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Soumya Raychaudhuri, Paul de Bakker and colleagues report fine mapping of the rheumatoid arthritis associations within the MHC by combining genome-wide SNP data and imputation of classical HLA alleles and SNPs across the MHC. They identify five amino acid positions in HLA-DRβ1, HLA-B and HLA-DPβ1 that together can explain most of the MHC association to seropositive rheumatoid arthritis. The genetic association of the major histocompatibility complex (MHC) to rheumatoid arthritis risk has commonly been attributed to alleles in HLA-DRB1 . However, debate persists about the identity of the causal variants in HLA-DRB1 and the presence of independent effects elsewhere in the MHC. Using existing genome-wide SNP data in 5,018 individuals with seropositive rheumatoid arthritis (cases) and 14,974 unaffected controls, we imputed and tested classical alleles and amino acid polymorphisms in HLA-A , HLA-B , HLA-C , HLA-DPA1 , HLA-DPB1 , HLA-DQA1 , HLA-DQB1 and HLA-DRB1 , as well as 3,117 SNPs across the MHC. Conditional and haplotype analyses identified that three amino acid positions (11, 71 and 74) in HLA-DRβ1 and single–amino-acid polymorphisms in HLA-B (at position 9) and HLA-DPβ1 (at position 9), which are all located in peptide-binding grooves, almost completely explain the MHC association to rheumatoid arthritis risk. This study shows how imputation of functional variation from large reference panels can help fine map association signals in the MHC.

•An unsupervised ML approach, SLED, was developed for T2* MWI data analysis.•SLED outperformed NLLS for MWF estimation using simulated phantom and real data.•SLED MWF sensitively measured graded hypomyelination in a novel KO mouse model.•SLED was efficiently trained on each dataset without the need for a signal library. Reconstruction of high quality myelin water imaging (MWI) maps is challenging, particularly for data acquired using multi-echo gradient echo (mGRE) sequences. A non-linear least squares fitting (NLLS) approach has often been applied for MWI. However, this approach may produce maps with limited detail and, in some cases, sub-optimal signal to noise ratio (SNR), due to the nature of the voxel-wise fitting. In this study, we developed a novel, unsupervised learning method called self-labelled encoder-decoder (SLED) to improve gradient echo-based MWI data fitting. Ultra-high resolution, MWI data was collected from five mouse brains with variable levels of myelination, using a mGRE sequence. Imaging data was acquired using a 7T preclinical MRI system. A self-labelled, encoder-decoder network was implemented in TensorFlow for calculation of myelin water fraction (MWF) based on the mGRE signal decay. A simulated MWI phantom was also created to evaluate the performance of MWF estimation. Compared to NLLS, SLED demonstrated improved MWF estimation, in terms of both stability and accuracy in phantom tests. In addition, SLED produced less noisy MWF maps from high resolution MR microscopy images of mouse brain tissue. It specifically resulted in lower noise amplification for all mouse genotypes that were imaged and yielded mean MWF values in white matter ROIs that were highly correlated with those derived from standard NLLS fitting. Lastly, SLED also exhibited higher tolerance to low SNR data. Due to its unsupervised and self-labeling nature, SLED offers a unique alternative to analyze gradient echo-based MWI data, providing accurate and stable MWF estimations.

•The role of MBP gene expression on white matter myelin elaboration was identified.•We validated a model-free reconstruction method for robust brain MWF mapping.•The findings were augmented with detailed, tract-wise measures of AWF.•Our hypomyelinated mouse models act as a tool for calibrated myelin-sensitive MRI. Non-invasive myelin water fraction (MWF) and g-ratio mapping using microstructural MRI have the potential to offer critical insights into brain microstructure and our understanding of neuroplasticity and neuroinflammation. By leveraging a unique panel of variably hypomyelinating mouse strains, we validated a high-resolution, model-free image reconstruction method for whole-brain MWF mapping. Further, by employing a bipolar gradient echo MRI sequence, we achieved high spatial resolution and robust mapping of MWF and g-ratio across the whole mouse brain. Our regional white matter-tract specific analyses demonstrated a graded decrease in MWF in white matter tracts which correlated strongly with myelin basic protein gene (Mbp) mRNA levels. Using these measures, we derived the first sensitive calibrations between MWF and Mbp mRNA in the mouse. Minimal changes in axonal density supported our hypothesis that observed MWF alterations stem from hypomyelination. Overall, our work strongly emphasizes the potential of non-invasive, MRI-derived MWF and g-ratio modeling for both preclinical model validation and ultimately translation to humans.

Genome-wide association (GWA) studies have identified numerous, replicable, genetic associations between common single nucleotide polymorphisms (SNPs) and risk of common autoimmune and inflammatory (immune-mediated) diseases, some of which are shared between two diseases. Along with epidemiological and clinical evidence, this suggests that some genetic risk factors may be shared across diseases-as is the case with alleles in the Major Histocompatibility Locus. In this work we evaluate the extent of this sharing for 107 immune disease-risk SNPs in seven diseases: celiac disease, Crohn's disease, multiple sclerosis, psoriasis, rheumatoid arthritis, systemic lupus erythematosus, and type 1 diabetes. We have developed a novel statistic for Cross Phenotype Meta-Analysis (CPMA) which detects association of a SNP to multiple, but not necessarily all, phenotypes. With it, we find evidence that 47/107 (44%) immune-mediated disease risk SNPs are associated to multiple-but not all-immune-mediated diseases (SNP-wise P(CPMA)<0.01). We also show that distinct groups of interacting proteins are encoded near SNPs which predispose to the same subsets of diseases; we propose these as the mechanistic basis of shared disease risk. We are thus able to leverage genetic data across diseases to construct biological hypotheses about the underlying mechanism of pathogenesis.

Primary sclerosing cholangitis (PSC) is a rare autoimmune bile duct disease that is strongly associated with immune-mediated disorders. In this study, we implemented multitrait joint analyses to genome-wide association summary statistics of PSC and numerous clinical and epidemiological traits to estimate the genetic contribution of each trait and genetic correlations between traits and to identify new lead PSC risk-associated loci. We identified seven new loci that have not been previously reported and one new independent lead variant in the previously reported locus. Functional annotation and fine-mapping nominated several potential susceptibility genes such as MANBA and IRF5 . Network-based in silico drug efficacy screening provided candidate agents for further study of pharmacological effect in PSC. The genetic basis of primary sclerosing cholangitis has only been partially uncovered. Here, the authors perform a multitrait genome-wide association study to provide insight into the genetic etiology of primary sclerosing cholangitis risk and possible therapeutic drug targets.

Myelin is composed of plasma membrane spirally wrapped around axons and compacted into dense sheaths by myelin-associated proteins. Myelin is elaborated by neuroepithelial derived oligodendrocytes in the central nervous system (CNS) and by neural crest derived Schwann cells in the peripheral nervous system (PNS). While some myelin proteins accumulate in only one lineage, myelin basic protein (Mbp) is expressed in both. Overlapping the Mbp gene is Golli, a transcriptional unit that is expressed widely both within and beyond the nervous system. A super-enhancer domain within the Golli/Mbp locus contains multiple enhancers shown previously to drive reporter construct expression specifically in oligodendrocytes or Schwann cells. In order to determine the contribution of each enhancer to the Golli/Mbp expression program, and to reveal if functional interactions occur among them, we derived mouse lines in which they were deleted, either singly or in different combinations, and relative mRNA accumulation was measured at key stages of early development and at maturity. Although super-enhancers have been shown previously to facilitate interaction among their component enhancers, the enhancers investigated here demonstrated largely additive relationships. However, enhancers demonstrating autonomous activity strictly in one lineage, when missing, were found to significantly reduce output in the other, thus revealing cryptic \"stealth\" activity. Further, in the absence of a key oligodendrocyte enhancer, Golli accumulation was markedly and uniformly attenuated in all cell types investigated. Our observations suggest a model in which enhancer-mediated DNA-looping and potential super-enhancer properties underlie Golli/Mbp regulatory organization.

How environmental factors combine with genetic risk at the molecular level to promote complex trait diseases such as multiple sclerosis (MS) is largely unknown. In mice, N -glycan branching by the Golgi enzymes Mgat1 and/or Mgat5 prevents T cell hyperactivity, cytotoxic T-lymphocyte antigen 4 (CTLA-4) endocytosis, spontaneous inflammatory demyelination and neurodegeneration, the latter pathologies characteristic of MS. Here we show that MS risk modulators converge to alter N -glycosylation and/or CTLA-4 surface retention conditional on metabolism and vitamin D 3 , including genetic variants in interleukin-7 receptor-α ( IL7RA *C), interleukin-2 receptor-α ( IL2RA *T), MGAT1 (IV A V T−T ) and CTLA-4 (Thr17Ala). Downregulation of Mgat1 by IL7RA *C and IL2RA *T is opposed by MGAT1 (IV A V T−T ) and vitamin D 3 , optimizing branching and mitigating MS risk when combined with enhanced CTLA-4 N -glycosylation by CTLA-4 Thr17. Our data suggest a molecular mechanism in MS whereby multiple environmental and genetic inputs lead to dysregulation of a final common pathway, namely N -glycosylation. Complex diseases such as multiple sclerosis have both genetic and environmental components. This study demonstrates that variants of genes implicated in multiple sclerosis, and alterations in cellular metabolism and vitamin D3 levels, alter N -glycosylation, a post-translational modification causal of the disease in mice.

Although genetic and non-genetic studies in mouse and human implicate the CD40 pathway in rheumatoid arthritis (RA), there are no approved drugs that inhibit CD40 signaling for clinical care in RA or any other disease. Here, we sought to understand the biological consequences of a CD40 risk variant in RA discovered by a previous genome-wide association study (GWAS) and to perform a high-throughput drug screen for modulators of CD40 signaling based on human genetic findings. First, we fine-map the CD40 risk locus in 7,222 seropositive RA patients and 15,870 controls, together with deep sequencing of CD40 coding exons in 500 RA cases and 650 controls, to identify a single SNP that explains the entire signal of association (rs4810485, P = 1.4×10(-9)). Second, we demonstrate that subjects homozygous for the RA risk allele have ∼33% more CD40 on the surface of primary human CD19+ B lymphocytes than subjects homozygous for the non-risk allele (P = 10(-9)), a finding corroborated by expression quantitative trait loci (eQTL) analysis in peripheral blood mononuclear cells from 1,469 healthy control individuals. Third, we use retroviral shRNA infection to perturb the amount of CD40 on the surface of a human B lymphocyte cell line (BL2) and observe a direct correlation between amount of CD40 protein and phosphorylation of RelA (p65), a subunit of the NF-κB transcription factor. Finally, we develop a high-throughput NF-κB luciferase reporter assay in BL2 cells activated with trimerized CD40 ligand (tCD40L) and conduct an HTS of 1,982 chemical compounds and FDA-approved drugs. After a series of counter-screens and testing in primary human CD19+ B cells, we identify 2 novel chemical inhibitors not previously implicated in inflammation or CD40-mediated NF-κB signaling. Our study demonstrates proof-of-concept that human genetics can be used to guide the development of phenotype-based, high-throughput small-molecule screens to identify potential novel therapies in complex traits such as RA.

Primary biliary cirrhosis is characterised by a progressive and destructive lymphocytic cholangitis, targeting small intra-hepatic bile ducts. In association with the histologic liver injury, patients characteristically express highly specific auto-antibodies that recognise a conserved epitope of the pyruvate dehydrogenase complex found on the inner membrane of the mitochondria. Family studies demonstrate a clear increased incidence and prevalence of associated autoimmune diseases; and historically, a clear HLA association with disease has been evident. With the use of a high-throughput whole-genome array technology, significant insights into the non-HLA loci associated with risk for disease development have been made. These studies, which have primarily incorporated genome-wide association screens and targeted analysis of immune genes, have highlighted the integral roles for immune cell development and function in disease risk. This has revealed the IL-12/JAK-STAT signalling pathway as a key etiologic factor. In conjunction with a better understanding of environmental triggers, such work lays the foundation for better disease insights mechanistically and, hopefully, therapeutically. Obstacles to uncovering all the associated genetic risk and the correlation between genotype and phenotype remain to be circumvented, as do better appreciation of the processes that underpin not only disease initiation but also presentation and outcome.

Rheumatoid arthritis (RA) is a highly heritable complex disease with unknown etiology. Multi-ancestry genetic research of RA promises to improve power to detect genetic signals, fine-mapping resolution and performances of polygenic risk scores (PRS). Here, we present a large-scale genome-wide association study (GWAS) of RA, which includes 276,020 samples from five ancestral groups. We conducted a multi-ancestry meta-analysis and identified 124 loci ( P  < 5 × 10 −8 ), of which 34 are novel. Candidate genes at the novel loci suggest essential roles of the immune system (for example, TNIP2 and TNFRSF11A ) and joint tissues (for example, WISP1 ) in RA etiology. Multi-ancestry fine-mapping identified putatively causal variants with biological insights (for example, LEF1 ). Moreover, PRS based on multi-ancestry GWAS outperformed PRS based on single-ancestry GWAS and had comparable performance between populations of European and East Asian ancestries. Our study provides several insights into the etiology of RA and improves the genetic predictability of RA. Multi-ancestry genome-wide association analyses identify 124 risk loci for rheumatoid arthritis, of which 34 are novel. A polygenic risk score based on multi-ancestry data showed comparable performance between populations of European and East Asian ancestries.