Genomic Footprinting Research Articles

The genomic footprint of social stratification in admixing American populations.

Cultural and socioeconomic differences stratify human societies and shape their genetic structure beyond the sole effect of geography. Despite mating being limited by sociocultural stratification, most demographic models in population genetics often assume random mating. Taking advantage of the correlation between sociocultural stratification and the proportion of genetic ancestry in admixed populations, we sought to infer the former process in the Americas. To this aim, we define a mating model where the individual proportions of the genome inherited from Native American, European, and sub-Saharan African ancestral populations constrain the mating probabilities through ancestry-related assortative mating and sex bias parameters. We simulate a wide range of admixture scenarios under this model. Then, we train a deep neural network and retrieve good performance in predicting mating parameters from genomic data. Our results show how population stratification, shaped by socially constructed racial and gender hierarchies, has constrained the admixture processes in the Americas since the European colonization and the subsequent Atlantic slave trade.

On convolutional neural networks for selection inference: Revealing the effect of preprocessing on model learning and the capacity to discover novel patterns.

A central challenge in population genetics is the detection of genomic footprints of selection. As machine learning tools including convolutional neural networks (CNNs) have become more sophisticated and applied more broadly, these provide a logical next step for increasing our power to learn and detect such patterns; indeed, CNNs trained on simulated genome sequences have recently been shown to be highly effective at this task. Unlike previous approaches, which rely upon human-crafted summary statistics, these methods are able to be applied directly to raw genomic data, allowing them to potentially learn new signatures that, if well-understood, could improve the current theory surrounding selective sweeps. Towards this end, we examine a representative CNN from the literature, paring it down to the minimal complexity needed to maintain comparable performance; this low-complexity CNN allows us to directly interpret the learned evolutionary signatures. We then validate these patterns in more complex models using metrics that evaluate feature importance. Our findings reveal that preprocessing steps, which determine how the population genetic data is presented to the model, play a central role in the learned prediction method. This results in models that mimic previously-defined summary statistics; in one case, the summary statistic itself achieves similarly high accuracy. For evolutionary processes that are less well understood than selective sweeps, we hope this provides an initial framework for using CNNs in ways that go beyond simply achieving high classification performance. Instead, we propose that CNNs might be useful as tools for learning novel patterns that can translate to easy-to-implement summary statistics available to a wider community of researchers.

ScATAC-seq preprocessing and imputation evaluation system for visualization, clustering and digital footprinting.

Single-cell ATAC-seq (scATAC-seq) is a recently developed approach that provides means to investigate open chromatin at single cell level, to assess epigenetic regulation and transcription factors binding landscapes. The sparsity of the scATAC-seq data calls for imputation. Similarly, preprocessing (filtering) may be required to reduce computational load due to the large number of open regions. However, optimal strategies for both imputation and preprocessing have not been yet evaluated together. We present SAPIEnS (scATAC-seq Preprocessing and Imputation Evaluation System), a benchmark for scATAC-seq imputation frameworks, a combination of state-of-the-art imputation methods with commonly used preprocessing techniques. We assess different types of scATAC-seq analysis, i.e. clustering, visualization and digital genomic footprinting, and attain optimal preprocessing-imputation strategies. We discuss the benefits of the imputation framework depending on the task and the number of the dataset features (peaks). We conclude that the preprocessing with the Boruta method is beneficial for the majority of tasks, while imputation is helpful mostly for small datasets. We also implement a SAPIEnS database with pre-computed transcription factor footprints based on imputed data with their activity scores in a specific cell type. SAPIEnS is published at: https://github.com/lab-medvedeva/SAPIEnS. SAPIEnS database is available at: https://sapiensdb.com.

Datamining approaches for examining the low prevalence of N-acetylglutamate synthase deficiency and understanding transcriptional regulation of urea cycle genes.

Ammonia, which is toxic to the brain, is converted into non-toxic urea, through a pathway of six enzymatically catalyzed steps known as the urea cycle. In this pathway, N-acetylglutamate synthase (NAGS, EC 2.3.1.1) catalyzes the formation of N-acetylglutamate (NAG) from glutamate and acetyl coenzyme A. NAGS deficiency (NAGSD) is the rarest of the urea cycle disorders, yet is unique in that ureagenesis can be restored with the drug N-carbamylglutamate (NCG). We investigated whether the rarity of NAGSD could be due to low sequence variation in the NAGS genomic region, high NAGS tolerance for amino acid replacements, and alternative sources of NAG and NCG in the body. We also evaluated whether the small genomic footprint of the NAGS catalytic domain might play a role. The small number of patients diagnosed with NAGSD could result from the absence of specific disease biomarkers and/or short NAGS catalytic domain. We screened for sequence variants in NAGS regulatory regions in patients suspected of having NAGSD and found a novel NAGS regulatory element in the first intron of the NAGS gene. We applied the same datamining approach to identify regulatory elements in the remaining urea cycle genes. In addition to the known promoters and enhancers of each gene, we identified several novel regulatory elements in their upstream regions and first introns. The identification of cis-regulatory elements of urea cycle genes and their associated transcription factors holds promise for uncovering shared mechanisms governing urea cycle gene expression and potentially leading to new treatments for urea cycle disorders.

Prioritizing cardiovascular disease-associated variants altering NKX2-5 and TBX5 binding through an integrative computational approach

Cardiovascular diseases (CVDs) are the leading cause of death worldwide and are heavily influenced by genetic factors. Genome-wide association studies (GWAS) have mapped > 90% of CVD-associated variants within the non-coding genome, which can alter the function of regulatory proteins, like transcription factors (TFs). However, due to the overwhelming number of GWAS single nucleotide polymorphisms (SNPs) (>500,000), prioritizing variants for in vitro analysis remains challenging. In this work, we implemented a computational approach that considers support vector machine (SVM)-based TF binding site classification and cardiac expression quantitative trait loci (eQTL) analysis to identify and prioritize potential CVD-causing SNPs. We identified 1,535 CVD-associated SNPs that occur within TF footprints and putative enhancers in the human heart and 14,218 variants in linkage disequilibrium (LD) with genotype-dependent gene expression in cardiac tissue. Using ChIP-seq data from two essential cardiac TFs (NKX2-5 and TBX5) in human induced pluripotent stem cell-derived cardiomyocytes (hIPSC-CM), we trained a large-scale gapped k-mer SVM (LS-GKM-SVM) model to identify CVD-associated SNPs that altered NKX2-5 and TBX5 binding sites. The model was tested by scoring human heart TF genomic footprints within putative enhancers and measuring in vitro binding through electrophoretic mobility shift assay (EMSA). Five variants predicted to alter NKX2-5 (rs59310144, rs6715570, and rs61872084) and TBX5 (rs7612445 and rs7790964) binding were prioritized for in vitro validation based on the magnitude of the predicted change in binding and because they are eQTLs in cardiac tissue. All five variants altered NKX2-5 and TBX5 DNA binding. In summary, we present a bioinformatic approach that considers tissue-specific eQTL analysis and SVM-based TF binding site classification to prioritize CVD-associated variants for in vitro experimental analysis.

Mutational processes of tobacco smoking and APOBEC activity generate protein-truncating mutations in cancer genomes.

Mutational signatures represent a genomic footprint of endogenous and exogenous mutational processes through tumor evolution. However, their functional impact on the proteome remains incompletely understood. We analyzed the protein-coding impact of single-base substitution (SBS) signatures in 12,341 cancer genomes from 18 cancer types. Stop-gain mutations (SGMs) (i.e., nonsense mutations) were strongly enriched in SBS signatures of tobacco smoking, APOBEC cytidine deaminases, and reactive oxygen species. These mutational processes alter specific trinucleotide contexts and thereby substitute serines and glutamic acids with stop codons. SGMs frequently affect cancer hallmark pathways and tumor suppressors such as TP53, FAT1, and APC. Tobacco-driven SGMs in lung cancer correlate with smoking history and highlight a preventable determinant of these harmful mutations. APOBEC-driven SGMs are enriched in YTCA motifs and associate with APOBEC3A expression. Our study exposes SGM expansion as a genetic mechanism by which endogenous and carcinogenic mutational processes directly contribute to protein loss of function, oncogenesis, and tumor heterogeneity.

Revelation of genetic diversity and genomic footprints of adaptation in Indian pig breeds

In the present study, the genetic diversity measures among four Indian domestic breeds of pig namely Agonda Goan, Ghurrah, Ghungroo, and Nicobari, of different agro-climatic regions of country were explored and compared with European commercial breeds, European wild boar and Chinese domestic breeds. The double digest restriction site-associated DNA sequencing (ddRADseq) data of Indian pigs (102) and Landrace (10 animals) were generated and whole genome sequencing data of exotic pigs (60 animals) from public data repository were used in the study. The principal component analysis (PCA), admixture analysis and phylogenetic analysis revealed that Indian breeds were closer in ancestry to Chinese breeds than European breeds. European breeds exhibited highest genetic diversity measures among all the considered breeds. Among Indian breeds, Agonda Goan and Ghurrah were found to be more genetically diverse than Nicobari and Ghungroo. The selection signature regions in Indian pigs were explored using iHS and XP-EHH, and during iHS analysis, it was observed that genes related to growth, reproduction, health, meat quality, sensory perception and behavior were found to be under selection pressure in Indian pig breeds. Strong selection signatures were recorded in 24.25–25.25 Mb region of SSC18, 123.25–124 Mb region of SSC15 and 118.75–119.5 Mb region of SSC2 in most of the Indian breeds upon pairwise comparison with European commercial breeds using XP-EHH. These regions were harboring some important genes such as EPHA4 for thermotolerance, TAS2R16, FEZF1, CADPS2 and PTPRZ1 for adaptability to scavenging system of rearing, TRIM36 and PGGT1B for disease resistance and CCDC112, PIAS1, FEM1B and ITGA11 for reproduction.

Genomic footprinting uncovers global transcription factor responses to amino acids in Escherichia coli.

Our knowledge of transcriptional responses to changes in nutrient availability comes primarily from few well-studied transcription factors (TFs), often lacking an unbiased genome-wide perspective. Leveraging recent advances allowing bacterial genomic footprinting, we comprehensively mapped the genome-wide regulatory responses of Escherichia coli to exogenous leucine, methionine, alanine, and lysine. The global TF Lrp was found to individually sense three amino acids and mount three different target gene responses. Overall, 531 genes had altered RNA polymerase occupancy, and 32 TFs responded directly or indirectly to the presence of amino acids, including regulators of membrane and osmotic pressure homeostasis. About 70% of the detected TF-DNA interactions had not been reported before. We thus identified 682 previously unknown TF-binding locations, for a subset of which the involved TFs were identified by affinity purification. This comprehensive map of amino acid regulation illustrates the incompleteness of the known transcriptional regulation network, even in E.coli.

The genomic footprint of whaling and isolation in fin whale populations

Twentieth century industrial whaling pushed several species to the brink of extinction, with fin whales being the most impacted. However, a small, resident population in the Gulf of California was not targeted by whaling. Here, we analyzed 50 whole-genomes from the Eastern North Pacific (ENP) and Gulf of California (GOC) fin whale populations to investigate their demographic history and the genomic effects of natural and human-induced bottlenecks. We show that the two populations diverged ~16,000 years ago, after which the ENP population expanded and then suffered a 99% reduction in effective size during the whaling period. In contrast, the GOC population remained small and isolated, receiving less than one migrant per generation. However, this low level of migration has been crucial for maintaining its viability. Our study exposes the severity of whaling, emphasizes the importance of migration, and demonstrates the use of genome-based analyses and simulations to inform conservation strategies.

Genomic footprints of rubber tree domestication.

Genomic footprints of rubber tree domestication.

Weak seed banks influence the signature and detectability of selective sweeps.

Seed banking (or dormancy) is a widespread bet-hedging strategy, generating a form of population overlap, which decreases the magnitude of genetic drift. The methodological complexity of integrating this trait implies it is ignored when developing tools to detect selective sweeps. But, as dormancy lengthens the ancestral recombination graph (ARG), increasing times to fixation, it can change the genomic signatures of selection. To detect genes under positive selection in seed banking species it is important to (1) determine whether the efficacy of selection is affected, and (2) predict the patterns of nucleotide diversity at and around positively selected alleles. We present the first tree sequence-based simulation program integrating a weak seed bank to examine the dynamics and genomic footprints of beneficial alleles in a finite population. We find that seed banking does not affect the probability of fixation and confirm expectations of increased times to fixation. We also confirm earlier findings that, for strong selection, the times to fixation are not scaled by the inbreeding effective population size in the presence of seed banks, but are shorter than would be expected. As seed banking increases the effective recombination rate, footprints of sweeps appear narrower around the selected sites and due to the scaling of the ARG are detectable for longer periods of time. The developed simulation tool can be used to predict the footprints of selection and draw statistical inference of past evolutionary events in plants, invertebrates, or fungi with seed banks.

Sex disparities in non-small cell lung cancer: mechanistic insights from a cRaf transgenic disease model

Women are at greater risk of developing non-small cell lung cancer (NSCLC), yet the underlying causes remain unclear. We performed whole genome scans in lung tumours of cRaf transgenic mice and identified miRNA, transcription factor and hormone receptor dependent gene regulations. We confirmed hormone receptors by immunohistochemistry and constructed regulatory gene networks by considering experimentally validated miRNA-gene and transcription factor-miRNA/gene targets. Bioinformatics, genomic foot-printing and gene enrichment analysis established sex-specific circuits of lung tumour growth. Translational research involved a large cohort of NSCLC patients. We evaluated commonalities in sex-specific NSCLC gene regulations between mice and humans and determined their prognostic value in Kaplan-Meier survival statistics and COX proportional hazard regression analysis. Overexpression of the cRaf kinase elicited an extraordinary 8-fold increase in tumour growth among females, and nearly 70% of the 112 differentially expressed genes (DEGs) were female specific. We identified oncogenes, oncomirs, tumour suppressors, cell cycle regulators and MAPK/EGFR signalling molecules, which prompted sex-based differences in NSCLC, and we deciphered a regulatory gene-network, which protected males from accelerated tumour growth. Strikingly, 41% of DEGs are targets of hormone receptors, and the majority (85%) are oestrogen receptor (ER) dependent. We confirmed the role of ER in a large cohort of NSCLC patients and validated 40% of DEGs induced by cRaf in clinical tumour samples. We report the molecular wiring that prompted sex disparities in tumour growth. This allowed us to propose the development of molecular targeted therapies by jointly blocking ER, CDK1 and arginase 2 in NSCLC. We gratefully acknowledge the financial support of the Lower Saxony Ministry of Culture and Sciences and Volkswagen Foundation, Germany to JB (25A.5-7251-99-3/00) and of the Chinese Scholarship Council to SZ (202008080022). This publication is funded by the Deutsche Forschungsgemeinschaft (DFG) as part of the "Open Access Publikationskosten" program.

Comparative population genomics analysis uncovers genomic footprints and genes influencing body weight trait in Chinese indigenous chicken

Body weight of chicken is a typical quantitative trait, which shows phenotypic variations due to selective breeding. Despite some QTL loci have been obtained, the body weight of native chicken breeds in different geographic regions varies greatly, its genetic basis remains unresolved questions. To address this issue, we analyzed 117 Chinese indigenous chickens from 10 breeds (Huiyang Bearded, Xinhua, Hotan Black, Baicheng You, Liyang, Yunyang Da, Jining Bairi, Lindian, Beijing You, Tibetan). We applied fixation index (FST) analysis to find selected genomic regions and genes associated with body weight traits. Our study suggests that NELL1, XYLT1, and NCAPG/LCORL genes are strongly selected in the body weight trait of Chinese indigenous chicken breeds. In addition, the IL1RAPL1 gene was strongly selected in large body weight chickens, while the PCDH17 and CADM2 genes were strongly selected in small body weight chickens. This result suggests that the patterns of genetic variation of native chicken and commercial chicken, and/or distinct local chicken breeds may follow different evolutionary mechanisms.

Genomic Instability Evolutionary Footprints on Human Health: Driving Forces or Side Effects?

In this work, we propose a comprehensive perspective on genomic instability comprising not only the accumulation of mutations but also telomeric shortening, epigenetic alterations and other mechanisms that could contribute to genomic information conservation or corruption. First, we present mechanisms playing a role in genomic instability across the kingdoms of life. Then, we explore the impact of genomic instability on the human being across its evolutionary history and on present-day human health, with a particular focus on aging and complex disorders. Finally, we discuss the role of non-coding RNAs, highlighting future approaches for a better living and an expanded healthy lifespan.

The genome of Acorus deciphers insights into early monocot evolution

Acorales is the sister lineage to all the other extant monocot plants. Genomic resource enhancement of this genus can help to reveal early monocot genomic architecture and evolution. Here, we assemble the genome of Acorus gramineus and reveal that it has ~45% fewer genes than the majority of monocots, although they have similar genome size. Phylogenetic analyses based on both chloroplast and nuclear genes consistently support that A. gramineus is the sister to the remaining monocots. In addition, we assemble a 2.2 Mb mitochondrial genome and observe many genes exhibit higher mutation rates than that of most angiosperms, which could be the reason leading to the controversies of nuclear genes- and mitochondrial genes-based phylogenetic trees existing in the literature. Further, Acorales did not experience tau (τ) whole-genome duplication, unlike majority of monocot clades, and no large-scale gene expansion is observed. Moreover, we identify gene contractions and expansions likely linking to plant architecture, stress resistance, light harvesting, and essential oil metabolism. These findings shed light on the evolution of early monocots and genomic footprints of wetland plant adaptations.

EPEN-03. ZFTA-RELA LOCALIZATION DYNAMICS REGULATE TRANSCRIPTIONAL CONTROL

Abstract ZFTA gene fusions are the most frequent driver events in supratentorial ependymoma (EPN). ZFTA-fusion proteins act as oncogenic transcription factors (TFs) to aberrantly activate oncogenic transcriptional programs. Our data indicates that ZFTA fusion proteins form distinct punctate structures with heterogeneous patterns of liquid-phase-like characteristics. These condensates are closely associated with transcription as seen by nascent RNA FISH and recruitment of various members of the transcription machinery, such as BRD4, MED1, and RNA POLII. Our genomic footprinting data indicates that many key transcription factors, such as Sox9, share similar genomic occupancy patterns with ZFTA fusions suggesting that these transcription factors are potentially recruited to these condensates, establishing transcriptional hubs. Our mutagenesis studies reveal that the ZFTA zinc finger domain is indispensable for condensate formation, and may bridge ZFTA fusion proteins to DNA at unique genomic loci. Condensate-forming deficient mutants fail to establish the same transcriptional program and are unable to drive tumor formation. This mechanistic data has led to our efforts to establish drug-chemical screen approaches against ZFTA-RELA localization/dynamics to identify novel therapeutic targets.

A genome-wide comparison between selected and unselected Valle del Belice sheep reveals differences in population structure and footprints of recent selection.

About three decades of breeding and selection in the Valle del Belìce sheep are expected to have left several genomic footprints related to milk production traits. In this study, we have assembled a dataset with 451 individuals of the Valle del Belìce sheep breed: 184 animals that underwent directional selection for milk production and 267 unselected animals, genotyped for 40,660 single-nucleotide polymorphisms (SNPs). Three different statistical approaches, both within (iHS and ROH) and between (Rsb) groups, were used to identify genomic regions potentially under selection. Population structure analyses separated all individuals according to their belonging to the two groups. A total of four genomic regions on two chromosomes were jointly identified by at least two statistical approaches. Several candidate genes for milk production were identified, corroborating the polygenic nature of this trait and which may provide clues to potential new selection targets. We also found candidate genes for growth and reproductive traits. Overall, the identified genes may explain the effect of selection to improve the performances related to milk production traits in the breed. Further studies using high-density array data, would be particularly relevant to refine and validate these results.

Timesweeper: accurately identifying selective sweeps using population genomic time series.

Despite decades of research, identifying selective sweeps, the genomic footprints of positive selection, remains a core problem in population genetics. Of the myriad methods that have been developed to tackle this task, few are designed to leverage the potential of genomic time-series data. This is because in most population genetic studies of natural populations, only a single period of time can be sampled. Recent advancements in sequencing technology, including improvements in extracting and sequencing ancient DNA, have made repeated samplings of a population possible, allowing for more direct analysis of recent evolutionary dynamics. Serial sampling of organisms with shorter generation times has also become more feasible due to improvements in the cost and throughput of sequencing. With these advances in mind, here we present Timesweeper, a fast and accurate convolutional neural network-based tool for identifying selective sweeps in data consisting of multiple genomic samplings of a population over time. Timesweeper analyzes population genomic time-series data by first simulating training data under a demographic model appropriate for the data of interest, training a one-dimensional convolutional neural network on said simulations, and inferring which polymorphisms in this serialized data set were the direct target of a completed or ongoing selective sweep. We show that Timesweeper is accurate under multiple simulated demographic and sampling scenarios, identifies selected variants with high resolution, and estimates selection coefficients more accurately than existing methods. In sum, we show that more accurate inferences about natural selection are possible when genomic time-series data are available; such data will continue to proliferate in coming years due to both the sequencing of ancient samples and repeated samplings of extant populations with faster generation times, as well as experimentally evolved populations where time-series data are often generated. Methodological advances such as Timesweeper thus have the potential to help resolve the controversy over the role of positive selection in the genome. We provide Timesweeper as a Python package for use by the community.

Genomic footprints of bottleneck in landlocked salmon population

At the end of the last ice age, several Atlantic salmon populations got caught up in the lakes and ponds of the Northern Hemisphere. Occasionally, the populations also got locked when the flow of rivers terminated from reaching the sea due to land upheaval. Therefore, the pattern of evolution shaping the landlocked salmon populations is different from the other anadromous salmons, which migrate between the sea and rivers. According to the theories of population genetics, the effect of genetic drift is expected to be more pronounced in the former compared to the latter. Here we examined this using the whole genome data of landlocked and anadromous salmon populations of Norway. Our results showed a 50–80% reduction in the genomic heterozygosity in the landlocked compared to anadromous salmon populations. The number and total size of the runs of homozygosity (RoH) segments of landlocked salmons were two to eightfold higher than those of their anadromous counterparts. We found the former had a higher ratio of nonsynonymous-to-synonymous diversities than the latter. The investigation also revealed a significant elevation of homozygous deleterious Single Nucleotide Variants (SNVs) in the landlocked salmon compared to the anadromous populations. All these results point to a significant reduction in the population size of the landlocked salmons. This process of reduction might have started recently as the phylogeny revealed a recent separation of the landlocked from the anadromous population. Previous studies on terrestrial vertebrates observed similar signatures of a bottleneck when the populations from Island and the mainland were compared. Since landlocked waterbody such as ponds and lakes are geographically analogous to Islands for fish populations, the findings of this study suggest the similarity in the patterns of evolution between the two.

Abstract 3505: Cancer disparities in race exposed through geospatial analysis of mutational signatures and environmental exposure

Abstract Background: Cancer has been disproportionally affecting minorities in the US due to socioeconomic, environmental, and genetic disparities. Cancer disparities are usually measured in incidence, mortality, survival, etc. Genomic-based cancer disparity analyses have been less common. In the past decade, mutational signatures were proposed as characteristic footprints of endogenous or exogenous carcinogens, which remarkably propelled the progress of genomic cancer research. The disparities caused by uneven exposure to environmental pollutants may be recorded in mutational signatures. Method: The Cancer Genome Altas is one of the richest genomic cancer datasets, empowering hundreds of secondary and tertiary cancer studies. One aspect of TCGA data that is seldomly utilized is geospatial information. Based on the contribution hospital location, we estimated the patients’ approximate location. Furthermore, utilizing data on 450 pollutants released by the Environmental Protection Agency from 2007-2017, we were able to establish associations between mutational signatures and certain pollutants. To remove the noise introduced by approximating the location and average pollution level, stringent statistical procedures were applied. Results: First, we showed that mutation frequencies varied substantially between different races. For example, TP53 was mutated in 46% of Black breast cancer patients, compared to 31% in Whites. Such differences in single genes translated to the level of mutational signatures. Liver cancer has aflatoxin as an affirmed carcinogen, and we compared the quantities of aflatoxin signature across races. It was found that aflatoxin contributed significantly higher (FDR = 0.002) to mutations in Asian than in Caucasians. In-depth analyses revealed that aflatoxin had a strong effect on hepatitis C virus infection in addition to the previously reported effect on hepatitis B virus infection. Similarly, as a known critical player in head and neck tumors, APOBEC signature was found at a significantly higher level (FDR = 0.01) in patients with positive infection of human papillomavirus. Furthermore, analyses from the geospatial perspective revealed several potential associations between pollutants and mutational signatures. For example, the pollutant carbofuran is positively associated with mutational signature SBS10a with the etiology of Polymerase epsilon exonuclease domain mutations (FDR = 0.02). Summary: We conducted a thorough mutational signature-based cancer disparity analysis. Our results reinforced previously known facts and expectations of oncogene mutations. The novel results demonstrated representative links between various forms of cancer disparity and genomic mutation footprints. Most importantly, such disparities can be interrogated at the mutational signature level and correlated to environmental pollutants. Citation Format: Yan Guo, Judy Bai, Katherine Ma, Shangyang Xia, Shuguang Leng, Hui Yu, Xi Gong. Cancer disparities in race exposed through geospatial analysis of mutational signatures and environmental exposure [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3505.