A global overview of pleiotropy and genetic architecture in complex traits

Genome-wide association study for lung cancer in 6531 African Americans reveals new susceptibility loci

Despite lung cancer affecting all races and ethnicities, disparities are observed in incidence and mortality rates among different ethnic groups in the United States. Non-Hispanic African Americans had a high incidence rate of lung cancer at 55.8 per 100 000 people, as well as the highest death rate at 37.2 per 100 000 people from 2016 to 2020. While previous genome-wide association studies (GWAS) have identified over 45 susceptibility risk loci that influence lung cancer development, few GWAS have investigated the etiology of lung cancer in African Americans. To address this gap in knowledge, we conducted GWAS of lung cancer focused on studying African Americans, comprising 2267 lung cancer cases and 4264 controls. We identified three loci associated with lung cancer, one with lung adenocarcinoma, and four with lung squamous cell carcinoma in this population at the genomic-wide significance level. Among them, three novel loci were identified near VWF at 12p13.31 for overall lung cancer and GACAT3 at 2p24.3 and LMAN1L at 15q24.1 for lung squamous cell carcinoma. In addition, we confirmed previously reported risk loci with known or new lead variants near CHRNA5 at 15q25.1 and CYP2A6 at 19q13.2 associated with lung cancer and TRIP13 at 5p15.33 and ERC1 at 12p13.33 associated with lung squamous cell carcinoma. Further multi-step functional analyses shed light on biological mechanisms underlying these associations of lung cancer in this population. Our study highlights the importance of ancestry-specific studies for the potential alleviation of lung cancer burden in African Americans.

Genome-wide association meta-analysis of age at onset of walking in over 70,000 infants of European ancestry

Age at onset of walking is an important early childhood milestone which is used clinically and in public health screening. In this genome-wide association study meta-analysis of age at onset of walking (N = 70,560 European-ancestry infants), we identified 11 independent genome-wide significant loci. SNP-based heritability was 24.13% (95% confidence intervals = 21.86-26.40) with ~11,900 variants accounting for about 90% of it, suggesting high polygenicity. One of these loci, in gene RBL2, co-localized with an expression quantitative trait locus (eQTL) in the brain. Age at onset of walking (in months) was negatively genetically correlated with ADHD and body-mass index, and positively genetically correlated with brain gyrification in both infant and adult brains. The polygenic score showed out-of-sample prediction of 3-5.6%, confirmed as largely due to direct effects in sib-pair analyses, and was separately associated with volume of neonatal brain structures involved in motor control. This study offers biological insights into a key behavioural marker of neurodevelopment.

Transdiagnostic and Disorder-Level GWAS Enhance Precision of Substance Use and Psychiatric Genetic Risk Profiles in African and European Ancestries

BACKGROUND

Substance use disorders (SUDs) and psychiatric disorders frequently co-occur, and their etiology likely reflects both transdiagnostic (i.e., common/shared) and disorder-level (i.e., independent/nonshared) genetic influences. Understanding the genetic influences that are shared and those that operate independently of the shared risk could enhance precision in diagnosis, prevention, and treatment, but this remains underexplored, particularly in non-European ancestry groups.

METHODS

We applied genomic structural equation modeling to examine the common and independent genetic architecture among SUDs and psychotic, mood, and anxiety disorders using summary statistics from genome-wide association studies (GWAS) conducted in European- (EUR) and African-ancestry (AFR) individuals. To characterize the biological and phenotypic associations, we used FUMA, conducted genetic correlations, and performed phenome-wide association studies (PheWAS).

RESULTS

In EUR individuals, transdiagnostic genetic factors represented SUDs, psychotic, and mood/anxiety disorders, with GWAS identifying two novel lead single-nucleotide polymorphisms (SNPs) for the mood factor. In AFR individuals, genetic factors represented SUDs and psychiatric disorders, and GWAS identified one novel lead SNP for the SUD factor. In EUR individuals, second-order factor models showed phenotypic and genotypic associations with a broad range of physical and mental health traits. Finally, genetic correlations and PheWAS highlighted how common and independent genetic factors for SUD and psychotic disorders were differentially associated with psychiatric, sociodemographic, and medical phenotypes.

CONCLUSIONS

Combining transdiagnostic and disorder-level genetic approaches can improve our understanding of co-occurring conditions and increase the specificity of genetic discovery, which is critical for identifying more effective prevention and treatment strategies to reduce the burden of these disorders.

The role of NUDT3 in lipid accumulation and its functional variants related to backfat thickness in pigs

NUDT3 is a leading candidate gene that strongly linked to pig fatness traits, however, its function in porcine adipocytes remains poorly understood. Here, the percentage of EdU+ cells was significantly reduced when NUDT3 was knocked down, as was the expression of cell cycle repressors. NUDT3 overexpression yielded the opposite outcome. Moreover, the knockdown of NUDT3 resulted in more lipid droplets in adipocytes, whereas its enforced expression had the reverse effect. In addition, exogenous expression of NUDT3 in adipose tissue significantly reduced fat expansion triggered by a high-fat diet in mice. At molecular level, integrative RIP-seq and RNA-seq analysis revealed that genes influenced by NUDT3 overexpression or knockdown were significantly enriched in the PI3K-AKT signaling pathway, and western blot confirmed that AKT phosphorylation was significantly increased by NUDT3 knockdown, while the phosphorylation levels of PI3K, AKT, and mTOR were significantly decreased by the enforced NUDT3 expression both ex vivo and in vivo. Notably, rs694899689 was identified as a potential genetic variant for modulates NUDT3 expression and impacting backfat thickness in pigs through analysis of multi-omics data, CRISPRi (CRISPR interference) and dual luciferase reporter assays. Overall, our work established NUDT3 as a novel negative regulator of adipogenesis and lipid deposition and revealed that rs694899689 might serve as a potential molecular marker for pig breeding.

Bone mineral density and cardiovascular diseases: a two-sample Mendelian randomization study

The link between BMD and cardiovascular disease (CVD) remains a topic of extensive debate in observational studies, with inconsistent reports regarding the causality of this relationship. This study implements robust methodologies to evaluate the causal relationship between BMD and various CVDs. Two sample Mendelian randomization (MR) method was used to estimate the relationship between genetically predicted BMD and seven key CVDs: atrial fibrillation and flutter, angina, ischemic heart disease, heart failure, hypertension, myocardial infarction, and non-ischemic cardiomyopathy. Data were obtained from independent publicly available genome-wide association studies (GWAS) for BMD and CVDs, using two separate datasets for the cardiovascular outcomes: the UK Biobank cohort (primary analysis) and the FinnGen cohort (validation analysis). The MR Pleiotropy RESidual Sum and Outlier test assessed the heterogeneity and pleiotropy of selected instrumental variables (IVs). We applied the inverse variance weighted model (IVW), weighted median, weighted mode method, and MR-Egger regression model to estimate causal effects. MR results indicate no relationship between BMD and atrial fibrillation and flutter (IVW, beta-estimate: 0.011, SE: 0.03, p = .73), angina (IVW, beta-estimate: 0.04, SE: 0.03, p = .17), chronic ischemic heart disease (IVW, beta-estimate: 0.009, SE: 0.03, p = .74), heart failure (IVW, beta-estimate: 0.004, SE: 0.04, p = .91), hypertension (IVW, beta-estimate: -0.01, SE: 0.01, p = .44), myocardial infarction (IVW, beta-estimate: 0.02, SE: 0.03, p = .36), or non-ischemic cardiomyopathy (IVW, beta-estimate: 0.1, SE: 0.08, p = .20). These findings remained consistent across all complementary analyses (MR-Egger, weighted median and weighted mode) and were validated using the FinnGen cohort GWAS dataset. This comprehensive analysis identified no evidence for a causal link between genetically predicted BMD and a range of key CVDs. Previously reported observational associations between bone and cardiovascular health likely represent shared risk factors rather than direct causal mechanisms.

Genome-Wide Assessment of Pleiotropy Across >1000 Traits from Global Biobanks

Large-scale genetic association studies have identified thousands of trait-associated risk loci, establishing the polygenic basis for common complex traits and diseases. Although prior studies suggest that many trait-associated loci are pleiotropic, the extent to which this pleiotropy reflects shared causal variants or confounding by linkage disequilibrium remains poorly characterized. To define a set of candidate loci with potentially pleiotropic associations, we performed genome-wide association study (GWAS) meta-analyses of up to 1,167 clinically relevant traits and diseases across 1,789,365 diverse individuals genetically similar to Admixed American (AMR, NMax = 60,756), African (AFR, NMax = 128,361), East Asian (EAS, NMax = 307,465), European (EUR, NMax = 1,283,907), and South Asian (SAS, NMax = 8,876) reference populations from the VA Million Veteran Program (MVP), UK Biobank (UKB), FinnGen, Biobank Japan (BBJ), Tohoku Medical Megabank (ToMMO), and Korean Genome and Epidemiology Study (KoGES). We identified 27,193 genome-wide significant locus-trait pairs (1MB region with PGWAMA < 5 × 10-8) in within-population analysis and 29,139 in multi-population analysis (PMR-MEGA < 5 × 10-8). Among these, 11.5% (n = 3,149) of locus-trait pairs in population-wise and 6.4% (n = 1,875) in multi-population analyses did not reach genome-wide significance in previously published GWAS. In aggregate, the genome-wide significant loci fell within 2,624 non-overlapping autosomal genomic windows on average ~600kb in size. Each locus contained genome-wide significant signals for a median of 6 traits (IQR 2 to 18), including 2,110 (80%) pleiotropic loci associated with >1 trait. Multi-trait colocalization identified 1,902 (72%) loci with high-confidence (posterior probability > 0.9) evidence of a shared causal variant across two or more traits. Variants in pleiotropic loci were significantly enriched for a broad spectrum of functional annotations compared to non-pleiotropic counterparts. Polygenic scores (PGS) developed from these data generally improved prediction compared to existing PGS and were broadly associated with both on- and off-target phenotypes. These results provide a contemporary map of genetic pleiotropy across the spectrum of human traits/diseases and genetic backgrounds.

metacp: a versatile software package for combining dependent or independent p-values

BACKGROUND

We present metacp an open-source software package which implements an abundance of statistical methods for the combination of both independent p-values, with methods such as Fisher's, Stouffer's and Edgington's, and dependent p-values, with methods such as Brown's method and the Cauchy Combination Test.

RESULTS

The tool is available in Python and STATA, it is very fast, and it is easy to use, requiring only minimal input. It offers a useful resource for combining both independent and dependent p-values, responding to diverse analytical needs for practitioners performing meta-analyses and bioinformaticians developing tools for a variety of applications. Depending on the input data it can be used for gene-based testing, for analysis of multiple traits in GWAS, or for combining diverse multi-omics data such as those of a TWAS, a colocalization or an RNA-seq study.

CONCLUSIONS

Compared to other similar packages (like poolr or metap), metacp implements the largest collection of statistical methods for this problem, offering users the flexibility to choose from a wide variety of approaches. Being available both as a standalone Python tool and as a STATA command, metacp is accessible to a broad and diverse audience, including practitioners conducting meta-analyses across various fields and bioinformaticians developing new tools where p-value combination is a crucial component.

PRED-LD: efficient imputation of GWAS summary statistics

BACKGROUND

Genome-wide association studies have identified connections between genetic variations and diseases, but they only examine a small portion of single nucleotide polymorphisms. To enhance genetic findings, researchers suggest imputing genotypes for unmeasured SNPs to improve coverage and statistical power. When this is not possible, summary statistics imputation can be used as an alternative. The available summary statistics imputation tools rely on reference panels, such as the 1000 Genomes Project, to estimate linkage disequilibrium (LD) between variants for accurate imputation. Tools like FAPI and SSIMP use these reference panels in variant call format (VCF) for this purpose, though this process can be time-consuming. A more effective approach for processing reference panels in summary statistics imputation was proposed in RAISS. In this approach, the LD among the variants is precomputed from the reference panel, prior to imputation, thereby reducing computational time.

RESULTS

We present PRED-LD, an imputation method for GWAS summary statistics that aims to enhance the resolution of genetic association analyses. The proposed method uses precomputed linkage disequilibrium statistics from HapMap, Pheno Scanner and TOP-LD to impute summary statistics, given beta coefficients and standard errors. The single-point approach that we describe provides a fast and accurate way to estimate associations for untyped single nucleotide polymorphisms that exhibit high linkage disequilibrium (LD). The proposed method is faster, provides accurate imputation compared to existing tools, and has been implemented in both a web service ( https://compgen.dib.uth.gr/PRED-LD/ ) and a command-line tool ( https://github.com/pbagos/PRED-LD ), making it a useful resource for the research community.

CONCLUSIONS

PRED-LD offers an efficient and accurate method for GWAS summary statistics imputation, providing faster performance, direct result interpretation, and the ability to use multiple reference panels. Also, the online version of PRED-LD simplifies obtaining LD information and performing imputation tasks without downloading reference panels and will be continuously updated to support tools for meta-analysis and fine-mapping in GWAS.

Pleiotropy increases parallel selection signatures during adaptation from standing genetic variation

The phenomenon of parallel evolution, whereby similar genomic and phenotypic changes occur across replicated pairs of populations or species, is widely studied. Nevertheless, the determining factors of parallel evolution remain poorly understood. Theoretical studies have proposed that pleiotropy, the influence of a single gene on multiple traits, is an important factor. In order to gain a deeper insight into the role of pleiotropy for parallel evolution from standing genetic variation, we characterized the interplay between parallelism, polymorphism, and pleiotropy. The present study examined the parallel gene expression evolution in 10 replicated populations of Drosophila simulans, which were adapted from standing variation to the same new temperature regime. The data demonstrate that the parallel evolution of gene expression from standing genetic variation is positively correlated with the strength of pleiotropic effects. The ancestral variation in gene expression is, however, negatively correlated with parallelism. Given that pleiotropy is also negatively correlated with gene expression variation, we conducted a causal analysis to distinguish cause and correlation and evaluate the role of pleiotropy. The causal analysis indicated that both direct (causative) and indirect (correlational) effects of pleiotropy contribute to parallel evolution. The indirect effect is mediated by historic selective constraint in response to pleiotropy. This results in parallel selection responses due to the reduced standing variation of pleiotropic genes. The direct effect of pleiotropy is likely to reflect a genetic correlation among adaptive traits, which in turn gives rise to synergistic effects and higher parallelism.

Local patterns of genetic sharing between neuropsychiatric and insulin resistance-related conditions

The co-occurrence of insulin resistance (IR)-related metabolic conditions with neuropsychiatric disorders is a major public health challenge. Evidence of the genetic links between these phenotypes is emerging, but little is currently known about the genomic regions and biological functions that are involved. To address this, we performed Local Analysis of [co]Variant Association (LAVA) using large-scale (N = 9,725-933,970) genome-wide association studies (GWASs) results for three IR-related conditions (type 2 diabetes mellitus, obesity, and metabolic syndrome) and nine neuropsychiatric disorders. Subsequently, positional and expression quantitative trait locus (eQTL)-based gene mapping and downstream functional genomic analyses were performed on the significant loci. Patterns of negative and positive local genetic correlations (|rg| = 0.21-1, pFDR < 0.05) were identified at 109 unique genomic regions across all phenotype pairs. Local correlations emerged even in the absence of global genetic correlations between IR-related conditions and Alzheimer's disease, bipolar disorder, and Tourette's syndrome. Genes mapped to the correlated regions showed enrichment in biological pathways integral to immune-inflammatory function, vesicle trafficking, insulin signalling, oxygen transport, and lipid metabolism. Colocalisation analyses further prioritised 10 genetically correlated regions for likely harbouring shared causal variants, displaying high deleterious or regulatory potential. These variants were found within or in close proximity to genes, such as SLC39A8 and HLA-DRB1, that can be targeted by supplements and already known drugs, including omega-3/6 fatty acids, immunomodulatory, antihypertensive, and cholesterol-lowering drugs. Overall, our findings highlight the complex genetic architecture of IR-neuropsychiatric multimorbidity, advocating for an integrated disease model and offering novel insights for research and treatment strategies in this domain.

The genetics of low and high birthweight and their relationship with cardiometabolic disease

AIMS/HYPOTHESIS

Low birthweight infants are at increased risk not only of mortality, but also of type 2 diabetes mellitus and CVD in later life. At the opposite end of the spectrum, high birthweight infants have increased risk of birth complications, such as shoulder dystocia, neonatal hypoglycaemia and obesity, and similarly increased risk of type 2 diabetes mellitus and CVD. However, previous genome-wide association studies (GWAS) of birthweight in the UK Biobank have primarily focused on individuals within the 'normal' range and have excluded individuals with high and low birthweight (<2.5 kg or >4.5 kg). The aim of this study was to investigate genetic variation associated within the tail ends of the birthweight distribution, to: (1) see whether the genetic factors operating in these regions were different from those that explained variation in birthweight within the normal range; (2) explore the genetic correlation between extremes of birthweight and cardiometabolic disease; and (3) investigate whether analysing the full distribution of birthweight values, including the extremes, improved the ability to detect genuine loci in GWAS.

METHODS

We performed case-control GWAS analysis of low (<2.5 kg) and high (>4.5 kg) birthweight in the UK Biobank using REGENIE software (Nlow=20,947; Nhigh=12,715; Ncontrols=207,506) and conducted three continuous GWAS of birthweight, one including the full range of birthweights, one involving a truncated GWAS including only individuals with birthweights between 2.5 and 4.5 kg and a third GWAS that winsorised birthweight values <2.5 kg and >4.5 kg. Additionally, we performed bivariate linkage disequilibrium (LD) score regression to estimate the genetic correlation between low/normal/high birthweight and cardiometabolic traits.

RESULTS

Bivariate LD score regression analyses suggested that high birthweight had a mostly similar genetic aetiology to birthweight within the normal range (genetic correlation coefficient [rG]=0.91, 95% CI 0.83, 0.99), whereas there was more evidence for a separate set of genes underlying low birthweight (rG=-0.74, 95% CI 0.66, 0.82). Low birthweight was also significantly positively genetically correlated with most cardiometabolic traits and diseases we examined, whereas high birthweight was mostly positively genetically correlated with adiposity and anthropometric-related traits. The winsorisation strategy performed best in terms of locus detection, with the number of independent genome-wide significant associations (p<5×10-8) increasing from 120 genetic variants at 94 loci in the truncated GWAS to 270 genetic variants at 178 loci, including 27 variants at 25 loci that had not been identified in previous birthweight GWAS. This included a novel low-frequency missense variant in the ABCC8 gene, a gene known to be involved in congenital hyperinsulinism, neonatal diabetes mellitus and MODY, that was estimated to be responsible for a 170 g increase in birthweight amongst carriers.

CONCLUSIONS/INTERPRETATION

Our results underscore the importance of genetic factors in the genesis of the phenotypic correlation between birthweight and cardiometabolic traits and diseases.

Context-specific eQTLs provide deeper insight into causal genes underlying shared genetic architecture of COVID-19 and idiopathic pulmonary fibrosis

Most genetic variants identified through genome-wide association studies (GWASs) are suspected to be regulatory in nature, but only a small fraction colocalize with expression quantitative trait loci (eQTLs, variants associated with expression of a gene). Therefore, it is hypothesized but largely untested that integration of disease GWAS with context-specific eQTLs will reveal the underlying genes driving disease associations. We used colocalization and transcriptomic analyses to identify shared genetic variants and likely causal genes associated with critically ill COVID-19 and idiopathic pulmonary fibrosis. We first identified five genome-wide significant variants associated with both diseases. Four of the variants did not demonstrate clear colocalization between GWAS and healthy lung eQTL signals. Instead, two of the four variants colocalized only in cell type- and disease-specific eQTL datasets. These analyses pointed to higher ATP11A expression from the C allele of rs12585036, in monocytes and in lung tissue from primarily smokers, which increased risk of idiopathic pulmonary fibrosis (IPF) and decreased risk of critically ill COVID-19. We also found lower DPP9 expression (and higher methylation at a specific CpG) from the G allele of rs12610495, acting in fibroblasts and in IPF lungs, and increased risk of IPF and critically ill COVID-19. We further found differential expression of the identified causal genes in diseased lungs when compared to non-diseased lungs, specifically in epithelial and immune cell types. These findings highlight the power of integrating GWASs, context-specific eQTLs, and transcriptomics of diseased tissue to harness human genetic variation to identify causal genes and where they function during multiple diseases.

Prediction of Skin Color Using Forensic DNA Phenotyping in Asian Populations: A Focus on Thailand

Forensic DNA phenotyping (FDP) has emerged as an essential tool in criminal investigations, enabling the prediction of physical traits based on genetic information. This review explores the genetic factors influencing skin pigmentation, particularly within Asian populations, with a focus on Thailand. Key genes such as Oculocutaneous Albinism II (OCA2), Dopachrome Tautomerase (DCT), KIT Ligand (KITLG), and Solute Carrier Family 24 Member 2 (SLC24A2) are examined for their roles in melanin production and variations that lead to different skin tones. The OCA2 gene is highlighted for its role in transporting ions that help stabilize melanosomes, while specific variants in the DCT gene, including single nucleotide polymorphisms (SNPs) rs2031526 and rs3782974, are discussed for their potential effects on pigmentation in Asian groups. The KITLG gene, crucial for developing melanocytes, includes the SNP rs642742, which is linked to lighter skin in East Asians. Additionally, recent findings on the SLC24A2 gene are presented, emphasizing its connection to pigmentation through calcium regulation in melanin production. Finally, the review addresses the ethical considerations of using FDP in Thailand, where advances in genetic profiling raise concerns about privacy, consent, and discrimination. Establishing clear guidelines is vital to balancing the benefits of forensic DNA applications with the protection of individual rights.

Genetic correlation, pleiotropic loci and shared risk genes between major depressive disorder and gastrointestinal tract disorders

BACKGROUND

Major depressive disorder (MDD) is associated with gastrointestinal tract (GIT) disorders, while genetic correlation, pleiotropic loci and shared risk genes remain to be explored.

METHODS

Leveraging genome-wide association study statistics for MDD (n = 170,756), peptic ulcer disease (PUD; n = 16,666), gastroesophageal reflux disease (GORD; n = 54,854), PUD and/or GORD and/or medications (PGM; n = 90,175), irritable bowel syndrome (IBS; n = 28,518), and inflammatory bowel disease (IBD; n = 7045), we determined global and local genetic correlations, identified pleiotropic loci, performed gene-level evaluations, and inferred causal associations using bidirectional Mendelian randomization.

RESULTS

We found global correlation of MDD with PUD (rg = 0.444, P = 3.135 × 10-24), GORD (rg = 0.459, P = 2.568 × 10-65), PGM (rg = 0.498, P = 6.094 × 10-114), IBS (rg = 0.621, P = 2.483 × 10-63), and IBD (rg = 0.171, P = 1.824 × 10-5). We identified 12 locally correlated regions between MDD and GIT disorders except for IBD, and one shared region (chr11:111985737-113,103,996) for PGM, GORD, and IBS. We found one pleiotropic locus for PUD, 12 for GORD, 30 for PGM, eight for IBS, and seven for IBD, and five shared loci (rs138786869, rs2284189, rs3130063, rs35789010, rs7568369) for GORD and PGM. We respectively observed 14 and 20 overlapping genes for MDD-GORD and MDD-PGM. We showed genetic liabilities to GORD, PGM, and IBS causally increase MDD risk, while all reverse causalities are significant.

CONCLUSIONS

Our work identifies genetic architectures shared between MDD and GIT disorders, contributes genetic insights to understand depression in the context of gut-brain interactions, and provides potential targets to treat gastrointestinal symptoms in depressive patients.

Rethinking GWAS: how lessons from genetic screens and artificial intelligence could reveal biological mechanisms

MOTIVATION

Modern single-cell omics data are key to unraveling the complex mechanisms underlying risk for complex diseases revealed by genome-wide association studies (GWAS). Phenotypic screens in model organisms have several important parallels to GWAS which the author explores in this essay.

RESULTS

The author provides the historical context of such screens, comparing and contrasting similarities to association studies, and how these screens in model organisms can teach us what to look for. Then the author considers how the results of GWAS might be exhaustively interrogated to interpret the biological mechanisms underpinning disease processes. Finally, the author proposes a general framework for tackling this problem computationally, and explore the data, mechanisms, and technology (both existing and yet to be invented) that are necessary to complete the task.

AVAILABILITY AND IMPLEMENTATION

There are no data or code associated with this article.

Transdiagnostic Polygenic Risk Models for Psychopathology and Comorbidity: Cross-Ancestry Analysis in the All of Us Research Program

Psychiatric disorders exhibit substantial genetic overlap, raising questions about the utility of transdiagnostic genetic risk models. Using data from the All of Us Research Program (N=102,091), we evaluated common psychiatric genetic (CPG) factor-based polygenic risk scores (PRSs) compared to standard disorder-specific PRSs. The CPG PRS consistently outperformed disorder-specific scores in predicting individual disorder risk, explaining 1.07 to 24.6 times more phenotypic variance across 11 psychiatric conditions. Meanwhile, many disorder-specific PRSs retained independent but smaller contributions, highlighting the complementary nature of shared and disorder-specific genetic risk. While alternative multi-factor models improved model fit, the CPG PRS provided comparable or superior predictive performance across most disorders, including overall comorbidity burden. Cross-ancestry analyses however revealed notable limitations of European-centric GWAS datasets for other populations due to ancestral differences in genetic architecture. These findings underscore the potential value of transdiagnostic PRSs for psychiatric genetics while highlighting the need for more equitable genetic risk models.

Canine genome-wide association study identifies DENND1B as an obesity gene in dogs and humans

Obesity is a heritable disease, but its genetic basis is incompletely understood. Canine population history facilitates trait mapping. We performed a canine genome-wide association study for body condition score-a measure of obesity-in 241 Labrador retrievers. Using a cross-species approach, we showed that canine obesity genes are also associated with rare and common forms of obesity in humans. The lead canine association was within the gene DENN domain containing 1B (DENND1B). Each copy of the alternate allele was associated with ~7.5% greater body fat. We demonstrate a role for this gene in regulating signaling and trafficking of melanocortin 4 receptor, a critical controller of energy homeostasis. Thus, canine genetics identified obesity genes and mechanisms relevant to both dogs and humans.

CUT&Tag recovers up to half of ENCODE ChIP-seq histone acetylation peaks

DNA-protein interactions have traditionally been profiled via chromatin immunoprecipitation followed by next-generation sequencing (ChIP-seq). Cleavage Under Targets & Tagmentation (CUT&Tag) is a rapidly expanding technique that enables the profiling of such interactions in situ at high sensitivity. However, thorough evaluation and benchmarking against established ChIP-seq datasets are lacking. Here, we comprehensively benchmarked CUT&Tag for H3K27ac and H3K27me3 against published ChIP-seq profiles from ENCODE in K562 cells. Combining multiple new and published CUT&Tag datasets, there was an average recall of 54% known ENCODE peaks for both histone modifications. We tested peak callers MACS2 and SEACR and identified optimal peak calling parameters. Overall, peaks identified by CUT&Tag represent the strongest ENCODE peaks and show the same functional and biological enrichments as ChIP-seq peaks identified by ENCODE. Our workflow systematically evaluates the merits of methodological adjustments, providing a benchmarking framework for the experimental design and analysis of CUT&Tag studies.

Unravelling Polygenic Risk and Environmental Interactions in Adolescent Polysubstance Use: a U.S. Population-Based Observational Study

Background

Polysubstance use (PSU), defined as the use of multiple psychoactive substances, is associated with a heightened risk of subsequent health issues, including substance use disorders. However, the interplay between genetic susceptibility and environmental exposures in PSU initiation during adolescence remains understudied.

Methods

We examined associations of polygenic scores (PGSs) for general addiction risk, environmental factors, and their joint interactions with PSU initiation among 11,868 adolescents (aged 11-15 years) from the Adolescent Brain and Cognitive Development study. PSU status was assessed through interviews and toxicology screenings.

Results

Our sample included 7,898 adolescents (mean age 12.9 [0.6] years; 4,150 [53%] male). Of these, 541 (6.8%) had initiated single substance use (SSU), and 162 (2.1%) reported PSU). PGSs for general addiction risk were significantly associated with PSU (Odds Ratios [OR]=1.62, 95% CI=1.30-2.01) but not with SSU. Key environmental risk factors for PSU included prenatal substance use and peer victimization, whereas protective factors included planned pregnancy and positive family dynamics. Notably, gene-environment interaction analyses revealed that peer victimization (OR=2.4, 95% CI=1.4-4.2), prenatal substance use (OR=2.1, 95% CI=1.2-3.6), and substance availability (OR=2.3, 95% CI=1.3-3.9) substantially increased PSU risk among adolescents with high genetic susceptibility, while having minimal influence at low genetic risk levels (all p < 0.05 after multiple testing correction).

Conclusions

This study provides novel evidence linking polygenic risk to PSU in early adolescence and highlights PSU as a more severe manifestation of substance use liability driven by heightened genetic vulnerability and adverse environmental exposures.

Uncovering the genetic architecture and evolutionary roots of androgenetic alopecia in African men

Androgenetic alopecia is a highly heritable trait. However, much of our understanding about the genetics of male-pattern baldness comes from individuals of European descent. Here, we examined a dataset comprising 2,136 men from Ghana, Nigeria, Senegal, and South Africa that were genotyped using the Men of African Descent and Carcinoma of the Prostate Array. We first tested how genetic predictions of baldness generalize from Europe to Africa and found that polygenic scores from European genome-wide association studies (GWASs) yielded area under the curve statistics that ranged from 0.513 to 0.546, indicating that genetic predictions of baldness generalized poorly from European to African populations. Subsequently, we conducted an African GWAS of androgenetic alopecia, focusing on self-reported baldness patterns at age 45. After correcting for age at recruitment, population structure, and study site, we identified 266 moderately significant associations, 51 of which were independent (p < 10-5, r2 < 0.2). Most baldness associations were autosomal, and the X chromosome does not seem to have a large impact on baldness in African men. Although Neanderthal alleles have previously been associated with skin and hair phenotypes, within the limits of statistical power, we did not find evidence that continental differences in the genetic architecture of baldness are due to Neanderthal introgression. While most loci that are associated with androgenetic alopecia do not have large integrative haplotype scores or fixation index statistics, multiple baldness-associated SNPs near the EDA2R and AR genes have large allele frequency differences between continents. Collectively, our findings illustrate how population genetic differences contribute to the limited portability of polygenic predictions across ancestries.

Update on the genetics of allergic diseases

The field of genetic etiology of allergic diseases has advanced significantly in recent years. Shared risk loci reflect the contribution of genetic factors to the sequential development of allergic conditions across the atopic march, while unique risk loci provide opportunities to understand tissue specific manifestations of allergic disease. Most identified risk variants are noncoding, indicating that they likely influence gene expression through gene regulatory mechanisms. Despite recent advances, challenges persist, particularly regarding the need for increased ancestral diversity in research populations. Further, while polygenic risk scores show promise for identifying individuals at higher genetic risk for allergic diseases, their predictive accuracy varies across different ancestries and can be difficult to translate to an individual's absolute risk of developing a disease. Methodologies, including "nearest gene," 3D chromatin interaction analysis, expression quantitative trait locus analysis, experimental screens, and integrative bioinformatic models, have established connections between genetic variants and their regulatory targets, enhancing our understanding of disease risk and phenotypic variability. In this review, we focus on the state of knowledge of allergic sensitization and 5 allergic diseases: asthma, atopic dermatitis, allergic rhinitis, food allergy, and eosinophilic esophagitis. We summarize recent progress and highlight opportunities for advancing our understanding of their genetic etiology.

Identification and catalog of viral transcriptional regulators in human diseases

Viral genomes encode viral transcriptional regulators (vTRs) that manipulate host gene expression to facilitate replication and evade immune detection. Nevertheless, their role in non-cancerous diseases remains largely underexplored. Here, we unveiled 268 new candidate vTRs from 14 of the 20 viral families we investigated. We mapped vTRs' genome-wide binding profiles and identified their potential human targets, which were enriched in immune-mediated pathways, neurodegenerative disorders, and cancers. Through vTR DNA-binding preference analysis, 283 virus-specific and human-like motifs were identified. Prioritized Epstein-Barr virus (EBV) vTR target genes were associated with multiple sclerosis (MS), rheumatoid arthritis, and systemic lupus erythematosus. The partitioned heritability study among 19 diseases indicated significant enrichment of these diseases in EBV vTR-binding sites, implicating EBV vTRs' roles in immune-mediated disorders. Finally, drug repurposing analysis pinpointed candidate drugs for MS, asthma, and Alzheimer disease. This study enhances our understanding of vTRs in diverse human diseases and identifies potential therapeutic targets for future investigation.

Nonlinear association of TSH with pulmonary ventilation: insights from bidirectional Mendelian randomization and cross-sectional study

BACKGROUND

Thyroid hormones play a crucial role in numerous physiological processes, including pulmonary function. However, the relationship between thyroid function and different patterns of pulmonary ventilation remains unclear.

METHODS

This study employed a bidirectional two-sample Mendelian randomization (MR) approach combined with a cross-sectional study from the National Health and Nutrition Examination Survey (NHANES) to explore the relationship between thyroid function and pulmonary ventilation indicators. We used genomic data from the ThyroidOmics Consortium and the UK Biobank to derive instrumental variables for thyroid and pulmonary functions. Adults from the NHANES 2007-2012 were included to validate the MR findings through weighted generalized linear model (GLM) regression and restricted cubic spline (RCS) analysis.

RESULTS

Genetically predicted thyroid-stimulating hormone (TSH) was associated with pulmonary ventilatory function (forced expiratory volume in 1 s (FEV1): β = 0.0223, 95% confidence interval (CI) 0.0040-0.0406, p-value = 0.0170), particularly with a restrictive ventilatory pattern (forced vital capacity (FVC): β = 0.0237, 95% CI 0.0047-0.0427, p-value = 0.0143). This association was more robust in the low TSH subgroup. Additionally, the NHANES data revealed a nonlinear relationship between both FEV1% predicted and FVC% predicted and TSH, characterized by a positive relationship at lower TSH ranges and a negative relationship at higher TSH ranges.

CONCLUSIONS

Our findings highlight a significant association between TSH levels and a restrictive ventilatory pattern, underscoring the importance of thyroid health in the clinical evaluation of certain pulmonary diseases. These insights may guide more personalized interventions in respiratory medicine.

Intergenerational transmission of polygenic predisposition for neuropsychiatric traits on emotional and behavioural difficulties in childhood

Childhood emotional and behavioural difficulties tend to co-occur and often precede diagnosed neuropsychiatric conditions. Identifying shared and specific risk factors for early-life mental health difficulties is therefore essential for prevention strategies. Here, we examine how parental risk factors shape their offspring's emotional and behavioural symptoms (e.g. feelings of anxiety, and restlessness) using data from 14,959 genotyped family trios from the Norwegian Mother, Father and Child Cohort Study (MoBa). We model maternal reports of emotional and behavioural symptoms, organizing them into general and specific domains. We then investigate the direct (genetically transmitted) and indirect (environmentally mediated) contributions of parental polygenic risk for neuropsychiatric-related traits and whether these are shared across symptoms. We observe evidence consistent with an environmental route to general symptomatology beyond genetic transmission, while also demonstrating domain-specific direct and indirect genetic contributions. These findings improve our understanding of early risk pathways that can be targeted in preventive interventions aiming to interrupt the intergenerational cycle of risk transmission.

Multi-Trait Polygenic Risk Score, Nongenetic Determinants, and Cardiovascular Disease Death: A Cohort Study of 14 086 Japanese Individuals

BACKGROUND

Although utility of composite trait-specific polygenic risk score (multi-trait PRS) has been examined among European ancestries, few studies investigated among East Asians and incorporated modifiable risk factors. We examined the associations of multi-trait PRS for cardiometabolic factors with cardiovascular disease mortality by integrating nongenetic determinants.

METHODS

A total of 14 086 Japanese participants (mean age, 55±9; 55.8% women) of the J-MICC (Japan Multi-Institutional Collaborative Cohort) study were analyzed in this study. We calculated 6 PRSs for cardiometabolic traits (systolic blood pressure, body mass index, triglycerides, low-density lipoprotein cholesterol, estimated glomerular filtration rate, and hemoglobin A1c). Based on these PRSs, we developed multi-trait PRS and considered as a primary exposure. Three nongenetic factors (smoking, alcohol drinking, and educational attainment) from the self-reported questionnaire were also examined.

RESULTS

During a median 12.1-year follow-up period, a total of 472 all-cause and 79 cardiovascular disease mortality cases were documented. Compared with 0% to 90% of multi-trait PRSs, an adjusted hazard ratio (HR) among the top 10% of multi-trait PRSs was 1.32 (95% CI, 1.00-1.73) for all-cause death and 2.63 (95% CI, 1.48-4.67) for cardiovascular disease death. Incorporation of educational attainment with multi-trait PRSs showed null associations in those who went beyond high school (HR, 2.07 [95% CI, 0.44-9.66]) even in the top 10% of multi-trait PRS.

CONCLUSIONS

Our analysis combining both genetic and nongenetic determinants highlighted that lifestyle factors and educational attainment can slightly reduce an individual's composite genetic risk for cardiovascular disease death.

The denitrosylase SCoR2 controls cardioprotective metabolic reprogramming

Acute myocardial infarction (MI) is a leading cause of morbidity and mortality, and therapeutic options remain limited. Endogenously generated nitric oxide (NO) is highly cardioprotective, but protection is not replicated by nitroso-vasodilators (e.g., nitrates, nitroprusside) used in clinical practice, highlighting specificity in NO-based signaling and untapped therapeutic potential. Signaling by NO is mediated largely by S-nitrosylation, entailing specific enzymes that form and degrade S-nitrosothiols in proteins (SNO-proteins), termed nitrosylases and denitrosylases, respectively. SNO-CoA Reductase 2 (SCoR2; product of the Akr1a1 gene) is a recently discovered protein denitrosylase. Genetic variants in SCoR2 have been associated with cardiovascular disease, but its function is unknown. Here we show that mice lacking SCoR2 exhibit robust protection in an animal model of MI. SCoR2 regulates ketolytic energy availability, antioxidant levels and polyol homeostasis via S-nitrosylation of key metabolic effectors. Human cardiomyopathy shows reduced SCoR2 expression and an S-nitrosylation signature of metabolic reprogramming, mirroring SCoR2-/- mice. Deletion of SCoR2 thus coordinately reprograms multiple metabolic pathways-ketone body utilization, glycolysis, pentose phosphate shunt and polyol metabolism-to limit infarct size, establishing SCoR2 as a novel regulator in the injured myocardium and a potential drug target.

Impact statement

Mice lacking the denitrosylase enzyme SCoR2/AKR1A1 demonstrate robust cardioprotection resulting from reprogramming of multiple metabolic pathways, revealing widespread, coordinated metabolic regulation by SCoR2.

Atlas of genetic and phenotypic associations across 42 female reproductive health diagnoses

The genetic background of many female reproductive health diagnoses remains uncharacterized, compromising our understanding of the underlying biology. Here, we map the genetic architecture across 42 female-specific health conditions using data from up to 293,618 women from two large population-based cohorts, the Estonian Biobank and the FinnGen study. Our study illustrates the utility of genetic analyses in understanding women's health better. As specific examples, we describe genetic risk factors for ovarian cysts that elucidate the genetic determinants of folliculogenesis and, by leveraging population-specific variants, uncover new candidate genes for uterine fibroids. We find that most female reproductive health diagnoses have a heritable component, with varying degrees of polygenicity and discoverability. Finally, we identify pleiotropic loci and genes that function in genital tract development (WNT4, PAX8, WT1, SALL1), hormonal regulation (FSHB, GREB1, BMPR1B, SYNE1/ESR1) and folliculogenesis (CHEK2), underlining their integral roles in female reproductive health.

A cis-regulatory element controls expression of histone deacetylase 9 to fine-tune inflammasome-dependent chronic inflammation in atherosclerosis

Common genetic variants in a conserved cis-regulatory element (CRE) at histone deacetylase (HDAC)9 are a major risk factor for cardiovascular disease, including stroke and coronary artery disease. Given the consistency of this association and its proinflammatory properties, we examined the mechanisms whereby HDAC9 regulates vascular inflammation. HDAC9 bound and mediated deacetylation of NLRP3 in the NACHT and LRR domains leading to inflammasome activation and lytic cell death. Targeted deletion of the critical CRE in mice increased Hdac9 expression in myeloid cells to exacerbate inflammasome-dependent chronic inflammation. In human carotid endarterectomy samples, increased HDAC9 expression was associated with atheroprogression and clinical plaque instability. Incorporation of TMP195, a class IIa HDAC inhibitor, into lipoprotein-based nanoparticles to target HDAC9 at the site of myeloid-driven vascular inflammation stabilized atherosclerotic plaques, implying a lower risk of plaque rupture and cardiovascular events. Our findings link HDAC9 to atherogenic inflammation and provide a paradigm for anti-inflammatory therapeutics for atherosclerosis.

Biomarker identification for Alzheimer's disease through integration of comprehensive Mendelian randomization and proteomics data

BACKGROUND

Alzheimer's disease (AD) is the main cause of dementia with few effective therapies. We aimed to identify potential plasma biomarkers or drug targets for AD by investigating the causal association between plasma proteins and AD by integrating comprehensive Mendelian randomization (MR) and multi-omics data.

METHODS

Using two-sample MR, cis protein quantitative trait loci (cis-pQTLs) for 1,916 plasma proteins were used as an exposure to infer their causal effect on AD liability in individuals of European ancestry, with two large-scale AD genome-wide association study (GWAS) datasets as the outcome for discovery and replication. Significant causal relationships were validated by sensitivity analyses, reverse MR analysis, and Bayesian colocalization analysis. Additionally, we investigated the causal associations at the transcriptional level with cis gene expression quantitative trait loci (cis-eQTLs) data across brain tissues and blood in European ancestry populations, as well as causal plasma proteins in African ancestry populations.

RESULTS

In those of European ancestry, the genetically predicted levels of five plasma proteins (BLNK, CD2AP, GRN, PILRA, and PILRB) were causally associated with AD. Among these five proteins, GRN was protective against AD, while the rest were risk factors. Consistent causal effects were found in the brain for cis-eQTLs of GRN, BLNK, and CD2AP, while the same was true for PILRA in the blood. None of the plasma proteins were significantly associated with AD in persons of African ancestry.

CONCLUSIONS

Comprehensive MR analyses with multi-omics data identified five plasma proteins that had causal effects on AD, highlighting potential biomarkers or drug targets for better diagnosis and treatment for AD.

Genomic Characterisation of the Relationship and Causal Links Between Vascular Calcification, Alzheimer's Disease, and Cognitive Traits

Background/Objectives: Observational studies suggest a link between vascular calcification and dementia or cognitive decline, but the evidence is conflicting, and the underlying mechanisms are unclear. Here, we investigate the shared genetic and causal relationships of vascular calcification-coronary artery calcification (CAC) and abdominal aortic calcification (AAC)-with Alzheimer's disease (AD), and five cognitive traits. Methods: We analyse large-scale genome-wide association studies (GWAS) summary statistics, using well-regarded methods, including linkage disequilibrium score regression (LDSC), Mendelian randomisation (MR), pairwise GWAS (GWAS-PW), and gene-based association analysis. Results: Our findings reveal a nominally significant positive genome-wide genetic correlation between CAC and AD, which becomes non-significant after excluding the APOE region. CAC and AAC demonstrate significant negative correlations with cognitive performance and educational attainment. MR found no causal association between CAC or AAC and AD or cognitive traits, except for a bidirectional borderline-significant association between AAC and fluid intelligence scores. Pairwise-GWAS analysis identifies no shared causal SNPs (posterior probability of association [PPA]3 < 0.5). However, we find pleiotropic loci (PPA4 > 0.9), particularly on chromosome 19, with gene association analyses revealing significant genes in shared regions, including APOE, TOMM40, NECTIN2, and APOC1. Moreover, we identify suggestively significant loci (PPA4 > 0.5) on chromosomes 1, 6, 7, 9 and 19, implicating pleiotropic genes, including NAV1, IPO9, PHACTR1, UFL1, FHL5, and FOCAD. Conclusions: Current findings reveal limited genetic correlation and no significant causal associations of CAC and AAC with AD or cognitive traits. However, significant pleiotropic loci, particularly at the APOE region, highlight the complex interplay between vascular calcification and neurodegenerative processes. Given APOE's roles in lipid metabolism, neuroinflammation, and vascular integrity, its involvement may link vascular and neurodegenerative disorders, pointing to potential targets for further investigation.

Genome-wide functional annotation of variants: a systematic review of state-of-the-art tools, techniques and resources

The recent advancement of sequencing technologies marks a significant shift in the character and complexity of the digital genomic data universe, encompassing diverse types of molecular data, screened through manifold technological platforms. As a result, a plethora of fully assembled genomes are generated that span vertically the evolutionary scale. Notwithstanding the tsunami of thriving innovations that accomplish unprecedented, nucleotide-level, structural and functional annotation, an exhaustive, systemic, massive genome-wide functional annotation remains elusive, particularly when the criterion is automation and efficiency in data-agnostic interpretation. The latter is of paramount importance for the elaboration of strategies for sophisticated, data-driven genome-wide annotation, which aim to impart a sustainable and comprehensive systemic approach to addressing whole genome variation. Therefore, it is essential to develop methods and tools that promote systematic functional genomic annotation, with emphasis on mechanistic information exceeding the limits of coding regions, and exploiting the chunks of pertinent information residing in non-coding regions, including promoter and enhancer sequences, non-coding RNAs, DNA methylation sites, transcription factor binding sites, transposable elements and more. This review provides an overview of the current state-of-the-art in genome-wide functional annotation of genetic variation, including existing bioinformatic tools, resources, databases and platforms currently available or reported in the literature. Particular emphasis is placed on the functional annotation of variants that lie outside protein-coding genomic regions (intronic or intergenic), their potential co-localization with regulatory element areas, such as putative non-coding RNA regions, and the assessment of their functional impact on the investigated phenotype. In addition, state-of-the-art tools that leverage data obtained from WGS and GWAS-based analyses are discussed, along with future bioinformatics directions and developments. These future directions emphasize efficient, comprehensive, and largely automated functional annotation of both coding and non-coding genomic variants, as well as their optimal evaluation.

Large-scale cross-trait genetic analysis highlights shared genetic backgrounds of autoimmune diseases

Disorders associated with the immune system burden multiple organs, although the shared biology exists across the diseases. Preceding family-based studies reveal that immune diseases are heritable to varying degrees, providing the basis for immunogenomics. The recent cost reduction in genetic analysis intensively promotes biobank-scale studies and the development of frameworks for statistical genetics. The accumulating multi-layer omics data, including genome-wide association studies (GWAS) and RNA-sequencing at single-cell resolution, enable us to dissect the genetic backgrounds of immune-related disorders. Although autoimmune and allergic diseases are generally categorized into different disease categories, epidemiological studies reveal the high incidence of autoimmune and allergic disease complications, suggesting the shared genetics and biology between the disease categories. Biobank resources and consortia cover multiple immune-related disorders to accumulate phenome-wide associations of genetic variants and enhance researchers to analyze the shared and heterogeneous genetic backgrounds. The emerging post-GWAS and integrative multi-omics analyses provide genetic and biological insights into the multicategorical disease associations.

Shared genetic architecture of type 2 diabetes with muscle mass and function and frailty reveals comorbidity etiology and pleiotropic druggable targets

BACKGROUND

Delineating the shared genetic architecture of type 2 diabetes with muscle mass and function and frailty is essential for unraveling the common etiology and developing holistic therapeutic strategies for these co-existing conditions.

METHODS

In this genome-wide pleiotropic association study, we performed multi-level pairwise trait pleiotropic analyses using genome-wide association study summary statistics from up to 461,026 European ancestry individuals to dissect the shared genetic factors and causal relationships of type 2 diabetes and seven glycemic traits with four muscle mass- and function-related phenotypes and the frailty index.

RESULTS

We first identified 27 pairs with significant genetic correlations through the linkage disequilibrium score regression and high-definition likelihood analysis. Then we determined 79 pleiotropic loci and 109 pleiotropic genes across linkage pairs via the pleiotropic analysis under the composite null hypothesis (PLACO), the colocalization, and the Multi-marker Analysis of GenoMic Annotation (MAGMA) analyses. We subsequently performed transcriptome-wide association study (TWAS) analyses using joint-tissue imputation, refined by gene-based integrative fine-mapping through a conditional TWAS approach, and identified 44 unique causal shared genes across 13 tissues in linkage pairs, including eight druggable genes (ABO, AOC1, FTO, GCKR, MTOR, POLK, PPARG, and APEH), with MTOR and PPARG categorized as clinically actionable. Two-sample Mendelian randomization analysis supported bidirectional causality between diabetes and frailty index and unidirectional causal effects of muscle phenotypes on glycemic profiles.

CONCLUSIONS

Our findings highlight the common genetic underpinnings between type 2 diabetes and muscle loss and frailty and inform drug targets with pleiotropic effects on both of these aging-related challenges.

Genetic insights into cardiac conduction disorders from genome-wide association studies

BACKGROUND

Substantial data support a heritable basis for cardiac conduction disorders (CCDs), but the genetic determinants and molecular mechanisms of these arrhythmias are poorly understood, therefore, we sought to identify genetic loci associated with CCDs.

METHODS

We performed meta-analyses of genome-wide association studies to identify genetic loci for atrioventricular block (AVB), left bundle branch block (LBBB), and right bundle branch block (RBBB) from public data from the UK Biobank and FinnGen consortium. We assessed evidence supporting the potential causal effects of candidate genes by analyzing relations between associated variants and cardiac gene expression, performing transcriptome-wide analyses, and ECG-wide phenome-wide associations for each indexed SNP.

RESULTS

Analysis comprised over 700,000 individuals for each trait. We identified 10, 4 and 0 significant loci for AVB (PLEKHA3, TTN, FNDC3B, SENP2, SCN10A, RRH, PPARGC1A, PKD2L2, NKX2-5 and TBX20), LBBB (PPARGC1A, HAND1, TBX5, and ADAMTS5) and RBBB, respectively. Transcriptome-wide association analysis supported an association between reduced predicted cardiac expression of SCN10A and AVB. Phenome-wide associations identified traits with both cardiovascular and non- cardiovascular traits with indexed SNPs.

CONCLUSIONS

Our analysis highlight gene regions associated with channel function, cardiac development, sarcomere function and energy modulation as important potential effectors of CCDs susceptibility.

Quantitative trait loci mapping of circulating metabolites in cerebrospinal fluid to uncover biological mechanisms involved in brain-related phenotypes

Genomic studies of molecular traits have provided mechanistic insights into complex disease, though these lag behind for brain-related traits due to the inaccessibility of brain tissue. We leveraged cerebrospinal fluid (CSF) to study neurobiological mechanisms in vivo, measuring 5543 CSF metabolites, the largest panel in CSF to date, in 977 individuals of European ancestry. Individuals originated from two separate cohorts including cognitively healthy subjects (n = 490) and a well-characterized memory clinic sample, the Amsterdam Dementia Cohort (ADC, n = 487). We performed metabolite quantitative trait loci (mQTL) mapping on CSF metabolomics and found 126 significant mQTLs, representing 65 unique CSF metabolites across 51 independent loci. To better understand the role of CSF mQTLs in brain-related disorders we integrated our CSF mQTL results with pre-existing summary statistics on brain traits, identifying 34 genetic associations between CSF metabolites and brain traits. Over 90% of significant mQTLs demonstrated colocalized associations with brain-specific gene expression, unveiling potential neurobiological pathways.

Genetic Correlates of Treatment-Resistant Depression

Importance

Treatment-resistant depression (TRD) is a major challenge in mental health, affecting a significant number of patients and leading to considerable burdens. The etiological factors contributing to TRD are complex and not fully understood.

Objective

To investigate the genetic factors associated with TRD using polygenic scores (PGS) across various traits and explore their potential role in the etiology of TRD using large-scale genomic data from the All of Us (AoU) Research Program.

Design, Setting, and Participants

This study was a cohort design with observational data from participants in the AoU Research Program who have both electronic health records and genomic data. Data analysis was performed from March 27 to October 24, 2024.

Exposures

PGS for 61 unique traits from 7 domains.

Main Outcomes and Measures

Logistic regressions to test if PGS was associated with treatment-resistant depression (TRD) compared with treatment-responsive major depressive disorder (trMDD). Cox proportional hazard model was used to determine if the progressions from MDD to TRD were associated with PGS.

Results

A total of 292 663 participants (median [IQR] age, 57 (41-69) years; 175 981 female [60.1%]) from the AoU Research Program were included in this analysis. In the discovery set (124 945 participants), 11 of the selected PGS were found to have stronger associations with TRD than with trMDD, encompassing PGS from domains in education, cognition, personality, sleep, and temperament. Genetic predisposition for insomnia (odds ratio [OR], 1.11; 95% CI, 1.07-1.15) and specific neuroticism (OR, 1.11; 95% CI, 1.07-1.16) traits were associated with increased TRD risk, whereas higher education (OR, 0.88; 95% CI, 0.85-0.91) and intelligence (OR, 0.91; 95% CI, 0.88-0.94) scores were protective. The associations held across different TRD definitions (meta-analytic R2 >83%) and were consistent across 2 other independent sets within AoU (the whole-genome sequencing Diversity dataset, 104 388, and Microarray dataset, 63 330). Among 28 964 individuals followed up over time, 3854 developed TRD within a mean of 944 days (95% CI, 883-992 days). All 11 previously identified and replicated PGS were found to be modulating the conversion rate from MDD to TRD.

Conclusions and Relevance

Results of this cohort study suggest that genetic predisposition related to neuroticism, cognitive function, and sleep patterns had a significant association with the development of TRD. These findings underscore the importance of considering psychosocial factors in managing and treating TRD. Future research should focus on integrating genetic data with clinical outcomes to enhance understanding of pathways leading to treatment resistance.

Correlation Analysis of Pyroptosis-Related Genes CASP1, NLRP3, AIM2, and NLRP1 With Lung Adenocarcinoma

Purpose: This study is aimed at exploring the role of pyroptosis-related genes in the development, immune infiltration, and clinical features of lung adenocarcinoma. Method: Pyroptosis-related genes were searched using online databases, including MSigDB, Gene, and GeneCards. We explored pyroptosis-related gene expression patterns in lung adenocarcinoma using the UALCAN database. Functional enrichment analysis of pyroptosis-related genes in lung adenocarcinoma was performed using the Metascape database. A protein-protein interaction network was constructed using the STRING database, and the outcomes were visualized using Cytoscape. The top five core genes were screened utilizing the MCC algorithm with its cytoHubba plugin. The correlation between immune cell infiltration, diagnosis, and prognosis of core genes in lung adenocarcinoma was explored using the TIMER 2.0, TCGA, and Kaplan-Meier plotter databases. A nomogram was constructed to predict the survival of patients with lung adenocarcinoma using Cox regression analysis, and its clinical value was validated. Samples of paraffin-embedded lung adenocarcinoma tissues were collected and subjected to immunohistochemical tests to verify the expression of core genes in lung adenocarcinoma and adjacent tissues. Results: Overall, 202 genes related to pyroptosis were identified, with 67 upregulated and 60 downregulated in lung adenocarcinomas. The top five core genes-namely, CASP1 (caspase1), PYCARD (PYD and CARD domain-containing protein), NLRP3 (NOD-like receptor protein 3), AIM2 (absent in melanoma 2), and NLRP1 (NOD-like receptor protein 1)-related to lung adenocarcinoma pyroptosis were selected. The correlation analysis of immune cell infiltration showed that CASP1, NLRP3, and AIM2, which showed that pyroptosis was involved in the infiltration of immune cells in the tumor microenvironment and NLRP1 exhibited high diagnostic efficacy, while PYCARD demonstrated poor diagnostic efficacy. High expression of CASP1, NLRP3, and NLRP1 correlated with a better prognosis (p < 0.05), while elevated AIM2 expression was associated with a poor prognosis (p < 0.05). However, PYCARD exhibited no significant correlation with prognosis (p > 0.05). The immunohistochemistry results showed that positive rates of CASP1, NLRP3, AIM2, and NLRP1 were 20%, 15%, 70%, and 10%, respectively, while in adjacent tissues, the positive rates were 60%, 60%, 20%, and70%, indicating high expression of AIM2 and low expression of CASP1, NLRP3, and NLRP1 in lung adenocarcinoma. Conclusion: CASP1, NLRP3, AIM2, and NLRP1 are core pyroptotic genes in lung adenocarcinoma and exhibit a strong correlation with immune cell infiltration, diagnosis, and prognosis of this condition. These genes may be useful in the clinical diagnosis and treatment of patients with lung adenocarcinoma.

Mapping the Genetic Landscape of Psychiatric Disorders With the MiXeR Toolset

Psychiatric disorders have complex genetic architectures with substantial genetic overlap across conditions, which may partially explain their high levels of comorbidity. This presents significant challenges to research. Genome-wide association studies (GWASs) have uncovered hundreds of loci associated with single disorders, but the genetic landscape of psychiatric disorders has remained largely obscure. Moving beyond the conventional infinitesimal model, uni-, bi-, and trivariate MiXeR tools, applied to GWAS summary statistics, has enabled us to more comprehensively describe the genetic architecture of complex disorders and traits and their overlap. Furthermore, the GSA-MiXeR tool improves biological interpretation of GWAS findings to better elucidate causal mechanisms. Here, we outline the methodology that underlies the MiXeR tools together with instructions for their optimal use. We review results from studies that have investigated the genetic architecture of psychiatric disorders and their overlap using the MiXeR toolset. These studies have revealed generally high polygenicity and low discoverability among psychiatric disorders, particularly in contrast to somatic disorders. There is also pervasive genetic overlap across psychiatric disorders and behavioral traits, while their overlap with somatic traits is smaller, consistent with differences in polygenicity. Finally, GSA-MiXeR has quantified the contribution of gene sets to the heritability of psychiatric disorders, prioritizing small, biologically coherent gene sets. Together, these findings have implications for our understanding of the complex relationships between psychiatric disorders and related traits. MiXeR tools have provided new insights into the genetic architecture of psychiatric disorders, generating a better understanding of their underlying biological mechanisms and potential for clinical utility.

A Large-Scale Genome-wide Association Study of Blood Pressure Accounting for Gene-Depressive Symptomatology Interactions in 564,680 Individuals from Diverse Populations

Background

Gene-environment interactions may enhance our understanding of hypertension. Our previous study highlighted the importance of considering psychosocial factors in gene discovery for blood pressure (BP) but was limited in statistical power and population diversity. To address these challenges, we conducted a multi-population genome-wide association study (GWAS) of BP accounting for gene-depressive symptomatology (DEPR) interactions in a larger and more diverse sample.

Results

Our study included 564,680 adults aged 18 years or older from 67 cohorts and 4 population backgrounds (African (5%), Asian (7%), European (85%), and Hispanic (3%)). We discovered seven novel gene-DEPR interaction loci for BP traits. These loci mapped to genes implicated in neurogenesis (TGFA, CASP3), lipid metabolism (ACSL1), neuronal apoptosis (CASP3), and synaptic activity (CNTN6, DBI). We also identified evidence for gene-DEPR interaction at nine known BP loci, further suggesting links between mood disturbance and BP regulation. Of the 16 identified loci, 11 loci were derived from African, Asian, or Hispanic populations. Post-GWAS analyses prioritized 36 genes, including genes involved in synaptic functions (DOCK4, MAGI2) and neuronal signaling (CCK, UGDH, SLC01A2). Integrative druggability analyses identified 11 druggable candidate gene targets, including genes implicated in pathways linked to mood disorders as well as gene products targeted by known antihypertensive drugs.

Conclusions

Our findings emphasize the importance of considering gene-DEPR interactions on BP, particularly in non-European populations. Our prioritized genes and druggable targets highlight biological pathways connecting mood disorders and hypertension and suggest opportunities for BP drug repurposing and risk factor prevention, especially in individuals with DEPR.

Gut Microbiota Metabolites Sensed by Host GPR41/43 Protect Against Hypertension

BACKGROUND

Fermentation of dietary fiber by the gut microbiota leads to the production of metabolites called short-chain fatty acids, which lower blood pressure and exert cardioprotective effects. Short-chain fatty acids activate host signaling responses via the functionally redundant receptors GPR41 (G-protein-coupled receptor 41) and GPR43 (G-protein-coupled receptor 43), which are highly expressed by immune cells. Whether and how these receptors protect against hypertension or mediate the cardioprotective effects of dietary fiber remains unknown.

METHODS

Cardiovascular phenotype was assessed in untreated and Ang II (angiotensin II) treated hypertensive wild-type and GPR41/43 knockout (KO) double knockout male mice fed diets with different levels of fiber content. Some mice received TLR4 (toll-like receptor 4)-antagonist treatment and bone marrow chimeras. SNPs (single-nucleotide polymorphisms) associated with GPR41 and GPR43 expression were assessed in UK Biobank participants.

RESULTS

Untreated GPR41/43KO mice had unaltered blood pressure but had greater cardiac and renal collagen deposition with higher macrophage numbers in the kidney compared with wild-type mice. Ang II-treated GPR41/43KO mice showed higher systolic blood pressure, cardiorenal weights and collagen deposition, and increased gut permeability, which allows the translocation of gastrointestinal bacterial components such as lipopolysaccharides into the circulation. The use of an antagonist to the lipopolysaccharide receptor, TLR4, a potent proinflammatory signaling molecule, restored the cardiovascular phenotype in GPR41/43KO mice. The lack of GPR41/43 expression in the immune compartment was sufficient to lead to a worsened hypertensive phenotype. We also demonstrate that GPR41/43 is, at least partially, responsible for the blood pressure-lowering and cardioprotective effects of a high-fiber diet. Finally, using the UK Biobank, we provide translational evidence that variants associated with lower expression of both GPR41 and GPR43 are more prevalent in participants with hypertension.

CONCLUSIONS

Our findings highlight that lack of short-chain fatty acid-receptor signaling via both GPR41 and GPR43 increases risk of high blood pressure, suggesting treatments that target these receptors could be a novel strategy to prevent or treat hypertension.

Comprehensive mapping of genetic variation at Epromoters reveals pleiotropic association with multiple disease traits

There is growing evidence that a wide range of human diseases and physiological traits are influenced by genetic variation of cis-regulatory elements. We and others have shown that a subset of promoter elements, termed Epromoters, also function as enhancer regulators of distal genes. This opens a paradigm in the study of regulatory variants, as single nucleotide polymorphisms (SNPs) within Epromoters might influence the expression of several (distal) genes at the same time, which could disentangle the identification of disease-associated genes. Here, we built a comprehensive resource of human Epromoters using newly generated and publicly available high-throughput reporter assays. We showed that Epromoters display intrinsic and epigenetic features that distinguish them from typical promoters. By integrating Genome-Wide Association Studies (GWAS), expression Quantitative Trait Loci (eQTLs) and 3D chromatin interactions, we found that regulatory variants at Epromoters are concurrently associated with more disease and physiological traits, as compared with typical promoters. To dissect the regulatory impact of Epromoter variants, we evaluated their impact on regulatory activity by analyzing allelic-specific high-throughput reporter assays and provided reliable examples of pleiotropic Epromoters. In summary, our study represents a comprehensive resource of regulatory variants supporting the pleiotropic role of Epromoters.

Genome data based deep learning identified new genes predicting pharmacological treatment response of attention deficit hyperactivity disorder

Although the efficacy of pharmacy in the treatment of attention deficit/hyperactivity disorder (ADHD) has been well established, the lack of predictors of treatment response poses great challenges for personalized treatment. The current study employed a comprehensive approach, combining genome-wide association analyses (GWAS) and deep learning (DL) methods, to elucidate the genetic underpinnings of pharmacological treatment response in ADHD. Based on genotype data of medication-naïve patients with ADHD who received pharmacological treatments for 12 weeks, the current study performed GWAS using the percentage changes in ADHD-RS score as phenotype. Then, DL models were constructed to predict percentage changes in symptom scores using genetic variants selected based on four different genome-wide P thresholds (E-02, E-03, E-04, E-05) as inputs. The current GWAS results identified two significant loci (rs10880574, P = 2.39E-09; rs2000900, P = 3.31E-09) which implicated two genes, TMEM117 and MYO5B, that were primarily associated with both brain- and gut-related disorders. The convolutional neural network (CNN) model, using variants with genome-wide P values less than E-02 (5516 SNPs), demonstrated the best performance with mean squared error (MSE) equals 0.012 (Accuracy = 0.83; Sensitivity = 0.90; Specificity = 0.75) in the validation dataset, 0.081 in an independent test dataset (Acc = 0.61, Sensitivity = 0.81; Specificity = 0.26). Notably, the variant that contributed most to the CNN model was NKAIN2, an ADHD-related gene, which is also associated with metabolic processes. To conclude, the integration of GWAS and DL methods revealed new genes contribute to ADHD pharmacological treatment responses, and underscored the interplay between neural systems and metabolic processes, potentially providing critical insights into precision treatment. Furthermore, our CNN model exhibited good performance in an independent dataset, encouraged future studies and implied potential clinical applications.

Discovery of Novel Pain Regulators Through Integration of Cross-Species High-Throughput Data

AIMS

Chronic pain is an impeding condition that affects day-to-day life and poses a substantial economic burden, surpassing many other health conditions. This study employs a cross-species integrated approach to uncover novel pain mediators/regulators.

METHODS

We used weighted gene coexpression network analysis to identify pain-enriched gene module. Functional analysis and protein-protein interaction (PPI) network analysis of the module genes were conducted. RNA sequencing compared pain model and control mice. PheWAS was performed to link genes to pain-related GWAS traits. Finally, candidates were prioritized based on node degree, differential expression, GWAS associations, and phenotype correlations.

RESULTS

A gene module significantly over-enriched with the pain reference set was identified (referred to as "pain module"). Analysis revealed 141 pain module genes interacting with 46 pain reference genes in the PPI network, which included 88 differentially expressed genes. PheWAS analysis linked 53 of these genes to pain-related GWAS traits. Expression correlation analysis identified Vdac1, Add2, Syt2, and Syt4 as significantly correlated with pain phenotypes across eight brain regions. NCAM1, VAMP2, SYT2, ADD2, and KCND3 were identified as top pain response/regulator genes.

CONCLUSION

The identified genes and molecular mechanisms may enhance understanding of pain pathways and contribute to better drug target identification.

A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies

Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally scalable analytical pipeline for functionally informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits, and further empowers rare variant association analysis by incorporating multiple functional annotations. We applied MultiSTAAR to jointly analyze three lipid traits in 61,838 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered and replicated new associations with lipid traits missed by single-trait analysis.

Prioritizing effector genes at trait-associated loci using multimodal evidence

Genome-wide association studies (GWAS) yield large numbers of genetic loci associated with traits and diseases. Predicting the effector genes that mediate these locus-trait associations remains challenging. Here we present the FLAMES (fine-mapped locus assessment model of effector genes) framework, which predicts the most likely effector gene in a locus. FLAMES creates machine learning predictions from biological data linking single-nucleotide polymorphisms to genes, and then evaluates these scores together with gene-centric evidence of convergence of the GWAS signal in functional networks. We benchmark FLAMES on gene-locus pairs derived by expert curation, rare variant implication and domain knowledge of molecular traits. We demonstrate that combining single-nucleotide-polymorphism-based and convergence-based modalities outperforms prioritization strategies using a single line of evidence. Applying FLAMES, we resolve the FSHB locus in the GWAS for dizygotic twinning and further leverage this framework to find schizophrenia risk genes that converge with rare coding evidence and are relevant in different stages of life.

Just a SNP away: The future of in vivo massively parallel reporter assay

The human genome is largely noncoding, yet the field is still grasping to understand how noncoding variants impact transcription and contribute to disease etiology. The massively parallel reporter assay (MPRA) has been employed to characterize the function of noncoding variants at unprecedented scales, but its application has been largely limited by the in vitro context. The field will benefit from establishing a systemic platform to study noncoding variant function across multiple tissue types under physiologically relevant conditions. However, to date, MPRA has been applied to only a handful of in vivo conditions. Given the complexity of the central nervous system and its widespread interactions with all other organ systems, our understanding of neuropsychiatric disorder-associated noncoding variants would be greatly advanced by studying their functional impact in the intact brain. In this review, we discuss the importance, technical considerations, and future applications of implementing MPRA in the in vivo space with the focus on neuropsychiatric disorders.

Haplotype rather than single causal variants effects contribute to regulatory gene expression associations in human myeloid cells

Genome-wide association studies typically identify hundreds to thousands of loci, many of which harbor multiple independent peaks, each parsimoniously assumed to be due to the activity of a single causal variant. Fine-mapping of such variants has become a priority and since most associations are located within regulatory regions, it is also assumed that they colocalize with regulatory variants that influence the expression of nearby genes. Here we examine these assumptions by using a moderate throughput expression CROPseq protocol in which Cas9 nuclease is used to induce small insertions and deletions across the credible set of SNPs that may account for expression quantitative trait loci (eQTL) for genes associated with inflammatory bowel disease (IBD). Of the 4,384 SNPs targeted in 88 loci (an average of 50 per locus), 439 were significant and further examined for validation. From these, 98 significantly altered target gene expression in HL-60 myeloid cell line, 74 in induced macrophages from these HL-60 cells, and 78 in induced neutrophils for a total of 201 validated effects (46%), 43 of which were observed in at least two of the cell types. Considering the observed sensitivity and specificity of the controls, we estimate that there are at least 150 true positives per cell type, an average of almost 2.4 for each of the 64 eQTL for which putative causal variants have been fine-mapped. This implies that haplotype effects are likely to explain many of the associations. We also demonstrate that the same approach can be used to investigate the activity of very rare variants in regulatory regions for 89 genes, providing a rapid strategy for establishing clinical relevance of non-coding mutations.

A genome-wide association study identifies genetic variants associated with hip pain in the UK Biobank cohort (N = 221,127)

Hip pain is a common musculoskeletal complaint that leads many people to seek medical attention. We conducted a primary genome-wide association study (GWAS) on the hip pain phenotype within the UK Biobank cohort. Sex-stratified GWAS analysis approach was also performed to explore sex specific variants associated with hip pain. We found seven different loci associated with hip pain at GWAS significance level, with the most significant single nucleotide polymorphism (SNP) being rs77641763 within the EXD3 (p value = 2.20 × 10-13). We utilized summary statistics from the FinnGen cohort and a previous GWAS meta-analysis on hip osteoarthritis as replication cohorts. Four loci (rs509345, rs73581564, rs9597759, rs2018384) were replicated with a p value less than 0.05. Sex-stratified GWAS analyses revealed a unique locus within the CUL1 gene (rs4726995, p = 2.56 × 10-9) in males, and three unique loci in females: rs1651359966 on chromosome 7 (p = 1.15 × 10-8), rs552965738 on chromosome 9 (p = 2.72 × 10-8), and rs1978969 on chromosome 13 (p = 2.87 × 10-9). This study has identified seven genetic loci associated with hip pain. Sex-stratified analysis also revealed sex specific variants associated with hip pain in males and females. This study has provided a foundation for advancing research of hip pain and hip osteoarthritis.

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.

The trait-specific timing of accelerated genomic change in the human lineage

Humans exhibit distinct characteristics compared to our primate and ancient hominin ancestors. To investigate genomic bursts in the evolution of these traits, we use two complementary approaches to examine enrichment among genome-wide association study loci spanning diseases and AI-based image-derived brain, heart, and skeletal tissue phenotypes with genomic regions reflecting four evolutionary divergence points. These regions cover epigenetic differences among humans and rhesus macaques, human accelerated regions (HARs), ancient selective sweeps, and Neanderthal-introgressed alleles. Skeletal traits such as pelvic width and limb proportions showed enrichment in evolutionary annotations that mirror morphological changes in the primate fossil record. Additionally, we observe enrichment of loci associated with the longitudinal fasciculus in human-gained epigenetic elements since macaques, the visual cortex in HARs, and the thalamus proper in Neanderthal-introgressed alleles, implying that associated cognitive functions such as language processing, decision-making, sensory signaling, and motor control are enriched at different evolutionary depths.