Genotypic variability-based genome-wide association study identifies non-additive loci HLA-C and IL12B for psoriasis

Detecting genetic effects on phenotype variability to capture gene-by-environment interactions: a systematic method comparison

Genetically associated phenotypic variability has been widely observed across organisms and traits, including in humans. Both gene-gene and gene-environment interactions can lead to an increase in genetically associated phenotypic variability. Therefore, detecting the underlying genetic variants, or variance Quantitative Trait Loci (vQTLs), can provide novel insights into complex traits. Established approaches to detect vQTLs apply different methodologies from variance-only approaches to mean-variance joint tests, but a comprehensive comparison of these methods is lacking. Here, we review available methods to detect vQTLs in humans, carry out a simulation study to assess their performance under different biological scenarios of gene-environment interactions, and apply the optimal approaches for vQTL identification to gene expression data. Overall, with a minor allele frequency (MAF) of less than 0.2, the squared residual value linear model (SVLM) and the deviation regression model (DRM) are optimal when the data follow normal and non-normal distributions, respectively. In addition, the Brown-Forsythe (BF) test is one of the optimal methods when the MAF is 0.2 or larger, irrespective of phenotype distribution. Additionally, a larger sample size and more balanced sample distribution in different exposure categories increase the power of BF, SVLM, and DRM. Our results highlight vQTL detection methods that perform optimally under realistic simulation settings and show that their relative performance depends on the phenotype distribution, allele frequency, sample size, and the type of exposure in the interaction model underlying the vQTL.

Disentangling the complexity of psoriasis in the post-genome-wide association era

In recent years, genome-wide association studies (GWAS) have been instrumental in unraveling the genetic architecture of complex diseases, including psoriasis. The application of large-scale GWA studies in psoriasis has illustrated several associated loci that participate in the cutaneous inflammation, however explaining a fraction of the disease heritability. With the advent of high-throughput sequencing technologies and functional genomics approaches, the post-GWAS era aims to unravel the functional mechanisms underlying the inter-individual variability in psoriasis patients. In this review, we present the key advances of psoriasis GWAS in under-represented populations, rare, non-coding and structural variants and epistatic phenomena that orchestrate the interplay between different cell types. We further review the gene-gene and gene-environment interactions contributing to the disease predisposition and development of comorbidities through Mendelian randomization studies and pleiotropic effects of psoriasis-associated loci. We finally examine the holistic approaches conducted in psoriasis through system genetics and state-of-the-art transcriptomic analyses, discussing their potential implication in the expanding field of precision medicine and characterization of comorbidities.

Association of Polymorphisms of Metabolism-Related Genes with Psoriasis Vulgaris in Han Chinese

Aim

Psoriasis is a chronic inflammatory disease with a complex etiology, and psoriasis vulgaris (PsV) is the most common type of psoriasis. Recent studies suggest the relationship between psoriasis and metabolic syndrome in different ethnicities. This study is aimed at evaluating the association of metabolism-related gene variants with the risk of PsV in Chinese Han population. Material and Methods. PsV patients (1030) and healthy controls (965) were enrolled in this study. Eighteen single-nucleotide polymorphisms (SNPs) previously reported to be significantly associated with metabolic syndrome were selected. SNPs were detected by next-generation sequencing.

Results

Seven SNPs were significantly associated with PsV: rs805303 (P = 0.012, OR = 0.85), rs3177928 (P = 1.37 × 10⁻¹⁵, OR = 2.51), and rs2247056 (P = 3.73 × 10⁻⁴, OR = 0.67) located in the HLA gene region; rs1047781 (P = 0.012, OR = 1.18), rs281379 (P = 0.014, OR = 1.71), and rs492602 (P = 0.005, OR = 1.86) located in the FUT2 region; and rs2303138 (P = 0.014, OR = 1.18) located in the LNPEP region. After stratified analysis, rs805303 (P = 0.017, OR = 0.74) and rs2303138 (P = 0.041, OR = 1.30) were associated with PsVs when HLA-C^∗06 : 02 was positive, and rs805303 (P = 5.62 × 10⁻⁵, OR = 0.68), rs3177928 (P = 0.003, OR = 1.75), rs281379 (P = 0.034, OR = 1.96), and rs492602 (P = 0.025, OR = 2.04) were associated with PsVs when HLA-C^∗06 : 02 was negative.

Conclusion

PsV and metabolic syndrome may have overlapped susceptible genes in Chinese Han population.

Role of the IL-23 pathway in the pathogenesis and treatment of enthesitis in psoriatic arthritis

INTRODUCTION

Enthesitis is a key feature of spondyloarthritis (SpA). Several studies have underlined the role of interleukin (IL)-23 in SpA development as a crucial cytokine in the pathogenesis of enthesitis.

AREA COVERED

This review summarizes recent evidence of the role of IL-23 in the pathogenesis of and as a target of the treatment of enthesitis. We review the definition, diagnosis and clinical impact of enthesitis and its connection with microbial infections, gut dysbiosis, and mechanical stress. We also review clinical trials and real-life studies of drugs targeting the p19 or p40 subunits of IL-23.

EXPERT OPINION

Novel therapies targeting the p19 or p40 subunit of IL-23 appear to be promising treatment options for patients with enthesitis. Although we are currently unable to identify the best therapeutic window to target IL-23 in SpA disease evolution, the promising ability of this therapy to control the gut-entheseal axis is increasing our knowledge of SpA pathogenesis.

Combining Understanding of Immunological Mechanisms and Genetic Variants Toward Development of Personalized Medicine for Psoriasis Patients

Psoriasis is multifactorial disease with complex genetic predisposition. Recent advances in genetics and genomics analyses have provided many insights into the relationship between specific genetic predisposition and the immunopathological mechanisms driving psoriasis manifestation. Novel approaches which utilize array-based genotyping technologies such as genome-wide association studies and bioinformatics tools for transcriptomics analysis have identified single nucleotide polymorphisms, genes and pathways that are associated with psoriasis. The discovery of these psoriasis-associated susceptibility loci, autoimmune targets and altered signaling pathways have provided opportunities to bridge the gap of knowledge from sequence to consequence, allowing new therapeutic strategies for the treatment of psoriasis to be developed. Here, we discuss recent advances in the field by highlighting how immune functions associated with psoriasis susceptibility loci may contribute to disease pathogenesis in different populations. Understanding the genetic variations in psoriasis and how these may influence the immunological pathways to cause disease will contribute to the efforts in developing novel and targeted personalized therapies for psoriasis patients.

Crop genome-wide association study: a harvest of biological relevance

With the advent of rapid genotyping and next-generation sequencing technologies, genome-wide association study (GWAS) has become a routine strategy for decoding genotype-phenotype associations in many species. More than 1000 such studies over the last decade have revealed substantial genotype-phenotype associations in crops and provided unparalleled opportunities to probe functional genomics. Beyond the many 'hits' obtained, this review summarizes recent efforts to increase our understanding of the genetic architecture of complex traits by focusing on non-main effects including epistasis, pleiotropy, and phenotypic plasticity. We also discuss how these achievements and the remaining gaps in our knowledge will guide future studies. Synthetic association is highlighted as leading to false causality, which is prevalent but largely underestimated. Furthermore, validation evidence is appealing for future GWAS, especially in the context of emerging genome-editing technologies.

Discovering Genetic Factors for psoriasis through exhaustively searching for significant second order SNP-SNP interactions

In this paper, we aim at discovering genetic factors of psoriasis through searching for statistically significant SNP-SNP interactions exhaustively from two real psoriasis genome-wide association study datasets (phs000019.v1.p1 and phs000982.v1.p1) downloaded from the database of Genotypes and Phenotypes. To deal with the enormous search space, our search algorithm is accelerated with eight biological plausible interaction patterns and a pre-computed look-up table. After our search, we have discovered several SNPs having a stronger association to psoriasis when they are in combination with another SNP and these combinations may be non-linear interactions. Among the top 20 SNP-SNP interactions being found in terms of pairwise p-value and improvement metric value, we have discovered 27 novel potential psoriasis-associated SNPs where most of them are reported to be eQTLs of a number of known psoriasis-associated genes. On the other hand, we have inferred a gene network after selecting the top 10000 SNP-SNP interactions in terms of improvement metric value and we have discovered a novel long distance interaction between XXbac-BPG154L12.4 and RNU6-283P which is not a long distance haplotype and may be a new discovery. Finally, our experiments with the synthetic datasets have shown that our pre-computed look-up table technique can significantly speed up the search process.