Large-scale Genetic Association Studies Research Articles

The Neurodegenerative Disease Knowledge Portal: Propelling Discovery Through the Sharing of Neurodegenerative Disease Genomic Resources.

Although large-scale genetic association studies have proven opportunistic for the delineation of neurodegenerative disease processes, we still lack a full understanding of the pathological mechanisms of these diseases, resulting in few appropriate treatment options and diagnostic challenges. To mitigate these gaps, the Neurodegenerative Disease Knowledge Portal (NDKP) was created as an open-science initiative with the aim to aggregate, enable analysis, and display all available genomic datasets of neurodegenerative disease, while protecting the integrity and confidentiality of the underlying datasets. The portal contains 218 genomic datasets, including genotyping and sequencing studies, of individuals across ten different phenotypic groups, including neurological conditions such as Alzheimer's disease, amyotrophic lateral sclerosis, Lewy body dementia, and Parkinson's disease. In addition to securely hosting large genomic datasets, the NDKP provides accessible workflows and tools to effectively utilize the datasets and assist in the facilitation of customized genomic analyses. Here, we summarize the genomic datasets currently included within the portal, the bioinformatics processing of the datasets, and the variety of phenotypes captured. We also present example use-cases of the various user interfaces and integrated analytic tools to demonstrate their extensive utility in enabling the extraction of high-quality results at the source, for both genomics experts and those in other disciplines. Overall, the NDKP promotes open-science and collaboration, maximizing the potential for discovery from the large-scale datasets researchers and consortia are expending immense resources to produce and resulting in reproducible conclusions to improve diagnostic and therapeutic care for neurodegenerative disease patients.

A cost-effective sequencing method for genetic studies combining high-depth whole exome and low-depth whole genome

Whole genome sequencing (WGS) at high-depth (30X) allows the accurate discovery of variants in the coding and non-coding DNA regions and helps elucidate the genetic underpinnings of human health and diseases. Yet, due to the prohibitive cost of high-depth WGS, most large-scale genetic association studies use genotyping arrays or high-depth whole exome sequencing (WES). Here we propose a cost-effective method which we call “Whole Exome Genome Sequencing” (WEGS), that combines low-depth WGS and high-depth WES with up to 8 samples pooled and sequenced simultaneously (multiplexed). We experimentally assess the performance of WEGS with four different depth of coverage and sample multiplexing configurations. We show that the optimal WEGS configurations are 1.7–2.0 times cheaper than standard WES (no-plexing), 1.8–2.1 times cheaper than high-depth WGS, reach similar recall and precision rates in detecting coding variants as WES, and capture more population-specific variants in the rest of the genome that are difficult to recover when using genotype imputation methods. We apply WEGS to 862 patients with peripheral artery disease and show that it directly assesses more known disease-associated variants than a typical genotyping array and thousands of non-imputable variants per disease-associated locus.

Understanding the consequences of leisure sedentary behavior on periodontitis: A two-step, multivariate Mendelian randomization study

BackgroundThe relationship between leisure sedentary behavior (LSB) and periodontitis risk remains unclear in terms of causality and the potential mediating effects of intermediate factors. Materials and methodsUsing the aggregate data of several large-scale genetic association studies from participants of European descent, we conducted a univariate, two-step, and multivariate Mendelian random (MR) analysis to infer the overall effect of LSB on periodontitis, and quantified the intermediary proportion of intermediary traits such as smoking. ResultsOur findings indicated that per 1-SD increase (1.87 h) in leisure screen time (LST), there was a 23 % increase in the risk of periodontitis. [odds ratios (95 % CI) = 1.23 (1.04–1.44), p = 0.013]. Smoking was found to partially mediate the overall causal effect of LST on periodontitis, with a mediation rate of 20.7 % (95 % CI: 4.9%–35.5 %). Multivariate MR analysis demonstrated that the causal effect of LST on periodontitis was weakened when adjusting for smoking, resulting in an odds ratio of 1.19 (95 % CI: 1.01–1.39, p = 0.049) for each 1 standard deviation increase in exposure. ConclusionThe study provides evidence of a potential causal relationship between LSB characterized by LST and periodontitis, thereby further supporting the notion that reducing LSB is beneficial for health. Furthermore, it confirms the role of smoking as a mediator in this process, suggesting that inhibiting smoking behavior among individuals with long-term LSB may serve as a strategy to mitigate the risk of periodontitis.

Excalibur: A new ensemble method based on an optimal combination of aggregation tests for rare-variant association testing for sequencing data.

The development of high-throughput next-generation sequencing technologies and large-scale genetic association studies produced numerous advances in the biostatistics field. Various aggregation tests, i.e. statistical methods that analyze associations of a trait with multiple markers within a genomic region, have produced a variety of novel discoveries. Notwithstanding their usefulness, there is no single test that fits all needs, each suffering from specific drawbacks. Selecting the right aggregation test, while considering an unknown underlying genetic model of the disease, remains an important challenge. Here we propose a new ensemble method, called Excalibur, based on an optimal combination of 36 aggregation tests created after an in-depth study of the limitations of each test and their impact on the quality of result. Our findings demonstrate the ability of our method to control type I error and illustrate that it offers the best average power across all scenarios. The proposed method allows for novel advances in Whole Exome/Genome sequencing association studies, able to handle a wide range of association models, providing researchers with an optimal aggregation analysis for the genetic regions of interest.

High-throughput identification of regulatory elements and functional assays to uncover susceptibility genes for nasopharyngeal carcinoma

Large-scale genetic association studies have identified multiple susceptibility loci for nasopharyngeal carcinoma (NPC), but the underlying biological mechanisms remain to be explored. To gain insights into the genetic etiology of NPC, we conducted a follow-up study encompassing 6,907 cases and 10,472 controls and identified two additional NPC susceptibility loci, 9q22.33 (rs1867277; OR= 0.74, 95% CI= 0.68-0.81, p= 3.08×10-11) and 17q12 (rs226241; OR= 1.42, 95% CI= 1.26-1.60, p= 1.62×10-8). The two additional loci, together with two previously reported genome-wide significant loci, 5p15.33 and 9p21.3, were investigated by high-throughput sequencing for chromatin accessibility, histone modification, and promoter capture Hi-C (PCHi-C) profiling. Using luciferase reporter assays and CRISPR interference (CRISPRi) to validate the functional profiling, we identified PHF2 at locus 9q22.33 as a susceptibility gene. PHF2 encodes a histone demethylase and acts as a tumor suppressor. The risk alleles of the functional SNPs reduced the expression of the target gene PHF2 by inhibiting the enhancer activity of its long-range (4.3 Mb) cis-regulatory element, which promoted proliferation of NPC cells. In addition, we identified CDKN2B-AS1 as a susceptibility gene at locus 9p21.3, and the NPC risk allele of the functional SNP rs2069418 promoted the expression of CDKN2B-AS1 by increasing its enhancer activity. The overexpression of CDKN2B-AS1 facilitated proliferation of NPC cells. In summary, we identified functional SNPs and NPC susceptibility genes, which provides additional explanations for the genetic association signals and helps to uncover the underlying genetic etiology of NPC development.

Human genetic basis of severe or critical illness in COVID-19.

Coronavirus Disease 2019 (COVID-19) caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to considerable morbidity and mortality worldwide. The clinical manifestation of COVID-19 ranges from asymptomatic or mild infection to severe or critical illness, such as respiratory failure, multi-organ dysfunction or even death. Large-scale genetic association studies have indicated that genetic variations affecting SARS-CoV-2 receptors (angiotensin-converting enzymes, transmembrane serine protease-2) and immune components (Interferons, Interleukins, Toll-like receptors and Human leukocyte antigen) are critical host determinants related to the severity of COVID-19. Genetic background, such as 3p21.31 and 9q34.2 loci were also identified to influence outcomes of COVID-19. In this review, we aimed to summarize the current literature focusing on human genetic factors that may contribute to the observed diversified severity of COVID-19. Enhanced understanding of host genetic factors and viral interactions of SARS-CoV-2 could provide scientific bases for personalized preventive measures and precision medicine strategies.

Genome-wide association study of leprosy in Malawi and Mali.

Leprosy is a chronic infection of the skin and peripheral nerves caused by Mycobacterium leprae. Despite recent improvements in disease control, leprosy remains an important cause of infectious disability globally. Large-scale genetic association studies in Chinese, Vietnamese and Indian populations have identified over 30 susceptibility loci for leprosy. There is a significant burden of leprosy in Africa, however it is uncertain whether the findings of published genetic association studies are generalizable to African populations. To address this, we conducted a genome-wide association study (GWAS) of leprosy in Malawian (327 cases, 436 controls) and Malian (247 cases, 368 controls) individuals. In that analysis, we replicated four risk loci previously reported in China, Vietnam and India; MHC Class I and II, LACC1 and SLC29A3. We further identified a novel leprosy susceptibility locus at 10q24 (rs2015583; combined p = 8.81 × 10-9; OR = 0.51 [95% CI 0.40 - 0.64]). Using publicly-available data we characterise regulatory activity at this locus, identifying ACTR1A as a candidate mediator of leprosy risk. This locus shows evidence of recent positive selection and demonstrates pleiotropy with established risk loci for inflammatory bowel disease and childhood-onset asthma. A shared genetic architecture for leprosy and inflammatory bowel disease has been previously described. We expand on this, strengthening the hypothesis that selection pressure driven by leprosy has shaped the evolution of autoimmune and atopic disease in modern populations. More broadly, our data highlights the importance of defining the genetic architecture of disease across genetically diverse populations, and that disease insights derived from GWAS in one population may not translate to all affected populations.

The Role of Klotho G395A Gene Polymorphism in Atherosclerotic Cardiovascular Disease and Mortality Risk Scores in Non-dialysis Chronic Kidney Disease

BACKGROUND: Cardiovascular disease (CVD) is the leading cause of death in patients with chronic kidney disease (CKD). Klotho expression was reduced in patients with CKD, leading to vascular calcification, endothelial dysfunction, and atherosclerosis. We investigated the role of the klotho G395A gene polymorphism and plasma klotho level in the ten-year risk of atherosclerotic cardiovascular disease (ASCVD) and CVD mortality in CKD patients.METHODS: We used the PCR-CTPP assay method to genotype klotho G395A single nucleotide polymorphism (SNP) in 72 non-dialysis CKD patients. The klotho level was determined using the enzyme-linked immunoassay (ELISA) method. Path analysis was used to determine the relationship between the klotho G395A SNP, plasma klotho level, ASCVD risk score, and CVD mortality risk score.RESULTS: Our results showed that the GA genotype had lower plasma klotho levels than the GG genotype (path coefficient=-0.185, p=0.000). There was a significant negative correlation between plasma klotho level and the ASCVD risk score (r=-0.243, p=0.040), but no significant correlation was found between plasma klotho level and the CVD mortality risk score (r=-0.145, p=0.225). Path analysis showed that plasma klotho level had a significant negative direct effect on ASCVD risk score (path coefficient=-0.272, p=0.000) and an indirect effect on CVD mortality risk score (path coefficient=0.187, p=0.005).CONCLUSION: Klotho G395A SNP might reduce lower plasma klotho levels, which increased ASCVD and CVD mortality risk scores in non-dialysis CKD patients. However, other risk factors such as age, CKD stages, hypertension, and smoking should be taken into consideration. Therefore, large-scale genetic association studies with adjusted variables could be conducted in various ethnic groups for a more robust result.KEYWORDS: klotho, single nucleotide polymorphism, cardiovascular disease, chronic kidney disease

Aggregative trans-eQTL analysis detects trait-specific target gene sets in whole blood

Large scale genetic association studies have identified many trait-associated variants and understanding the role of these variants in the downstream regulation of gene-expressions can uncover important mediating biological mechanisms. Here we propose ARCHIE, a summary statistic based sparse canonical correlation analysis method to identify sets of gene-expressions trans-regulated by sets of known trait-related genetic variants. Simulation studies show that compared to standard methods, ARCHIE is better suited to identify “core”-like genes through which effects of many other genes may be mediated and can capture disease-specific patterns of genetic associations. By applying ARCHIE to publicly available summary statistics from the eQTLGen consortium, we identify gene sets which have significant evidence of trans-association with groups of known genetic variants across 29 complex traits. Around half (50.7%) of the selected genes do not have any strong trans-associations and are not detected by standard methods. We provide further evidence for causal basis of the target genes through a series of follow-up analyses. These results show ARCHIE is a powerful tool for identifying sets of genes whose trans-regulation may be related to specific complex traits.

The human genetic epidemiology of COVID-19.

Human genetics can inform the biology and epidemiology of coronavirus disease 2019 (COVID-19) by pinpointing causal mechanisms that explain why some individuals become more severely affected by the disease upon infection by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Large-scale genetic association studies, encompassing both rare and common genetic variants, have used different study designs and multiple disease phenotype definitions to identify several genomic regions associated with COVID-19. Along with a multitude of follow-up studies, these findings have increased our understanding of disease aetiology and provided routes for management of COVID-19. Important emergent opportunities include the clinical translatability of genetic risk prediction, the repurposing of existing drugs, exploration of variable host effects of different viral strains, study of inter-individual variability in vaccination response and understanding the long-term consequences of SARS-CoV-2 infection. Beyond the current pandemic, these transferrable opportunities are likely to affect the study of many infectious diseases.

Functional coding haplotypes and machine-learning feature elimination identifies predictors of Methotrexate Response in Rheumatoid Arthritis patients.

SummaryBackgroundMajor challenges in large scale genetic association studies include not only the identification of causative single nucleotide polymorphisms (SNPs), but also accounting for SNP-SNP interactions. This study thus proposes a novel feature engineering approach integrating potentially functional coding haplotypes (pfcHap) with machine-learning (ML) feature selection to identify biologically meaningful, possibly causative genetic factors, that take into consideration potential SNP-SNP interactions within the pfcHap, to best predict for methotrexate (MTX) response in rheumatoid arthritis (RA) patients.MethodsExome sequencing from 349 RA patients were analysed, of which they were split into training and unseen test set. Inferred pfcHaps were combined with 30 non-genetic features to undergo ML recursive feature elimination with cross-validation using the training set. Predictive capacity and robustness of the selected features were assessed using six popular machine learning models through a train set cross-validation and evaluated in an unseen test set.FindingsSignificantly, 100 features (95 pfcHaps, 5 non-genetic factors) were identified to have good predictive performance (AUC: 0.776-0.828; Sensitivity: 0.656-0.813; Specificity: 0.684-0.868) across all six ML models in an unseen test dataset for the prediction of MTX response in RA patients.InterpretationMajority of the predictive pfcHap SNPs were predicted to be potentially functional and some of the genes in which the pfcHap resides in were identified to be associated with previously reported MTX/RA pathways.FundingSingapore Ministry of Health's National Medical Research Council (NMRC) [NMRC/CBRG/0095/2015; CG12Aug17; CGAug16M012; NMRC/CG/017/2013]; National Cancer Center Research Fund and block funding Duke-NUS Medical School.; Singapore Ministry of Education Academic Research Fund Tier 2 grant MOE2019-T2-1-138.

GTQC: Automated Genotyping Array Quality Control and Report.

Genotyping array is the most economical approach for conducting large-scale genome-wide genetic association studies. Thorough quality control is key to generating high integrity genotyping data and robust results. Quality control of genotyping array is generally a complicated process, as it requires intensive manual labor in implementing the established protocols and curating a comprehensive quality report. There is an urgent need to reduce manual intervention via an automated quality control process. Based on previously established protocols and strategies, we developed an R package GTQC (GenoTyping Quality Control) to automate a majority of the quality control steps for general array genotyping data. GTQC covers a comprehensive spectrum of genotype data quality metrics and produces a detailed HTML report comprising tables and figures. Here, we describe the concepts underpinning GTQC and demonstrate its effectiveness using a real genotyping dataset. R package GTQC streamlines a majority of the quality control steps and produces a detailed HTML report on a plethora of quality control metrics, thus enabling a swift and rigorous data quality inspection prior to downstream GWAS and related analyses. By significantly cutting down on the time on genotyping quality control procedures, GTQC ensures maximum utilization of available resources and minimizes waste and inefficient allocation of manual efforts. GTQC tool can be accessed at https://github.com/slzhao/GTQC.

Analysing an allelic series of rare missense variants of CACNA1I in a Swedish schizophrenia cohort.

CACNA1I is implicated in the susceptibility to schizophrenia by large-scale genetic association studies of single nucleotide polymorphisms. However, the channelopathy of CACNA1I in schizophrenia is unknown. CACNA1I encodes CaV3.3, a neuronal voltage-gated calcium channel that underlies a subtype of T-type current that is important for neuronal excitability in the thalamic reticular nucleus and other regions of the brain. Here, we present an extensive functional characterization of 57 naturally occurring rare and common missense variants of CACNA1I derived from a Swedish schizophrenia cohort of more than 10 000 individuals. Our analysis of this allelic series of coding CACNA1I variants revealed that reduced CaV3.3 channel current density was the dominant phenotype associated with rare CACNA1I coding alleles derived from control subjects, whereas rare CACNA1I alleles from schizophrenia patients encoded CaV3.3 channels with altered responses to voltages. CACNA1I variants associated with altered current density primarily impact the ionic channel pore and those associated with altered responses to voltage impact the voltage-sensing domain. CaV3.3 variants associated with altered voltage dependence of the CaV3.3 channel and those associated with peak current density deficits were significantly segregated across affected and unaffected groups (Fisher’s exact test, P = 0.034). Our results, together with recent data from the SCHEMA (Schizophrenia Exome Sequencing Meta-Analysis) cohort, suggest that reduced CaV3.3 function may protect against schizophrenia risk in rare cases. We subsequently modelled the effect of the biophysical properties of CaV3.3 channel variants on thalamic reticular nucleus excitability and found that compared with common variants, ultrarare CaV3.3-coding variants derived from control subjects significantly decreased thalamic reticular nucleus excitability (P = 0.011). When all rare variants were analysed, there was a non-significant trend between variants that reduced thalamic reticular nucleus excitability and variants that either had no effect or increased thalamic reticular nucleus excitability across disease status. Taken together, the results of our functional analysis of an allelic series of >50 CACNA1I variants in a schizophrenia cohort reveal that loss of function of CaV3.3 is a molecular phenotype associated with reduced disease risk burden, and our approach may serve as a template strategy for channelopathies in polygenic disorders.

Genetic association tests in family samples for multi-category phenotypes

BackgroundAdvancements in statistical methods and sequencing technology have led to numerous novel discoveries in human genetics in the past two decades. Among phenotypes of interest, most attention has been given to studying genetic associations with continuous or binary traits. Efficient statistical methods have been proposed and are available for both types of traits under different study designs. However, for multinomial categorical traits in related samples, there is a lack of efficient statistical methods and software.ResultsWe propose an efficient score test to analyze a multinomial trait in family samples, in the context of genome-wide association/sequencing studies. An alternative Wald statistic is also proposed. We also extend the methodology to be applicable to ordinal traits. We performed extensive simulation studies to evaluate the type-I error of the score test, Wald test compared to the multinomial logistic regression for unrelated samples, under different allele frequency and study designs. We also evaluate the power of these methods. Results show that both the score and Wald tests have a well-controlled type-I error rate, but the multinomial logistic regression has an inflated type-I error rate when applied to family samples. We illustrated the application of the score test with an application to the Framingham Heart Study to uncover genetic variants associated with diabesity, a multi-category phenotype.ConclusionBoth proposed tests have correct type-I error rate and similar power. However, because the Wald statistics rely on computer-intensive estimation, it is less efficient than the score test in terms of applications to large-scale genetic association studies. We provide computer implementation for both multinomial and ordinal traits.

Common genetic variants on chromosome 9p21 confers risk of ischemic stroke: A large-scale genetic association study.

The objective of this experiment was to observe the allele frequency and genotype distribution of some 9p21 SNPs in the Anhui Han population, and to study its relationship with the susceptibility to ischemic stroke. For this purpose, a collection of 992 patients with ischemic stroke confirmed and hospitalized in our hospital from October 2017 to October 2020 were used as the IS case group, and 951 normal people who had a healthy physical examination in the physical examination center of our hospital during the same period were selected as the control group. After informed consent, cubital venous blood of all subjects was collected, and epidemiological data of the subjects were collected; the rs2383206, rs2383207, rs10757274, and rs1333049 on chromosome 9p21 as the sites to be tested, using Sequenom Mass Array system for genotyping, using Haploview4.2 software to calculate whether the genotype distribution meets Hardy-Weinberg equilibrium. Results showed that there were no significant differences between the two groups in gender, age, and smoking history. There are significant differences in the levels of hypertension, diabetes and hyperlipidemia, total cholesterol, triglycerides, HDL-C and apolipoprotein A1 between the two groups of study subjects. The genotype frequencies of the participating populations were in a balanced state. The results of the association analysis between SNPs and IS susceptibility showed that rs2383207, rs10757274, rs1333049 and rs2383206 are the susceptibility sites of ischemic stroke. It concluded that in Anhui, China, the inheritance of chromosome 9p21 region is associated with ischemic stroke.

Using de novo assembly to identify structural variation of eight complex immune system gene regions.

Driven by the necessity to survive environmental pathogens, the human immune system has evolved exceptional diversity and plasticity, to which several factors contribute including inheritable structural polymorphism of the underlying genes. Characterizing this variation is challenging due to the complexity of these loci, which contain extensive regions of paralogy, segmental duplication and high copy-number repeats, but recent progress in long-read sequencing and optical mapping techniques suggests this problem may now be tractable. Here we assess this by using long-read sequencing platforms from PacBio and Oxford Nanopore, supplemented with short-read sequencing and Bionano optical mapping, to sequence DNA extracted from CD14+ monocytes and peripheral blood mononuclear cells from a single European individual identified as HV31. We use this data to build a de novo assembly of eight genomic regions encoding four key components of the immune system, namely the human leukocyte antigen, immunoglobulins, T cell receptors, and killer-cell immunoglobulin-like receptors. Validation of our assembly using k-mer based and alignment approaches suggests that it has high accuracy, with estimated base-level error rates below 1 in 10 kb, although we identify a small number of remaining structural errors. We use the assembly to identify heterozygous and homozygous structural variation in comparison to GRCh38. Despite analyzing only a single individual, we find multiple large structural variants affecting core genes at all three immunoglobulin regions and at two of the three T cell receptor regions. Several of these variants are not accurately callable using current algorithms, implying that further methodological improvements are needed. Our results demonstrate that assessing haplotype variation in these regions is possible given sufficiently accurate long-read and associated data. Continued reductions in the cost of these technologies will enable application of these methods to larger samples and provide a broader catalogue of germline structural variation at these loci, an important step toward making these regions accessible to large-scale genetic association studies.

Cohort profile: DOLORisk Dundee: a longitudinal study of chronic neuropathic pain

PurposeNeuropathic pain is a common disorder of the somatosensory system that affects 7%–10% of the general population. The disorder places a large social and economic burden on patients as well...

Genome-Wide Association Studies of CKD and Related Traits.

The past few years have seen major advances in genome-wide association studies (GWAS) of CKD and kidney function-related traits in several areas: increases in sample size from >100,000 to >1 million, enabling the discovery of >250 associated genetic loci that are highly reproducible; the inclusion of participants not only of European but also of non-European ancestries; and the use of advanced computational methods to integrate additional genomic and other unbiased, high-dimensional data to characterize the underlying genetic architecture and prioritize potentially causal genes and variants. Together with other large-scale biobank and genetic association studies of complex traits, these GWAS of kidney function-related traits have also provided novel insight into the relationship of kidney function to other diseases with respect to their genetic associations, genetic correlation, and directional relationships. A number of studies also included functional experiments using model organisms or cell lines to validate prioritized potentially causal genes and/or variants. In this review article, we will summarize these recent GWAS of CKD and kidney function-related traits, explain approaches for downstream characterization of associated genetic loci and the value of such computational follow-up analyses, and discuss related challenges along with potential solutions to ultimately enable improved treatment and prevention of kidney diseases through genetics.

Association study between X-linked susceptibility genes and clinical features in Chinese female patients with systemic lupus erythematosus

Recent large-scale genetic association studies have identified that several single nucleotide polymorphisms (SNPs) on X chromosome are correlated with risk of systemic lupus erythematosus (SLE) in Chinese population. The aim of this study was to estimate association between these loci and clinical features in female patients with SLE. Six SNPs identified in previous studies were genotyped. Odds ratio (OR) was calculated with adjusting for potential confounding factors. A total of 772 SLE patients were included in the final analysis. The data showed that 3 SNPs (rs5914778, rs3853839 and rs1059702) were marginally associated with several clinical subphenotypes. Furthermore, consistent associations were also found in two independent cohorts. However, the cumulative genetic risk score (GRS) was not associated with clinical manifestations as well as the disease activity index at disease diagnosis. In summary, genetic variants in X-linked genes may be potentially associated with presence of specific clinical feature. Further studies in distinct ethnical populations are required to clarify the association between these loci and presence of SLE clinical features.

Update on the Genetics of Systemic Lupus Erythematosus: Genome-Wide Association Studies and Beyond.

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease of complex etiology that primarily affects women of childbearing age. The development of SLE is attributed to the breach of immunological tolerance and the interaction between SLE-susceptibility genes and various environmental factors, resulting in the production of pathogenic autoantibodies. Working in concert with the innate and adaptive arms of the immune system, lupus-related autoantibodies mediate immune-complex deposition in various tissues and organs, leading to acute and chronic inflammation and consequent end-organ damage. Over the past two decades or so, the impact of genetic susceptibility on the development of SLE has been well demonstrated in a number of large-scale genetic association studies which have uncovered a large fraction of genetic heritability of SLE by recognizing about a hundred SLE-susceptibility loci. Integration of genetic variant data with various omics data such as transcriptomic and epigenomic data potentially provides a unique opportunity to further understand the roles of SLE risk variants in regulating the molecular phenotypes by various disease-relevant cell types and in shaping the immune systems with high inter-individual variances in disease susceptibility. In this review, the catalogue of SLE susceptibility loci will be updated, and biological signatures implicated by the SLE-risk variants will be critically discussed. It is optimistically hoped that identification of SLE risk variants will enable the prognostic and therapeutic biomarker armamentarium of SLE to be strengthened, a major leap towards precision medicine in the management of the condition.