The Post-GWAS Era: From Association to Function

Racial disparity in prostate cancer: an outlook in genetic and molecular landscape

Prostate cancer (PCa) incidence, morbidity, and mortality rates are significantly impacted by racial disparities. Despite innovative therapeutic approaches and advancements in prevention, men of African American (AA) ancestry are at a higher risk of developing PCa and have a more aggressive and metastatic form of the disease at the time of initial PCa diagnosis than other races. Research on PCa has underlined the biological and molecular basis of racial disparity and emphasized the genetic aspect as the fundamental component of racial inequality. Furthermore, the lower enrollment rate, limited access to national-level cancer facilities, and deferred treatment of AA men and other minorities are hurdles in improving the outcomes of PCa patients. This review provides the most up-to-date information on various biological and molecular contributing factors, such as the single nucleotide polymorphisms (SNPs), mutational spectrum, altered chromosomal loci, differential gene expression, transcriptome analysis, epigenetic factors, tumor microenvironment (TME), and immune modulation of PCa racial disparities. This review also highlights future research avenues to explore the underlying biological factors contributing to PCa disparities, particularly in men of African ancestry.

The PTK2B gene is associated with obesity, adiposity, and leptin levels in children and adolescents

Previous studies determined that Pyk2 is involved in several diseases in which the symptomatology presents alterations in energy balance. However, its role in obesity is poorly understood. To evaluate the metabolic role of the Pyk2 gene (PTK2B) in children and adolescents with obesity we measured its mRNA expression levels in peripheral blood mononuclear cells. For that we performed a cross-sectional study involving 130 Caucasian subjects that was divided into two groups according to BMI. Data showed increased PTK2B mRNA expression in children and adolescents with obesity. Interestingly, a positive correlation has been found between the levels of PTK2B with weight, BMI, BMI Z score, fat mass, waist circumference, waist to height ratio, diastolic blood pressure, and leptin. In addition, it is indicated that high levels of PTK2B gene expression might be a predictor of obesity development. This work provides important insights into the previously undescribed role of Pyk2 in obesity.

Identifying key genes in COPD risk via multiple population data integration and gene prioritization

Chronic obstructive pulmonary disease (COPD) is a highly prevalent disease, making it a leading cause of death worldwide. Several genome-wide association studies (GWAS) have been conducted to identify loci associated with COPD. However, different ancestral genetic compositions for the same disease across various populations present challenges in studies involving multi-population data. In this study, we aimed to identify protein-coding genes associated with COPD by prioritizing genes for each population's GWAS data, and then combining these results instead of performing a common meta-GWAS due to significant sample differences in different population cohorts. Lung function measurements are often used as indicators for COPD risk prediction; therefore, we used lung function GWAS data from two populations, Japanese and European, and re-evaluated them using a multi-population gene prioritization approach. This study identified significant single nucleotide variants (SNPs) in both Japanese and European populations. The Japanese GWAS revealed nine significant SNPs and four lead SNPs in three genomic risk loci. In comparison, the European population showed five lead SNPs and 17 independent significant SNPs in 21 genomic risk loci. A comparative analysis of the results found 28 similar genes in the prioritized gene lists of both populations. We also performed a standard meta-analysis for comparison and identified 18 common genes in both populations. Our approach demonstrated that trans-ethnic linkage disequilibrium (LD) could detect some significant novel associations and genes that have yet to be reported or were missed in previous analyses. The study suggests that a gene prioritization approach for multi-population analysis using GWAS data may be a feasible method to identify new associations in data with genetic diversity across different populations. It also highlights the possibility of identifying generalized and population-specific treatment and diagnostic options.

ATXN2 loss of function results in glaucoma-related features supporting a role for Ataxin-2 in primary open-angle glaucoma (POAG) pathogenesis

Glaucoma is a leading cause of irreversible blindness worldwide. The most common form, primary open-angle glaucoma (POAG), is a genetically complex trait with high heritability. Genome-wide association studies have identified significant POAG and IOP association of a genomic region on chromosome 12 that includes ATXN2 as well as 7 other genes. Association of protein disrupting ATXN2 variants in the NEIGHBORHOOD case-control cohort and the UK Biobank suggests that ATXN2 is a key gene in this locus. To investigate functional effects, we utilized a zebrafish (Danio rerio) CRISPR/Cas9 edited atxn2-knockdown line to show that loss of atxn2 results in reduced eye size, diminished retinal ganglion cells (RGC), increased intraocular pressure (IOP), and impaired visual function in zebrafish. Complementation assays supported functional effects for 14 POAG-associated human ATXN2 missense variants. These results suggest a loss-of-function mechanism underlying a potential role for ATXN2 in POAG pathogenesis.

Genome wide association studies are enriched for interacting genes

Background

With recent advances in single cell technology, high-throughput methods provide unique insight into disease mechanisms and more importantly, cell type origin. Here, we used multi-omics data to understand how genetic variants from genome-wide association studies influence development of disease. We show in principle how to use genetic algorithms with normal, matching pairs of single-nucleus RNA- and ATAC-seq, genome annotations, and protein-protein interaction data to describe the genes and cell types collectively and their contribution to increased risk.

Results

We used genetic algorithms to measure fitness of gene-cell set proposals against a series of objective functions that capture data and annotations. The highest information objective function captured protein-protein interactions. We observed significantly greater fitness scores and subgraph sizes in foreground vs.matching sets of control variants. Furthermore, our model reliably identified known targets and ligand-receptor pairs, consistent with prior studies.

Conclusions

Our findings suggested that application of genetic algorithms to association studies can generate a coherent cellular model of risk from a set of susceptibility variants. Further, we showed, using breast cancer as an example, that such variants have a greater number of physical interactions than expected due to chance.

Genetic knock-in of EIF2AK3 variants reveals differences in PERK activity in mouse liver and pancreas under endoplasmic reticulum stress

Common single-nucleotide variants (SNVs) of eukaryotic translation initiation factor 2 alpha kinase 3 (EIF2AK3) slightly increase the risk of disorders in the periphery and the central nervous system. EIF2AK3 encodes protein kinase RNA-like endoplasmic reticulum kinase (PERK), a key regulator of ER stress. Three exonic EIF2AK3 SNVs form the PERK-B haplotype, which is present in 28% of the global population. Importantly, the precise impact of these SNVs on PERK activity remains elusive. In this study, we demonstrate that PERK-B SNVs do not alter PERK expression or basal activity in vitro and in the novel triple knock-in mice expressing the exonic PERK-B SNVs in vivo. However, the kinase activity of PERK-B protein is higher than that of PERK-A in a cell-free assay and in mouse liver homogenates. Pancreatic tissue in PERK-B/B mice also exhibit increased susceptibility to apoptosis under acute ER stress. Monocyte-derived macrophages from PERK-B/B mice exhibit higher PERK activity than those from PERK-A/A mice, albeit with minimal functional consequences at acute timepoints. The subtle PERK-B-driven effects observed in liver and pancreas during acute stress implicate PERK as a contributor to disease susceptibility. The novel PERK-B mouse model provides valuable insights into ER stress-induced PERK activity, aiding the understanding of the genetic basis of disorders associated with ER stress.

GWAShug: a comprehensive platform for decoding the shared genetic basis between complex traits based on summary statistics

The shared genetic basis offers very valuable insights into the etiology, diagnosis and therapy of complex traits. However, a comprehensive resource providing shared genetic basis using the accessible summary statistics is currently lacking. It is challenging to analyze the shared genetic basis due to the difficulty in selecting parameters and the complexity of pipeline implementation. To address these issues, we introduce GWAShug, a platform featuring a standardized best-practice pipeline with four trait level methods and three molecular level methods. Based on stringent quality control, the GWAShug resource module includes 539 high-quality GWAS summary statistics for European and East Asian populations, covering 54 945 pairs between a measurement-based and a disease-based trait and 43 902 pairs between two disease-based traits. Users can easily search for shared genetic basis information by trait name, MeSH term and category, and access detailed gene information across different trait pairs. The platform facilitates interactive visualization and analysis of shared genetic basic results, allowing users to explore data dynamically. Results can be conveniently downloaded via FTP links. Additionally, we offer an online analysis module that allows users to analyze their own summary statistics, providing comprehensive tables, figures and interactive visualization and analysis. GWAShug is freely accessible at http://www.gwashug.com.

Connecting dementia risk loci to the CSF proteome identifies pathophysiological leads for dementia

Genome-wide association studies have successfully identified many genetic risk loci for dementia, but exact biological mechanisms through which genetic risk factors contribute to dementia remains unclear. Integrating CSF proteomic data with dementia risk loci could reveal intermediate molecular pathways connecting genetic variance to the development of dementia. We tested to what extent effects of known dementia risk loci can be observed in CSF levels of 665 proteins [proximity extension-based (PEA) immunoassays] in a deeply-phenotyped mixed memory clinic cohort [n = 502, mean age (standard deviation, SD) = 64.1 (8.7) years, 181 female (35.4%)], including patients with Alzheimer's disease (AD, n = 213), dementia with Lewy bodies (DLB, n = 50) and frontotemporal dementia (FTD, n = 93), and controls (n = 146). Validation was assessed in independent cohorts (n = 99 PEA platform, n = 198, mass reaction monitoring-targeted mass spectroscopy and multiplex assay). We performed additional analyses stratified according to diagnostic status (AD, DLB, FTD and controls separately), to explore whether associations between CSF proteins and genetic variants were specific to disease or not. We identified four AD risk loci as protein quantitative trait loci (pQTL): CR1-CR2 (rs3818361, P = 1.65 × 10-8), ZCWPW1-PILRB (rs1476679, P = 2.73 × 10-32), CTSH-CTSH (rs3784539, P = 2.88 × 10-24) and HESX1-RETN (rs186108507, P = 8.39 × 10-8), of which the first three pQTLs showed direct replication in the independent cohorts. We identified one AD-specific association between a rare genetic variant of TREM2 and CSF IL6 levels (rs75932628, P = 3.90 × 10-7). DLB risk locus GBA showed positive trans effects on seven inter-related CSF levels in DLB patients only. No pQTLs were identified for FTD loci, either for the total sample as for analyses performed within FTD only. Protein QTL variants were involved in the immune system, highlighting the importance of this system in the pathophysiology of dementia. We further identified pQTLs in stratified analyses for AD and DLB, hinting at disease-specific pQTLs in dementia. Dissecting the contribution of risk loci to neurobiological processes aids in understanding disease mechanisms underlying dementia.

Multimorbidity in neurodegenerative diseases: a network analysis

The socioeconomic costs of neurodegenerative diseases (NDs) are highly affected by comorbidities. This study aims to enhance our understanding of the prevalent complications of NDs through the lens of network analysis. A multimorbidity network (MN) was constructed based on a longitudinal EHR dataset of 93,647,498 diagnoses of 824,847 patients. The association between the conditions was measured by two metrics, i.e. Phi-correlation and Cosine Index (CI). Based on multiple network centrality measures, a fused ranking list of the prevalent multimorbidities was provided. Finally, class-level networks depicting the prevalence and strength of diseases in different classes were constructed. The general MN included 928 diseases and 337,253 associations. Considering a 99% confidence level, two networks of 575 relationships were constructed based on Phi-correlations (73 diseases) and CI (102 diseases). Five out of 19 ICD-9 categories did not appear in either of the networks. Also, ND's immediate MNs for the top 50% of the significant associations included 42 relationships, whereas the Phi-correlation and CI networks included 36 and 34 diseases, respectively. Thirteen diseases were identified as the most notable multimorbidities based on various centrality measures. The analysis framework helps practitioners toward better resource allocations, more effective preventive screenings, and improved quality of life for ND patients and caregivers.

Stability and change in maternal wellbeing and illbeing from pregnancy to three years postpartum

PURPOSE

Motherhood affects women's mental health, encompassing aspects of both wellbeing and illbeing. This study investigated stability and change in wellbeing (i.e., relationship satisfaction and positive affect) and illbeing (i.e., depressive and anxiety symptoms) from pregnancy to three years postpartum. We further investigated the mutual and dynamic relations between these constructs over time and the role of genetic propensities in their time-invariant stability.

DATA AND METHODS

This four-wave longitudinal study included 83,124 women from the Norwegian Mother, Father, and Child Cohort Study (MoBa) linked to the Medical Birth Registry of Norway. Data were collected during pregnancy (30 weeks) and at 6, 18 and 36 months postpartum. Wellbeing and illbeing were based on the Relationship Satisfaction Scale, the Differential Emotions Scale and Hopkins Symptoms Checklist-8. Genetics were measured by the wellbeing spectrum polygenic index. Analyses were based on random intercept cross-lagged panel models using R.

RESULTS

All four outcomes showed high stability and were mutually interconnected over time, with abundant cross-lagged predictions. The period of greatest instability was from pregnancy to 6 months postpartum, followed by increasing stability. Prenatal relationship satisfaction played a crucial role in maternal mental health postpartum. Women's genetic propensity to wellbeing contributed to time-invariant stability of all four constructs.

CONCLUSION

Understanding the mutual relationship between different aspects of wellbeing and illbeing allows for identifying potential targets for health promotion interventions. Time-invariant stability was partially explained by genetics. Maternal wellbeing and illbeing develop in an interdependent way from pregnancy to 36 months postpartum.

Exponential family measurement error models for single-cell CRISPR screens

CRISPR genome engineering and single-cell RNA sequencing have accelerated biological discovery. Single-cell CRISPR screens unite these two technologies, linking genetic perturbations in individual cells to changes in gene expression and illuminating regulatory networks underlying diseases. Despite their promise, single-cell CRISPR screens present considerable statistical challenges. We demonstrate through theoretical and real data analyses that a standard method for estimation and inference in single-cell CRISPR screens-"thresholded regression"-exhibits attenuation bias and a bias-variance tradeoff as a function of an intrinsic, challenging-to-select tuning parameter. To overcome these difficulties, we introduce GLM-EIV ("GLM-based errors-in-variables"), a new method for single-cell CRISPR screen analysis. GLM-EIV extends the classical errors-in-variables model to responses and noisy predictors that are exponential family-distributed and potentially impacted by the same set of confounding variables. We develop a computational infrastructure to deploy GLM-EIV across hundreds of processors on clouds (e.g. Microsoft Azure) and high-performance clusters. Leveraging this infrastructure, we apply GLM-EIV to analyze two recent, large-scale, single-cell CRISPR screen datasets, yielding several new insights.

Identification of candidate causal variants and target genes at 41 breast cancer risk loci through differential allelic expression analysis

Understanding breast cancer genetic risk relies on identifying causal variants and candidate target genes in risk loci identified by genome-wide association studies (GWAS), which remains challenging. Since most loci fall in active gene regulatory regions, we developed a novel approach facilitated by pinpointing the variants with greater regulatory potential in the disease's tissue of origin. Through genome-wide differential allelic expression (DAE) analysis, using microarray data from 64 normal breast tissue samples, we mapped the variants associated with DAE (daeQTLs). Then, we intersected these with GWAS data to reveal candidate risk regulatory variants and analysed their cis-acting regulatory potential. Finally, we validated our approach by extensive functional analysis of the 5q14.1 breast cancer risk locus. We observed widespread gene expression regulation by cis-acting variants in breast tissue, with 65% of coding and noncoding expressed genes displaying DAE (daeGenes). We identified over 54 K daeQTLs for 6761 (26%) daeGenes, including 385 daeGenes harbouring variants previously associated with BC risk. We found 1431 daeQTLs mapped to 93 different loci in strong linkage disequilibrium with risk-associated variants (risk-daeQTLs), suggesting a link between risk-causing variants and cis-regulation. There were 122 risk-daeQTL with stronger cis-acting potential in active regulatory regions with protein binding evidence. These variants mapped to 41 risk loci, of which 29 had no previous report of target genes and were candidates for regulating the expression levels of 65 genes. As validation, we identified and functionally characterised five candidate causal variants at the 5q14.1 risk locus targeting the ATG10 and ATP6AP1L genes, likely acting via modulation of alternative transcription and transcription factor binding. Our study demonstrates the power of DAE analysis and daeQTL mapping to identify causal regulatory variants and target genes at breast cancer risk loci, including those with complex regulatory landscapes. It additionally provides a genome-wide resource of variants associated with DAE for future functional studies.

Investigating the association of the effect of genetically proxied PCSK9i with mood disorders using cis-pQTLs: A drug-target Mendelian randomization study

PCSK9-inhibitors (PCSK9i) are new drugs recently approved to lower LDL-cholesterol levels. However, due to the lack of long-term clinical data, the potential adverse effects of long-term use are still unknown. The PCSK9 genetic locus has been recently implicated in mood disorders and hence we wanted to assess if the effect of PCSK9i that block the PCSK9 protein can lead to an increase in the incidence of mood disorders. We used genetically-reduced PCSK9 protein levels (pQTLs) in plasma, serum, cerebrospinal fluid as a proxy for the effect of PCSK9i. We performed Mendelian randomization analyses using PCSK9 levels as exposure and mood disorder traits major depressive disorder, mood instability, and neuroticism score as outcomes. We find no association of PCSK9 levels with mood disorder traits in serum, plasma, and cerebrospinal fluid. We can conclude that genetically proxied on-target effect of pharmacological PCSK9 inhibition is unlikely to contribute to mood disorders.

Multi-step gene set analysis identified HTR3 family genes involving childhood acute lymphoblastic leukemia susceptibility

In our previous conventional genome-wide association study (GWAS), WWOX was a susceptibility gene associated with acute lymphoblastic leukemia (ALL) development. Nowadays, advancements in genetic association analyses promote an in-depth exploration of ALL genomics. We conducted a two-step enrichment analysis at both gene and pathway levels based on ALL GWAS data including 269 cases and 1039 controls of Chinese descent. The following functional prediction and experiments were used to evaluate the genetic biology of candidate variants and genes. The serotonin-activated cation-selective channel complex gene-set was a potential biological pathway involved in ALL occurrence. Of which, individuals carrying the T allele of rs33940208 exhibited a prominent reduced risk of ALL [odds ratio (OR) = 0.71, 95% confidence interval (CI) = 0.53 to 0.96, P = 2.81 × 10-2], whereas those with the A allele of rs6779545 demonstrated an increased risk (OR = 1.23, 95% CI = 1.01 to 1.51, P = 4.11 × 10-2). Notably, the two variants displayed a better prediction capability when combined, that the risk of developing childhood ALL increased by 131% in subjects with 2-4 risk alleles compared to those with 0-1 risk alleles (Ptrend = 2.05 × 10-3). In addition, the T allele of rs33940208 could reduce HTR3A mRNA level, while the A allele of rs6779545 increased HTR3D mRNA expression. In this study, we identified HTR3A rs33940208 and HTR3D rs6779545 as potential susceptibility loci for ALL in Chinese children. Future validation and functional research will elucidate the underlying molecular processes, refining preventive strategies for this disease.

Context-aware single-cell multiomics approach identifies cell-type-specific lung cancer susceptibility genes

Genome-wide association studies (GWAS) identified over fifty loci associated with lung cancer risk. However, underlying mechanisms and target genes are largely unknown, as most risk-associated variants might regulate gene expression in a context-specific manner. Here, we generate a barcode-shared transcriptome and chromatin accessibility map of 117,911 human lung cells from age/sex-matched ever- and never-smokers to profile context-specific gene regulation. Identified candidate cis-regulatory elements (cCREs) are largely cell type-specific, with 37% detected in one cell type. Colocalization of lung cancer candidate causal variants (CCVs) with these cCREs combined with transcription factor footprinting prioritize the variants for 68% of the GWAS loci. CCV-colocalization and trait relevance score indicate that epithelial and immune cell categories, including rare cell types, contribute to lung cancer susceptibility the most. A multi-level cCRE-gene linking system identifies candidate susceptibility genes from 57% of the loci, where most loci display cell-category-specific target genes, suggesting context-specific susceptibility gene function.

Genotype inference from aggregated chromatin accessibility data reveals genetic regulatory mechanisms

Background

Understanding the genetic causes for variability in chromatin accessibility can shed light on the molecular mechanisms through which genetic variants may affect complex traits. Thousands of ATAC-seq samples have been collected that hold information about chromatin accessibility across diverse cell types and contexts, but most of these are not paired with genetic information and come from diverse distinct projects and laboratories.

Results

We report here joint genotyping, chromatin accessibility peak calling, and discovery of quantitative trait loci which influence chromatin accessibility (caQTLs), demonstrating the capability of performing caQTL analysis on a large scale in a diverse sample set without pre-existing genotype information. Using 10,293 profiling samples representing 1,454 unique donor individuals across 653 studies from public databases, we catalog 23,381 caQTLs in total. After joint discovery analysis, we cluster samples based on accessible chromatin profiles to identify context-specific caQTLs. We find that caQTLs are strongly enriched for annotations of gene regulatory elements across diverse cell types and tissues and are often strongly linked with genetic variation associated with changes in expression (eQTLs), indicating that caQTLs can mediate genetic effects on gene expression. We demonstrate sharing of causal variants for chromatin accessibility and diverse complex human traits, enabling a more complete picture of the genetic mechanisms underlying complex human phenotypes.

Conclusions

Our work provides a proof of principle for caQTL calling from previously ungenotyped samples, and represents one of the largest, most diverse caQTL resources currently available, informing mechanisms of genetic regulation of gene expression and contribution to disease.

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

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

Liver eQTL meta-analysis illuminates potential molecular mechanisms of cardiometabolic traits

Understanding the molecular mechanisms of complex traits is essential for developing targeted interventions. We analyzed liver expression quantitative-trait locus (eQTL) meta-analysis data on 1,183 participants to identify conditionally distinct signals. We found 9,013 eQTL signals for 6,564 genes; 23% of eGenes had two signals, and 6% had three or more signals. We then integrated the eQTL results with data from 29 cardiometabolic genome-wide association study (GWAS) traits and identified 1,582 GWAS-eQTL colocalizations for 747 eGenes. Non-primary eQTL signals accounted for 17% of all colocalizations. Isolating signals by conditional analysis prior to coloc resulted in 37% more colocalizations than using marginal eQTL and GWAS data, highlighting the importance of signal isolation. Isolating signals also led to stronger evidence of colocalization: among 343 eQTL-GWAS signal pairs in multi-signal regions, analyses that isolated the signals of interest resulted in higher posterior probability of colocalization for 41% of tests. Leveraging allelic heterogeneity, we predicted causal effects of gene expression on liver traits for four genes. To predict functional variants and regulatory elements, we colocalized eQTL with liver chromatin accessibility QTL (caQTL) and found 391 colocalizations, including 73 with non-primary eQTL signals and 60 eQTL signals that colocalized with both a caQTL and a GWAS signal. Finally, we used publicly available massively parallel reporter assays in HepG2 to highlight 14 eQTL signals that include at least one expression-modulating variant. This multi-faceted approach to unraveling the genetic underpinnings of liver-related traits could lead to therapeutic development.

Gene regulatory networks in disease and ageing

The precise control of gene expression is required for the maintenance of cellular homeostasis and proper cellular function, and the declining control of gene expression with age is considered a major contributor to age-associated changes in cellular physiology and disease. The coordination of gene expression can be represented through models of the molecular interactions that govern gene expression levels, so-called gene regulatory networks. Gene regulatory networks can represent interactions that occur through signal transduction, those that involve regulatory transcription factors, or statistical models of gene-gene relationships based on the premise that certain sets of genes tend to be coexpressed across a range of conditions and cell types. Advances in experimental and computational technologies have enabled the inference of these networks on an unprecedented scale and at unprecedented precision. Here, we delineate different types of gene regulatory networks and their cell-biological interpretation. We describe methods for inferring such networks from large-scale, multi-omics datasets and present applications that have aided our understanding of cellular ageing and disease mechanisms.

A bootstrap model comparison test for identifying genes with context-specific patterns of genetic regulation

Understanding how genetic variation affects gene expression is essential for a complete picture of the functional pathways that give rise to complex traits. Although numerous studies have established that many genes are differentially expressed in distinct human tissues and cell types, no tools exist for identifying the genes whose expression is differentially regulated. Here we introduce DRAB (differential regulation analysis by bootstrapping), a gene-based method for testing whether patterns of genetic regulation are significantly different between tissues or other biological contexts. DRAB first leverages the elastic net to learn context-specific models of local genetic regulation and then applies a novel bootstrap-based model comparison test to check their equivalency. Unlike previous model comparison tests, our proposed approach can determine whether population-level models have equal predictive performance by accounting for the variability of feature selection and model training. We validated DRAB on mRNA expression data from a variety of human tissues in the Genotype-Tissue Expression (GTEx) Project. DRAB yielded biologically reasonable results and had sufficient power to detect genes with tissue-specific regulatory profiles while effectively controlling false positives. By providing a framework that facilitates the prioritization of differentially regulated genes, our study enables future discoveries on the genetic architecture of molecular phenotypes.

Genetic Variants in p53 Pathway Genes Affect Survival of Patients with HBV-Related Hepatocellular Carcinoma

Purpose

P53 is a suppressor gene closely related to carcinogenesis. However, the associations between genetic variants in the p53 signaling pathway and prognosis in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) remain unknown. The current study aims to analyze associations between the single nucleotide polymorphisms (SNPs) in p53 pathway-related genes and survival of patients with HBV-HCC.

Methods

We evaluated the associations between 4698 SNPs in 70 genes of the p53 pathway and overall survival (OS) of 866 patients in additive genetic models by using Cox proportional hazards regression analysis. Stepwise multivariable Cox regression analysis was conducted to determine the independent effects of identified SNPs in single-locus analyses. The expression of quantitative trait loci (eQTL) was also analyzed using data from GTEx and 1000 Genomes Project, and functional prediction of SNPs was performed by using RegulomeDB v2.2, 3DSNP v2.0, HaploReg v4.2 and VannoPortal.

Results

We found that two novel SNPs of CD82 rs7925603 A > G and PMAIP1 rs4396625 A > T, were significantly and independently associated with OS [adjusted hazards ratios (HRs) and 95% confidence intervals (CI) were 1.27 (1.10-1.48) and 0.77 (0.66-0.91), respectively; P = 0.001 and = 0.002, respectively] and that the combined risk genotypes of these SNPs showed a significant association with OS in patients with HBV-HCC (P trend < 0.001). Further eQTL analysis in the GTEx dataset showed that the rs7925603 G allele was associated with lower CD82 mRNA expression levels, while the rs4396625 T allele was associated with higher PMAIP1 mRNA expression levels in whole blood cells.

Conclusion

We identified two observed survival-associated SNPs in CD82 and PMAIP1 in the p53 pathway, which influenced HBV-HCC survival possibly through a mechanism of altering mRNA expression. Large studies are warranted to validate our findings.

Inferring causal direction between two traits using R2 with application to transcriptome-wide association studies

In Mendelian randomization, two single SNP-trait correlation-based methods have been developed to infer the causal direction between an exposure (e.g., a gene) and an outcome (e.g., a trait), called MR Steiger's method and its recent extension called Causal Direction-Ratio (CD-Ratio). Here we propose an approach based on R2, the coefficient of determination, to combine information from multiple (possibly correlated) SNPs to simultaneously infer the presence and direction of a causal relationship between an exposure and an outcome. Our proposed method generalizes Steiger's method from using a single SNP to multiple SNPs as IVs. It is especially useful in transcriptome-wide association studies (TWASs) (and similar applications) with typically small sample sizes for gene expression (or another molecular trait) data, providing a more flexible and powerful approach to inferring causal directions. It can be applied to GWAS summary data with a reference panel. We also discuss the influence of invalid IVs and introduce a new approach called R2S to select and remove invalid IVs (if any) to enhance the robustness. We compared the performance of the proposed method with existing methods in simulations to demonstrate its advantages. We applied the methods to identify causal genes for high/low-density lipoprotein cholesterol (HDL/LDL) using the individual-level GTEx gene expression data and UK Biobank GWAS data. The proposed method was able to confirm some well-known causal genes while identifying some novel ones. Additionally, we illustrated an application of the proposed method to GWAS summary to infer causal relationships between HDL/LDL and stroke/coronary artery disease (CAD).

Nutrition, Other Environmental Influences, and Genetics in the Determination of Human Stature

Linear growth during three distinct stages of life determines attained stature in adulthood: namely, in utero, early postnatal life, and puberty and the adolescent period. Individual host factors, genetics, and the environment, including nutrition, influence attained human stature. Each period of physical growth has its specific biological and environmental considerations. Recent epidemiologic investigations reveal a strong influence of prenatal factors on linear size at birth that in turn influence the postnatal growth trajectory. Although average population height changes have been documented in high-income regions, stature as a complex human trait is not well understood or easily modified. This review summarizes the biology of linear growth and its major drivers, including nutrition from a life-course perspective, the genetics of programmed growth patterns or height, and gene-environment interactions that determine human stature in toto over the life span. Implications for public health interventions and knowledge gaps are discussed.

Single-nucleus multiomics reveals the disrupted regulatory programs in three brain regions of sporadic early-onset Alzheimer's disease

Sporadic early-onset Alzheimer's disease (sEOAD) represents a significant but less-studied subtype of Alzheimer's disease (AD). Here, we generated a single-nucleus multiome atlas derived from the postmortem prefrontal cortex, entorhinal cortex, and hippocampus of nine individuals with or without sEOAD. Comprehensive analyses were conducted to delineate cell type-specific transcriptomic changes and linked candidate cis-regulatory elements (cCREs) across brain regions. We prioritized seven conservative transcription factors in glial cells in multiple brain regions, including RFX4 in astrocytes and IKZF1 in microglia, which are implicated in regulating sEOAD-associated genes. Moreover, we identified the top 25 altered intercellular signaling between glial cells and neurons, highlighting their regulatory potential on gene expression in receiver cells. We reported 38 cCREs linked to sEOAD-associated genes overlapped with late-onset AD risk loci, and sEOAD cCREs enriched in neuropsychiatric disorder risk loci. This atlas helps dissect transcriptional and chromatin dynamics in sEOAD, providing a key resource for AD research.

Genome-scale CRISPR-Cas9 screening in stem cells: theories, applications and challenges

Due to the rapid development of stem cell technology, there have been tremendous advances in molecular biological and pathological research, cell therapy as well as organoid technologies over the past decades. Advances in genome editing technology, particularly the discovery of clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-related protein 9 (Cas9), have further facilitated the rapid development of stem cell researches. The CRISPR-Cas9 technology now goes beyond creating single gene editing to enable the inhibition or activation of endogenous gene loci by fusing inhibitory (CRISPRi) or activating (CRISPRa) domains with deactivated Cas9 proteins (dCas9). These tools have been utilized in genome-scale CRISPRi/a screen to recognize hereditary modifiers that are synergistic or opposing to malady mutations in an orderly and fair manner, thereby identifying illness mechanisms and discovering novel restorative targets to accelerate medicinal discovery investigation. However, the application of this technique is still relatively rare in stem cell research. There are numerous specialized challenges in applying large-scale useful genomics approaches to differentiated stem cell populations. Here, we present the first comprehensive review on CRISPR-based functional genomics screening in the field of stem cells, as well as practical considerations implemented in a range of scenarios, and exploration of the insights of CRISPR-based screen into cell fates, disease mechanisms and cell treatments in stem cell models. This review will broadly benefit scientists, engineers and medical practitioners in the areas of stem cell research.

Integrating plasma proteomics with genome-wide association data to identify novel drug targets for inflammatory bowel disease

Inflammatory bowel disease (IBD) is a chronic disease that includes Crohn's disease (CD) and ulcerative colitis (UC). Although genome-wide association studies (GWASs) have identified many relevant genetic risk loci, the impact of these loci on protein abundance and their potential utility as clinical therapeutic targets remain uncertain. Therefore, this study aimed to investigate the pathogenesis of IBD and identify effective therapeutic targets through a comprehensive and integrated analysis. We systematically integrated GWAS data related to IBD, UC and CD (N = 25,305) by the study of de Lange KM with the human blood proteome (N = 7213) by the Atherosclerosis Risk in Communities (ARIC) study. Proteome-wide association study (PWAS), mendelian randomisation (MR) and Bayesian colocalisation analysis were used to identify proteins contributing to the risk of IBD. Integrative analysis revealed that genetic variations in IBD, UC and CD affected the abundance of five (ERAP2, RIPK2, TALDO1, CADM2 and RHOC), three (VSIR, HGFAC and CADM2) and two (MST1 and FLRT3) cis-regulated plasma proteins, respectively (P < 0.05). Among the proteins identified via Bayesian colocalisation analysis, CADM2 was found to be an important common protein between IBD and UC. A drug and five druggable target genes were identified from DGIdb after Bayesian colocalisation analysis. Our study's findings from genetic and proteomic approaches have identified compelling proteins that may serve as important leads for future functional studies and potential drug targets for IBD (UC and CD).

Post-GWAS Validation of Target Genes Associated with HbF and HbA2 Levels

Genome-Wide Association Studies (GWASs) have identified a huge number of variants associated with different traits. However, their validation through in vitro and in vivo studies often lags well behind their identification. For variants associated with traits or diseases of biomedical interest, this gap delays the development of possible therapies. This issue also impacts beta-hemoglobinopathies, such as beta-thalassemia and sickle cell disease (SCD). The definitive cures for these diseases are currently bone marrow transplantation and gene therapy. However, limitations regarding their effective use restrict their worldwide application. Great efforts have been made to identify whether modulators of fetal hemoglobin (HbF) and, to a lesser extent, hemoglobin A2 (HbA2) are possible therapeutic targets. Herein, we performed the post-GWAS in vivo validation of two genes, cyclin D3 (CCND3) and nuclear factor I X (NFIX), previously associated with HbF and HbA2 levels. The absence of Ccnd3 expression in vivo significantly increased g (HbF) and d (HbA2) globin gene expression. Our data suggest that CCND3 is a possible therapeutic target in sickle cell disease. We also confirmed the association of Nfix with γ-globin gene expression and present data suggesting a possible role for Nfix in regulating Kruppel-like transcription factor 1 (Klf1), a master regulator of hemoglobin switching. This study contributes to filling the gap between GWAS variant identification and target validation for beta-hemoglobinopathies.

A systematic strategy for identifying causal single nucleotide polymorphisms and their target genes on Juvenile arthritis risk haplotypes

BACKGROUND

Although genome-wide association studies (GWAS) have identified multiple regions conferring genetic risk for juvenile idiopathic arthritis (JIA), we are still faced with the task of identifying the single nucleotide polymorphisms (SNPs) on the disease haplotypes that exert the biological effects that confer risk. Until we identify the risk-driving variants, identifying the genes influenced by these variants, and therefore translating genetic information to improved clinical care, will remain an insurmountable task. We used a function-based approach for identifying causal variant candidates and the target genes on JIA risk haplotypes.

METHODS

We used a massively parallel reporter assay (MPRA) in myeloid K562 cells to query the effects of 5,226 SNPs in non-coding regions on JIA risk haplotypes for their ability to alter gene expression when compared to the common allele. The assay relies on 180 bp oligonucleotide reporters ("oligos") in which the allele of interest is flanked by its cognate genomic sequence. Barcodes were added randomly by PCR to each oligo to achieve > 20 barcodes per oligo to provide a quantitative read-out of gene expression for each allele. Assays were performed in both unstimulated K562 cells and cells stimulated overnight with interferon gamma (IFNg). As proof of concept, we then used CRISPRi to demonstrate the feasibility of identifying the genes regulated by enhancers harboring expression-altering SNPs.

RESULTS

We identified 553 expression-altering SNPs in unstimulated K562 cells and an additional 490 in cells stimulated with IFNg. We further filtered the SNPs to identify those plausibly situated within functional chromatin, using open chromatin and H3K27ac ChIPseq peaks in unstimulated cells and open chromatin plus H3K4me1 in stimulated cells. These procedures yielded 42 unique SNPs (total = 84) for each set. Using CRISPRi, we demonstrated that enhancers harboring MPRA-screened variants in the TRAF1 and LNPEP/ERAP2 loci regulated multiple genes, suggesting complex influences of disease-driving variants.

CONCLUSION

Using MPRA and CRISPRi, JIA risk haplotypes can be queried to identify plausible candidates for disease-driving variants. Once these candidate variants are identified, target genes can be identified using CRISPRi informed by the 3D chromatin structures that encompass the risk haplotypes.

Distinct genetic liability profiles define clinically relevant patient strata across common diseases

Stratified medicine holds great promise to tailor treatment to the needs of individual patients. While genetics holds great potential to aid patient stratification, it remains a major challenge to operationalize complex genetic risk factor profiles to deconstruct clinical heterogeneity. Contemporary approaches to this problem rely on polygenic risk scores (PRS), which provide only limited clinical utility and lack a clear biological foundation. To overcome these limitations, we develop the CASTom-iGEx approach to stratify individuals based on the aggregated impact of their genetic risk factor profiles on tissue specific gene expression levels. The paradigmatic application of this approach to coronary artery disease or schizophrenia patient cohorts identified diverse strata or biotypes. These biotypes are characterized by distinct endophenotype profiles as well as clinical parameters and are fundamentally distinct from PRS based groupings. In stark contrast to the latter, the CASTom-iGEx strategy discovers biologically meaningful and clinically actionable patient subgroups, where complex genetic liabilities are not randomly distributed across individuals but rather converge onto distinct disease relevant biological processes. These results support the notion of different patient biotypes characterized by partially distinct pathomechanisms. Thus, the universally applicable approach presented here has the potential to constitute an important component of future personalized medicine paradigms.

A Multilayered Post-Genome-Wide Association Study Analysis Pipeline Defines Functional Variants and Target Genes for Systemic Lupus Erythematosus

OBJECTIVE

Systemic lupus erythematosus (SLE), an autoimmune disease with incompletely understood etiology, has a strong genetic component. Although genome-wide association studies (GWASs) have revealed multiple SLE susceptibility loci and associated single-nucleotide polymorphisms (SNPs), the precise causal variants, target genes, cell types, tissues, and mechanisms of action remain largely unknown.

METHODS

Here, we report a comprehensive post-GWAS analysis using extensive bioinformatics, molecular modeling, and integrative functional genomic and epigenomic analyses to optimize fine-mapping. We compile and cross-reference immune cell-specific expression quantitative trait loci (cis- and trans-expression quantitative trait loci) with promoter capture high-throughput capture chromatin conformation (PCHi-C), allele-specific chromatin accessibility, and massively parallel reporter assay data to define predisposing variants and target genes. We experimentally validate a predicted locus using CRISPR/Cas9 genome editing, quantitative polymerase chain reaction, and Western blot.

RESULTS

Anchoring on 452 index SNPs, we selected 9,931 high linkage disequilibrium (r2 > 0.8) SNPs and defined 182 independent non-human leukocyte antigen (HLA) SLE loci. The 3,746 SNPs from 143 loci were identified as regulating 564 unique genes. Target genes are enriched in lupus-related tissues and associated with other autoimmune diseases. Of these, 329 SNPs (106 loci) showed significant allele-specific chromatin accessibility and/or enhancer activity, indicating regulatory potential. Using CRISPR/Cas9, we validated reference SNP identifier 57668933 (rs57668933) as a functional variant regulating multiple targets, including SLE-risk gene ELF1 in B cells.

CONCLUSION

We demonstrate and validate post-GWAS strategies for using multidimensional data to prioritize likely causal variants with cognate gene targets underlying SLE pathogenesis. Our results provide a catalog of significantly SLE-associated SNPs and loci, target genes, and likely biochemical mechanisms to guide experimental characterization.

Single-nucleus multiomics reveals the disrupted regulatory programs in three brain regions of sporadic early-onset Alzheimer's disease

Sporadic early-onset Alzheimer's disease (sEOAD) represents a significant but less-studied subtype of Alzheimer's disease (AD). Here, we generated a single-nucleus multiome atlas derived from the postmortem prefrontal cortex, entorhinal cortex, and hippocampus of nine individuals with or without sEOAD. Comprehensive analyses were conducted to delineate cell type-specific transcriptomic changes and linked candidate cis- regulatory elements (cCREs) across brain regions. We prioritized seven conservative transcription factors in glial cells in multiple brain regions, including RFX4 in astrocytes and IKZF1 in microglia, which are implicated in regulating sEOAD-associated genes. Moreover, we identified the top 25 altered intercellular signaling between glial cells and neurons, highlighting their regulatory potential on gene expression in receiver cells. We reported 38 cCREs linked to sEOAD-associated genes overlapped with late-onset AD risk loci, and sEOAD cCREs enriched in neuropsychiatric disorder risk loci. This atlas helps dissect transcriptional and chromatin dynamics in sEOAD, providing a key resource for AD research.

Advances in regulation of homeostasis through chromatin modifications by airway commensals

Commensal bacteria are residents of the human airway where they interact with both colonizing pathogens and host respiratory epithelial cells of this mucosal surface. It is here that commensals exert their influence through host signaling cascades, host transcriptional responses and host immunity, all of which are rooted in chromatin remodeling and histone modifications. Recent studies show that airway commensals impact host chromatin, but compared the what is known for gut commensals, the field remains in its infancy. The mechanisms by which airway commensals regulate respiratory health and homeostasis through chromatin modifications is of increasing interest, specifically since their displacement precedes the increased potential for respiratory disease. Herein we will discuss recent advances and intriguing avenues of future work aimed at deciphering how airway commensals protect and influence respiratory health.

Identify Regulatory eQTLs by Multiome Sequencing in Prostate Single Cells

While genome-wide association studies and expression quantitative trait loci (eQTL) analysis have made significant progress in identifying noncoding variants associated with prostate cancer risk and bulk tissue transcriptome changes, the regulatory effect of these genetic elements on gene expression remains largely unknown. Recent developments in single-cell sequencing have made it possible to perform ATAC-seq and RNA-seq profiling simultaneously to capture functional associations between chromatin accessibility and gene expression. In this study, we tested our hypothesis that this multiome single-cell approach allows for mapping regulatory elements and their target genes at prostate cancer risk loci. We applied a 10X Multiome ATAC + Gene Expression platform to encapsulate Tn5 transposase-tagged nuclei from multiple prostate cell lines for a total of 65,501 high quality single cells from RWPE1, RWPE2, PrEC, BPH1, DU145, PC3, 22Rv1 and LNCaP cell lines. To address data sparsity commonly seen in the single-cell sequencing, we performed targeted sequencing to enrich sequencing data at prostate cancer risk loci involving 2,730 candidate germline variants and 273 associated genes. Although not increasing the number of captured cells, the targeted multiome data did improve eQTL gene expression abundance by about 20% and chromatin accessibility abundance by about 5%. Based on this multiomic profiling, we further associated RNA expression alterations with chromatin accessibility of germline variants at single cell levels. Cross validation analysis showed high overlaps between the multiome associations and the bulk eQTL findings from GTEx prostate cohort. We found that about 20% of GTEx eQTLs were covered within the significant multiome associations (p-value ≤ 0.05, gene abundance percentage ≥ 5%), and roughly 10% of the multiome associations could be identified by significant GTEx eQTLs. We also analyzed accessible regions with available heterozygous SNP reads and observed more frequent association in genomic regions with allelically accessible variants (p = 0.0055). Among these findings were previously reported regulatory variants including rs60464856-RUVBL1 (multiome p-value = 0.0099 in BPH1) and rs7247241-SPINT2 (multiome p-value = 0.0002- 0.0004 in 22Rv1). We also functionally validated a new regulatory SNP and its target gene rs2474694-VPS53 (multiome p-value = 0.00956 in BPH1 and 0.00625 in DU145) by reporter assay and SILAC proteomics sequencing. Taken together, our data demonstrated the feasibility of the multiome single-cell approach for identifying regulatory SNPs and their regulated genes.

Plasma proteome analysis implicates novel proteins as potential therapeutic targets for chronic kidney disease: A proteome-wide association study

Chronic kidney disease (CKD) is prevalent globally with limited therapeutic drugs available. To systemically identify novel proteins involved in the pathogenesis of CKD and possible therapeutic targets, we integrated human plasma proteomes with the genome-wide association studies (GWASs) of CKD, estimated glomerular filtration rate (eGFR) and blood urea nitrogen (BUN) to perform proteome-wide association study (PWAS), Mendelian Randomization and Bayesian colocalization analyses. The single-cell RNA sequencing data of healthy human and mouse kidneys were analyzed to explore the cell-type specificity of identified genes. Functional enrichment analysis was conducted to investigate the involved signaling pathways. The PWAS identified 22 plasma proteins significantly associated with CKD. Of them, the significant associations of three proteins (INHBC, LMAN2, and SNUPN) were replicated in the GWASs of eGFR, and BUN. Mendelian Randomization analyses showed that INHBC and SNUPN were causally associated with CKD, eGFR, and BUN. The Bayesian colocalization analysis identified shared causal variants for INHBC in CKD, eGFR, and BUN (all PP4 > 0.75). The single-cell RNA sequencing revealed that the INHBC gene was sparsely scattered within the kidney cells. This proteomic study revealed that INHBC, LMAN2, and SNUPN may be involved in the pathogenesis of CKD, which represent novel therapeutic targets and warrant further exploration in future research.

A transcriptome-wide association study identified susceptibility genes for hepatocellular carcinoma in East Asia

Background

Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide and is prevalent in East Asia. Although genome-wide association studies (GWASs) of HCC have identified 23 risk regions, the susceptibility genes underlying these associations largely remain unclear. To identify novel candidate genes for HCC, we conducted liver single-tissue and cross-tissue transcriptome-wide association studies (TWASs) in two populations of East Asia.

Methods

GWAS summary statistics of 2,514 subjects (1,161 HCC cases and 1,353 controls) from the Chinese Qidong cohort and 161,323 subjects (2,122 HCC cases and 159,201 controls) from the BioBank Japan project were used to conduct TWAS analysis. The single-tissue and cross-tissue TWAS approaches were both used to detect the association between susceptible genes and the risk of HCC. TWAS identified genes were further annotated by Metascape, UALCAN, GEPIA2, and DepMap.

Results

We identified 22 novel genes at 16 independent loci significantly associated with HCC risk after Bonferroni correction. Of these, 13 genes were located in novel regions. Besides, we found 83 genes overlapped in the Chinese and Japanese cohorts with P < 0.05, of which, three genes (NUAK2, HLA-DQA1, and ATP6V1G2) were discerned by both single-tissue and cross-tissue TWAS approaches. Among the genes identified through TWAS, a significant proportion of them exhibit a credible role in HCC biology, such as FAM96B, HSPA5, POLRMT, MPHOSPH10, and RABL2A. HLA-DQA1, NUAK2, and HSPA5 associated with the process of carcinogenesis in HCC as previously reported.

Conclusions

Our findings highlight the value of leveraging the gene expression data to identify new candidate genes beyond the GWAS associations and could further provide a genetic insight for the biology of HCC.

Genopyc: a Python library for investigating the functional effects of genomic variants associated to complex diseases

MOTIVATION

Integrative Biomedicl Informatics, Research Program on Biomedical Informatics (IBI - GRIB), Hospital Del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences, Universitat Pompeu Fabra (UPF) C/ del Dr. Aiguader 88 Barcelona 08003 Spain.Understanding the genetic basis of complex diseases is one of the main challenges in modern genomics. However, current tools often lack the versatility to efficiently analyze the intricate relationships between genetic variations and disease outcomes. To address this, we introduce Genopyc, a novel Python library designed for comprehensive investigation of how the variants associated to complex diseases affects downstream pathways. Genopyc offers an extensive suite of functions for heterogeneous data mining and visualization, enabling researchers to delve into and integrate biological information from large-scale genomic datasets.

RESULTS

In this work, we present the Genopyc library through application to real-world genome wide association studies variants. Using Genopyc to investigate the functional consequences of variants associated to intervertebral disc degeneration enabled a deeper understanding of the potential dysregulated pathways involved in the disease, which can be explored and visualized by exploiting the functionalities featured in the package. Genopyc emerges as a powerful asset for researchers, facilitating the investigation of complex diseases paving the way for more targeted therapeutic interventions.

AVAILABILITY AND IMPLEMENTATION

Genopyc is available on pip https://pypi.org/project/genopyc/.The source code of Genopyc is available at https://github.com/freh-g/genopyc. A tutorial notebook is available at https://github.com/freh-g/genopyc/blob/main/tutorials/Genopyc_tutorial_notebook.ipynb. Finally, a detailed documentation is available at: https://genopyc.readthedocs.io/en/latest/.

An overview of detecting gene-trait associations by integrating GWAS summary statistics and eQTLs

Detecting genes that affect specific traits (such as human diseases and crop yields) is important for treating complex diseases and improving crop quality. A genome-wide association study (GWAS) provides new insights and directions for understanding complex traits by identifying important single nucleotide polymorphisms. Many GWAS summary statistics data related to various complex traits have been gathered recently. Studies have shown that GWAS risk loci and expression quantitative trait loci (eQTLs) often have a lot of overlaps, which makes gene expression gradually become an important intermediary to reveal the regulatory role of GWAS. In this review, we review three types of gene-trait association detection methods of integrating GWAS summary statistics and eQTLs data, namely colocalization methods, transcriptome-wide association study-oriented approaches, and Mendelian randomization-related methods. At the theoretical level, we discussed the differences, relationships, advantages, and disadvantages of various algorithms in the three kinds of gene-trait association detection methods. To further discuss the performance of various methods, we summarize the significant gene sets that influence high-density lipoprotein, low-density lipoprotein, total cholesterol, and triglyceride reported in 16 studies. We discuss the performance of various algorithms using the datasets of the four lipid traits. The advantages and limitations of various algorithms are analyzed based on experimental results, and we suggest directions for follow-up studies on detecting gene-trait associations.

SABER: Statistical Identification of Loci of Interest in GWAS Summary Statistics using a Bayesian Gaussian Mixture Model

Genome-wide association studies (GWAS) remain a popular method for identifying novel genetic associations with human phenotypes and have provided many insights into the etiology of many diseases. However, GWAS provide limited support for how a genetic association might contribute to disease due to inherent limitations, such as linkage disequilibrium. As such, many methods that operate on GWAS summary statistics have been developed to generate evidence for functional pathways or for variants of interest, but they require defining the genomic region bounds for loci of interest. At present, there are limited methods for determining these bounds in a rigorous, reproducible way. We present a novel statistical method, Statistical Analysis for Bayesian Estimation of Regions (SABER), that uses Bayesian Gaussian mixture models to reproducibly generate ratios that quantify whether particular genomic positions represent the bounds of loci of interest and can be used to delineate genomic regions for downstream analyses.

Characterizing the Relationship between Expression Quantitative Trait Loci (eQTLs), DNA Methylation Quantitative Trait Loci (mQTLs), and Breast Cancer Risk Variants

PURPOSE

To assess the association of a polygenic risk score (PRS) for functional genetic variants with the risk of developing breast cancer.

METHODS

Summary data-based Mendelian randomization (SMR) and heterogeneity in dependent instruments (HEIDI) were used to identify breast cancer risk variants associated with gene expression and DNA methylation levels. A new SMR-based PRS was computed from the identified variants (functional PRS) and compared to an established 313-variant breast cancer PRS (GWAS PRS). The two scores were evaluated in 3560 breast cancer cases and 3383 non-cancer controls and also in a prospective study (n = 10,213) comprising 418 cases.

RESULTS

We identified 149 variants showing pleiotropic association with breast cancer risk (eQTLHEIDI > 0.05 = 9, mQTLHEIDI > 0.05 = 165). The discriminatory ability of the functional PRS (AUCcontinuous [95% CI]: 0.540 [0.526 to 0.553]) was found to be lower than that of the GWAS PRS (AUCcontinuous [95% CI]: 0.609 [0.596 to 0.622]). Even when utilizing 457 distinct variants from both the functional and GWAS PRS, the combined discriminatory performance remained below that of the GWAS PRS (AUCcontinuous, combined [95% CI]: 0.561 [0.548 to 0.575]). A binary high/low-risk classification based on the 80th centile PRS in controls revealed a 6% increase in cases using the GWAS PRS compared to the functional PRS. The functional PRS identified an additional 12% of high-risk cases but also led to a 13% increase in high-risk classification among controls. Similar findings were observed in the SCHS prospective cohort, where the GWAS PRS outperformed the functional PRS, and the highest-performing PRS, a combined model, did not significantly improve over the GWAS PRS.

CONCLUSIONS

While this study identified potentially functional variants associated with breast cancer risk, their inclusion did not substantially enhance the predictive accuracy of the GWAS PRS.

Digenic Inheritance in Rare Disorders and Mitochondrial Disease-Crossing the Frontier to a More Comprehensive Understanding of Etiology

Our understanding of rare disease genetics has been shaped by a monogenic disease model. While the traditional monogenic disease model has been successful in identifying numerous disease-associated genes and significantly enlarged our knowledge in the field of human genetics, it has limitations in explaining phenomena like phenotypic variability and reduced penetrance. Widening the perspective beyond Mendelian inheritance has the potential to enable a better understanding of disease complexity in rare disorders. Digenic inheritance is the simplest instance of a non-Mendelian disorder, characterized by the functional interplay of variants in two disease-contributing genes. Known digenic disease causes show a range of pathomechanisms underlying digenic interplay, including direct and indirect gene product interactions as well as epigenetic modifications. This review aims to systematically explore the background of digenic inheritance in rare disorders, the approaches and challenges when investigating digenic inheritance, and the current evidence for digenic inheritance in mitochondrial disorders.

Brain and Blood Transcriptome-Wide Association Studies Identify Five Novel Genes Associated with Alzheimer's Disease

INTRODUCTION

Transcriptome-wide Association Studies (TWAS) extend genome-wide association studies (GWAS) by integrating genetically-regulated gene expression models. We performed the most powerful AD-TWAS to date, using summary statistics from cis -eQTL meta-analyses and the largest clinically-adjudicated Alzheimer's Disease (AD) GWAS.

METHODS

We implemented the OTTERS TWAS pipeline, leveraging cis -eQTL data from cortical brain tissue (MetaBrain; N=2,683) and blood (eQTLGen; N=31,684) to predict gene expression, then applied these models to AD-GWAS data (Cases=21,982; Controls=44,944).

RESULTS

We identified and validated five novel gene associations in cortical brain tissue ( PRKAG1 , C3orf62 , LYSMD4 , ZNF439 , SLC11A2 ) and six genes proximal to known AD-related GWAS loci (Blood: MYBPC3 ; Brain: MTCH2 , CYB561 , MADD , PSMA5 , ANXA11 ). Further, using causal eQTL fine-mapping, we generated sparse models that retained the strength of the AD-TWAS association for MTCH2 , MADD , ZNF439 , CYB561 , and MYBPC3 .

DISCUSSION

Our comprehensive AD-TWAS discovered new gene associations and provided insights into the functional relevance of previously associated variants.

Reviewing the essential roles of remote phenotyping, GWAS and explainable AI in practical marker-assisted selection for drought-tolerant winter wheat breeding

Marker-assisted selection (MAS) plays a crucial role in crop breeding improving the speed and precision of conventional breeding programmes by quickly and reliably identifying and selecting plants with desired traits. However, the efficacy of MAS depends on several prerequisites, with precise phenotyping being a key aspect of any plant breeding programme. Recent advancements in high-throughput remote phenotyping, facilitated by unmanned aerial vehicles coupled to machine learning, offer a non-destructive and efficient alternative to traditional, time-consuming, and labour-intensive methods. Furthermore, MAS relies on knowledge of marker-trait associations, commonly obtained through genome-wide association studies (GWAS), to understand complex traits such as drought tolerance, including yield components and phenology. However, GWAS has limitations that artificial intelligence (AI) has been shown to partially overcome. Additionally, AI and its explainable variants, which ensure transparency and interpretability, are increasingly being used as recognised problem-solving tools throughout the breeding process. Given these rapid technological advancements, this review provides an overview of state-of-the-art methods and processes underlying each MAS, from phenotyping, genotyping and association analyses to the integration of explainable AI along the entire workflow. In this context, we specifically address the challenges and importance of breeding winter wheat for greater drought tolerance with stable yields, as regional droughts during critical developmental stages pose a threat to winter wheat production. Finally, we explore the transition from scientific progress to practical implementation and discuss ways to bridge the gap between cutting-edge developments and breeders, expediting MAS-based winter wheat breeding for drought tolerance.

Combined SNPs sequencing and allele specific proteomics capture reveal functional causality underpinning the 2p25 prostate cancer susceptibility locus

Genome wide association studies (GWASs) have identified numerous risk loci associated with prostate cancer, yet unraveling their functional significance remains elusive. Leveraging our high-throughput SNPs-seq method, we pinpointed rs4519489 within the multi-ancestry GWAS-discovered 2p25 locus as a potential functional SNP due to its significant allelic differences in protein binding. Here, we conduct a comprehensive analysis of rs4519489 and its associated gene, NOL10, employing diverse cohort data and experimental models. Clinical findings reveal a synergistic effect between rs4519489 genotype and NOL10 expression on prostate cancer prognosis and severity. Through unbiased proteomics screening, we reveal that the risk allele A of rs4519489 exhibits enhanced binding to USF1, a novel oncogenic transcription factor (TF) implicated in prostate cancer progression and prognosis, resulting in elevated NOL10 expression. Furthermore, we elucidate that NOL10 regulates cell cycle pathways, fostering prostate cancer progression. The concurrent expression of NOL10 and USF1 correlates with aggressive prostate cancer characteristics and poorer prognosis. Collectively, our study offers a robust strategy for functional SNP screening and TF identification through high-throughput SNPs-seq and unbiased proteomics, highlighting the rs4519489-USF1-NOL10 regulatory axis as a promising biomarker or therapeutic target for clinical diagnosis and treatment of prostate cancer.

Synergizing drug repurposing and target identification for neurodegenerative diseases

Despite dedicated research efforts, the absence of disease-curing remedies for neurodegenerative diseases (NDDs) continues to jeopardize human society and stands as a challenge. Drug repurposing is an attempt to find new functionality of existing drugs and take it as an opportunity to discourse the clinically unmet need to treat neurodegeneration. However, despite applying this approach to rediscover a drug, it can also be used to identify the target on which a drug could work. The primary objective of target identification is to unravel all the possibilities of detecting a new drug or repurposing an existing drug. Lately, scientists and researchers have been focusing on specific genes, a particular site in DNA, a protein, or a molecule that might be involved in the pathogenesis of the disease. However, the new era discusses directing the signaling mechanism involved in the disease progression, where receptors, ion channels, enzymes, and other carrier molecules play a huge role. This review aims to highlight how target identification can expedite the whole process of drug repurposing. Here, we first spot various target-identification methods and drug-repositioning studies, including drug-target and structure-based identification studies. Moreover, we emphasize various drug repurposing approaches in NDDs, namely, experimental-based, mechanism-based, and in silico approaches. Later, we draw attention to validation techniques and stress on drugs that are currently undergoing clinical trials in NDDs. Lastly, we underscore the future perspective of synergizing drug repurposing and target identification in NDDs and present an unresolved question to address the issue.

Specific isoforms of the ubiquitin ligase gene WWP2 are targets of osteoarthritis genetic risk via a differentially methylated DNA sequence

BACKGROUND

Transitioning from a genetic association signal to an effector gene and a targetable molecular mechanism requires the application of functional fine-mapping tools such as reporter assays and genome editing. In this report, we undertook such studies on the osteoarthritis (OA) risk that is marked by single nucleotide polymorphism (SNP) rs34195470 (A > G). The OA risk-conferring G allele of this SNP associates with increased DNA methylation (DNAm) at two CpG dinucleotides within WWP2. This gene encodes a ubiquitin ligase and is the host gene of microRNA-140 (miR-140). WWP2 and miR-140 are both regulators of TGFβ signaling.

METHODS

Nucleic acids were extracted from adult OA (arthroplasty) and foetal cartilage. Samples were genotyped and DNAm quantified by pyrosequencing at the two CpGs plus 14 flanking CpGs. CpGs were tested for transcriptional regulatory effects using a chondrocyte cell line and reporter gene assay. DNAm was altered using epigenetic editing, with the impact on gene expression determined using RT-qPCR. In silico analysis complemented laboratory experiments.

RESULTS

rs34195470 genotype associates with differential methylation at 14 of the 16 CpGs in OA cartilage, forming a methylation quantitative trait locus (mQTL). The mQTL is less pronounced in foetal cartilage (5/16 CpGs). The reporter assay revealed that the CpGs reside within a transcriptional regulator. Epigenetic editing to increase their DNAm resulted in altered expression of the full-length and N-terminal transcript isoforms of WWP2. No changes in expression were observed for the C-terminal isoform of WWP2 or for miR-140.

CONCLUSIONS

As far as we are aware, this is the first experimental demonstration of an OA association signal targeting specific transcript isoforms of a gene. The WWP2 isoforms encode proteins with varying substrate specificities for the components of the TGFβ signaling pathway. Future analysis should focus on the substrates regulated by the two WWP2 isoforms that are the targets of this genetic risk.

Comprehensive functional interrogation of susceptibility loci in GWASs identified KIAA0391 as a novel oncogenic driver via regulating pyroptosis in NSCLC

Approximately 51 non-small-cell lung cancer (NSCLC) risk loci have been identified by genome-wide association studies (GWASs). We conducted a high throughput RNA-interference (RNAi) screening to identify the candidate causal genes in NSCLC risk loci. KIAA0391 at 14q13.1 had the highest score and could promote proliferation and metastasis of NSCLC in vitro and in vivo. We next prioritized rs3783313 as a causal variant at 14q13.1, by integrating a large-scale population study consisting of 27,120 lung cancer cases and 27,355 controls, functional annotation, and expression quantitative trait locus (eQTL) analysis. Then we found that rs3783313 could facilitate a promoter-enhancer interaction to upregulate KIAA0391 expression by affecting the affinity of transcription factor NFYA. Mechanistically, KIAA0391 knockdown dramatically influenced pyroptosis-related pathways and increased the expression of CASP1. And KIAA0391 transcriptionally repressed CASP1 by binding to SMAD2 and induced an anti-pyroptosis phenotype, promoting tumorigenesis of NSCLC, which provides new insights and potential target for NSCLC.

Omics-based construction of regulatory variants can be applied to help decipher pig liver-related traits

Genetic variants can influence complex traits by altering gene expression through changes to regulatory elements. However, the genetic variants that affect the activity of regulatory elements in pigs are largely unknown, and the extent to which these variants influence gene expression and contribute to the understanding of complex phenotypes remains unclear. Here, we annotate 90,991 high-quality regulatory elements using acetylation of histone H3 on lysine 27 (H3K27ac) ChIP-seq of 292 pig livers. Combined with genome resequencing and RNA-seq data, we identify 28,425 H3K27ac quantitative trait loci (acQTLs) and 12,250 expression quantitative trait loci (eQTLs). Through the allelic imbalance analysis, we validate two causative acQTL variants in independent datasets. We observe substantial sharing of genetic controls between gene expression and H3K27ac, particularly within promoters. We infer that 46% of H3K27ac exhibit a concomitant rather than causative relationship with gene expression. By integrating GWAS, eQTLs, acQTLs, and transcription factor binding prediction, we further demonstrate their application, through metabolites dulcitol, phosphatidylcholine (PC) (16:0/16:0) and published phenotypes, in identifying likely causal variants and genes, and discovering sub-threshold GWAS loci. We provide insight into the relationship between regulatory elements and gene expression, and the genetic foundation for dissecting the molecular mechanism of phenotypes.

Genetic control of DNA methylation is largely shared across European and East Asian populations

DNA methylation is an ideal trait to study the extent of the shared genetic control across ancestries, effectively providing hundreds of thousands of model molecular traits with large QTL effect sizes. We investigate cis DNAm QTLs in three European (n = 3701) and two East Asian (n = 2099) cohorts to quantify the similarities and differences in the genetic architecture across populations. We observe 80,394 associated mQTLs (62.2% of DNAm probes with significant mQTL) to be significant in both ancestries, while 28,925 mQTLs (22.4%) are identified in only a single ancestry. mQTL effect sizes are highly conserved across populations, with differences in mQTL discovery likely due to differences in allele frequency of associated variants and differing linkage disequilibrium between causal variants and assayed SNPs. This study highlights the overall similarity of genetic control across ancestries and the value of ancestral diversity in increasing the power to detect associations and enhancing fine mapping resolution.