Author Correction: Approaches and advances in the genetic causes of autoimmune disease and their implications.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Publisher Correction: Approaches and advances in the genetic causes of autoimmune disease and their implications.

In the version of this article initially published, the bibliographic information for reference 2 was incorrect in the reference list, and reference 2 was cited incorrectly at the end of the second sentence in the second paragraph ("...were identified2."). The correct reference 2 is as follows: "Kong, A. et al. The nature of nurture: Effects of parental genotypes. Science 359, 424-428 (2018)." The reference that should be cited at the end of the aforementioned sentence, which should be numbered '5' ("...were identified5."), is as follows: "Okada, Y. et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506, 376-381 (2014)." All subsequent references (5-161) should be renumbered accordingly (6-162) in the list and text. Also, several of the gene symbols in Table 2 were formatted incorrectly (without commas); the correct gene symbols are as follows: column 3 row 13, RBM17, IL2RA; column 3 row 30, DEXI, CLEC16A; column 3 row 39, UBASH3A, ICOSLG; column 4 row 15, PTEN, KLLN; column 4 row 21, CLEC7A, CLEC9A; and column 5 rows 7-9, AL391559.1, ENSG00000238747, RP11-63K6.7, RP3-512E2.2. The errors have been corrected in the HTML and PDF version of the article.

Lineage-Specific Genome Architecture Links Enhancers and Non-coding Disease Variants to Target Gene Promoters.

Long-range interactions between regulatory elements and gene promoters play key roles in transcriptional regulation. The vast majority of interactions are uncharted, constituting a major missing link in understanding genome control. Here, we use promoter capture Hi-C to identify interacting regions of 31,253 promoters in 17 human primary hematopoietic cell types. We show that promoter interactions are highly cell type specific and enriched for links between active promoters and epigenetically marked enhancers. Promoter interactomes reflect lineage relationships of the hematopoietic tree, consistent with dynamic remodeling of nuclear architecture during differentiation. Interacting regions are enriched in genetic variants linked with altered expression of genes they contact, highlighting their functional role. We exploit this rich resource to connect non-coding disease variants to putative target promoters, prioritizing thousands of disease-candidate genes and implicating disease pathways. Our results demonstrate the power of primary cell promoter interactomes to reveal insights into genomic regulatory mechanisms underlying common diseases.

Approaches and advances in the genetic causes of autoimmune disease and their implications.

Genome-wide association studies are transformative in revealing the polygenetic basis of common diseases, with autoimmune diseases leading the charge. Although the field is just over 10 years old, advances in understanding the underlying mechanistic pathways of these conditions, which result from a dense multifactorial blend of genetic, developmental and environmental factors, have already been informative, including insights into therapeutic possibilities. Nevertheless, the challenge of identifying the actual causal genes and pathways and their biological effects on altering disease risk remains for many identified susceptibility regions. It is this fundamental knowledge that will underpin the revolution in patient stratification, the discovery of therapeutic targets and clinical trial design in the next 20 years. Here we outline recent advances in analytical and phenotyping approaches and the emergence of large cohorts with standardized gene-expression data and other phenotypic data that are fueling a bounty of discovery and improved understanding of human physiology.

Rare variant associations with plasma protein levels in the UK Biobank.

Integrating human genomics and proteomics can help elucidate disease mechanisms, identify clinical biomarkers and discover drug targets1-4. Because previous proteogenomic studies have focused on common variation via genome-wide association studies, the contribution of rare variants to the plasma proteome remains largely unknown. Here we identify associations between rare protein-coding variants and 2,923 plasma protein abundances measured in 49,736 UK Biobank individuals. Our variant-level exome-wide association study identified 5,433 rare genotype-protein associations, of which 81% were undetected in a previous genome-wide association study of the same cohort5. We then looked at aggregate signals using gene-level collapsing analysis, which revealed 1,962 gene-protein associations. Of the 691 gene-level signals from protein-truncating variants, 99.4% were associated with decreased protein levels. STAB1 and STAB2, encoding scavenger receptors involved in plasma protein clearance, emerged as pleiotropic loci, with 77 and 41 protein associations, respectively. We demonstrate the utility of our publicly accessible resource through several applications. These include detailing an allelic series in NLRC4, identifying potential biomarkers for a fatty liver disease-associated variant in HSD17B13 and bolstering phenome-wide association studies by integrating protein quantitative trait loci with protein-truncating variants in collapsing analyses. Finally, we uncover distinct proteomic consequences of clonal haematopoiesis (CH), including an association between TET2-CH and increased FLT3 levels. Our results highlight a considerable role for rare variation in plasma protein abundance and the value of proteogenomics in therapeutic discovery.

Plasma proteomic associations with genetics and health in the UK Biobank.

The Pharma Proteomics Project is a precompetitive biopharmaceutical consortium characterizing the plasma proteomic profiles of 54,219 UK Biobank participants. Here we provide a detailed summary of this initiative, including technical and biological validations, insights into proteomic disease signatures, and prediction modelling for various demographic and health indicators. We present comprehensive protein quantitative trait locus (pQTL) mapping of 2,923 proteins that identifies 14,287 primary genetic associations, of which 81% are previously undescribed, alongside ancestry-specific pQTL mapping in non-European individuals. The study provides an updated characterization of the genetic architecture of the plasma proteome, contextualized with projected pQTL discovery rates as sample sizes and proteomic assay coverages increase over time. We offer extensive insights into trans pQTLs across multiple biological domains, highlight genetic influences on ligand-receptor interactions and pathway perturbations across a diverse collection of cytokines and complement networks, and illustrate long-range epistatic effects of ABO blood group and FUT2 secretor status on proteins with gastrointestinal tissue-enriched expression. We demonstrate the utility of these data for drug discovery by extending the genetic proxied effects of protein targets, such as PCSK9, on additional endpoints, and disentangle specific genes and proteins perturbed at loci associated with COVID-19 susceptibility. This public-private partnership provides the scientific community with an open-access proteomics resource of considerable breadth and depth to help to elucidate the biological mechanisms underlying proteo-genomic discoveries and accelerate the development of biomarkers, predictive models and therapeutics1.

Publisher Correction: Whole-genome sequencing of a sporadic primary immunodeficiency cohort.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Whole-genome sequencing of a sporadic primary immunodeficiency cohort.

Primary immunodeficiency (PID) is characterized by recurrent and often life-threatening infections, autoimmunity and cancer, and it poses major diagnostic and therapeutic challenges. Although the most severe forms of PID are identified in early childhood, most patients present in adulthood, typically with no apparent family history and a variable clinical phenotype of widespread immune dysregulation: about 25% of patients have autoimmune disease, allergy is prevalent and up to 10% develop lymphoid malignancies1-3. Consequently, in sporadic (or non-familial) PID genetic diagnosis is difficult and the role of genetics is not well defined. Here we address these challenges by performing whole-genome sequencing in a large PID cohort of 1,318 participants. An analysis of the coding regions of the genome in 886 index cases of PID found that disease-causing mutations in known genes that are implicated in monogenic PID occurred in 10.3% of these patients, and a Bayesian approach (BeviMed4) identified multiple new candidate PID-associated genes, including IVNS1ABP. We also examined the noncoding genome, and found deletions in regulatory regions that contribute to disease causation. In addition, we used a genome-wide association study to identify loci that are associated with PID, and found evidence for the colocalization of-and interplay between-novel high-penetrance monogenic variants and common variants (at the PTPN2 and SOCS1 loci). This begins to explain the contribution of common variants to the variable penetrance and phenotypic complexity that are observed in PID. Thus, using a cohort-based whole-genome-sequencing approach in the diagnosis of PID can increase diagnostic yield and further our understanding of the key pathways that influence immune responsiveness in humans.

Whole-genome sequencing of patients with rare diseases in a national health system.

Most patients with rare diseases do not receive a molecular diagnosis and the aetiological variants and causative genes for more than half such disorders remain to be discovered1. Here we used whole-genome sequencing (WGS) in a national health system to streamline diagnosis and to discover unknown aetiological variants in the coding and non-coding regions of the genome. We generated WGS data for 13,037 participants, of whom 9,802 had a rare disease, and provided a genetic diagnosis to 1,138 of the 7,065 extensively phenotyped participants. We identified 95 Mendelian associations between genes and rare diseases, of which 11 have been discovered since 2015 and at least 79 are confirmed to be aetiological. By generating WGS data of UK Biobank participants2, we found that rare alleles can explain the presence of some individuals in the tails of a quantitative trait for red blood cells. Finally, we identified four novel non-coding variants that cause disease through the disruption of transcription of ARPC1B, GATA1, LRBA and MPL. Our study demonstrates a synergy by using WGS for diagnosis and aetiological discovery in routine healthcare.

A minimal role for synonymous variation in human disease.

Synonymous mutations change the DNA sequence of a gene without affecting the amino acid sequence of the encoded protein. Although some synonymous mutations can affect RNA splicing, translational efficiency, and mRNA stability, studies in human genetics, mutagenesis screens, and other experiments and evolutionary analyses have repeatedly shown that most synonymous variants are neutral or only weakly deleterious, with some notable exceptions. Based on a recent study in yeast, there have been claims that synonymous mutations could be as important as nonsynonymous mutations in causing disease, assuming the yeast findings hold up and translate to humans. Here, we argue that there is insufficient evidence to overturn the large, coherent body of knowledge establishing the predominant neutrality of synonymous variants in the human genome.

The effects of pathogenic and likely pathogenic variants for inherited hemostasis disorders in 140 214 UK Biobank participants.

Rare genetic diseases affect millions, and identifying causal DNA variants is essential for patient care. Therefore, it is imperative to estimate the effect of each independent variant and improve their pathogenicity classification. Our study of 140 214 unrelated UK Biobank (UKB) participants found that each of them carries a median of 7 variants previously reported as pathogenic or likely pathogenic. We focused on 967 diagnostic-grade gene (DGG) variants for rare bleeding, thrombotic, and platelet disorders (BTPDs) observed in 12 367 UKB participants. By association analysis, for a subset of these variants, we estimated effect sizes for platelet count and volume, and odds ratios for bleeding and thrombosis. Variants causal of some autosomal recessive platelet disorders revealed phenotypic consequences in carriers. Loss-of-function variants in MPL, which cause chronic amegakaryocytic thrombocytopenia if biallelic, were unexpectedly associated with increased platelet counts in carriers. We also demonstrated that common variants identified by genome-wide association studies (GWAS) for platelet count or thrombosis risk may influence the penetrance of rare variants in BTPD DGGs on their associated hemostasis disorders. Network-propagation analysis applied to an interactome of 18 410 nodes and 571 917 edges showed that GWAS variants with large effect sizes are enriched in DGGs and their first-order interactors. Finally, we illustrate the modifying effect of polygenic scores for platelet count and thrombosis risk on disease severity in participants carrying rare variants in TUBB1 or PROC and PROS1, respectively. Our findings demonstrate the power of association analyses using large population datasets in improving pathogenicity classifications of rare variants.

Prevalence of CFTR variants in primary immunodeficiency patients with bronchiectasis is an important modifying cofactor.

BACKGROUND: Cystic fibrosis (CF) is one of the most common life-limiting autosomal-recessive disorders and is caused by genetic defects in the CF transmembrane conductance regulator (CFTR) gene. Some of the features of this multisystem disease can be present in primary immunodeficiency (PID). OBJECTIVE: We hypothesized that a carrier CFTR status might be associated with worse outcome regarding structural lung disease in patients with PID. METHODS: A within-cohort and population-level statistical genomic analysis of a large European cohort of PID patients was performed using genome sequence data. Genomic analysis of variant pathogenicity was performed. RESULTS: Compared to the general population, p.Phe508del carriage was enriched in lung-related PID. Additionally, carriage of several pathogenic CFTR gene variants were increased in PID associated with structural lung damage compared to PID patients without the structural lung damage. We identified 3 additional biallelic cases, including several variants not traditionally considered to cause CF. CONCLUSION: Genome sequencing identified cases of CFTR dysfunction in PID, driving an increased susceptibility to infection. Large national genomic services provide an opportunity for precision medicine by interpreting subtle features of genomic diversity when treating traditional Mendelian disorders.

Fine mapping chromatin contacts in capture Hi-C data.

BACKGROUND: Hi-C and capture Hi-C (CHi-C) are used to map physical contacts between chromatin regions in cell nuclei using high-throughput sequencing. Analysis typically proceeds considering the evidence for contacts between each possible pair of fragments independent from other pairs. This can produce long runs of fragments which appear to all make contact with the same baited fragment of interest. RESULTS: We hypothesised that these long runs could result from a smaller subset of direct contacts and propose a new method, based on a Bayesian sparse variable selection approach, which attempts to fine map these direct contacts. Our model is conceptually novel, exploiting the spatial pattern of counts in CHi-C data. Although we use only the CHi-C count data in fitting the model, we show that the fragments prioritised display biological properties that would be expected of true contacts: for bait fragments corresponding to gene promoters, we identify contact fragments with active chromatin and contacts that correspond to edges found in previously defined enhancer-target networks; conversely, for intergenic bait fragments, we identify contact fragments corresponding to promoters for genes expressed in that cell type. We show that long runs of apparently co-contacting fragments can typically be explained using a subset of direct contacts consisting of <10% of the number in the full run, suggesting that greater resolution can be extracted from existing datasets. CONCLUSIONS: Our results appear largely complementary to those from a per-fragment analytical approach, suggesting that they provide an additional level of interpretation that may be used to increase resolution for mapping direct contacts in CHi-C experiments.

Negligible impact of rare autoimmune-locus coding-region variants on missing heritability.

Genome-wide association studies (GWAS) have identified common variants of modest-effect size at hundreds of loci for common autoimmune diseases; however, a substantial fraction of heritability remains unexplained, to which rare variants may contribute. To discover rare variants and test them for association with a phenotype, most studies re-sequence a small initial sample size and then genotype the discovered variants in a larger sample set. This approach fails to analyse a large fraction of the rare variants present in the entire sample set. Here we perform simultaneous amplicon-sequencing-based variant discovery and genotyping for coding exons of 25 GWAS risk genes in 41,911 UK residents of white European origin, comprising 24,892 subjects with six autoimmune disease phenotypes and 17,019 controls, and show that rare coding-region variants at known loci have a negligible role in common autoimmune disease susceptibility. These results do not support the rare-variant synthetic genome-wide-association hypothesis (in which unobserved rare causal variants lead to association detected at common tag variants). Many known autoimmune disease risk loci contain multiple, independently associated, common and low-frequency variants, and so genes at these loci are a priori stronger candidates for harbouring rare coding-region variants than other genes. Our data indicate that the missing heritability for common autoimmune diseases may not be attributable to the rare coding-region variant portion of the allelic spectrum, but perhaps, as others have proposed, may be a result of many common-variant loci of weak effect.

Dissection of a Complex Disease Susceptibility Region Using a Bayesian Stochastic Search Approach to Fine Mapping.

Identification of candidate causal variants in regions associated with risk of common diseases is complicated by linkage disequilibrium (LD) and multiple association signals. Nonetheless, accurate maps of these variants are needed, both to fully exploit detailed cell specific chromatin annotation data to highlight disease causal mechanisms and cells, and for design of the functional studies that will ultimately be required to confirm causal mechanisms. We adapted a Bayesian evolutionary stochastic search algorithm to the fine mapping problem, and demonstrated its improved performance over conventional stepwise and regularised regression through simulation studies. We then applied it to fine map the established multiple sclerosis (MS) and type 1 diabetes (T1D) associations in the IL-2RA (CD25) gene region. For T1D, both stepwise and stochastic search approaches identified four T1D association signals, with the major effect tagged by the single nucleotide polymorphism, rs12722496. In contrast, for MS, the stochastic search found two distinct competing models: a single candidate causal variant, tagged by rs2104286 and reported previously using stepwise analysis; and a more complex model with two association signals, one of which was tagged by the major T1D associated rs12722496 and the other by rs56382813. There is low to moderate LD between rs2104286 and both rs12722496 and rs56382813 (r2 ≃ 0:3) and our two SNP model could not be recovered through a forward stepwise search after conditioning on rs2104286. Both signals in the two variant model for MS affect CD25 expression on distinct subpopulations of CD4+ T cells, which are key cells in the autoimmune process. The results support a shared causal variant for T1D and MS. Our study illustrates the benefit of using a purposely designed model search strategy for fine mapping and the advantage of combining disease and protein expression data.

Integration of disease association and eQTL data using a Bayesian colocalisation approach highlights six candidate causal genes in immune-mediated diseases.

The genes and cells that mediate genetic associations identified through genome-wide association studies (GWAS) are only partially understood. Several studies that have investigated the genetic regulation of gene expression have shown that disease-associated variants are over-represented amongst expression quantitative trait loci (eQTL) variants. Evidence for colocalisation of eQTL and disease causal variants can suggest causal genes and cells for these genetic associations. Here, we used colocalisation analysis to investigate whether 595 genetic associations to ten immune-mediated diseases are consistent with a causal variant that regulates, in cis, gene expression in resting B cells, and in resting and stimulated monocytes. Previously published candidate causal genes were over-represented amongst genes exhibiting colocalisation (odds ratio > 1.5), and we identified evidence for colocalisation (posterior odds > 5) between cis eQTLs in at least one cell type and at least one disease for six genes: ADAM15, RGS1, CARD9, LTBR, CTSH and SYNGR1. We identified cell-specific effects, such as for CTSH, the expression of which in monocytes, but not in B cells, may mediate type 1 diabetes and narcolepsy associations in the chromosome 15q25.1 region. Our results demonstrate the utility of integrating genetic studies of disease and gene expression for highlighting causal genes and cell types.

Detection and correction of artefacts in estimation of rare copy number variants and analysis of rare deletions in type 1 diabetes.

Copy number variants (CNVs) have been proposed as a possible source of 'missing heritability' in complex human diseases. Two studies of type 1 diabetes (T1D) found null associations with common copy number polymorphisms, but CNVs of low frequency and high penetrance could still play a role. We used the Log-R-ratio intensity data from a dense single nucleotide polymorphism (SNP) array, ImmunoChip, to detect rare CNV deletions (rDELs) and duplications (rDUPs) in 6808 T1D cases, 9954 controls and 2206 families with T1D-affected offspring. Initial analyses detected CNV associations. However, these were shown to be false-positive findings, failing replication with polymerase chain reaction. We developed a pipeline of quality control (QC) tests that were calibrated using systematic testing of sensitivity and specificity. The case-control odds ratios (OR) of CNV burden on T1D risk resulting from this QC pipeline converged on unity, suggesting no global frequency difference in rDELs or rDUPs. There was evidence that deletions could impact T1D risk for a small minority of cases, with enrichment for rDELs longer than 400 kb (OR = 1.57, P = 0.005). There were also 18 de novo rDELs detected in affected offspring but none for unaffected siblings (P = 0.03). No specific CNV regions showed robust evidence for association with T1D, although frequencies were lower than expected (most less than 0.1%), substantially reducing statistical power, which was examined in detail. We present an R-package, plumbCNV, which provides an automated approach for QC and detection of rare CNVs that can facilitate equivalent analyses of large-scale SNP array datasets.

Genome-wide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls.

Copy number variants (CNVs) account for a major proportion of human genetic polymorphism and have been predicted to have an important role in genetic susceptibility to common disease. To address this we undertook a large, direct genome-wide study of association between CNVs and eight common human diseases. Using a purpose-designed array we typed approximately 19,000 individuals into distinct copy-number classes at 3,432 polymorphic CNVs, including an estimated approximately 50% of all common CNVs larger than 500 base pairs. We identified several biological artefacts that lead to false-positive associations, including systematic CNV differences between DNAs derived from blood and cell lines. Association testing and follow-up replication analyses confirmed three loci where CNVs were associated with disease-IRGM for Crohn's disease, HLA for Crohn's disease, rheumatoid arthritis and type 1 diabetes, and TSPAN8 for type 2 diabetes-although in each case the locus had previously been identified in single nucleotide polymorphism (SNP)-based studies, reflecting our observation that most common CNVs that are well-typed on our array are well tagged by SNPs and so have been indirectly explored through SNP studies. We conclude that common CNVs that can be typed on existing platforms are unlikely to contribute greatly to the genetic basis of common human diseases.

Robust associations of four new chromosome regions from genome-wide analyses of type 1 diabetes.

The Wellcome Trust Case Control Consortium (WTCCC) primary genome-wide association (GWA) scan on seven diseases, including the multifactorial autoimmune disease type 1 diabetes (T1D), shows associations at P < 5 x 10(-7) between T1D and six chromosome regions: 12q24, 12q13, 16p13, 18p11, 12p13 and 4q27. Here, we attempted to validate these and six other top findings in 4,000 individuals with T1D, 5,000 controls and 2,997 family trios independent of the WTCCC study. We confirmed unequivocally the associations of 12q24, 12q13, 16p13 and 18p11 (P(follow-up)

A method for gene-based pathway analysis using genomewide association study summary statistics reveals nine new type 1 diabetes associations.

Pathway analysis can complement point-wise single nucleotide polymorphism (SNP) analysis in exploring genomewide association study (GWAS) data to identify specific disease-associated genes that can be candidate causal genes. We propose a straightforward methodology that can be used for conducting a gene-based pathway analysis using summary GWAS statistics in combination with widely available reference genotype data. We used this method to perform a gene-based pathway analysis of a type 1 diabetes (T1D) meta-analysis GWAS (of 7,514 cases and 9,045 controls). An important feature of the conducted analysis is the removal of the major histocompatibility complex gene region, the major genetic risk factor for T1D. Thirty-one of the 1,583 (2%) tested pathways were identified to be enriched for association with T1D at a 5% false discovery rate. We analyzed these 31 pathways and their genes to identify SNPs in or near these pathway genes that showed potentially novel association with T1D and attempted to replicate the association of 22 SNPs in additional samples. Replication P-values were skewed (P=9.85×10-11) with 12 of the 22 SNPs showing P<0.05. Support, including replication evidence, was obtained for nine T1D associated variants in genes ITGB7 (rs11170466, P=7.86×10-9), NRP1 (rs722988, 4.88×10-8), BAD (rs694739, 2.37×10-7), CTSB (rs1296023, 2.79×10-7), FYN (rs11964650, P=5.60×10-7), UBE2G1 (rs9906760, 5.08×10-7), MAP3K14 (rs17759555, 9.67×10-7), ITGB1 (rs1557150, 1.93×10-6), and IL7R (rs1445898, 2.76×10-6). The proposed methodology can be applied to other GWAS datasets for which only summary level data are available.