Due to methodological reasons, the X-chromosome has not been featured in the major genome-wide association studies on Alzheimer's Disease (AD). To address this and better characterize the genetic landscape of AD, we performed an in-depth X-Chromosome-Wide Association Study (XWAS) in 115,841 AD cases or AD proxy cases, including 52,214 clinically-diagnosed AD cases, and 613,671 controls. We considered three approaches to account for the different X-chromosome inactivation (XCI) states in females, i.e. random XCI, skewed XCI, and escape XCI. We did not detect any genome-wide significant signals (P ≤ 5 × 10-8) but identified seven X-chromosome-wide significant loci (P ≤ 1.6 × 10-6). The index variants were common for the Xp22.32, FRMPD4, DMD and Xq25 loci, and rare for the WNK3, PJA1, and DACH2 loci. Overall, this well-powered XWAS found no genetic risk factors for AD on the non-pseudoautosomal region of the X-chromosome, but it identified suggestive signals warranting further investigations.

Imaging genetics is one of the important keys to precision medicine that leads to personalized treatment based on a patient's genetics, phenotype, or psychosocial characteristics. It deepens the understanding of the mechanisms through which genetic variations contribute to neurological and psychiatric disorders. This systematic review overviews the methods and applications of imaging genetics in the context of neurological diseases, mentioning its potential role in personalized medicine. Following PRISMA guidelines, this review systematically analyzes 28 studies integrating genetic and neuroimaging data to explore disease mechanisms and their implications for precision medicine. Selected research included multiple neurological disorders, including frontotemporal dementia, Alzheimer's disease, bipolar disorder, schizophrenia, Parkinson's disease, and others. Voxel-based morphometry was the most common imaging technique, while frequently examined genetic variants included APOE, C9orf72, MAPT, GRN, COMT, and BDNF. Associations between these variants and regional gray matter loss (e.g., frontal, temporal, or subcortical regions) suggest that genetic risk factors play a key role in disease pathophysiology. Integrating genetic and neuroimaging analyses enhances our understanding of disease mechanisms and supports advancements in precision medicine.

The rapid growth of population-scale whole-genome resequencing, RNA sequencing, bisulfite sequencing, and metabolomic and proteomic profiling has led quantitative genetics into the era of big omics data. Association analyses of omics data, such as genome-, transcriptome-, proteome-, and methylome-wide association studies, along with integrative analyses of multiple omics datasets, require various bioinformatics tools, which rely on advanced programming skills and command-line interfaces and thus pose challenges for wet-lab biologists. Here, we present EasyOmics, a stand-alone R Shiny application with a user-friendly interface that enables wet-lab biologists to perform population-scale omics data association, integration, and visualization. The toolkit incorporates multiple functions designed to meet the increasing demand for population-scale omics data analyses, including data quality control, heritability estimation, genome-wide association analysis, conditional association analysis, omics quantitative trait locus mapping, omics-wide association analysis, omics data integration, and visualization. A wide range of publication-quality graphs can be prepared in EasyOmics by pointing and clicking. EasyOmics is a platform-independent software that can be run under all operating systems, with a docker container for quick installation. It is freely available to non-commercial users at Docker Hub https://hub.docker.com/r/yuhan2000/easyomics.

Among the more than 90 identified genetic risk loci for late-onset Alzheimer's disease (AD) and related dementias, the apolipoprotein E gene ( APOE ) ɛ2/ɛ3/ɛ4 polymorphism remains the longstanding benchmark for genetic disease risk with a consistently large effect across studies 1-10 . Despite this massive signal, the exact mechanisms for how ɛ4 increases and for how ɛ2 decreases dementia risk is not well-understood. Importantly, recent trials of anti-amyloid therapies suggest less efficacy and higher risks of severe side effects in ε4 carriers 11-13 , hampering the treatment of those with the highest unmet need. To improve our understanding of the genetic architecture of AD in the context of its main genetic driver, we performed genome-wide association studies (GWASs) stratified by ε4 and ε2 carrier status. Such insights may help to understand and overcome side effects, to impact clinical trial enrolment strategies, and to create the scientific basis for targeted mechanism-driven therapies in neurodegenerative diseases.

Pancreatic Ductal Adenocarcinoma (PDAC) is the third leading cause of cancer-related deaths in the U.S. Both rare and common germline variants contribute to PDAC risk. Here, we fine-map and functionally characterize a common PDAC risk signal at chr1p36.33 (tagged by rs13303010) identified through a genome wide association study (GWAS). One of the fine-mapped SNPs, rs13303160 (OR = 1.23 (95% CI 1.15-1.32), P-value = 2.74×10-9, LD r2 = 0.93 with rs13303010 in 1000 G EUR samples) demonstrated allele-preferential gene regulatory activity in vitro and binding of JunB and JunD in vitro and in vivo. Expression Quantitative Trait Locus (eQTL) analysis identified KLHL17 as a likely target gene underlying the signal. Proteomic analysis identified KLHL17 as a member of the Cullin-E3 ubiquitin ligase complex with vimentin and nestin as candidate substrates for degradation in PDAC-derived cells. In silico differential gene expression analysis of high and low KLHL17 expressing GTEx pancreas samples suggested an association between lower KLHL17 levels (risk associated) and pro-inflammatory pathways. We hypothesize that KLHL17 may mitigate cell injury and inflammation by recruiting nestin and vimentin for ubiquitination and degradation thereby influencing PDAC risk.

Tuberculosis (TB) continues to be a leading cause of morbidity and mortality worldwide. Past genome-wide association studies (GWAS) have explored TB susceptibility across various ethnic groups, yet a significant portion of TB heritability remains unexplained.

We conducted GWAS in the Singapore Chinese and Vietnamese, followed by a comprehensive meta-analysis incorporating 4 independent East Asian datasets, resulting in a total of 11,841 cases and 197,373 population controls.

We identified a novel susceptibility locus for pulmonary TB (PTB) at 22q12.2 in East Asians [rs6006426, OR (95%Cl) =1.097(1.066, 1.130), P meta =3.31×10 -10 ]. The association was further validated in Europeans [OR (95%Cl) =1.101(1.002, 1.211), P =0.046] and was strengthened in the combined meta-anlaysis including 12,736 PTB cases and 673,864 controls [OR (95%Cl) =1.098(1.068, 1.129), P meta =4.33×10 -11 ]. rs6006426 affected SF3A1 expression in various immune cells ( P from 0.003 to 6.17×10 -18 ) and OSM expression in monocytes post lipopolysaccharide stimulation ( P =5.57×10 -4 ). CRISPR-Cas9 edited zebrafish embryos with osm depletion resulted in decreased burden of Mycobacterium marinum ( M.marinum ) in infected embryos ( P =0.047).

Our findings offer novel insights into the genetic factors underlying TB and reveals new avenues for understanding its etiology.

Genotype-phenotype association tests are typically adjusted for population stratification using principal components that are estimated genome-wide. This lacks resolution when analyzing populations with fine structure and/or individuals with fine levels of admixture. This can affect power and precision, and is a particularly relevant consideration when control individuals are recruited using geographic selection criteria. Such is the case in France where we have recently created reference panels of individuals anchored to different geographic regions. To make correct comparisons against case groups, who would likely be gathered from large urban areas, new methods are needed. We present SURFBAT (a surrogate family based association test), which performs an approximation of the transmission-disequilibrium test. Our method hinges on the application of genotype imputation algorithms to match similar haplotypes between the case and control groups. This permits us to approximate local ancestry informed posterior probabilities of un-transmitted parental alleles of each case individual. This is achieved by assuming haplotypes from the imputation panel are well-matched for ancestry with the case individuals. When the first haplotype of an individual from the imputation panel matches that of a case individual, it is assumed that the second haplotype of the same reference individual can be used as a locally ancestry matched control haplotype and to approximately impute un-transmitted parental alleles. SURFBAT provides an association test that is inherently robust to fine-scale population stratification and opens up the possibility of efficiently using large imputation reference panels as control groups for association testing. In contrast to other methods for association testing that incorporate local-ancestry inference, SURFBAT does not require a set of ancestry groups to be defined, nor for local ancestry to be explicitly estimated. We demonstrate the interest of our tool on simulated datasets, as well as on a real-data example for a group of case individuals affected by Brugada syndrome.

One of the trending fields in almost all areas of science and technology is artificial intelligence. Computational biology and artificial intelligence can help gene therapy in many steps including: gene identification, gene editing, vector design, development of new macromolecules and modeling of gene delivery. There are various tools used by computational biology and artificial intelligence in this field, such as genomics, transcriptomic and proteomics data analysis, machine learning algorithms and molecular interaction studies. These tools can introduce new gene targets, novel vectors, optimized experiment conditions, predict the outcomes and suggest the best solutions to avoid undesired immune responses following gene therapy treatment.

Current methodologies of genome-wide single-nucleotide polymorphism (SNP) genotyping produce large amounts of missing data that may affect statistical inference and bias the outcome of experiments. Genotype imputation is routinely used in well-studied species to buffer the impact in downstream analysis, and several algorithms are available to fill in missing genotypes. The lack of reference haplotype panels precludes the use of these methods in genomic studies on non-model organisms. As an alternative, machine learning algorithms are employed to explore the genotype data and to estimate the missing genotypes. Here, we propose an imputation method based on self-organizing maps (SOM), a widely used neural networks formed by spatially distributed neurons that cluster similar inputs into close neurons. The method explores genotype datasets to select SNP loci to build binary vectors from the genotypes, and initializes and trains neural networks for each query missing SNP genotype. The SOM-derived clustering is then used to impute the best genotype. To automate the imputation process, we have implemented gtImputation, an open-source application programmed in Python3 and with a user-friendly GUI to facilitate the whole process. The method performance was validated by comparing its accuracy, precision and sensitivity on several benchmark genotype datasets with other available imputation algorithms. Our approach produced highly accurate and precise genotype imputations even for SNPs with alleles at low frequency and outperformed other algorithms, especially for datasets from mixed populations with unrelated individuals.

Recurrent urinary tract infections (rUTIs) are a major clinical challenge and their increasing prevalence underscores the need to define host-microbiome interactions underlying susceptibility. How the urinary microbiota engages with the biochemical environment of the urogenital tract is yet to be fully defined. Here, we leverage paired metagenomic and quantitative metabolomic data to establish a microbe-metabolite association network of the female urinary microbiome and define metabolic signatures of rUTI. We observe unique metabolic networks of uropathogens and uroprotective species, highlighting potential metabolite-driven ecological shifts influencing rUTI susceptibility. We find distinct metabolites are associated with urinary microbiome diversity and identify a lipid signature of active rUTI that accurately distinguishes cases from controls. Finally, we identify deoxycholic acid as a prognostic indicator for UTI recurrence. Together these findings provide insight into microbiome-metabolite interactions within the female urinary tract and highlight new biomarkers for the development of new diagnostic tools to improve patient outcomes.

Psychiatric disorders like schizophrenia, bipolar disorder, and major depressive disorder exhibit substantial genetic and clinical overlap. However, their molecular architecture remains elusive due to their polygenic nature and complex brain cell interactions. We integrated clinical data with genetic susceptibility to investigate gene expression and chromatin accessibility in the orbitofrontal cortex of 92 postmortem human brain samples at the single-nucleus (sn) level. Using snRNA-seq and snATAC-seq, we analyzed ~800,000 and 400,000 nuclei, respectively. We observed cell-type-specific dysregulation related to clinical diagnosis and genetic risk. Dysregulation in gene expression and chromatin accessibility associated with diagnosis was pronounced in excitatory neurons. Conversely, genetic risk predominantly affected glial and endothelial cells. Notably, INO80E and HCN2 genes exhibited dysregulation in excitatory neurons' superficial layers 2/3 influenced by schizophrenia polygenic risk. This study unveils the complex genetic and epigenetic landscape of psychiatric disorders, emphasizing the importance of cell-type-specific analyses in understanding their pathogenesis and contrasting genetic predisposition with clinical diagnosis.

Hypertrophic cardiomyopathy (HCM) is an important cause of morbidity and mortality, with pathogenic variants found in about a third of cases. Large-scale genome-wide association studies (GWAS) demonstrate that common genetic variation contributes to HCM risk. Here we derive polygenic scores (PGS) from HCM GWAS and genetically correlated traits and test their performance in the UK Biobank, 100,000 Genomes Project, and clinical cohorts. We show that higher PGS significantly increases the risk of HCM in the general population, particularly among pathogenic variant carriers, where HCM penetrance differs 10-fold between those in the highest and lowest PGS quintiles. Among relatives of HCM probands, PGS stratifies risks of developing HCM and adverse outcomes. Finally, among HCM cases, PGS strongly predicts the risk of adverse outcomes and death. These findings support the broad utility of PGS across clinical settings, enabling tailored screening and surveillance and stratification of risk of adverse outcomes.

The nicotine metabolite ratio (NMR; 3'hydroxycotinine/cotinine) is a stable biomarker for CYP2A6 enzyme activity and nicotine clearance, with demonstrated clinical utility in personalizing smoking cessation treatment. Common genetic variation in the CYP2A6 region is strongly associated with NMR in smokers. Here, we investigated this regional association in more detail. We evaluated the association of CYP2A6 single-nucleotide polymorphisms (SNPs) and * alleles with NMR among African American smokers (N = 953) from two clinical trials of smoking cessation. Stepwise conditional analysis and Bayesian fine-mapping were undertaken. Putative causal variants were incorporated into an existing African ancestry-specific genetic risk score (GRS) for NMR, and the performance of the updated GRS was evaluated in both African American (n = 953) and European ancestry smokers (n = 933) from these clinical trials. Five independent associations with NMR in the CYP2A6 region were identified using stepwise conditional analysis, including the deletion variant CYP2A6*4 (beta = -0.90, p = 1.55 × 10-11). Six putative causal variants were identified using Bayesian fine-mapping (posterior probability, PP = 0.67), with the top causal configuration including CYP2A6*4, rs116670633, CYP2A6*9, rs28399451, rs8192720, and rs10853742 (PP = 0.09). Incorporating these putative causal variants into an existing ancestry-specific GRS resulted in comparable prediction of NMR within African American smokers, and improved trans-ancestry portability of the GRS to European smokers. Our findings suggest that both * alleles and SNPs underlie the association of the CYP2A6 region with NMR among African American smokers, identify a shortlist of variants that may causally influence nicotine clearance, and suggest that portability of GRSs across populations can be improved through inclusion of putative causal variants.

Kidney dysfunction is a major cause of mortality, but its genetic architecture remains elusive. In this study, we conducted a multiancestry genome-wide association study in 2.2 million individuals and identified 1026 (97 previously unknown) independent loci. Ancestry-specific analysis indicated an attenuation of newly identified signals on common variants in European ancestry populations and the power of population diversity for further discoveries. We defined genotype effects on allele-specific gene expression and regulatory circuitries in more than 700 human kidneys and 237,000 cells. We found 1363 coding variants disrupting 782 genes, with 601 genes also targeted by regulatory variants and convergence in 161 genes. Integrating 32 types of genetic information, we present the "Kidney Disease Genetic Scorecard" for prioritizing potentially causal genes, cell types, and druggable targets for kidney disease.

The challenges faced by today's agronomists, plant breeders, and their managers encompass adapting sustainably to climate variability while working with limited budgets. Besides, managers are dealing with a multitude of issues with different organizations working on similar initiatives and projects, leading to a lack of a sustainable impact on smallholder farmers. To transform the current food systems as a more sustainable and resilient model efficient solutions are needed to deliver and convey results. Challenges such as logistics, labour, infrastructure, and equity, must be addressed alongside adapting to increasingly unstable climate conditions which affect the life cycle of transboundary pathogens and pests. In this context, transforming food systems go far beyond just farmers and plant breeders and it requires substantial contributions from industry, global finances, transportation, energy, education, and country developmental sectors including legislators. As a result, a holistic approach is essential for achieving sustainable and resilient food systems to sustain a global population anticipated to reach 9.7 billion by 2050 and 11.2 billion by 2100. As of 2021, nearly 193 million individuals were affected by food insecurity, 40 million more than in 2020. Meanwhile, the digital world is rapidly advancing with the digital economy estimated at about 20% of the global gross domestic product, suggesting that digital technologies are increasingly accessible even in areas affected by food insecurity. Leveraging these technologies can facilitate the development of climate-smart cultivars that adapt effectively to climate variation, meet consumer preferences, and address human and livestock nutritional needs. Most economically important traits in crops are controlled by multiple loci often with recessive alleles. Considering particularly Africa, this continent has several agro-climatic zones, hence crops need to be adapted to these. Therefore, targeting specific loci using modern tools offers a precise and efficient approach. This review article aims to address how these new technologies can provide a better support to smallholder farmers.

Sunitinib exhibits considerable interindividual variability in exposure. While the target total plasma concentration of sunitinib and its active metabolite is 50-87.5 ng/mL for the intermittent dosing schedule, ~10-21% of patients experience higher exposures (>87.5 ng/mL), correlated with an increased risk for toxicity. Previous research identified single nucleotide variants (SNVs) in genes from the sunitinib pharmacokinetic pathway to be associated with efficacy and toxicity. However, significant interindividual variability in exposure remains unexplained. Our aim was to identify genetic variants associated with supratherapeutic exposure of sunitinib.

This was a genome-wide association study. Cases were identified during routine therapeutic drug monitoring and consisted of patients with dose-normalized sunitinib plasma concentrations >87.5 ng/mL (intermittent dosing) or >75 ng/mL (continuous dosing). Controls were sampled from the historical cohort EuroTARGET who tolerated the standard dose of 50 mg in an intermittent schedule. SNVs were tested for an association with sunitinib exposure. A P-value ≤5 × 10-8 was considered significant and a P-value between 5 × 10-8 and 5 × 10-6 was considered suggestive.

Sixty-nine cases and 345 controls were included for association analysis. One SNV (rs6923761), located on the gene glucagon-like peptide 1 receptor, was significantly associated with increased sunitinib exposure (P = 7.86 × 10-19). Twelve SNVs were suggestive for an association with sunitinib exposure (P ≤ 5 × 10-6).

While rs6923761 is associated with high sunitinib exposure, the underlying mechanism is not yet clarified and warrants further investigation. [Corrections made on 23 September 2024, after first online publication: In the preceding sentence, identifier rs6923671 has been changed to rs6923761 in this version.] We could not confirm the earlier found associations between SNVs in candidate genes involved in the pharmacokinetic pathway of sunitinib and its efficacy and toxicity.

The phenotypic variability of inherited conditions can be due to several factors including environmental, epigenetic, and genetic. One of those genetic factors is the presence of modifying loci which alter the phenotypic expression of a primary disease or phenotype-causing variant. Modifiers are known to affect penetrance, dominance, expressivity, and pleiotropy of disease.

We review the literature to highlight the impact of modifiers on inherited retinal diseases.

Modifiers have been identified or associated with phenotypic variation in many inherited retinal diseases including retinitis pigmentosa and Stargardt disease. Despite being notoriously difficult to identify, proposed candidate modifiers have been identified using multiple methods including GWAS, family and population studies, and variant calling methods.

Overall, modifiers present themselves as an interesting target for further understanding of underlying disease pathways that could ultimately lead to therapeutic targets.

Disordered eating and self-harm commonly co-occur in young people suggesting potential for shared underlying causes. Body image dissatisfaction (BID) has been recognised as a psychological correlate of body size, associated with both disordered eating and self-harm. However, the investigation into etiological pathways early in the lifecourse to provide detail on how body size and BID may foster disordered eating and self-harm remains largely unexplored. Employing data from two large population-based cohorts, the UK Biobank and the Avon Longitudinal Study of Parents And Children (ALSPAC), we conducted bidirectional Mendelian randomization (MR) to determine the causal direction of effect between genetically predicted prepubertal body size and two measures of BID indicating (i) desire to be smaller, and (ii) desire to be larger. We then used multivariable regression followed by counterfactual mediation analyses. Bidirectional MR indicated robust evidence that increased genetically predicted prepubertal body size increased desire to be smaller and decreased desire to be larger. Evidence for the reverse causal direction was negligible. These findings remained very similar across sensitivity analyses. In females and males, multivariable regression analyses demonstrated that being overweight increased the risk of disordered eating (risk ratio (RR), 95% confidence interval (CI): 1.19, 1.01 to 1.40 and 1.98, 1.28 to 3.05, respectively) and self-harm (RR, 95% CI: 1.35, 1.04 to 1.77 and 1.55, 0.86 to 2.81, respectively), while being underweight was protective against disordered eating (RR, 95% CI: 0.57, 0.40 to 0.81 and 0.81, 0.38 to 1.73, respectively). There was weak evidence of an increase in the risk of self-harm among underweight individuals. Mediation analyses indicated that the relationship between being overweight and subsequent disordered eating was largely mediated by the desire to be smaller. Our research carries important public health implications, suggesting distinct risk profiles for self-harm and disordered eating in relation to weight and body image. In addition, a better understanding of genetically predicted prepubertal BID may be valuable in the prevention and treatment of disordered eating and self-harm in adolescence.

Pancreatic cysts, particularly intraductal papillary mucinous neoplasms (IPMNs), pose a potential risk for progressing to pancreatic cancer (PC). This study investigates the genetic architecture of benign pancreatic cysts and its potential connection to PC using genome-wide association studies (GWAS). The discovery GWAS identified significant genetic variants associated with benign cysts, specifically the rs142409042 variant near the OPCML gene. A pairwise GWAS comparing PC to benign cysts revealed the rs7190458 variant near the BCAR1 and CTRB1 genes. Further analysis with identified GWAS genes highlighted the Actin Related Protein (Arp) 2/3 complex as a potentially important molecular mechanism connecting benign cysts and PC. The Arp2/3 complex-associated genes were significantly upregulated in PC, suggesting their role in the malignant transformation of pancreatic cysts. Differential expression of these genes was observed across various cell types in PC, indicating their involvement in the tumor microenvironment. These findings suggest that the Arp2/3 complex-associated genes can serve as potential biomarkers for predicting the malignant transformation of pancreatic cysts, opening new avenues for targeted therapies and early detection strategies.

In the present study, we investigated the association of genetic predisposition with specific dimensions of dementia pathophysiology for global and domain-specific cognitive decline in older adults. The sample was drawn from the Hellenic Longitudinal Investigation of Aging and Diet (HELIAD) study, comprising 512 cognitively normal individuals over 64 years of age, with a mean follow-up of 2.9 years. Cognitive function was evaluated through a neuropsychological test battery, while genetic predisposition was assessed based on two distinct Polygenic Risk Scores (PRS) for amyloid-beta 42 (Aβ42) and white matter hyperintensities (WMH). The association of each PRS with the cognitive decline rate was examined using generalized estimating equation models. In the whole sample, higher PRSs Aβ42 (β = -0.042) and WMH (β =-0.029) were associated with a higher rate of global cognitive decline per year, an association which remained significant in age, sex, and education subgroups. Moreover, higher PRSs Aβ42 and WMH were related to significant memory decline only in females, older, and highly educated participants. Thus, while the association of both PRSs with global cognitive decline over time was independent of age, sex, or education, the relationship of the specific PRSs with the memory decline rate appeared to vary depending on these factors.

There is a paucity of evidence on the association between genetic propensity for hippocampal atrophy with cognitive outcomes. Therefore, we examined the relationship of the polygenic risk score for hippocampal atrophy (PRShp) with the incidence of amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD) as well as the rates of cognitive decline.

Participants were drawn from the population-based HELIAD cohort. Comprehensive neuropsychological assessments were performed at baseline and at follow-up. PRShp was derived from the summary statistics of a large genome-wide association study for hippocampal volume. Cox proportional hazards models as well as generalized estimating equations (GEEs) were used to evaluate the association of PRShp with the combined incidence of aMCI/AD and cognitive changes over time, respectively. All models were adjusted for age, sex, education, and apolipoprotein E (APOE) genotype.

Our analysis included 618 older adults, among whom 73 developed aMCI/AD after an average follow-up of 2.96 ± 0.8 years. Each additional SD of PRShp elevated the relative hazard for incident aMCI/AD by 46%. Participants at the top quartile of PRShp had an almost three times higher risk of converting to aMCI/AD compared to the lowest quartile group. Higher PRShp scores were also linked to steeper global cognitive and memory decline. The impact of PRShp was greater among women and younger adults.

Our findings support the association of PRShp with aMCI/AD incidence and with global cognitive and memory decline over time. The PRS association was sex- and age-dependent, suggesting that these factors should be considered in genetic modelling for AD.

We have reanalyzed the genomic data from the International Collaboration for the Genomics of HIV (ICGH), focusing on HIV-1 Elite Controllers (EC).

A genome-wide association study (GWAS) was performed, comparing 543 HIV-1 EC individuals with 3,272 uninfected controls (CTR) of European ancestry. 8 million single nucleotide polymorphisms (SNPs) and HLA class I and class II gene alleles were imputed to compare EC and CTR.

Two thousand six hundred twenty-six SNPs were associated with EC (p<5.10-8), all located within the Major Histocompatibility Complex (MHC) region. Stepwise regression analysis narrowed this list to 17 SNPs. In parallel, 22 HLA class I and II alleles were associated with EC. Through meticulous mapping of the LD between all identified signals and employing reciprocal covariate analyses, we delineated a final set of 6 independent SNPs and 3 HLA class I gene alleles that accounted for most of the associations observed with EC. Our study revealed the presence of cumulative haploblock effects (SNP rs9264942 contributing to the HLA-B*57:01 effect) and that several HLA allele associations were in fact caused by SNPs in linkage disequilibrium (LD). Upon investigating SNPs in LD with the selected 6 SNPs and 3 HLA class I alleles for their impact on protein function (either damaging or differential expression), we identified several compelling mechanisms potentially explaining EC among which: a multi-action mechanism of HLA-B*57:01 involving MICA mutations and MICB differential expression overcoming the HIV-1 blockade of NK cell response, and overexpression of ZBTB12 with a possible anti-HIV-1 effect through HERV-K interference; a deleterious mutation in PPP1R18 favoring viral budding associated with rs1233396.

Our results show that MHC influence on EC likely extends beyond traditional HLA class I or class II allele associations, encompassing other MHC SNPs with various biological impacts. They point to the key role of NK cells in preventing HIV-1 infection. Our analysis shows that HLA-B*57:01 is indeed associated with partially functional MICA/MICB proteins which could also explain this marker's involvement in other diseases such as psoriasis. More broadly, our findings suggest that within any HLA class I and II association in diseases, there may exist distinct causal SNPs within this crucial, gene-rich, and LD-rich MHC region.

The NHGRI-EBI GWAS Catalog serves as a vital resource for the genetic research community, providing access to the most comprehensive database of human GWAS results. Currently, it contains close to 7 000 publications for >15 000 traits, from which more than 625 000 lead associations have been curated. Additionally, 85 000 full genome-wide summary statistics datasets-containing association data for all variants in the analysis-are available for downstream analyses such as meta-analysis, fine-mapping, Mendelian randomisation or development of polygenic risk scores. As a centralised repository for GWAS results, the GWAS Catalog sets and implements standards for data submission and harmonisation, and encourages the use of consistent descriptors for traits, samples and methodologies. We share processes and vocabulary with the PGS Catalog, improving interoperability for a growing user group. Here, we describe the latest changes in data content, improvements in our user interface, and the implementation of the GWAS-SSF standard format for summary statistics. We address the challenges of handling the rapid increase in large-scale molecular quantitative trait GWAS and the need for sensitivity in the use of population and cohort descriptors while maintaining data interoperability and reusability.