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

RNA-Based Antipsoriatic Gene Therapy: An Updated Review Focusing on Evidence from Animal Models

Psoriasis presents as a complex genetic skin disorder, characterized by the interaction between infiltrated immune cells and keratinocytes. Substantial progress has been made in understanding the molecular mechanisms of both coding and non-coding genes, which has positively impacted clinical treatment approaches. Despite extensive research into the genetic aspects of psoriasis pathogenesis, fully grasping its epigenetic component remains a challenging endeavor. In response to the pressing demand for innovative treatments to alleviate inflammatory skin disorders, various novel strategies are under consideration. These include gene therapy employing antisense nucleotides, silencing RNA complexes, stem cell therapy, and antibody-based therapy. There is a pressing requirement for a psoriasis-like animal model that replicates human psoriasis to facilitate early preclinical evaluations of these novel treatments. The authors conduct a comprehensive review of various gene therapy in different psoriasis-like animal models utilized in psoriasis research. The animals included in the list underwent skin treatments such as imiquimod application, as well as genetic and biologic injections, and the results of these interventions are detailed. Animal models play a crucial role in translating drug discoveries from the laboratory to clinical practice, and these models aid in improving the reproducibility and clinical applicability of preclinical data. Numerous animal models with characteristics similar to those of human psoriasis have proven to be useful in understanding the development of psoriasis. In this review, the article focuses on RNA-based gene therapy exploration in different types of psoriasis-like animal models to improve the treatment of psoriasis.

Multi-trait analysis characterizes the genetics of thyroid function and identifies causal associations with clinical implications

To date only a fraction of the genetic footprint of thyroid function has been clarified. We report a genome-wide association study meta-analysis of thyroid function in up to 271,040 individuals of European ancestry, including reference range thyrotropin (TSH), free thyroxine (FT4), free and total triiodothyronine (T3), proxies for metabolism (T3/FT4 ratio) as well as dichotomized high and low TSH levels. We revealed 259 independent significant associations for TSH (61% novel), 85 for FT4 (67% novel), and 62 novel signals for the T3 related traits. The loci explained 14.1%, 6.0%, 9.5% and 1.1% of the total variation in TSH, FT4, total T3 and free T3 concentrations, respectively. Genetic correlations indicate that TSH associated loci reflect the thyroid function determined by free T3, whereas the FT4 associations represent the thyroid hormone metabolism. Polygenic risk score and Mendelian randomization analyses showed the effects of genetically determined variation in thyroid function on various clinical outcomes, including cardiovascular risk factors and diseases, autoimmune diseases, and cancer. In conclusion, our results improve the understanding of thyroid hormone physiology and highlight the pleiotropic effects of thyroid function on various diseases.

A multi-threaded approach to genotype pattern mining for detecting digenic disease genes

To locate disease-causing DNA variants on the human gene map, the customary approach has been to carry out a genome-wide association study for one variant after another by testing for genotype frequency differences between individuals affected and unaffected with disease. So-called digenic traits are due to the combined effects of two variants, often on different chromosomes, while individual variants may have little or no effect on disease. Machine learning approaches have been developed to find variant pairs underlying digenic traits. However, many of these methods have large memory requirements so that only small datasets can be analyzed. The increasing availability of desktop computers with large numbers of processors and suitable programming to distribute the workload evenly over all processors in a machine make a new and relatively straightforward approach possible, that is, to evaluate all existing variant and genotype pairs for disease association. We present a prototype of such a method with two components, Vpairs and Gpairs, and demonstrate its advantages over existing implementations of such well-known algorithms as Apriori and FP-growth. We apply these methods to published case-control datasets on age-related macular degeneration and Parkinson disease and construct an ROC curve for a large set of genotype patterns.

Matrix Metalloproteinases and Glaucoma

Matrix metalloproteinases (MMPs) are enzymes that decompose extracellular matrix (ECM) proteins. MMPs are thought to play important roles in cellular processes, such as cell proliferation, differentiation, angiogenesis, migration, apoptosis, and host defense. MMPs are distributed in almost all intraocular tissues and are involved in physiological and pathological mechanisms of the eye. MMPs are also associated with glaucoma, a progressive neurodegenerative disease of the eyes. MMP activity affects intraocular pressure control and apoptosis of retinal ganglion cells, which are the pathological mechanisms of glaucoma. It also affects the risk of glaucoma development based on genetic pleomorphism. In addition, MMPs may affect the treatment outcomes of glaucoma, including the success rate of surgical treatment and side effects on the ocular surface due to glaucoma medications. This review discusses the various relationships between MMP and glaucoma.

Break on through: The role of innate immunity and barrier defence in atopic dermatitis and psoriasis

The human skin can be affected by a multitude of diseases including inflammatory conditions such as atopic dermatitis and psoriasis. Here, we describe how skin barrier integrity and immunity become dysregulated during these two most common inflammatory skin conditions. We summarise recent advances made in the field of the skin innate immune system and its interaction with adaptive immunity. We review gene variants associated with atopic dermatitis and psoriasis that affect innate immune mechanisms and skin barrier integrity. Finally, we discuss how current and future therapies may affect innate immune responses and skin barrier integrity in a generalized or more targeted approach in order to ameliorate disease in patients.

Common and Rare 5′UTR Variants Altering Upstream Open Reading Frames in Cardiovascular Genomics

High-throughput sequencing (HTS) technologies are revolutionizing the research and molecular diagnosis landscape by allowing the exploration of millions of nucleotide sequences at an unprecedented scale. These technologies are of particular interest in the identification of genetic variations contributing to the risk of rare (Mendelian) and common (multifactorial) human diseases. So far, they have led to numerous successes in identifying rare disease-causing mutations in coding regions, but few in non-coding regions that include introns, untranslated (UTR), and intergenic regions. One class of neglected non-coding variations is that of 5′UTR variants that alter upstream open reading frames (upORFs) of the coding sequence (CDS) of a natural protein coding transcript. Following a brief summary of the molecular bases of the origin and functions of upORFs, we will first review known 5′UTR variations altering upORFs and causing rare cardiovascular disorders (CVDs). We will then investigate whether upORF-affecting single nucleotide polymorphisms could be good candidates for explaining association signals detected in the context of genome-wide association studies for common complex CVDs.

Genetic Polymorphisms of IFNG, IFNGR1, and Androgen Receptor and Chronic Prostatitis/Chronic Pelvic Pain Syndrome in a Chinese Han Population

BACKGROUND

Chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) refers to a common disorder with unclear etiology and unsatisfactory treatment, which reduces the male's quality of life.

OBJECTIVE

To examine the effects of genetic polymorphisms of IFNG, IFNGR1, and androgen receptor (AR) on CP/CPPS.

METHODS

The single nucleotide polymorphisms (SNPs) of IFNG, IFNGR1, and AR were genotyped with the improved multiplex ligation detection reaction. The GTEx, RegulomeDB, HaploReg, and 3DSNP databases were adopted to predict the regulatory functions of the genotyped SNPs. The correlation between SNPs and CP/CPPS was analyzed with the χ 2 test, logistic regression, and two genetic models (codominant and log-additive models). The nomogram was built to predict the risk of CP/CPPS occurrence.

RESULTS

On the whole, 130 CP/CPPS patients and 125 healthy controls were recruited in the study, and 18 SNPs of IFNG, IFNGR1, and AR were genotyped. The results of functional annotation indicated that the 18 genotyped SNPs might have regulatory effects in the whole blood. The rs144488434 was correlated with the elevated CP/CPPS risk (odds ratio (OR): 2.40, 95% confidence interval (CI): 1.12-5.13, χ 2 = 5.37, and P = 0.021) by the χ 2 test. In the built genetic models, rs10457655 was correlated with the elevated National Institutes of Health Chronic Prostatitis Symptom Index (NIH-CPSI) scores (codominant model: GA/GG: crude mean difference (MD) = 0.98, 95% CI: -1.71-3.67 and AA/GG: crude MD = 9.10, 95% CI: 0.58-17.62, P = 0.10). In subgroup analysis, rs2069718 was correlated with the elevated CP/CPPS risk (log-additive model: crude OR = 2.18, 95% CI: 1.03-4.64, and P = 0.034) in patients ≥ 35 years. The nomogram integrating age, rs2069718, rs10457655, and rs144488434 showed good performance to predict the risk of CP/CPPS.

CONCLUSIONS

Genetic polymorphisms of IFNG, IFNGR1, and AR might act as the genetic factors for CP/CPPS susceptibility, which deserved further explorations.

Identifying Novel Psoriatic Disease Drug Targets Using a Genetics-Based Priority Index Pipeline

BACKGROUND

Despite numerous genome-wide association studies conducted in psoriasis and psoriatic arthritis, only a small fraction of the identified genes has been therapeutically targeted.

OBJECTIVE

We sought to identify and analyze potential therapeutic targets for psoriasis and psoriatic arthritis (PsA) using the priority index (Pi), a genetics-dependent drug target prioritization approach.

METHODS

Significant genetic variants from GWAS for psoriasis, PsA, and combined psoriatic disease were annotated and run through the Pi pipeline. Potential drug targets were identified based on genomic predictors, annotation predictors, pathway enrichment, and pathway crosstalk.

RESULTS

Several gene targets were identified for psoriasis and PsA that demonstrated biological associations to their respective diseases. Some are currently being explored as potential therapeutic targets (i.e. ICAM1, NF-kB, REV3L, ADRA1B for psoriasis; CCL11 for PsA); others have not yet been investigated (i.e. LNPEP, LCE3 for psoriasis; UBLCP1 for PsA). Additionally, many nodal points of potential intervention were identified as promising therapeutic targets. Of these, some are currently being studied such as TYK2 for psoriasis, and others have yet to be explored (i.e. PPP2CA, YAP1, PI3K, AKT, FOXO1, RELA, CSF2, IFNGR1, IFNGR2 for psoriasis; GNAQ, PLCB1, GNAI2 for PsA).

CONCLUSION

Through Pi, we identified data-driven candidate therapeutic gene targets and pathways for psoriasis and PsA. Given the sparse PsA specific genetic studies and PsA specific drug targets, this analysis could prove to be particularly valuable in the pipeline for novel psoriatic therapies.

Comprehensive analysis of the major histocompatibility complex in systemic sclerosis identifies differential HLA associations by clinical and serological subtypes

OBJECTIVE

The greatest genetic effect reported for systemic sclerosis (SSc) lies in the major histocompatibility complex (MHC) locus. Leveraging the largest SSc genome-wide association study, we aimed to fine-map this region to identify novel human leucocyte antigen (HLA) genetic variants associated with SSc susceptibility and its main clinical and serological subtypes.

METHODS

9095 patients with SSc and 17 584 controls genome-wide genotyped were used to impute and test single-nucleotide polymorphisms (SNPs) across the MHC, classical HLA alleles and their composite amino acid residues. Additionally, patients were stratified according to their clinical and serological status, namely, limited cutaneous systemic sclerosis (lcSSc), diffuse cutaneous systemic sclerosis (dcSSc), anticentromere (ACA), antitopoisomerase (ATA) and anti-RNApolIII autoantibodies (ARA).

RESULTS

Sequential conditional analyses showed nine SNPs, nine classical alleles and seven amino acids that modelled the observed associations with SSc. This confirmed previously reported associations with HLA-DRB1*11:04 and HLA-DPB1*13:01, and revealed a novel association of HLA-B*08:01. Stratified analyses showed specific associations of HLA-DQA1*02:01 with lcSSc, and an exclusive association of HLA-DQA1*05:01 with dcSSc. Similarly, private associations were detected in HLA-DRB1*08:01 and confirmed the previously reported association of HLA-DRB1*07:01 with ACA-positive patients, as opposed to the HLA-DPA1*02:01 and HLA-DQB1*03:01 alleles associated with ATA presentation.

CONCLUSIONS

This study confirms the contribution of HLA class II and reveals a novel association of HLA class I with SSc, suggesting novel pathways of disease pathogenesis. Furthermore, we describe specific HLA associations with SSc clinical and serological subtypes that could serve as biomarkers of disease severity and progression.

Genome-Wide Search for SNP Interactions in GWAS Data: Algorithm, Feasibility, Replication Using Schizophrenia Datasets

In this study, we looked for potential gene-gene interaction in susceptibility to schizophrenia by an exhaustive searching for SNP-SNP interactions in 3 GWAS datasets (phs000021:phg000013, phs000021:phg000014, phs000167) using our recently published algorithm. The search space for SNP-SNP interaction was confined to 8 biologically plausible ways of interaction under dominant-dominant or recessive-recessive modes. First, we performed our search of all pair-wise combination of 729,454 SNPs after filtering by SNP genotype quality. All possible pairwise interactions of any 2 SNPs (5 × 10¹¹) were exhausted to search for significant interaction which was defined by p-value of chi-square tests. Nine out the top 10 interactions, protein coding genes were partnered with non-coding RNA (ncRNA) which suggested a new alternative insight into interaction biology other than the frequently sought-after protein-protein interaction. Therefore, we extended to look for replication among the top 10,000 interaction SNP pairs and high proportion of concurrent genes forming the interaction pairs were found. The results indicated that an enrichment of signals over noise was present in the top 10,000 interactions. Then, replications of SNP-SNP interaction were confirmed for 14 SNPs-pairs in both replication datasets. Biological insight was highlighted by a potential binding between FHIT (protein coding gene) and LINC00969 (lncRNA) which showed a replicable interaction between their SNPs. Both of them were reported to have expression in brain. Our study represented an early attempt of exhaustive interaction analysis of GWAS data which also yield replicated interaction and new insight into understanding of genetic interaction in schizophrenia.

Detecting Shared Genetic Architecture Among Multiple Phenotypes by Hierarchical Clustering of Gene-Level Association Statistics

Emerging large-scale biobanks pairing genotype data with phenotype data present new opportunities to prioritize shared genetic associations across multiple phenotypes for molecular validation. Past research, by our group and others, has shown gene-level tests of association produce biologically interpretable characterization of the genetic architecture of a given phenotype. Here, we present a new method, Ward clustering to identify Internal Node branch length outliers using Gene Scores (WINGS), for identifying shared genetic architecture among multiple phenotypes. The objective of WINGS is to identify groups of phenotypes, or "clusters," sharing a core set of genes enriched for mutations in cases. We validate WINGS using extensive simulation studies and then combine gene-level association tests with WINGS to identify shared genetic architecture among 81 case-control and seven quantitative phenotypes in 349,468 European-ancestry individuals from the UK Biobank. We identify eight prioritized phenotype clusters and recover multiple published gene-level associations within prioritized clusters.

Psoriasis and Genetics

Psoriasis is a common inflammatory skin disease caused by the interplay between multiple genetic and environmental risk factors. This review summarises recent progress in elucidating the genetic basis of psoriasis, particularly through large genome-wide association studies. We illustrate the power of genetic analyses for disease stratification. Psoriasis can be stratified by phenotype (common plaque versus rare pustular variants), or by outcome (prognosis, comorbidities, response to treatment); recent progress has been made in delineating the genetic contribution in each of these areas. We also highlight how genetic data can directly inform the development of effective psoriasis treatments.

Defining Early Life Stress as a Precursor for Autoimmune Disease

Childhood exposure to traumatic events, termed early life stress (ELS), is now widely recognized for causing long-term negative health effects that may not manifest until adulthood. Allostatic load (AL) describes the cumulative "wear-and-tear" effects of chronic stress on the body that may adversely affect human health by accelerating other disease processes. Recent epidemiological studies have reported higher stress levels in industrialized countries and trends of increasing prevalence in autoimmune diseases during recent decades. To elucidate mechanisms of stress-related immune dysregulation, most animal studies up to now have focused on AL and stress-triggered events occurring in adults but have not explored ELS in the context of autoimmune disorders. We have identified a current gap in understanding the impact of ELS on immune system ontogeny and its potential for priming genetically susceptible individuals who are at increased risk for autoimmune diseases later in life, through mechanisms involving neuroendocrine-immune cross talk. In this review, we highlight the intersection between stress and immune function, with a focus on ELS as consequential for increased autoimmune disorder risks later in life.