10 Years of GWAS Discovery: Biology, Function, and Translation

Predicting Lung Cancer in Korean Never-Smokers With Polygenic Risk Scores

In the last few decades, genome-wide association studies (GWAS) with more than 10,000 subjects have identified several loci associated with lung cancer and these loci have been used to develop novel risk prediction tools for cancer. The present study aimed to establish a lung cancer prediction model for Korean never-smokers using polygenic risk scores (PRSs); PRSs were calculated using a pruning-thresholding-based approach based on 11 genome-wide significant single nucleotide polymorphisms (SNPs). Overall, the odds ratios tended to increase as PRSs were larger, with the odds ratio of the top 5% PRSs being 1.71 (95% confidence interval: 1.31-2.23) using the 40%-60% percentile group as the reference, and the area under the curve (AUC) of the prediction model being of 0.76 (95% confidence interval: 0.747-0.774). The receiver operating characteristic (ROC) curves of the prediction model with and without PRSs as covariates were compared using DeLong's test, and a significant difference was observed. Our results suggest that PRSs can be valuable tools for predicting the risk of lung cancer.

Impacts of Methylenetetrahydrofolate Reductase Genotypes on Hallux Valgus

BACKGROUND/AIM

Hallux valgus (HV) is the most common deformity of the forefoot. Although HV has been strongly associated with a family history, its genetic underpinnings remain unclear. Few studies have examined the relationship between folic acid metabolism, which is critical in normal bone development, and HV. The study aimed to investigate the contribution of methylenetetrahydrofolate reductase (MTHFR) genotypes to the risk of HV.

MATERIALS AND METHODS

The MTHFR rs1801133 and rs1801131 genotypes were analyzed in 150 patients with HV and 600 controls without HV, using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP).

RESULTS

The results highlighted a significant difference in the genotypic frequency distributions of MTHFR rs1801133 between the HV cases and non-HV controls (p for trend=0.0024). Specifically, individuals with the homozygous TT genotype at MTHFR rs1801133 exhibited a 2.57-fold increased risk of HV (95% confidence interval=1.49-4.42, p=0.0009). However, those with the CT genotype did not show an elevated risk. Stratified analysis showed no correlation between MTHFR rs1801133 genotypic distributions and different age groups (below or above 51 years) or sex (both p>0.05). Furthermore, no associations were identified between MTHFR rs1801133 and height, weight, or body mass index in relation to HV risk.

CONCLUSION

The TT genotype of MTHFR rs1801133 is associated with an increased risk of HV. Subgrouping HV patients based on their MTHFR genotypes and related comorbidities, such as rheumatoid arthritis, may offer a new approach to diagnosis.

Leveraging Distributed Brain Signal at Rest to Predict Internalizing Symptoms in Youth: Deriving a Polyneuro Risk Score From the ABCD Study Cohort

BACKGROUND

The prevalence of internalizing psychopathology rises precipitously from early to mid-adolescence, yet the underlying neural phenotypes that give rise to depression and anxiety during this developmental period remain unclear.

METHODS

Youths from the Adolescent Brain Cognitive Development (ABCD) Study (ages 9-10 years at baseline) with a resting-state functional magnetic resonance imaging scan and mental health data were eligible for inclusion. Internalizing subscale scores from the Brief Problem Monitor-Youth Form were combined across 2 years of follow-up to generate a cumulative measure of internalizing symptoms. The total sample (N = 6521) was split into a large discovery dataset and a smaller validation dataset. Brain-behavior associations of resting-state functional connectivity with internalizing symptoms were estimated in the discovery dataset. The weighted contributions of each functional connection were aggregated using multivariate statistics to generate a polyneuro risk score (PNRS). The predictive power of the PNRS was evaluated in the validation dataset.

RESULTS

The PNRS explained 10.73% of the observed variance in internalizing symptom scores in the validation dataset. Model performance peaked when the top 2% functional connections identified in the discovery dataset (ranked by absolute β weight) were retained. The resting-state functional connectivity networks that were implicated most prominently were the default mode, dorsal attention, and cingulo-parietal networks. These findings were significant (p < 1 × 10-6) as accounted for by permutation testing (n = 7000).

CONCLUSIONS

These results suggest that the neural phenotype associated with internalizing symptoms during adolescence is functionally distributed. The PNRS approach is a novel method for capturing relationships between resting-state functional connectivity and behavior.

Heritable polygenic editing: the next frontier in genomic medicine?

Polygenic genome editing in human embryos and germ cells is predicted to become feasible in the next three decades. Several recent books and academic papers have outlined the ethical concerns raised by germline genome editing and the opportunities that it may present1-3. To date, no attempts have been made to predict the consequences of altering specific variants associated with polygenic diseases. In this Analysis, we show that polygenic genome editing could theoretically yield extreme reductions in disease susceptibility. For example, editing a relatively small number of genomic variants could make a substantial difference to an individual's risk of developing coronary artery disease, Alzheimer's disease, major depressive disorder, diabetes and schizophrenia. Similarly, large changes in risk factors, such as low-density lipoprotein cholesterol and blood pressure, could, in theory, be achieved by polygenic editing. Although heritable polygenic editing (HPE) is still speculative, we completed calculations to discuss the underlying ethical issues. Our modelling demonstrates how the putatively positive consequences of gene editing at an individual level may deepen health inequalities. Further, as single or multiple gene variants can increase the risk of some diseases while decreasing that of others, HPE raises ethical challenges related to pleiotropy and genetic diversity. We conclude by arguing for a collectivist perspective on the ethical issues raised by HPE, which accounts for its effects on individuals, their families, communities and society4.

Using GWAS and Machine Learning to Identify and Predict Genetic Variants Associated with Foodborne Bacteria Phenotypic Traits

One of the main challenges in food microbiology is to prevent the risk of outbreaks by avoiding the distribution of food contaminated by bacteria. This requires constant monitoring of the circulating strains throughout the food production chain. Bacterial genomes contain signatures of natural evolution and adaptive markers that can be exploited to better understand the behavior of pathogen in the food industry. The monitoring of foodborne strains can therefore be facilitated by the use of these genomic markers capable of rapidly providing essential information on isolated strains, such as the source of contamination, risk of illness, potential for biofilm formation, and tolerance or resistance to biocides. The increasing availability of large genome datasets is enhancing the understanding of the genetic basis of complex traits such as host adaptation, virulence, and persistence. Genome-wide association studies have shown very promising results in the discovery of genomic markers that can be integrated into rapid detection tools. In addition, machine learning has successfully predicted phenotypes and classified important traits. Genome-wide association and machine learning tools have therefore the potential to support decision-making circuits intending at reducing the burden of foodborne diseases. The aim of this chapter review is to provide knowledge on the use of these two methods in food microbiology and to recommend their use in the field.

Structural framework to address variant-gene relationship in primary open-angle glaucoma

Primary open-angle glaucoma (POAG) is a complex, multifactorial disease leading to progressive optic neuropathy and irreversible vision loss. Genome-Wide Association Studies (GWAS) have significantly advanced our understanding of the genetic loci associated with POAG. Expanding on these findings, Exome-Wide Association Studies (ExWAS) refine the genetic landscape by identifying rare coding variants with potential functional relevance. Post-GWAS in silico analyses, including fine-mapping, gene-based association testing, and pathway analysis, offer insights into target genes and biological mechanisms underlying POAG. This review aims to provide a comprehensive roadmap for the post-GWAS characterization of POAG genes. We integrate current knowledge from GWAS, ExWAS, and post-GWAS analyses, highlighting key genetic variants and pathways implicated in POAG. Recent advancements in genomics, such as ATAC-seq, CUT&RUN, and Hi-C, are crucial for identifying disease-relevant gene regulatory elements by profiling chromatin accessibility, histone modifications, and three-dimensional chromatin architecture. These approaches help pinpoint regulatory elements that influence gene expression in POAG. Expression Quantitative Trait Loci (eQTL) analysis and Transcriptome-Wide Association Studies (TWAS) elucidate the impact of these elements on gene expression and disease risk, while functional validations like enhancer reporter assays confirm their relevance. The integration of high-resolution genomics with functional assays and the characterization of genes in vivo using animal models provides a robust framework for unraveling the complex genetic architecture of POAG. This roadmap is essential for advancing our understanding and identification of genes and regulatory networks involved in POAG pathogenesis.

Associations of IL6R and IL10 Gene Polymorphisms with Susceptibility to Ankylosing Spondylitis with or without Acute Anterior Uveitis

BACKGROUND

This research aims to explore the associations between ten candidate single nucleotide polymorphisms (SNPs) on Interleukin-6 receptor (IL6R) and Interleukin-10 (IL10) genes and ankylosing spondylitis (AS) patients with or without acute anterior uveitis (AAU).

METHODS

This study involved a case-control approach that examined 354 cases with AS and AAU, 377 AS cases without AAU, and 918 healthy controls. Genotyping of ten SNPs of IL10 and IL6R genes was performed using iPLEX Gold genotyping method. The allele and genotype frequencies of cases and healthy individuals were contrasted using the chi-square test. The IL10 mRNA level in various IL10 genotypes was tested using real-time PCR.

RESULTS

Two loci associated with AS with AAU were identified: IL10//rs3790622 (OR = 0.664; 95%CI = 0.503-0.878; Pc = 0.038); IL10//rs3021094 (OR = 1.365; 95%CI = 1.110-1.679; Pc = 0.032). The other eight loci located on IL10 and IL6R did not show significant associations with the diseases. Additionally, as shown by functional experiments, there was no correlation between the mRNA expression of IL10 and various genotypes.

CONCLUSION

Our study suggests that the IL10 gene contributes to the susceptibility of the Chinese population to AS with AAU.

Shedding Light on Antisocial Behavior Through Genetically Informed Research

Antisocial behavior (ASB) refers to a set of behaviors that violate social norms and disregard the well-being and rights of others. In this review, we synthesize evidence from studies using genetically informed designs to investigate the genetic and environmental contributions to individual differences in ASB. We review evidence from studies using family data (twin and adoption studies) and measured DNA (candidate gene and genome-wide association studies) that have informed our understanding of ASB. We describe how genetically informative designs are especially suited to investigate the nature of environmental risk and the forms of gene-environment interplay. We also highlight clinical and legal implications, including how insights from genetically informed research can help inform prevention and intervention, and we discuss some challenges and opportunities within this field of research.

Refining fine-mapping: Effect sizes and regional heritability

Recent statistical approaches have shown that the set of all available genetic variants explains considerably more phenotypic variance of complex traits and diseases than the individual variants that are robustly associated with these phenotypes. However, rapidly increasing sample sizes constantly improve detection and prioritization of individual variants driving the associations between genomic regions and phenotypes. Therefore, it is useful to routinely estimate how much phenotypic variance the detected variants explain for each region by taking into account the correlation structure of variants and the uncertainty in their causal status. Here we extend the FINEMAP software to estimate the effect sizes and regional heritability under the probabilistic model that assumes a handful of causal variants per region. Using the UK Biobank (UKB) data to simulate genomic regions, we demonstrate that FINEMAP provides higher precision and enables more detailed decomposition of regional heritability into individual variants than the variance component model implemented in BOLT or the fixed-effect model implemented in HESS, particularly when there are only a few causal variants in the fine-mapped region. Using data from 2,940 plasma proteins from the UKB study, we observed that on average FINEMAP identified 2.5 causal variants at an association signal and captured 36% more regional heritability than the variant with the lowest P-value. We estimate that in genomic regions with notable contribution to the total heritability, FINEMAP captures on average 13% and 40% more heritability than BOLT and HESS respectively. Our analysis shows how FINEMAP, BOLT and HESS relate to each other in cases where inference of a variant-level picture of the regional genetic architecture is possible.

Hierarchical selection of genetic and gene by environment interaction effects in high-dimensional mixed models

Interactions between genes and environmental factors may play a key role in the etiology of many common disorders. Several regularized generalized linear models have been proposed for hierarchical selection of gene by environment interaction effects, where a gene-environment interaction effect is selected only if the corresponding genetic main effect is also selected in the model. However, none of these methods allow to include random effects to account for population structure, subject relatedness and shared environmental exposure. In this article, we develop a unified approach based on regularized penalized quasi-likelihood estimation to perform hierarchical selection of gene-environment interaction effects in sparse regularized mixed models. We compare the selection and prediction accuracy of our proposed model with existing methods through simulations under the presence of population structure and shared environmental exposure. We show that for all simulation scenarios, including and additional random effect to account for the shared environmental exposure reduces the false positive rate and false discovery rate of our proposed method for selection of both gene-environment interaction and main effects. Using the F 1 score as a balanced measure of the false discovery rate and true positive rate, we further show that in the hierarchical simulation scenarios, our method outperforms other methods for retrieving important gene-environment interaction effects. Finally, we apply our method to a real data application using the Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) study, and found that our method retrieves previously reported significant loci.

A Mendelian randomization study on associations between plasma lipidome, circulating inflammatory proteins, and erectile dysfunction

Background

Some studies suggest a potential association between plasma lipidome and erectile dysfunction (ED), but the underlying mechanism and whether circulating inflammatory proteins act as mediators remain unclear. The purpose of this study was to investigate the potential causal relationships between plasma lipidome, inflammatory proteins, and ED.

Methods

Plasma lipidome, circulating inflammatory proteins, and ED cases were identified based on the summary data from several large-scale genome-wide association studies (GWAS). The causal relationships of plasma lipidome and circulating inflammatory proteins with ED were explored by a bidirectional two-sample, two-sample Mendelian randomization (MR) method. The inverse variance weighted (IVW) method was used as the primary analytical method. MR-Egger and the weighted median (IVW) methods were utilized as supplementary analytical techniques. Sensitivity analyses including MR-Pleiotropy RESidual Sum and Outlier method (PRESSO), Cochran's Q test, and MR-Egger intercept test were conducted to address heterogeneity and horizontal pleiotropy.

Results

Ceramide (d42:2) and triacylglycerol (56:3) were found to be associated with an increased risk of ED, while phosphatidylethanolamine (O-18:1_18:2) and phosphatidylinositol (18:1_18:1) were linked to a decreased risk of ED. Interleukin-1α (IL-1α), IL-7, IL-17C, and the tumor necrosis factor (TNF) receptor superfamily member 9 (TNFRSF9) positively affected ED. Conversely, leukemia inhibitory factor and urokinase-type plasminogen activator (uPA) showed a negative impact. Mediation analysis indicated that the uPA mediated between Triacylglycerol (56:3) and ED, accounting for a mediation proportion of -14.71%.

Conclusions

There was a causal relationship between plasma lipidome and circulating inflammatory proteins with ED. Circulating inflammatory proteins appeared to mediate between triacylglycerol (56:3) levels and ED.

Review on GPU accelerated methods for genome-wide SNP-SNP interactions

Detecting genome-wide SNP-SNP interactions (epistasis) efficiently is essential to harnessing the vast data now available from modern biobanks. With millions of SNPs and genetic information from hundreds of thousands of individuals, researchers are positioned to uncover new insights into complex disease pathways. However, this data scale brings significant computational and statistical challenges. To address these, recent approaches leverage GPU-based parallel computing for high-throughput, cost-effective analysis and refine algorithms to improve time and memory efficiency. In this survey, we systematically review GPU-accelerated methods for exhaustive epistasis detection, detailing the statistical models used and the computational strategies employed to enhance performance. Our findings indicate substantial speedups with GPU implementations over traditional CPU approaches. We conclude that while GPU-based solutions hold promise for advancing genomic research, continued innovation in both algorithm design and hardware optimization is necessary to meet future data challenges in the field.

Evaluation of Bayesian Linear Regression derived gene set test methods

BACKGROUND

Gene set tests can pinpoint genes and biological pathways that exert small to moderate effects on complex diseases like Type 2 Diabetes (T2D). By aggregating genetic markers based on biological information, these tests can enhance the statistical power needed to detect genetic associations.

RESULTS

Our goal was to develop a gene set test utilizing Bayesian Linear Regression (BLR) models, which account for both linkage disequilibrium (LD) and the complex genetic architectures intrinsic to diseases, thereby increasing the detection power of genetic associations. Through a series of simulation studies, we demonstrated how the efficacy of BLR derived gene set tests is influenced by several factors, including the proportion of causal markers, the size of gene sets, the percentage of genetic variance explained by the gene set, and the genetic architecture of the traits. By using KEGG pathways, eQTLs, and regulatory elements as different kinds of gene sets with T2D results, we also assessed the performance of gene set tests in explaining more about real phenotypes.

CONCLUSIONS

Comparing our method with other approaches, such as the gold standard MAGMA (Multi-marker Analysis of Genomic Annotation) approach, our BLR gene set test showed superior performance. Combining performance of our method in simulated and real phenotypes, this suggests that our BLR-based approach could more accurately identify genes and biological pathways underlying complex diseases.

Mathematical bounds on r 2 and the effect size in case-control genome-wide association studies

Case-control genome-wide association studies (GWAS) are often used to find associations between genetic variants and diseases. When case-control GWAS are conducted, researchers must make decisions regarding how many cases and how many controls to include in the study. Depending on differing availability and cost of controls and cases, varying case fractions are used in case-control GWAS. Connections between variants and diseases are made using association statistics, includingχ 2 . Previous work in population genetics has shown that LD statistics, includingr 2 , are bounded by the allele frequencies in the population being studied. Since varying the case fraction changes sample allele frequencies, we extend use the known bounds onr 2 to explore how variation in the fraction of cases included in a study can impact statistical power to detect associations. We analyze a simple mathematical model and use simulations to study a quantity proportional to theχ 2 noncentrality parameter, which is closely related tor 2 , under various conditions. Varying the case fraction changes theχ 2 noncentrality parameter, and by extension the statistical power, with effects depending on the dominance, penetrance, and frequency of the risk allele. Our framework explains previously observed results, such as asymmetries in power to detect risk vs. protective alleles, and the fact that a balanced sample of cases and controls does not always give the best power to detect associations, particularly for highly penetrant minor risk alleles that are either dominant or recessive. We show by simulation that our results can be used as a rough guide to statistical power for association tests other thanχ 2 tests of independence.

Longitudinal joint effects of polycyclic aromatic hydrocarbons exposure and genetic susceptibility on fasting plasma glucose: A prospective cohort study of general Chinese urban adults

The effects of environmental polycyclic aromatic hydrocarbons (PAHs) exposure on glycemic regulation and the underlying genetic mechanism were still unclear. This study aimed to analyze the longitudinal joint effects of PAHs exposure and genetic susceptibility on fasting plasma glucose (FPG) through a longitudinal study. We included 4104 observations (2383 baseline participants and 1721 6-year follow-up participants) from Wuhan-Zhuhai cohort. Ten urinary PAHs metabolites and FPG were measured at both baseline and follow-up. We constructed the polygenic risk scores (PRS) of FPG from the corresponding genome-wide association studies. Linear mixed models were used to explore the associations of urinary PAHs metabolites or FPG-PRS on FPG levels in the repeated-measure analysis. Besides, the longitudinal relationships of urinary PAHs metabolites, FPG-PRS, and their joint effects on FPG change over 6 years were evaluated by linear regression models. Compared with participants with persistent low levels of urinary total PAHs metabolites, hydroxynaphthalene, and hydroxyphenanthrene, participants with persistent high levels had average decreases of 0.173, 0.188, and 0.263 mmol/L for FPG change over 6 years, respectively. Each 1-unit increase of FPG-PRS was associated with a 0.531 mmol/L for FPG change over 6 years. Besides, compared with participants with high FPG-PRS and persistent low levels of urinary total hydroxynaphthalene, hydroxyfluorene, and hydroxyphenanthrene, participants with low FPG-PRS and persistent high levels had average decreases of 0.322, 0.567, and 0.419 mmol/L for FPG change over 6 years. Our findings demonstrated that high-level PAHs exposure was longitudinally associated with an average decrease of FPG over 6 years, and low FPG genetic risk can enhance the above associations. Our findings emphasized the hypoglycemic effect of PAHs exposure, shed new light on the complex effects between PAHs exposure and genetic factors in the prevention of high FPG, and might provide some clues for the development of potential hypoglycemic agents.

Cell type and dynamic state govern genetic regulation of gene expression in heterogeneous differentiating cultures

Identifying the molecular effects of human genetic variation across cellular contexts is crucial for understanding the mechanisms underlying disease-associated loci, yet many cell types and developmental stages remain underexplored. Here, we harnessed the potential of heterogeneous differentiating cultures (HDCs), an in vitro system in which pluripotent cells asynchronously differentiate into a broad spectrum of cell types. We generated HDCs for 53 human donors and collected single-cell RNA sequencing data from over 900,000 cells. We identified expression quantitative trait loci in 29 cell types and characterized regulatory dynamics across diverse differentiation trajectories. This revealed novel regulatory variants for genes involved in key developmental and disease-related processes while replicating known effects from primary tissues and dynamic regulatory effects associated with a range of complex traits.

Genetic Analysis of Psychosis Biotypes: Shared Ancestry-Adjusted Polygenic Risk and Unique Genomic Associations

The Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) created psychosis Biotypes based on neurobiological measurements in a multi-ancestry sample. These Biotypes cut across DSM diagnoses of schizophrenia, schizoaffective disorder and bipolar disorder with psychosis. Two recently developed post hoc ancestry adjustment methods of Polygenic Risk Scores (PRSs) generate Ancestry-Adjusted PRSs (AAPRSs), which allow for PRS analysis of multi-ancestry samples. Applied to schizophrenia PRS, we found the Khera AAPRS method to show superior portability and comparable prediction accuracy as compared with the Ge method. The three Biotypes of psychosis disorders had similar AAPRSs across ancestries. In genomic analysis of Biotypes, 12 genes and isoforms showed significant genomic associations with specific Biotypes in Transcriptome-Wide Association Study (TWAS) of genetically regulated expression (GReX) in adult brain and fetal brain. TWAS inflation was addressed by inclusion of genotype principal components in the association analyses. Seven of these 12 genes/isoforms satisfied Mendelian Randomization (MR) criteria for putative causality, including four genes TMEM140, ARTN, C1orf115, CYREN, and three transcripts ENSG00000272941, ENSG00000257176, ENSG00000287733. These genes are enriched in the biological pathways of Rearranged during Transfection (RET) signaling, Neural Cell Adhesion Molecule 1 (NCAM1) interactions, and NCAM signaling for neurite out-growth. The specific associations with Biotypes suggest that pharmacological clinical trials and biological investigations might benefit from analyzing Biotypes separately.

A review: simulation tools for genome-wide interaction studies

Genome-wide association study (GWAS) is essential for investigating the genetic basis of complex diseases; nevertheless, it usually ignores the interaction of multiple single nucleotide polymorphisms (SNPs). Genome-wide interaction studies provide crucial means for exploring complex genetic interactions that GWAS may miss. Although many interaction methods have been proposed, challenges still persist, including the lack of epistasis models and the inconsistency of benchmark datasets. SNP data simulation is a pivotal intermediary between interaction methods and real applications. Therefore, it is important to obtain epistasis models and benchmark datasets by simulation tools, which is helpful for further improving interaction methods. At present, many simulation tools have been widely employed in the field of population genetics. According to their basic principles, these existing tools can be divided into four categories: coalescent simulation, forward-time simulation, resampling simulation, and other simulation frameworks. In this paper, their basic principles and representative simulation tools are compared and analyzed in detail. Additionally, this paper provides a discussion and summary of the advantages and disadvantages of these frameworks and tools, offering technical insights for the design of new methods, and serving as valuable reference tools for researchers to comprehensively understand GWAS and genome-wide interaction studies.

Allele frequency impacts the cross-ancestry portability of gene expression prediction in lymphoblastoid cell lines

Population-level genetic studies are overwhelmingly biased toward European ancestries. Transferring genetic predictions from European ancestries to other ancestries results in a substantial loss of accuracy. Yet, it remains unclear how much various genetic factors, such as causal effect differences, linkage disequilibrium (LD) differences, or allele frequency differences, contribute to the loss of prediction accuracy across ancestries. In this study, we used gene expression levels in lymphoblastoid cell lines to understand how much each genetic factor contributes to lowered portability of gene expression prediction from European to African ancestries. We found that cis-genetic effects on gene expression are highly similar between European and African individuals. However, we found that allele frequency differences of causal variants have a striking impact on prediction portability. For example, portability is reduced by more than 32% when the causal cis-variant is common (minor allele frequency, MAF >5%) in European samples (training population) but is rarer (MAF <5%) in African samples (prediction population). While large allele frequency differences can decrease portability through increasing LD differences, we also determined that causal allele frequency can significantly impact portability when the impact from LD is substantially controlled. This observation suggests that improving statistical fine-mapping alone does not overcome the loss of portability resulting from differences in causal allele frequency. We conclude that causal cis-eQTL effects are highly similar in European and African individuals, and allele frequency differences have a large impact on the accuracy of gene expression prediction.

Local genetic correlation via knockoffs reduces confounding due to cross-trait assortative mating

Local genetic correlation analysis is an important tool for identifying genetic loci with shared biology across traits. Recently, Border et al. have shown that the results of these analyses are confounded by cross-trait assortative mating (xAM), leading to many false-positive findings. Here, we describe LAVA-Knock, a local genetic correlation method that builds off an existing genetic correlation method, LAVA, and augments it by generating synthetic data in a way that preserves local and long-range linkage disequilibrium (LD), allowing us to reduce the confounding induced by xAM. We show in simulations based on a realistic xAM model and in genome-wide association study (GWAS) applications for 630 trait pairs that LAVA-Knock can greatly reduce the bias due to xAM relative to LAVA. Furthermore, we show a significant positive correlation between the reduction in local genetic correlations and estimates in the literature of cross-mate phenotype correlations; in particular, pairs of traits that are known to have high cross-mate phenotype correlation values have a significantly higher reduction in the number of local genetic correlations compared with other trait pairs. A few representative examples include education and intelligence, education and alcohol consumption, and attention-deficit hyperactivity disorder and depression. These results suggest that LAVA-Knock can reduce confounding due to both short-range LD and long-range LD induced by xAM.

A One-Shot Lossless Algorithm for Cross-Cohort Learning in Mixed-Outcomes Analysis

In cross-cohort studies, integrating diverse datasets, such as electronic health records (EHRs), is both essential and challenging due to cohort-specific variations, distributed data storage, and data privacy concerns. Traditional methods often require data pooling or complex data harmonization, which can reduce efficiency and limit the scope of cross-cohort learning. We introduce mixWAS, a one-shot, lossless algorithm that efficiently integrates distributed EHR datasets via summary statistics. Unlike existing approaches, mixWAS preserves cohort-specific covariate associations and supports simultaneous mixed-outcome analyses. Simulations demonstrate that mixWAS outperforms conventional methods in accuracy and efficiency across various scenarios. Applied to EHR data from seven cohorts in the US, mixWAS identified 4,534 significant cross-cohort genetic associations among traits such as blood lipids, BMI, and circulatory diseases. Validation with an independent UK EHR dataset confirmed 97.7% of these associations, underscoring the algorithm's robustness. By enabling lossless cross-cohort integration, mixWAS improves the precision of multi-outcome analyses and expands the potential for actionable insights in healthcare research.

Identification of rare disease genes as drivers of common diseases through tissue-specific gene regulatory networks

Genetic variants identified through genome-wide association studies (GWAS) are typically non-coding, exerting small regulatory effects on downstream genes. However, which downstream genes are ultimately impacted and how they confer risk remains mostly unclear. By contrast, variants that cause rare Mendelian diseases are often coding and have a more direct impact on disease development. Here we demonstrate that common and rare genetic diseases can be linked by studying how common disease-associated variants influence gene co-expression in 57 different tissues and cell types. We implemented this method in a framework called Downstreamer and applied it to 88 GWAS traits. We find that predicted downstream "genes" are enriched with Mendelian disease genes, e.g. key genes for height are enriched for genes that cause skeletal abnormalities and Ehlers-Danlos syndromes. 78% of these key genes are located outside of GWAS loci, suggesting that they result from complex trans regulation rather than being impacted by disease-associated variants in cis. Based on our findings, we discuss the challenges in reconstructing gene regulatory networks and provide a roadmap to improve the identification of these highly connected genes linked to common traits and diseases.

Genome-wide association analysis of body conformation traits in Chinese Holstein Cattle

BACKGROUND

The body conformation traits of dairy cattle are closely related to their production performance and health. The present study aimed to identify gene variants associated with body conformation traits in Chinese Holstein cattle and provide marker loci for genomic selection in dairy cattle breeding. These findings could offer robust theoretical support for optimizing the health of dairy cattle and enhancing their production performance.

RESULTS

This study involved 586 Chinese Holstein cattle and used the predicted transmitting abilities (PTAs) of 17 body conformation traits evaluated by the Council on Dairy Cattle Breeding in the USA as phenotypic values. These traits were categorized into body size traits, rump traits, feet/legs traits, udder traits, and dairy characteristic traits. On the basis of the genomic profiling results from the Genomic Profiler Bovine 100 K SNP chip, genotype data were quality controlled via PLINK software, and 586 individuals and 80,713 SNPs were retained for further analysis. Genome-wide association studies (GWASs) were conducted via GEMMA software, which employs both univariate linear mixed models (LMMs) and multivariate linear mixed models (mvLMMs). The Bonferroni method was used to determine the significance threshold, identifying gene variants significantly associated with body conformation traits in Chinese Holstein cattle. The single-trait GWAS identified 24 SNPs significantly associated with body conformation traits (P < 0.01), with annotation leading to the identification of 21 candidate genes. The multi-trait GWAS identified 54 SNPs, which were annotated to 57 candidate genes, including 39 new SNPs not identified in the single-trait GWAS. Additionally, 14 SNPs in the 86.84-87.41 Mb region of chromosome 6 were significantly associated with multiple traits, such as body size, udder, and dairy characteristics. Four genes-SLC4A4, GC, NPFFR2, and ADAMTS3-were annotated in this region.

CONCLUSIONS

A total of 63 SNPs were identified as significantly associated with 17 body conformation traits in Chinese Holstein cattle through both single-trait and multi-trait GWAS analyses. Sixty-six candidate genes were annotated, with 12 genes identified by both methods, such as SLC4A4, GC, NPFFR2, and ADAMTS3, which are involved in pathways such as growth hormone synthesis and secretion, sphingolipid signaling, and dopaminergic synapse pathways. These findings provide potential genetic marker information related to body conformation traits for the breeding of Chinese Holstein cattle.

Multiome-wide Association Studies: Novel Approaches for Understanding Diseases

The rapid development of multiome (transcriptome, proteome, cistrome, imaging, and regulome)-wide association study methods have opened new avenues for biologists to understand the susceptibility genes underlying complex diseases. Thorough comparisons of these methods are essential for selecting the most appropriate tool for a given research objective. This review provides a detailed categorization and summary of the statistical models, use cases, and advantages of recent multiome-wide association studies. In addition, to illustrate gene-disease association studies based on transcriptome-wide association study (TWAS), we collected 478 disease entries across 22 categories from 235 manually reviewed publications. Our analysis reveals that mental disorders are the most frequently studied diseases by TWAS, indicating its potential to deepen our understanding of the genetic architecture of complex diseases. In summary, this review underscores the importance of multiome-wide association studies in elucidating complex diseases and highlights the significance of selecting the appropriate method for each study.

Expression quantitative trait loci influence DNA damage-induced apoptosis in cancer

BACKGROUND

Genomic instability and evading apoptosis are two fundamental hallmarks of cancer and closely linked to DNA damage response (DDR). By analyzing expression quantitative trait loci (eQTL) upon cell stimulation (called exposure eQTL (e2QTL)) it is possible to identify context specific gene regulatory variants and connect them to oncological diseases based on genome-wide association studies (GWAS).

RESULTS

We isolate CD8+ T cells from 461 healthy donors and stimulate them with high doses of 5 different carcinogens to identify regulatory mechanisms of DNA damage-induced apoptosis. Across all stimuli, we find 5,373 genes to be differentially expressed, with 85% to 99% of these genes being suppressed. While upregulated genes are specific to distinct stimuli, downregulated genes are shared across conditions but exhibit enrichment in biological processes depending on the DNA damage type. Analysis of eQTL reveals 654 regulated genes across conditions. Among them, 47 genes are significant e2QTL, representing a fraction of 4% to 5% per stimulus. To unveil disease relevant genetic variants, we compare eQTL and e2QTL with GWAS risk variants. We identify gene regulatory variants for KLF2, PIP4K2A, GPR160, RPS18, ARL17B and XBP1 that represent risk variants for oncological diseases.

CONCLUSION

Our study highlights the relevance of gene regulatory variants influencing DNA damage-induced apoptosis in cancer. The results provide new insights in cellular mechanisms and corresponding genes contributing to inter-individual effects in cancer development.

Deciphering the genetic basis of behavioral traits in dogs: Observed-trait GWAS and latent-trait GWAS analysis reveal key genes and variants

Dogs exhibit remarkable phenotypic diversity, particularly in behavioral traits, making them an excellent model for studying the genetic basis of complex behaviors. Behavioral traits such as aggression and fear are highly heritable among different dog breeds, but their genetic basis is largely unknown. We used the genome-wide association study (GWAS) to identify candidate genes associated with nine behavioral traits including; stranger-directed aggression (SDA), owner-directed aggression (ODA), dog-directed aggression (DDA), stranger-directed fear (SDF), nonsocial fear (NF), dog-directed fear (DDF), touch sensitivity (TS), separation-related behavior (SRB) and attachment attention-seeking (AAS). The observed behavioral traits were collected from 38,714 to 40,460 individuals across 108 modern dog breeds. We performed a GWAS based on a latent trait extracted using the confirmatory factor analysis (CFA) method with nine observable behavioral traits and compared the results with those from the GWAS of the observed traits. Using both observed-trait and latent-trait GWAS, we identified 41 significant SNPs that were common between both GWAS methods, of which 26 were pleiotropic, as well as 10 SNPs unique to the latent-trait GWAS, and 5 SNPs unique to the observed-trait GWAS discovered. These SNPs were associated with 21 genes in latent-trait GWAS and 22 genes in the observed-trait GWAS, with 19 genes shared by both. According to previous studies, some of the genes from this study have been reported to be related to behavioral and neurological functions in dogs. In the human population, these identified genes play a role in either the formation of the nervous system or are linked to various mental health conditions. Taken together, our findings suggest that latent-trait GWAS for behavioral traits in dogs identifies significant latent genes that are neurologically prioritized.

Biological and therapeutic implications of the cancer-related germline mutation landscape in lung cancer

Although smoking is the primary cause of lung cancer, only about 15% of lifelong smokers develop the disease. Moreover, a substantial proportion of lung cancer cases occur in never-smokers, highlighting the potential role of inherited genetic factors in the cause of lung cancer. Lung cancer is significantly more common among those with a positive family history, especially for early-onset disease. Therefore, the presence of pathogenic germline variants might act synergistically with environmental factors. The incorporation of next-generation sequencing in routine clinical practice has led to the identification of cancer-predisposing mutations in an increasing proportion of patients with lung cancer. This Review summarises the landscape of germline susceptibility in lung cancer and highlights the importance of germline testing in patients diagnosed with the disease, which has the potential to identify individuals at risk, with implications for tailored therapeutic approaches and successful prevention through genetic counselling and screening.

Identification of genetic basis of brain imaging by group sparse multi-task learning leveraging summary statistics

Brain imaging genetics is an evolving neuroscience topic aiming to identify genetic variations related to neuroimaging measurements of interest. Traditional linear regression methods have shown success, but their reliance on individual-level imaging and genetic data limits their applicability. Herein, we proposed S-GsMTLR, a group sparse multi-task linear regression method designed to harness summary statistics from genome-wide association studies (GWAS) of neuroimaging quantitative traits. S-GsMTLR directly employs GWAS summary statistics, bypassing the requirement for raw imaging genetic data, and applies multivariate multi-task sparse learning to these univariate GWAS results. It amalgamates the strengths of conventional sparse learning methods, including sophisticated modeling techniques and efficient feature selection. Additionally, we implemented a rapid optimization strategy to alleviate computational burdens by identifying genetic variants associated with phenotypes of interest across the entire chromosome. We first evaluated S-GsMTLR using summary statistics derived from the Alzheimer's Disease Neuroimaging Initiative. The results were remarkably encouraging, demonstrating its comparability to conventional methods in modeling and identification of risk loci. Furthermore, our method was evaluated with two additional GWAS summary statistics datasets: One focused on white matter microstructures and the other on whole brain imaging phenotypes, where the original individual-level data was unavailable. The results not only highlighted S-GsMTLR's ability to pinpoint significant loci but also revealed intriguing structures within genetic variations and loci that went unnoticed by GWAS. These findings suggest that S-GsMTLR is a promising multivariate sparse learning method in brain imaging genetics. It eliminates the need for original individual-level imaging and genetic data while demonstrating commendable modeling and feature selection capabilities.

Enhancing Equity in Genomics: Incorporating Measures of Structural Racism, Discrimination, and Social Determinants of Health

The everyday harms of structural racism and discrimination, perpetuated through institutions, laws, policies, and practices, constitute social determinants of health, but measures that account for their debilitating effects are largely missing in genetic studies of complex diseases. Drawing on insights from the social sciences and public health, we propose critical methodologies for incorporating tools that measure structural racism and discrimination within genetic analyses. We illustrate how including these measures may strengthen the accuracy and utility of findings for diverse communities, clarify elusive relationships between genetics and environment in a racialized society, and support greater equity within genomics and precision health research. This approach may also support efforts to build and sustain vital partnerships with communities and with other fields of research inquiry, centering community expertise and lived experiences and drawing on valuable knowledge from practitioners in the social sciences and public health to innovate biomedical and genomic study designs aimed at community health priorities.

Gene editing of angiotensin for blood pressure management

Arterial hypertension has remained the world's leading cause of morbidity and mortality for more than 20 years. While early Genome-Wide Association Studies raised the hypothesis that a precision medicine approach could be implemented in the treatment of hypertension, the large number of single nucleotide polymorphisms that were found to be associated with blood pressure and their limited impact on the blood pressure values have initially hampered these expectations. With the development and refinement of gene-editing and RNA-based approaches allowing selective and organ-specific modulation of critical systems involved in blood pressure regulation, a renewed interest in genetic treatments for hypertension has emerged. The CRISPR-Cas9 system, antisense oligonucleotides (ASO) and small interfering RNA (siRNA) have been used to specifically target the hepatic angiotensinogen (AGT) production, with the scope of safely but effectively reducing the activation of the renin-angiotensin system, ultimately leading to an effective reduction of the blood pressure with extremely simplified treatment regimens that involve weekly, monthly or even once-in-life injection of the drugs. Among the various approaches, siRNA and ASO that reduce hepatic AGT production are in advanced development, with phase I and II clinical trials showing their safety and effectiveness. In the current manuscript, we review the mode of action of these new approaches to hypertension treatment, discussing the results of the clinical trials and their potential to revolutionize the management of hypertension.

Genetics of hypertension-related sex differences and hypertensive disorders of pregnancy

Background: Hypertension and hypertensive disorders of pregnancy (HDP) cause a significant burden of disease on societies and individuals by increasing cardiovascular disease risk. Environmental risk factors alone do not explain the observed sexual dimorphism in lifetime blood pressure (BP) trajectories nor inter-individual variation in HDP risk.

Methods: In this short review, we focus on the genetics of hypertension-related sex differences and HDP and discuss the importance of genetics utilization for sex-specific hypertension risk prediction.

Results: Population and twin studies estimate that 28-66% of variation in BP levels and HDP is explained by genetic variation, while genomic wide association studies suggest that BP traits and HDP partly share a common genetic background. Moreover, environmental and epigenetic regulation of these genes differ by sex and oestrogen receptors in particular are shown to convey cardio- and vasculoprotective effects through epigenetic regulation of DNA. The majority of known genetic variation in hypertension and HDP is polygenic. Polygenic risk scores for BP display stronger associations with hypertension risk in women than in men and are associated with sex-specific age of hypertension onset. Monogenic forms of hypertension are rare and mostly present equally in both sexes.

Conclusion: Despite recent genetic discoveries providing new insights into HDP and sex differences in BP traits, further research is needed to elucidate the underlying biology. Emphasis should be placed on demonstrating the added clinical value of these genetic discoveries, which may eventually facilitate genomics-based personalized treatments for hypertension and HDP.

Understanding the Genetic Architecture of Vitamin Status Biomarkers in the Genome-Wide Association Study Era: Biological Insights and Clinical Significance

Vitamins play an intrinsic role in human health and are targets for clinical intervention through dietary or pharmacological approaches. Biomarkers of vitamin status are complex traits, measurable phenotypes that arise from an interplay between dietary and other environmental factors with a genetic component that is polygenic, meaning many genes are plausibly involved. Studying these genetic influences will improve our knowledge of fundamental vitamin biochemistry, refine estimates of the effects of vitamins on human health, and may in future prove clinically actionable. Here, we evaluate genetic studies of circulating and excreted biomarkers of vitamin status in the era of hypothesis-free genome-wide association studies (GWAS) that have provided unprecedented insights into the genetic architecture of these traits. We found that the most comprehensive and well-powered GWAS currently available were for circulating status biomarkers of vitamin A, C, D, and a subset of the B vitamins (B9 and B12). The biology implicated by GWAS of measured biomarkers of each vitamin is then discussed, both in terms of key genes and higher-order processes. Across all major vitamins, there were genetic signals revealed by GWAS that could be directly linked with known vitamin biochemistry. We also outline how genetic variants associated with vitamin status biomarkers have been already extensively used to estimate causal effects of vitamins on human health outcomes, which is particularly important given the large number of randomized control trials of vitamin related interventions with null findings. Finally, we discuss the current evidence for the clinical applicability of findings from vitamin GWAS, along with future directions for the field to maximize the utility of these data.

Genetic association of type 2 diabetes mellitus and glycaemic factors with primary tumours of the central nervous system

Type 2 diabetes mellitus (T2DM) is a pivotal chronic disease with an increasing prevalence. Recent studies have found associations between T2DM and the development of central nervous system (CNS) tumours, a special class of solid tumours with an unclear pathogenesis. In this study, we aimed to explore the relationship between T2DM and certain glycaemic factors with common CNS tumours by using genetic data to conduct Mendelian randomization (MR) and co-localisation analysis. We found a causal relationship between T2DM and glioblastoma, fasting glucose and spinal cord tumours, glycated haemoglobin and spinal cord tumours, and insulin-like growth factor-1 and spinal cord tumours, pituitary tumours, and craniopharyngiomas. These results clarify the relationship between T2DM, glucose-related factors, and common CNS tumours, and they provide valuable insight into further clinical and basic research on CNS tumours, as well as new ideas for their diagnosis and treatment.

A multi-regional human brain atlas of chromatin accessibility and gene expression facilitates promoter-isoform resolution genetic fine-mapping

Brain region- and cell-specific transcriptomic and epigenomic features are associated with heritability for neuropsychiatric traits, but a systematic view, considering cortical and subcortical regions, is lacking. Here, we provide an atlas of chromatin accessibility and gene expression profiles in neuronal and non-neuronal nuclei across 25 distinct human cortical and subcortical brain regions from 6 neurotypical controls. We identified extensive gene expression and chromatin accessibility differences across brain regions, including variation in alternative promoter-isoform usage and enhancer-promoter interactions. Genes with distinct promoter-isoform usage across brain regions were strongly enriched for neuropsychiatric disease risk variants. Moreover, we built enhancer-promoter interactions at promoter-isoform resolution across different brain regions and highlighted the contribution of brain region-specific and promoter-isoform-specific regulation to neuropsychiatric disorders. Including promoter-isoform resolution uncovers additional distal elements implicated in the heritability of diseases, thereby increasing the power to fine-map risk genes. Our results provide a valuable resource for studying molecular regulation across multiple regions of the human brain and underscore the importance of considering isoform information in gene regulation.

LOGOWheat: deep learning-based prediction of regulatory effects for noncoding variants in wheats

Identifying the regulatory effects of noncoding variants presents a significant challenge. Recently, the accumulation of epigenomic profiling data in wheat has provided an opportunity to model the functional impacts of these variants. In this study, we introduce Language of Genome for Wheat (LOGOWheat), a deep learning-based tool designed to predict the regulatory effects of noncoding variants in wheat. LOGOWheat initially employs a self-attention-based, contextualized pretrained language model to acquire bidirectional representations of the unlabeled wheat reference genome. Epigenomic profiling data are also collected and utilized to fine-tune the model, enabling it to discern the regulatory code inherent in genomic sequences. The test results suggest that LOGOWheat is highly effective in predicting multiple chromatin features, achieving an average area under the receiver operating characteristic (AUROC) of 0.8531 and an average area under the precision-recall curve (AUPRC) of 0.7633. Two case studies illustrate and demonstrate the main functions provided by LOGOWheat: assigning scores and prioritizing causal variants within a given variant set and constructing a saturated mutagenesis map in silico to discover high-impact sites or functional motifs in a given sequence. Finally, we propose the concept of extracting potential functional variations from the wheat population by integrating evolutionary conservation information. LOGOWheat is available at http://logowheat.cn/.

Polygenic prediction for underrepresented populations through transfer learning by utilizing genetic similarity shared with European populations

Because current genome-wide association studies are primarily conducted in individuals of European ancestry and information disparities exist among different populations, the polygenic score derived from Europeans thus exhibits poor transferability. Borrowing the idea of transfer learning, which enables the utilization of knowledge acquired from auxiliary samples to enhance learning capability in target samples, we propose transPGS, a novel polygenic score method, for genetic prediction in underrepresented populations by leveraging genetic similarity shared between the European and non-European populations while explaining the trans-ethnic difference in linkage disequilibrium (LD) and effect sizes. We demonstrate the usefulness and robustness of transPGS in elevated prediction accuracy via individual-level and summary-level simulations and apply it to seven continuous phenotypes and three diseases in the African, Chinese, and East Asian populations of the UK Biobank and Genetic Epidemiology Research Study on Adult Health and Aging cohorts. We further reveal that distinct LD and minor allele frequency patterns across ancestral groups are responsible for the dissatisfactory portability of PGS.

Learning genotype-phenotype associations from gaps in multi-species sequence alignments

Understanding the genetic basis of phenotypic variation is fundamental to biology. Here we introduce GAP, a novel machine learning framework for predicting binary phenotypes from gaps in multi-species sequence alignments. GAP employs a neural network to predict the presence or absence of phenotypes solely from alignment gaps, contrasting with existing tools that require additional and often inaccessible input data. GAP can be applied to three distinct problems: predicting phenotypes in species from known associated genomic regions, pinpointing positions within such regions that are important for predicting phenotypes, and extracting sets of candidate regions associated with phenotypes. We showcase the utility of GAP by exploiting the well-known association between the L-gulonolactone oxidase (Gulo) gene and vitamin C synthesis, demonstrating its perfect prediction accuracy in 34 vertebrates. This exceptional performance also applies more generally, with GAP achieving high accuracy and power on a large simulated dataset. Moreover, predictions of vitamin C synthesis in species with unknown status mirror their phylogenetic relationships, and positions with high predictive importance are consistent with those identified by previous studies. Last, a genome-wide application of GAP identifies many additional genes that may be associated with vitamin C synthesis, and analysis of these candidates uncovers functional enrichment for immunity, a widely recognized role of vitamin C. Hence, GAP represents a simple yet useful tool for predicting genotype-phenotype associations and addressing diverse evolutionary questions from data available in a broad range of study systems.

CSGDN: contrastive signed graph diffusion network for predicting crop gene-phenotype associations

Positive and negative association prediction between gene and phenotype helps to illustrate the underlying mechanism of complex traits in organisms. The transcription and regulation activity of specific genes will be adjusted accordingly in different cell types, developmental timepoints, and physiological states. There are the following two problems in obtaining the positive/negative associations between gene and phenotype: (1) high-throughput DNA/RNA sequencing and phenotyping are expensive and time-consuming due to the need to process large sample sizes; (2) experiments introduce both random and systematic errors, and, meanwhile, calculations or predictions using software or models may produce noise. To address these two issues, we propose a Contrastive Signed Graph Diffusion Network, CSGDN, to learn robust node representations with fewer training samples to achieve higher link prediction accuracy. CSGDN uses a signed graph diffusion method to uncover the underlying regulatory associations between genes and phenotypes. Then, stochastic perturbation strategies are used to create two views for both original and diffusive graphs. Lastly, a multiview contrastive learning paradigm loss is designed to unify the node presentations learned from the two views to resist interference and reduce noise. We perform experiments to validate the performance of CSGDN in three crop datasets: Gossypium hirsutum, Brassica napus, and Triticum turgidum. The results show that the proposed model outperforms state-of-the-art methods by up to 9. 28% AUC for the prediction of link sign in the G. hirsutum dataset. The source code of our model is available at https://github.com/Erican-Ji/CSGDN.

Leveraging large-scale multi-omics evidences to identify therapeutic targets from genome-wide association studies

BACKGROUND

Therapeutic targets supported by genetic evidence from genome-wide association studies (GWAS) show higher probability of success in clinical trials. GWAS is a powerful approach to identify links between genetic variants and phenotypic variation; however, identifying the genes driving associations identified in GWAS remains challenging. Integration of molecular quantitative trait loci (molQTL) such as expression QTL (eQTL) using mendelian randomization (MR) and colocalization analyses can help with the identification of causal genes. Careful interpretation remains warranted because eQTL can affect the expression of multiple genes within the same locus.

METHODS

We used a combination of genomic features that include variant annotation, activity-by-contact maps, MR, and colocalization with molQTL to prioritize causal genes across 4,611 disease GWAS and meta-analyses from biobank studies, namely FinnGen, Estonian Biobank and UK Biobank.

RESULTS

Genes identified using this approach are enriched for gold standard causal genes and capture known biological links between disease genetics and biology. In addition, we find that eQTL colocalizing with GWAS are statistically enriched for corresponding disease-relevant tissues. We show that predicted directionality from MR is generally consistent with matched drug mechanism of actions (> 85% for approved drugs). Compared to the nearest gene mapping method, genes supported by multi-omics evidences displayed higher enrichment in approved therapeutic targets (risk ratio 1.75 vs. 2.58 for genes with the highest level of support). Finally, using this approach, we detected anassociation between the IL6 receptor signal transduction gene IL6ST and polymyalgia rheumatica, an indication for which sarilumab, a monoclonal antibody against IL-6, has been recently approved.

CONCLUSIONS

Combining variant annotation, activity-by-contact maps, and molQTL increases performance to identify causal genes, while informing on directionality which can be translated to successful target identification and drug development.

Deciphering the tissue-specific functional effect of Alzheimer risk SNPs with deep genome annotation

Alzheimer's disease (AD) is a highly heritable brain dementia, along with substantial failure of cognitive function. Large-scale genome-wide association studies (GWASs) have led to a set of SNPs significantly associated with AD and related traits. GWAS hits usually emerge as clusters where a lead SNP with the highest significance is surrounded by other less significant neighboring SNPs. Although functionality is not guaranteed even with the strongest associations in GWASs, lead SNPs have historically been the focus of the field, with the remaining associations inferred to be redundant. Recent deep genome annotation tools enable the prediction of function from a segment of a DNA sequence with significantly improved precision, which allows in-silico mutagenesis to interrogate the functional effect of SNP alleles. In this project, we explored the impact of top AD GWAS hits around APOE region on chromatin functions and whether it will be altered by the genetic context (i.e., alleles of neighboring SNPs). Our results showed that highly correlated SNPs in the same LD block could have distinct impacts on downstream functions. Although some GWAS lead SNPs showed dominant functional effects regardless of the neighborhood SNP alleles, several other SNPs did exhibit enhanced loss or gain of function under certain genetic contexts, suggesting potential additional information hidden in the LD blocks.

Uncovering hidden gene-trait patterns through biclustering analysis of the UK Biobank

The growing availability of genome-wide association studies (GWAS) and large-scale biobanks provides an unprecedented opportunity to explore the genetic basis of complex traits and diseases. However, with this vast amount of data comes the challenge of interpreting numerous associations across thousands of traits, especially given the high polygenicity and pleiotropy underlying complex phenotypes. Traditional clustering methods, which identify global patterns in data, lack the resolution to capture overlapping associations relevant to subsets of traits or genes. Consequently, there is a critical need for innovative analytic approaches capable of revealing local, biologically meaningful patterns that could advance our understanding of trait comorbidities and gene-trait interactions. Here, we applied BiBit, a biclustering algorithm, to transcriptome-wide association study (TWAS) results from PhenomeXcan, a large resource of gene-trait associations derived from the UK Biobank. BiBit allows simultaneous grouping of traits and genes, identifying biclusters that represent local, overlapping associations. Our analyses uncovered biologically interpretable patterns, including asthma-related biclusters enriched for immune-related gene sets, connections between eye traits and blood pressure, and associations between dietary traits, high cholesterol, and specific loci on chromosome 19. These biclusters highlight gene-trait relationships and patterns of trait co-occurrence that may otherwise be obscured by traditional methods. Our findings demonstrate that biclustering can provide a nuanced view of the genetic architecture of complex traits, offering insights into pleiotropy and disease mechanisms. By enabling the exploration of complex, overlapping patterns within biobank-scale datasets, this approach provides a valuable framework for advancing research on genetic associations, comorbidities, and polygenic traits.

Polygenic risk for alcohol use disorder affects cellular responses to ethanol exposure in a human microglial cell model

Polygenic risk scores (PRSs) assess genetic susceptibility to alcohol use disorder (AUD), yet their molecular implications remain underexplored. Neuroimmune interactions, particularly in microglia, are recognized as notable contributors to AUD pathophysiology. We investigated the interplay between AUD PRS and ethanol in human microglia derived from iPSCs from individuals with AUD high-PRS (diagnosed with AUD) or low-PRS (unaffected). Ethanol exposure induced elevated CD68 expression and morphological changes in microglia, with differential responses between high-PRS and low-PRS microglial cells. Transcriptomic analysis revealed expression differences in MHCII complex and phagocytosis-related genes following ethanol exposure; high-PRS microglial cells displayed enhanced phagocytosis and increased CLEC7A expression, unlike low-PRS microglial cells. Synapse numbers in cocultures of induced neurons with microglia after alcohol exposure were lower in high-RPS cocultures, suggesting possible excess synapse pruning. This study provides insights into the intricate relationship between AUD PRS, ethanol, and microglial function, potentially influencing neuronal functions in developing AUD.

ZNF350 gene polymorphisms promote the response to Peg-IFNα therapy through JAK-STAT signaling pathway in patients with chronic hepatitis B

Background

The varying individual responses to Pegylated interferon-α (Peg-IFNα) in patients with chronic hepatitis B (CHB) pose significant hurdles in treatment optimization, and the underlying mechanisms remain unclear.

Objective

We aimed to identify genetic polymorphisms influencing the efficacy of Peg-IFNα in patients with HBeAg-positive CHB, with the goal to predict Peg-IFNα response before treatment.

Methods

We employed an Asian Screening Array analysis involving 124 HBeAg-positive CHB patients treated with Peg-IFNα. We conducted assessment of immunological markers and mRNA expression of pivotal genes, establishing correlations with SNPs, functional genes of SNPs, and efficacy of Peg-IFNα therapy. In vitro experiments were performed to verify the functional involvement of the candidate SNPs.

Results

The G allele presented in rs2278420 and rs6509607 were significantly more common in patients who achieved a complete response (CR) compared to those who had a suboptimal response (SR), and linked to an increased rates of HBeAg loss following Peg-IFNα treatment (all p < 0.05). Additionally, the mRNA level of ZNF350 varied notably across different genotypes of both SNPs as determined by eQTL analysis, and showed higher expression in patients achieved a SR (all p < 0.05). In vitro investigations with IFNα stimulation showed that the mRNA level of SOCS3 was elevated in patients with rs2278420 genotype AA, similarly, mRNA levels of PKR, STAT2, SOCS1, SOCS3, PIAS1, PTPN6 and TRIM8 were heightened in patients with rs6509607 genotype AA compared to those with genotypes (AG+GG) (all p < 0.05).

Conclusion

The G allele of rs2278420 and rs6509607 were associated with mRNA level of ZNF350, with an increased probability of Peg-IFNα response in HBeAg-positive CHB patients, likely through the modulation of JAK-STAT signaling pathway.

The genetic landscape of neuro-related proteins in human plasma

Understanding the genetic basis of neuro-related proteins is essential for dissecting the molecular basis of human behavioural traits and the disease aetiology of neuropsychiatric disorders. Here the SCALLOP Consortium conducted a genome-wide association meta-analysis of over 12,000 individuals for 184 neuro-related proteins in human plasma. The analysis identified 125 cis-regulatory protein quantitative trait loci (cis-pQTL) and 164 trans-pQTL. The mapped pQTL capture on average 50% of each protein's heritability. At the cis-pQTL, multiple proteins shared a genetic basis with human behavioural traits such as alcohol and food intake, smoking and educational attainment, as well as neurological conditions and psychiatric disorders such as pain, neuroticism and schizophrenia. Integrating with established drug information, the causal inference analysis validated 52 out of 66 matched combinations of protein targets and diseases or side effects with available drugs while suggesting hundreds of repurposing and new therapeutic targets.

A brief guide to analyzing expression quantitative trait loci

Molecular quantitative trait locus (molQTL) mapping has emerged as an important approach for elucidating the functional consequences of genetic variants and unraveling the causal mechanisms underlying diseases or complex traits. However, the variety of analysis tools and sophisticated methodologies available for molQTL studies can be overwhelming for researchers with limited computational expertise. Here, we provide a brief guideline with a curated list of methods and software tools for analyzing expression quantitative trait loci, the most widely studied type of molQTL.

A 30-nation investigation of lay heritability beliefs

Lay beliefs about human trait heritability are consequential for cooperation and social cohesion, yet there has been no global characterisation of these beliefs. Participants from 30 countries (N = 6128) reported heritability beliefs for intelligence, personality, body weight and criminality, and transnational factors that could influence these beliefs were explored using public nation-level data. Globally, mean lay beliefs differ from published heritability (h2) estimated by twin studies, with a worldwide majority overestimating the heritability of personality and intelligence, and underestimating body weight and criminality. Criminality was seen as substantially less attributable to genes than other traits. People from countries with high infant mortality tended to ascribe greater heritability for most traits, relative to people from low infant mortality countries. This study provides the first systematic foray into worldwide lay heritability beliefs. Future research must incorporate diverse global perspectives to further contextualise and extend upon these findings.

MR Corge: sensitivity analysis of Mendelian randomization based on the core gene hypothesis for polygenic exposures

SUMMARY

Mendelian randomization is being utilized to assess causal effects of polygenic exposures, where many genetic instruments are subject to horizontal pleiotropy. Existing methods for detecting and correcting for horizontal pleiotropy have important assumptions that may not be fulfilled. Built upon the core gene hypothesis, we developed MR Corge for performing sensitivity analysis of Mendelian randomization. MR Corge identifies a small number of putative core instruments that are more likely to affect genes with a direct biological role in an exposure and obtains causal effect estimates based on these instruments, thereby reducing the risk of horizontal pleiotropy. Using positive and negative controls, we demonstrated that MR Corge estimates aligned with established biomedical knowledge and the results of randomized controlled trials. MR Corge may be widely applied to investigate polygenic exposure-outcome relationships.

AVAILABILITY AND IMPLEMENTATION

An open-sourced R package is available at https://github.com/zhwm/MRCorge.

Next-generation data filtering in the genomics era

Genomic data are ubiquitous across disciplines, from agriculture to biodiversity, ecology, evolution and human health. However, these datasets often contain noise or errors and are missing information that can affect the accuracy and reliability of subsequent computational analyses and conclusions. A key step in genomic data analysis is filtering - removing sequencing bases, reads, genetic variants and/or individuals from a dataset - to improve data quality for downstream analyses. Researchers are confronted with a multitude of choices when filtering genomic data; they must choose which filters to apply and select appropriate thresholds. To help usher in the next generation of genomic data filtering, we review and suggest best practices to improve the implementation, reproducibility and reporting standards for filter types and thresholds commonly applied to genomic datasets. We focus mainly on filters for minor allele frequency, missing data per individual or per locus, linkage disequilibrium and Hardy-Weinberg deviations. Using simulated and empirical datasets, we illustrate the large effects of different filtering thresholds on common population genetics statistics, such as Tajima's D value, population differentiation (FST), nucleotide diversity (π) and effective population size (Ne).

Mendelian randomization study on the causal relationship between chronic hepatitis B/C virus infection and idiopathic pulmonary fibrosis

Background

The pathogenesis of idiopathic pulmonary fibrosis (IPF) is not well understood. Given the known role of hepatitis C virus (HCV) in inducing cirrhosis, the virus has also received attention in the study of IPF. An earlier retrospective study found an increased incidence of IPF in patients with HCV, supported by evidence in the alveolar lavage fluid of the patients, whereas another set of observational studies did not find an association, which prompted us to explore a causal relationship. It is well known that HCV and hepatitis B virus (HBV) have some similarities: both are RNA viruses, and both have a strong ability to induce cirrhosis, which in turn leads to poor prognosis and increased mortality in patients with viral hepatitis. This factor also inspired us to start exploring whether there is a causal relationship between HBV and IPF. Due to the inherent limitations of previous studies, causality between chronic HBV/HCV infection and IPF is yet to be established. Mendelian randomization (MR) uses genetic variation as exposure and can be used to determine the causal effect of exposure on outcomes. Therefore, we used a two-sample MR study to determine if there is a causal relationship between viral hepatitis and IPF risk.

Methods

Single nucleotide polymorphisms (SNPs) were used as instrumental variables (IVs), with chronic HBV and HCV infections as exposure factors and IPF as the outcome variable. Three methods, inverse variance weighting (IVW), weighted median (WM), and MR-Egger regression, were employed for the bidirectional MR. Sensitivity analyses, including horizontal pleiotropy analysis, Cochran's Q test, and leave-one-out evaluation of result reliability, were conducted. Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) and MR-Egger regression tests were used to monitor potential horizontal pleiotropic effects. Odds ratios (ORs) and 95% confidence intervals (CIs) were used to interpret the causal relationship between chronic HBV and HCV infections and IPF. Finally, reverse MR analysis was performed to validate the robustness of the results.

Results

The results of the IVW suggested that there was no causal relationship between chronic HBV infection (OR =1.039, 95% CI: 0.935-1.154, P=0.48) and chronic HCV infection (OR =1.146, 95% CI: 0.834-1.576, P=0.40) and the risk of IPF. Sensitivity analysis showed no evidence of reverse causation, horizontal pleiotropy, and heterogeneity.

Conclusions

This study, using the bidirectional MR, provides preliminary evidence that chronic HBV and HCV infections are not causally related to IPF at the genetic level. However, this conclusion requires support from larger sample sizes in genome-wide association study (GWAS) databases for further MR analysis, and additional clinical studies and animal experiments are needed for validation.

Genetic studies through the lens of gene networks

Understanding the genetic basis of complex traits is a longstanding challenge in the field of genomics. Genome-wide association studies (GWAS) have identified thousands of variant-trait associations, but most of these variants are located in non-coding regions, making the link to biological function elusive. While traditional approaches, such as transcriptome-wide association studies (TWAS), have advanced our understanding by linking genetic variants to gene expression, they often overlook gene-gene interactions. Here, we review current approaches to integrate different molecular data, leveraging machine learning methods to identify gene modules based on co-expression and functional relationships. These integrative approaches, like PhenoPLIER, combine TWAS and drug-induced transcriptional profiles to effectively capture biologically meaningful gene networks. This integration provides a context-specific understanding of disease processes while highlighting both core and peripheral genes. These insights pave the way for novel therapeutic targets and enhance the interpretability of genetic studies in personalized medicine.