Nonparametric approaches for population structure analysis

The genetic status and rescue measure for a geographically isolated population of Amur tigers

The Amur tiger is currently confronted with challenges of anthropogenic development, leading to its population becoming fragmented into two geographically isolated groups: smaller and larger ones. Small and isolated populations frequently face a greater extinction risk, yet the small tiger population's genetic status and survival potential have not been assessed. Here, a total of 210 samples of suspected Amur tiger feces were collected from this small population, and the genetic background and population survival potentials were assessed by using 14 microsatellite loci. Our results demonstrated that the mean number of alleles in all loci was 3.7 and expected heterozygosity was 0.6, indicating a comparatively lower level of population genetic diversity compared to previously reported studies on other subspecies. The genetic estimates of effective population size (Ne) and the Ne/N ratio were merely 7.6 and 0.152, respectively, representing lower values in comparison to the Amur tiger population in Sikhote-Alin (the larger group). However, multiple methods have indicated the possibility of genetic divergence within our isolated population under study. Meanwhile, the maximum kinship recorded was 0.441, and the mean inbreeding coefficient stood at 0.0868, both of which are higher than those observed in other endangered species, such as the African lion and the grey wolf. Additionally, we have identified a significant risk of future extinction if the lethal equivalents were to reach 6.26, which is higher than that of other large carnivores. Further, our simulation results indicated that an increase in the number of breeding females would enhance the prospects of this population. In summary, our findings provide a critical theoretical basis for further bailout strategies concerning Amur tigers.

Phenotypic plasticity and genetic diversity shed light on endemism of rare Boechera perstellata and its potential vulnerability to climate warming

The rapid pace of contemporary environmental change puts many species at risk, especially rare species constrained by limited capacity to adapt or migrate due to low genetic diversity and/or fitness. But the ability to acclimate can provide another way to persist through change. We compared the capacity of rare Boechera perstellata (Braun's rockcress) and widespread B. laevigata to acclimate to change. We investigated the phenotypic plasticity of growth, biomass allocation, and leaf morphology of individuals of B. perstellata and B. laevigata propagated from seed collected from several populations throughout their ranges in a growth chamber experiment to assess their capacity to acclimate. Concurrently, we assessed the genetic diversity of sampled populations using 17 microsatellite loci to assess evolutionary potential. Plasticity was limited in both rare B. perstellata and widespread B. laevigata, but differences in the plasticity of root traits between species suggest that B. perstellata may have less capacity to acclimate to change. In contrast to its widespread congener, B. perstellata exhibited no plasticity in response to temperature and weaker plastic responses to water availability. As expected, B. perstellata also had lower levels of observed heterozygosity than B. laevigata at the species level, but population-level trends in diversity measures were inconsistent due to high heterogeneity among B. laevigata populations. Overall, the ability of phenotypic plasticity to broadly explain the rarity of B. perstellata versus commonness of B. laevigata is limited. However, some contextual aspects of our plasticity findings compared with its relatively low genetic variability may shed light on the narrow range and habitat associations of B. perstellata and suggest its vulnerability to climate warming due to acclimatory and evolutionary constraints.

Evaluation of the Genetic Diversity, Population Structure and Selection Signatures of Three Native Chinese Pig Populations

Indigenous pig populations in Hainan Province live in tropical climate conditions and a relatively closed geographical environment, which has contributed to the formation of some excellent characteristics, such as heat tolerance, strong disease resistance and excellent meat quality. Over the past few decades, the number of these pig populations has decreased sharply, largely due to a decrease in growth rate and poor lean meat percentage. For effective conservation of these genetic resources (such as heat tolerance, meat quality and disease resistance), the whole-genome sequencing data of 78 individuals from 3 native Chinese pig populations, including Wuzhishan (WZS), Tunchang (TC) and Dingan (DA), were obtained using a 150 bp paired-end platform, and 25 individuals from two foreign breeds, including Landrace (LR) and Large White (LW), were downloaded from a public database. A total of 28,384,282 SNPs were identified, of which 27,134,233 SNPs were identified in native Chinese pig populations. Both genetic diversity statistics and linkage disequilibrium (LD) analysis indicated that indigenous pig populations displayed high genetic diversity. The result of population structure implied the uniqueness of each native Chinese pig population. The selection signatures were detected between indigenous pig populations and foreign breeds by using the population differentiation index (F_ST) method. A total of 359 candidate genes were identified, and some genes may affect characteristics such as immunity (IL-2, IL-21 and ZFYVE16), adaptability (APBA1), reproduction (FGF2, RNF17, ADAD1 and HIPK4), meat quality (ABCA1, ADIG, TLE4 and IRX5), and heat tolerance (VPS13A, HSPA4). Overall, the findings of this study will provide some valuable insights for the future breeding, conservation and utilization of these three Chinese indigenous pig populations.

Oocyte degeneration in a cohort adversely affects clinical outcomes in conventional IVF cycles: a propensity score matching study

Background

Oocyte degeneration was mostly described in intracytoplasmic sperm injection (ICSI) cycles; there is no report showing the relationship between oocyte degeneration and clinical outcomes in conventional in vitro fertilization (IVF) cycles. This retrospective study using the propensity score (PS) matching method aimed to explore whether the presence of oocyte degeneration in conventional IVF cycles would affect the sibling embryo development potential and clinical outcomes.

Methods

Patients with at least one oocyte degenerated after short-term insemination and stripping were defined as the degeneration (DEG) group, while patients with no oocyte degenerated were defined as the non-degeneration (NONDEG) group. The PS matching method was used to control for potential confounding factors, and a multivariate logistic regression analysis was made to evaluate whether the presence of oocyte degeneration would affect the cumulative live birth rate (CLBR).

Results

After PS matching, basic characteristics were similar between the two groups, oocyte yield was significantly higher in the DEG group than the NON-DEG group (P < 0.05), mature oocyte number, 2 pronuclear (2PN) embryo number, 2PN embryo clearage rate, "slow" embryo number, "accelerated" embryo number, rate of cycles with total day 3 embryo extended culture, number of frozen embryo transfer (FET) cycles, transferred embryo stage, transferred embryo number, and live birth rate in fresh embryo transfer cycles were all similar between the two groups (P > 0.05), but the 2PN fertilization rate, available embryo number, high-quality embryo number, "normal" embryo number, frozen embryo number, blastocyst formation rate, and no available embryo cycle rate were all significantly lower in the DEG group than the NON-DEG group (P < 0.05). The cumulative live birth rate was also significantly lower in the DEG group than in the NON-DEG group (70.2% vs. 74.0%, P = 0.0019). Multivariate logistic regression analysis further demonstrated that the presence of oocyte degeneration in conventional IVF cycles adversely affects the CLBR both before (OR = 0.83, 95% CI: 0.75-0.92) and after (OR = 0.82, 95% CI: 0.72-0.93) PS matching.

Conclusion

Our findings together revealed that the presence of oocyte degeneration in a cohort of oocytes may adversely affect subsequent embryo development potential and clinical outcomes in conventional IVF cycles.

The Mediating Effect of Operative Approach on Racial Disparities in Bariatric Surgery Complications

INTRODUCTION

A laparoscopic approach to bariatric surgeries confers a favorable side-effect profile as compared to an open approach. However, literature regarding the independent association of race with access to and postoperative outcomes in laparoscopic Roux-en-Y gastric bypass (RYGB) and sleeve gastrectomy (GS) is scarce.

MATERIALS AND METHODS

All RYGB and GS cases recorded in American College of Surgeons National Quality Improvement Program data from 2012 to 2020 were subjected to propensity score matching to assess the independent association between Black self-identified race on access to a laparoscopic approach and postoperative complications. Finally, a series of logistic regressions enabled evaluation of the mediating effect of operative approach on racial disparities in postoperative complications.

RESULTS

55,846 cases of RYGB and 94,209 cases of GS were identified. Following propensity score matching, logistic regression identified Black race as an independent predictor of open approach to RYGB (P < 0.001) and GS (P = 0.019). Black patients had increased incidence of any, minor and severe postoperative complications and unplanned readmissions in both RYGB (P < 0.001, P < 0.001, P = 0.0412, and P < 0.001, respectively) and GS (P < 0.001, P < 0.001, P = 0.0037, and P < 0.001, respectively). Open approach to RYGB was identified as a partial mediator of the independent association between Black race and any complication, minor complications, and unplanned readmission.

CONCLUSIONS

This methodology identified racial disparities in complications following RYGB and GS. Interestingly, reduced access to a laparoscopic approach mediated racial disparities in complications following RYGB but not GS. Further research might elucidate upstream determinants of health that catalyze these disparities.

Racial disparities in minimally invasive esophagectomy and gastrectomy for upper GI malignancies

BACKGROUND

Esophageal cancer and gastric cancer are two important causes of upper GI malignancies. Literature has shown that minimally invasive esophagectomies (MIE) and gastrectomies (MIG), have shorter length of stay and fewer complications. However, limited literature exists about the association between race and access to MIE and MIG. This study aims to identify the racial disparities in the different approaches to esophagectomy and gastrectomy. We further evaluate the relationship between the race and postoperative complications.

METHODS

This IRB-approved retrospective study utilized data from the American College of Surgeons National Quality Improvement Program. All recorded cases of MIE, MIG, open gastrectomy, and esophagectomy between 2012 and 2019 were isolated. Propensity score matching and univariate analysis was performed to assess the independent effect of black self-identified race on access and outcomes. p < 0.05 was required to achieve statistical significance.

RESULTS

7891 cases of esophagectomy and 5,132 cases of gastrectomy cases were identified. Using Propensity and logistic regression, we identified that black self-reported race is an independent predictor of open approach to gastrectomy (OR 1.6871943, 95% CI 1.431464-1.989829, p < 0.001). Black self-reported race was not predictive of operative approach among esophagectomy patients (OR 0.7942576, 95% CI 0.5698645-1.124228, p = 0.183). In contrast, black self-reported is an independent predictor of postoperative complications among esophagectomy patients only. Esophagectomy patients of black self-reported race were more likely to experience any complication (OR 1.4373437, 95% CI 1.1129239-1.8557096, p = 0.00537), severe complications (OR 1.3818966, 95% CI 1.0653087-1.7888454, p = 0.0144), and death (OR 2.00779762, 95% CI 1.08034921-3.56117535, p = 0.0211) within 30 days of their surgeries.

CONCLUSION

Our analysis revealed a significant racial disparity in access to MIG and a higher incidence of post-operative complications amongst esophagectomy patients. Minimally invasive techniques are underutilized in racial minorities. The findings herein warrant further investigation to eliminate barriers and disparities.

A machine learning-based SNP-set analysis approach for identifying disease-associated susceptibility loci

Identifying disease-associated susceptibility loci is one of the most pressing and crucial challenges in modeling complex diseases. Existing approaches to biomarker discovery are subject to several limitations including underpowered detection, neglect for variant interactions, and restrictive dependence on prior biological knowledge. Addressing these challenges necessitates more ingenious ways of approaching the “missing heritability” problem. This study aims to discover disease-associated susceptibility loci by augmenting previous genome-wide association study (GWAS) using the integration of random forest and cluster analysis. The proposed integrated framework is applied to a hepatitis B virus surface antigen (HBsAg) seroclearance GWAS data. Multiple cluster analyses were performed on (1) single nucleotide polymorphisms (SNPs) considered significant by GWAS and (2) SNPs with the highest feature importance scores obtained using random forest. The resulting SNP-sets from the cluster analyses were subsequently tested for trait-association. Three susceptibility loci possibly associated with HBsAg seroclearance were identified: (1) SNP rs2399971, (2) gene LINC00578, and (3) locus 11p15. SNP rs2399971 is a biomarker reported in the literature to be significantly associated with HBsAg seroclearance in patients who had received antiviral treatment. The latter two loci are linked with diseases influenced by the presence of hepatitis B virus infection. These findings demonstrate the potential of the proposed integrated framework in identifying disease-associated susceptibility loci. With further validation, results herein could aid in better understanding complex disease etiologies and provide inputs for a more advanced disease risk assessment for patients.

Quantitative evaluation of nonlinear methods for population structure visualization and inference

Population structure (also called genetic structure and population stratification) is the presence of a systematic difference in allele frequencies between subpopulations in a population as a result of nonrandom mating between individuals. It can be informative of genetic ancestry, and in the context of medical genetics, it is an important confounding variable in genome-wide association studies. Recently, many nonlinear dimensionality reduction techniques have been proposed for the population structure visualization task. However, an objective comparison of these techniques has so far been missing from the literature. In this article, we discuss the previously proposed nonlinear techniques and some of their potential weaknesses. We then propose a novel quantitative evaluation methodology for comparing these nonlinear techniques, based on populations for which pedigree is known a priori either through artificial selection or simulation. Based on this evaluation metric, we find graph-based algorithms such as t-SNE and UMAP to be superior to principal component analysis, while neural network-based methods fall behind.

Trends, Predictors, and Outcomes of 30-Day Readmission With Heart Failure After Transcatheter Aortic Valve Replacement: Insights From the US Nationwide Readmission Database

BACKGROUND

Data on trends, predictors, and outcomes of heart failure (HF) readmissions after transcatheter aortic valve replacement (TAVR) remain limited. Moreover, the relationship between hospital TAVR discharge volume and HF readmission outcomes has not been established.

METHODS AND RESULTS

The Nationwide Readmission Database was used to identify 30‐day readmissions for HF after TAVR from October 1, 2015, to November 30, 2018, using International Classification of Diseases, Tenth Revision, Clinical Modification (ICD‐10‐CM) codes. A total of 167 345 weighted discharges following TAVR were identified. The all‐cause readmission rate within 30 days of discharge was 11.4% (19 016). Of all the causes of 30‐day rehospitalizations, HF comprised 31.4% (5962) of all causes. The 30‐day readmission rate for HF did not show a significant decline during the study period (P_trend=0.06); however, all‐cause readmission rates decreased significantly (P_trend=0.03). HF readmissions were comparable between high‐ and low‐volume TAVR centers. Charlson Comorbidity Index >8, length of stay >4 days during the index hospitalization, chronic obstructive pulmonary disease, atrial fibrillation, chronic HF, preexisting pacemaker, complete heart block during index hospitalization, paravalvular regurgitation, chronic kidney disease, and end‐stage renal disease were independent predictors of 30‐day HF readmission after TAVR. HF readmissions were associated with higher mortality rates when compared with non‐HF readmissions (4.9% versus 3.3%; P<0.01). Each HF readmission within 30 days was associated with an average increased cost of $13 000 more than for each non‐HF readmission.

CONCLUSIONS

During the study period from 2015 to 2018, 30‐day HF readmissions after TAVR remained steady despite all‐cause readmissions decreasing significantly. All‐cause readmission mortality and HF readmission mortality also showed a nonsignificant downtrend. HF readmissions were comparable across low‐, medium‐, and high‐volume TAVR centers. HF readmission was associated with increased mortality and resource use attributed to the increased costs of care compared with non‐HF readmission. Further studies are needed to identify strategies to decrease the burden of HF readmissions and related mortality after TAVR.

A Neural Network-Based Spectral Approach for the Assignment of Individual Trees to Genetically Differentiated Subpopulations

Studying population structure has made an essential contribution to understanding evolutionary processes and demographic history in forest ecology research. This inference process basically involves the identification of common genetic variants among individuals, then grouping the similar individuals into subpopulations. In this study, a spectral-based classification of genetically differentiated groups was carried out using a provenance–progeny trial of Eucalyptus cladocalyx. First, the genetic structure was inferred through a Bayesian analysis using single-nucleotide polymorphisms (SNPs). Then, different machine learning models were trained with foliar spectral information to assign individual trees to subpopulations. The results revealed that spectral-based classification using the multilayer perceptron method was very successful at classifying individuals into their respective subpopulations (with an average of 87% of correct individual assignments), whereas 85% and 81% of individuals were assigned to their respective classes correctly by convolutional neural network and partial least squares discriminant analysis, respectively. Notably, 93% of individual trees were assigned correctly to the class with the smallest size using the spectral data-based multi-layer perceptron classification method. In conclusion, spectral data, along with neural network models, are able to discriminate and assign individuals to a given subpopulation, which could facilitate the implementation and application of population structure studies on a large scale.

Whole Transcriptome Sequencing Unveils the Genomic Determinants of Putative Somaclonal Variation in Mint (Mentha L.)

Mint (Mentha L., Lamiaceae) is a strongly scented herb of the family Lamiaceae that is grown mostly by clonal propagation, making it a valuable species for the study of somaclonal variation and its phenotypic consequences. The recent introduction of a few species of mint in South America, followed by a presumably rampant propagation, make this region particularly ideal for studying the extent of somaclonal genetic diversity. Hence, the objective of this work was to offer a preliminary characterization of somaclonal genetically coding diversity of the mint in the northern Andes in order to address the question of whether somaclonal variants may have emerged despite relatively recent introductions in a region where mint is not native. A total of 29 clonally propagated specimens, collected in mint export farms in the province of Antioquia, a major region for mint production in the northwest Andes of Colombia, were genotyped using RNA sequencing (RNA-Seq). SNP calling was carried out from the leaves’ transcriptome profiles of each plant by combining the GATK4 and TRINITY protocols, obtaining a total of 2033 loci across 912 transcripts with a minimum read depth of 20X and 4% of missing data. Unsupervised machine learning algorithms considered the K-means, AGNES and UPGMA approaches, all of which suggested three genetic clusters for M. spicata and a unique cluster for M. × piperita. The results indicate that at least two different origins of M. spicata reached the eastern region of the Antioquia province, clonally propagated in the locality ever since for local consumption and export. One of these ancestries had more population structure, possibly due to environmental or anthropological pressures that intervened in the fragmentation of this genetic group or to a higher somaclonal mutation rate. This work offers a first step into the study of the accumulation and transmission of presumably quasi-neutral somatic mutations at coding regions in an herbaceous clonally propagated scented species such as mint, likely favored by an expected population expansion after its Andean introduction. These ad hoc hypotheses warrant further study as part of future research.

Population-based biobank for analyzing the frequencies of clinically relevant DNA markers in the Russian population: bioinformatic aspects

One of the tasks of population-based biobanks is to determine the frequencies of clinically relevant genetic polymorphisms in the population. The population of Russia is very heterogeneous both ethnically and genetically. Therefore, the frequencies of genetic markers are in demand not in one sample, but in a series of samples reflecting the heterogeneity of the gene pool of different peoples and regions.

Aim. To divide the population of Russia and neighboring countries into population groups meeting certain conditions, as well as having a representative sample in existing data and biobanks.

Material and methods. We developed a method for combining populations into larger groups with maintaining intragroup homogeneity based on the principal components analysis with K-means clustering, followed by refinement of clustering for higher homogeneity and a more equal distribution of group sizes using FST distances. The technology has been adjusted using the example of the Biobank of Northern Eurasia. Therefore, the material was the genome-wide data on 4.5 million genetic markers for 1,883 samples representing 247 populations of Russia and neighboring countries from this biobank. The developed approach, the resulting set of populations and related map can be applied for other collections of biomaterials from Russian populations.

Results. Application of this approach made it possible to divide the entire population of Russia and neighboring countries into 29 ethnogeographic groups, characterized by relative genetic homogeneity. This set of populations is recommended as a baseline for population screenings to identify the frequency of any genetic markers among the population of Russia. A map has been constructed showing the division of population into 29 ethnogeographic areas.

Conclusion. On the basis of a reliable genome-wide data, the zoning of gene pool of the Russian population was carried out. We identified ethnogeographic groups with intergroup contrasting allele frequencies, but at the same time with relatively homogeneous intragroup parameters. The resulting map and register of groups can be used in population genetic, medical genetic and pharmacogenetic studies.

A Deep Learning Approach to Population Structure Inference in Inbred Lines of Maize

Analysis of population genetic variation and structure is a common practice for genome-wide studies, including association mapping, ecology, and evolution studies in several crop species. In this study, machine learning (ML) clustering methods, K-means (KM), and hierarchical clustering (HC), in combination with non-linear and linear dimensionality reduction techniques, deep autoencoder (DeepAE) and principal component analysis (PCA), were used to infer population structure and individual assignment of maize inbred lines, i.e., dent field corn (n = 97) and popcorn (n = 86). The results revealed that the HC method in combination with DeepAE-based data preprocessing (DeepAE-HC) was the most effective method to assign individuals to clusters (with 96% of correct individual assignments), whereas DeepAE-KM, PCA-HC, and PCA-KM were assigned correctly 92, 89, and 81% of the lines, respectively. These findings were consistent with both Silhouette Coefficient (SC) and Davies-Bouldin validation indexes. Notably, DeepAE-HC also had better accuracy than the Bayesian clustering method implemented in InStruct. The results of this study showed that deep learning (DL)-based dimensional reduction combined with ML clustering methods is a useful tool to determine genetically differentiated groups and to assign individuals into subpopulations in genome-wide studies without having to consider previous genetic assumptions.

Racial Disparities in Outcomes After Common Abdominal Surgical Procedures-The Impact of Access to a Minimally Invasive Approach

BACKGROUND

It is presently considered the standard of care to perform many routine intra-abdominal operations using a minimally invasive approach. The authors recently identified a racial disparity in access to a laparoscopic approach to inguinal hernia repair, cholecystectomy, appendectomy, and colectomy. The present study further evaluates this patient cohort to assess the relationship between the race and postoperative complications and test the mediating effect of the selected surgical approach.

METHODS

After institutional review board approval, patients in the American College of Surgeons National Surgical Quality Improvement Program database who underwent inguinal hernia repair, cholecystectomy, appendectomy, or colectomy in 2016 were identified. Patient demographics, including the self-reported race and ethnicity, as well as clinical, operative, and postoperative variables were recorded. After the exclusion of cases associated with diagnoses of cancer, a 4:1 propensity score matching algorithm generated a clinically balanced cohort of patients of white and black self-reported race. The mediating effect of an open approach to surgery on the relationship between black self-reported race and postoperative complications was evaluated via a series of regressions.

RESULTS

There were 41,340 unilateral inguinal hernia repairs, 3182 bilateral inguinal hernia repairs, 60,444 cholecystectomies, 50,523 appendectomies, and 58,012 colectomies included in the database in 2017. Exclusion of cases associated with cancer and subsequent propensity score matching returned 17,540 unilateral hernia repairs, 890 bilateral hernia repairs, 23,865 cholecystectomies, 11,660 appendectomies, and 12,320 colectomies. On mediation analysis, any complication, severe complication, and death were significant when regressed on black self-reported race (any: odds ratio [OR] = 1.210, 95% confidence interval [CI] = 1.132-1.291, P < 0.001; severe: OR = 1.352, 95% CI = 1.245-1.466, P < 0.001; death: OR = 1.358, 95% CI = 1.000-1.818, P = 0.044), and open surgery was a significant mediator in the incidence of any complication and severe complication (any: OR = 1.180, 95% CI = 1.105-1.260, P < 0.001 and severe: OR = 1.307, 95% CI = 1.203-1.418, P < 0.001).

CONCLUSIONS

These findings underscore the importance of access to a minimally invasive approach to surgery. However, other factors may contribute to racial disparities in postoperative complications after common abdominal operations.

Heterogeneity in the distribution of 159 drug-response related SNPs in world populations and their genetic relatedness

Interethnic variability in drug response arises from genetic differences associated with drug metabolism, action and transport. These genetic variations can affect drug efficacy as well as cause adverse drug reactions (ADRs). We retrieved drug-response related single nucleotide polymorphism (SNP) associated data from databases and analyzed to elucidate population specific distribution of 159 drug-response related SNPs in twenty six populations belonging to five super-populations (African, Admixed Americans, East Asian, European and South Asian). Significant interpopulation differences exist in the minor (variant) allele frequencies (MAFs), linkage disequilibrium (LD) and haplotype distributions among these populations. 65 of the drug-response related alleles, which are considered as minor (variant) in global population, are present as the major alleles (frequency ≥0.5) in at least one or more populations. Populations that belong to the same super-population have similar distribution pattern for majority of the variant alleles. These drug response related variant allele frequencies and their pairwise LD measure (r²) can clearly distinguish the populations in a way that correspond to the known evolutionary history of human and current geographic distributions, while D' cannot. The data presented here may aid in identifying drugs that are more appropriate and/or require pharmacogenetic testing in these populations. Our findings emphasize on the importance of distinct, ethnicity-specific clinical guidelines, especially for the African populations, to avoid ADRs and ensure effective drug treatment.

Genomic biosurveillance of forest invasive alien enemies: A story written in code

The world's forests face unprecedented threats from invasive insects and pathogens that can cause large irreversible damage to the ecosystems. This threatens the world's capacity to provide long-term fiber supply and ecosystem services that range from carbon storage, nutrient cycling, and water and air purification, to soil preservation and maintenance of wildlife habitat. Reducing the threat of forest invasive alien species requires vigilant biosurveillance, the process of gathering, integrating, interpreting, and communicating essential information about pest and pathogen threats to achieve early detection and warning and to enable better decision-making. This process is challenging due to the diversity of invasive pests and pathogens that need to be identified, the diverse pathways of introduction, and the difficulty in assessing the risk of establishment. Genomics can provide powerful new solutions to biosurveillance. The process of invasion is a story written in four chapters: transport, introduction, establishment, and spread. The series of processes that lead to a successful invasion can leave behind a DNA signature that tells the story of an invasion. This signature can help us understand the dynamic, multistep process of invasion and inform management of current and future introductions. This review describes current and future application of genomic tools and pipelines that will provide accurate identification of pests and pathogens, assign outbreak or survey samples to putative sources to identify pathways of spread, and assess risk based on traits that impact the outbreak outcome.