Biology subjects

Kim, S. S.

Publications and source records attributed to Kim, S. S..

At least 19 recordsLinked to original sources

bioRxiv2026

Structure-guided design of a CD81-binding mini-protein that blocks hepatitis C virus entry

Despite the success of direct-acting antivirals, preventing hepatitis C virus (HCV) reinfection remains a critical global challenge. To address this, we leveraged deep learning-based de novo protein design to engineer mini-proteins targeting the large extracellular loop (LEL) of the HCV co-receptor CD81. These mini-proteins are predicted to precisely dock into CD81-LEL, occluding the critical binding interface required for the HCV E2 glycoprotein. Through biophysical screening, we identified mini-19, a lead candidate with sub-nanomolar affinity (KD = 0.5 nM) and high thermostability up to 95{degrees}C. Flow cytometry and super-resolution imaging confirmed that mini-19 specifically recognizes the native topology of CD81 on cells and extracellular vesicles. Functionally, mini-19 neutralized HCVcc infection (IC50 = 1.2 nM). Molecular dynamics simulations demonstrated that mini-19 acts as a structural clamp, restricting the fusogenic conformational plasticity of the receptor. By targeting a conserved host factor rather than the rapidly mutating viral envelope, mini-19 provides a high genetic barrier to resistance. Beyond offering a prophylactic strategy against HCV, our findings establish a stable biologic platform for targeting tetraspanin-enriched microdomains and modulating host-pathogen interactions.

bioengineering↗

bioRxiv2026

HiChIA-Rep quantifies the similarity between enrichment-based chromatin interactions datasets

3D genome mapping technologies ChIA-PET, HiChIP, PLAC-seq, HiCAR, and ChIATAC yield pairwise contacts and a one-dimensional signal indicating protein binding or chromatin accessibility. However, a lack of computational tools to quantify the reproducibility of these enrichment-based 3C data prevents rigorous data quality assessment and interpretation. We developed HiChIA-Rep, an algorithm incorporating both 1D and 2D signals to measure similarity via graph signal processing methods. HiChIA-Rep can distinguish biological replicates from non-replicates, cell lines, and protein factors, outperforming tools designed for Hi-C data. With a large amount of multi-ome datasets being generated, HiChIA-Rep will likely be a fundamental tool for the 3D genomics community.

bioinformatics↗

bioRxiv2025

Vector coordinate transformation in the Drosophila navigation system

Animals navigational behaviors rely on internal representations of space, yet how these representations guide behaviors is poorly understood. Here, we determined how neurons in the brain of the fruit fly, Drosophila melanogaster, transform a world-centric (allocentric) reference frame to one that is body-centric (egocentric), which is essential for navigating to a goal. PFL1 neurons in the flys central complex, the navigation center, have two dendrites; one of these receives input from neurons that encode a head direction vector, while the other receives input from neurons in a neuropil called the fan-shaped body (FB). Therefore, we investigated whether PFL1 neurons transform allocentric vectors to an egocentric reference frame. We developed a computational model that shows how PFL1 can transform an allocentric direction vector encoded by FB neurons to an egocentric reference frame by nonlinearly combining it with the head direction input. In tethered flying flies, we simultaneously recorded the head direction input to as well as the output of PFL1, which we used to constrain and fit our model. This allowed us to infer that PFL1 neurons receive a stored direction that persists across behavioral trials for the same fly but appears to be random across flies, fundamental characteristics of a goal. Our results describe how a population of neurons performs a coordinate transformation that can facilitate the pursuit of a goal, a fundamental computation for any neural system containing representations in multiple reference frames.

neuroscience↗

bioRxiv2025

MIA-Jet: Multi-scale Identification Algorithm of Chromatin Jets

The mammalian genome is organized into large-scale chromosome territories, compartments, domains, and at the smallest scale, chromatin loops and stripes. The newest element is a chromatin jet, a diffused line perpendicular to the main diagonal in the Hi-C contact map, which was reported in quiescent mammalian lymphocytes supporting a two-sided symmetric cohesin loop extrusion model. A similar structure is observed in Repli-HiC and related data, where relatively thin and straight chromatin fountains indicate coupling of DNA replication forks. However, the precise biological implications of these jet-like structures are unknown due to the limitations in computational methods. We developed MIA-Jet, a multi-scale ridge detection algorithm that can accurately detect jets of variable lengths, widths, and angles. When tested on Hi-C, Repli-HiC, ChIA-PET, ChIA-Drop, and Micro-C data in mouse, human, roundworm, and zebrafish cells, MIA-Jet outperformed existing methods. In human cells, jets were enriched in cohesin loading sites and early replication initiation zones. Applying MIA-Jet to Hi-C data generated from protein-degraded cells revealed that jets are dependent on cohesin and that depleting CTCF results in longer and less angled jets than wild-type. We envision MIA-Jet to be broadly applicable to any 3D genome mapping data, thereby providing new insights into the functional roles of chromatin jets.

bioinformatics↗

bioRxiv2024

Characterization and analysis of neuronal signaling using microelectrode array combined with rapid and localized cooling device for cryo-neuromodulation

Cryoanesthesia--a purely physical anesthesia treatment that freezes tissue and attenuate nerve activity--can provide fast treatment through freezing and thawing of cryo-machine and is inexpensive compared to other anesthetics. However, cryoanesthesia has not been widely adopted because securing safe and effective conditions requires quantitative measurement and analysis of neuronal signaling during freezing and recovery, for which research tools are limited. A lack of rapid and localized cooling technologies for quantitative cellular level analysis, in particular, hinders research on not only the optimal cryo-modulation of neuronal activities but also its influence to neighboring cells via cellular networks. Here, we introduce a novel cryo-neuromodulation platform, a high-speed precision probe-type cooling device ([~]20{degrees}C/s at cooling) that provides localized cooling combined with a microelectrode array (MEA) system. We explored the temperature conditions for efficient silencing and recovery of neuronal activities without cell damage. We found that electrical activities of neurons were fully recovered within 1 minute of cooling duration with the maximum cooling speed, which was also confirmed with calcium imaging. The impact of silenced neurons on the neighboring neural network was explored using the localized cooling and we perceived that its influence can be transmitted if the neuronal network is well organized. Our new cryo-device provides rapid and reversible control of neural activities, which allows not just quantitative analysis of the network dynamics, but also new applications in clinical settings.

neuroscience↗

bioRxiv2023

Connectomic reconstruction predicts the functional organization of visual inputs to the navigation center of the Drosophila brain

Many animals, including humans, navigate their surroundings by visual input, yet we understand little about how visual information is transformed and integrated by the navigation system. In Drosophila melanogaster, compass neurons in the donut-shaped ellipsoid body of the central complex generate a sense of direction by integrating visual input from ring neurons, a part of the anterior visual pathway (AVP). Here, we densely reconstruct all neurons in the AVP using FlyWire, an AI-assisted tool for analyzing electron-microscopy data. The AVP comprises four neuropils, sequentially linked by three major classes of neurons: MeTu neurons, which connect the medulla in the optic lobe to the small unit of anterior optic tubercle (AOTUsu) in the central brain; TuBu neurons, which connect the anterior optic tubercle to the bulb neuropil; and ring neurons, which connect the bulb to the ellipsoid body. Based on neuronal morphologies, connectivity between different neural classes, and the locations of synapses, we identified non-overlapping channels originating from four types of MeTu neurons, which we further divided into ten subtypes based on the presynaptic connections in medulla and postsynaptic connections in AOTUsu. To gain an objective measure of the natural variation within the pathway, we quantified the differences between anterior visual pathways from both hemispheres and between two electron-microscopy datasets. Furthermore, we infer potential visual features and the visual area from which any given ring neuron receives input by combining the connectivity of the entire AVP, the MeTu neurons dendritic fields, and presynaptic connectivity in the optic lobes. These results provide a strong foundation for understanding how distinct visual features are extracted and transformed across multiple processing stages to provide critical information for computing the flys sense of direction.

neuroscience↗

bioRxiv2023

Neuronal "parts list" and wiring diagram for a visual system

A catalog of neuronal cell types has often been called a "parts list" of the brain, and regarded as a prerequisite for understanding brain function. In the optic lobe of Drosophila, rules of connectivity between cell types have already proven essential for understanding fly vision. Here we analyze the fly connectome to complete the list of cell types intrinsic to the optic lobe, as well as the rules governing their connectivity. We more than double the list of known types. Most new cell types contain between 10 and 100 cells, and integrate information over medium distances in the visual field. Some existing type families (Tm, Li, and LPi) at least double in number of types. We introduce a new Sm interneuron family, which contains more types than any other, and three new families of cross-neuropil types. Self-consistency of cell types is demonstrated through automatic assignment of cells to types by distance in high-dimensional feature space, and further validation is provided by algorithms that select small subsets of discriminative features. Cell types with similar connectivity patterns divide into clusters that are interpretable in terms of motion, object, and color vision. Our work showcases the advantages of connectomic cell typing: complete and unbiased sampling, a rich array of features based on connectivity, and reduction of the connectome to a drastically simpler wiring diagram of cell types, with immediate relevance for brain function and development.

neuroscience↗

bioRxiv2023

Conformation-specific synthetic antibodies discriminate multiple functional states of the ion channel CorA

CorA, the primary magnesium ion channel in prokaryotes and archaea, is a prototypical homopentameric ion channel that undergoes ion-dependent conformational transitions. CorA adopts five-fold symmetric non-conductive states in the presence of high concentrations of Mg2+, and highly asymmetric flexible states in its complete absence. However, the latter were of insufficient resolution to be thoroughly characterized. In order to gain additional insights into the relationship between asymmetry and channel activation, we exploited phage display selection strategies to generate conformation-specific synthetic antibodies (sABs) against CorA in the absence of Mg2+. Two sABs from these selections, C12 and C18, showed different degrees of Mg2+-sensitivity. Through structural, biochemical, and biophysical characterization, we found the sABs are both conformation-specific but probe different features of the channel under open-like conditions. C18 is highly specific to the Mg2+-depleted state of CorA and through negative-stain electron microscopy (ns-EM), we show sAB binding reflects the asymmetric arrangement of CorA protomers in Mg2+-depleted conditions. We used X-ray crystallography to determine a structure at 2.0 [A] resolution of sAB C12 bound to the soluble N-terminal regulatory domain of CorA. The structure shows C12 is a competitive inhibitor of regulatory magnesium binding through its interaction with the divalent cation sensing site. We subsequently exploited this relationship to capture and visualize asymmetric CorA states in different [Mg2+] using ns-EM. We additionally utilized these sABs to provide insights into the energy landscape that governs the ion-dependent conformational transitions of CorA.

biochemistry↗

bioRxiv2023

Functional aberration of cortical neuronal network induced by Aβ42 oligomer

Alzheimers disease (AD) is a multifactorial disorder that affects cognitive functioning, behavior, and neuronal properties. The neuronal dysfunction is primarily responsible for cognitive decline in AD patients, with many causal factors including plaque accumulation of A{beta}42. Neural hyperactivity induced by A{beta}42 deposition cause abnormalities in neural networks, leading to alterations in synaptic activity and interneuron dysfunction. Even though neuroimaging techniques elucidated the underlying mechanism in the neural connectivity, precise understanding in cellular level is still elusive. Previously, a few multielectrode array studies examined the neuronal network modulation in vitro cultures revealing relevance of ion channels and the chemical modulators in the presence of A{beta}42. In this study, we investigated neuronal connectivity and dynamic changes with high density multielectrode array, particularly in relation to network-wide parameter changes over time. By comparing the neuronal network between normal and A{beta}42 treated neuronal cultures, it was possible to discover the direct pathological effect of the A{beta}42 oligomer altering the network characteristics. The application of graph theory and center of activity trajectory analysis assessed the consolidation and disassociation of neural networks under A{beta}42 oligomer exposure over time. This result can enhance our understanding of how neural networks are affected during AD progression.

biophysics↗

bioRxiv2023

Alteration of neural network and hippocampal slice activation through exosomes derived from 5XFAD nasal lavage fluid

Exosomes contain various intracellular biomarkers reflecting the condition of cells, organs, and subjects. Under neurodegenerative conditions, they contrive in detrimental neuronal communications leading to the initiation and propagation of neurodegenerative symptoms. Since the exosomes in olfactory fluid are readily accessible to collect in ample amount noninvasively and highly enriched in neural biomarkers, they can be a primary biomarker if we can verify precise pathophysiological information from them. Here we show that exosomes from nasal lavage fluid (NLF) of the neurodegeneration model animal, 5XFAD mice, induce the pathological network activity in neuronal cultures. We separated intact NLF exosomes from the 5XFAD and wild-type mice via a high-efficacy microfluidic system and applied them to the primary cortical neurons and organotypic hippocampal slice cultures (OHSC), whose neuronal activities were monitored by a high-density microelectrode array system. We found that NLF exosomes from 5XFAD mice increased the firing rate of neuronal spikes with augmentation of neuronal connectivity similar to the effect of pathological amyloid beta oligomer treatment. Furthermore, the current source densities, computed from the local field potentials, were elevated in OHSCs incubated with the exosomes, suggesting a pathological shift in synaptic and membrane currents. Those results demonstrate that NLF exosomes from neurodegeneration model can effectively modify neuronal networks and suggest that this property can serve as a functional biomarker for Alzheimers disease.

neuroscience↗

bioRxiv2022

AlphaFold2 reveals commonalities and novelties in protein structure space for 21 model organisms

Over the last year, there have been substantial improvements in protein structure prediction, particularly in methods like DeepMinds AlphaFold2 (AF2) that exploit deep learning strategies. Here we report a new CATH-Assign protocol which is used to analyse the first tranche of AF2 models predicted for 21 model organisms and discuss insights these models bring on the nature of protein structure space. We analyse good quality models and those with no unusual structural characteristics, i.e., features rarely seen in experimental structures. For the [~]370,000 models that meet these criteria, we observe that 92% can be assigned to evolutionary superfamilies in CATH. The remaining domains cluster into 2,367 putative novel superfamilies. Detailed manual analysis on a subset of 618 of those which had at least one human relative revealed some extremely remote homologies and some further unusual features, but 26 could be confirmed as novel superfamilies and one of these has an alpha-beta propeller architectural arrangement never seen before. By clustering both experimental and predicted AF2 domain structures into distinct global fold groups, we observe that the new AF2 models in CATH increase information on structural diversity by 36%. This expansion in structural diversity will help to reveal associated functional diversity not previously detected. Our novel CATH-Assign protocol scales well and will be able to harness the huge expansion (at least 100 million models) in structural data promised by DeepMind to provide more comprehensive coverage of even the most diverse superfamilies to help rationalise evolutionary changes in their functions.

bioinformatics↗

bioRxiv2021

Quaternary structure independent folding of voltage-gated ion channel pore domain subunits

Every voltage-gated ion channel (VGIC) superfamily member has an ion conducting pore consisting of four pore domain (PD) subunits that are each built from a common plan comprising an antiparallel transmembrane helix pair, a short, obliquely positioned helix (the pore helix), and selectivity filter. The extent to which this structure, the VGIC-PD fold, relies on the extensive quaternary interactions observed in PD assemblies is unclear. Here, we present crystal structures of three bacterial voltage-gated sodium channel (BacNaO_SCPLOWVC_SCPLOW) pores that adopt a surprising set of non-canonical quaternary structures and yet maintain the native tertiary structure of the PD monomer. This context-independent structural robustness demonstrates that the VGIC-PD fold, the fundamental VGIC structural building block, can adopt its native-like tertiary fold independent of native quaternary interactions. In line with this observation, we find that the VGIC-PD fold is not only present throughout the VGIC superfamily and other channel classes but has homologs in diverse transmembrane and soluble proteins. Characterization of the structures of two synthetic Fabs (sFabs) that recognize the VGIC-PD fold shows that such sFabs can bind purified full-length channels and indicates that non-canonical quaternary PD assemblies can occur in the context of complete VGICs. Together, our data demonstrate that the VGIC-PD structure can fold independently of higher-order assembly interactions and suggest that full-length VGIC PDs can access previously unknown non-canonical quaternary states. These PD properties have deep implications for understanding how the complex quaternary architectures of VGIC superfamily members are achieved and point to possible evolutionary origins of this fundamental VGIC structural element.

biophysics↗

bioRxiv2021

DNAJB1-PRKACA in HEK293T cells induces LINC00473 overexpression that depends on PKA signaling

Fibrolamellar carcinoma (FLC) is a primary liver cancer that most commonly arises in adolescents and young adults in a background of normal liver tissue and has an poor prognosis due to lack of effective chemotherapeutic agents. The DNAJB1-PRKACA gene fusion (DP) has been reported in the majority of FLC tumors, however its oncogenic mechanisms remain unclear. Given the paucity of cellular models, in particular FLC tumor cell lines, we hypothesized that engineering the DP fusion gene in HEK293T cells would provide insight into the cellular effects of the fusion gene. We used CRISPR/Cas9 to engineer HEK293T clones expressing DP fusion gene (HEK-DP) and performed transcriptomic, proteomic, and mitochondrial studies to characterize this cellular model. Proteomic analysis of DP interacting partners identified mitochondrial proteins as well as proteins in other subcellular compartments. HEK-DP cells demonstrated significantly elevated mitochondrial fission, which suggests a role for DP in altering mitochondrial dynamics. Transcriptomic analysis of HEK-DP cells revealed a significant increase in LINC00473 expression, similar to what has been observed in primary FLC samples. LINC00473 overexpression was reversible with siRNA targeting of PRKACA as well as pharmacologic targeting of PKA and Hsp40 in HEK-DP cells. Therefore, our model suggests that LINC00473 is a candidate marker for DP activity.

cancer biology↗

bioRxiv2021

Leveraging gene co-regulation to identify gene sets enriched for disease heritability

Identifying gene sets that are associated to disease can provide valuable biological knowledge, but a fundamental challenge of gene set analyses of GWAS data is linking disease-associated SNPs to genes. Transcriptome-wide association studies (TWAS) can be used to detect associations between the genetically predicted expression of a gene and disease risk, thus implicating candidate disease genes. However, causal disease genes at TWAS-associated loci generally remain unknown due to gene co-regulation, which leads to correlations across genes in predicted expression. We developed a new method, gene co-regulation score (GCSC) regression, to identify gene sets that are enriched for disease heritability explained by the predicted expression of causal disease genes in the gene set. GCSC regresses TWAS chi-square statistics on gene co-regulation scores reflecting correlations in predicted gene expression; GCSC determines that a gene set is enriched for disease heritability if genes with high co-regulation to the gene set have higher TWAS chi-square statistics than genes with low co-regulation to the gene set, beyond what is expected based on co-regulation to all genes. We verified via simulations that GCSC is well-calibrated, and well-powered to identify gene sets that are enriched for disease heritability explained by predicted expression. We applied GCSC to gene expression data from GTEx (48 tissues) and GWAS summary statistics for 43 independent diseases and complex traits (average N =344K), analyzing a broad set of biological pathways and specifically expressed gene sets. We identified many enriched gene sets, recapitulating known biology. For Alzheimers disease, we detected evidence of an immune basis, and specifically a role for antigen presentation, in analyses of both biological pathways and specifically expressed gene sets. Our results highlight the advantages of leveraging gene co-regulation within the TWAS framework to identify gene sets associated to disease.

genetics↗

bioRxiv2021

Leveraging single-cell ATAC-seq to identify disease-critical fetal and adult brain cell types

Prioritizing disease-critical cell types by integrating genome-wide association studies (GWAS) with functional data is a fundamental goal. Single-cell chromatin accessibility (scATAC-seq) and gene expression (scRNA-seq) have characterized cell types at high resolution, and early work on integrating GWAS with scRNA-seq has shown promise, but work on integrating GWAS with scATAC-seq has been limited. Here, we identify disease-critical fetal and adult brain cell types by integrating GWAS summary statistics from 28 brain-related diseases and traits (average N =298K) with 3.2 million scATAC-seq and scRNA-seq profiles from 83 cell types. We identified disease-critical fetal (resp. adult) brain cell types for 22 (resp. 23) of 28 traits using scATAC-seq data, and for 8 (resp. 17) of 28 traits using scRNA-seq data. Notable findings using scATAC-seq data included highly significant enrichments of fetal photoreceptor cells for major depressive disorder, fetal ganglion cells for BMI, fetal astrocytes for ADHD, and adult VGLUT2 excitatory neurons for schizophrenia. Our findings improve our understanding of brain-related diseases and traits, and inform future analyses of other diseases/traits.

genetics↗

bioRxiv2020

Sex-biased ZRSR2 mutations in myeloid malignancies impair plasmacytoid dendritic cell activation and apoptosis

Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is an aggressive leukemia of plasmacytoid dendritic cells (pDCs). BPDCN occurs at least three times more frequently in men than women, but the reasons for this sex bias are unknown. Here, studying genomics of primary BPDCN and modeling disease-associated mutations, we link acquired alterations in RNA splicing to abnormal pDC development and inflammatory response through Toll-like receptors. Loss-of-function mutations in ZRSR2, an X chromosome gene encoding a splicing factor, are enriched in BPDCN and nearly all mutations occur in males. ZRSR2 mutation impairs pDC activation and apoptosis after inflammatory stimuli, associated with intron retention and inability to upregulate the transcription factor IRF7. In vivo, BPDCN-associated mutations promote pDC expansion and signatures of decreased activation. These data support a model in which male-biased mutations in hematopoietic progenitors alter pDC function and confer protection from apoptosis, which may impair immunity and predispose to leukemic transformation. STATEMENT OF SIGNIFICANCESex bias in cancer is well recognized but the underlying mechanisms are incompletely defined. We connect X chromosome mutations in ZRSR2 to an extremely male-predominant leukemia. Aberrant RNA splicing induced by ZRSR2 mutation impairs dendritic cell inflammatory signaling, interferon production, and apoptosis, revealing a sex- and lineage-related tumor suppressor pathway.

cancer biology↗

bioRxiv2020

Integrative approaches to improve the informativeness of deep learning models for human complex diseases

Deep learning models have achieved great success in predicting genome-wide regulatory effects from DNA sequence, but recent work has reported that SNP annotations derived from these predictions contribute limited unique information for human complex disease. Here, we explore three integrative approaches to improve the disease informativeness of allelic-effect annotations (predicted difference between reference and variant alleles) constructed using several previously trained deep learning models: DeepSEA, Basenji and DeepBind (and a related machine learning model, deltaSVM). First, we employ gradient boosting to learn optimal combinations of deep learning annotations, using fine-mapped SNPs and matched control SNPs (on held-out chromosomes) for training. Second, we improve the specificity of these annotations by restricting them to SNPs implicated by (proximal and distal) SNP-to-gene (S2G) linking strategies, e.g. prioritizing SNPs involved in gene regulation. Third, we predict gene expression (and derive allelic-effect annotations) from deep learning annotations at SNPs implicated by S2G linking strategies -- generalizing the previously proposed ExPecto approach, which incorporates deep learning annotations based on distance to TSS. We evaluated these approaches using stratified LD score regression, using functional data in blood and focusing on 11 autoimmune diseases and blood-related traits (average N =306K). We determined that the three approaches produced SNP annotations that were uniquely informative for these diseases/traits, despite the fact that linear combinations of the underlying DeepSEA, Basenji, DeepBind and deltaSVM blood annotations were not uniquely informative for these diseases/traits. Our results highlight the benefits of integrating SNP annotations produced by deep learning models with other types of data, including data linking SNPs to genes.

genetics↗

bioRxiv2020

Unique contribution of enhancer-driven and master-regulator genes to autoimmune disease revealed using functionally informed SNP-to-gene linking strategies

Gene regulation is known to play a fundamental role in human disease, but mechanisms of regulation vary greatly across genes. Here, we explore the contributions to disease of two types of genes: genes whose regulation is driven by enhancer regions as opposed to promoter regions (enhancer-related) and genes that regulate other genes in trans (candidate master-regulator). We link these genes to SNPs using a comprehensive set of SNP-to-gene (S2G) strategies and apply stratified LD score regression to the resulting SNP annotations to draw three main conclusions about 11 autoimmune diseases and blood cell traits (average Ncase=13K across 6 autoimmune diseases, average N =443K across 5 blood cell traits). First, several characterizations of enhancer-related genes defined in blood using functional genomics data (e.g. ATAC-seq, RNA-seq, PC-HiC) are conditionally informative for autoimmune disease heritability, after conditioning on a broad set of regulatory annotations from the baseline-LD model. Second, candidate master-regulator genes defined using trans-eQTL in blood are also conditionally informative for autoimmune disease heritability. Third, integrating enhancer-related and candidate master-regulator gene sets with protein-protein interaction (PPI) network information magnified their disease signal. The resulting PPI-enhancer gene score produced >2x stronger conditional signal (maximum standardized SNP annotation effect size ({tau}*) = 2.0 (s.e. 0.3) vs. 0.91 (s.e. 0.21)), and >2x stronger gene-level enrichment for approved autoimmune disease drug targets (5.3x vs. 2.1x), as compared to the recently proposed Enhancer Domain Score (EDS). In each case, using functionally informed S2G strategies to link genes to SNPs that may regulate them produced much stronger disease signals (4.1x-13x larger{tau} * values) than conventional window-based S2G strategies. We conclude that our characterizations of enhancer-related and candidate master-regulator genes identify gene sets that are important for autoimmune disease, and that combining those gene sets with functionally informed S2G strategies enables us to identify SNP annotations in which disease heritability is concentrated.

genetics↗