•Genome-wide association studies have been conducted worldwide and identified multiple loci associated with psoriasis.•The functional annotations and clinical applications of the identified psoriasis ...risk loci have not been fully elucidated.•Mendelian randomization and drug repositioning are effective for translating genome-wide association studies results into clinical medicine.
Psoriasis is an immune-mediated disease associated with skin and joint inflammation that affects large proportions of populations worldwide. It is a heritable disease: individuals’ genetic backgrounds modulate their susceptibility. In genetics, multiple human leukocyte antigen (HLA) genes are most strongly associated with the risk of psoriasis, especially HLA-C*06:02. In the last 10 years, large-scale genome-wide association studies (GWASs) of psoriasis have been conducted in multiple populations, and these have substantially increased the number of genetic loci associated with psoriasis susceptibility (n > 80). Understanding the genetic background of psoriasis is important for understanding the disease’s biology, identifying clinical biomarkers, discovering novel drug targets, and accelerating the journey towards personalized medicine. However, the application of whole-genome and long-read sequencing technology in psoriasis genetic analysis is still developing. Moreover, achieving practical strategies for translating psoriasis risk-associated genetic variants into functional annotations and clinical applications remains challenging. In this review, we detail the current and future landscape of psoriasis genetics and introduce the cutting-edge use of large-scale GWAS data, especially in the Japanese population.
Variations of human leukocyte antigen (HLA) genes in the major histocompatibility complex region (MHC) significantly affect the risk of various diseases, especially autoimmune diseases. Fine-mapping ...of causal variants in this region was challenging due to the difficulty in sequencing and its inapplicability to large cohorts. Thus, HLA imputation, a method to infer HLA types from regional single nucleotide polymorphisms, has been developed and has successfully contributed to MHC fine-mapping of various diseases. Different HLA imputation methods have been developed, each with its own advantages, and recent methods have been improved in terms of accuracy and computational performance. Additionally, advances in HLA reference panels by next-generation sequencing technologies have enabled higher resolution and a more reliable imputation, allowing a finer-grained evaluation of the association between sequence variations and disease risk. Risk-associated variants in the MHC region would affect disease susceptibility through complicated mechanisms including alterations in peripheral responses and central thymic selection of T cells. The cooperation of reliable HLA imputation methods, informative fine-mapping, and experimental validation of the functional significance of MHC variations would be essential for further understanding of the role of the MHC in the immunopathology of autoimmune diseases.
Polygenic risk scores (PRS) are poised to improve biomedical outcomes via precision medicine. However, the major ethical and scientific challenge surrounding clinical implementation of PRS is that ...those available today are several times more accurate in individuals of European ancestry than other ancestries. This disparity is an inescapable consequence of Eurocentric biases in genome-wide association studies, thus highlighting that-unlike clinical biomarkers and prescription drugs, which may individually work better in some populations but do not ubiquitously perform far better in European populations-clinical uses of PRS today would systematically afford greater improvement for European-descent populations. Early diversifying efforts show promise in leveling this vast imbalance, even when non-European sample sizes are considerably smaller than the largest studies to date. To realize the full and equitable potential of PRS, greater diversity must be prioritized in genetic studies, and summary statistics must be publically disseminated to ensure that health disparities are not increased for those individuals already most underserved.
Study of the genetics of rheumatoid arthritis (RA) began about four decades ago with the discovery of HLA-DRB1. Since the beginning of this century, a number of non-HLA risk loci have been identified ...through genome-wide association studies (GWAS). We now know that over 100 loci are associated with RA risk. Because genetic information implies a clear causal relationship to the disease, research into the pathogenesis of RA should be promoted. However, only 20% of GWAS loci contain coding variants, with the remaining variants occurring in non-coding regions, and therefore, the majority of causal genes and causal variants remain to be identified. The use of epigenetic studies, high-resolution mapping of open chromatin, chromosomal conformation technologies and other approaches could identify many of the missing links between genetic risk variants and causal genetic components, thus expanding our understanding of RA genetics.
Abstract
Summary
Making use of accumulated genetic knowledge for clinical practice is our next goal in human genetics. Here we introduce GREP (Genome for REPositioning drugs), a standalone python ...software to quantify an enrichment of the user-defined set of genes in the target of clinical indication categories and to capture potentially repositionable drugs targeting the gene set. We show that genes identified by the large-scale genome-wide association studies were robustly enriched in the approved drugs to treat the trait of interest. This enrichment analysis was also highly applicable to other sets of biological genes such as those identified by gene expression studies and genes somatically mutated in cancers. This software should accelerate investigators to reposition drugs to other indications with the guidance of known genomics.
Availability and implementation
GREP is available at https://github.com/saorisakaue/GREP as a python source code.
Supplementary information
Supplementary data are available at Bioinformatics online.
Interpreting the susceptible loci documented by genome-wide association studies (GWASs) is of utmost importance in the post-GWAS era. Since most complex traits are contributed by multiple tissues, ...analyzing tissue-specific effects of expression quantitative trait loci (eQTLs) is a promising approach. Here we describe "opposite eQTL effects", i.e., gene expression effects of eQTLs that are in the opposite direction between different tissues, as the biologically meaningful annotations of genes and genetic variants for understanding the GWAS loci. The genes and single-nucleotide polymorphisms (SNPs) associated with the opposite eQTL effects (opp-multi-eQTL-Genes and opp-multi-eQTL-SNPs) were extracted from the largest eQTL database provided by the Genotype-Tissue Expression (GTEx) project (release version 7). The opposite eQTL effects were detected even between closely related tissues such as cerebellum and brain cortex, and a significant proportion of the genes having eQTLs were annotated as the opp-multi-eQTL-Genes (2,323 out of 31,212; 7.4%). The opp-multi-eQTL-SNPs showed locational enrichment at the transcription start site and also possible involvement of epigenetic regulation. The biological importance of the opposite eQTL effects was also assessed using the SNPs reported in GWASs (GWAS-SNPs), which demonstrated that a high proportion of the opp-multi-eQTL-SNPs are in linkage disequilibrium with the GWAS-SNPs (2,498 out of 9,290; 26.9%). Based on the results, the opposite eQTL effects can be a common phenomenon in the tissue-specific gene regulation with a possible contribution to the development of complex traits.
Conventional human leukocyte antigen (HLA) imputation methods drop their performance for infrequent alleles, which is one of the factors that reduce the reliability of trans-ethnic major ...histocompatibility complex (MHC) fine-mapping due to inter-ethnic heterogeneity in allele frequency spectra. We develop DEEP*HLA, a deep learning method for imputing HLA genotypes. Through validation using the Japanese and European HLA reference panels (n = 1,118 and 5,122), DEEP*HLA achieves the highest accuracies with significant superiority for low-frequency and rare alleles. DEEP*HLA is less dependent on distance-dependent linkage disequilibrium decay of the target alleles and might capture the complicated region-wide information. We apply DEEP*HLA to type 1 diabetes GWAS data from BioBank Japan (n = 62,387) and UK Biobank (n = 354,459), and successfully disentangle independently associated class I and II HLA variants with shared risk among diverse populations (the top signal at amino acid position 71 of HLA-DRβ1; P = 7.5 × 10
). Our study illustrates the value of deep learning in genotype imputation and trans-ethnic MHC fine-mapping.
Sensing and clearance of dysfunctional lysosomes is critical for cellular homeostasis. Here we show that transcription factor EB (TFEB)-a master transcriptional regulator of lysosomal biogenesis and ...autophagy-is activated during the lysosomal damage response, and its activation is dependent on the function of the ATG conjugation system, which mediates LC3 lipidation. In addition, lysosomal damage triggers LC3 recruitment on lysosomes, where lipidated LC3 interacts with the lysosomal calcium channel TRPML1, facilitating calcium efflux essential for TFEB activation. Furthermore, we demonstrate the presence and importance of this TFEB activation mechanism in kidneys in a mouse model of oxalate nephropathy accompanying lysosomal damage. A proximal tubule-specific TFEB-knockout mouse exhibited progression of kidney injury induced by oxalate crystals. Together, our results reveal unexpected mechanisms of TFEB activation by LC3 lipidation and their physiological relevance during the lysosomal damage response.
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