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PubMed2025

Genetic-epigenetic interactions (meQTLs) in orofacial clefts etiology.

OBJECTIVES: Nonsyndromic orofacial clefts (OFCs) involve complex genetic and environmental factors, with over 60 risk loci accounting for only a minority of estimated heritability and residing in non-coding regions with unclear functional relevance. We hypothesize that some genetic variants alter orofacial cleft risk by modifying DNA methylation (DNAm) at regulatory sequences essential for craniofacial development, acting as methylation quantitative trait loci (meQTLs). METHODS: We analyzed 10 well-established OFC-associated SNPs against genome-wide DNAm profiles in 409 cases and 456 controls, identifying 23 potential meQTLs. We validated findings using 358 cleft-discordant sibling pairs analyzed with quantitative MethyLight assays. Cross-referencing with the mQTL Database assessed temporal patterns across human development. Functional annotation used GeneHancer and craniofacial enhancer databases. RESULTS: Nine meQTLs were successfully replicated, including the highly significant rs987525 (8q24) - cg16561172 (MYC) association (P = 9.610E-6). This association mapped to a mesendoderm-active enhancer upstream of MYC, providing mechanistic explanation for the longstanding 8q24 cleft locus. Additional validated associations involved MAFB-PLCG1, NOG-PPM1E, FOXE1-FRZB, and SPRY2-LGR4 interactions. Independent differential methylation analysis revealed significant differences between discordant siblings at three CpG sites. Cross-referencing confirmed concordance with population-level methylation effects, with childhood representing the critical developmental window for most associations. CONCLUSIONS: This systematic meQTL characterization in OFCs demonstrates that genetic variants influence disease risk through epigenetic mechanisms. The 8q24-MYC regulatory pathway evidence provides crucial mechanistic insight into a major OFC risk locus. These findings bridge genetic associations with functional consequences, address missing heritability challenges, and suggest potential biomarkers and therapeutic targets for OFC prevention and treatment.

Journal Article↗
PubMed2025

Generation of C9orf72 repeat knock-in iPSC lines for modelling ALS and FTD.

Induced pluripotent stem cell (iPSC) models are powerful tools for neurodegenerative disease modelling, as they allow mechanistic studies in a human genetic environment and they can be differentiated into a range of neuronal and non-neuronal cells. However, these models come with inherent challenges due to line-to-line and clonal variability. To combat this issue, the iPSC Neurodegenerative Disease Initiative (iNDI) has generated an iPSC repository using a single clonal reference line, KOLF2.1J, into which disease-causing mutations and revertants are introduced via gene editing. Here we describe the generation and validation of lines carrying the most common causative mutation for amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), a repeat expansion in the C9orf72 gene, for the iNDI collection of neurodegenerative iPSC models. We demonstrate that these C9orf72 knock-in lines differentiate efficiently into neurons and display characteristic C9orf72-associated pathologies, including reduced C9orf72 levels and the presence of dipeptide repeat proteins (DPRs) and RNA foci, which increase in abundance over time in culture. These pathologies are not present in revertant cells lacking the repeat expansion. These repeat expansion and revertant cell lines are now available to academic and for-profit institutions through the JAX iPS cell repository and will help to facilitate and standardise iPSC-based ALS/FTD research.

Journal Article↗
PubMed2025

A single cluster of RNA Polymerase II molecules is stably associated with active genes.

In eukaryotic nuclei, transcription is associated with the clustering of RNA Polymerase II (RNAPII) molecules. The mechanisms underlying cluster formation, their interactions with genes, and their impact on transcriptional activity remain heavily debated. Here we take advantage of the naturally occurring increase in transcriptional activity during Zygotic Genome Activation (ZGA) in Drosophila melanogaster embryos to characterize the functional roles of RNAPII clusters in a developmental context. Using single-molecule tracking and lattice light-sheet microscopy, we find that RNAPII cluster formation depends on transcription initiation, and that cluster lifetimes depend on transcriptional activity when not constrained by interphase duration. We show that single clusters are stably associated with active gene loci during transcription and that cluster intensities are strongly correlated with transcriptional output. Collectively our data and simulations on cluster formation kinetics show that RNAPII clusters reflect local accumulations of transcriptionally engaged polymerases and do not form through higher-order mechanisms such as phase separation.

Journal Article↗
PubMed2025

SpaceBar enables clone tracing in spatial transcriptomic data.

We report a cellular barcoding strategy, SpaceBar, that enables simultaneous clone tracing and spatial transcriptomics profiling. Our approach uses a library of 96 synthetic barcode sequences that can be robustly detected by imaging based spatial transcriptomics (seqFISH), delivered such that each cell is labeled with a combination of barcodes. We used these barcodes to label melanoma cells in a tumor xenograft model and profiled both clone identity and spatial gene expression in situ. We developed a gene scoring metric that quantifies how strongly gene expression is driven by intrinsic cellular cues or extrinsic environmental signals. Our framework distinguishes between clonal dynamics and environmentally-driven transcriptional regulation in complex tissue contexts.

Journal Article↗
PubMed2025

Identification and classification of ion-channels across the tree of life provide functional insights into understudied CALHM channels.

The ion channel (IC) genes encoded in the human genome play fundamental roles in cellular functions and disease and are one of the largest classes of druggable proteins. However, limited knowledge of the diverse molecular and cellular functions carried out by ICs presents a major bottleneck in developing selective chemical probes for modulating their functions in disease states. The wealth of sequence data available on ICs from diverse organisms provides a valuable source of untapped information for illuminating the unique modes of channel regulation and functional specialization. However, the extensive diversification of IC sequences and the lack of a unified resource present a challenge in effectively using existing data for IC research. Here, we perform integrative mining of available sequence, structure, and functional data on 419 human ICs across disparate sources, including extensive literature mining by leveraging advances in large language models to annotate and curate the full complement of the "channelome". We employ a well-established orthology inference approach to identify and extend the IC orthologs across diverse organisms to above 48,000. We show that the depth of conservation and taxonomic representation of IC sequences can further be translated to functional similarities by clustering them into functionally relevant groups, which can be used for downstream functional prediction on understudied members. We demonstrate this by delineating co-conserved patterns characteristic of the understudied family of the Calcium Homeostasis Modulator (CALHM) family of ICs. Through mutational analysis of co-conserved residues altered in human diseases and electrophysiological studies, we show that these evolutionarily-constrained residues play an important role in channel gating functions. Thus, by providing new tools and resources for performing large comparative analyses on ICs, this study addresses the unique needs of the IC community and provides the groundwork for accelerating the functional characterization of dark channels for therapeutic intervention.

CALHM1↗
PubMed2025

Scalable Deep Learning of Histology Images Reveals Genetic and Phenotypic Determinants of Adipocyte Hypertrophy.

BACKGROUND: White adipose tissue dysfunction has emerged as a critical factor in cardiometabolic disease development, yet the cellular microstructure and genetic architecture of adipocyte morphology remain poorly explored. METHODS: We introduce Adipocyte U-Net 2.0, an advanced deep learning method for the semantic segmentation of adipose tissue histology, enabling analysis of over 27 million adipocytes from 2,667 individuals. FINDINGS: Our approach revealed that adipocyte hypertrophy associates with metabolic dysfunction, including increased fasting glucose, glycated hemoglobin, leptin, and triglycerides, with decreased adiponectin and HDL cholesterol levels. Through the largest genome-wide association study of adipocyte size to date (NSubcutaneous = 2,066, NVisceral = 1,878), we identified four genome-wide significant loci: two in sex-combined analysis (rs73184721 in NAALADL2 and rs200047724 in NRXN3) and two female-specific variants (rs140503338 and rs11656704 in ULK2). Notably, these genetic associations showed congruent relationships with cardiometabolic traits, suggesting shared biological mechanisms. INTERPRETATION: Our findings demonstrate the utility of deep learning for adipocyte phenotyping at scale and provide novel insights into the genetic basis of adipocyte morphology and its relationship to metabolic disease.

Journal Article↗
PubMed2025

Mutational scanning of TnpB reveals latent activity for genome editing.

TnpB is a diverse family of RNA-guided endonucleases associated with prokaryotic transposons. Due to their small size and putative evolutionary relationship to CRISPR-Cas12, TnpB enzymes hold significant potential for genome editing. However, most TnpBs lack robust gene editing activity, and unbiased profiling of mutational effects on editing activity has not been explored. Here, we mapped comprehensive sequence-function landscapes of a TnpB ribonucleoprotein and discovered many activating mutations in both the protein and RNA. One- and two-position RNA mutants outperform existing variants, highlighting the utility of systematic RNA scaffold mutagenesis. Leveraging the protein's mutational landscape, we identified enhanced TnpB variants from a combinatorial library of activating mutations. These variants enhanced editing in human cells, N. benthamiana, pepper, and rice, with up to a fifty-fold increase compared to wild-type TnpB. These findings highlight previously unknown elements critical for regulating TnpB endonuclease activity and reveal surprising latent activity accessible through mutation.

Journal Article↗
PubMed2025

The human IG heavy chain constant gene locus is enriched for large structural variants and coding polymorphisms that vary among human populations.

The human immunoglobulin heavy chain constant (IGHC) domain of antibodies (Ab) is responsible for effector functions critical to immunity. This domain is encoded by genes in the IGHC locus, where descriptions of genomic diversity remain incomplete. We utilized long-read sequencing to build an IGHC haplotype/variant catalog from 105 individuals of diverse ancestry. We discovered uncharacterized single nucleotide variants (SNV) and large structural variants (SVs, n=7), representing new genes and alleles enriched for non-synonymous substitutions, highlighting potential functional effects. Of the 221 identified IGHC alleles, 192 were novel. SNV, SV, and gene allele/genotype frequencies revealed population differentiation, including (i) hundreds of SNVs in African and East Asian populations exceeding a fixation index (FST) of 0.3, and (ii) an IGHG4 haplotype carrying coding variants uniquely enriched in Asian populations. Our results illuminate missing signatures of IGHC diversity and establish a new foundation for investigating IGHC germline variation in Ab function and disease.

Journal Article↗
PubMed2025

Tracing the evolution and genomic dynamics of mating-type loci in Cryptococcus pathogens and closely related species.

Sexual reproduction in basidiomycete fungi is governed by MAT loci (P/R and HD), which exhibit remarkable evolutionary plasticity, characterized by expansions, rearrangements, and gene losses often associated with mating system transitions. The sister genera Cryptococcus and Kwoniella provide a powerful framework for studying MAT loci evolution owing to their diverse reproductive strategies and distinct architectures, spanning bipolar and tetrapolar systems with either linked or unlinked MAT loci. Building on recent comparative genomic analyses, we generated additional chromosome-level assemblies, uncovering distinct trajectories shaping MAT loci organization. Contrasting with the small-scale expansions and gene acquisitions observed in Kwoniella, our analyses revealed independent expansions of the P/R locus in tetrapolar Cryptococcus, possibly driven by pheromone gene duplications. Notably, these expansions coincided with a pronounced GC-content reduction best explained by reduced GC-biased gene conversion following recombination suppression, rather than relaxed codon usage selection. Diverse modes of MAT locus linkage were also identified, including three previously unrecognized transitions: one resulting in a pseudobipolar arrangement and two leading to bipolarity. All three transitions involved translocations. In the pseudobipolar configuration, the P/R and HD loci remained on the same chromosome but genetically unlinked, whereas the bipolar transitions additionally featured rearrangements that fused the two loci into a nonrecombining region. Mating assays confirmed a sexual cycle in C. decagattii, demonstrating its ability to undergo mating and sporulation. Progeny analysis in K. mangrovensis revealed substantial ploidy variation and aneuploidy, likely stemming from haploid-diploid mating, yet evidence of recombination and loss of heterozygosity indicates that meiotic exchange occurs despite irregular chromosome segregation. Our findings underscore the importance of continued diversity sampling and provide further evidence for convergent evolution of fused MAT loci in basidiomycetes, offering new insights into the genetic and chromosomal changes driving reproductive transitions.

MAT genes↗
PubMed2025

Synthetic community Hi-C benchmarking provides a baseline for virus-host inferences.

Microbiomes influence diverse ecosystems, and viruses increasingly appear to impose key constraints. While viromics has expanded genomic catalogs, host identification for these viruses remains challenging due to the limitations in scaling cultivation-based approaches and the uncertain reliability and relative low resolution of in silico predictions - particularly for understudied viral taxa. Towards this, Hi-C proximity ligation uses sequenced, cross-linked virus and host genomic fragments to infer virus-host linkages and has now been applied in at least ten studies. However, its accuracy remains unknown. Here we assess Hi-C performance in recovering virus-host interactions using synthetic communities (SynComs) composed of four marine bacterial strains and nine phages with known interactions and then apply optimized bioinformatic protocols to natural soil samples. In SynComs, standard Hi-C sample preparations and analyses showed poor normalized contact score performance (26% specificity, 100% sensitivity, incorrect matches up to class level) that could be dramatically improved by Z-score filtering (Z ≥ 0.5, 99% specificity), though at reduced sensitivity (62% down from 100%). Detection limits were established as reproducibility was poor below minimal phage abundances of 105 PFU/mL. Applying optimized bioinformatic protocols to natural soil samples, we compared virus-host linkages inferred from proximity-ligated Hi-C sequencing with predictions generated by in silico homology-based and machine learning-based bioinformatic approaches. Prior to Z-score thresholding, agreement was relatively high at the phylum to family levels (72%), but not at the genus (43%) or species (15%) levels. Z-score thresholding reduced sensitivity (only 34% of predictions were retained), with only modest improvements in congruence with bioinformatic methods (48% or 18% at genus or species levels, respectively). Regardless, this led to 79 genus-level-congruent virus-host linkages and 293 new ones revealed by Hi-C alone - i.e., providing many new virus-host interactions to explore in already well-studied climate-critical soils. Overall, these findings provide empirical benchmarks and methodological guidelines to improve the accuracy and reliability of Hi-C for virus-host linkage studies in complex microbial communities.

Genomics↗
PubMed2025

Effects of Lamina-Chromatin Attachment on Super Long-Range Chromatin Interactions.

The interactions between chromatin and lamin proteins localized on the nuclear envelope play a crucial role in the three-dimensional (3D) organization of the genome. This study investigates the influence of lamin associated domains (LADs) on genome organization at the chromosome level using 3D polymer models of mouse embryonic fibroblasts (MEFs) and embryonic stem cells (mESCs). By integrating genome-wide LAD maps from DamID assays, we simulated chromatin conformations with and without LAD attachment to the nuclear envelope. Our results show that incorporating LAD-lamin interactions yields a radial chromatin distribution consistent with experimental observations. Moreover, LAD-lamin interactions induce significant super long-range chromatin contacts across distant genomic regions. These findings suggest two distinct mechanisms driving induction of chromatin interactions by LAD-lamin attachment.

3D single cell conformations↗
PubMed2025

A General Framework for Branch Length Estimation in Ancestral Recombination Graphs.

Inference of Ancestral Recombination Graphs (ARGs) is of central interest in the analysis of genomic variation. ARGs can be specified in terms of topologies and coalescence times. The coalescence times are usually estimated using an informative prior derived from coalescent theory, but this may generate biased estimates and can also complicate downstream inferences based on ARGs. Here we introduce, POLEGON, a novel approach for estimating branch lengths for ARGs which uses an uninformative prior. Using extensive simulations, we show that this method provides improved estimates of coalescence times and lead to more accurate inferences of effective population sizes under a wide range of demographic assumptions (population expansion, bottleneck, split, etc). It also improves other downstream inferences including estimates of mutation rates. We apply the method to data from the 1000 Genomes Project to investigate population size histories and differential mutation signatures across populations. We also estimate coalescence times in the HLA region, and show that they exceed 30 million years in multiple segments.

Ancestral Recombination Graph↗
PubMed2025

Mechanisms of enhanced or impaired DNA target selectivity driven by protein dimerization.

Successful DNA transcription demands coordination between proteins that bind DNA while simultaneously binding to one another to form dimers or higher-order complexes. For proteins with numerous DNA targets throughout the genome, measurements that report on their dwell time or occupancy thus represent a convolution over a population interacting with specific DNA, nonspecific DNA, or protein partners on DNA. Dimerization is known to add contacts that can help a single protein to stably bind DNA. However, we show here that dimerization can also impair measured dwell times and occupancy on target sequences because the population redistributes across DNA. We combine mass-action kinetic models of pairwise reversible reactions between proteins and DNA with theory and spatial stochastic simulations to isolate the role of dimerization on observed DNA dwell times, occupancy, and spatial distribution of proteins on DNA. Three key themes emerge: (i) Protein-protein interactions, in addition to protein-DNA interactions, can localize a protein to DNA, and relative binding rates can thus widely tune dwell times. (ii) Dimensional reduction achieved through nonspecific binding and subsequent 1D diffusion controls the order-of-magnitude of enhancements despite nucleosome barriers. (iii) Dimerization enhances selectivity for locally clustered targets and often impairs binding to widely-spaced targets by sequestration. Compared with ChIP-seq data, our model explains how the distribution of the essential GAF protein throughout the genome is highly selective for clustered targets due to protein interactions. This model framework predicts when even weak dimerization can redistribute and stabilize proteins on DNA as a necessary part of transcription.

DNA binding↗
PubMed2025

A model for background selection in non-equilibrium populations.

In many taxa, levels of genetic diversity are observed to vary along their genome. The framework of background selection models this variation in terms of linkage to constrained sites, and recent applications have been able to explain a large portion of the variation in human genomes. However, these studies have also yielded conflicting results, stemming from two key limitations. First, existing models are inaccurate in a critical region of parameter space (), where the local reduction in diversity is sharpest. Second, they assume a constant population size over time. Here, we develop predictions for diversity under background selection based on the Hill-Robertson system of two-locus statistics, which allows for population size changes. We treat the joint effect of multiple selected loci independently, but we show that interference among them is well captured through local rescaling of mutation, recombination and selection in an iterative procedure that converges quickly. We further accommodate existing background selection theory to non-equilibrium demography, bridging the gap between weak and strong selection. Simulations show that our predictions are accurate across the entire range of selection coefficients. We characterize the temporal dynamics of linked selection under population size changes and demonstrate that patterns of diversity can be misinterpreted by other models. Specifically, biases due to the incorrect assumption of equilibrium carry over to downstream inferences of the distribution of fitness effects and deleterious mutation rate. Jointly modeling demography and linked selection therefore improves our understanding of the genomic landscape of diversity, which will help refine inferences of linked selection in humans and other species.

Journal Article↗
PubMed2025

Molecular alterations in TP53, WNT, PI3K, TGF-Beta and RTK/RAS pathways in gastric cancer among ethnically heterogeneous cohorts.

BACKGROUND/OBJECTIVES: Gastric cancer (GC) remains a leading cause of cancer-related mortality worldwide, with significant racial and ethnic disparities in incidence, molecular characteristics, and patient outcomes. However, genomic studies focusing on Hispanic/Latino (H/L) populations remain scarce, limiting our understanding of ethnicity-specific molecular alterations. This study aims to characterize pathway-specific mutations in TP53, WNT, PI3K, TGF-Beta and RTK/RAS signaling pathways in GC and compare mutation frequencies between H/L and Non-Hispanic White (NHW) patients. Additionally, we evaluate the impact of these alterations on overall survival using publicly available datasets. METHODS: We conducted a bioinformatics analysis using publicly available GC datasets to assess mutation frequencies in TP53, WNT, PI3K, TGF-Beta and RTK/RAS pathway genes. A total of 800 patients were included in the analysis, comprising 83 H/L patients and 717 NHW patients. Patients were stratified by ethnicity (H/L vs. NHW) to evaluate differences in mutation prevalence. Chi-squared tests were performed to compare mutation rates between groups, and Kaplan-Meier survival analysis was used to assess overall survival differences based on pathway alterations among both H/L and NHW patients. RESULTS: Significant differences were observed in the TP53 pathway and related genes when comparing GC in H/L patients to NHW patients. TP53 mutations were less prevalent in H/L patients (9.6% vs. 19%, p = 0.03). Borderline significant differences were noted in the WNT pathway when comparing GC in H/L patients to NHW GC patients, with WNT alterations more frequent in H/L GC (8.4% vs. 4%, p = 0.08), and APC mutations significantly higher (3.6% vs. 0.8%, p = 0.05). Although alterations in PI3K, TGF-Beta and RTK/RAS pathways were not statistically significant, borderline significance was observed in genes related to these pathways, including EGFR (p = 0.07), FGFR1 (p = 0.05), FGFR2 (p = 0.05), and PTPN11 (p = 0.05) in the PI3K pathway, and SMAD4 (p = 0.08) in the TGF-Beta pathway. Survival analysis revealed no significant differences among H/L patients. However, NHW patients with TP53 and PI3K pathway alterations exhibited significant differences in overall survival, while those without TGF-Beta pathway alterations also showed a significant survival impact. In contrast, WNT pathway alterations were not associated with significant survival differences. These findings suggest that TP53, PI3K, and TGF-Beta pathway disruptions may have distinct prognostic implications in NHW GC patients. CONCLUSIONS: This study provides one of the first ethnicity-focused analyses of TP53, WNT, PI3K, TGF-Beta and RTK/RAS pathway alterations in GC, revealing significant racial/ethnic differences in pathway dysregulation. The findings suggest that TP53 and WNT alterations may play a critical role in GC among H/L patients, while PI3K and TGF-Beta alterations may have greater prognostic significance in NHW patients. These insights emphasize the need for precision medicine approaches that account for genetic heterogeneity and ethnicity-specific pathway alterations to improve cancer care and outcomes for underrepresented populations.

PI3K pathway↗
PubMed2025

Cdk1 and PP2A constitute a molecular switch controlling orderly degradation of atypical E2Fs.

Dynamic oscillations in the phosphorylation and ubiquitination of key proliferative regulators are defining features of the eukaryotic cell cycle. Resetting the cell cycle at the mitosis-to-G1 transition requires activation of the E3 ubiquitin ligase Anaphase-Promoting Complex/Cyclosome (APC/C), which ensures cell cycle irreversibility by targeting dozens of substrates for degradation, safeguarding genome integrity. However, the overall coupling of substrate phosphorylation with target recognition and degradation by the APC/C remains relatively unexplored. As a paradigm for further defining these rules, we focused on E2F7 and E2F8 - atypical E2F-family proteins that coordinate cell cycle gene expression by restraining the pro-proliferative transcriptional activity of E2F1. Leveraging complementary cell and cell-free systems, we demonstrate that flexible domains in the amino-termini of E2F7 and E2F8 contain APC/C recognition motifs adjacent to critical Thr residues, whose phosphorylation by Cdk1 is rate limiting for degradation. The removal of this phosphorylation by PP2A phosphatase serves as a molecular switch, coupling the degradation of E2F7 and E2F8 to the G1 phase, coinciding with the rise of E2F1. Collectively, these findings highlight a critical role for Cdk1-PP2A signaling in controlling the orderly degradation of APC/C substrates, ensuring precisely timed assembly of the transcriptional infrastructure that coordinates cell cycle commitment and progression.

APC/C substrate↗
PubMed2025

Computational modeling of human genetic variants in mice.

Mouse models represent a powerful platform to study genes and variants associated with human diseases. While genome editing technologies have increased the rate and precision of model development, predicting and installing specific types of mutations in mice that mimic the native human genetic context is complicated. Computational tools can identify and align orthologous wild-type genetic sequences from different species; however, predictive modeling and engineering of equivalent mouse variants that mirror the nucleotide and/or polypeptide change effects of human variants remains challenging. Here, we present H2M (human-to-mouse), a computational pipeline to analyze human genetic variation data to systematically model and predict the functional consequences of equivalent mouse variants. We show that H2M can integrate mouse-to-human and paralog-to-paralog variant mapping analyses with precision genome editing pipelines to devise strategies tailored to model specific variants in mice. We leveraged these analyses to establish a database containing > 3 million human-mouse equivalent mutation pairs, as well as in silico-designed base and prime editing libraries to engineer 4,944 recurrent variant pairs. Using H2M, we also found that predicted pathogenicity and immunogenicity scores were highly correlated between human-mouse variant pairs, suggesting that variants with similar sequence change effects may also exhibit broad interspecies functional conservation. Overall, H2M fills a gap in the field by establishing a robust and versatile computational framework to identify and model homologous variants across species while providing key experimental resources to augment functional genetics and precision medicine applications. The H2M database (including software package and documentation) can be accessed at https://human2mouse.com.

Journal Article↗
PubMed2025

Gene dosage architecture across complex traits.

UNLABELLED: Copy number variants (CNVs) have large effects on complex traits, but they are rare and remain challenging to study. As a result, our understanding of biological functions linking gene dosage to complex traits remains limited, and whether these functions sensitive to gene dosage are similar to those underlying the effects of rare single nucleotide variants (SNVs) and common variants remains unknown. METHODS: We developed FunBurd, a functional burden analysis, to test the association of CNVs aggregated within functional gene sets. We applied this approach in 500,000 individuals from the UK Biobank to associate 43 complex traits with CNVs disrupting 172 gene sets across tissues and cell types. We compared CNV findings with those from common variants and LoF (Loss of Function) SNVs in the same cohort using the same functional gene sets. RESULTS: All 43 traits showed FDR significant associations with CNVs. Brain tissue and neuronal cell-types showed the highest levels of pleiotropy. Most of the functional gene set associations could, in part, be explained by genetic constraint, except for brain related processes. Shared genetic contributions between pairs of traits were concordant across types of variants, but on average 2-fold higher, for rare CNVs and SNVs compared to common variants.Functional enrichment across traits found limited overlap between CNVs and common variants. Moreover, the effects of deletions and duplications were negatively correlated for most traits.In conclusion, we present new methods to separate the contributions of genetic constraint and gene function to the associations of CNVs with complex traits. Overall, the functional convergence between different types of variants -even between deletions and duplications-remains limited.

Journal Article↗