Beyond GWAS: from simple associations to functional insights

Enhanced diagnosis of multi-drug-resistant microbes using group association modeling and machine learning

New solutions are needed to detect genotype-phenotype associations involved in microbial drug resistance. Herein, we describe a Group Association Model (GAM) that accurately identifies genetic variants linked to drug resistance and mitigates false-positive cross-resistance artifacts without prior knowledge. GAM analysis of 7,179 Mycobacterium tuberculosis (Mtb) isolates identifies gene targets for all analyzed drugs, revealing comparable performance but fewer cross-resistance artifacts than World Health Organization (WHO) mutation catalogue approach, which requires expert rules and precedents. GAM also reveals generalizability, demonstrating high predictive accuracy with 3,942 S. aureus isolates. GAM refinement by machine learning (ML) improves predictive accuracy with small or incomplete datasets. These findings were validated using 427 Mtb isolates from three sites, where GAM inputs are also found to be more suitable in ML prediction models than WHO inputs. GAM + ML could thus address the limitations of current drug resistance prediction methods to improve treatment decisions for drug-resistant microbial infections.

An overview on Sjögren's syndrome and systemic lupus erythematosus' genetics

Major autoimmune rheumatic disorders, such as systemic lupus erythematosus and Sjögren's syndrome, are defined by the presence of autoantibodies. These diseases are brought on by immune system dysregulation, which can present clinically in a wide range of ways. The etiologies of these illnesses are complex and heavily impacted by a variety of genetic and environmental variables. The most powerful susceptibility element for each of these disorders is still the human leukocyte antigen (HLA) area, that was the initial locus found to be associated. This region is primarily responsible for the HLA class II genes, such as DQA1, DQB1, and DRB1, however class I genes have also been linked. Numerous genetic variants that do not pose a risk to HLA have been found as a result of intensive research into the genetic component of these diseases conducted over the last 20 years. Furthermore, it is generally acknowledged that autoimmune rheumatic illnesses have similar genetic backgrounds and share molecular pathways of disease, including the interferon (IFN) type I routes. Pleiotropic sites for autoimmune rheumatic illnesses comprise TNIP1, DNASEL13, IRF5, the HLA region, and others. It remains a challenge to determine the causative biological mechanisms beneath the genetic connections. Nonetheless, functional analyses of the loci and mouse models have produced recent advancements. With an emphasis on the HLA region, we present an updated summary of the structure of genes underpinning both of these autoimmune rheumatic illnesses here.

Critical genes in genitourinary embryogenesis are related to the development of adult hydrocele

Despite being a common urologic disorder with potentially complicated sequela, the genetic background of adult hydrocele has not previously been described. We performed a multi-population genome-wide association study of 363,460 men in the United Kingdom BioBank and FinnGen cohorts. We identified 6,548 adult men with hydrocele. We analyzed common variants (minor allele frequency > 0.01) associated with hydrocele and set the threshold for genome-wide significance at p < 5 × 10- 8. Meta-analysis of genome-wide association studies identified 7 genome-wide significant loci which mapped to 24 genes. Multiple gene prioritization strategies highlighted PAX8, INHBB, AMHR2, and SHH, all known to be critical to genitourinary embryogenesis and associated with Mendelian genitourinary syndromes and model organism phenotypes. Identified loci affect gene expression in genitourinary structures and are associated with multiple markers of renal function. These common variants in genes critical for genitourinary embryogenesis are associated with adult hydrocele, suggesting these genes may maintain normal scrotal anatomy in adults. This large study of nearly 400,000 men is the first genomic study of idiopathic hydrocele and defines our current understanding of the genetic background of this common condition.

The goldmine of GWAS summary statistics: a systematic review of methods and tools

Genome-wide association studies (GWAS) have revolutionized our understanding of the genetic architecture of complex traits and diseases. GWAS summary statistics have become essential tools for various genetic analyses, including meta-analysis, fine-mapping, and risk prediction. However, the increasing number of GWAS summary statistics and the diversity of software tools available for their analysis can make it challenging for researchers to select the most appropriate tools for their specific needs. This systematic review aims to provide a comprehensive overview of the currently available software tools and databases for GWAS summary statistics analysis. We conducted a comprehensive literature search to identify relevant software tools and databases. We categorized the tools and databases by their functionality, including data management, quality control, single-trait analysis, and multiple-trait analysis. We also compared the tools and databases based on their features, limitations, and user-friendliness. Our review identified a total of 305 functioning software tools and databases dedicated to GWAS summary statistics, each with unique strengths and limitations. We provide descriptions of the key features of each tool and database, including their input/output formats, data types, and computational requirements. We also discuss the overall usability and applicability of each tool for different research scenarios. This comprehensive review will serve as a valuable resource for researchers who are interested in using GWAS summary statistics to investigate the genetic basis of complex traits and diseases. By providing a detailed overview of the available tools and databases, we aim to facilitate informed tool selection and maximize the effectiveness of GWAS summary statistics analysis.

Identification of an Allele-Specific Transcription Factor Binding Interaction that May Regulate PLA2G2A Gene Expression

The secreted phospholipase A2 (sPLA2) isoform, sPLA2-IIA, has been implicated in a variety of diseases and conditions, including bacteremia, cardiovascular disease, COVID-19, sepsis, adult respiratory distress syndrome, and certain cancers. Given its significant role in these conditions, understanding the regulatory mechanisms impacting its levels is crucial. Genome-wide association studies (GWAS) have identified several single nucleotide polymorphisms (SNPs), including rs11573156, that are associated with circulating levels of sPLA2-IIA. The work in the manuscript leveraged 4 publicly available datasets to investigate the mechanism by which rs11573156 influences sPLA2-IIA levels via bioinformatics and modeling analysis. Through genotype-tissue expression (GTEx), 234 expression quantitative trait loci (eQTLs) were identified for the gene that encodes for sPLA2-IIA, PLA2G2A. SNP2TFBS was used to ascertain the binding affinities between transcription factors (TFs) to both the reference and alternative alleles of identified eQTL SNPs. Subsequently, candidate TF-SNP interactions were cross-referenced with the ChIP-seq results in matched tissues from ENCODE. SP1-rs11573156 emerged as the significant TF-SNP pair in the liver. Further analysis revealed that the upregulation of PLA2G2A transcript levels through the rs11573156 variant was likely affected by tissue SP1 protein levels. Using an ordinary differential equation based on Michaelis-Menten kinetic assumptions, we modeled the dependence of PLA2G2A transcription on SP1 protein levels, incorporating the SNP influence. Collectively, our analysis strongly suggests that the difference in the binding dynamics of SP1 to different rs11573156 alleles may underlie the allele-specific PLA2G2A expression in different tissues, a mechanistic model that awaits future direct experimental validation. This mechanism likely contributes to the variation in circulating sPLA2-IIA protein levels in the human population, with implications for a wide range of human diseases.

From GWASs toward Mechanistic Understanding with Case Studies in Dermatogenetics

Many human skin diseases result from the complex interplay of genetic and environmental mechanisms that are largely unknown. GWASs have yielded insight into the genetic aspect of complex disease by highlighting regions of the genome or specific genetic variants associated with disease. Leveraging this information to identify causal genes and cell types will provide insight into fundamental biology, inform diagnostics, and aid drug discovery. However, the etiological mechanisms from genetic variant to disease are still unestablished in most cases. There now exists an unprecedented wealth of data and computational methods for variant interpretation in a functional context. It can be challenging to decide where to start owing to a lack of consensus on the best way to identify causal genetic mechanisms. This article highlights 3 key aspects of genetic variant interpretation: prioritizing causal genes, cell types, and pathways. We provide a practical overview of the main methods and datasets, giving examples from recent atopic dermatitis studies to provide a blueprint for variant interpretation. A collection of resources, including brief description and links to the packages and web tools, is provided for researchers looking to start in silico follow-up genetic analysis of associated genetic variants.

Changes in environmental exposures over decades may influence the genetic architecture of severe spermatogenic failure

STUDY QUESTION

Do the genetic determinants of idiopathic severe spermatogenic failure (SPGF) differ between generations?

SUMMARY ANSWER

Our data support that the genetic component of idiopathic SPGF is impacted by dynamic changes in environmental exposures over decades.

WHAT IS KNOWN ALREADY

The idiopathic form of SPGF has a multifactorial etiology wherein an interaction between genetic, epigenetic, and environmental factors leads to the disease onset and progression. At the genetic level, genome-wide association studies (GWASs) allow the analysis of millions of genetic variants across the genome in a hypothesis-free manner, as a valuable tool for identifying susceptibility risk loci. However, little is known about the specific role of non-genetic factors and their influence on the genetic determinants in this type of conditions.

STUDY DESIGN, SIZE, DURATION

Case-control genetic association analyses were performed including a total of 912 SPGF cases and 1360 unaffected controls.

PARTICIPANTS/MATERIALS, SETTING, METHODS

All participants had European ancestry (Iberian and German). SPGF cases were diagnosed during the last decade either with idiopathic non-obstructive azoospermia (n = 547) or with idiopathic non-obstructive oligozoospermia (n = 365). Case-control genetic association analyses were performed by logistic regression models considering the generation as a covariate and by in silico functional characterization of the susceptibility genomic regions.

MAIN RESULTS AND THE ROLE OF CHANCE

This analysis revealed 13 novel genetic association signals with SPGF, with eight of them being independent. The observed associations were mostly explained by the interaction between each lead variant and the age-group. Additionally, we established links between these loci and diverse non-genetic factors, such as toxic or dietary habits, respiratory disorders, and autoimmune diseases, which might potentially influence the genetic architecture of idiopathic SPGF.

LARGE SCALE DATA

GWAS data are available from the authors upon reasonable request.

LIMITATIONS, REASONS FOR CAUTION

Additional independent studies involving large cohorts in ethnically diverse populations are warranted to confirm our findings.

WIDER IMPLICATIONS OF THE FINDINGS

Overall, this study proposes an innovative strategy to achieve a more precise understanding of conditions such as SPGF by considering the interactions between a variable exposome through different generations and genetic predisposition to complex diseases.

STUDY FUNDING/COMPETING INTEREST(S)

This work was supported by the "Plan Andaluz de Investigación, Desarrollo e Innovación (PAIDI 2020)" (ref. PY20_00212, P20_00583), the Spanish Ministry of Economy and Competitiveness through the Spanish National Plan for Scientific and Technical Research and Innovation (ref. PID2020-120157RB-I00 funded by MCIN/ AEI/10.13039/501100011033), and the 'Proyectos I+D+i del Programa Operativo FEDER 2020' (ref. B-CTS-584-UGR20). ToxOmics-Centre for Toxicogenomics and Human Health, Genetics, Oncology and Human Toxicology, is also partially supported by the Portuguese Foundation for Science and Technology (Projects: UIDB/00009/2020; UIDP/00009/2020). The authors declare no competing interests.

TRIAL REGISTRATION NUMBER

N/A.

Association Between Multiple Single Nucleotide Polymorphisms in Folate Metabolism Pathway and Breast Cancer Risk in Georgian Women: A Case-Control Study

Background

The folate metabolism pathway plays an integral part in DNA synthesis, methylation, and repair. Methylenetetrahydrofolate reductase (MTHFR) and methylenetetrahydrofolate dehydrogenase (MTHFD1) are both enzymes that are involved in this pathway, and the single nucleotide polymorphisms (SNPs) in genes coding for them have modulatory effects on DNA expression. This study aimed to investigate the relationship between MTHFR C677T (rs1801133) and MTHFD1 G1958A (rs2236225) polymorphisms and the risk of developing breast cancer in Georgian women.

Methods

A case-control study was performed examining the MTHFR C677T and MTHFD1 G1958A SNP in breast cancer-confirmed cases and healthy matched controls. Real time-polymerase chain reaction (PCR) was used to genotype SNPs. The case individuals' pathology reports were obtained following surgeries for cancer characteristic data. Statistical analysis was performed to investigate the significance of the acquired data.

Results

Statistical analysis of MTHFR C677T SNP revealed that the CT genotype increased the risk of breast cancer by 2.17 folds in the over-dominant model. Statistical analysis of MTHFD1 G1958A SNP showed that the GA genotype increased the risk of breast cancer by 4.12 folds in the codominant model and 2.41 folds in the over-dominant model. No statistically significant link was found between genotypes and lymph node status, however, patients with the CT genotype had higher percentages of proliferative activity.

Conclusions

Breast cancer seems to have a statistically significant association with the CT genotype in MTHFR C677T and the GA genotype in MTHFD1 G1958A in Georgian women.

Identification of an allele-specific transcription factor binding interaction that regulates PLA2G2A gene expression

The secreted phospholipase A 2 (sPLA 2 ) isoform, sPLA 2 -IIA, has been implicated in a variety of diseases and conditions, including bacteremia, cardiovascular disease, COVID-19, sepsis, adult respiratory distress syndrome, and certain cancers. Given its significant role in these conditions, understanding the regulatory mechanisms impacting its levels is crucial. Genome-wide association studies (GWAS) have identified several single nucleotide polymorphisms (SNPs), including rs11573156, that are associated with circulating levels of sPLA 2 -IIA. Through Genotype-Tissue Expression (GTEx), 234 expression quantitative trait loci (eQTLs) were identified for the gene that encodes for sPLA 2 -IIA, PLA2G2A . SNP2TFBS ( https://ccg.epfl.ch/snp2tfbs/ ) was utilized to ascertain the binding affinities between transcription factors (TFs) to both the reference and alternative alleles of identified SNPs. Subsequently, ChIP-seq peaks highlighted the TF combinations that specifically bind to the SNP, rs11573156. SP1 emerged as a significant TF/SNP pair in liver cells, with rs11573156/SP1 interaction being most prominent in liver, prostate, ovary, and adipose tissues. Further analysis revealed that the upregulation of PLA2G2A transcript levels through the rs11573156 variant was affected by tissue SP1 protein levels. By leveraging an ordinary differential equation, structured upon Michaelis-Menten enzyme kinetics assumptions, we modeled the PLA2G2A transcription's dependence on SP1 protein levels, incorporating the SNP's influence. Collectively, these data strongly suggest that the binding affinity differences of SP1 for the different rs11573156 alleles can influence PLA2G2A expression. This, in turn, can modulate sPLA2-IIA levels, impacting a wide range of human diseases.

Transcription factor-binding k-mer analysis clarifies the cell type dependency of binding specificities and cis-regulatory SNPs in humans

BACKGROUND

Transcription factors (TFs) exhibit heterogeneous DNA-binding specificities in individual cells and whole organisms under natural conditions, and de novo motif discovery usually provides multiple motifs, even from a single chromatin immunoprecipitation-sequencing (ChIP-seq) sample. Despite the accumulation of ChIP-seq data and ChIP-seq-derived motifs, the diversity of DNA-binding specificities across different TFs and cell types remains largely unexplored.

RESULTS

Here, we applied MOCCS2, our k-mer-based motif discovery method, to a collection of human TF ChIP-seq samples across diverse TFs and cell types, and systematically computed profiles of TF-binding specificity scores for all k-mers. After quality control, we compiled a set of TF-binding specificity score profiles for 2,976 high-quality ChIP-seq samples, comprising 473 TFs and 398 cell types. Using these high-quality samples, we confirmed that the k-mer-based TF-binding specificity profiles reflected TF- or TF-family dependent DNA-binding specificities. We then compared the binding specificity scores of ChIP-seq samples with the same TFs but with different cell type classes and found that half of the analyzed TFs exhibited differences in DNA-binding specificities across cell type classes. Additionally, we devised a method to detect differentially bound k-mers between two ChIP-seq samples and detected k-mers exhibiting statistically significant differences in binding specificity scores. Moreover, we demonstrated that differences in the binding specificity scores between k-mers on the reference and alternative alleles could be used to predict the effect of variants on TF binding, as validated by in vitro and in vivo assay datasets. Finally, we demonstrated that binding specificity score differences can be used to interpret disease-associated non-coding single-nucleotide polymorphisms (SNPs) as TF-affecting SNPs and provide candidates responsible for TFs and cell types.

CONCLUSIONS

Our study provides a basis for investigating the regulation of gene expression in a TF-, TF family-, or cell-type-dependent manner. Furthermore, our differential analysis of binding-specificity scores highlights noncoding disease-associated variants in humans.

A body map of super-enhancers and their function in pig

Introduction

Super-enhancers (SEs) are clusters of enhancers that act synergistically to drive the high-level expression of genes involved in cell identity and function. Although SEs have been extensively investigated in humans and mice, they have not been well characterized in pigs.

Methods

Here, we identified 42,380 SEs in 14 pig tissues using chromatin immunoprecipitation sequencing, and statistics of its overall situation, studied the composition and characteristics of SE, and explored the influence of SEs characteristics on gene expression.

Results

We observed that approximately 40% of normal enhancers (NEs) form SEs. Compared to NEs, we found that SEs were more likely to be enriched with an activated enhancer and show activated functions. Interestingly, SEs showed X chromosome depletion and short interspersed nuclear element enrichment, implying that SEs play an important role in sex traits and repeat evolution. Additionally, SE-associated genes exhibited higher expression levels and stronger conservation than NE-associated genes. However, genes with the largest SEs had higher expression levels than those with the smallest SEs, indicating that SE size may influence gene expression. Moreover, we observed a negative correlation between SE gene distance and gene expression, indicating that the proximity of SEs can affect gene activity. Gene ontology enrichment and motif analysis revealed that SEs have strong tissue-specific activity. For example, the CORO2B gene with a brain-specific SE shows strong brain-specific expression, and the phenylalanine hydroxylase gene with liver-specific SEs shows strong liver-specific expression.

Discussion

In this study, we illustrated a body map of SEs and explored their functions in pigs, providing information on the composition and tissue-specific patterns of SEs. This study can serve as a valuable resource of gene regulatory and comparative analyses to the scientific community and provides a theoretical reference for genetic control mechanisms of important traits in pigs.

VEXAS syndrome: Current clinical, diagnostic and treatment approaches

VEXAS syndrome, is a hemato-inflammatory chronic disease characterized with predominantly rheumatic and hematologic systemic involvement. It was first described in 2020 by a group of researchers in the United States. VEXAS syndrome is a rare condition that primarily affects adult males and is caused by a mutation in the UBA1 gene located on the X chromosome. Its pathogenesis is related to the somatic mutation affecting methionine-41 (p.Met41) in UBA1, the major E1 enzyme that initiates ubiquitylation. Mutant gene lead to decreased ubiquitination and activated innate immune pathways and systemic inflammation occur. The specific mechanism by which the UBA1 mutation leads to the clinical features of VEXAS syndrome is not yet fully understood. VEXAS is a newly define adult-onset inflammatory syndrome manifested with treatment-refractory fevers, arthritis, chondritis, vasculitis, cytopenias, typical vacuoles in hematopetic precursor cells, neutrophilic cutaneous and pulmonary inflammation. Diagnosing VEXAS syndrome can be challenging due to its rarity and the overlap of symptoms with other inflammatory conditions. Genetic testing to identify the UBA1 gene mutation is essential for definitive diagnosis. Currently, there is no known cure for VEXAS syndrome, and treatment mainly focuses on managing the symptoms. This may involve the use of anti-inflammatory medications, immunosuppressive drugs, and supportive therapies tailored to the individual patient's needs. Due to the recent discovery of VEXAS syndrome, ongoing research is being conducted to better understand its pathogenesis, clinical features, and potential treatment options. In this review article, the clinical, diagnostic and treatment approaches of VEXAS syndrome were evaluated in the light of the latest literature data.

Strategies for activity analysis of single nucleotide polymorphisms associated with human diseases

Genome-wide association studies (GWAS) have identified a large number of single nucleotide polymorphism (SNP) sites associated with human diseases. In the annotation of human diseases, especially cancers, SNPs, as an important component of genetic factors, have gained increasing attention. Given that most of the SNPs are located in non-coding regions, the functional verification of these SNPs is a great challenge. The key to functional annotation for risk SNPs is to screen SNPs with regulatory activity from thousands of disease associated-SNPs. In this review, we systematically recapitulate the characteristics and functional roles of SNP sites, discuss three parallel reporter screening strategies in detail based on barcode tag classification, and recommend the common in silico strategies to help supplement the annotation of SNP sites with epigenetic activity analysis, prediction of target genes and trans-acting factors. We hope that this review will contribute to this exuberant research field by providing robust activity analysis strategies that can facilitate the translation of GWAS results into personalized diagnosis and prevention measures for human diseases.

Sequencing technology as a major impetus in the advancement of studies into rheumatism: A bibliometric study

Background

Rheumatism covers a wide range of diseases with complex clinical manifestations and places a tremendous burden on humans. For many years, our understanding of rheumatism was seriously hindered by technology constraints. However, the increasing application and rapid advancement of sequencing technology in the past decades have enabled us to study rheumatism with greater accuracy and in more depth. Sequencing technology has made huge contributions to the field and is now an indispensable component and powerful tool in the study of rheumatism.

Methods

Articles on sequencing and rheumatism, published from 1 January 2000 to 25 April 2022, were retrieved from the Web of Science™ (Clarivate™, Philadelphia, PA, USA) database. Bibliometrix, the open-source tool, was used for the analysis of publication years, countries, authors, sources, citations, keywords, and co-words.

Results

The 1,374 articles retrieved came from 62 countries and 350 institutions, with a general increase in article numbers during the last 22 years. The leading countries in terms of publication numbers and active cooperation with other countries were the USA and China. The most prolific authors and most popular documents were identified to establish the historiography of the field. Popular and emerging research topics were assessed by keywords and co-occurrence analysis. Immunological and pathological process in rheumatism, classification, risks and susceptibility, and biomarkers for diagnosis were among the hottest themes for research.

Conclusions

Sequencing technology has been widely applied in the study of rheumatism and propells research in the area of discovering novel biomarkers, related gene patterns and physiopathology. We suggest that further efforts be made to advance the study of genetic patterns related to rheumatic susceptibility, pathogenesis, classification and disease activity, and novel biomarkers.

Pan-Genomic Regulation of Gene Expression in Normal and Pathological Human Placentas

In this study, we attempted to find genetic variants affecting gene expression (eQTL = expression Quantitative Trait Loci) in the human placenta in normal and pathological situations. The analysis of gene expression in placental diseases (Pre-eclampsia and Intra-Uterine Growth Restriction) is hindered by the fact that diseased placental tissue samples are generally taken at earlier gestations compared to control samples. The difference in gestational age is considered a major confounding factor in the transcriptome regulation of the placenta. To alleviate this significant problem, we propose here a novel approach to pinpoint disease-specific cis-eQTLs. By statistical correction for gestational age at sampling as well as other confounding/surrogate variables systematically searched and identified, we found 43 e-genes for which proximal SNPs influence expression level. Then, we performed the analysis again, removing the disease status from the covariates, and we identified 54 e-genes, 16 of which are identified de novo and, thus, possibly related to placental disease. We found a highly significant overlap with previous studies for the list of 43 e-genes, validating our methodology and findings. Among the 16 disease-specific e-genes, several are intrinsic to trophoblast biology and, therefore, constitute novel targets of interest to better characterize placental pathology and its varied clinical consequences. The approach that we used may also be applied to the study of other human diseases where confounding factors have hampered a better understanding of the pathology.

A Summary on the Genetics of Systemic Lupus Erythematosus, Rheumatoid Arthritis, Systemic Sclerosis, and Sjögren's Syndrome

Systemic lupus erythematosus, systemic sclerosis, rheumatoid arthritis, and Sjögren's syndrome are four major autoimmune rheumatic diseases characterized by the presence of autoantibodies, caused by a dysregulation of the immune system that leads to a wide variety of clinical manifestations. These conditions present complex etiologies strongly influenced by multiple environmental and genetic factors. The human leukocyte antigen (HLA) region was the first locus identified to be associated and still represents the strongest susceptibility factor for each of these conditions, particularly the HLA class II genes, including DQA1, DQB1, and DRB1, but class I genes have also been associated. Over the last two decades, the genetic component of these disorders has been extensively investigated and hundreds of non-HLA risk genetic variants have been uncovered. Furthermore, it is widely accepted that autoimmune rheumatic diseases share molecular disease pathways, such as the interferon (IFN) type I pathways, which are reflected in a common genetic background. Some examples of well-known pleiotropic loci for autoimmune rheumatic diseases are the HLA region, DNASEL13, TNIP1, and IRF5, among others. The identification of the causal molecular mechanisms behind the genetic associations is still a challenge. However, recent advances have been achieved through mouse models and functional studies of the loci. Here, we provide an updated overview of the genetic architecture underlying these four autoimmune rheumatic diseases, with a special focus on the HLA region.