Biology subjects

Huang, K.-l.

Publications and source records attributed to Huang, K.-l..

18 recordsLinked to original sources

bioRxiv2026

Multispecific nanobody degraders co-deplete membrane receptors and enable targeted delivery of diverse payloads

Targeting membrane receptors underlies the success of antibody-drug conjugates (ADCs), yet single-receptor formats can be limited by heterogeneous expression, compensatory signaling, and variable internalization. Here we developed Multivalent Interchangeable Nanobody Degradation System (MINDS), a modular nanobody-Fc chassis that co-engages multiple membrane receptors, promotes their lysosomal co-depletion, and enables delivery of diverse intracellular payloads. As a proof of concept, we generated Tritazumab, a trispecific nanobody-Fc targeting three oncogenic receptors EGFR, cMET, and TfR1. Tritazumab incorporates a high-affinity, non-transferrin-competing anti-TfR1 nanobody that drives efficient uptake and lysosomal trafficking, enabling coordinated depletion of all three receptors. Across non-small cell lung cancer models, Tritazumab achieved rapid and sustained multi-receptor surface loss with picomolar degradation potency, reaching near-maximal depletion within approximately 1.5 hours. Conjugation of Tritazumab to MMAE preserved receptor binding and produced substantially greater antiproliferative activity and improved tumor selectivity relative to clinical ADCs in matched cell models, along with potent in vivo tumor growth inhibition and acceptable tolerability in a xenograft model. Extending the platform beyond cytotoxic payloads, a BRD4 molecular glue conjugate improved the selectivity window by > 100-fold and showed marked in vivo efficacy, while an EZH2-targeting PROTAC conjugate achieved an approximately 1,000-fold increase in intracellular degradation potency relative to the free PROTAC. These findings establish MINDS as a modular multispecific degrader-payload platform that integrates receptor co-depletion to enhance anticancer selectivity and efficacy.

cancer biology↗

bioRxiv2026

Agent-Driven Validation of Oncology Therapeutic Targets

Selecting the correct target is critical in drug development, yet systematic replication of published target claims is rarely performed. Here, we introduce a replication-focused AI agent framework to evaluate 31 gene target-disease hypotheses, including context-specific oncology targets from both retracted and non-retracted papers. Each target claim was translated into a zero-shot validation prompt executed by a biomedical research agent in one round, and all agent-driven analyses were validated and scored by domain expert. Compared to retracted targets (2/17 validated, 11.8%), non-retracted targets (9/14 validated, 64.3%) were 17-fold more likely to show context-specific dependency in agent-driven analyses. The replicated targets include WRN in microsatellite stable cancer, PRMT5 in MTAP-deleted cancer, as well as more recent discoveries such as PTGES3, HASPIN, SLC5A3, PKMYT1, FAM126B, and PAPSS1. These results demonstrate that agent-human collaboration can conduct data-driven validation at scale, improve target prioritization, and systematically reduce translational risk for drug development.

genomics↗

bioRxiv2026

A Unified Agent-Enabled Platform for Drug Repurposing across Molecular, Phenotypic, and Clinical Scales

Drug repurposing offers an effective path to new therapies, yet existing computational approaches rely on a single line of evidence and are rarely validated across biological scales. We present LinkD, an integrated framework that unifies diffusion-based affinity prediction, proteome-wide selectivity scoring, phenotypic validation, and population-scale clinical evidence. LinkD-Bind predicts binding across 14,981 drugs and 20,385 human targets, ranking first in 8 of 9 BindingDB, Davis, and KIBA evaluations, with the largest gains under cold-start conditions. LinkD-Select recovers 95.3% of known drug-target pairs by combining selectivity scoring and molecular docking. LinkD-Pheno integrates drug-sensitivity and CRISPR dependency data across 960 cancer cell lines, identifying 34 novel drug-gene pairs and recovering [~]85% of known targets among the top 50 candidates. Across 11.5 million individuals from Mount Sinai and UK Biobank, LinkD-prioritized {beta}-blockers propranolol (HR 0.82) and carvedilol (HR 0.92) reduced 5-year prostate cancer incidence relative to metoprolol, corroborated by ADRB2 docking and LNCaP growth inhibition. LinkD-Agent, which can effectively orchestrate all evidence layers, is served on a publicly available web platform (https://linkd-agent.onrender.com/), enabling a wide range of users to derive new drug repurposing opportunities through natural language queries.

bioinformatics↗

bioRxiv2026

Tissue-Specific Prevalence and Clonal Architecture of BRCA1/2 LOH-Inducing Chromosomal Aneuploidy

Germline pathogenic variants in BRCA1 and BRCA2 confer disproportionately elevated cancer risks in breast and ovarian tissues, yet the basis for this tissue specificity remains incompletely understood. Here, we integrate bulk-tumor aneuploidy analysis across 340,824 cancer cases from three independent cohorts (TCGA, ICGC PCAWG, and FoundationCore) with single-cell whole-genome sequencing from two independent studies to investigate whether tissue-specific patterns of chromosomal deletion contribute to this phenomenon. We find that breast and ovarian cancers are consistently enriched for deletions of chromosome arms 17q and 13q--harboring the BRCA1 and BRCA2 genes, respectively--relative to other solid tumor types, and that mutational timing analysis independently places these deletions among the earliest somatic events in these cancers. Phylogenetic reconstruction of single-cell data reveals that in pre-malignant breast tissue from germline BRCA1/2 carriers, chr17q and chr13q deletions appear as localized subclonal events within small clades against a largely diploid background. In established malignancies, these same deletions are found within dominant clonal lineages accompanied by widespread genomic instability--consistent with clonal sweeps originating from early deletion events. These findings suggest that breast and ovarian cellular environments confer a selective advantage for chr17q and chr13q deletions, providing a mechanism that may contribute to the tissue-specific cancer risk observed in gBRCA1/2 carriers.

cancer biology↗

bioRxiv2026

fastVEP: A Fast, Comprehensive Variant Effect Predictor Written in Rust

Annotating genomic variants with their predicted functional consequences is a required step in genomics research and clinical diagnostics. The Ensembl Variant Effect Predictor (VEP) is the community standard for this task, but its Perl implementation struggles with the variant call sets that modern sequencing studies produce. Here we present fastVEP, a complete reimplementation of the VEP variant annotation engine in Rust, a systems programming language that combines memory safety with C-level performance. fastVEP annotates the complete GIAB HG002 clinical whole-genome sequencing benchmark (4.05 million high-confidence variants) against the full Ensembl GRCh38 gene model (508,530 transcripts) in 86 seconds. Across five organisms and complete gold-standard call sets, including 26 million Mouse Genomes Project variants and 12.9 million Arabidopsis 1001 Genomes variants, throughput holds between 47,000 and 86,000 variants per second. In single-threaded head-to-head runs on the same whole genome, fastVEP is 22-24x faster than Ensembl VEP v115.1, which needs approximately 77 minutes for the equivalent consequence-only run. Accuracy was checked field by field against Ensembl VEP release 115.1: all 23 compared annotation fields matched on 2,340 shared transcript-allele pairs. Beyond core consequence prediction, fastVEP provides a supplementary annotation framework (fastSA) with a native binary format for direct integration with ClinVar, gnomAD, dbSNP, COSMIC, 1000 Genomes, TOPMed, and MitoMap. It also supplies prediction and conservation scores including PhyloP, GERP, REVEL, SpliceAI, PrimateAI, and SIFT/PolyPhen via dbNSFP; structural variant annotation (DEL, DUP, INV, CNV, BND) with SV-specific consequence prediction; gene-level annotation from OMIM and gnomAD gene constraint metrics; a filter_vep-compatible expression filter engine; multi-sample genotype parsing; regulatory region detection; and mitochondrial-specific variant handling. fastVEP supports both GRCh38 and GRCh37, predicts 49 Sequence Ontology consequence terms, writes 47 VEP-compatible CSQ annotation fields, generates HGVS nomenclature, and outputs VCF, tab-delimited, or JSON. It ships as a single 3.3 MB statically linked binary with no external dependencies and includes a built-in web interface for interactive annotation; a hosted server is available at https://fastVEP.org fastVEP is open source under the Apache 2.0 license at https://github.com/Huang-lab/fastVEP

genomics↗

bioRxiv2026

RastQC: High-Performance Sequencing Quality Control Written in Rust

Quality control (QC) of high-throughput sequencing data is a critical first step in genomics analysis pipelines. FastQC has served as the de facto standard for sequencing QC for over a decade, but its Java runtime dependency introduces startup overhead, elevated memory consumption, and deployment complexity. Meanwhile, the growing adoption of long-read sequencing platforms from Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio) has created a pressing demand for QC tools capable of handling both short and long reads. However, existing solutions require separate tools for each data type and an additional aggregation tool, such as MultiQC, to consolidate results across samples. Here we present RastQC, a unified sequencing QC tool written in Rust that combines FastQC-compatible short-read QC, long-read-specific metrics, built-in multi-sample summary, native MultiQC JSON export, and a web-based report viewer in a single 2.1 MB static binary. RastQC implements all 12 standard FastQC modules with matching algorithms, plus 3 long-read modules (Read Length N50, Quality Stratified Length, and Homopolymer Content), achieving 100% module-level concordance with FastQC across 55 out of 55 calls on five model organisms. RastQCs streaming parallel pipeline with adaptive batch sizing delivers 1.8-3.2x speedup on short-read Illumina data and 4.7-6.5x speedup on long-read ONT/PacBio data, while using 8-9x less memory on small files and comparable memory on large files. RastQC is freely available and is available as an AI agent skill at https://github.com/Huang-lab/RastQC under the MIT license.

bioinformatics↗

bioRxiv2025

Widespread Epistasis between Cancer Driver Mutations and Allele-Specific Copy Number Variations

Cancer driver mutations alone are often insufficient to fully explain tumorigenesis. We demonstrate that these mutations cooperate with somatic copy number variations (CNVs) in a tissue-specific pattern of genomic epistasis. Analyzing 93,462 tumors, we identified 54 gene-cancer type pairs with significant co-occurrence of somatic mutations and CNVs. Our new Binoculars algorithm, which resolved phased DNA/RNA reads, revealed frequent preferential amplification in oncogenic mutation alleles, including AKT1 p.E17K, BRAF p.V600E, KRAS p.G12C/D/V, NRAS p.Q61K, and a fraction of gain-of-function TP53 p.R175H. Conversely, deletions selectively targeted the reference alleles, leading to loss of heterozygosity of IDH1 p.R132H and tumor suppressor mutations, including CDKN2A and TP53 truncations. Lung cancer patients carrying co-occurrences of somatic mutation-CNVs in TP53 and KRAS showed poorer survival than those carrying the same gene mutations. These findings reveal epistasis of cancer mutations and CNVs at an allelic resolution, suggesting specific genomic events to enhance patient stratification and therapeutic targeting.

genomics↗

bioRxiv2025

Are Different Populations Fairly Represented in Single-Cell Omic Atlases?

Single-cell Omic atlases are transforming biology and medicine, yet their demographic representativeness has not been systematically evaluated. We analyzed >13,500 samples from the Human Cell Atlas (HCA), Human Tumor Atlas Network (HTAN), and PsychAD Consortium. Benchmarking against global and US general and disease-prevalence data, we found a striking, pervasive European over-representation and under-representation of Asian and Latino individuals. Nearly 70% of HCA samples lacked ancestry annotation, and among annotated samples, Europeans were over-represented sixfold. PsychAD was nearly two-thirds European. HTAN tumors were 69% European, with several cancer types showing sex skews beyond expected incidence. These disparities highlight that current single-cell resources risk embedding inequities into AI foundational models, biomarker discovery, and therapeutic development. We contextualize these findings within the structural, economic, and regulatory, survey the growing ecosystem of diversity-focused initiatives, and provide an actionable, field-specific checklist to help research teams design single-cell studies whose benefits extend equitably across populations.

genomics↗

bioRxiv2024

Master regulators governing protein abundance across ten human cancer types

Protein abundance correlates only moderately with mRNA levels, and are modulated post-transcriptionally by a network of regulators including ribosomes, RNA-binding proteins (RBPs), and the proteasome. Here, we identified Master Protein abundance Regulators (MaPRs) across ten cancer types by devising a new computational pipeline that jointly analyzed transcriptomes and proteomes from 1,305 tumor samples. We identified 232 to 1,394 MaPRs per cancer type, mediating up to 79% of post- transcriptional regulatory networks. MaPRs exhibit high network connectivity, strong genetic dependency in cancer cells, and significant enrichment for RBPs. Combining tumor up-regulation, druggability, and target network analyses identified cancer-specific vulnerabilities. MaPRs predict tumor proteomic subtypes more accurately than other proteins. Finally, significant portions of RBP MaPR-target relationships were validated by experimental evidence from eCLIP binding and knockdown assays. Our findings uncover central MaPRs that govern post-transcriptional networks, highlighting diverse processes underlying human proteome regulation and identifying key regulators in cancer biology.

systems biology↗

bioRxiv2024

Expression-Driven Genetic Dependency Reveals Targets for Precision Medicine

Cancer cells are heterogeneous, each harboring distinct molecular aberrations and are dependent on different genes for their survival and proliferation. While successful targeted therapies have been developed based on driver DNA mutations, many patient tumors lack druggable mutations and have limited treatment options. Here, we hypothesize that new precision oncology targets may be identified through "expression-driven dependency", whereby cancer cells with high expression of a targeted gene are more vulnerable to the knockout of that gene. We introduce a Bayesian approach, BEACON, to identify such targets by jointly analyzing global transcriptomic and proteomic profiles with genetic dependency data of cancer cell lines across 17 tissue lineages. BEACON identifies known druggable genes, e.g., BCL2, ERBB2, EGFR, ESR1, MYC, while revealing new targets confirmed by both mRNA- and protein-expression driven dependency. Notably, the identified genes show an overall 3.8-fold enrichment for approved drug targets and enrich for druggable oncology targets by 7 to 10-fold. We experimentally validate that the depletion of GRHL2, TP63, and PAX5 effectively reduce tumor cell growth and survival in their dependent cells. Overall, we present the catalog of express-driven dependency targets as a resource for identifying novel therapeutic targets in precision oncology.

genomics↗

bioRxiv2024

Deletions Rate-Limit Breast and Ovarian Cancer Initiation

Optimizing prevention and early detection of cancer requires understanding the number, types and timing of driver mutations. To quantify this, we exploited the elevated cancer incidence and mutation rates in germline BRCA1 and BRCA2 (gBRCA1/2) carriers. Using novel statistical models, we identify genomic deletions as the likely rate-limiting mutational processes, with 1-3 deletions required to initiate breast and ovarian tumors. gBRCA1/2-driven hereditary and sporadic tumors undergo convergent evolution to develop a similar set of driver deletions, and deletions explain the elevated cancer risk of gBRCA1/2-carriers. Orthogonal mutation timing analysis identifies deletions of chromosome 17 and 13q as early, recurrent events. Single-cell analyses confirmed deletion rate differences in gBRCA1/2 vs. non-carrier tumors as well as cells engineered to harbor gBRCA1/2. The centrality of deletion-associated chromosomal instability to tumorigenesis shapes interpretation of the somatic evolution of non-malignant tissue and guides strategies for precision prevention and early detection.

cancer biology↗

bioRxiv2023

Mutation Impact on mRNA Versus Protein Expression across Human Cancers

Cancer mutations are often assumed to alter proteins, thus promoting tumorigenesis. However, how mutations affect protein expression has rarely been systematically investigated. We conduct a comprehensive analysis of mutation impacts on mRNA- and protein-level expressions of 953 cancer cases with paired genomics and global proteomic profiling across six cancer types. Protein-level impacts are validated for 47.2% of the somatic expression quantitative trait loci (seQTLs), including mutations from likely "long-tail" driver genes. Devising a statistical pipeline for identifying somatic protein-specific QTLs (spsQTLs), we reveal several gene mutations, including NF1 and MAP2K4 truncations and TP53 missenses showing disproportional influence on protein abundance not readily explained by transcriptomics. Cross-validating with data from massively parallel assays of variant effects (MAVE), TP53 missenses associated with high tumor TP53 proteins were experimentally confirmed as functional. Our study demonstrates the importance of considering protein-level expression to validate mutation impacts and identify functional genes and mutations.

genomics↗

bioRxiv2023

Tumor-intrinsic LKB1-LIF signaling axis establishes a myeloid niche to promote immune evasion and tumor growth

Tumor mutations can influence the surrounding microenvironment leading to suppression of anti-tumor immune responses and thereby contributing to tumor progression and failure of cancer therapies. Here we use genetically engineered lung cancer mouse models and patient samples to dissect how LKB1 mutations accelerate tumor growth by reshaping the immune microenvironment. Comprehensive immune profiling of LKB1-mutant vs wildtype tumors revealed dramatic changes in myeloid cells, specifically enrichment of Arg1+ interstitial macrophages and SiglecFHi neutrophils. We discovered a novel mechanism whereby autocrine LIF signaling in Lkb1-mutant tumors drives tumorigenesis by reprogramming myeloid cells in the immune microenvironment. Inhibiting LIF signaling in Lkb1-mutant tumors, via gene targeting or with a neutralizing antibody, resulted in a striking reduction in Arg1+ interstitial macrophages and SiglecFHi neutrophils, expansion of antigen specific T cells, and inhibition of tumor progression. Thus, targeting LIF signaling provides a new therapeutic approach to reverse the immunosuppressive microenvironment of LKB1-mutant tumors.

cancer biology↗

bioRxiv2021

Spatially Interacting Phosphorylation Sites and Mutations in Cancer

Advances in mass-spectrometry have generated increasingly large-scale proteomics datasets containing tens of thousands of phosphorylation sites (phosphosites) that require prioritization. We develop a bioinformatics tool called HotPho and systematically discover 3D co-clustering of phosphosites and cancer mutations on protein structures. HotPho identifies 474 such hybrid clusters containing 1,255 co-clustering phosphosites, including RET p.S904/Y928, the conserved HRAS/KRAS p.Y96, and IDH1 p.Y139/IDH2 p.Y179 that are adjacent to recurrent mutations on protein structures not found by linear proximity approaches. Hybrid clusters, enriched in histone and kinase domains, frequently include expression-associated mutations experimentally shown as activating and conferring genetic dependency. Approximately 300 co-clustering phosphosites are verified in patient samples of 5 cancer types or previously implicated in cancer, including CTNNB1 p.S29/Y30, EGFR p.S720, MAPK1 p.S142, and PTPN12 p.S275. In summary, systematic 3D clustering analysis highlights nearly 3,000 likely functional mutations and over 1,000 cancer phosphosites for downstream investigation and evaluation of potential clinical relevance.

bioinformatics↗

bioRxiv2020

Cancer-specific associations of driver genes with immunotherapy outcome

Genomic features such as microsatellite instability (MSI) and tumor mutation burden (TMB) are predictive of immune checkpoint inhibitor (ICI) response. However, they do not account for the functional effects of specific driver gene mutations, which may alter the immune microenvironment and influence immunotherapy outcomes. By analyzing a multi-cancer cohort of 1,525 ICI-treated patients, we identified 12 driver genes in 6 cancer types associated with treatment outcomes, including genes involved in oncogenic signaling pathways (NOTCH, WNT, FGFR) and chromatin remodeling. Mutations of PIK3CA, PBRM1, SMARCA4, and KMT2D were associated with worse outcomes across multiple cancer types. In comparison, genes showing cancer-specific associations--such as KEAP1, BRAF, and RNF43--harbored distinct variant types and variants, some of which were individually associated with outcomes. In colorectal cancer, a common RNF43 indel was a putative neoantigen associated with higher immune infiltration and favorable ICI outcomes. Finally, we showed that selected mutations were associated with PD-L1 status and could further stratify patient outcomes beyond MSI or TMB, highlighting their potential as biomarkers for immunotherapy.

genomics↗

bioRxiv2020

Ancestry-Specific Predisposing Germline Variants in Cancer

BackgroundCancer risk differs across ancestries and these differences may result from differing prevalence of inherited genetic predisposition. Yet, most germline genomic studies performed to date have focused on individuals of European ancestry. Ancestry-specific analyses of germline genomes are required to inform cancer genetic risk and prognosis for each ancestral group. Here, we investigate potentially germline pathogenic variants in cancer predisposition genes (CPG) and their somatic effects in patients across diverse ancestral backgrounds. MethodsWe performed a retrospective analysis of germline genomic data of 9,899 patients from 33 cancer types generated by The Cancer Genome Atlas (TCGA) project along with matching somatic genomic and transcriptomic data. By collapsing pathogenic and likely pathogenic variants to the gene level, we analyzed the association between variants in CPGs and cancer types within each ancestry. We also identified ancestry- specific predisposing variants and their associated somatic two-hit events and gene expression levels. ResultsRecent genetic ancestry analysis classified the cohort of 9,899 cancer cases into individuals of primarily European, (N = 8,184, 82.7%), African (N = 966, 9.8%), East Asian (N = 649, 6.6%), South Asian (N=48, 0.5%), Native/Latin American (N=41, 0.4%), and admixed (N=11, 0.1%) ancestries. In the African ancestry, we discovered a potentially novel association of BRCA2 in lung squamous cell carcinoma (OR = 41.4 [95% CI, 6.1-275.6]; FDR = 0.002) along with the previously identified association of BRCA2 in ovarian serous cystadenocarcinoma (OR=8.5 [95% CI, 1.5-47.4]; FDR=0.045). Similarly, in the East Asian ancestry, we discovered one previously known association of BRIP1 in stomach adenocarcinoma (OR=12.8 [95% CI, 1.8-90.84]; FDR=0.038). Rare variant burden analysis further identified 7 suggestive associations for cancer-gene pairs in African ancestry individuals previously well described in European ancestry including SDHB in pheochromocytoma and paraganglioma, ATM in prostate adenocarcinoma, VHL in kidney renal clear cell carcinoma, FH in kidney renal papillary cell carcinoma, and PTEN in uterine corpus endometrial carcinoma. Loss of heterozygosity was identified for 7 out of the 15 African ancestry carriers of predisposing variants. Further, tumors from the SDHB or BRCA2 carriers showed simultaneous allelic specific expression and low gene expression of their respective affected genes; and FH splice-site variant carriers showed mis-splicing of FH. ConclusionWhile several predisposing genes are shared across patients, many pathogenic variants are found to be ancestry-specific and trigger somatic effects. Analysis of larger diverse ancestries genomic cohorts are required to pinpoint ancestry- specific genetic predisposition to inform personalized diagnosis and screening strategies.

genomics↗

bioRxiv2019

A Genotype Concordance Study between Consumer Genetic Testing Services

The consumer genomics industry is steadily growing and delivering genetic information to over 10 million individuals. Yet, the implications of using data from different services remain unclear. We investigated the genotyped sites, concordance, and genetic risk estimation using data from three major consumer services. We found low overlap among reported genotyped sites (2.7% across all three). While there was a high concordance between overlapping sites of the two SNP-array services (99.6%), there was a lower concordance between them and a low-pass whole-genome service (73.0%). Finally, we demonstrated that the discrepancy resulted in distinct APOE genotypes and genetic risk scores of Alzheimers disease determined using these data. Our results demonstrate genotype results across consumer genomics platforms may lead to different genetic risk estimates, highlighting the need for careful quality control and interpretation.

genetics↗

bioRxiv2017

A common haplotype lowers SPI1 (PU.1) expression in myeloid cells and delays age at onset for Alzheimer’s disease

In this study we used age at onset of Alzheimers disease (AD), cerebrospinal fluid (CSF) biomarkers, and cis-expression quantitative trait loci (cis-eQTL) datasets to identify candidate causal genes and mechanisms underlying AD GWAS loci. In a genome-wide survival analysis of 40,255 samples, eight of the previously reported AD risk loci are significantly (P < 5x10-8) or suggestively (P < 1x10-5) associated with age at onset-defined survival (AAOS) and a further fourteen novel loci reached suggestive significance. Using stratified LD score regression we demonstrated a significant enrichment of AD heritability in hematopoietic cells of the myeloid and B-lymphoid lineage. We then investigated the impact of these 22 AAOS-associated variants on CSF biomarkers and gene expression in cells of the myeloid lineage. In particular, the minor allele of rs1057233 (G), within the previously reported CELF1 AD risk locus, shows association with higher age at onset of AD (P=8.40x10-6), higher CSF levels of A{beta}42 (P=1.2x10-4), and lower expression of SPI1 in monocytes (P=1.50x10-105) and macrophages (P=6.41x10-87). SPI1 encodes PU.1, a transcription factor critical for myeloid cell development and function. AD heritability is enriched within the SPI1 cistromes of monocytes and macrophages, implicating a myeloid PU.1 target gene network in the etiology of AD. Finally, experimentally altered PU.1 levels are correlated with phagocytic activity of BV2 mouse microglial cells and specific changes in the expression of multiple myeloid-expressed genes, including the mouse orthologs of AD-associated genes, APOE, CLU/APOJ, CD33, MS4A4A/MS4A6A, and TYROBP. Our results collectively suggest that lower SPI1 expression reduces AD risk by modulating myeloid cell gene expression and function.

neuroscience↗