Genome-wide association study for systemic lupus erythematosus in an egyptian population

Systemic lupus erythematosus (SLE) susceptibility has a strong genetic component. Genome-wide association studies (GWAS) across trans-ancestral populations show both common and distinct genetic variants of susceptibility across European and Asian ancestries, while many other ethnic populations remain underexplored. We conducted the first SLE GWAS on Egyptians–an admixed North African/Middle Eastern population–using 537 patients and 883 controls. To identify novel susceptibility loci and replicate previously known loci, we performed imputation-based association analysis with 6,382,276 SNPs while accounting for individual admixture. We validated the association analysis using adaptive permutation tests (n = 10⁹). We identified a novel genome-wide significant locus near IRS1/miR-5702 (P_corrected = 1.98 × 10⁻⁸) and eight novel suggestive loci (P_corrected < 1.0 × 10⁻⁵). We also replicated (P_perm < 0.01) 97 previously known loci with at least one associated nearby SNP, with ITGAM, DEF6-PPARD and IRF5 the top three replicated loci. SNPs correlated (r² > 0.8) with lead SNPs from four suggestive loci (ARMC9, DIAPH3, IFLDT1, and ENTPD3) were associated with differential gene expression (3.5 × 10⁻⁹⁵ < p < 1.0 × 10⁻²) across diverse tissues. These loci are involved in cellular proliferation and invasion—pathways prominent in lupus and nephritis. Our study highlights the utility of GWAS in an admixed Egyptian population for delineating new genetic associations and for understanding SLE pathogenesis.

1 Introduction

Systemic lupus erythematosus (SLE) is a chronic, complex, multi-system autoimmune disease with substantial mortality and morbidity. Prevalence, severity, and sub-clinical manifestations vary significantly across ethnically diverse populations—with an outsized burden on individuals with African, Hispanic, and Asian backgrounds compared to Caucasians (Carter et al., 2016; Lewis and Jawad, 2017; Pisetsky et al., 2017). Additionally, SLE shows remarkable gender bias, affecting ∼9–10 times as many women as men.

In addition to epigenetic and environmental contributions, SLE has a very strong genetic component. This is evidenced by high heritability (∼66%), familial clustering (sibling recurrence risk ratio ∼30), twin studies (∼10 times higher concordance rate in monozygotic versus dizygotic twins) (Deapen et al., 1992; Block, 2006; Mak and Tay, 2014; Kuo et al., 2015), and by a growing number of susceptibility loci identified by genome-wide association studies (GWAS) and high-density candidate gene studies [e.g., ImmunoChip (Cortes and Brown, 2011)]. Despite the identification of ∼180 SLE susceptibility loci (Bentham et al., 2015; Sun et al., 2016; Langefeld et al., 2017; Molineros et al., 2017; Julia et al., 2018; Wang et al., 2018; Tangtanatakul et al., 2020; Ha et al., 2022), these account for only ∼30% of genetic heritability (Yin et al., 2020), indicating that many genes and pathways remain incompletely mapped or even undiscovered.

The statistical power and locus resolution of GWAS and candidate-gene studies can be increased in several ways: larger sample size, deeper genotyping of existing samples, improved imputation and statistical fine-mapping, and most importantly, introducing samples with different allele and haplotype usage than existing samples. Critically, most lupus studies—and most genetic studies in general—are built mainly upon individuals with European (and to a lesser extent Asian) ancestries (Ha et al., 2022). However, many alleles occur at low frequencies in these ethnicities, preventing statistically meaningful measurement of their effects. The use of underrepresented ethnicities with different allelic usage and recombination hotspots is the single most effective way to increase association power (Wojcik et al., 2019). In addition to providing sufficient power to resolve additional ancestry-independent signals, such studies also illuminate ancestry-specific signals (Goulielmos et al., 2018)—wherein a locus is risk only in a specific ethno-genetic background. Such studies are particularly important in autoimmune diseases like lupus, which afflict non-white ethnicities at much higher rates than whites.

Lack of diversity in the available human genomes deposited in public databases limits our understanding of the genetic underpinnings of complex traits, hinders precision medicine, and contributes to health disparities (Popejoy and Fullerton, 2016; Ben-Eghan et al., 2020). Africans and Middle Easterners are among the populations the most underrepresented in genetic association studies, despite comprising over 1.5 billion people and coming from the birthplace of humanity. Moreover, Africa is home to more genetic diversity than the rest of the world combined (Tishkoff et al., 2009) and as such constitutes a woefully underutilized pool for genetic discoveries. Meanwhile, the Middle East and North Africa were both the cradle of civilization and the largest crossroads of migration in the ancient world, bringing together Africa, Europe, and Asia as empires rose and fell. A recent population admixture study (Schuenemann et al., 2017) showed that Ancient Egyptians were more closely related to Middle Easterners and Europeans than to Africans, with African ancestry increasing after the fall of the Roman Empire. In SLE, striking racial/ethnic differences exist in incidence, disease course and clinical manifestations; genetic studies will be helpful to delineate these disparities. However, very few genetic association studies on Egyptians have been performed thus far—and most feature only a few SNPs from a handful of candidate genes (Elghzaly et al., 2015). Therefore, a large-scale genome-wide wide association study is required to understand the SLE genetic landscape in Egyptians—here, we perform the first such study. The major findings of our study are: 1) nine newly uncovered SLE susceptibility loci with one genome-wide significant, 2) replication of several previously known SLE loci, and 3) remarkable similarities in minor allele frequencies at risk loci between Egyptians and Europeans.

2 Materials and methods

2.1 Systemic lupus erythematosus patients and controls

The overview and design of the study is shown in Figure 1. Patients were informed of the nature of the study, and only those who gave their consent were included in the study. SLE was diagnosed according to the 1997 update of the Revised American College of Rheumatology (ACR) classification criteria for SLE (Hochberg, 1997) or the “Systemic Lupus International Collaborating Clinic” (SLICC) Criteria for Classification of Systemic Lupus Erythematosus (Petri et al., 2012). Our rheumatologists used both diagnostic criteria, because we had patients of long disease history with established diagnosis based on earlier criteria that preceded the SLICC standard. Patients who were identified as SLE positive after the introduction of the SLICC criteria were diagnosed accordingly and the two subsets, being included in each center’s registries, were recruited for the study as definite cases of SLE. Note that we lack ACR/SLICC information and some demographic data for 26 SLE patients who have left physicians’ care and are no longer available for verification. Patients’ recruitment, clinical examinations, diagnostic laboratory investigations, and data collection were all performed in their corresponding medical centers by expert rheumatology teams and in specialized university laboratories. Data was collected in an ad hoc unified Excel spreadsheet and reviewed by senior rheumatology experts. Exclusion criteria included the presence of overlap features or the presence of other autoimmune diseases except for Sjögren’s syndrome. The control group was selected from the same geographical location as the corresponding center. All participants provided informed consent, and the study was approved by the Institutional Review Boards from Oklahoma Medical Research Foundation (OMRF), Oklahoma City, United States (IRB approval number 14–16), and Mansoura University Ethics committee/IRB (MFM-IRB code number R/16.04.81). The recruiting hospitals/universities included Cairo University, Mansoura University, Tanta University, Alazhar University–females only, Beni-Suef University, Benha University, Menofia University, Alexandria University, Ain Shams University, South Valley University, and Assuit University.

FIGURE 1

FIGURE 1. Overview and study design.

Initially, 537 Egyptian SLE patients and 434 controls were recruited for the GWAS study, and DNA samples were extracted from 5 ml whole blood samples collected in EDTA-containing tubes and stored at −20°C. DNA extraction for all samples was done in Germany, according to manufacturer’s instructions using the Qiamp DNA Blood Mini Kit (QIAGEN, Hilden, Germany). To increase statistical power, 449 additional unaffected Egyptian controls [already genotyped in Germany for another GWAS study (Bejaoui et al., 2019)] were added to the study. These controls were recruited from the blood banks of Cairo and Mansoura Universities and approved by the IRBs of both Universities (as mentioned above).

2.2 Demographics

Gender and age distributions for SLE cases and controls (after quality control, described below) are shown in Supplementary Table S1. Overall, females constituted 90% and 64% of cases and controls, respectively. Average ages of cases and controls are comparable. The distribution of major ACR criteria within SLE patients are shown in Supplementary Table S1. Almost all (99%) SLE patients were positive for anti-nuclear antibodies (ANA).

2.3 Genotyping and quality control

Initial samples (537 SLE patients and 434 controls) were genotyped on the Illumina Multi-Ethnic Genotyping Array (MEGA) at the genotyping core facility of OMRF, Oklahoma City, United States. The 449 additional out-of-study controls were already genotyped on the Illumina Infinium Global Screening Array. The out-of-study controls have no genotyping data on the X-chromosome; thus we decided to focus only on autosomal loci. We hope to obtain this missing genotyping data for use in future studies on X-linked traits. We performed strict quality control (QC) on genotyped SNPs in each dataset separately (cases and two sets of controls) as follows: (a) we excluded SNPs with bad clusters due to poor genotyping calls, (b) we removed SNPs with missing genotype rate ≥ 0.05, and (c) we filtered out SNPs out of Hardy-Weinberg equilibrium (p < 0.0001) and/or minor allele frequency (MAF) < 0.5%. After QC, 295,981 autosomal (chromosomes 1–22) SNPs that overlapped both genotyping arrays remained for further analysis. Of the 537 cases and 883 controls, the GCTA algorithm (Yang et al., 2011) selected 500 cases and 815 controls as being unrelated from one another; the others were excluded. As a final QC step, 42 cases and 46 controls were removed from the cohort due to being outliers from principal components analysis—leaving 458 cases and 769 controls for this study. The inflation factor λ was calculated from the case-control association test on the final, clean cohort (458 cases and 769 controls).

2.4 Statistical power for detecting association

To estimate the ability of our GWAS cohort to support statistically significant associations, we performed a statistical power calculation. The Genetic Association Study Power Calculator server (Johnson and Abecasis, 2017), derived from the CaTS power calculator for two-stage association studies (Skol et al., 2006), was used. We used our final cohort (458 cases, 769 controls) to assess statistical power, while setting the parameter prevalence for SLE to 0.3% (Wang Y. F. et al., 2021).

2.5 Imputation for GWAS samples

We used the Michigan Imputation Server (Das et al., 2016) for imputation on the 22 autosomes. 1,000 Genomes Project data (Phase 3 Integrated Release Version 5 Haplotypes) was used as the reference panel. After imputation, we performed strict QC on post-imputed SNPs as described above (Hardy-Weinberg equilibrium p > 0.0001 in controls and/or MAF ≥ 0.5%). About 6.36 million post-imputed SNPs with high imputation quality (Rsq > 0.7 for MAF ≥ 3%) were used for downstream analysis.

2.6 Admixture-corrected association analysis with imputed data

To quantify individual ancestry proportion in our Egyptian cohort, we complemented our dataset with published samples from the ancient Near East (Lazaridis et al., 2016) (the historical Fertile Crescent and Levant regions, corresponding to modern-day Egypt, Turkey, Iran, and surrounding nations.) The Near East samples include 294 ancient and 2,068 modern individuals spanning a range of nationalities and ethnicities. In both the Egyptian dataset and Near East public dataset, we excluded SNPs with A/T or C/G alleles and identified ∼33,000 common unrelated SNPs (MAF > 5% and linkage disequilibrium, LD r² < 0.2) for analysis. Principal components analysis was performed with GCTA (Yang et al., 2011), and genetic ancestry composition was estimated with ADMIXTURE (Alexander et al., 2009).

We performed imputation-based association analyses using mach2dat (Li et al., 2009) before and after admixture correction. (A/T and C/G alleles were recovered during imputation.) ADMIXTURE uses a maximum-likelihood approach to determine admixture proportions of individuals by assuming there are K hypothetical underlying populations among them. We selected K = 5 as the best model for the number of ancestral populations and took the first four ancestry proportions as covariates for admixture correction. The inflation factor (λ) was estimated using all admixture-corrected association results from QC-passed SNPs.

We performed multiple-testing correction using permutation (Gao, 2011)—considered the gold standard in GWAS studies. Note that we specifically avoided Bonferroni correction as this assumes independence, which is failed when any SNPs are in linkage disequilibrium—thus producing unacceptably conservative associations (Johnson et al., 2010). Besides, Bonferroni correction suffers several shortcomings, most notably low statistical power for rejecting the null hypothesis (Cohen, 1990, 1994; Perneger, 1998; Nakagawa, 2004) and also performing large amounts of unnecessary testing (Fadista et al., 2016). Critically, permutation analysis controls for low sample-size effects, as all permutations have identical n as the proposal—comparisons are between permutations with identical statistical power a priori to produce an association. Thus, we do not fear spurious associations despite our relatively small sample sizes.

2.7 Replication of previously known variants and loci

To replicate previously reported SLE susceptibility loci, we followed a recent study (Ha et al., 2022) that collated 179 statistically-independent non-human leukocyte antigen (HLA) loci as underlying SLE susceptibility. At each risk locus, we defined a boundary as ± 150 kb around the nominated SNP and collated the full set of SNPs in these intervals. Then, we compared this (enlarged) SNP set with the admixture-corrected association results. Using an ancestry-corrected permutation-based analysis, we set the threshold for replication at P_perm < 0.01—stricter than the more commonly used 0.05 threshold, to better exclude false positive associations.

2.8 SNP functional annotation

Functional interpretation of the associated SNP under any GWAS peak is of critical importance in follow-up analysis. Like most other GWAS studies, many of our identified SNPs concentrate in non-coding DNA, e.g., transcriptional regulatory regions (promoters, enhancers) and non-coding RNA. Therefore, analyzing tissue-specific effects of expression quantitative trait loci (eQTLs) is a promising approach. We used Qtlizer (Munz et al., 2020) for the top SNPs in the novel loci to obtain their eQTLs from several public databases, including GTEx v7 (Consortium, 2013), GEUVADIS (Lappalainen et al., 2013), GRASP (Leslie et al., 2014), Haploreg (Ward and Kellis, 2012), SCAN (Gamazon et al., 2009), seeQTL (Xia et al., 2011), Blood eQTL Browser (Westra et al., 2013), pGWAS (Suhre et al., 2017), ExSNP (Yu et al., 2016), and BRAINEAC (Ramasamy et al., 2014). Qtlizer allowed exploration of QTL data for a given list of variants (indels and SNPs) in a fast and efficient manner by integrating many QTL datasets.

2.9 Annotation of long non-coding RNA targets and microRNAs

Target sites of lncRNAs were predicted using the LncRRIsearch server (http://rtools.cbrc.jp/LncRRIsearch) (Fukunaga et al., 2019), built upon RIblast (Fukunaga and Hamada, 2017), which computationally identifies lncRNA-mRNA interactions through a seed-and-extension approach. For miRNAs, we identified similar sequences in the human genome (GRCh37/hg19) with BLAT on the UCSC Genome Browser (GRCh37/hg19).

3 Results

3.1 Study population and statistical power

After rigorous QC, 458 cases and 769 controls were identified by GCTA (Yang et al., 2011) as unrelated samples and were used for association (Figure 1, Section 2). Due to the small sample size, our study had moderate power (68%) for detection of significant (p < 1 × 10⁻⁵) variants with odds ratio (OR) < 1.5 (Supplementary Figure S1). However, for common (minor allele frequency, MAF ≥ 10%) SNPs with large effect (OR ≥ 1.6), our study had high (up to 90%) power. The inflation factor (λ) between cases and controls was 1.06; the QQ plot is shown in Supplementary Figure S2.

3.2 Genome-wide association of SLE

To discover novel SLE susceptibility loci and to replicate previously known SLE risk loci in our Egyptian cohort, we performed a genome-wide imputation-based association study with 6,382,276 SNPs while adjusting for individual admixture (see Section 2). The Manhattan plot showing -log₁₀ p values for SNP associations for our GWAS is shown in Figure 2.

FIGURE 2

FIGURE 2. Manhattan plot for imputation-based association with individual ancestry correction (green, black, and red gene names represent known, novel suggestive, and novel genome-wide significant loci, respectively).

3.2.1 Discovery of novel SLE loci

We identified 28 SNPs in nine novel loci with P_corrected < 1 × 10⁻⁵ (Table 1; Supplementary Table S2; Figure 3). The most significant signal, which passed genome-wise significance (rs5839171, P_corrected = 1.98 × 10⁻⁸, P_perm = 3.30 × 10⁻⁸), is downstream of the Insulin receptor substrate 1 gene (IRS1) and miR-5702, a microRNA involved in cellular proliferation and cancer risk (Li K. et al., 2019). IRS1 is a signaling adapter protein linking activity of insulin and insulin-like growth factor receptors to intracellular signaling cascades, most notably the PI3K/Akt and Erk MAP kinase pathways (Leslie et al., 2014). IRS1 has been flagged as a risk locus for insulin resistance (Rung et al., 2009) and type II diabetes (Rung et al., 2009; Voight et al., 2010), and also several cancers (Slattery et al., 2004). The best characterized target of miR-5702 is Zinc finger E-box-binding homeobox 1 (ZEB1) (Zhang et al., 2018), a transcription factor critically involved in T-lymphocyte differentiation and interleukin-2 signaling (Guan et al., 2018). At this locus, another nearby (1.7 kb) SNP, rs10804331 (P_corrected = 8.5 × 10⁻⁸), is a significant eQTL for IRS1 in CD4⁺ naïve T-cells (p = 0.002) and CD16⁺ neutrophils (p = 0.01) (Supplementary Table S3). This SNP is also a significant mQTL for methylated CpG cg07514207 in the IRS1 promoter (Chen et al., 2016), potentially modulating transcription.

TABLE 1

TABLE 1. Association (genome-wide significant and suggestive) signals at the lead SNPs with p < 1.0x10⁻⁵.

FIGURE 3

FIGURE 3. (A–I). LocusZoom plot near the IRS1/miR-5702 locus, which reached genome-wide significance, and 8 suggestive loci.

The second signal (rs2265634; P_corrected = 1.14 × 10⁻⁶) is between Diaphanous homolog 1 (DIAPH3), which modulates mTOR signaling and cellular proliferation (Wan et al., 2021), and Tudor domain-containing protein 3 (TDRD3), a chromatin modulator also involved in proliferation (Morettin et al., 2017); it is also close to long non-coding RNA LINC00434. LINC00434 appears to target several genes involved in cancer and autoimmunity (Supplementary Table S4). This SNP is a significant (p = 4.78 × 10⁻⁸) expression quantitative trait locus (eQTL) for DIAPH3, which was a nominally significant (p = 7.23 × 10⁻³) SLE risk locus in a previous GWAS study (Lee et al., 2014).

The third signal (rs6761645; P_corrected = 3.35 × 10⁻⁶) is upstream of Armadillo repeat containing 9 [ARMC9, encoding KU-MEL-1 (Kiniwa et al., 2001)], a ciliary basal body protein, mutations of which underlie the systemic inflammatory disorder Vogt-Koyanagi-Harada disease (VKH) (Ohno et al., 2019) and the neurodevelopmental disorder Joubert syndrome (Van De Weghe et al., 2017). KU-MEL-1, over-expressed in melanocytes, appears to be a primary autoantigen in VKH (Otani et al., 2006) and is also associated with vitiligo and autoimmune uveitis. These top three hits were all located in active chromatin, with rs6761645, in particular, overlapping promoter signals in 17 tissues and enhancers in 12 tissues (Ward and Kellis, 2012); having 14 significant eQTL hits (Supplementary Table S3); and being associated with transcription factor binding sites for Hypoxia-inducible factor 1 (HIF1), a critical regulator of inflammation and the DNA damage response (Dehne and Brüne, 2009; Ramachandran et al., 2021). rs6761645 is an eQTL for PSMD1 (26S proteasome non-ATPase regulatory subunit 1) (Supplementary Table S3), another proliferation marker (Okumura et al., 2017).

The five other novel signals localized to ENTPD3/ENTPD3-AS1 (a tumor suppressor (Li M. et al., 2019) that strongly signals through HIF1 (Wang J. et al., 2021) and its associated antisense RNA), NCK2 [a critical immune adaptor protein linking B-cell receptor activation to PI3K signaling (Cannons et al., 2013)], IFLTD1 [a ciliary organization protein and cancer risk gene (Wang et al., 2005), also known as LMNTD1 and PAS1C1], AGXT2 [a risk locus of premature myocardial infarct (Yuanfeng et al., 2016)], DOCK10 [critical in B-cell activation and proliferation (Yelo et al., 2008)], and the microRNA MIR548AG1.

Given the prominent role played by microRNAs in cancer (Calin and Croce, 2006) and autoimmunity (Xiao and Rajewsky, 2009), and the fact that miR-548ag1 had the largest effect size of our novel loci [odds ratio = 2.45 (95% confidence interval 1.67–3.59)], we closely examined the apparent involvement of this miRNA in SLE. The miR-548 family was first discovered in colorectal expression arrays (Cummins et al., 2006) and was expanded through bioinformatic searches for miRNAs overlapping repetitive elements (Piriyapongsa and Jordan, 2007). Both the microRNA family itself and putative target sites derive from the Made1 repetitive element; these sequences form very stable hairpin structures of ∼37 bp in length. miR-548ag1 was discovered from a B-cell tumor line (Jima et al., 2010), suggesting immune involvement. Many other family members are implicated in cancer (Shi et al., 2015; Ke et al., 2016) and autoimmunity (Yu et al., 2017; Cakmak Genc et al., 2018; Li et al., 2018). To further elucidate the biological roles of miR-548ag1, we searched the human genome for miR-548ag1-like sequences (see Section 2), restricting ourselves to loci exhibiting primate-specific insertions [consistent with other verified miR-548 family members (Piriyapongsa and Jordan, 2007)]. We found closely related sequences in UTRs and introns of ten genes—usually overlapping enhancers and/or transcription factor binding sites (Supplementary Table S5). Strikingly, all ten genes have clear immune involvement (Supplementary Table S5). Five of the ten hits are expressed sequence tags (ESTs) of ∼90 bp–100 bp and almost certainly represent previously unannotated members of the miR-548 family. Of the remaining five, it is not immediately clear which are microRNAs themselves and which are potential miRNA target sites. Together, these results emphasize the immune system relevance of our SLE risk locus miR-548ag1 and related miR-548 family members.

3.2.2 Replication of known SLE loci

We compared our association results with 179 SLE known loci (see Section 2) and found 97 known loci replicated with at least one significantly (P_perm < 0.01) associated SNP. Whenever available, we showed LD with the top published variant to identify independent associations at known loci (Table 2). The most significant SNPs at each locus are listed in Table 2. Among them, prominent (P_perm < 5.0 × 10⁻⁵) signals include ITGAM-ITGAX (rs71391210; P_perm = 2.0 × 10⁻⁷), IRF5 (rs80086012; P_perm = 3.5 × 10⁻⁶), DEF6-PPARD (rs2395623; P_perm = 4.70 × 10⁻⁶), XKR6 (rs2409660; P_perm = 4.55 × 10⁻⁵), IRF1 (rs10586626; P_perm = 5.43 × 10⁻⁵), and TYK2 (rs77389625; P_perm = 1.91 × 10⁻⁴). All replicated (P_perm < 0.01) SNPs and their nearest genes are shown in Table 2.

TABLE 2

TABLE 2. Replication of 97 published loci at P_perm<0.01.

3.2.3 Minor allele frequency of associated SNPs and ethnicity

Allelic abundance—most often reported as minor allele frequency (MAF)—is an important measure both to estimate the effect of given SNPs on specific populations and more broadly to compare ancestry and population stratification between diverse populations. Using the 97 total replicated lead SNPs, we compared MAFs in our Egyptian cohort with those of Africans, Europeans, and Asians. We found a remarkable similarity between the MAFs of Egyptians and Europeans (R² = 93%)—i.e., the MAF variance in Egyptians was well explained by the MAF variance in Europeans. Conversely, correlation with Africans (R² = 51%) and Asians (R² = 59%) was markedly lower (Figure 4). Although surprising prima facie, these results were supported by our admixture analysis (Supplementary Figure S3).

FIGURE 4

FIGURE 4. Comparison of MAFs for the associated SNPs from our Egyptian controls and 1,000 Genomes data.

3.2.4 Functional SNPs from some notable loci

Having identified several new SLE-associated loci, most of which have known roles in both immune homeostasis and other autoimmune disorders, we sought to better establish possible mechanisms for both the loci and the associated SNPs. For our nine novel loci, we first looked for significant (P < 1 × 10⁻⁵) eQTLs for both the top SNPs and the larger set of SNPs in linkage disequilibrium (r² > 0.8) with them. We found several significant (3.5 × 10⁻⁹⁵ < P < 1 × 10⁻²) eQTLs from our new loci, particularly from ARMC9 (43 SNPs; 25 tissues), IFLTD1 (5 SNPs; 4 tissues), DIAPH3 (6 SNPs; 1 tissue), and ENTPD3 (1 SNP; 10 tissues) (Figure 5A). IFLTD1 is enriched in the brain, sex organs, and digestive tract (https://www.proteinatlas.org), and most eQTL signals came from these tissues. ARMC9 is very strongly expressed in natural killer (NK) cells (https://www.proteinatlas.org) and melanocytes (Otani et al., 2006); interestingly, the strongest eQTL signals arose from vascular endothelium and thyroid. Known loci also contained many eQTL SNPs; for example, IRF1 (58 SNPs; 23 tissues) showed particularly strong eQTL signals. IRF1 is quite immune cell-specific, and accordingly, the strongest eQTL signals came from blood (Figure 5B).

FIGURE 5

FIGURE 5. (A,B). eQTL plots for the related SNPs (r² > 0.8 with top SNPs) in two notable loci.

4 Discussion

Our study represents the first lupus GWAS in North Africans, an underrepresented population in human genetic studies. Previous studies indicated that Egyptians are highly admixed, with substantial ancestry arising from the Middle East, Europe, and Africa—with the extent of European and Caucasus hunter-gatherer (Lazaridis et al., 2016) contribution being higher, and the African contribution being lower, than might be expected from geography. Greater European/Middle Eastern than African ancestry is also supported by whole-genome sequencing (ElHefnawi et al., 2021). Importantly, modern Egyptians are quite divergent from ancient populations. The inclusion of more diverse participants in genomics studies has been shown to significantly increase fine-mapping resolution (Zaitlen et al., 2010; Asimit et al., 2016). Further studies in Egyptians (and other North African and Middle Eastern populations) could potentially reveal more genetic associations, with implications for understanding, diagnosing, and treating disease for Egyptians and all humans.

In this study, we identified nine novel loci; one is genome-wide significant for lupus susceptibility and eight are suggestive. Among 179 known non-HLA SLE susceptibility loci, 97 were successfully replicated—indicating that most lupus susceptibility in Egyptians is ancestry-independent. We also found several loci not previously reported in any populations. Intriguingly, the novel loci were substantially enriched in proteins involved in cellular proliferation and cancer risk. Specifically, Insulin receptor substrate 1 (IRS1), the microRNA miR-5702, Diaphanous homolog 1 (DIAPH3), Armadillo repeat containing 9 (ARMC9, a.k.a. KU-MEL-1), Intermediate filament tail domain containing 1 (IFLTD1, a.k.a. LMNTD1), immune adaptor protein NCK2 (Labelle-Côté et al., 2011), and Ectonucleoside triphosphate diphosphohydrolase 3 (ENTPD3) (Wang J. et al., 2021) all have well-documented roles in cellular proliferation and tumor invasion. Proliferation factors play a prominent role in lupus, particularly in invasion of specific organs in sub-clinical phenotypes, most notably lupus nephritis (Balomenos et al., 2000; Treamtrakanpon et al., 2012).

The novel and replicated loci in this study share other features in addition to association with cellular proliferation—for instance, regulation of and by microRNAs, another hallmark of SLE (Dai et al., 2007; Carlsen et al., 2013). Notably, both IRS1 and ARMC9 (along with LRIG2, PSPH, and SKP2) are direct targets down-regulated by miRNA-150 (Zhang et al., 2021). miRNA-150 is a critical immune regulator, for instance driving expression of the inflammatory receptor TREM-1 (Triggering receptor expressed on myeloid cells 1) in dendritic cells and contributing to lupus-like symptoms in mice (Gao et al., 2017). More recently, miRNA-150 (along with miRNA-148b) has been proposed as a specific biomarker for lupus nephritis (Alkhateeb and Altamemi, 2021). Two of our novel risk loci even interact at the protein level: NCK2 directly binds to IRS1 to create an intracellular signal transduction mechanism for signaling through insulin and insulin-like growth factor receptors (Tu et al., 1998; Tu et al., 2001). We performed a sequence search with our novel risk miRNA miR548AG1 and discovered ten closely related sequences, all of them in introns or UTRs of immune-critical genes (Supplementary Table S5) –many overlapping enhancers and/or transcription factor binding sites. Furthermore, five of these ten hits are ESTs of ∼90 bp–100 bp and almost certainly constitute previously unannotated members of the miR-548 family. The clear immune association of all ten closely related sequences further confirms the relevance of our SLE risk locus miR548AG1.

Our study very carefully controlled for admixture in estimating effect size and significance level. This was a critical step, as multiple loci became more significant or less significant once controlled for admixture (Tables 1, 2; Supplementary Table S1). The most substantive change was at the novel locus IRS1/miR-5702, where the top SNP rs5839171 achieved genome-wide significance after admixture correction (P_uncorrected = 1.70 × 10⁻⁷, P_corrected = 1.98 × 10⁻⁸). This remains significant (P_permuted = 3.3 × 10⁻⁸) after 10⁹ permutations. This emphasizes the utility of admixed populations, like Egyptians, in studying novel gene discovery.

To assess the replication of previously known loci [Table 1 from (Ha et al., 2022)], we implemented a very large (10⁷–10⁹ combinations) ancestry-adjusted permutation analysis on our data. Critically, permutation analysis controls for low sample-size effects, as all permutations have identical n as the proposal—comparisons are between permutations with identical statistical power a priori to produce an association. Using the ancestry-corrected permutation-based analysis, we set threshold for replication at p < 0.01. Here we further explain our reasoning behind the robustness of our permutation analysis and some of its findings.

First, a recent large-scale (n > 208,000) SLE GWAS meta-analysis on East-Asian populations identified rs956237 (GWAS lead variant; intron of LEF1, Lymphoid enhancer-binding factor 1—a leukocyte transcription factor) at 4q25 as a novel susceptibility locus (Yin et al., 2020; p < 4.0 × 10⁻¹¹). Reassuringly, a companion transcriptome-wide association study (TWAS) using data from Asian samples also strongly indicated LEF1 as the primary gene underlying 4q25 association (Yin et al., 2020; p < 1.3 × 10⁻¹⁰). However, our primary reference (Table 1 from Ha et al., 2022) nominated rs58107865 instead of rs956237 as lead SNP. In our study, we found that rs58107865 does not exist in the African/Egyptian population. Instead, we discovered an association with another intronic LEF1 SNP (rs10516550, p = 0.008), common in African/European/Hispanic populations (but not Asian). Of course, this SNP needs to be independently replicated in a population with similar ethnic background. But we believe that it’s appropriate to cautiously interpret this in the replication category.

Second, NCF2 is a very well-known SLE locus, where multiple studies (Cunninghame Graham et al., 2011; Yin et al., 2020) including ours (Kim-Howard et al., 2014) have reported non-coding and at least 3 independent ethnicity-specific coding variants within NCF2 passing genome-wide significance in Asian/Hispanic (rs13306575), European/African-American (rs17849502) and African-American (rs35937854) populations. This locus was represented by rs13306575 in our reference data (Table 1 from Ha et al., 2022). In our data, we found evidence of association (ancestry-adjusted permutation p = 0.004) with nearby (< 150 bp) rs17849502. Thus, we have found essentially the same result as the prior studies; however, this real association falls below the strict threshold set by the Bonferroni correction (p = 0.05/179 = 0.00027). Our non-parametric permutation analysis, however, detects this locus as a significant association, which we believe is supported by the preponderance of evidence.

Although the sample sizes in the current study are not overly large, we had the power to attain multiple noteworthy discoveries for several reasons: 1) The highly admixed Egyptian population gave us the power to discover nine novel loci, one of which achieved genome-wide significance. 2) Our data attained a remarkable replication rate (97 out of 179 loci; 54%)—increasing confidence in both known and novel loci, and supporting the notion that despite different genetic backgrounds, underlying loci and risk alleles are broadly conserved across ethnicities (Marigorta and Navarro, 2013). Over 80% of our replicated loci arose from GWASs on Europeans, indicating broad concordance of genetic risk. However, it is important to note that the studies included in Ha et al. (2022) were performed on populations with large sample sizes and statistical power. Egyptians have a stronger contribution from Middle Eastern ancestries than European (Wohlers et al., 2020), and their population history and admixture proportions are different. Moreover, our sample size was relatively small. Future studies will boost sample sizes and further address the other aspects. 3) We found several common pathways shared by the known and new loci, increasing the robustness of the observations, and laying out the foundation for further experimental studies to establish a clear mechanistic basis for these loci contributing to SLE risk and progression.

However, we also acknowledge that lack of an independent replication, especially for the novel associations, is a limitation of this study. Therefore, in addition to strengthening the observations and establishing mechanisms, further studies with larger sample sizes will clearly delineate the ancestry-independent and ancestry-specific components of SLE association.

Institutional Review Board Statement: The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of the Oklahoma Medical Research Foundation (IRB approval number 14–16), and Mansoura University (Egypt) Ethics committee/IRB: MFM-IRB code number R/16.04.81.

Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Materials, further inquiries can be directed to the corresponding authors.

Ethics statement

The studies involving human participants were reviewed and approved by Institutional Review Board of the Oklahoma Medical Research Foundation And Mansoura University Ethics Committee/Institutional Review Board. The patients/participants provided their written informed consent to participate in this study.

Author contributions

Conceptualization, GR and SN; Data curation, AAE, CS, LL and MTH; Formal analysis, CS and SN; Funding acquisition, SI and SN; Investigation, ZT, MA, WA, IE-S, ME, AnA, SH, NS, AH and ENA; Methodology, CS, LL and SN; Resources, NMK, MB, MTH, MAH, MNS, EE, ZT, MA, WA, IE-S, ME, AnA, SH, NS, AS, AmE, AmH, KR, HE-T, AY, FE, AAH, AsA, EmA, DF, SE-B, ME, ENA, and SN; Software, CS, LL, NK, MB and SN; Supervision, NK, SI, GR and SN; Validation, AAE, CS, MAH, MNS and SI; Visualization, AAE, SI, GR and SN; Writing–review and editing, AAE, LL, CS, SE-K, GR and SN. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the University of Lübeck and by the National Institutes of Health, grant numbers MD007909 and AR060366.

Acknowledgments

We thank all patients with SLE and healthy controls who participated in this study. We also thank the research assistants, coordinators, and physicians who helped in the recruitment of subjects for this project.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2022.948505/full#supplementary-material.

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