Variants in BANK1 are associated with lupus nephritis of European ancestry.

The genetic background of lupus nephritis (LN) has not been completely elucidated. We performed a case-only study of 2886 SLE patients, including 947 (33%) with LN. Renal biopsies were available from 396 patients. The discovery cohort (Sweden, n = 1091) and replication cohort 1 (US, n = 962) were genotyped on the Immunochip and replication cohort 2 (Denmark/Norway, n = 833) on a custom array. Patients with LN, proliferative nephritis, or LN with end-stage renal disease were compared with SLE without nephritis. Six loci were associated with LN (p < 1 × 10-4, NFKBIA, CACNA1S, ITGA1, BANK1, OR2Y, and ACER3) in the discovery cohort. Variants in BANK1 showed the strongest association with LN in replication cohort 1 (p = 9.5 × 10-4) and proliferative nephritis in a meta-analysis of discovery and replication cohort 1. There was a weak association between BANK1 and LN in replication cohort 2 (p = 0.052), and in the meta-analysis of all three cohorts the association was strengthened (p = 2.2 × 10-7). DNA methylation data in 180 LN patients demonstrated methylation quantitative trait loci (meQTL) effects between a CpG site and BANK1 variants. To conclude, we describe genetic variations in BANK1 associated with LN and evidence for genetic regulation of DNA methylation within the BANK1 locus. This indicates a role for BANK1 in LN pathogenesis.

Whole-genome sequencing identifies complex contributions to genetic risk by variants in genes causing monogenic systemic lupus erythematosus.

Systemic lupus erythematosus (SLE, OMIM 152700) is a systemic autoimmune disease with a complex etiology. The mode of inheritance of the genetic risk beyond familial SLE cases is currently unknown. Additionally, the contribution of heterozygous variants in genes known to cause monogenic SLE is not fully understood. Whole-genome sequencing of DNA samples from 71 Swedish patients with SLE and their healthy biological parents was performed to investigate the general genetic risk of SLE using known SLE GWAS risk loci identified using the ImmunoChip, variants in genes associated to monogenic SLE, and the mode of inheritance of SLE risk alleles in these families. A random forest model for predicting genetic risk for SLE showed that the SLE risk variants were mainly inherited from one of the parents. In the 71 patients, we detected a significant enrichment of ultra-rare ( ≤ 0.1%) missense and nonsense mutations in 22 genes known to cause monogenic forms of SLE. We identified one previously reported homozygous nonsense mutation in the C1QC (Complement C1q C Chain) gene, which explains the immunodeficiency and severe SLE phenotype of that patient. We also identified seven ultra-rare, coding heterozygous variants in five genes (C1S, DNASE1L3, DNASE1, IFIH1, and RNASEH2A) involved in monogenic SLE. Our findings indicate a complex contribution to the overall genetic risk of SLE by rare variants in genes associated with monogenic forms of SLE. The rare variants were inherited from the other parent than the one who passed on the more common risk variants leading to an increased genetic burden for SLE in the child. Higher frequency SLE risk variants are mostly passed from one of the parents to the offspring affected with SLE. In contrast, the other parent, in seven cases, contributed heterozygous rare variants in genes associated with monogenic forms of SLE, suggesting a larger impact of rare variants in SLE than hitherto reported.

Genome-wide DNA methylation analysis in multiple tissues in primary Sjögren's syndrome reveals regulatory effects at interferon-induced genes.

OBJECTIVES: Increasing evidence suggests an epigenetic contribution to the pathogenesis of autoimmune diseases, including primary Sjögren's Syndrome (pSS). The aim of this study was to investigate the role of DNA methylation in pSS by analysing multiple tissues from patients and controls. METHODS: Genome-wide DNA methylation profiles were generated using HumanMethylation450K BeadChips for whole blood, CD19+ B cells and minor salivary gland biopsies. Gene expression was analysed in CD19+ B cells by RNA-sequencing. Analysis of genetic regulatory effects on DNA methylation at known pSS risk loci was performed. RESULTS: We identified prominent hypomethylation of interferon (IFN)-regulated genes in whole blood and CD19+ B cells, including at the genes MX1, IFI44L and PARP9, replicating previous reports in pSS, as well as identifying a large number of novel associations. Enrichment for genomic overlap with histone marks for enhancer and promoter regions was observed. We showed for the first time that hypomethylation of IFN-regulated genes in pSS B cells was associated with their increased expression. In minor salivary gland biopsies we observed hypomethylation of the IFN-induced gene OAS2. Pathway and disease analysis resulted in enrichment of antigen presentation, IFN signalling and lymphoproliferative disorders. Evidence for genetic control of methylation levels at known pSS risk loci was observed. CONCLUSIONS: Our study highlights the role of epigenetic regulation of IFN-induced genes in pSS where replication is needed for novel findings. The association with altered gene expression suggests a functional mechanism for differentially methylated CpG sites in pSS aetiology.

Allelic expression mapping across cellular lineages to establish impact of non-coding SNPs.

Most complex disease-associated genetic variants are located in non-coding regions and are therefore thought to be regulatory in nature. Association mapping of differential allelic expression (AE) is a powerful method to identify SNPs with direct cis-regulatory impact (cis-rSNPs). We used AE mapping to identify cis-rSNPs regulating gene expression in 55 and 63 HapMap lymphoblastoid cell lines from a Caucasian and an African population, respectively, 70 fibroblast cell lines, and 188 purified monocyte samples and found 40-60% of these cis-rSNPs to be shared across cell types. We uncover a new class of cis-rSNPs, which disrupt footprint-derived de novo motifs that are predominantly bound by repressive factors and are implicated in disease susceptibility through overlaps with GWAS SNPs. Finally, we provide the proof-of-principle for a new approach for genome-wide functional validation of transcription factor-SNP interactions. By perturbing NFκB action in lymphoblasts, we identified 489 cis-regulated transcripts with altered AE after NFκB perturbation. Altogether, we perform a comprehensive analysis of cis-variation in four cell populations and provide new tools for the identification of functional variants associated to complex diseases.

Contributions of de novo variants to systemic lupus erythematosus.

By performing whole-genome sequencing in a Swedish cohort of 71 parent-offspring trios, in which the child in each family is affected by systemic lupus erythematosus (SLE, OMIM 152700), we investigated the contribution of de novo variants to risk of SLE. We found de novo single nucleotide variants (SNVs) to be significantly enriched in gene promoters in SLE patients compared with healthy controls at a level corresponding to 26 de novo promoter SNVs more in each patient than expected. We identified 12 de novo SNVs in promoter regions of genes that have been previously implicated in SLE, or that have functions that could be of relevance to SLE. Furthermore, we detected three missense de novo SNVs, five de novo insertion-deletions, and three de novo structural variants with potential to affect the expression of genes that are relevant for SLE. Based on enrichment analysis, disease-affecting de novo SNVs are expected to occur in one-third of SLE patients. This study shows that de novo variants in promoters commonly contribute to the genetic risk of SLE. The fact that de novo SNVs in SLE were enriched to promoter regions highlights the importance of using whole-genome sequencing for identification of de novo variants.

Shared and Unique Patterns of DNA Methylation in Systemic Lupus Erythematosus and Primary Sjögren's Syndrome.

Objectives: To perform a cross-comparative analysis of DNA methylation in patients with systemic lupus erythematosus (SLE), patients with primary Sjögren's syndrome (pSS), and healthy controls addressing the question of epigenetic sharing and aiming to detect disease-specific alterations. Methods: DNA extracted from peripheral blood from 347 cases with SLE, 100 cases with pSS, and 400 healthy controls were analyzed on the Human Methylation 450k array, targeting 485,000 CpG sites across the genome. A linear regression model including age, sex, and blood cell type distribution as covariates was fitted, and association p-values were Bonferroni corrected. A random forest machine learning classifier was designed for prediction of disease status based on DNA methylation data. Results: We established a combined set of 4,945 shared differentially methylated CpG sites (DMCs) in SLE and pSS compared to controls. In pSS, hypomethylation at type I interferon induced genes was mainly driven by patients who were positive for Ro/SSA and/or La/SSB autoantibodies. Analysis of differential methylation between SLE and pSS identified 2,244 DMCs with a majority of sites showing decreased methylation in SLE compared to pSS. The random forest classifier demonstrated good performance in discerning between disease status with an area under the curve (AUC) between 0.83 and 0.96. Conclusions: The majority of differential DNA methylation is shared between SLE and pSS, however, important quantitative differences exist. Our data highlight neutrophil dysregulation as a shared mechanism, emphasizing the role of neutrophils in the pathogenesis of systemic autoimmune diseases. The current study provides evidence for genes and molecular pathways driving common and disease-specific pathogenic mechanisms.

Novel risk genes for systemic lupus erythematosus predicted by random forest classification.

Genome-wide association studies have identified risk loci for SLE, but a large proportion of the genetic contribution to SLE still remains unexplained. To detect novel risk genes, and to predict an individual's SLE risk we designed a random forest classifier using SNP genotype data generated on the "Immunochip" from 1,160 patients with SLE and 2,711 controls. Using gene importance scores defined by the random forest classifier, we identified 15 potential novel risk genes for SLE. Of them 12 are associated with other autoimmune diseases than SLE, whereas three genes (ZNF804A, CDK1, and MANF) have not previously been associated with autoimmunity. Random forest classification also allowed prediction of patients at risk for lupus nephritis with an area under the curve of 0.94. By allele-specific gene expression analysis we detected cis-regulatory SNPs that affect the expression levels of six of the top 40 genes designed by the random forest analysis, indicating a regulatory role for the identified risk variants. The 40 top genes from the prediction were overrepresented for differential expression in B and T cells according to RNA-sequencing of samples from five healthy donors, with more frequent over-expression in B cells compared to T cells.

Transancestral mapping and genetic load in systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) is an autoimmune disease with marked gender and ethnic disparities. We report a large transancestral association study of SLE using Immunochip genotype data from 27,574 individuals of European (EA), African (AA) and Hispanic Amerindian (HA) ancestry. We identify 58 distinct non-HLA regions in EA, 9 in AA and 16 in HA (∼50% of these regions have multiple independent associations); these include 24 novel SLE regions (P<5 × 10-8), refined association signals in established regions, extended associations to additional ancestries, and a disentangled complex HLA multigenic effect. The risk allele count (genetic load) exhibits an accelerating pattern of SLE risk, leading us to posit a cumulative hit hypothesis for autoimmune disease. Comparing results across the three ancestries identifies both ancestry-dependent and ancestry-independent contributions to SLE risk. Our results are consistent with the unique and complex histories of the populations sampled, and collectively help clarify the genetic architecture and ethnic disparities in SLE.

Single nucleotide polymorphisms with cis-regulatory effects on long non-coding transcripts in human primary monocytes.

We applied genome-wide allele-specific expression analysis of monocytes from 188 samples. Monocytes were purified from white blood cells of healthy blood donors to detect cis-acting genetic variation that regulates the expression of long non-coding RNAs. We analysed 8929 regions harboring genes for potential long non-coding RNA that were retrieved from data from the ENCODE project. Of these regions, 60% were annotated as intergenic, which implies that they do not overlap with protein-coding genes. Focusing on the intergenic regions, and using stringent analysis of the allele-specific expression data, we detected robust cis-regulatory SNPs in 258 out of 489 informative intergenic regions included in the analysis. The cis-regulatory SNPs that were significantly associated with allele-specific expression of long non-coding RNAs were enriched to enhancer regions marked for active or bivalent, poised chromatin by histone modifications. Out of the lncRNA regions regulated by cis-acting regulatory SNPs, 20% (n = 52) were co-regulated with the closest protein coding gene. We compared the identified cis-regulatory SNPs with those in the catalog of SNPs identified by genome-wide association studies of human diseases and traits. This comparison identified 32 SNPs in loci from genome-wide association studies that displayed a strong association signal with allele-specific expression of non-coding RNAs in monocytes, with p-values ranging from 6.7×10(-7) to 9.5×10(-89). The identified cis-regulatory SNPs are associated with diseases of the immune system, like multiple sclerosis and rheumatoid arthritis.

Powerful identification of cis-regulatory SNPs in human primary monocytes using allele-specific gene expression.

A large number of genome-wide association studies have been performed during the past five years to identify associations between SNPs and human complex diseases and traits. The assignment of a functional role for the identified disease-associated SNP is not straight-forward. Genome-wide expression quantitative trait locus (eQTL) analysis is frequently used as the initial step to define a function while allele-specific gene expression (ASE) analysis has not yet gained a wide-spread use in disease mapping studies. We compared the power to identify cis-acting regulatory SNPs (cis-rSNPs) by genome-wide allele-specific gene expression (ASE) analysis with that of traditional expression quantitative trait locus (eQTL) mapping. Our study included 395 healthy blood donors for whom global gene expression profiles in circulating monocytes were determined by Illumina BeadArrays. ASE was assessed in a subset of these monocytes from 188 donors by quantitative genotyping of mRNA using a genome-wide panel of SNP markers. The performance of the two methods for detecting cis-rSNPs was evaluated by comparing associations between SNP genotypes and gene expression levels in sample sets of varying size. We found that up to 8-fold more samples are required for eQTL mapping to reach the same statistical power as that obtained by ASE analysis for the same rSNPs. The performance of ASE is insensitive to SNPs with low minor allele frequencies and detects a larger number of significantly associated rSNPs using the same sample size as eQTL mapping. An unequivocal conclusion from our comparison is that ASE analysis is more sensitive for detecting cis-rSNPs than standard eQTL mapping. Our study shows the potential of ASE mapping in tissue samples and primary cells which are difficult to obtain in large numbers.