Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases

Are complex traits underpinned by polygenic molecular traits? A reflection on the complexity of gene expression

Variation in complex traits is controlled by multiple genes. The prevailing assumption is that such polygenic complex traits are underpinned by variation in elementary molecular traits, such as gene expression, which themselves have a simple genetic basis. Here, we review recent advances that reveal the captivating complexity of gene regulation: the cell type, time point, and magnitude of gene expression are not merely dependent on a couple of regulators; rather, they result from a probabilistic process shaped by cis- and trans-regulatory elements collaboratively integrating internal and external cues with the tightly regulated dynamics of DNA. In addition, the finding that genetic variants linked to complex diseases in humans often do not co-localize with quantitative trait loci modulating gene expression, along with the role of nonfunctional transcription factor (TF) binding sites, suggests that some of the genetic effects influencing gene expression variation may be indirect. If the number of genomic positions responsible for TF binding, TF binding site search time, DNA conformation and accessibility as well as regulation of all trans-acting factors is indeed vast, is it plausible that the complexity of elementary molecular traits approaches the complexity of higher-level organismal traits? Although it is hard to know the answer to this question, we motivate it by reviewing the complexity of the molecular machinery further.

Characterizing somatic mutations in ovarian cancer germline risk regions

Epithelial ovarian cancer (EOC) genetics research has been focused on germline or somatic mutations independently. Emerging evidence suggests that the somatic mutational landscape can be shaped by the germline genetic background. In this study, we aim to unravel the role of somatic alterations within EOC germline susceptibility regions by incorporating functional annotations. We investigate somatic events, including mutational signatures, point mutations, copy number alterations, and transcription factor binding disruptions, within 33 EOC germline susceptibility regions. Our analysis identifies significant associations between candidate germline susceptibility genes and somatic mutational signatures known to be key risk factors for EOC, such as mismatch repair deficiency, age-related mutagenesis, and homologous recombination deficiency. In addition, we find somatic point mutations and copy number alterations are significantly enriched in histotype-specific active enhancers and promoters within EOC risk loci. Furthermore, we examine the impact of germline variants and somatic mutations on transcription factor binding sites, identifying cancer developmental transcription factor motifs frequently affected by both types of mutations. Overall, our study highlights the importance of integrating germline and somatic mutations with regulatory and epigenomic data to gain insights into the genetic basis of EOC.

Single-cell analysis of the epigenome and 3D chromatin architecture in the human retina

Most genetic risk variants linked to ocular diseases are non-protein coding and presumably contribute to disease through dysregulation of gene expression, however, deeper understanding of their mechanisms of action has been impeded by an incomplete annotation of the transcriptional regulatory elements across different retinal cell types. To address this knowledge gap, we carried out single-cell multiomics assays to investigate gene expression, chromatin accessibility, DNA methylome and 3D chromatin architecture in human retina, macula, and retinal pigment epithelium (RPE)/choroid. We identified 420,824 unique candidate regulatory elements and characterized their chromatin states in 23 sub-classes of retinal cells. Comparative analysis of chromatin landscapes between human and mouse retina cells further revealed both evolutionarily conserved and divergent retinal gene-regulatory programs. Leveraging the rapid advancements in deep-learning techniques, we developed sequence-based predictors to interpret non-coding risk variants of retina diseases. Our study establishes retina-wide, single-cell transcriptome, epigenome, and 3D genome atlases, and provides a resource for studying the gene regulatory programs of the human retina and relevant diseases.

The identification of blood-derived response eQTLs reveals complex effects of regulatory variants on inflammatory and infectious disease risk

Hundreds of risk loci for immune mediated inflammatory and infectious diseases have been identified by genome-wide association studies (GWAS). Yet, what causal variants and genes in risk loci underpin the observed associations remains poorly understood for most. The identification of colocalized cis-expression Quantitative Trait Loci (cis-eQTLs) is a promising way to identify candidate causative genes. The catalogue of cis-eQTLs of the immune system is likely incomplete as many cis-eQTLs may be context-specific. We built a large cohort of 406 healthy individuals and expanded the immune cis-regulome through their whole blood transcriptome obtained after stimulation with specific toll-like receptor (TLR) agonists and T-cell receptor (TCR) antagonist. We report three mechanisms that may explain why an eQTL could only be revealed after immune stimulation. More than half of the cis-eQTLs detected in this study would have been overlooked without specific immune stimulations. We then mined this new catalogue of response (r)eQTLs, with public GWAS summary statistics of three diseases through a colocalization approach: inflammatory bowel diseases, rheumatoid arthritis and COVID-19 disease. We identified reQTL-specific colocalizations for risk loci for which no matching eQTL were reported before, revealing interesting new candidate causal genes.

Neuropsychiatric manifestations in systemic lupus erythematosus and Sjogren's disease

INTRODUCTION

Autoimmune diseases often present in a systemic manner, affecting various organs and tissues. Involvement of the central and peripheral nervous system is not uncommon in these conditions and is associated with high morbidity and mortality. Therefore, early recognition of the neuropsychiatric manifestations associated with rheumatologic diseases is essential for the introduction of appropriate therapies with the objective of providing a better quality of life for individuals.

OBJECTIVE

To provide a literature review of the neuropsychiatric manifestations related to Systemic Lupus Erythematosus (SLE) and primary Sjögren's Disease (pSD), through the description of signs, symptoms, and immunological variables associated with these conditions.

METHODS

A literature review was conducted by searching for national and international articles available in the SciELO and PubMed databases related to the description of neurological and psychiatric manifestations in patients with the rheumatologic diseases of interest in this study.

RESULTS

The main NP manifestations presented in SLE and pSD are discussed, focusing on clinical presentation and etiology. Treatment option are, however, mainly based on expert opinion, since a few randomized controlled trials have been done.

CONCLUSIONS

There is a high prevalence of neuropsychiatric manifestations associated with SLE and pSD. The variety of physiopathology pathways may explain the variety of symptoms, however pathological findings are rare. Multicenter studies on attribution protocols and treatment are necessary to address the current gaps.

The ulcerative colitis risk gene adenylyl cyclase 7 restrains the T-helper 2 phenotype and Class II antigen presentation

BACKGROUND AND AIMS

Genome-wide association studies have shown that the most risk-conferring genetic polymorphism for ulcerative colitis (UC) outside the human leukocyte antigen locus is the amino acid substitution p.Asp439Glu in the adenylyl cyclase 7 gene (ADCY7). ADCY7 is the main isoform in the hematopoietic system and produces the second messenger cyclic AMP (cAMP) downstream of G protein-coupled receptor signaling. Our aim was to determine the contribution of this polymorphism to UC risk by analyzing its effect on ADCY7 function in cell-based assays.

METHODS

We characterized the p.Asp439Glu variant in cell lines using western blots, immunofluorescence, cAMP assay, and luciferase assay. We modeled this variant using siRNA knock-down in human primary CD4+ T cells and characterized them by RNA-seq, viability assay, flow cytometry, cAMP assay, and ELISA.

RESULTS

The p.Asp439Glu variant is deficient in protein expression but retains membrane localization. This results in a 40% reduction in cAMP synthesis and luciferase reporter expression. Knock-down of ADCY7 in T cells reduces the expression of ribosomal proteins and cAMP signaling proteins, while skewing cytokine production toward a T-helper 2 pattern and upregulating antigen presentation accompanied by increased surface expression of major histocompatibility complex Class II and CD86.

CONCLUSIONS

The UC risk-conferring variant, p.Asp439Glu, in ADCY7 reduces cyclic AMP signaling, leading to modifications in cytokine profile and antigen presentation. Medications that enhance cyclic AMP by direct activation of ADCY7 or by phosphodiesterase inhibition may be beneficial in this disease.

Evaluation of genomic selection models using whole genome sequence data and functional annotation in Belgian Blue cattle

BACKGROUND

The availability of large cohorts of whole-genome sequenced individuals, combined with functional annotation, is expected to provide opportunities to improve the accuracy of genomic selection (GS). However, such benefits have not often been observed in initial applications. The reference population for GS in Belgian Blue Cattle (BBC) continues to grow. Combined with the availability of reference panels of sequenced individuals, it provides an opportunity to evaluate GS models using whole genome sequence (WGS) data and functional annotation.

RESULTS

Here, we used data from 16,508 cows, with phenotypes for five muscular development traits and imputed at the WGS level, in combination with in silico functional annotation and catalogs of putative regulatory variants obtained from experimental data. We evaluated first GS models using the entire WGS data, with or without functional annotation. At this marker density, we were able to run two approaches, assuming either a highly polygenic architecture (GBLUP) or allowing some variants to have larger effects (BayesRR-RC, a Bayesian mixture model), and observed an increased reliability compared to the official GBLUP model at medium marker density (on average 0.016 and 0.018 for GBLUP and BayesRR-RC, respectively). When functional annotation was used, we observed slightly higher reliabilities with an extension of GBLUP that included multiple polygenic terms (one per functional group), while reliabilities decreased with BayesRR-RC. We then used large subsets of variants selected based on functional information or with a linkage disequilibrium (LD) pruning approach, which allowed us to evaluate two additional approaches, BayesCπ and Bayesian Sparse Linear Mixed Model (BSLMM). Reliabilities were higher for these panels than for the WGS data, with the highest accuracies obtained when markers were selected based on functional information. In our setting, BSLMM systematically achieved higher reliabilities than other methods.

CONCLUSIONS

GS with large panels of functional variants selected from WGS data allowed a significant increase in reliability compared to the official genomic evaluation approach. However, the benefits of using WGS and functional data remained modest, indicating that there is still room for improvement, for example by further refining the functional annotation in the BBC breed.

A western dietary pattern during pregnancy is associated with neurodevelopmental disorders in childhood and adolescence

Despite the high prevalence of neurodevelopmental disorders, the influence of maternal diet during pregnancy on child neurodevelopment remains understudied. Here we show that a western dietary pattern during pregnancy is associated with child neurodevelopmental disorders. We analyse self-reported maternal dietary patterns at 24 weeks of pregnancy and clinically evaluated neurodevelopmental disorders at 10 years of age in the COPSAC2010 cohort (n = 508). We find significant associations with attention-deficit hyperactivity disorder (ADHD) and autism diagnoses. We validate the ADHD findings in three large, independent mother-child cohorts (n = 59,725, n = 656 and n = 348) through self-reported dietary modelling, maternal blood metabolomics and foetal blood metabolomics. Metabolome analyses identify 15 mediating metabolites in pregnancy that improve ADHD prediction. Longitudinal blood metabolome analyses, incorporating five time points per cohort in two independent cohorts, reveal that associations between western dietary pattern metabolite scores and neurodevelopmental outcomes are consistently significant in early-mid-pregnancy. These findings highlight the potential for targeted prenatal dietary interventions to prevent neurodevelopmental disorders and emphasise the importance of early intervention.

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.

Impact of life adversity and gene expression on psychiatric symptoms in children and adolescents: findings from the Brazilian high risk cohort study

Introduction

While the influence of both genetic and environmental factors on the development of psychiatric symptoms is well-recognized, the precise nature of their interaction throughout development remains a subject of ongoing debate. This study investigated the association between the expression of 78 candidate genes, previously associated with psychiatric phenotypes, in peripheral blood and both adversity and psychopathology in a sample of 298 young individuals assessed at two time points from the Brazilian High Risk Cohort Study for Mental Conditions (BHRCS).

Methods

Psychopathology was assessed using the Child Behavior Checklist (CBCL), considering the total CBCL, p-factor (i.e. general factor of psychopathology), and internalizing and externalizing symptoms as clinical variables. The life adversities considered in this study includes four composite variables: child maltreatment, stressful life events, threat and deprivation. Gene expression was measured using next-generation sequencing for target genes and differential gene expression was analyzed with the DESeq2 package.

Results

Mixed models revealed six genes associated with internalizing symptoms: NR3C1, HSPBP1, SIN3A, SMAD4, and CRLF3 genes exhibited a negative correlation with these symptoms, while FAR1 gene showed a positive correlation. Additionally, we also found a negative association between USP38 gene expression and externalizing symptoms. Finally, DENND11 and PRRC1 genes were negatively associated with deprivation, a latent factor characterized by neglect, parental absence, and measures of material forms of deprivation. No mediation or moderation effect was observed of gene expression on the association between life adversities and psychiatric symptoms, meaning that they might influence distinct pathways.

Discussion

Among these nine genes, NR3C1, which encodes a glucocorticoid receptor, is by far the most investigated, being associated with depressive symptoms, early life adversity, and stress. While further research is needed to fully understand the complex relationship between gene expression, life adversities, and psychopathology, our findings provide valuable insights into the molecular mechanisms underlying mental disorders.

Multi-locus CRISPRi targeting with a single truncated guide RNA

A critical goal in functional genomics is evaluating which non-coding elements contribute to gene expression, cellular function, and disease. Functional characterization remains a challenge due to the abundance and complexity of candidate elements. Here, we develop a CRISPRi-based approach for multi-locus screening of putative transcription factor binding sites with a single truncated guide. A truncated guide with hundreds of sequence match sites can reliably disrupt enhancer activity, which expands the targeting scope of CRISPRi while maintaining repressive efficacy. We screen over 13,000 possible CTCF binding sites with 24 guides at 10 nucleotides in spacer length. These truncated guides direct CRISPRi-mediated deposition of repressive H3K9me3 marks and disrupt transcription factor binding at most sequence match target sites. This approach can be a valuable screening step for testing transcription factor binding motifs or other repeated genomic sequences and is easily implemented with existing tools.

Integrating Polygenic Risk Scores (PRS) for Personalized Diabetes Care: Advancing Clinical Practice with Tailored Pharmacological Approaches

The rising global prevalence of diabetes poses a serious threat to public health, national economies, and the healthcare system. Despite a high degree of disease heterogeneity and advancing techniques, there is still an unclear diagnosis of patients with diabetes compounded by the array of long-term microvascular and macrovascular complications associated with the disease. In addition to environmental variables, diabetes susceptibility is significantly influenced by genetic components. The risk stratification of genetically predisposed individuals may play an important role in disease diagnosis and management. Precision medicine methods are crucial to reducing this global burden by delivering a more personalised and patient-centric approach. Compared to the European population, genetic susceptibility variants of type 2 diabetes mellitus (T2DM) are still not fully understood in other major populations, including South Asians, Latinos, and people of African descent. Polygenic risk scores (PRS) can be used to identify individuals who are more susceptible to complex diseases such as diabetes. PRS is selective and effective in developing novel diagnostic interventions. This comprehensive predictive approach facilitates the understanding of distinct response profiles, resulting in the development of more effective management strategies. The targeted implementation of PRS is especially advantageous for people who fall into a higher-risk category for diabetes. Through early risk assessment and the creation of individualised diabetes treatment plans, the integration of PRS in clinical practice shows potential for reducing the prevalence of diabetes and its complications. Diabetes self-management depends significantly on patient empowerment, with behavioural monitoring emerging as a vital facilitator. The main aim of this review article is to formulate a more structured intervention strategy by advocating for increased awareness of the clinical utility of PRS and counseling among healthcare practitioners, patients, and individuals at risk of diabetes.

Computational analysis of congenital heart disease associated SNPs: unveiling their impact on the gene regulatory system

Congenital heart disease (CHD) is a prevalent condition characterized by defective heart development, causing premature death and stillbirths among infants. Genome-wide association studies (GWASs) have provided insights into the role of genetic variants in CHD pathogenesis through the identification of a comprehensive set of single-nucleotide polymorphisms (SNPs). Notably, 90-95% of these variants reside in the noncoding genome, complicating the understanding of their underlying mechanisms. Here, we developed a systematic computational pipeline for the identification and analysis of CHD-associated SNPs spanning both coding and noncoding regions of the genome. Initially, we curated a thorough dataset of SNPs from GWAS-catalog and ClinVar database and filtered them based on CHD-related traits. Subsequently, these CHD-SNPs were annotated and categorized into noncoding and coding regions based on their location. To study the functional implications of noncoding CHD-SNPs, we cross-validated them with enhancer-specific histone modification marks from developing human heart across 9 Carnegie stages and identified potential cardiac enhancers. This approach led to the identification of 2,056 CHD-associated putative enhancers (CHD-enhancers), 38.9% of them overlapping with known enhancers catalogued in human enhancer disease database. We identified heart-related transcription factor binding sites within these CHD-enhancers, offering insights into the impact of SNPs on TF binding. Conservation analysis further revealed that many of these CHD-enhancers were highly conserved across vertebrates, suggesting their evolutionary significance. Utilizing heart-specific expression quantitative trait loci data, we further identified a subset of 63 CHD-SNPs with regulatory potential distributed across various cardiac tissues. Concurrently, coding CHD-SNPs were represented as a protein interaction network and its subsequent binding energy analysis focused on a pair of proteins within this network, pinpointed a deleterious coding CHD-SNP, rs770030288, located in C2 domain of MYBPC3 protein. Overall, our findings demonstrate that SNPs have the potential to disrupt gene regulatory systems, either by affecting enhancer sequences or modulating protein-protein interactions, which can lead to abnormal developmental processes contributing to CHD pathogenesis.

Machine learning-driven identification of critical gene programs and key transcription factors in migraine

BACKGROUND

Migraine is a complex neurological disorder characterized by recurrent episodes of severe headaches. Although genetic factors have been implicated, the precise molecular mechanisms, particularly gene expression patterns in migraine-associated brain regions, remain unclear. This study applies machine learning techniques to explore region-specific gene expression profiles and identify critical gene programs and transcription factors linked to migraine pathogenesis.

METHODS

We utilized single-nucleus RNA sequencing (snRNA-seq) data from 43 brain regions, along with genome-wide association study (GWAS) data, to investigate susceptibility to migraine. The cell-type-specific expression (CELLEX) algorithm was employed to calculate specific expression profiles for each region, while non-negative matrix factorization (NMF) was applied to decompose gene programs within the single-cell data from these regions. Following the annotation of brain region expression profiles and gene programs to the genome, we employed stratified linkage disequilibrium score regression (S-LDSC) to assess the associations between brain regions, gene programs, and migraine-related SNPs. Key transcription factors regulating critical gene programs were identified using a random forest model based on regulatory networks derived from the GTEx consortium.

RESULTS

Our analysis revealed significant enrichment of migraine-associated single nucleotide polymorphisms (SNPs) in the posterior nuclear complex-medial geniculate nuclei (PoN_MG) of the thalamus, highlighting this region's crucial role in migraine pathogenesis. Gene program 1, identified through NMF, was enriched in the calcium signaling pathway, a known contributor to migraine pathophysiology. Random forest analysis predicted ARID3A as the top transcription factor regulating gene program 1, suggesting its potential role in modulating calcium-related genes involved in migraine.

CONCLUSION

This study provides new insights into the molecular mechanisms underlying migraine, emphasizing the importance of the PoN_MG thalamic region, calcium signaling pathways, and key transcription factors like ARID3A. These findings offer potential avenues for developing targeted therapeutic strategies for migraine treatment.

Multi-trait Genome-Wide Analysis Identified 20 Novel Loci for Sarcopenia-Related Traits in UK Biobank

This study aims to identify novel loci associated with sarcopenia-related traits in UK Biobank (UKB) through multi-trait genome-wide analysis. To identify novel loci associated with sarcopenia, we integrated the genome-wide association studies (GWAS) of usual walking pace (UWP) and hand grip strength (HGS) to conduct a joint association study known as multi-trait analysis of GWAS (MTAG). We performed a transcriptome-wide association study (TWAS) to analyze the results of MTAG in relation to mRNA expression data for genes identified in skeletal muscle. Additionally, we utilized Weighted Gene Co-Expression Network Analysis (WGCNA) and Protein-Protein Interaction (PPI) networks to explore the relationships between the identified genes and hub genes related to sarcopenia. We identified 15 novel loci associated with UWP and 5 novel loci associated with HGS at the genome wide significance level (GWS, p < 5 × 10 - 8 ). After TWAS (p TWAS < 6.659 × 10 - 6 , 0.05 / 7509 ), we found two significant genes: PPP1R3A, located at 7q31.1 and associated with HGS, and ZBTB38, located at 3q23 and associated with UWP. 11 identified genes associated with hub genes for sarcopenia were obtained through WGCNA. Our findings offer new insights into biological mechanisms underlying sarcopenia and identify several novel genes related to sarcopenia that warrant in-depth study.

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.

Funmap: integrating high-dimensional functional annotations to improve fine-mapping

MOTIVATION

Fine-mapping aims to prioritize causal variants underlying complex traits by accounting for the linkage disequilibrium of genome-wide association study risk locus. The expanding resources of functional annotations serve as auxiliary evidence to improve the power of fine-mapping. However, existing fine-mapping methods tend to generate many false positive results when integrating a large number of annotations.

RESULTS

In this study, we propose a unified method to integrate high-dimensional functional annotations with fine-mapping (Funmap). Funmap can effectively improve the power of fine-mapping by borrowing information from hundreds of functional annotations. Meanwhile, it relates the annotation to the causal probability with a random effects model that avoids the over-fitting issue, thereby producing a well-controlled false positive rate. Paired with a fast algorithm, Funmap enables scalable integration of a large number of annotations to facilitate prioritizing multiple causal single nucleotide polymorphisms. Our comprehensive simulations across a wide range of annotation relevance settings demonstrate that Funmap is the only method that produces well-calibrated false discovery rate under the setting of high-dimensional annotations while achieving better or comparable power gains as compared to existing methods. By integrating genome-wide association studies of 4 lipid traits with 187 functional annotations, Funmap consistently identified more variants that can be replicated in an independent cohort, achieving 15.5%-26.2% improvement over the runner-up in terms of replication rate.

AVAILABILITY AND IMPLEMENTATION

The Funmap software and all analysis code are available at https://github.com/LeeHITsz/Funmap.

Co-expression-wide association studies link genetically regulated interactions with complex traits

Transcriptome- and proteome-wide association studies (TWAS/PWAS) have proven successful in prioritizing genes and proteins whose genetically regulated expression modulates disease risk, but they ignore potential co-expression and interaction effects. To address this limitation, we introduce the co-expression-wide association study (COWAS) method, which can identify pairs of genes or proteins whose genetically regulated co-expression is associated with complex traits. COWAS first trains models to predict expression and co-expression conditional on genetic variation, and then tests for association between imputed co-expression and the trait of interest while also accounting for direct effects from each exposure. We applied our method to plasma proteomic concentrations from the UK Biobank, identifying dozens of interacting protein pairs associated with cholesterol levels, Alzheimer's disease, and Parkinson's disease. Notably, our results demonstrate that co-expression between proteins may affect complex traits even if neither protein is detected to influence the trait when considered on its own. We also show how COWAS can help disentangle direct and interaction effects, providing a richer picture of the molecular networks that mediate genetic effects on disease outcomes.

Systematic identification of pathogenic variants of non-small cell lung cancer in the promoters of DNA-damage repair genes

BACKGROUND

Deficiency in DNA-damage repair (DDR) genes, often due to disruptive coding variants, is linked to higher cancer risk. Our previous study has revealed the association between rare loss-of-function variants in DDR genes and the risk of lung cancer. However, it is still challenging to study the predisposing role of rare regulatory variants of these genes.

METHODS

Based on whole-genome sequencing data from 2984 patients with non-small cell lung cancer (NSCLC) and 3020 controls, we performed massively parallel reporter assays on 1818 rare variants located in the promoters of DDR genes. Pathway- or gene-level burden analyses were performed using Firth's logistic regression or generalized linear model.

FINDINGS

We identified 750 rare functional regulatory variants (frVars) that showed allelic differences in transcriptional activity within the promoter regions of DDR genes. Interestingly, the burden of frVars was significantly elevated in cases (odds ratio [OR] = 1.17, p = 0.026), whereas the burden of variants prioritized solely based on bioinformatics annotation was comparable between cases and controls (OR = 1.04, p = 0.549). Among the frVars, 297 were down-regulated transcriptional activity (dr-frVars) and 453 were up-regulated transcriptional activity (ur-frVars); especially, dr-frVars (OR = 1.30, p = 0.008) rather than ur-frVars (OR = 1.06, p = 0.495) were significantly associated with risk of NSCLC. Individuals with NSCLC carried more dr-frVars from Fanconi anemia, homologous recombination, and nucleotide excision repair pathways. In addition, we identified seven genes (i.e., BRCA2, GTF2H1, DDB2, BLM, ALKBH2, APEX1, and RAD51B) with promoter dr-frVars that were associated with lung cancer susceptibility.

INTERPRETATION

Our findings indicate that functional promoter variants in DDR genes, in addition to protein-truncating variants, can be pathogenic and contribute to lung cancer susceptibility.

FUNDING

National Natural Science Foundation of China, Youth Foundation of Jiangsu Province, Research Unit of Prospective Cohort of Cardiovascular Diseases and Cancer of Chinese Academy of Medical Sciences, and Natural Science Foundation of Jiangsu Province.

Disease-modifying therapies for Parkinson disease: lessons from multiple sclerosis

The development of disease-modifying therapies (DMTs) for neurological disorders is an important goal in modern neurology, and the associated challenges are similar in many chronic neurological conditions. Major advances have been made in the multiple sclerosis (MS) field, with a range of DMTs being approved for relapsing MS and the introduction of the first DMTs for progressive MS. By contrast, people with Parkinson disease (PD) still lack such treatment options, relying instead on decades-old therapeutic approaches that provide only symptomatic relief. To address this unmet need, an in-person symposium was held in Toronto, Canada, in November 2022 for international researchers and experts in MS and PD to discuss strategies for advancing DMT development. In this Roadmap article, we highlight discussions from the symposium, which focused on therapeutic targets and preclinical models, disease spectra and subclassifications, and clinical trial design and outcome measures. From these discussions, we propose areas for novel or deeper exploration in PD using lessons learned from therapeutic development in MS. In addition, we identify challenges common to the PD and MS fields that need to be addressed to further advance the discovery and development of effective DMTs.

Dissecting shared genetic architecture between depression and body mass index

BACKGROUND

A growing body of evidence supports the comorbidity between depression (DEP) and obesity, yet the genetic mechanisms underlying this association remain unclear. Our study explored the shared genetic architecture and causal associations of DEP with BMI.

METHODS

We investigated the multigene overlap and genetic correlation between DEP (N > 1.3 million) and BMI (N = 806,834) based on genome-wide association studies (GWAS) and using the bivariate causal mixture model and linkage disequilibrium score regression (LDSC). The causal association was explored by bi-directional Mendelian randomization (MR). Common risk loci were identified through cross-trait meta-analyses. Stratified LDSC and multi-marker gene annotation analyses were applied to investigate single-nucleotide polymorphisms enrichment across tissue types, cell types, and functional categories. Finally, we explored shared functional genes by Summary Data-Based Mendelian Randomization (SMR) and further detected differential expression genes (DEG) in brain tissues of individuals with depression and obesity.

RESULTS

We found a positive genetic correlation between DEP and BMI (rg = 0.19, P = 4.07 × 10-26), which was more evident in local genomic regions. Cross-trait meta-analyses identified 16 shared genetic loci, 5 of which were newly identified, and they had influence on both diseases in the same direction. MR analysis showed a bidirectional causal association between DEP and BMI, with comparable effect sizes estimated in both directions. Combined with gene expression information, we found that genetic correlations between DEP and BMI were enriched in 6 brain regions, predominantly in the nucleus accumbens and anterior cingulate cortex. Moreover, 6 specific cell types and 23 functional genes were found to have an impact on both DEP and BMI across the brain regions. Of which, NEGR1 was identified as the most significant functional gene and associated with DEP and BMI at the genome-wide significance level (P < 5 × 10-8). Compared with healthy controls, the expression levels of NEGR1 gene were significant lower in brain tissues of individuals with depression and obesity.

CONCLUSIONS

Our study reveals shared genetic basis underpinnings between DEP and BMI, including genetic correlations and common genes. These insights offer novel opportunities and avenues for future research into their comorbidities.

Powerful mapping of cis-genetic effects on gene expression across diverse populations reveals novel disease-critical genes

While disease-associated variants identified by genome-wide association studies (GWAS) most likely regulate gene expression levels, linking variants to target genes is critical to determining the functional mechanisms of these variants. Genetic effects on gene expression have been extensively characterized by expression quantitative trait loci (eQTL) studies, yet data from non-European populations is limited. This restricts our understanding of disease to genes whose regulatory variants are common in European populations. While previous work has leveraged data from multiple populations to improve GWAS power and polygenic risk score (PRS) accuracy, multi-ancestry data has not yet been used to better estimate cis-genetic effects on gene expression. Here, we present a new method, Multi-Ancestry Gene Expression Prediction Regularized Optimization (MAGEPRO), which constructs robust genetic models of gene expression in understudied populations or cell types by fitting a regularized linear combination of eQTL summary data across diverse cohorts. In simulations, our tool generates more accurate models of gene expression than widely-used LASSO and the state-of-the-art multi-ancestry PRS method, PRS-CSx, adapted to gene expression prediction. We attribute this improvement to MAGEPRO's ability to more accurately estimate causal eQTL effect sizes (p < 3.98 × 10-4, two-sided paired t-test). With real data, we applied MAGEPRO to 8 eQTL cohorts representing 3 ancestries (average n = 355) and consistently outperformed each of 6 competing methods in gene expression prediction tasks. Integration with GWAS summary statistics across 66 complex traits (representing 22 phenotypes and 3 ancestries) resulted in 2,331 new gene-trait associations, many of which replicate across multiple ancestries, including PHTF1 linked to white blood cell count, a gene which is overexpressed in leukemia patients. MAGEPRO also identified biologically plausible novel findings, such as PIGB, an essential component of GPI biosynthesis, associated with heart failure, which has been previously evidenced by clinical outcome data. Overall, MAGEPRO is a powerful tool to enhance inference of gene regulatory effects in underpowered datasets and has improved our understanding of population-specific and shared genetic effects on complex traits.

Neuronal enhancers fine-tune adaptive circuit plasticity

Neuronal activity-regulated gene expression plays a crucial role in sculpting neural circuits that underpin adaptive brain function. Transcriptional enhancers are now recognized as key components of gene regulation that orchestrate spatiotemporally precise patterns of gene transcription. We propose that the dynamics of enhancer activation uniquely position these genomic elements to finely tune activity-dependent cellular plasticity. Enhancer specificity and modularity can be exploited to gain selective genetic access to specific cell states, and the precise modulation of target gene expression within restricted cellular contexts enabled by targeted enhancer manipulation allows for fine-grained evaluation of gene function. Mounting evidence also suggests that enduring stimulus-induced changes in enhancer states can modify target gene activation upon restimulation, thereby contributing to a form of cell-wide metaplasticity. We advocate for focused exploration of activity-dependent enhancer function to gain new insight into the mechanisms underlying brain plasticity and cognitive dysfunction.

A metagene based similarity network fusion approach for multi-omics data integration identified novel subtypes in renal cell carcinoma

Renal cell carcinoma (RCC) ranks among the most prevalent cancers worldwide, with both incidence and mortality rates increasing annually. The heterogeneity among RCC patients presents considerable challenges for developing universally effective treatment strategies, emphasizing the necessity of in-depth research into RCC's molecular mechanisms, understanding the variations among RCC patients and further identifying distinct molecular subtypes for precise treatment. We proposed a metagene-based similarity network fusion (Meta-SNF) method for RCC subtype identification with multi-omics data, using a non-negative matrix factorization technique to capture alternative structures inherent in the dataset as metagenes. These latent metagenes were then integrated to construct a fused network under the Similarity Network Fusion (SNF) framework for more precise subtyping. We conducted simulation studies and analyzed real-world data from two RCC datasets, namely kidney renal clear cell carcinoma (KIRC) and kidney renal papillary cell carcinoma (KIRP) to demonstrate the utility of Meta-SNF. The simulation studies indicated that Meta-SNF achieved higher accuracy in subtype identification compared with the original SNF and other state-of-the-art methods. In analyses of real data, Meta-SNF produced more distinct and well-separated clusters, classifying both KIRC and KIRP into four subtypes with significant differences in survival outcomes. Subsequently, we performed comprehensive bioinformatics analyses focused on subtypes with poor prognoses in KIRC and KIRP and identified several potential biomarkers. Meta-SNF offers a novel strategy for subtype identification using multi-omics data, and its application to RCC datasets has yielded diverse biological insights which are highly valuable for informing clinical decision-making processes in the treatment of RCC.

The genetic architecture of biological age in nine human organ systems

Investigating the genetic underpinnings of human aging is essential for unraveling the etiology of and developing actionable therapies for chronic diseases. Here, we characterize the genetic architecture of the biological age gap (BAG; the difference between machine learning-predicted age and chronological age) across nine human organ systems in 377,028 participants of European ancestry from the UK Biobank. The BAGs were computed using cross-validated support vector machines, incorporating imaging, physical traits and physiological measures. We identify 393 genomic loci-BAG pairs (P < 5 × 10-8) linked to the brain, eye, cardiovascular, hepatic, immune, metabolic, musculoskeletal, pulmonary and renal systems. Genetic variants associated with the nine BAGs are predominantly specific to the respective organ system (organ specificity) while exerting pleiotropic links with other organ systems (interorgan cross-talk). We find that genetic correlation between the nine BAGs mirrors their phenotypic correlation. Further, a multiorgan causal network established from two-sample Mendelian randomization and latent causal variance models revealed potential causality between chronic diseases (for example, Alzheimer's disease and diabetes), modifiable lifestyle factors (for example, sleep duration and body weight) and multiple BAGs. Our results illustrate the potential for improving human organ health via a multiorgan network, including lifestyle interventions and drug repurposing strategies.

Identification of DNase I hypersensitive sites in the human genome by multiple sequence descriptors

DNase I hypersensitive sites (DHSs) are chromatin regions highly sensitive to DNase I enzymes. Studying DHSs is crucial for understanding complex transcriptional regulation mechanisms and localizing cis-regulatory elements (CREs). Numerous studies have indicated that disease-related loci are often enriched in DHSs regions, underscoring the importance of identifying DHSs. Although wet experiments exist for DHSs identification, they are often labor-intensive. Therefore, there is a strong need to develop computational methods for this purpose. In this study, we used experimental data to construct a benchmark dataset. Seven feature extraction methods were employed to capture information about human DHSs. The F-score was applied to filter the features. By comparing the prediction performance of various classification algorithms through five-fold cross-validation, random forest was proposed to perform the final model construction. The model could produce an overall prediction accuracy of 0.859 with an AUC value of 0.837. We hope that this model can assist scholars conducting DNase research in identifying these sites.

Pleiotropy, epistasis and the genetic architecture of quantitative traits

Pleiotropy (whereby one genetic polymorphism affects multiple traits) and epistasis (whereby non-linear interactions between genetic polymorphisms affect the same trait) are fundamental aspects of the genetic architecture of quantitative traits. Recent advances in the ability to characterize the effects of polymorphic variants on molecular and organismal phenotypes in human and model organism populations have revealed the prevalence of pleiotropy and unexpected shared molecular genetic bases among quantitative traits, including diseases. By contrast, epistasis is common between polymorphic loci associated with quantitative traits in model organisms, such that alleles at one locus have different effects in different genetic backgrounds, but is rarely observed for human quantitative traits and common diseases. Here, we review the concepts and recent inferences about pleiotropy and epistasis, and discuss factors that contribute to similarities and differences between the genetic architecture of quantitative traits in model organisms and humans.

Single-cell analysis of chromatin and expression reveals age- and sex-associated alterations in the human heart

Sex differences and age-related changes in the human heart at the tissue, cell, and molecular level have been well-documented and many may be relevant for cardiovascular disease. However, how molecular programs within individual cell types vary across individuals by age and sex remains poorly characterized. To better understand this variation, we performed single-nucleus combinatorial indexing (sci) ATAC- and RNA-Seq in human heart samples from nine donors. We identify hundreds of differentially expressed genes by age and sex and find epigenetic signatures of variation in ATAC-Seq data in this discovery cohort. We then scale up our single-cell RNA-Seq analysis by combining our data with five recently published single nucleus RNA-Seq datasets of healthy adult hearts. We find variation such as metabolic alterations by sex and immune changes by age in differential expression tests, as well as alterations in abundance of cardiomyocytes by sex and neurons with age. In addition, we compare our adult-derived ATAC-Seq profiles to analogous fetal cell types to identify putative developmental-stage-specific regulatory factors. Finally, we train predictive models of cell-type-specific RNA expression levels utilizing ATAC-Seq profiles to link distal regulatory sequences to promoters, quantifying the predictive value of a simple TF-to-expression regulatory grammar and identifying cell-type-specific TFs. Our analysis represents the largest single-cell analysis of cardiac variation by age and sex to date and provides a resource for further study of healthy cardiac variation and transcriptional regulation at single-cell resolution.

Bayesian Estimation of Allele-Specific Expression in the Presence of Phasing Uncertainty

Motivation

Allele-specific expression (ASE) analyses aim to detect imbalanced expression of maternal versus paternal copies of an autosomal gene. Such allelic imbalance can result from a variety of cis-acting causes, including disruptive mutations within one copy of a gene that impact the stability of transcripts, as well as regulatory variants outside the gene that impact transcription initiation. Current methods for ASE estimation suffer from a number of shortcomings, such as relying on only one variant within a gene, assuming perfect phasing information across multiple variants within a gene, or failing to account for alignment biases and possible genotyping errors.

Results

We developed BEASTIE, a Bayesian hierarchical model designed for precise ASE quantification at the gene level, based on given genotypes and RNA-Seq data. BEASTIE addresses the complexities of allelic mapping bias, genotyping error, and phasing errors by incorporating empirical phasing error rates derived from Genome-in-a-Bottle individual NA12878. BEASTIE surpasses existing methods in accuracy, especially in scenarios with high phasing errors. This improvement is critical for identifying rare genetic variants often obscured by such errors. Through rigorous validation on simulated data and application to real data from the 1000 Genomes Project, we establish the robustness of BEASTIE. These findings underscore the value of BEASTIE in revealing patterns of ASE across gene sets and pathways.

Availability and Implementation

The software is freely available from https://github.com/x811zou/BEASTIE . BEASTIE is available as Python source code and as a Docker image.

Supplementary information

Additional information is available online.

Identification of KSR2 Variants in Pediatric Patients with Severe Early-Onset Obesity from Qatar

The kinase suppressor of Ras 2 (KSR2) gene is associated with monogenic obesity, and loss-of-function variants in KSR2 have been identified in individuals with severe early-onset obesity. This study investigated KSR2 variants in 9 pediatric patients with severe early-onset obesity in Qatar using whole genome sequencing among a cohort of 240 individuals. We focused on KSR2 variants with a minor allele frequency (MAF) below 1% and a Combined Annotation Dependent Depletion (CADD) score above 13 to identify potential causative variants. Our analysis identified four KSR2 variants: one intronic (c.1765-8G>A) and three missense variants (c.1057G>A, c.1673G>A, and c.923T>C) in nine patients. The intronic variant c.1765-8G>A was the most frequent (seen in six individuals) and had a CADD score of 21.10, suggesting possible pathogenicity. This variant showed a significantly higher allele frequency in the Qatari population compared to the Genome Aggregation Database (gnomAD), indicating a possible founder effect. Molecular modeling of the missense variants revealed structural changes in the protein structure. The study concludes that these four KSR2 variants are associated with monogenic obesity, with an autosomal dominant inheritance pattern. The c.1765-8G>A variant's prevalence in Qatar underscores its importance in genetic screening for severe obesity. This research advances the understanding of genetic factors in severe early-onset obesity and may inform better management strategies.

Evaluation of heritability partitioning approaches in livestock populations

BACKGROUND

Heritability partitioning approaches estimate the contribution of different functional classes, such as coding or regulatory variants, to the genetic variance. This information allows a better understanding of the genetic architecture of complex traits, including complex diseases, but can also help improve the accuracy of genomic selection in livestock species. However, methods have mainly been tested on human genomic data, whereas livestock populations have specific characteristics, such as high levels of relatedness, small effective population size or long-range levels of linkage disequilibrium.

RESULTS

Here, we used data from 14,762 cows, imputed at the whole-genome sequence level for 11,537,240 variants, to simulate traits in a typical livestock population and evaluate the accuracy of two state-of-the-art heritability partitioning methods, GREML and a Bayesian mixture model. In simulations where a single functional class had increased contribution to heritability, we observed that the estimators were unbiased but had low precision. When causal variants were enriched in variants with low (< 0.05) or high (> 0.20) minor allele frequency or low (below 1st quartile) or high (above 3rd quartile) linkage disequilibrium scores, it was necessary to partition the genetic variance into multiple classes defined on the basis of allele frequencies or LD scores to obtain unbiased results. When multiple functional classes had variable contributions to heritability, estimators showed higher levels of variation and confounding between certain categories was observed. In addition, estimators from small categories were particularly imprecise. However, the estimates and their ranking were still informative about the contribution of the classes. We also demonstrated that using methods that estimate the contribution of a single category at a time, a commonly used approach, results in an overestimation. Finally, we applied the methods to phenotypes for muscular development and height and estimated that, on average, variants in open chromatin regions had a higher contribution to the genetic variance (> 45%), while variants in coding regions had the strongest individual effects (> 25-fold enrichment on average). Conversely, variants in intergenic or intronic regions showed lower levels of enrichment (0.2 and 0.6-fold on average, respectively).

CONCLUSIONS

Heritability partitioning approaches should be used cautiously in livestock populations, in particular for small categories. Two-component approaches that fit only one functional category at a time lead to biased estimators and should not be used.

A Western Dietary Pattern during Pregnancy is Associated with Neurodevelopmental Disorders in Childhood and Adolescence

Despite the high prevalence of neurodevelopmental disorders, there is a notable gap in clinical studies exploring the impact of maternal diet during pregnancy on child neurodevelopment. This observational clinical study examined the association between pregnancy dietary patterns and neurodevelopmental disorders, as well as their symptoms, in a prospective cohort of 10-year-old children (n=508). Data-driven dietary patterns were derived from self-reported food frequency questionnaires. A Western dietary pattern in pregnancy (per SD change) was significantly associated with attention-deficit / hyperactivity disorder (ADHD) (OR 1.66 [1.21 - 2.27], p=0.002) and autism diagnosis (OR 2.22 [1.33 - 3.74], p=0.002) and associated symptoms (p<0.001). Findings for ADHD were validated in three large (n=59725, n=656, n=348), independent mother-child cohorts. Objective blood metabolome modelling at 24 weeks gestation identified 15 causally mediating metabolites which significantly improved ADHD prediction in external validation. Temporal analyses across five blood metabolome timepoints in two independent mother-child cohorts revealed that the association of Western dietary pattern metabolite scores with neurodevelopmental outcomes was consistently significant in early to mid-pregnancy, independent of later child timepoints. These findings underscore the importance of early intervention and provide robust evidence for targeted prenatal dietary interventions to prevent neurodevelopmental disorders in children.

Integrative multi-omics analyses to identify the genetic and functional mechanisms underlying ovarian cancer risk regions

To identify credible causal risk variants (CCVs) associated with different histotypes of epithelial ovarian cancer (EOC), we performed genome-wide association analysis for 470,825 genotyped and 10,163,797 imputed SNPs in 25,981 EOC cases and 105,724 controls of European origin. We identified five histotype-specific EOC risk regions (p value <5 × 10-8) and confirmed previously reported associations for 27 risk regions. Conditional analyses identified an additional 11 signals independent of the primary signal at six risk regions (p value <10-5). Fine mapping identified 4,008 CCVs in these regions, of which 1,452 CCVs were located in ovarian cancer-related chromatin marks with significant enrichment in active enhancers, active promoters, and active regions for CCVs from each EOC histotype. Transcriptome-wide association and colocalization analyses across histotypes using tissue-specific and cross-tissue datasets identified 86 candidate susceptibility genes in known EOC risk regions and 32 genes in 23 additional genomic regions that may represent novel EOC risk loci (false discovery rate <0.05). Finally, by integrating genome-wide HiChIP interactome analysis with transcriptome-wide association study (TWAS), variant effect predictor, transcription factor ChIP-seq, and motifbreakR data, we identified candidate gene-CCV interactions at each locus. This included risk loci where TWAS identified one or more candidate susceptibility genes (e.g., HOXD-AS2, HOXD8, and HOXD3 at 2q31) and other loci where no candidate gene was identified (e.g., MYC and PVT1 at 8q24) by TWAS. In summary, this study describes a functional framework and provides a greater understanding of the biological significance of risk alleles and candidate gene targets at EOC susceptibility loci identified by a genome-wide association study.

Functional dissection of complex and molecular trait variants at single nucleotide resolution

Identifying the causal variants and mechanisms that drive complex traits and diseases remains a core problem in human genetics. The majority of these variants have individually weak effects and lie in non-coding gene-regulatory elements where we lack a complete understanding of how single nucleotide alterations modulate transcriptional processes to affect human phenotypes. To address this, we measured the activity of 221,412 trait-associated variants that had been statistically fine-mapped using a Massively Parallel Reporter Assay (MPRA) in 5 diverse cell-types. We show that MPRA is able to discriminate between likely causal variants and controls, identifying 12,025 regulatory variants with high precision. Although the effects of these variants largely agree with orthogonal measures of function, only 69% can plausibly be explained by the disruption of a known transcription factor (TF) binding motif. We dissect the mechanisms of 136 variants using saturation mutagenesis and assign impacted TFs for 91% of variants without a clear canonical mechanism. Finally, we provide evidence that epistasis is prevalent for variants in close proximity and identify multiple functional variants on the same haplotype at a small, but important, subset of trait-associated loci. Overall, our study provides a systematic functional characterization of likely causal common variants underlying complex and molecular human traits, enabling new insights into the regulatory grammar underlying disease risk.

MPH: fast REML for large-scale genome partitioning of quantitative genetic variation

MOTIVATION

Genome partitioning of quantitative genetic variation is useful for dissecting the genetic architecture of complex traits. However, existing methods, such as Haseman-Elston regression and linkage disequilibrium score regression, often face limitations when handling extensive farm animal datasets, as demonstrated in this study.

RESULTS

To overcome this challenge, we present MPH, a novel software tool designed for efficient genome partitioning analyses using restricted maximum likelihood. The computational efficiency of MPH primarily stems from two key factors: the utilization of stochastic trace estimators and the comprehensive implementation of parallel computation. Evaluations with simulated and real datasets demonstrate that MPH achieves comparable accuracy and significantly enhances convergence, speed, and memory efficiency compared to widely used tools like GCTA and LDAK. These advancements facilitate large-scale, comprehensive analyses of complex genetic architectures in farm animals.

AVAILABILITY AND IMPLEMENTATION

The MPH software is available at https://jiang18.github.io/mph/.

Genetic mapping across autoimmune diseases reveals shared associations and mechanisms

Autoimmune and inflammatory diseases are polygenic disorders of the immune system. Many genomic loci harbor risk alleles for several diseases, but the limited resolution of genetic mapping prevents determining whether the same allele is responsible, indicating a shared underlying mechanism. Here, using a collection of 129,058 cases and controls across 6 diseases, we show that ~40% of overlapping associations are due to the same allele. We improve fine-mapping resolution for shared alleles twofold by combining cases and controls across diseases, allowing us to identify more expression quantitative trait loci driven by the shared alleles. The patterns indicate widespread sharing of pathogenic mechanisms but not a single global autoimmune mechanism. Our approach can be applied to any set of traits and is particularly valuable as sample collections become depleted.

Cross-ancestry genetic architecture and prediction for cholesterol traits

While cholesterol is essential, a high level of cholesterol is associated with the risk of cardiovascular diseases. Genome-wide association studies (GWASs) have proven successful in identifying genetic variants that are linked to cholesterol levels, predominantly in white European populations. However, the extent to which genetic effects on cholesterol vary across different ancestries remains largely unexplored. Here, we estimate cross-ancestry genetic correlation to address questions on how genetic effects are shared across ancestries. We find significant genetic heterogeneity between ancestries for cholesterol traits. Furthermore, we demonstrate that single nucleotide polymorphisms (SNPs) with concordant effects across ancestries for cholesterol are more frequently found in regulatory regions compared to other genomic regions. Indeed, the positive genetic covariance between ancestries is mostly driven by the effects of the concordant SNPs, whereas the genetic heterogeneity is attributed to the discordant SNPs. We also show that the predictive ability of the concordant SNPs is significantly higher than the discordant SNPs in the cross-ancestry polygenic prediction. The list of concordant SNPs for cholesterol is available in GWAS Catalog. These findings have relevance for the understanding of shared genetic architecture across ancestries, contributing to the development of clinical strategies for polygenic prediction of cholesterol in cross-ancestral settings.

The impact of genetic variants related to vitamin D and autoimmunity: A systematic review

Over the past few years, there has been a notable increment in scientific literature aimed at unraveling the genetic foundations of vitamin D signaling and its implications for susceptibility to autoimmunity, however, most of them address isolated diseases. Here, we conducted a systematic review of genetic variants related to vitamin D and autoimmune diseases and we discussed the current landscape of susceptibility and outcomes. Of 65 studies analyzed, most variants cited are in vitamin D binding protein (VDBP; rs2282679 GC gene), 25-hydroxylase (rs10751657 CYP2R1), 1α-hydroxylase (rs10877012, CYP27B1) and the nuclear hormone receptor superfamily [FokI (rs2228570), BsmI (rs1544410), ApaI (rs7975232), and TaqI (rs731236) in VDR gene]. Therefore, our findings confirmed the associations of several genetic variants of vitamin D signaling with a broad spectrum of autoimmune diseases/traits. In addition, given the low number of papers found with functional analysis, further studies to elucidate the real effect that the variants exert on Vitamin D signaling are recommended.

Chromatin accessibility variation provides insights into missing regulation underlying immune-mediated diseases

Most genetic loci associated with complex traits and diseases through genome-wide association studies (GWAS) are noncoding, suggesting that the causal variants likely have gene regulatory effects. However, only a small number of loci have been linked to expression quantitative trait loci (eQTLs) detected currently. To better understand the potential reasons for many trait-associated loci lacking eQTL colocalization, we investigated whether chromatin accessibility QTLs (caQTLs) in lymphoblastoid cell lines (LCLs) explain immune-mediated disease associations that eQTLs in LCLs did not. The power to detect caQTLs was greater than that of eQTLs and was less affected by the distance from the transcription start site of the associated gene. Meta-analyzing LCL eQTL data to increase the sample size to over a thousand led to additional loci with eQTL colocalization, demonstrating that insufficient statistical power is still likely to be a factor. Moreover, further eQTL colocalization loci were uncovered by surveying eQTLs of other immune cell types. Altogether, insufficient power and context-specificity of eQTLs both contribute to the 'missing regulation.'

Investigating the shared genetic architecture between breast and ovarian cancers

High heritability and strong correlation have been observed in breast and ovarian cancers. However, their shared genetic architecture remained unclear. Linkage disequilibrium score regression (LDSC) and heritability estimation from summary statistics (ρ-HESS) were applied to estimate heritability and genetic correlations. Bivariate causal mixture model (MiXeR) was used to qualify the polygenic overlap. Then, stratified-LDSC (S-LDSC) was used to identify tissue and cell type specificity. Meanwhile, the adaptive association test called MTaSPUsSet was performed to identify potential pleiotropic genes. The Single Nucleotide Polymorphisms (SNP) heritability was 13% for breast cancer and 5% for ovarian cancer. There was a significant genetic correlation between breast and ovarian cancers (rg=0.21). Breast and ovarian cancers exhibited polygenic overlap, sharing 0.4 K out 2.8 K of causal variants. Tissue and cell type specificity displayed significant enrichment in female breast mammary, uterus, kidney tissues, and adipose cell. Moreover, the 74 potential pleiotropic genes were identified between breast and ovarian cancers, which were related to the regulation of cell cycle and cell death. We quantified the shared genetic architecture between breast and ovarian cancers and shed light on the biological basis of the co-morbidity. Ultimately, these findings facilitated the understanding of disease etiology.

Improved estimation of functional enrichment in SNP heritability using feasible generalized least squares

Functional enrichment results typically implicate tissue or cell-type-specific biological pathways in disease pathogenesis and as therapeutic targets. We propose generalized linkage disequilibrium score regression (g-LDSC) that requires only genome-wide association studies (GWASs) summary-level data to estimate functional enrichment. The method adopts the same assumptions and regression model formulation as stratified linkage disequilibrium score regression (s-LDSC). Although s-LDSC only partially uses LD information, our method uses the whole LD matrix, which accounts for possible correlated error structure via a feasible generalized least-squares estimation. We demonstrate through simulation studies under various scenarios that g-LDSC provides more precise estimates of functional enrichment than s-LDSC, regardless of model misspecification. In an application to GWAS summary statistics of 15 traits from the UK Biobank, estimates of functional enrichment using g-LDSC were lower and more realistic than those obtained from s-LDSC. In addition, g-LDSC detected more significantly enriched functional annotations among 24 functional annotations for the 15 traits than s-LDSC (118 vs. 51).

wMKL: multi-omics data integration enables novel cancer subtype identification via weight-boosted multi-kernel learning

BACKGROUND

Cancer is a heterogeneous disease driven by complex molecular alterations. Cancer subtypes determined from multi-omics data can provide novel insight into personalised precision treatment. It is recognised that incorporating prior weight knowledge into multi-omics data integration can improve disease subtyping.

METHODS

We develop a weighted method, termed weight-boosted Multi-Kernel Learning (wMKL) which incorporates heterogeneous data types as well as flexible weight functions, to boost subtype identification. Given a series of weight functions, we propose an omnibus combination strategy to integrate different weight-related P-values to improve subtyping precision.

RESULTS

wMKL models each data type with multiple kernel choices, thus alleviating the sensitivity and robustness issue due to selecting kernel parameters. Furthermore, wMKL integrates different data types by learning weights of different kernels derived from each data type, recognising the heterogeneous contribution of different data types to the final subtyping performance. The proposed wMKL outperforms existing weighted and non-weighted methods. The utility and advantage of wMKL are illustrated through extensive simulations and applications to two TCGA datasets. Novel subtypes are identified followed by extensive downstream bioinformatics analysis to understand the molecular mechanisms differentiating different subtypes.

CONCLUSIONS

The proposed wMKL method provides a novel strategy for disease subtyping. The wMKL is freely available at https://github.com/biostatcao/wMKL .

Moderating effects of impulsivity and morning cortisol on the genotype-phenotype relationship of attention deficit hyperactivity disorder in young adults

Dysregulation of the morning cortisol response in young adults with attention deficit hyperactivity disorder (ADHD) has been shown to underlie several of the alterations present in their lives. Thus, the interaction of this mechanism with genetic and behavioural characteristics could explain a large proportion of the aetiology of ADHD in this population. For these reasons, the present study explores the associations of 30 single nucleotide polymorphisms (SNPs) previously identified as significant (after correction for multiple comparisons) in the aetiology of ADHD with an assessment of morning cortisol and impulsivity traits in a group of 120 adults aged 18-24 years. Participants were recruited through private centres of neuropsychology and psychiatry, as well as through events in local universities. Morning cortisol within 30 min of awakening and motor impulsivity traits were shown to moderate the effect of SNP rs10129500 on the severity of the symptoms of ADHD measured by the Adult Self-Report Scale. This variant associated with cortisol-binding globulin would explain the low concentrations of this hormone found in young adults with high symptoms of ADHD, which is accentuated when there are high levels of impulsivity. The proposed model allows for transferring the theoretical relationships between the dimensions that explain the aetiology of ADHD to an applied exploratory model with good performance.

Enhancer-promoter activation by the Kaposi sarcoma-associated herpesvirus episome maintenance protein LANA

Higher-order genome structure influences the transcriptional regulation of cellular genes through the juxtaposition of regulatory elements, such as enhancers, close to promoters of target genes. While enhancer activation has emerged as an important facet of Kaposi sarcoma-associated herpesvirus (KSHV) biology, the mechanisms controlling enhancer-target gene expression remain obscure. Here, we discover that the KSHV genome tethering protein latency-associated nuclear antigen (LANA) potentiates enhancer-target gene expression in primary effusion lymphoma (PEL), a highly aggressive B cell lymphoma causally associated with KSHV. Genome-wide analyses demonstrate increased levels of enhancer RNA transcription as well as activating chromatin marks at LANA-bound enhancers. 3D genome conformation analyses identified genes critical for latency and tumorigenesis as targets of LANA-occupied enhancers, and LANA depletion results in their downregulation. These findings reveal a mechanism in enhancer-gene coordination and describe a role through which the main KSHV tethering protein regulates essential gene expression in PEL.

The genetic architecture of multimodal human brain age

The complex biological mechanisms underlying human brain aging remain incompletely understood. This study investigated the genetic architecture of three brain age gaps (BAG) derived from gray matter volume (GM-BAG), white matter microstructure (WM-BAG), and functional connectivity (FC-BAG). We identified sixteen genomic loci that reached genome-wide significance (P-value < 5×10-8). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG displayed the most pronounced heritability enrichment in genetic variants within conserved regions. Oligodendrocytes and astrocytes, but not neurons, exhibited notable heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several chronic diseases on brain aging, such as type 2 diabetes on GM-BAG and AD on WM-BAG. Our results provide insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at https://labs.loni.usc.edu/medicine .

Genetic and molecular architecture of complex traits

Human genetics has emerged as one of the most dynamic areas of biology, with a broadening societal impact. In this review, we discuss recent achievements, ongoing efforts, and future challenges in the field. Advances in technology, statistical methods, and the growing scale of research efforts have all provided many insights into the processes that have given rise to the current patterns of genetic variation. Vast maps of genetic associations with human traits and diseases have allowed characterization of their genetic architecture. Finally, studies of molecular and cellular effects of genetic variants have provided insights into biological processes underlying disease. Many outstanding questions remain, but the field is well poised for groundbreaking discoveries as it increases the use of genetic data to understand both the history of our species and its applications to improve human health.

Quantifying negative selection in human 3' UTRs uncovers constrained targets of RNA-binding proteins

Many non-coding variants associated with phenotypes occur in 3' untranslated regions (3' UTRs), and may affect interactions with RNA-binding proteins (RBPs) to regulate gene expression post-transcriptionally. However, identifying functional 3' UTR variants has proven difficult. We use allele frequencies from the Genome Aggregation Database (gnomAD) to identify classes of 3' UTR variants under strong negative selection in humans. We develop intergenic mutability-adjusted proportion singleton (iMAPS), a generalized measure related to MAPS, to quantify negative selection in non-coding regions. This approach, in conjunction with in vitro and in vivo binding data, identifies precise RBP binding sites, miRNA target sites, and polyadenylation signals (PASs) under strong selection. For each class of sites, we identify thousands of gnomAD variants under selection comparable to missense coding variants, and find that sites in core 3' UTR regions upstream of the most-used PAS are under strongest selection. Together, this work improves our understanding of selection on human genes and validates approaches for interpreting genetic variants in human 3' UTRs.

Minimum entropy framework identifies a novel class of genomic functional elements and reveals regulatory mechanisms at human disease loci

We introduce CoRE-BED, a framework trained using 19 epigenomic features in 33 major cell and tissue types to predict cell-type-specific regulatory function. CoRE-BED identifies nine functional classes de-novo, capturing both known and new regulatory categories. Notably, we describe a previously undercharacterized class that we term Development Associated Elements (DAEs), which are highly enriched in cell types with elevated regenerative potential and distinguished by the dual presence of either H3K4me2 and H3K9ac (an epigenetic signature associated with kinetochore assembly) or H3K79me3 and H4K20me1 (a signature associated with transcriptional pause release). Unlike bivalent promoters, which represent a transitory state between active and silenced promoters, DAEs transition directly to or from a non-functional state during stem cell differentiation and are proximal to highly expressed genes. CoRE-BED's interpretability facilitates causal inference and functional prioritization. Across 70 complex traits, distal insulators account for the largest mean proportion of SNP heritability (~49%) captured by the GWAS. Collectively, our results demonstrate the value of exploring non-conventional ways of regulatory classification that enrich for trait heritability, to complement existing approaches for cis-regulatory prediction.

Dynamic regulatory elements in single-cell multimodal data implicate key immune cell states enriched for autoimmune disease heritability

In autoimmune diseases such as rheumatoid arthritis, the immune system attacks the body's own cells. Developing a precise understanding of the cell states where noncoding autoimmune risk variants impart causal mechanisms is critical to developing curative therapies. Here, to identify noncoding regions with accessible chromatin that associate with cell-state-defining gene expression patterns, we leveraged multimodal single-nucleus RNA and assay for transposase-accessible chromatin (ATAC) sequencing data across 28,674 cells from the inflamed synovial tissue of 12 donors. Specifically, we used a multivariate Poisson model to predict peak accessibility from single-nucleus RNA sequencing principal components. For 14 autoimmune diseases, we discovered that cell-state-dependent ('dynamic') chromatin accessibility peaks in immune cell types were enriched for heritability, compared with cell-state-invariant ('cs-invariant') peaks. These dynamic peaks marked regulatory elements associated with T peripheral helper, regulatory T, dendritic and STAT1+CXCL10+ myeloid cell states. We argue that dynamic regulatory elements can help identify precise cell states enriched for disease-critical genetic variation.