regions where these single nucleotide polymorphisms (SNPs) reside have also been associated with autoimmune disease (AID) susceptibility, suggesting there may be pleiotropy (genetic sharing) between loci controlling both N-glycosylation and AIDs. We investigated this by testing variants associated with levels of IgG N-glycosylation for association with rheumatoid arthritis (RA) susceptibility using a Mendelian randomisation study, and testing a subset of these variants in a less well-powered study of treatment response and severity.Methods SNPs showing association with IgG N-glycosylation were analysed for association with RA susceptibility in 14 361 RA cases and 43 923 controls. Five SNPs were tested for association with response to anti-tumour necrosis factor (TNF) therapy in 1081 RA patient samples and for association with radiological disease severity in 342 patients.Results Only one SNP (rs9296009) associated with N-glycosylation showed an association (p=6.92x10(-266)) with RA susceptibility, although this was due to linkage disequilibrium with causal human leukocyte antigen (HLA) variants. Four regions of the genome harboured SNPs associated with both traits (shared loci); although statistical analysis indicated that the associations observed for the two traits are independent. No SNPs showed association with response to anti-TNF therapy. One SNP rs12342831 was modestly associated with Larsen score (p=0.05).Conclusions In a large, well-powered cohort of RA patients, we show SNPs driving levels of N-glycosylation have no association with RA susceptibility, indicating colocalisation of associated SNPs are not necessarily indicative of a shared genetic background or a role for glycosylation in disease susceptibility.

Drug R&D requires a disciplined approach anchored in causal human biology through proof-of-concept clinical trials. Our modern health care system demands therapeutic interventions that improve the lives of patients. Unfortunately, decreased productivity in therapeutics research and development (R&D) has driven drug costs up while delivering insufficient value to patients. Here, I discuss a model of translational medicine that connects four components of the early R&D pipeline—causal human biology, therapeutic modality, biomarkers of target modulation, and proof-of-concept clinical trials. Whereas the individual components of this model are not new, technological advances and a disciplined approach to integrating all four areas offer hope for improving R&D productivity.

Nature Communications 7: Article number: 12460 (2016); Published: 23 August 2016; Updated: 10 October 2016. The HTML version of this Article incorrectly duplicated the authors S. Louis Bridges, Lindsey Criswell, Larry Moreland, Lars Klareskog, Saedis Saevarsdottir, Leonid Padyukov, Peter K. Gregersen, Stephen Friend, Robert Plenge, Gustavo Stolovitzky, Baldo Oliva, Yuanfang Guan and Lara M.

Our modern health care system demands therapeutic interventions that improve the lives of patients. Unfortunately, decreased productivity in therapeutics research and development (R&D) has driven drug costs up while delivering insufficient value to patients. Here, I discuss a model of translational medicine that connects four components of the early R&D pipeline-causal human biology, therapeutic modality, biomarkers of target modulation, and proof-of-concept clinical trials. Whereas the individual components of this model are not new, technological advances and a disciplined approach to integrating all four areas offer hope for improving R&D productivity.