Biology subjects

Capra, J. A.

Publications and source records attributed to Capra, J. A..

At least 19 recordsLinked to original sources

bioRxiv2026

Identifying human-specific transcription factor binding site gains and losses that contribute to human-specific phenotypes

Identifying the genetic changes responsible for human-specific traits remains a central challenge in evolutionary genomics. Although thousands of modern human-specific variants have been identified through comparisons with Neanderthal, Denisovan, and great ape genomes, the phenotypic consequences of most of these changes remain unknown, particularly for non-coding variants. Here, we predicted transcription factor binding site (TFBS) gains and losses for 16,883 human-specific high frequency variants. We integrated these predictions with experimentally annotated cis-regulatory elements (cCREs), target genes, and phenotype annotations to identify regulatory changes associated with human-specific traits. We identified 3,357 human-specific variants within cCREs that modify TFBS motifs and prioritized variants for which both the target gene and the affected transcription factor are independently associated with the same phenotype. This identified 128 candidate human-specific regulatory variants involving 155 genes and 58 skeletal traits known to differ between modern humans and archaic hominins. Beyond skeletal morphology, enrichment analyses suggested regulatory changes also affect soft tissues that are not represented in the fossil record, including the brain, vocal cords, testes, and other reproductive organs. Independent support for the functional relevance of these candidates comes from their significant enrichment in modern human-derived differentially methylated regions and among variants experimentally shown to alter gene expression in massively parallel reporter assays. Furthermore, candidate variants preferentially occur within unusually large regions depleted of archaic introgression, consistent with the hypothesis that some regulatory changes contributed to reduced fitness following admixture with Neanderthals and Denisovans. Together, our results provide a prioritized catalogue of human-specific regulatory variants and reveal candidate molecular mechanisms underlying traits that distinguish modern humans from archaic hominins.

genetics↗

bioRxiv2026

EXCAVATE-HT: A Bioinformatic Pipeline to Identify Targetable Genomic Variants for Allele-Specific Editing

Allele-specific CRISPR/Cas editing is a powerful tool with great potential for treating genetic diseases and for uncovering the effects of allelic diversity. By targeting commonly inherited single nucleotide polymorphisms (SNPs), a small number of gRNAs can treat many more individuals than targeting rare disease mutations. However, current tools for identifying common targetable variants and generating CRISPR guide RNAs (gRNA) have fundamental conceptual and technical limitations. Here, we introduce EXCAVATE-HT (EXtracting Common Allelic VAriants for Targeted Editing in High-Throughput) a bioinformatic tool that mines population variant data to generate CRISPR libraries targeting genomic loci for allele-specific editing. Users define their loci of interest, Cas species, and SNP frequency, then EXCAVATE-HT outputs an annotated list of allele-specific gRNAs. EXCAVATE-HT can also generate libraries of gRNA pairs to enable excision. We illustrate the use of EXCAVATE-HT to design and characterize multiple gRNA libraries for allele-specific targeting of the disease gene, Cone-Rod Homeobox (CRX). EXCAVATE-HT revealed multiple excisions that could treat >30-fold more patients than targeting a single CRX disease mutation.

bioinformatics↗

bioRxiv2025

Fine-Tuning Protein Language Models on Human Spatial Constraint Yields State-of-the-Art Variant Effect Prediction

Protein language models (PLMs) achieve state-of-the-art performance in predicting effects of missense variants, yet they do not explicitly consider variation within the human population. Here, we introduce Human Spatial Constraint (HuSC), a framework for quantifying intraspecies constraint on missense variants that integrates population-scale human genetic variation with 3D protein structures. We then fine-tune PLMs on HuSC scores. HuSC models the expected frequency of missense variation under neutral evolution and compares it to observed variation, accounting for both variation in mutational processes and 3D structural context. HuSC outperforms traditional inter- and intraspecies conservation metrics in predicting pathogenic variants. By focusing on intraspecies variation, HuSC reveals protein sites under human-specific constraint that cannot be captured by interspecies models. Integrating this intraspecies perspective into PLMs by fine-tuning on HuSC scores improves the prediction of variant fitness from deep mutational scans across diverse taxa and functional assay types. The improvement after fine-tuning comes largely from reducing bias toward wild-type sequences in regions that tolerate variation. Together, these results demonstrate that combining intraspecies constraint with cross-species PLMs improves their performance in variant-effect interpretation.

genetics↗

bioRxiv2025

Comparing Neanderthal introgression maps reveals core agreement but substantial heterogeneity

Statistical methods to identify Neanderthal ancestry in modern human genomes rest on varying assumptions and inputs. Nonetheless, most studies of introgression use only a single method to define Neanderthal ancestry. Due to a lack of "ground truth," we have a limited understanding of the accuracy, comparative strengths and weaknesses, and the sensitivity of downstream conclusions for these methods. Here, we performed large-scale comparisons of genome-wide introgression maps from 12 representative Neanderthal introgression detection algorithms. These span methods that consider archaic and human reference genomes not from Africa (ArchaicSeeker2, CRF, DICAL-ADMIX), only archaic genomes (S*, Sprime, HMM, SARGE, ARGWeaver-D), only human reference genomes, including from Africa (IBDmix), or simulated data (ArchIE). Our results highlight a core set of regions predicted by nearly all methods, as well as substantial heterogeneity in commonly used Neanderthal introgression maps. Furthermore, we find that downstream analyses may result in different conclusions depending on the map used. Thus, we recommend careful consideration of map(s) chosen for an analysis and support the use of multiple maps to ensure robustness of conclusions. We make integrated prediction sets available, enabling further understanding of Neanderthal introgressions legacy on modern humans.

evolutionary biology↗

bioRxiv2025

Evaluating the Contribution of Genome 3D Folding to Variation in Human Height Using Machine Learning

Genome-wide association studies (GWAS) have identified thousands of variants associated with complex traits, yet the majority lie in noncoding regions, making it difficult to determine their functional impact. Alterations to the three-dimensional (3D) spatial interactions among gene regulatory elements are increasingly recognized as a mechanism by which genetic variants influence gene expression. However, experimentally evaluating whether variants disrupt 3D-genome structure is not feasible at GWAS scale. To address this, we developed a computational framework that integrates GWAS summary statistics with predictions from the Akita sequence-based deep learning model of 3D chromatin contacts. We applied the framework to 9,917 genomic regions associated with human height, assessing both individual variants and haplotypes for their predicted impact on 3D genome architecture. Only a small fraction of height-associated haplotypes had substantial predicted disruption of 3D folding (17 regions, 0.17%, exceeded a disruption score of 0.1). Considering all common variants in a haplotype together generally produced greater perturbations than individual variants, but several highly divergent regions were driven by single variants. We highlight a variant that disrupts the binding motif at a confirmed CTCF binding site and is predicted to modify 3D genome contacts with the LCOR promoter, suggesting that 3D-genome-mediated disruption of gene regulation underlies the association with height. This work presents a scalable and interpretable strategy for integrating 3D genome modeling with GWAS, enabling investigation of this important regulatory mechanism in the connection of non-coding genetic variation to complex traits.

genetics↗

bioRxiv2025

A catalog of ancient proxies for modern genetic variants

The ability to observe the genomes of past human populations using ancient DNA provides an extraordinary perspective on many fundamental questions in human genetics, including understanding the evolutionary history of variants that underlie human disease and other phenotypes. However, ancient DNA is often damaged and degraded, yielding low-coverage of most nucleotides. Further, many publicly available genotypes for ancient humans are limited to [~]1.23 million specific loci. Thus, variants of interest often fall outside these specific positions, limiting the ability of ancient DNA to shed light on many loci. Here, we address this challenge by quantifying linkage disequilibrium (LD) between modern variants and ancient genotyped variants (AGVs) to generate a catalog enabling rapid identification of proxy variants. We identified 260,732,675 pairs of AGVs and modern variants with a minimum LD threshold hold of R2 [≥] 0.2. Even at R2 [≥] 0.9, [≥] 60% of common variants were linked to an AGV in non-African ancestry groups, as were 34% of common variants in Africans. We evaluated the accuracy of the genotypes inferred from proxy variants in two high-coverage ancient genomes finding that > 90% of genotypes were correctly predicted, even in a 45,000 year old individual. We also find that AGVs are significantly older than expected and that many likely are evolving neutrally. We integrate these results in a database that researchers can easily query to identify ancient proxy variants if their variant of interest is not directly genotyped in ancient humans.

genomics↗

bioRxiv2024

Haplotype editing with CRISPR/Cas9 as a therapeutic approach for dominant-negative missense mutations in NEFL

Inactivation of disease alleles by allele-specific editing is a promising approach to treat dominant-negative genetic disorders, provided the causative gene is haplo-sufficient. We previously edited a dominant NEFL missense mutation with inactivating frameshifts and rescued disease-relevant phenotypes in induced pluripotent stem cell (iPSC)-derived motor neurons. However, a multitude of different NEFL missense mutations cause disease. Here, we addressed this challenge by targeting common single-nucleotide polymorphisms in cis with NEFL disease mutations for gene excision. We validated this haplotype editing approach for two different missense mutations and demonstrated its therapeutic potential in iPSC-motor neurons. Surprisingly, our analysis revealed that gene inversion, a frequent byproduct of excision editing, failed to reliably disrupt mutant allele expression. We deployed alternative strategies and novel molecular assays to increase therapeutic editing outcomes while maintaining specificity for the mutant allele. Finally, population genetics analysis demonstrated the power of haplotype editing to enable therapeutic development for the greatest number of patients. Our data serve as an important case study for many dominant genetic disorders amenable to this approach.

genetics↗

bioRxiv2024

Methylation Clocks Do Not Predict Age or Alzheimer's Disease Risk Across Genetically Admixed Individuals

Epigenetic aging clocks based on DNA methylation patterns across the genome have emerged as a potential biomarker for risk of age-related diseases, like Alzheimers disease (AD), and environmental and social stressors. However, methylation clocks have not been comprehensively validated in genetically diverse individuals. Here we evaluate a set of first-, second-, and third-generation methylation clocks in 621 AD patients and matched controls from African American, Hispanic, and White cohorts. The clocks are less accurate at predicting age in genetically admixed cohorts compared to the White cohort, especially for those with substantial African ancestry. This decreased accuracy holds in >2,500 individuals of European and African ancestry from three additional datasets. The clocks also fail to consistently identify age acceleration in admixed AD cases compared to controls. To explore potential causes for the lack of generalization of the clocks, we intersected clock CpGs with methylation, germline genetic variants, and methylation QTL (meQTL) data from global populations. We find differential methylation between African and European ancestry individuals is common for clock CpGs. Genetic variants rarely disrupt clock CpGs between populations, but a substantial fraction of clock CpGs have meQTL with significantly higher frequencies in African genetic ancestries. Our results demonstrate that methylation clocks often fail to predict age and AD risk when applied across populations and suggest avenues for improving their portability by considering differences in genetic and epigenetic patterns across human populations.

genomics↗

bioRxiv2024

Identifying deleterious noncoding variation through gain and loss of CTCF binding activity

Noncoding single nucleotide variants are the predominant class of genetic variation in whole genome sequencing and are key drivers of phenotypic variation. However, their functional annotation remains challenging. To address this, we develop a hypothesis-driven functional annotation scheme for CTCF binding sites given CTCFs critical roles in gene regulation and extensive profiling in regulatory datasets. We synthesize CTCFs binding patterns at 1,063,879 genomic loci across 214 biological contexts into a summary metric, which we refer to as binding activity. We find that binding activity is significantly enriched for both conserved nucleotides (Pearson R = 0.31, p < 2.2 x 10-16) and sequences that contain high-quality CTCF binding motifs (Pearson R = 0.63, p = 2.9 x 10-12). We then integrate binding activity with high confidence change in precision weight matrix scores. By applying this framework to 1,253,330 SNVs in gnomAD, we explore signatures of selection acting against the disruption of CTCF binding. We find a strong, positive relationship between the mutability adjusted proportion of singletons (MAPS) metric and the loss of CTCF binding at loci with high in vitro activity (Pearson R = 0.67, p = 1.5 x 10-14). To contextualize these findings, we apply MAPS to other functional classes of variation and find that a subset of 198,149 loss of CTCF binding variants are observed as infrequently as missense variants. This work implicates these thousands of rare, noncoding variants that disrupt CTCF binding for further functional studies while providing a blueprint for the interpretable annotation of noncoding variants.

genetics↗

bioRxiv2024

VUStruct: a compute pipeline for high throughput and personalized structural biology

Effective diagnosis and treatment of rare genetic disorders requires the interpretation of a patients genetic variants of unknown significance (VUSs). Today, clinical decision-making is primarily guided by gene-phenotype association databases and DNA-based scoring methods. Our web-accessible variant analysis pipeline, VUStruct, supplements these established approaches by deeply analyzing the downstream molecular impact of variation in context of 3D protein structure. VUStructs growing impact is fueled by the co-proliferation of protein 3D structural models, gene sequencing, compute power, and artificial intelligence. Contextualizing VUSs in protein 3D structural models also illuminates longitudinal genomics studies and biochemical bench research focused on VUS, and we created VUStruct for clinicians and researchers alike. We now introduce VUStruct to the broad scientific community as a mature, web-facing, extensible, High-Performance Computing (HPC) software pipeline. VUStruct maps missense variants onto automatically selected protein structures and launches a broad range of analyses. These include energy-based assessments of protein folding and stability, pathogenicity prediction through spatial clustering analysis, and machine learning (ML) predictors of binding surface disruptions and nearby post-translational modification sites. The pipeline also considers the entire input set of VUS and identifies genes potentially involved in digenic disease. VUStructs utility in clinical rare disease genome interpretation has been demonstrated through its analysis of over 175 Undiagnosed Disease Network (UDN) Patient cases. VUStruct-leveraged hypotheses have often informed clinicians in their consideration of additional patient testing, and we report here details from two cases where VUStruct was key to their solution. We also note successes with academic research collaborators, for whom VUStruct has informed research directions in both computational genomics and wet lab studies.

biochemistry↗

bioRxiv2024

Mapping kinase domain resistance mechanisms for the MET receptor tyrosine kinase via deep mutational scanning

Mutations in the kinase and juxtamembrane domains of the MET Receptor Tyrosine Kinase are responsible for oncogenesis in various cancers and can drive resistance to MET-directed treatments. Determining the most effective inhibitor for each mutational profile is a major challenge for MET-driven cancer treatment in precision medicine. Here, we used a deep mutational scan (DMS) of [~]5,764 MET kinase domain variants to profile the growth of each mutation against a panel of 11 inhibitors that are reported to target the MET kinase domain. We validate previously identified resistance mutations, pinpoint common resistance sites across type I, type II, and type I [1/2] inhibitors, unveil unique resistance and sensitizing mutations for each inhibitor, and verify non-cross-resistant sensitivities for type I and type II inhibitor pairs. We augment a protein language model with biophysical and chemical features to improve the predictive performance for inhibitor-treated datasets. Together, our study demonstrates a pooled experimental pipeline for identifying resistance mutations, provides a reference dictionary for mutations that are sensitized to specific therapies, and offers insights for future drug development.

cancer biology↗

bioRxiv2023

Machine learning reveals the diversity of human 3D chromatin contact patterns

Understanding variation in chromatin contact patterns across human populations is critical for interpreting non-coding variants and their ultimate effects on gene expression and phenotypes. However, experimental determination of chromatin contacts at a population-scale is prohibitively expensive. To overcome this challenge, we develop and validate a machine learning method to quantify the diversity 3D chromatin contacts at 2 kilobase resolution from genome sequence alone. We then apply this approach to thousands of diverse modern humans and the inferred human-archaic hominin ancestral genome. While patterns of 3D contact divergence genome-wide are qualitatively similar to patterns of sequence divergence, we find that 3D divergence in local 1-megabase genomic windows does not follow sequence divergence. In particular, we identify 392 windows with significantly greater 3D divergence than expected from sequence. Moreover, 26% of genomic windows have rare 3D contact variation observed in a small number of individuals. Using in silico mutagenesis we find that most sequence changes to do not result in changes to 3D chromatin contacts. However in windows with substantial 3D divergence, just one or a few variants can lead to divergent 3D chromatin contacts without the individuals carrying those variants having high sequence divergence. In summary, inferring 3D chromatin contact maps across human populations reveals diverse contact patterns. We anticipate that these genetically diverse maps of 3D chromatin contact will provide a reference for future work on the function and evolution of 3D chromatin contact variation across human populations.

genomics↗

bioRxiv2023

Cell-Specific Transposable Element Gene Expression Analysis Identifies Associations with Systemic Lupus Erythematosus Phenotypes

There is an established yet unexplained link between interferon (IFN) and systemic lupus erythematosus (SLE). The expression of sequences derived from transposable elements (TEs) may contribute to production of type I IFNs and generation of autoantibodies. We profiled cell-sorted RNA-seq data (CD4+ T cells, CD14+ monocytes, CD19+ B cells, and NK cells) from PBMCs of 120 SLE patients and quantified TE expression identifying 27,135 TEs. We tested for differential TE expression across 10 SLE phenotypes including autoantibody production and disease activity and discovered 731 differentially expressed (DE) TEs whose effects were mostly cell-specific and phenotype-specific. DE TEs were enriched for specific families and viral genes encoded in TE sequences. Increased expression of DE TEs was associated with genes involved in antiviral activity such as LY6E, ISG15, TRIM22 and pathways such as interferon signaling. These findings suggest that expression of TEs contributes to activation of SLE-related mechanisms in a cell-specific manner, which can impact disease diagnostics and therapeutics.

molecular biology↗

bioRxiv2023

Sequence-based machine learning reveals 3D genome differences between bonobos and chimpanzees

Phenotypic divergence between closely related species, including bonobos and chimpanzees (genus Pan), is largely driven by variation in gene regulation. The 3D structure of the genome mediates gene expression; however, genome folding differences in Pan are not well understood. Here, we apply machine learning to predict genome-wide 3D genome contact maps from DNA sequence for 56 bonobos and chimpanzees, encompassing all five extant lineages. We use a pairwise approach to estimate 3D divergence between individuals from the resulting contact maps in 4,420 1 Mb genomic windows. While most pairs were similar,[~] 17% were predicted to be substantially divergent in genome folding. The most dissimilar maps were largely driven by single individuals with rare variants that pro-duce unique 3D genome folding in a region. We also identified 89 genomic windows where bonobo and chimpanzee contact maps substantially diverged, including several windows harboring genes associated with traits implicated in Pan phenotypic divergence. We used in silico mutagenesis to identify 51 3D-modifying variants in these bonobo-chimpanzee diver-gent windows, finding that 34 or 66.67% induce genome folding changes via CTCF binding motif disruption. Our results reveal 3D genome variation at the population-level and identify genomic regions where changes in 3D folding may contribute to phenotypic differences in our closest living relatives.

genomics↗

bioRxiv2023

Illuminating the Function of the Orphan Transporter, SLC22A10 in Humans and Other Primates

SLC22A10 is classified as an orphan transporter with unknown substrates and function. Here we describe the discovery of the substrate specificity and functional characteristics of SLC22A10. The human SLC22A10 tagged with green fluorescent protein was found to be absent from the plasma membrane, in contrast to the SLC22A10 orthologs found in great apes. Estradiol-17{beta}-glucuronide accumulated in cells expressing great ape SLC22A10 orthologs (over 4-fold, p<0.001). In contrast, human SLC22A10 displayed no uptake function. Sequence alignments revealed two amino acid differences including a proline at position 220 of the human SLC22A10 and a leucine at the same position of great ape orthologs. Site-directed mutagenesis yielding the human SLC22A10-P220L produced a protein with excellent plasma membrane localization and associated uptake function. Neanderthal and Denisovan genomes show human-like sequences at proline 220 position, corroborating that SLC22A10 were rendered nonfunctional during hominin evolution after the divergence from the pan lineage (chimpanzees and bonobos). These findings demonstrate that human SLC22A10 is a unitary pseudogene and was inactivated by a missense mutation that is fixed in humans, whereas orthologs in great apes transport sex steroid conjugates.

pharmacology and toxicology↗

bioRxiv2023

High-throughput functional mapping of variants in an arrhythmia gene, KCNE1, reveals novel biology

BackgroundKCNE1 encodes a 129-residue cardiac potassium channel (IKs) subunit. KCNE1 variants are associated with long QT syndrome and atrial fibrillation. However, most variants have insufficient evidence of clinical consequences and thus limited clinical utility. ResultsHere, we demonstrate the power of variant effect mapping, which couples saturation mutagenesis with high-throughput sequencing, to ascertain the function of thousands of protein coding KCNE1 variants. We comprehensively assayed KCNE1 variant cell surface expression (2,554/2,709 possible single amino acid variants) and function (2,539 variants). We identified 470 loss-of-surface expression and 588 loss-of-function variants. Out of the 588 loss-of-function variants, only 155 had low cell surface expression. The latter half of the protein is dispensable for protein trafficking but essential for channel function. 22 of the 30 KCNE1 residues (73%) highly intolerant of variation were in predicted close contact with binding partners KCNQ1 or calmodulin. Our data were highly concordant with gold standard electrophysiological data ({rho} = -0.65), population and patient cohorts (32/38 concordant variants), and computational metrics ({rho} = -0.55). Our data provide moderate-strength evidence for the ACMG/AMP functional criteria for benign and pathogenic variants. ConclusionsComprehensive variant effect maps of KCNE1 can both provide insight into IKs channel biology and help reclassify variants of uncertain significance.

genomics↗

bioRxiv2023

Comparing chromatin contact maps at scale: methods and insights

Comparing chromatin contact maps is an essential step in quantifying how three-dimensional (3D) genome organization shapes development, evolution, and disease. However, no gold standard exists for comparing contact maps, and even simple methods often disagree. In this study, we propose novel comparison methods and evaluate them alongside existing approaches using genome-wide Hi-C data and 22,500 in silico predicted contact maps. We also quantify the robustness of methods to common sources of biological and technical variation, such as boundary size and noise. We find that simple difference-based methods such as mean squared error are suitable for initial screening, but biologically informed methods are necessary to identify why maps diverge and propose specific functional hypotheses. We provide a reference guide, codebase, and benchmark for rapidly comparing chromatin contact maps at scale to enable biological insights into the 3D organization of the genome.

bioinformatics↗

bioRxiv2023

Human gene regulatory evolution is driven by the divergence of regulatory element function in both cis and trans

Gene regulatory divergence between species can result from cis-acting local changes to regulatory element DNA sequences or global trans-acting changes to the regulatory environment. Understanding how these mechanisms drive regulatory evolution has been limited by challenges in identifying trans-acting changes. We present a comprehensive approach to directly identify cis- and trans-divergent regulatory elements between human and rhesus macaque lymphoblastoid cells using ATAC-STARR-seq. In addition to thousands of cis changes, we discover an unexpected number (~10,000) of trans changes and show that cis and trans elements exhibit distinct patterns of sequence divergence and function. We further identify differentially expressed transcription factors that underlie >50% of trans differences and trace how cis changes can produce cascades of trans changes. Overall, we find that most divergent elements (67%) experienced changes in both cis and trans, revealing a substantial role for trans divergence--alone and together with cis changes--to regulatory differences between species.

genomics↗