Genetic advances in systemic lupus erythematosus: an update.

TLDR: Gene expression and epigenetic databases provide a valuable resource to interpret genetic association in SLE and expansion of such resources to include disease status and multiple ancestries will further aid the exploration of the biology underlying the genetics.

Abstract: Purpose of reviewMore than 80 susceptibility loci are now reported to show robust genetic association with systemic lupus erythematosus (SLE) The differential functional effects of the risk alleles for the majority of these loci remain to be defined Here, we review current SLE association findings

Figures

Figure 2. Box plots of GRS across the five major population groups_Previously published

Figure 1. SLE risk loci in genomic context_Original

Figure 4. Schematic overview of fine mapping causal SNPs by integrating genetics and epigenetics_Original

Figure 3. Overview of co-localisation analysis of GWAS and eQTL_Original

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DOI:
10.1097/BOR.0000000000000411
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Citation for published version (APA):
Chen, L., Morris, D. L., & Vyse, T. J. (2017). Genetic advances in systemic lupus erythematosus: an update.
Current Opinion in Rheumatology, 29(5), 423-433. https://doi.org/10.1097/BOR.0000000000000411
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Current Opinion in Rheumatology
Genetic Advances in SLE: an update
--Manuscript Draft--
Manuscript Number:
Full Title: Genetic Advances in SLE: an update
Article Type: Review Article
Corresponding Author: Timothy Vyse, Ph.D. M.D.
UNITED KINGDOM
Corresponding Author Secondary
Information:
Corresponding Author's Institution:
Corresponding Author's Secondary
Institution:
First Author: Lingyan Chen, MSc
First Author Secondary Information:
Order of Authors: Lingyan Chen, MSc
David Lester Morris, PhD
Timothy Vyse, Ph.D. M.D.
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Genetic Advances in SLE: an update
Lingyan Chen
1
, David L Morris
1
, Timothy J Vyse
1,2*
1 Division of Genetics and Molecular Medicine, 2 Division of Immunology, Infection & Inflammatory Disease,
King’s College London, Guy’s Hospital, London, SE1 9RT, UK.
Correspondence to Timothy J Vyse, PhD, FRCP, FMedSci, Professor of Molecular Medicine, King’s College
London, Consultant in Rheumatology, Guy’s and St Thomas’ NHS Trust. Tel: +44 20 7848 8517; email:
tim.vyse@kcl.ac.uk.
Abstract
Purpose of review – More than 80 loci are now reported to show robust genetic association
with Systemic Lupus Erythematosus (SLE). The differential functional effects of the risk
alleles for the majority of these loci remain to be defined. Here, we review current SLE
association findings and the recent progress in the annotation of non-coding regions of the
human genome as well as the new technologies and statistical methods that can be applied to
further the understanding of SLE genetics.
Recent findings – Genome-wide association studies (GWAS) have markedly expanded the
catalogue of genetic signals contributing to SLE development; we can now explain more than
50% of the disease’s heritability. Expression quantitative trait loci (eQTL) mapping with co-
localisation analysis of GWAS results help to identify the underlying causal genes. The
ENCODE, Roadmap Epigenome and the Blueprint Epigenome projects have jointly
annotated more than 80% of the noncoding genome, providing a wealth of information (from
healthy individuals) to define the functional elements within the risk loci. Technologies, such
as next-generation sequencing, chromatin structure determination and genome editing, will
help elucidate the actual mechanisms that underpin SLE risk alleles.
Review_Genetic advances in SLE-an update_LC-DLM-TJV
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Summary – Gene expression and epigenetic databases provide a valuable resource to
interpret genetic association in SLE. Expansion of such resources to include disease and
multiple ancestries will further aid the exploration of the biology underlying the genetics.
Keywords: Systemic lupus erythematosus; GWAS; expression quantitative trait loci;
epigenome; causal variants
Introduction
Systemic lupus erythematosus (SLE) is a chronic inflammatory autoimmune disease
associated with a wide range of signs and symptoms varying among affected individuals and
can involve many organs and systems, including the skin, joints, kidneys, lungs, central
nervous system, and hematopoietic system. The population prevalence varies with ancestry,
being more prevalent in non-European populations with a significant gender disparity
towards women (9:1) during the years between menarche and menopause [1]. Although the
exact etiology of lupus is not fully understood, a strong genetic link has been identified
through the application of family and large-scale genome-wide association studies (GWAS).
The concordance rate in monozygotic twins (24%) is approximately 10 fold higher than in
dizygotic twins (2%) [2,3]. A recent study from Taiwan reported that the heritability was
43.9% and the proportion of phenotypic variance explained by shared and non-shared
environmental factors was 25.8% and 30.3%, respectively, suggesting non-heritable factors
may play a considerable role in disease pathogenesis [4].
There are now more than 80 loci reported to be associated with the susceptibility of SLE.
Here, we review current SLE association findings and the recent progress in the annotation of
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the non-coding region of the human genome as well as new technologies and statistical
methods, in order to apply this knowledge to the understanding of SLE genetics.
Insights from GWAS
Genetic linkage analysis and candidate gene association studies identified several SLE
susceptibility loci (e.g. HLA-DR2/DR3) [5]. Nevertheless, the advent and application of
GWAS dramatically advanced knowledge of the genetic aetiology of SLE.
There have been seven SLE GWAS in European population [6–12], six Asian GWAS [13–
18], subsequent meta-analysis and large-scale replication studies [19–22], published since
2008. Currently, 84 genetic loci are implicated as SLE risk (Figure 1: The CIRCOS plot [23]
and supplementary Table 1), which, in order to avoid likely spurious associations, includes
genetic associations with a P value less than 5  10
-8
tested in a total sample size of at least
1000 individuals. The interactive version of a continually updated resource with details on
SLE associations can be access through the following link: http://insidegen.com/insidegen-
LUPUS-Associations.html.
With the caveat that the majority of mechanisms remain to be elucidated, it appears that the
risk loci associated with SLE influence immune cell function. Although functional studies
are designed with a priori hypotheses in mind, key pathogenic pathways that are likely
influenced by SLE-associated gene products include: immune complex processing and
phagocytosis; DNA degradation, apoptosis and clearance of cellular debris; neutrophil and
monocytes signalling; Toll-like receptor and/or type I interferon signalling; nuclear factor
kappaB activation; B and T-cell function and signalling. Some genes associated with SLE
may act through several pathways. For example, TNFAIP3, encoding the ubiquitin-editing
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Frequently asked questions

Q1. What contributions have the authors mentioned in the paper "Genetic advances in sle: an update" ?

Here, the authors review current SLE association findings and the recent progress in the annotation of non-coding regions of the human genome as well as the new technologies and statistical methods that can be applied to further the understanding of SLE genetics. Recent findings – Genome-wide association studies ( GWAS ) have markedly expanded the catalogue of genetic signals contributing to SLE development ; the authors can now explain more than 50 % of the disease ’ s heritability. The ENCODE, Roadmap Epigenome and the Blueprint Epigenome projects have jointly annotated more than 80 % of the noncoding genome, providing a wealth of information ( from healthy individuals ) to define the functional elements within the risk loci. Expansion of such resources to include disease and multiple ancestries will further aid the exploration of the biology underlying the genetics. 

Q2. What can be used to infer that the disease association and eQTL have the same?

Co-localisation methods, like the regulatory trait concordance (RTC) [39], conditionalanalysis [30], and Bayesian co-localisation [40], can be employed to infer that the diseaseassociation and eQTL have the same allelic basis. 

Q3. What is the important part of the ENCODE project?

The Encyclopedia of DNA elements (ENCODE) project (https://www.encodeproject.org/)[44] has systematically mapped regions of transcription, transcription factor association,chromatin structure and histone modification, and assigns biochemical functions for 80% ofthe genome, in particular outside of the protein-coding regions. 

Q4. What is the role of IKBKE in the genome research?

Next generation sequencing (NGS) in the genome researchWith the development of NGS, high-throughput technologies that are now widely used ingenome research, any part of the genome can be sequenced. 

Q5. What is the role of functional annotations in the genome?

Comprehensive sets of functional annotations (ENCODE, Roadmap and Blueprint projects)in the context of complex genomic structure can be used to predict function and guideexperimentation, such as precision genome editing with the CRISPR-Cas (Clusteredregulatory interspaced short palindromic repeats/CRISPR-associated) [63,64], to address thelong standing question of disease mechanism and heterogeneity. 

Q6. What are the cells of closestimmune relevance in ENCODE Tier 1 and Tier 2?

The cells of closestimmune relevance in ENCODE Tier 1 and Tier 2 are LCLs (GM12878), B cells (CD20+) andmonocytes (CD14+), as well as T cells (CD4+) and peripheral blood mononuclear cell(PBMC) in Tier 3. 

Q7. How many GWAS have been published since 2008?

There have been seven SLE GWAS in European population [6–12], six Asian GWAS [13–18], subsequent meta-analysis and large-scale replication studies [19–22], published since2008. 

Q8. How many variants are missing from the genome?

Application of eQTL mapping to GWAS resultsAssisted by dense genome coverage of the reference panel from the 1000 Genome project[36], imputation and Bayesian inference provided evidence for missense variantsunderpinning association for eight genes, including PTPN22, FCGR2A, NCF2, IFIH1,WDFY4, ITGAM, PLD2, and TYK2 [11]. 

Q9. How did the GWAS study explain the heritability of the risk loci?

by using all genotyped SNPs (DNA chip) to calculate heritabilityexplained, the explained variation (Vg) increase to 28% in Chinese subjects and 27%Europeans using the GCTA algorithm [30]. 

Q10. What are the three projects that have jointlyannotated more than 80 loci?

Roadmap Epigenome and the Blueprint Epigenome projects have jointlyannotated more than 80% of the noncoding genome, providing a wealth of information (fromhealthy individuals) to define the functional elements within the risk loci. 

Q11. How did they find evidence to support the role of causal genes at agiven locus?

By integrating the results of eQTLand RTC analysis, they found evidence to support the role of causal genes as candidates at agiven locus. 

Q12. How is the function of the SNPs in the human genome determined?

As the majority of variants withincausal credibility sets are non-coding [34,35], function is inferred using gene transcriptexpression data and epigenetic modification data (as described below) (Figure 3 and Figure4). 

Q13. What are the technologies that underpin SLE?

suchas next-generation sequencing, chromatin structure determination and genome editing, willhelp elucidate the actual mechanisms that underpin SLE risk alleles. 

Q14. What are the key pathogenic pathways that are likely affected by SLE?

Although functional studiesare designed with a priori hypotheses in mind, key pathogenic pathways that are likelyinfluenced by SLE-associated gene products include: immune complex processing andphagocytosis; DNA degradation, apoptosis and clearance of cellular debris; neutrophil andmonocytes signalling; 

Q15. How can the authors integrate the disease association and eQTL data?

In order to highlight the potential causal genes at the susceptibility loci robustly, it isessential to integrate the disease association and eQTL data using a co-localisation approach. 

Q16. What is the way to explain the potential causal genes at the susceptibility loci?

With RNA-seq, transcript profiling can be done on the gene1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65Page 9 of 23level, exon-level and splice-junction-level, which is more effective in explaining potentialregulatory mechanisms. 

Q17. What is the purpose of the map?

This map is extremely useful for studies of genome interpretation, gene regulation,cellular differentiation, genome evolution, genetic variation and human disease. 

Q18. What is the recent update of the SLE association resource?

The interactive version of a continually updated resource with details onSLE associations can be access through the following link: http://insidegen.com/insidegen-LUPUS-Associations.html.With the caveat that the majority of mechanisms remain to be elucidated, it appears that therisk loci associated with SLE influence immune cell function. 

Q19. What is the role of eqtl in SLE?

Recent studies by Morris et al [11,21] and Odhams et al [41] examined the functionaloutcome of SLE associated variants through the integration of GWAS and eQTL data fromvarious cell types ex vivo, involving T cells, B cells, NK cells, stimulated and restingmonocytes, as well as lymphoblastoid cell lines (LCL). 

Q20. What is the significance of the study?

The study reveals evidence of sharing of genetic risk loci between ancestries as well asevidence that each individual population carries unique genetic risk factors at the locus leveland at the allelic level. 

Q21. What is the significance of the risk alleles mapped at these loci?

Irrespective of the underlying causal genes, the authors canconclude that the heritability explained by the risk alleles mapped at these loci is 15.3%,which is a large increase over the 8.7% reported by So et al [29] in 2011 using the samemeasure. 

Q22. How many genetic loci are implicated in SLE?

84 genetic loci are implicated as SLE risk (Figure 1: The CIRCOS plot [23]and supplementary Table 1), which, in order to avoid likely spurious associations, includes genetic associations with a P value less than 5 10-8 tested in a total sample size of at least1000 individuals.