The Post-GWAS Era: From Association to Function

Brain and blood transcriptome-wide association studies identify five novel genes associated with Alzheimer's disease

BackgroundGenome-wide association studies (GWAS) have identified numerous genetic variants associated with Alzheimer's disease (AD), but their functional implications remain unclear. Transcriptome-wide association studies (TWAS) offer enhanced statistical power by analyzing genetic associations at the gene level rather than at the variant level, enabling assessment of how genetically-regulated gene expression influences AD risk. However, previous AD-TWAS have been limited by small expression quantitative trait loci (eQTL) reference datasets or reliance on AD-by-proxy phenotypes.ObjectiveTo perform the most powerful AD-TWAS to date using summary statistics from the largest available brain and blood cis-eQTL meta-analyses applied to the largest clinically-adjudicated AD GWAS.MethodsWe implemented the OTTERS TWAS pipeline to predict gene expression using the largest available cis-eQTL data from cortical brain tissue (MetaBrain; N = 2683) and blood (eQTLGen; N = 31,684), and then applied these models to AD-GWAS data (Cases = 21,982; Controls = 44,944).ResultsWe 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.ConclusionsOur comprehensive AD-TWAS discovered new gene associations and provided insights into the functional relevance of previously associated variants, which enables us to further understand the genetic architecture underlying AD risk.

Integrating large-scale meta-GWAS and PigGTEx resources to decipher the genetic basis of 232 complex traits in pigs

Understanding the molecular and cellular mechanisms underlying complex traits in pigs is crucial for enhancing genetic gain via artificial selection and utilizing pigs as models for human disease and biology. Here, we conducted comprehensive genome-wide association studies (GWAS) followed by a cross-breed meta-analysis for 232 complex traits and a within-breed meta-analysis for 12 traits, using 28.3 million imputed sequence variants in 70 328 animals across 14 pig breeds. We identified 6878 quantitative trait loci (QTL) for 139 complex traits. Leveraging the Pig Genotype-Tissue Expression resource, we systematically investigated the biological context and regulatory mechanisms behind these trait-QTLs, ultimately prioritizing 14 829 variant-gene-tissue-trait regulatory circuits. For instance, rs344053754 regulates UGT2B31 expression in the liver and intestines, potentially by modulating enhancer activity, ultimately influencing litter weight at weaning in pigs. Furthermore, we observed conservation of certain genetic and regulatory mechanisms underlying complex traits between humans and pigs. Overall, our cross-breed meta-GWAS in pigs provides invaluable resources and novel insights into the genetic regulatory and evolutionary mechanisms of complex traits in mammals.

Dissect Gender-Dependent Susceptibility SNPs in Progressive Osteoarthritis Using Regulator Motif Candidate of Genetic Association Strategy (RMCGA)

The role of gender in osteoarthritis (OA) has been reported. However, knowledge on whether gender-specific regulatory SNPs are determining factors in OA is limited. We aimed to identify susceptible gender-specific SNPs of transcription factor binding sites in OA. We used a modified NF-κB binding motif from an RNA sequencing data-inferred OA-associated upstream regulator to define genome-wide potential NF-κB binding sites, which were aligned to the Taiwan BioBank SNP database to identify susceptible SNPs. A case-control study was conducted to verify SNPs with OA determined by a logistic model. The functional assessment was validated using the Genotype-Tissue Expression Portal database. We collected 533 OA patients and 614 healthy controls. Two of nine novel OA-associated SNPs were identified to be significant. For males, the variant of rs73164856 in the aldose reductase gene enhancer was identified to be a protective factor of severe OA patients [odds ratio (OR): 0.17, 95% confidence interval (CI): 0.04-0.73]. For females, the variant of the rs545654 in the neuronal NOS (nNOS) gene was identified to be a detrimental factor of severe OA patients (OR: 2.07, 95% CI: 1.15-3.73). The gene expression analysis demonstrated a lower expression of the AKR1B15 gene (p = 0.00019) upon the rs73164856 T allele; meanwhile, it showed a higher expression of the nNOS gene (p = 1.2 × 10-17) upon the rs545654 T allele. This study identifies susceptible gender-specific SNPs of NF-κB binding sites in severe OA and validates the RMCGA, which sheds light on genetic determinants by gender in advanced OA.

Integration of functional genomics and statistical fine-mapping systematically characterizes adult-onset and childhood-onset asthma genetic associations

BACKGROUND

Genome-wide association studies (GWAS) have identified hundreds of loci underlying adult-onset asthma (AOA) and childhood-onset asthma (COA). However, the causal variants, regulatory elements, and effector genes at these loci are largely unknown.

METHODS

We performed heritability enrichment analysis to determine relevant cell types for AOA and COA, respectively. Next, we fine-mapped putative causal variants at AOA and COA loci. To improve the resolution of fine-mapping, we integrated ATAC-seq data in blood and lung cell types to annotate variants in candidate cis-regulatory elements (CREs). We then computationally prioritized candidate CREs underlying asthma risk, experimentally assessed their enhancer activity by massively parallel reporter assay (MPRA) in bronchial epithelial cells (BECs) and further validated a subset by luciferase assays. Combining chromatin interaction data and expression quantitative trait loci, we nominated genes targeted by candidate CREs and prioritized effector genes for AOA and COA.

RESULTS

Heritability enrichment analysis suggested a shared role of immune cells in the development of both AOA and COA while highlighting the distinct contribution of lung structural cells in COA. Functional fine-mapping uncovered 21 and 67 credible sets for AOA and COA, respectively, with only 16% shared between the two. Notably, one-third of the loci contained multiple credible sets. Our CRE prioritization strategy nominated 62 and 169 candidate CREs for AOA and COA, respectively. Over 60% of these candidate CREs showed open chromatin in multiple cell lineages, suggesting their potential pleiotropic effects in different cell types. Furthermore, COA candidate CREs were enriched for enhancers experimentally validated by MPRA in BECs. The prioritized effector genes included many genes involved in immune and inflammatory responses. Notably, multiple genes, including TNFSF4, a drug target undergoing clinical trials, were supported by two independent GWAS signals, indicating widespread allelic heterogeneity. Four out of six selected candidate CREs demonstrated allele-specific regulatory properties in luciferase assays in BECs.

CONCLUSIONS

We present a comprehensive characterization of causal variants, regulatory elements, and effector genes underlying AOA and COA genetics. Our results supported a distinct genetic basis between AOA and COA and highlighted regulatory complexity at many GWAS loci marked by both extensive pleiotropy and allelic heterogeneity.

Time-series analysis reveals metabolic and transcriptional dynamics during mulberry fruit development and ripening

Understanding the global transcriptomic and metabolic changes during mulberry growth and development is essential for the enhancing fruit quality and optimizing breeding strategies. By integrating phenotypic, metabolomic, and transcriptomic data across 18 developmental and ripening stages of Da10 mulberry fruit, a global map of gene expression and metabolic changes was generated. Analysis revealed a gradual progression of morphological, metabolic, and transcriptional changes throughout the development and ripening phases. In this study, a new transcriptome transition, which was highly related to stress resistance, was observed after the full ripening stage. Moreover, a novel method was devised by integrating metabolome and phenotypic data to assess fruit quality and determine optimal harvest times early in the supply chain. Phase-specific co-expression networks involved in photosynthesis, quality regulation, and plant immunity were also constructed. Notably, eight flavonoids and six hub genes emerged as potential natural edible coatings or gene-editing targets for mulberry fruit to enhance resistance against biotic and abiotic stress. These findings should facilitate further research on stress resistance, post-harvest management, and sustainable agricultural development.

The Role of Exerkines in the Treatment of Knee Osteoarthritis: From Mechanisms to Exercise Strategies

With the increasing prevalence of knee osteoarthritis (KOA), the limitations of traditional treatments, such as their limited efficacy in halting disease progression and their potential side effects, are becoming more evident. This situation has prompted scientists to seek more effective strategies. In recent years, exercise therapy has gained prominence in KOA treatment due to its safety, efficacy, and cost-effectiveness, which are underpinned by the molecular actions of exerkines. Unlike conventional therapies, exerkines offer specific advantages by targeting inflammatory responses, enhancing chondrocyte proliferation, and slowing cartilage degradation at the molecular level. This review explores the potential mechanisms involved in and application prospects of exerkines in KOA treatment and provides a comprehensive analysis of their role. Studies show that appropriate exercise not only promotes overall health, but also positively impacts KOA by stimulating exerkine production. The effectiveness of exerkines, however, is influenced by exercise modality, intensity, and duration of exercise, making the development of personalized exercise plans crucial for KOA patients. Based on these insights, this paper proposes targeted exercise strategies designed to maximize exerkine benefits, aiming to provide novel perspectives for KOA prevention and treatment.

Genotype inference from aggregated chromatin accessibility data reveals genetic regulatory mechanisms

BACKGROUND

Understanding the genetic causes underlying 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 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 1454 unique donor individuals across 653 studies from public databases, we catalog 24,159 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 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 across 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.

Assessing the influence of plasma metabolites on chronic skin ulcer risk: a two-sample Mendelian randomization study

Chronic skin ulcers, although rare, pose severe and debilitating challenges. The identification of causal metabolite biomarkers presents an opportunity to refine effective risk assessment strategies for this condition. In this study, we conducted a comprehensive Two-Sample Mendelian Randomization (TSMR) investigation to delineate the potential causal effects of plasma metabolites on chronic skin ulcer risk. Exposure data comprised 14,296 participants with 913 metabolites from INTERVAL/EPIC-Norfolk, and 8,299 participants with 1,091 metabolites and 309 ratios from the Canadian Longitudinal Study on Aging (CLSA). Outcome data came from the finngen_R9_L12_CHRONICULCEROFSKIN (1,840 cases, 353,088 controls) and UK Biobank Chronic ulcer of skin (495 cases, 455,853 controls) cohorts. Leveraging the inverse-variance weighted (IVW) method, alongside MR-Egger and MR-PRESSO sensitivity analyses, we evaluated metabolite associations with chronic skin ulcer risk. Further assessment involved a phenome-wide MR (Phe-MR) analysis to explore potential repercussions of targeting identified metabolites for intervention. Our study identified 12 distinct metabolites significantly associated with chronic skin ulcers, demonstrating consistent and replicable results. Notably, X-19,141 exhibited the highest reproducibility. These findings highlight novel plasma metabolites relevant to chronic skin ulcers, offering theoretical underpinnings for mechanistic research and clinical strategies in prevention and treatment.

Genome-wide association studies and transcriptomics reveal mechanisms explaining the diversity of wheat root responses to nutrient availability

Nutrient availability profoundly influences plant root system architecture, which critically determines crop productivity. While Arabidopsis has provided important insights into the genetic responses to nutrient deficiency, translating this knowledge to crops, particularly wheat, remains a subject of inquiry. Here, examining a diverse wheat population under varying nitrogen (N), phosphorus (P), potassium (K), and iron (Fe) levels, we uncover a spectrum of root responses, spanning from growth inhibition to stimulation, highlighting genotype-specific strategies. Furthermore, we reveal a nuanced interplay between macronutrient deficiency (N, P, and K) and Fe availability, emphasizing the central role of Fe in modulating root architecture. Through genome-wide association mapping, we identify 11 quantitative trait loci underlying root traits under varying nutrient availabilities, including homologous genes previously validated in Arabidopsis, supporting our findings. In addition, utilizing transcriptomics, reactive oxygen species (ROS) imaging, and antioxidant treatment, we uncover that wheat root growth inhibition by nutrient deficiency is attributed to ROS accumulation, akin to the role of ROS in governing Arabidopsis root responses to nutrient deficiency. Therefore, our study reveals the conservation of molecular and physiological mechanisms between Arabidopsis and wheat to adjust root growth to nutrient availability, paving the way for targeted crop improvement strategies aimed at increasing nutrient use efficiency.

Massively parallel reporter assays identify functional enhancer variants at QT interval GWAS loci

Genome-wide association studies (GWAS) have identified >30 loci with multiple common noncoding variants explaining interindividual electrocardiographic QT interval (QTi) variation. Of the many types of noncoding functional elements, here we sought to identify transcriptional enhancers with sequence variation and their cognate transcription factors (TFs) that alter the expression of proximal cardiac genes to affect QTi variation. We used massively parallel reporter assays (MPRA) in mouse cardiomyocyte HL-1 cells to screen for functional enhancer variants among 1,018 QTi-associated GWAS variants that overlap candidate cardiac enhancers across 31 loci. We identified 445 GWAS variant-containing enhancers of which 79 showed significant allelic difference in enhancer activity across 21 GWAS loci, with multiple enhancer variants per locus. Of these, we predicted differential binding by cardiac TFs, including AP-1, ATF-1, GATA2, MEF2, NKX2.5, SRF and TBX5 which are known to play key roles in development and homeostasis, at 49 enhancer variants. Finally, we used expression quantitative trait locus mapping and predicted promoter-enhancer contacts to identify 14 candidate target genes through analyses of 36 enhancer variants at 16 loci. This study provides strong evidence for 14 cardiac genes, 10 of them novel, impacting on QTi variation, beyond explaining observed genetic associations.

Integrative approaches to m6A and m5C RNA modifications in autism spectrum disorder revealing potential causal variants

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that currently affects approximately 1-2% of the global population. Genome-wide studies have identified several loci associated with ASD; however, pinpointing causal variants remains elusive. Therefore, functional studies are essential to discover potential therapeutics for ASD. RNA modification plays a crucial role in the post-transcriptional regulation of mRNA, with m6A and m5C being the most prevalent internal modifications. Recent research indicates their involvement in the regulation of key genes associated with ASD. In this study, we conducted an integrative genomic analysis of ASD, incorporating m6A and m5C variants, followed by cis-eQTL, gene differential expression, and gene enrichment analyses to identify causal variants from a genome-wide study of ASD. We identified 20,708 common m6A-SNPs and 2,407 common m5C-SNPs. Among these, 647 m6A-SNPs exhibited cis-eQTL signals with a p-value < 0.05, while only 81 m5C-SNPs with a p-value < 0.05 showed cis-eQTL signals. Most of these were functional loss variants, with 38 variants representing the most significant common m6A/m5C-SNPs associated with key ASD-related genes. In the gene differential expression analysis, seven proximal genes corresponding to significant m6A/m5C-SNPs were differentially expressed in at least one of the three microarray gene expression profiles of ASD. Key differentially expressed genes corresponding to m6A/m5C cis-variants included KIAA1671 (rs5752063, rs12627825), INTS1 (rs67049052, rs10237910), VSIG10 (rs7965350), TJP2 (rs3812536), FAM167A (rs9693108), TMEM8A (rs1802752), and NUP43 (rs3924871, rs7818, rs9383844, rs9767113). Cell-specific cis-eQTL analysis for proximal gene identification, combined with gene expression datasets from single-cell RNA-seq analysis, would validate the causal relationship of gene regulation in brain-specific regions, and experimental validation in cell lines would achieve the goal of precision medicine.

Gene-level analysis reveals the genetic aetiology and therapeutic targets of schizophrenia

Genome-wide association studies (GWASs) have reported multiple risk loci for schizophrenia (SCZ). However, the majority of the associations were from populations of European ancestry. Here we conducted a large-scale GWAS in Eastern Asian populations (29,519 cases and 44,392 controls) and identified ten Eastern Asian-specific risk loci, two of which have not been previously reported. A further cross-ancestry GWAS meta-analysis (96,806 cases and 492,818 controls) including populations from diverse ancestries identified 61 previously unreported risk loci. Systematic variant-level analysis, including fine mapping, functional genomics and expression quantitative trait loci, prioritized potential causal variants. Gene-level analyses, including transcriptome-wide association study, proteome-wide association study and Mendelian randomization, nominated the potential causal genes. By integrating evidence from layers of different analyses, we prioritized the most plausible causal genes for SCZ, such as ACE, CNNM2, SNAP91, ABCB9 and GATAD2A. Finally, drug repurposing showed that ACE, CA14, MAPK3 and MAPT are potential therapeutic targets for SCZ. Our study not only showed the power of cross-ancestry GWAS in deciphering the genetic aetiology of SCZ, but also uncovered new genetic risk loci, potential causal variants and genes and therapeutic targets for SCZ.

Functional analysis of cancer-associated germline risk variants

Single-nucleotide variants (SNVs) in regulatory DNA are linked to inherited cancer risk. Massively parallel reporter assays of 4,041 SNVs linked to 13 neoplasms comprising >90% of human malignancies were performed in pertinent primary human cell types and then integrated with matching chromatin accessibility, DNA looping and expression quantitative trait loci data to nominate 380 potentially regulatory SNVs and their putative target genes. The latter highlighted specific protein networks in lifetime cancer risk, including mitochondrial translation, DNA damage repair and Rho GTPase activity. A CRISPR knockout screen demonstrated that a subset of germline putative risk genes also enables the growth of established cancers. Editing one SNV, rs10411210 , showed that its risk allele increases rhophilin RHPN2 expression and stimulus-responsive RhoA activation, indicating that individual SNVs may upregulate cancer-linked pathways. These functional data are a resource for variant prioritization efforts and further interrogation of the mechanisms underlying inherited risk for cancer.

Gene expression variation across genetically identical individuals predicts reproductive traits

In recent decades, genome-wide association studies (GWAS) have been the major approach to understand the biological basis of individual differences in traits and diseases. However, GWAS approaches have limited predictive power to explain individual differences, particularly for complex traits and diseases in which environmental factors play a substantial role in their etiology. Indeed, individual differences persist even in genetically identical individuals, although fully separating genetic and environmental causation is difficult in most organisms. To understand the basis of individual differences in the absence of genetic differences, we measured two quantitative reproductive traits in 180 genetically identical young adult Caenorhabditis elegans roundworms in a shared environment and performed single-individual transcriptomics on each worm. We identified hundreds of genes for which expression variation was strongly associated with reproductive traits, some of which depended on individuals' historical environments and some of which was random. Multiple small sets of genes together were highly predictive of reproductive traits, explaining on average over half and over a quarter of variation in the two traits. We manipulated mRNA levels of predictive genes to identify a set of causal genes, demonstrating the utility of this approach for both prediction and understanding underlying biology. Finally, we found that the chromatin environment of predictive genes was enriched for H3K27 trimethylation, suggesting that gene expression variation may be driven in part by chromatin structure. Together, this work shows that individual, non-genetic differences in gene expression are both highly predictive and causal in shaping reproductive traits.

Integration of functional genomics and statistical fine-mapping systematically characterizes adult-onset and childhood-onset asthma genetic associations

Background

Genome-wide association studies (GWAS) have identified hundreds of loci underlying adult-onset asthma (AOA) and childhood-onset asthma (COA). However, the causal variants, regulatory elements, and effector genes at these loci are largely unknown.

Methods

We performed heritability enrichment analysis to determine relevant cell types for AOA and COA, respectively. Next, we fine-mapped putative causal variants at AOA and COA loci. To improve the resolution of fine-mapping, we integrated ATAC-seq data in blood and lung cell types to annotate variants in candidate cis-regulatory elements (CREs). We then computationally prioritized candidate CREs underlying asthma risk, experimentally assessed their enhancer activity by massively parallel reporter assay (MPRA) in bronchial epithelial cells (BECs) and further validated a subset by luciferase assays. Combining chromatin interaction data and expression quantitative trait loci, we nominated genes targeted by candidate CREs and prioritized effector genes for AOA and COA.

Results

Heritability enrichment analysis suggested a shared role of immune cells in the development of both AOA and COA while highlighting the distinct contribution of lung structural cells in COA. Functional fine-mapping uncovered 21 and 67 credible sets for AOA and COA, respectively, with only 16% shared between the two. Notably, one-third of the loci contained multiple credible sets. Our CRE prioritization strategy nominated 62 and 169 candidate CREs for AOA and COA, respectively. Over 60% of these candidate CREs showed open chromatin in multiple cell lineages, suggesting their potential pleiotropic effects in different cell types. Furthermore, COA candidate CREs were enriched for enhancers experimentally validated by MPRA in BECs. The prioritized effector genes included many genes involved in immune and inflammatory responses. Notably, multiple genes, including TNFSF4, a drug target undergoing clinical trials, were supported by two independent GWAS signals, indicating widespread allelic heterogeneity. Four out of six selected candidate CREs demonstrated allele-specific regulatory properties in luciferase assays in BECs.

Conclusions

We present a comprehensive characterization of causal variants, regulatory elements, and effector genes underlying AOA and COA genetics. Our results supported a distinct genetic basis between AOA and COA and highlighted regulatory complexity at many GWAS loci marked by both extensive pleiotropy and allelic heterogeneity.

The evaluation of targeted exome sequencing of candidate genes in a Han Chinese population with primary open-angle glaucoma

Primary open-angle glaucoma (POAG), known as a common ocular disease with genetic heterogeneity, is characterized by progressive optic disc atrophy and visual field defects. This study aimed to assess the contribution of previously reported POAG-associated genes and investigate potential functional variations and genotype-phenotype correlations in a Han Chinese population. DNA from 500 cases and 500 controls was pooled and sequenced using a customized panel of 398 candidate genes. After prioritization, 21 SNPs from 16 genes were genotyped in the first replication cohort (500 cases and 500 controls), and 9 SNPs were genotyped in the second replication cohort (500 cases and 500 controls). Allelic associations and odds ratios were adjusted for age and sex, while linear regression assessed SNP correlations with POAG endophenotypes. Haplotype analysis and linkage disequilibrium were performed using Haploview. In silico prediction tools were used to predict pathogenicity and function. SNPs from MFN2, DGKG, PKHD1, PTPRJ, and LTBP2 were associated with POAG in at least one cohort, and SNPs from EXOC2, PTPRJ, and LTBP2 showed significant correlations with intraocular pressure. Additionally, haplotype analysis revealed a significant association between the EXOC2 TGC haplotype and POAG risk. We validated several candidate genes and identified novel SNPs, providing further insight into the genetic architecture of POAG in the Han Chinese population.

Knockoff procedure improves causal gene identifications in conditional transcriptome-wide association studies

Transcriptome-wide association studies (TWASs) have been developed to nominate candidate genes associated with complex traits by integrating genome-wide association studies (GWASs) with expression quantitative trait loci (eQTL) data. However, most existing TWAS methods evaluate the marginal association between a single gene and the trait of interest without accounting for other genes within the same genomic region or the same gene from different tissues. Additionally, false-positive gene-trait pairs can arise due to correlations with the direct effects of genetic variants. In this study, we introduce TWASKnockoff, a new knockoff-based framework for detecting causal gene-tissue pairs using GWAS summary statistics and eQTL data. Unlike marginal testing in traditional TWAS methods, TWASKnockoff examines the conditional independence for each gene-trait pair, considering both correlations in cis-predicted expression across genes and correlations between gene expression levels and genetic variants. TWASKnockoff estimates the theoretical correlation matrix for all genetic elements (cis-predicted expression across genes and genotypes for genetic variants) by averaging estimations from parametric boot-strap samples and then performs knockoff-based inference to detect causal gene-trait pairs while controlling the false discovery rate (FDR). Through empirical simulations and an application to type 2 diabetes (T2D) data, we demonstrate that TWASKnockoff achieves superior FDR control and improves the average power in detecting causal gene-trait pairs at a fixed FDR level.

A mechanism-informed deep neural network enables prioritization of regulators that drive cell state transitions

Cells are regulated at multiple levels, from regulations of individual genes to interactions across multiple genes. Some recent neural network models can connect molecular changes to cellular phenotypes, but their design lacks modeling of regulatory mechanisms, limiting the decoding of regulations behind key cellular events, such as cell state transitions. Here, we present regX, a deep neural network incorporating both gene-level regulation and gene-gene interaction mechanisms, which enables prioritizing potential driver regulators of cell state transitions and providing mechanistic interpretations. Applied to single-cell multi-omics data on type 2 diabetes and hair follicle development, regX reliably prioritizes key transcription factors and candidate cis-regulatory elements that drive cell state transitions. Some regulators reveal potential new therapeutic targets, drug repurposing possibilities, and putative causal single nucleotide polymorphisms. This method to analyze single-cell multi-omics data demonstrates how the interpretable design of neural networks can better decode biological systems.

An Evolutionary Perspective on Dog Behavioral Genetics

Dogs have played an outsized role in the field of behavioral genetics since its earliest days. Their unique evolutionary history and ubiquity in the modern world make them a potentially powerful model system for discovering how genetic changes lead to changes in behavior. Genomic technology has supercharged this potential by enabling scientists to sequence the DNA of thousands of dogs and test for correlations with behavioral traits. However, fractures in the early history of animal behavior between biological and psychological subfields may be impeding progress. In addition, canine behavioral genetics has included almost exclusively dogs from modern breeds, who represent just a small fraction of all dog diversity. By expanding the scope of dog behavior studies, and incorporating an evolutionary perspective on canine behavioral genetics, we can move beyond associations to understanding the complex interactions between genes and environment that lead to dog behavior.

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.

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.

Omics based technology application in poultry meat research

Omics techniques, including genomics, transcriptomics, proteomics, metabolomics, and lipidomics, analyze entire sets of biological molecules to seek comprehensive knowledge on a particular phenotype. These approaches have been extensively utilized to identify both biomarkers and biological mechanisms for various physiological conditions in livestock and poultry. The purpose of this symposium was not only to focus on how recent omics technologies can be used to gather, integrate, and interpret data produced by various methodologies in poultry research, but also to highlight how omics and bioinformatics have increased our understanding of poultry meat quality problems and other complex traits. This Poultry Science Association symposium paper includes 5 sections that cover: 1) functional annotation of cis-regulatory elements in the genome informs genetic control of complex traits in poultry, 2) mass spectrometry for proteomics, metabolomics, and lipidomics, 3) proteomic approaches to investigate meat quality, 4) spatial transcriptomics and metabolomics studies of wooden breast disease, and 5) multiomics analyses on chicken meat quality and spaghetti meat. These topics provide insights into the molecular components that contribute to the structure, function, and dynamics of the underlying mechanisms influencing meat quality traits, including chicken breast myopathies. This information will ultimately contribute to improving the quality and composition of poultry products.

Adipose tissue eQTL meta-analysis highlights the contribution of allelic heterogeneity to gene expression regulation and cardiometabolic traits

Complete characterization of the genetic effects on gene expression is needed to elucidate tissue biology and the etiology of complex traits. In the present study, we analyzed 2,344 subcutaneous adipose tissue samples and identified 34,774 conditionally distinct expression quantitative trait locus (eQTL) signals at 18,476 genes. Over half of eQTL genes exhibited at least two eQTL signals. Compared with primary eQTL signals, nonprimary eQTL signals had lower effect sizes, lower minor allele frequencies and less promoter enrichment; they corresponded to genes with higher heritability and higher tolerance for loss of function. Colocalization of eQTLs with genome-wide association study (GWAS) signals for 28 cardiometabolic traits identified 1,835 genes. Inclusion of nonprimary eQTL signals increased discovery of colocalized GWAS-eQTL signals by 46%. Furthermore, 21 genes with ≥2 colocalized GWAS-eQTL signals showed a mediating gene dosage effect on the GWAS trait. Thus, expanded eQTL identification reveals more mechanisms underlying complex traits and improves understanding of the complexity of gene expression regulation.

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.

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.

Decoding the Therapeutic Target SVEP1: Harnessing Molecular Trait GWASs to Unravel Mechanisms of Human Disease

Although human genetics has substantial potential to illuminate novel disease pathways and facilitate drug development, identifying causal variants and deciphering their mechanisms remain challenging. We believe these challenges can be addressed, in part, by creatively repurposing the results of molecular trait genome-wide association studies (GWASs). In this review, we introduce techniques related to molecular GWASs and unconventionally apply them to understanding SVEP1, a human coronary artery disease risk locus. Our analyses highlight SVEP1's causal link to cardiometabolic disease and glaucoma, as well as the surprising discovery of SVEP1 as the first known physiologic ligand for PEAR1, a critical receptor governing platelet reactivity. We further employ these techniques to dissect the interactions between SVEP1, PEAR1, and the Ang/Tie pathway, with therapeutic implications for a constellation of diseases. This review underscores the potential of molecular GWASs to guide drug discovery and unravel the complexities of human health and disease by demonstrating an integrative approach that grounds mechanistic research in human biology.

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.

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.

Functional investigation suggests CNTNAP5 involvement in glaucomatous neurodegeneration obtained from a GWAS in primary angle closure glaucoma

Primary angle closure glaucoma (PACG) affects more than 20 million people worldwide, with an increased prevalence in south-east Asia. In a prior haplotype-based Genome Wide Association Study (GWAS), we identified a novel CNTNAP5 genic region, significantly associated with PACG. In the current study, we have extended our perception of CNTNAP5 involvement in glaucomatous neurodegeneration in a zebrafish model, through investigating phenotypic consequences pertinent to retinal degeneration upon knockdown of cntnap5 by translation-blocking morpholinos. While cntnap5 knockdown was successfully validated using an antibody, immunofluorescence followed by western blot analyses in cntnap5-morphant (MO) zebrafish revealed increased expression of acetylated tubulin indicative of perturbed cytoarchitecture of retinal layers. Moreover, significant loss of Nissl substance is observed in the neuro-retinal layers of cntnap5-MO zebrafish eye, indicating neurodegeneration. Additionally, in spontaneous movement behavioural analysis, cntnap5-MO zebrafish have a significantly lower average distance traversed in light phase compared to mismatch-controls, whereas no significant difference was observed in the dark phase, corroborating with vision loss in the cntnap5-MO zebrafish. This study provides the first direct functional evidence of a putative role of CNTNAP5 in visual neurodegeneration.

Transcriptome-wide association identifies KLC1 as a regulator of mitophagy in non-syndromic cleft lip with or without palate

This study investigated pathogenic genes associated with non-syndromic cleft lip with or without cleft palate (NSCL/P) through transcriptome-wide association studies (TWAS). By integrating expression quantitative trait loci (eQTL) data with genome-wide association study (GWAS) data, we identified key susceptibility genes, including KLC1. Notably, the variant rs12884809 G>A was associated with an increased risk of NSCL/P by enhancing the binding of the transcription factor ELK1 to the KLC1 promoter, thereby activating its expression. This alteration in KLC1 expression subsequently impacted mitophagy, leading to significant changes in cellular behavior and zebrafish morphology. Our findings illuminate the genetic mechanisms underlying NSCL/P and provide valuable insights for future prevention strategies and a deeper understanding of this condition.

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/.

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.

Mapping chromatin interactions at melanoma susceptibility loci and cell-type specific dataset integration uncovers distant gene targets of cis-regulation

Genome-wide association studies (GWAS) of melanoma risk have identified 68 independent signals at 54 loci. For most loci, specific functional variants and their respective target genes remain to be established. Capture-HiC is an assay that links fine-mapped risk variants to candidate target genes by comprehensively mapping cell-type specific chromatin interactions. We performed a melanoma GWAS region-focused capture-HiC assay in human primary melanocytes to identify physical interactions between fine-mapped risk variants and potential causal melanoma susceptibility genes. Overall, chromatin interaction data alone nominated potential causal genes for 61 of the 68 melanoma risk signals, identifying many candidates beyond those reported by previous studies. We further integrated these data with cell-type specific epigenomic (chromatin state, accessibility), gene expression (eQTL/TWAS), DNA methylation (meQTL/MWAS), and massively parallel reporter assay (MPRA) data to prioritize potentially cis-regulatory variants and their respective candidate gene targets. From the set of fine-mapped variants across these loci, we identified 140 prioritized candidate causal variants linked to 195 candidate genes at 42 risk signals. In addition, we developed an integrative scoring system to facilitate candidate gene prioritization, integrating melanocyte and melanoma datasets. Notably, at several GWAS risk signals we observed long-range chromatin connections (500 kb to >1 Mb) with distant candidate target genes. We validated several such cis-regulatory interactions using CRISPR inhibition, providing evidence for known cancer driver genes MDM4 and CBL, as well as the SRY-box transcription factor SOX4, as likely melanoma risk genes.

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.

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.

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.

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.

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.

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.

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.