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Prostate cancer (PCa) is the second most common cause of cancer death in American men. Metastatic castration-resistant prostate cancer (mCRPC) is the most lethal form of PCa and preferentially metastasizes to the bones through incompletely understood molecular mechanisms. Herein, we processed RNA sequencing data from patients with mCRPC (n = 60) and identified 14 gene clusters (modules) highly correlated with mCRPC bone metastasis. We used a novel combination of weighted gene co-expression network analysis (WGCNA) and upstream regulator and gene ontology analyses of clinically annotated transcriptomes to identify the genes. The cyan module (M14) had the strongest positive correlation (0.81, p = 4 × 10−15) with mCRPC bone metastasis. It was associated with two significant biological pathways through KEGG enrichment analysis (parathyroid hormone synthesis, secretion, and action and protein digestion and absorption). In particular, we identified 10 hub genes (ALPL, PHEX, RUNX2, ENPP1, PHOSPHO1, PTH1R, COL11A1, COL24A1, COL22A1, and COL13A1) using cytoHubba of Cytoscape. We also found high gene expression for collagen formation, degradation, absorption, cell-signaling peptides, and bone regulation processes through Gene Ontology (GO) enrichment analysis.

While overall cancer mortality has steadily decreased in recent decades, cancer health disparities among racial and ethnic population groups persist. Here we studied the relationship between cancer survival disparities (CSD), genetic ancestry (GA), and tumor molecular signatures across 33 cancers in a cohort of 9,818 patients. GA correlated with race and ethnicity but showed observable differences in effects on CSD, with significant associations identified in four cancer types: breast invasive carcinoma (BRCA), head and neck squamous cell carcinoma (HNSCC), kidney renal clear cell carcinoma (KIRC), and skin cutaneous carcinoma (SKCM). Differential gene expression and methylation between ancestry groups associated cancer-related genes with CSD, of which, seven protein-coding genes [progestin and adipoQ receptor family member 6 (PAQR6), Lck-interacting transmembrane adaptor 1 (LIME1), Sin3A-associated protein 25 (SAP25), MAX dimerization protein 3 (MXD3), coiled-coil glutamate rich protein 2 (CCER2), refilin A (RFLNA), and cathepsin W (CTSW)] significantly interacted with GA and exacerbated observed survival disparities. These findings indicated that regulatory changes mediated by epigenetic mechanisms have a greater contribution to CSD than population-specific mutations. Overall, we uncovered various molecular mechanisms through which GA might impact CSD, revealing potential population-specific therapeutic targets for groups disproportionately burdened by cancer.Significance:This large-cohort, multicancer study identifies four cancer types with cancer survival disparities and seven cancer-related genes that interact with genetic ancestry and contribute to disparities.

Supplementary Table from Association of Genetic Ancestry and Molecular Signatures with Cancer Survival Disparities: A Pan-Cancer Analysis

Supplementary Data from Association of Genetic Ancestry and Molecular Signatures with Cancer Survival Disparities: A Pan-Cancer Analysis

PDF file - 858KB, Supplementary Methods, Supplementary References, Figure S1-6, Table S1-5. Figure S1. Identification of a new miR in FHIT intron. Figure S2. The SNP variant around the Pri-C2-miR region in FHIT did not affect the C2-miR expression. Figure S3. C2-miR and FHIT are under the same transcriptional control. Figure S4. C2-miR inhibiting human tumor cell growth was not through promoting apoptosis. Figure S5. C2-miR inhibits human tumor cell growth through targeting CCND1, ERBB2, DNMT3A or DNMT3B. Figure S6. Multiple members of the miR-548 family could target the same oncogenes. Table S1. The documented miR-548 family members. Table S2. Quantities of FHIT or C2-miR in total cell RNA (20 ng). Table S3. Expression comparison of pri-miRs or mature miRs in 293FT cells. Table S4. Primers used in this study. Table S5. NanoString probes for detecting 29 members of the miR-548 family.

Loss or attenuated expression of the tumor-suppressor gene FHIT is associated paradoxically with poor progression of human tumors. Fhit promotes apoptosis and regulates reactive oxygen species; however, the mechanism by which Fhit inhibits tumor growth in animals remains unclear. In this study, we used a multidisciplinary approach based on bioinformatics, small RNA library screening, human tissue analysis, and a xenograft mouse model to identify a novel member of the miR-548 family in the fourth intron of the human FHIT gene. Characterization of this human-specific microRNA illustrates the importance of this class of microRNAs in tumor suppression and may influence interpretation of Fhit action in human cancer. Cancer Res; 74(8); 2283–94. ©2014 AACR.

The UK Biobank (UKB), a large-scale biomedical database that includes demographic and electronic health record data for more than half a million ethnically diverse participants, is a potentially valuable resource for the study of health disparities. However, publicly accessible databases that catalog health disparities in the UKB do not exist. We developed the UKB Health Disparities Browser with the aims of (i) facilitating the exploration of the landscape of health disparities in the UK and (ii) directing the attention to areas of disparities research that might have the greatest public health impact. Health disparities were characterized for UKB participant groups defined by age, country of residence, ethnic group, sex and socioeconomic deprivation. We defined disease cohorts for UKB participants by mapping participant International Classification of Diseases, Tenth Revision (ICD-10) diagnosis codes to phenotype codes (phecodes). For each of the population attributes used to define population groups, disease percent prevalence values were computed for all groups from phecode case–control cohorts, and the magnitude of the disparities was calculated by both the difference and ratio of the range of disease prevalence values among groups to identify high- and low-prevalence disparities. We identified numerous diseases and health conditions with disparate prevalence values across population attributes, and we deployed an interactive web browser to visualize the results of our analysis: https://ukbatlas.health-disparities.org. The interactive browser includes overall and group-specific prevalence data for 1513 diseases based on a cohort of >500 000 participants from the UKB. Researchers can browse and sort by disease prevalence and prevalence differences to visualize health disparities for each of the five population attributes, and users can search for diseases of interest by disease names or codes. Database URL https://ukbatlas.health-disparities.org/

This study assesses racial and ethnic differences in overall burden of firearm-related mortality and in change in firearm-related mortality among youths from 1999 to 2020.

Objective The goal of this study was to investigate the relationship between comorbidities and ethnic health disparities in a diverse, cosmopolitan population. Materials and Methods We used the UK Biobank (UKB), a large progressive cohort study of the UK population. Study participants self-identified with 1 of 5 ethnic groups and participant comorbidities were characterized using the 31 disease categories captured by the Elixhauser Comorbidity Index. Ethnic disparities in comorbidities were quantified as the extent to which disease prevalence within categories varies across ethnic groups and the extent to which pairs of comorbidities co-occur within ethnic groups. Disease-risk factor comorbidity pairs were identified where one comorbidity is known to be a risk factor for a co-occurring comorbidity. Results The Asian ethnic group shows the greatest average number of comorbidities, followed by the Black and then White groups. The Chinese group shows the lowest average number of comorbidities. Comorbidity prevalence varies significantly among the ethnic groups for almost all disease categories, with diabetes and hypertension showing the largest differences across groups. Diabetes and hypertension both show ethnic-specific comorbidities that may contribute to the observed disease prevalence disparities. Discussion These results underscore the extent to which comorbidities vary among ethnic groups and reveal group-specific disease comorbidities that may underlie ethnic health disparities. Conclusion The study of comorbidity distributions across ethnic groups can be used to inform targeted group-specific interventions to reduce ethnic health disparities.

Genome-enabled approaches to molecular epidemiology have become essential to public health agencies and the microbial research community. We developed the algorithm STing to provide turn-key solutions for molecular typing and gene detection directly from next generation sequence data of microbial pathogens. Our implementation of STing uses an innovative k-mer search strategy that eliminates the computational overhead associated with the time-consuming steps of quality control, assembly, and alignment, required by more traditional methods. We compared STing to six of the most widely used programs for genome-based molecular typing and demonstrate its ease of use, accuracy, speed and efficiency. STing shows superior accuracy and performance for standard multilocus sequence typing schemes, along with larger genome-scale typing schemes, and it enables rapid automated detection of antimicrobial resistance and virulence factor genes. STing determines the sequence type of traditional 7-gene MLST with 100% accuracy in less than 10 seconds per isolate. We hope that the adoption of STing will help to democratize microbial genomics and thereby maximize its benefit for public health.

Biobank projects around the world are generating genomic data for many thousands and even millions of individuals. Computational methods are needed to handle these massive data sets, including tools for genetic ancestry (GA) inference. Current methods for GA inference are generally accurate, but they are slow and do not scale to biobank-size genomic datasets. Here we present Rye – a new algorithm for GA inference at biobank scale. We compare the accuracy and runtime performance of Rye to the widely used RFMix and ADMIXTURE programs, and we apply it to a dataset of 488,221 genome-wide variant samples from the UK Biobank. Rye infers GA based on principal component analysis (PCA) of genomic variant samples from ancestral reference populations and query individuals. The algorithm’s accuracy is powered by Metropolis-Hastings optimization and its speed is provided by non-negative least squares (NNLS) regression. Rye produces highly accurate GA estimates for three-way admixed populations – African, European, and Native American – compared to RFMix and ADMIXTURE (R2 = 0.998 – 1.00), and shows 50x runtime improvement compared to ADMIXTURE on the UK Biobank dataset. Rye analysis of UK Biobank samples demonstrates how it can be used to infer GA at different levels of relatedness. We discuss user consideration and options for the use of Rye; the program and its documentation are distributed on the GitHub repository: https://github.com/healthdisparities/rye.

Genetic ancestry inference can be used to stratify patient cohorts and to model pharmacogenomic variation within and between populations. We provide a detailed guide to genetic ancestry inference using genome-wide genetic variant datasets, with an emphasis on two widely used techniques: principal components analysis (PCA) and ADMIXTURE analysis. PCA can be used for patient stratification and categorical ancestry inference, whereas ADMIXTURE is used to characterize genetic ancestry as a continuous variable. Visualization methods are critical for the interpretation of genetic ancestry inference methods, and we provide instructions for how the results of PCA and ADMIXTURE can be effectively visualized.

Summary Next-generation sequencing (NGS) enables reliable detection of resistance mutations in minority variants of human immunodeficiency virus type 1 (HIV-1). There is paucity of evidence for the association of minority resistance to treatment failure, and this requires evaluation. However, the tools for analyzing HIV-1 drug resistance (HIVDR) testing data are mostly web-based which requires uploading data to webservers. This is a challenge for laboratories with internet connectivity issues and instances with restricted data transfer across networks. We present QuasiFlow, a pipeline for reproducible analysis of NGS-based HIVDR testing data across different computing environments. Since QuasiFlow entirely depends on command-line tools and a local copy of the reference database, it eliminates challenges associated with uploading HIV-1 NGS data onto webservers. The pipeline takes raw sequence reads in FASTQ format as input and generates a user-friendly report in PDF/HTML format. The drug resistance scores obtained using QuasiFlow were 100% and 99.12% identical to those obtained using web-based HIVdb program and HyDRA web respectively at a mutation detection threshold of 20%. Availability and implementation QuasiFlow and corresponding documentation are publicly available at https://github.com/AlfredUg/QuasiFlow. The pipeline is implemented in Nextflow and requires regular updating of the Stanford HIV drug resistance interpretation algorithm. Supplementary information Supplementary data are available at Bioinformatics Advances online.

Despite a substantial overall decrease in mortality, disparities among ethnic minorities in developed countries persist. This study investigated mortality disparities and their associated risk factors for the three largest ethnic groups in the United Kingdom: Asian, Black, and White. Study participants were sampled from the UK Biobank (UKB), a prospective cohort enrolled between 2006 and 2010. Genetics, biological samples, and health information and outcomes data of UKB participants were downloaded and data-fields were prioritized based on participants with death registry records. Kaplan-Meier method was used to evaluate survival differences among ethnic groups; survival random forest feature selection followed by Cox proportional-hazard modeling was used to identify and estimate the effects of shared and ethnic group-specific mortality risk factors. The White ethnic group showed significantly worse survival probability than the Asian and Black groups. In all three ethnic groups, endoscopy and colonoscopy procedures showed significant protective effects on overall mortality. Asian and Black women show lower relative risk of mortality than men, whereas no significant effect of sex was seen for the White group. The strongest ethnic group-specific mortality associations were ischemic heart disease for Asians, COVID-19 for Blacks, and cancers of respiratory/intrathoracic organs for Whites. Mental health-related diagnoses, including substance abuse, anxiety, and depression, were a major risk factor for overall mortality in the Asian group. The effect of mental health on Asian mortality, particularly for digestive cancers, was exacerbated by an observed hesitance to answer mental health questions, possibly related to cultural stigma. C-reactive protein (CRP) serum levels were associated with both overall and cause-specific mortality due to COVID-19 and digestive cancers in the Black group, where elevated CRP has previously been linked to psychosocial stress due to discrimination. Our results point to mortality risk factors that are group-specific and modifiable, supporting targeted interventions towards greater health equity.

The World Health Organization recently defined hypertension and type 2 diabetes (T2D) as modifiable comorbidities leading to dementia and Alzheimer’s disease. In the United States (US), hypertension and T2D are health disparities, with higher prevalence seen for Black and Hispanic minority groups compared to the majority White population. We hypothesized that elevated prevalence of hypertension and T2D risk factors in Black and Hispanic groups may be associated with dementia disparities. We interrogated this hypothesis using a cross-sectional analysis of participant data from the All of Us (AoU) Research Program, a large observational cohort study of US residents. The specific objectives of our study were: (1) to compare the prevalence of dementia, hypertension, and T2D in the AoU cohort to previously reported prevalence values for the US population, (2) to investigate the association of hypertension, T2D, and race/ethnicity with dementia, and (3) to investigate whether race/ethnicity modify the association of hypertension and T2D with dementia. AoU participants were recruited from 2018 to 2019 as part of the initial project cohort (R2019Q4R3). Participants aged 40–80 with electronic health records and demographic data (age, sex, race, and ethnicity) were included for analysis, yielding a final cohort of 125,637 individuals. AoU participants show similar prevalence of hypertension (32.1%) and T2D (13.9%) compared to the US population (32.0% and 10.5%, respectively); however, the prevalence of dementia for AoU participants (0.44%) is an order of magnitude lower than seen for the US population (5%). AoU participants with dementia show a higher prevalence of hypertension (81.6% vs. 31.9%) and T2D (45.9% vs. 11.4%) compared to non-dementia participants. Dominance analysis of a multivariable logistic regression model with dementia as the outcome shows that hypertension, age, and T2D have the strongest associations with dementia. Hispanic was the only race/ethnicity group that showed a significant association with dementia, and the association of sex with dementia was non-significant. The association of T2D with dementia is likely explained by concurrent hypertension, since > 90% of participants with T2D also had hypertension. Black race and Hispanic ethnicity interact with hypertension, but not T2D, to increase the odds of dementia. This study underscores the utility of the AoU participant cohort to study disease prevalence and risk factors. We do notice a lower participation of aged minorities and participants with dementia, revealing an opportunity for targeted engagement. Our results indicate that targeting hypertension should be a priority for risk factor modifications to reduce dementia incidence.

Foodborne pathogens are a major public health burden in the United States, leading to 9.4 million illnesses annually. Since 1996, a national laboratory-based surveillance program, PulseNet, has used molecular subtyping and serotyping methods with the aim to reduce the burden of foodborne illness through early detection of emerging outbreaks. PulseNet affiliated laboratories have used pulsed-field gel electrophoresis (PFGE) and immunoassays to subtype and serotype bacterial isolates. Widespread use of serotyping and PFGE for foodborne illness surveillance over the years has resulted in the accumulation of a wealth of routine surveillance and outbreak epidemiological data. This valuable source of data has been used to understand seasonal frequency, geographic distribution, demographic information, exposure information, disease severity, and source of foodborne isolates. In 2019, PulseNet adopted whole genome sequencing (WGS) at a national scale to replace PFGE with higher-resolution methods such as the core genome multilocus sequence typing. Consequently, PulseNet's recent shift to genome-based subtyping methods has rendered the vast collection of historic surveillance data associated with serogroups and PFGE patterns potentially unusable. The goal of this study was to develop a bioinformatics method to associate the WGS data that are currently used by PulseNet for bacterial pathogen subtyping to previously characterized serogroup and PFGE patterns. Previous efforts to associate WGS to PFGE patterns relied on predicting DNA molecular weight based on restriction site analysis. However, these approaches failed owing to the non-uniform usage of genomic restriction sites by PFGE restriction enzymes. We developed a machine learning approach to classify isolates to their most probable serogroup and PFGE pattern, based on comparisons of genomic k-mer signatures. We applied our WGS classification method to 5,970 Shiga toxin-producing Escherichia coli (STEC) isolates collected as part of PulseNet's routine foodborne surveillance activities between 2003 and 2018. Our machine learning classifier is able to associate STEC WGS to higher-level serogroups with very high accuracy and lower-level PFGE patterns with somewhat lower accuracy. Taken together, these classifications support the ability of public health investigators to associate currently generated WGS data with historical epidemiological knowledge linked to serogroups and PFGE patterns in support of outbreak surveillance for food safety and public health.

Humans have migrated from their ancestral homelands in Africa to nearly every part of the world. Human migration is characterized by a recurrent process of physical isolation and genetic diversification followed by admixture, whereby previously isolated populations come together and exchange genes. Admixture results in the introgression of alleles from ancestral source populations into hybrid admixed populations, and introgression can facilitate rapid, adaptive evolution by introducing beneficial alleles at intermediate frequencies. We provide examples of adaptive introgression between archaic and modern human populations and for admixed populations in the Americas, which were formed relatively recently via admixture among African, European, and Indigenous American ancestral populations. Adaptive introgression has had an outsized effect on the human immune system. In light of the ubiquity of admixture in human evolution, we propose that adaptive introgression is a fundamentally important mechanism for driving rapid, adaptive evolution in human populations.

While overall cancer mortality has steadily decreased in recent decades, cancer health disparities among racial and ethnic population groups persist. Here we studied the relationship between cancer survival disparities (CSD), genetic ancestry (GA), and tumor molecular signatures across 33 cancers in a cohort of 9,818 patients. GA correlated with race and ethnicity but showed observable differences in effects on CSD, with significant associations identified in four cancer types: breast invasive carcinoma (BRCA), head and neck squamous cell carcinoma (HNSCC), kidney renal clear cell carcinoma (KIRC), and skin cutaneous carcinoma (SKCM). Differential gene expression and methylation between ancestry groups associated cancer-related genes with CSD, of which, seven protein-coding genes [progestin and adipoQ receptor family member 6 (PAQR6), Lck-interacting transmembrane adaptor 1 (LIME1), Sin3A-associated protein 25 (SAP25), MAX dimerization protein 3 (MXD3), coiled-coil glutamate rich protein 2 (CCER2), refilin A (RFLNA), and cathepsin W (CTSW)] significantly interacted with GA and exacerbated observed survival disparities. These findings indicated that regulatory changes mediated by epigenetic mechanisms have a greater contribution to CSD than population-specific mutations. Overall, we uncovered various molecular mechanisms through which GA might impact CSD, revealing potential population-specific therapeutic targets for groups disproportionately burdened by cancer. Significance: This large-cohort, multicancer study identifies four cancer types with cancer survival disparities and seven cancer-related genes that interact with genetic ancestry and contribute to disparities.

Histoplasmosis is a systemic fungal disease caused by the pathogen Histoplasma spp. that results in significant morbidity and mortality in persons with HIV/AIDS and can also affect immunocompetent individuals. Although some PCR and antigen-detection assays have been developed, conventional diagnosis has largely relied on culture, which can take weeks. Our aim was to provide a proof of principle for rationally designing and standardizing PCR assays based on Histoplasma-specific genomic sequences. Via automated comparisons of aligned genome contigs/scaffolds and gene (sub)sequences, we identified protein-coding genes that are present in existing sequences of Histoplasma strains but not in other genera. Two of the genes, PPK and CFP4, were used for designing primer sets for conventional and real-time PCR assays. Both resulted in a 100% analytical specificity in vitro and detected 62/62 H. capsulatum isolates using purified DNA. We also obtained positive detections of 2/2 confirmed H. capsulatum clinical FFPE (formalin-fixed paraffin-embedded) samples using both primer sets. Positive control plasmid 10-fold serial dilutions confirmed the analytical sensitivity of the assays. The findings suggest that these novel primer sets should allow for detection sensitivity and reduce false positive results/cross-reactions. New assays for detecting pathogenic fungi, constructed along these lines, could be simple and affordable to implement.

Previous studies have shown that the sugarcane microbiome harbors diverse plant growth promoting (PGP) microorganisms, including nitrogen-fixing bacteria, and the objective of this study was to design a genome-enabled approach to prioritize sugarcane associated nitrogen-fixing bacteria according to their potential as biofertilizers. Using a systematic high throughput approach, 22 pure cultures of nitrogen-fixing bacteria were isolated and tested for diazotrophic potential by PCR amplification of nitrogenase (nifH) genes, common molecular markers for nitrogen fixation capacity. Genome sequencing confirmed the presence of intact nitrogenasenifHgenes and operons in the genomes of 18 of the isolates. Isolate genomes also encoded operons for phosphate solubilization, siderophore production operons, and other PGP phenotypes.Klebsiella pneumoniaestrains comprised 14 of the 22 nitrogen-fixing isolates, and four others were members of closely related genera toKlebsiella. A computational phenotyping approach was developed to rapidly screen for strains that have high potential for nitrogen fixation and other PGP phenotypes while showing low risk for virulence and antibiotic resistance. The majority of sugarcane isolates were below a genotypic and phenotypic threshold, showing uniformly low predicted virulence and antibiotic resistance compared to clinical isolates. Six prioritized strains were experimentally evaluated for PGP phenotypes: nitrogen fixation, phosphate solubilization, and the production of siderophores, gibberellic acid and indole acetic acid. Results from the biochemical assays were consistent with the computational phenotype predictions for these isolates. Our results indicate that computational phenotyping is a promising tool for the assessment of benefits and risks associated with bacteria commonly detected in agricultural ecosystems.IMPORTANCEA genome-enabled approach was developed for the prioritization of native bacterial isolates with the potential to serve as biofertilizers for sugarcane fields in Colombia’s Cauca Valley. The approach is based on computational phenotyping, which entails predictions related to traits of interest based on bioinformatic analysis of whole genome sequences. Bioinformatic predictions of the presence of plant growth promoting traits were validated with experimental assays and more extensive genome comparisons, thereby demonstrating the utility of computational phenotyping for assessing the benefits and risks posed by bacterial isolates that can be used as biofertilizers. The quantitative approach to computational phenotyping developed here for the discovery of biofertilizers has the potential for use with a broad range of applications in environmental and industrial microbiology, food safety, water quality, and antibiotic resistance studies.

The inclusion of ethnicity in equations for estimating the glomerular filtration rate (eGFR) from serum creatinine levels has been challenged since ethnicity is socially defined and therefore a poor proxy for biological differences. We hypothesized that genetic ancestry (GA) would be more strongly associated with creatinine levels among healthy individuals than self-identified ethnicity. We studied a diverse cohort of 35,590 participants characterized as part of the UK Biobank, grouped by self-reported ethnicity: Black, East Asian, Mixed, Other, South Asian, and White. We used multivariable modeling to test for associations between ethnicity, GA, socioeconomic deprivation, and serum creatinine levels, including covariates for age, sex, height, and body mass index. Model fit comparisons and relative importance analysis were used to compare the effects of ethnicity and GA on creatinine levels. Black ethnicity shows a positive effect on participant serum creatinine levels (β = 9.36 ± 0.38), whereas East Asian (β = -1.80 ± 0.66) and South Asian (β = -0.28 ± 0.36) ethnicity show negative effects on creatinine. Male sex (β = 17.69 ± 0.34) and height (β = 0.13 ± 0.02) also show high positive associations with creatinine levels, while socioeconomic deprivation (β = -0.04 ± 0.04) shows no significant association. African ancestry has the highest association (β = 13.81 ± 0.52) with creatinine levels. Overall, GA (9.06%) explains significantly more of the variation in creatinine levels than ethnicity (4.96%), with African ancestry (6.36%) alone explaining more of the variation than ethnicity. We found that GA explains more of the variation in serum creatinine levels than socioeconomic deprivation, suggesting the possibility that ethnic differences in creatinine are shaped by genetic rather than social factors.

Alzheimer’s disease (AD) is the most common cause of dementia in the elderly, affecting over 50 million people worldwide in 2020 and this number will triple to 152 million by 2050. Much of the increase will be in developing countries like Colombia. In familial forms, highly penetrant mutations have been identified in three genes, APP, PSEN1, and PSEN2, supporting a role for amyloid-β peptide. In sporadic forms, more than 30 risk genes involved in the lipid metabolism, the immune system, and synaptic functioning mechanisms. We used whole-exome sequencing (WES) to evaluate a family of 97 members, spanning three generations, with a familiar AD, and without mutations in APP, PSEN1, or PSEN2. We sequenced two affected and one unaffected member with the aim of identifying genetic variants that could explain the presence of the disease in the family and the candidate variants were validated in eleven members. We also built a structural model to try to determine the effect on protein function. WES analysis identified two rare variants in SORL1 and MTHFD1L genes segregating in the family with other potential risk variants in APOE, ABCA7, and CHAT, suggesting an oligogenic inheritance. Additionally, the structural 3D models of SORL1 and MTHFD1L variants shows that these variants produce polarity changes that favor hydrophobic interactions, resulting in local structural changes that could affect the protein function and may contribute to the development of the disease in this family.

Prostate cancer (PCa) is the second most cancer-related cause of death in men. Most PCa are characterized as slow-growing or indolent, though some patients are at a high risk of for recurrent and eventually metastatic disease. The most lethal form of PCa is metastatic castration-resistant prostate cancer (mCRPC) that has progressed to the bone. The molecular mechanism underlying PCa progression to bone has not been fully elucidated. Here, we identify gene networks associated with CRPC bone metastasis and survival risk. Networks were investigated by using a novel weighted gene network co-expression analysis (WGCNA) method and examining overrepresentation of upstream regulators and signaling pathways within co-expressed transcriptome modules across clinically annotated transcriptomes from mCRPC patients (N=60). Functional Enrichment analysis was used, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and protein-protein interaction analysis, to identify biological functions of related hub genes overrepresented in our module of interest. WGCNA identified that cyan module was significantly associated with castration-resistant prostate cancer (CRPC) bone metastasis. KEGG and GO analysis results revealed genes in blue module were mainly related to collagen formation, cell signaling peptides, and bone regulation processes, including alkaline phosphatase, osteoblast formation, and endochondral ossification. Genes positively correlated with bone metastasis exhibited the following biological pathways: PI3K-Akt signaling, ECM-receptor interaction, and protein digestion and absorption pathways. This study provides novel insights into the biological pathways associated with CPRC metastasis to the bone. The module associated with bone metastasis and overall survival represent both known and novel pathways. Citation Format: Lawrence P. McKinney, Rajesh Singh, I. King Jordan, Sooryanarayana Varambally, Martin G. Sanda, Eric B. Dammer, James W. Lillard. Multivariate transcriptome analysis identifies networks and key drivers of metastatic castration-resistant prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2701.

Background Type 2 diabetes (T2D) is a complex common disease that disproportionately impacts minority ethnic groups in the United Kingdom (UK). Socioeconomic deprivation (SED) is widely considered as a potential explanation for T2D ethnic disparities in the UK, whereas the effect of genetic ancestry (GA) on such disparities has yet to be studied. Methods We leveraged data from the UK Biobank prospective cohort study, with participants enrolled between 2006 and 2010, to model the relationship between SED (Townsend index), GA (clustering principal components of whole genome genotype data), and T2D status (ICD-10 codes) across the three largest ethnic groups in the UK – Asian, Black, and White – using multivariable logistic regression. Findings The Asian group shows the highest T2D prevalence (17·9%), followed by the Black (11·7%) and White (5·5%) ethnic groups. We find that both SED (OR: 1·11, 95% CI: 1·10–1·11) and non-European GA (OR South Asian versus European: 4·37, 95% CI: 4·10–4·66; OR African versus European: 2·52, 95% CI: 2·23–2·85) are significantly associated with the observed T2D disparities. GA and SED show significant interaction effects on T2D, with SED being a relatively greater risk factor for T2D for individuals with South Asian and African ancestry, compared to those with European ancestry. Interpretation The significant interactions between SED and GA underscore how the effects of environmental risk factors can differ among ancestry groups, suggesting the need for group-specific interventions. Funding This work was supported by the National Institutes of Health (NIH) Distinguished Scholars Program (DSP) to LMR and the Division of Intramural Research (DIR) of the National Institute on Minority Health and Health Disparities (NIMHD) at NIH.

Genome-wide association studies have uncovered thousands of genetic variants that are associated with a wide variety of human traits. Knowledge of how trait-associated variants are distributed within and between populations can provide insight into the genetic basis of group-specific phenotypic differences, particularly for health-related traits. We analyzed the genetic divergence levels for 1) individual trait-associated variants and 2) collections of variants that function together to encode polygenic traits, between two neighboring populations in Colombia that have distinct demographic profiles: Antioquia (Mestizo) and Chocó (Afro-Colombian). Genetic ancestry analysis showed 62% European, 32% Native American, and 6% African ancestry for Antioquia compared with 76% African, 10% European, and 14% Native American ancestry for Chocó, consistent with demography and previous results. Ancestry differences can confound cross-population comparison of polygenic risk scores (PRS); however, we did not find any systematic bias in PRS distributions for the two populations studied here, and population-specific differences in PRS were, for the most part, small and symmetrically distributed around zero. Both genetic differentiation at individual trait-associated single nucleotide polymorphisms and population-specific PRS differences between Antioquia and Chocó largely reflected anthropometric phenotypic differences that can be readily observed between the populations along with reported disease prevalence differences. Cases where population-specific differences in genetic risk did not align with observed trait (disease) prevalence point to the importance of environmental contributions to phenotypic variance, for both infectious and complex, common disease. The results reported here are distributed via a web-based platform for searching trait-associated variants and PRS divergence levels at http://map.chocogen.com (last accessed August 12, 2020).

Background Hispanic/Latino (HL) populations bear a disproportionately high burden of type 2 diabetes (T2D). The ability to predict T2D genetic risk using polygenic risk scores (PRS) offers great promise for improved screening and prevention. However, there are a number of complications related to the accurate inference of genetic risk across HL populations with distinct ancestry profiles. We investigated how ancestry affects the inference of T2D genetic risk using PRS in diverse HL populations from Colombia and the United States (US). In Colombia, we compared T2D genetic risk for the Mestizo population of Antioquia to the Afro-Colombian population of Chocó, and in the US, we compared European-American versus Mexican-American populations. Methods Whole genome sequences and genotypes from the 1000 Genomes Project and the ChocoGen Research Project were used for genetic ancestry inference and for T2D polygenic risk score (PRS) calculation. Continental ancestry fractions for HL genomes were inferred via comparison with African, European, and Native American reference genomes, and PRS were calculated using T2D risk variants taken from multiple genome-wide association studies (GWAS) conducted on cohorts with diverse ancestries. A correction for ancestry bias in T2D risk inference based on the frequencies of ancestral versus derived alleles was developed and applied to PRS calculations in the HL populations studied here. Results T2D genetic risk in Colombian and US HL populations is positively correlated with African and Native American ancestry and negatively correlated with European ancestry. The Afro-Colombian population of Chocó has higher predicted T2D risk than Antioquia, and the Mexican-American population has higher predicted risk than the European-American population. The inferred relative risk of T2D is robust to differences in the ancestry of the GWAS cohorts used for variant discovery. For trans-ethnic GWAS, population-specific variants and variants with same direction effects across populations yield consistent results. Nevertheless, the control for bias in T2D risk prediction confirms that explicit consideration of genetic ancestry can yield more reliable cross-population genetic risk inferences. Conclusions T2D associations that replicate across populations provide for more reliable risk inference, and modeling population-specific frequencies of ancestral and derived risk alleles can help control for biases in PRS estimation.

Prostate cancer (PCa) is the second most cancer-related cause of death in men. The most lethal form of PCa is metastatic castration-resistant prostate cancer (CRPC) that has progressed to the bone. The molecular mechanism underlying PCa progression to bone has not been fully elucidated. Here, we identify gene networks and characterize the cell types that contribute to the heterogeneity and complexity of CRPC associated with bone metastasis. Networks were investigated by using a novel weighted gene network co-expression analysis (WGCNA) method and examining overrepresentation of upstream regulators and signaling pathways within co-expressed transcriptome modules across clinically annotated transcriptomes from mCRPC patients (N=60). Functional Enrichment analysis was used, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and protein-protein interaction analysis to identify biological functions of related hub genes overrepresented in our module of interest. WGCNA identified significant modules that are associated with CRPC bone metastasis. KEGG and GO analysis results revealed genes in these modules were mainly related to collagen formation, cell signaling peptides, and bone regulation processes. Genes positively correlated with bone metastasis exhibited the following biological pathways: PI3K-Akt signaling, ECM-receptor interaction, and protein digestion and absorption pathways. This study provides novel insights into the biological pathways associated with CRPC metastasis to the bone and its tumor immune microenvironment (TIME) niche. The modules associated with bone metastasis and overall survival represent both known and novel pathways. Supported by NIH/NCI Grant: MSM/Tuskegee U/UAB Comp. Cancer Center Partnership Name & Number of Grant Award: NCI - U54CA118638

Insertional activity of transposable elements (TEs) has had a major impact on the human genome; approximately one-half to two-thirds of the genome sequence is likely to be derived from TE insertions. Several families of human TEs - primarily Alu, L1 and SVA - continue to actively transpose, thereby generating insertional polymorphisms among individual genomes. The impact that TE insertions have on their human hosts' fitness, and accordingly the role that natural selection plays in shaping patterns of TE polymorphisms among populations, have yet to be systematically evaluated using whole genome sequence data. We present here a population genomic study of the effects of natural selection on human genetic variation that results from the recent activity of TEs. We developed a genome-wide scan for selection on human TE polymorphisms and applied it to a dataset of 14,384 locus-specific TE insertions characterized for 1511 individuals from 15 populations. Our TE selection scan looks for anomalously high population-specific TE insertion allele frequencies that are consistent with the action of positive (adaptive) selection. To control for the effects of demographic history, we compared the observed patterns of population-specific TE insertion allele frequencies to a neutral evolutionary model generated using time forward simulation of TE insertion allele frequencies among human population groups. This approach uncovered seven cases of polymorphic TE insertions that appear to have increased in frequency within specific human populations owing to the effects of positive selection. Five of the seven putatively selected TE insertions map to tissue-specific enhancers, and two cases correspond to expression quantitative trait loci that are associated with inter-individual gene regulatory differences. This study represents the first report of recent, local adaptation acting on polymorphic human TEs.

Human populations from around the world show striking phenotypic variation across a wide variety of traits. Genome-wide association studies (GWAS) are used to uncover genetic variants that influence the expression of heritable human traits; accordingly, population-specific distributions of GWAS-implicated variants may shed light on the genetic basis of human phenotypic diversity. With this in mind, we developed the GlobAl Distribution of GEnetic Traits web server (GADGET http://gadget.biosci.gatech.edu). The GADGET web server provides users with a dynamic visual platform for exploring the relationship between worldwide genetic diversity and the genetic architecture underlying numerous human phenotypes. GADGET integrates trait-implicated single nucleotide polymorphisms (SNPs) from GWAS, with population genetic data from the 1000 Genomes Project, to calculate genome-wide polygenic trait scores (PTS) for 818 phenotypes in 2504 individual genomes. Population-specific distributions of PTS are shown for 26 human populations across 5 continental population groups, with traits ordered based on the extent of variation observed among populations. Users of GADGET can also upload custom trait SNP sets to visualize global PTS distributions for their own traits of interest.

A convergence of novel genome analysis technologies is enabling population genomic studies of human transposable elements (TEs). Population surveys of human genome sequences have uncovered thousands of individual TE insertions that segregate as common genetic variants, i.e. TE polymorphisms. These recent TE insertions provide an important source of naturally occurring human genetic variation. Investigators are beginning to leverage population genomic data sets to execute genome-scale association studies for assessing the phenotypic impact of human TE polymorphisms. For example, the expression quantitative trait loci (eQTL) analytical paradigm has recently been used to uncover hundreds of associations between human TE insertion variants and gene expression levels. These include population-specific gene regulatory effects as well as coordinated changes to gene regulatory networks. In addition, analyses of linkage disequilibrium patterns with previously characterized genome-wide association study (GWAS) trait variants have uncovered TE insertion polymorphisms that are likely causal variants for a variety of common complex diseases. Gene regulatory mechanisms that underlie specific disease phenotypes have been proposed for a number of these trait associated TE polymorphisms. These new population genomic approaches hold great promise for understanding how ongoing TE activity contributes to functionally relevant genetic variation within and between human populations.

The DNA is cells is continuously exposed to reactive oxygen species resulting in toxic and mutagenic DNA damage. Although the repair of oxidative DNA damage occurs primarily through the base excision repair (BER) pathway, the nucleotide excision repair (NER) pathway processes some of the same lesions. In addition, damage tolerance mechanisms, such as recombination and translesion synthesis, enable cells to tolerate oxidative DNA damage, especially when BER and NER capacities are exceeded. Thus, disruption of BER alone or disruption of BER and NER in Saccharomyces cerevisiae leads to increased mutations as well as large-scale genomic rearrangements. Previous studies demonstrated that a particular region of chromosome II is susceptible to chronic oxidative stress-induced chromosomal rearrangements, suggesting the existence of DNA damage and/or DNA repair hotspots. Here we investigated the relationship between oxidative damage and genomic instability utilizing chromatin immunoprecipitation combined with DNA microarray technology to profile DNA repair sites along yeast chromosomes under different oxidative stress conditions. We targeted the major yeast AP endonuclease Apn1 as a representative BER protein. Our results indicate that Apn1 target sequences are enriched for cytosine and guanine nucleotides. We predict that BER protects these sites in the genome because guanines and cytosines are thought to be especially susceptible to oxidative attack, thereby preventing large-scale genome destabilization from chronic accumulation of DNA damage. Information from our studies should provide insight into how regional deployment of oxidative DNA damage management systems along chromosomes protects against large-scale rearrangements. Copyright © 2017 John Wiley & Sons, Ltd.

Transposable element (TE)-derived sequences comprise more than half of the human genome, and their presence has been documented to alter gene expression in a number of different ways, including the generation of alternatively spliced transcript isoforms. Alternative splicing has been associated with tumorigenesis for a number of different cancers. The objective of this study was to broadly characterize the role of human TEs in generating alternatively spliced transcript isoforms in cancer. To do so, we screened for the presence of TE-derived sequences co-located with alternative splice sites that are differentially used in normal versus cancer tissues. We analysed a comprehensive set of alternative splice variants characterized for 614 matched normal-tumour tissue pairs across 13 cancer types, resulting in the discovery of 4820 TE-generated alternative splice events distributed among 723 cancer-associated genes. Short interspersed nuclear elements (Alu) and long interspersed nuclear elements (L1) were found to contribute the majority of TE-generated alternative splice sites in cancer genes. A number of cancer-associated genes, including MYH11 , WHSC1 and CANT1 , were shown to have overexpressed TE-derived isoforms across a range of cancer types. TE-derived isoforms were also linked to cancer-specific fusion transcripts, suggesting a novel mechanism for the generation of transcriptome diversity via trans -splicing mediated by dispersed TE repeats. This article is part of a discussion meeting issue ‘Crossroads between transposons and gene regulation'.

Genome-enabled approaches to molecular epidemiology have become essential to public health agencies and the microbial research community. We developed the algorithm STing to provide turn-key solutions for molecular typing and gene detection directly from next-generation sequence data of microbial pathogens. Our implementation of STing uses an innovative k-mer search strategy that eliminates the computational overhead associated with the time consuming steps of quality control, assembly, and alignment required by more traditional methods. We compared STing to six of the most widely used programs for genome-based molecular typing and demonstrate its ease of use, accuracy, speed, and efficiency. STing shows superior accuracy and performance for standard multilocus sequence typing schemes, along with larger genome-scale typing schemes, and it enables rapid automated detection of antimicrobial resistance and virulence factor genes. We hope that the adoption of STing will help to democratize microbial genomics and thereby maximize its benefit for public health.

BackgroundAdmixture occurs when previously isolated populations come together and exchange genetic material. We hypothesized that admixture can enable rapid adaptive evolution in human populations by introducing novel genetic variants (haplotypes) at intermediate frequencies, and we tested this hypothesis via the analysis of whole genome sequences sampled from admixed Latin American populations in Colombia, Mexico, Peru, and Puerto Rico.ResultsOur screen for admixture-enabled selection relies on the identification of loci that contain more or less ancestry from a given source population than would be expected given the genome-wide ancestry frequencies. We employed a combined evidence approach to evaluate levels of ancestry enrichment at (1) single loci across multiple populations and (2) multiple loci that function together to encode polygenic traits. We found cross-population signals of African ancestry enrichment at the major histocompatibility locus on chromosome 6, consistent with admixture-enabled selection for enhanced adaptive immune response. Several of the human leukocyte antigen genes at this locus (HLA-A,HLA-DRB51andHLA-DRB5) showed independent evidence of positive selection prior to admixture, based on extended haplotype homozygosity in African populations. A number of traits related to inflammation, blood metabolites, and both the innate and adaptive immune system showed evidence of admixture-enabled polygenic selection in Latin American populations.ConclusionsThe results reported here, considered together with the ubiquity of admixture in human evolution, suggest that admixture serves as a fundamental mechanism that drives rapid adaptive evolution in human populations.

Volume 84, no. 4, e01512-17, 2018, [https://doi.org/10.1128/AEM.01512-17][1]. Page 14, Acknowledgments: The following data attribution should be added at the end of the first paragraph: “Data are publicly available through the Gulf of Mexico Research Initiative Information and Data Cooperative (

While genomic approaches to precision medicine hold great promise, they remain prohibitively expensive for developing countries. The precision public health paradigm, whereby healthcare decisions are made at the level of populations as opposed to individuals, provides one way for the genomics revolution to directly impact health outcomes in the developing world. Genomic approaches to precision public health require a deep understanding of local population genomics, which is still missing for many developing countries. We are investigating the population genomics of genetic variants that mediate drug response in an effort to inform healthcare decisions in Colombia. Our work focuses on two neighboring populations with distinct ancestry profiles: Antioquia and Chocó. Antioquia has primarily European genetic ancestry followed by Native American and African components, whereas Chocó shows mainly African ancestry with lower levels of Native American and European admixture. We performed a survey of the global distribution of pharmacogenomic variants followed by a more focused study of pharmacogenomic allele frequency differences between the two Colombian populations. Worldwide, we found pharmacogenomic variants to have both unusually high minor allele frequencies and high levels of population differentiation. A number of these pharmacogenomic variants also show anomalous effect allele frequencies within and between the two Colombian populations, and these differences were found to be associated with their distinct genetic ancestry profiles. For example, the C allele of the single nucleotide polymorphism (SNP) rs4149056 [Solute Carrier Organic Anion Transporter Family Member 1B1 (SLCO1B1)∗5], which is associated with an increased risk of toxicity to a commonly prescribed statin, is found at relatively high frequency in Antioquia and is associated with European ancestry. In addition to pharmacogenomic alleles related to increased toxicity risk, we also have evidence that alleles related to dosage and metabolism have large frequency differences between the two populations, which are associated with their specific ancestries. Using these findings, we have developed and validated an inexpensive allele-specific PCR assay to test for the presence of such population-enriched pharmacogenomic SNPs in Colombia. These results serve as an example of how population-centered approaches to pharmacogenomics can help to realize the promise of precision medicine in resource-limited settings.

Haemophilu s haemolyticus has been recently discovered to have the potential to cause invasive disease. It is closely related to nontypeable Haemophilus influenzae (NT H. influenzae ). NT H. influenzae and H. haemolyticus are often misidentified because none of the existing tests targeting the known phenotypes of H. haemolyticus are able to specifically identify H. haemolyticus . Through comparative genomic analysis of H. haemolyticus and NT H. influenzae , we identified genes unique to H. haemolyticus that can be used as targets for the identification of H. haemolyticus . A real-time PCR targeting purT (encoding phosphoribosylglycinamide formyltransferase 2 in the purine synthesis pathway) was developed and evaluated. The lower limit of detection was 40 genomes/PCR; the sensitivity and specificity in detecting H. haemolyticus were 98.9% and 97%, respectively. To improve the discrimination of H. haemolyticus and NT H. influenzae , a testing scheme combining two targets ( H. haemolyticus purT and H. influenzae hpd , encoding protein D lipoprotein) was also evaluated and showed 96.7% sensitivity and 98.2% specificity for the identification of H. haemolyticus and 92.8% sensitivity and 100% specificity for the identification of H. influenzae , respectively. The dual-target testing scheme can be used for the diagnosis and surveillance of infection and disease caused by H. haemolyticus and NT H. influenzae .

Colistin is an important last-line antibiotic to treat highly resistant Enterobacter infections. Resistance to colistin has emerged among clinical isolates but has been associated with a significant growth defect. Here, we describe a clinical Enterobacter isolate with a deletion of mgrB, a regulator of colistin resistance, leading to high-level resistance in the absence of a growth defect. The identification of a path to resistance unrestrained by growth defects suggests colistin resistance could become more common in Enterobacter.

Transposable element (TE) derived sequences comprise more than half of the human genome, and their presence has been documented to alter gene expression in a number of different ways, including the generation of alternatively spliced transcript isoforms. Alternative splicing has been associated with tumorigenesis for a number of different cancers. The objective of this study was to broadly characterize the role of human TEs in generating alternatively spliced transcript isoforms in cancer. To do so, we screened for the presence of TE-derived sequences co-located with alternative splice sites that are differentially utilized in normal versus cancer tissues. We analyzed a comprehensive set of alternative splice variants characterized for 614 matched normal-tumor tissue pairs across 13 cancer types, resulting in the discovery of 4,820 TE-generated alternative splice events distributed among 723 cancer-associated genes. SINEs (Alu) and LINEs (L1) were found to contribute the majority of TE-generated alternative splice sites in cancer genes. A number of cancer-associated genes - including MYH11, WHSC1, and CANT1 - were shown to have overexpressed TE-derived isoforms across a range of cancer types. TE-derived isoforms were also linked to cancer-specific fusion transcripts, suggesting a novel mechanism for the generation of transcriptome diversity via trans-splicing mediated by dispersed TE repeats. Citation Format: Evan A. Clayton, Lavanya Rishishwar, Tzu-Chuan Huang, Saurabh Gulati, Dongjo Ban, John F. McDonald, I. King Jordan. An atlas of transposable element derived alternative splicing in cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2115.

BackgroundLike many bacteria, Vibrio cholerae, which causes fatal cholera, deploys a harpoon-like Type VI Secretion System (T6SS) to compete against other microbes in environmental and host settings. The T6SS punctures adjacent cells and delivers toxic effector proteins that are harmless to bacteria carrying cognate immunity factors. Only four effector/immunity pairs encoded on one large and three auxiliary gene clusters have been characterized from largely clonal, patient-derived strains of V. cholerae.ResultsWe sequenced two dozen V. cholerae strain genomes from diverse sources and developed a novel and adaptable bioinformatic tool based on Hidden Markov Models. We identified two new T6SS auxiliary gene clusters; one, Aux 5, is described here. Four Aux 5 loci are present in the host strain, each with an atypical effector/immunity gene organization. Structural prediction of the putative effector indicated it is a lipase, which we name TleV1 (Type VI lipase effector Vibrio, TleV1). Ectopic TleV1 expression induced toxicity in E. coli, which was rescued by co-expression of the TleV1 immunity factor. A clinical V. cholerae reference strain expressing the Aux 5 cluster used TleV1 to lyse its parental strain upon contact via its T6SS but was unable to kill parental cells expressing TleV1’s immunity factor.ConclusionWe developed a novel bioinformatic method and identified new T6SS gene clusters in V. cholerae. We also showed the TleV1 toxin is delivered in a T6SS-manner by V. cholerae and can lyse other bacterial cells. Our web-based tool may be modified to identify additional novel T6SS genomic loci in diverse bacterial species.

The convergence of hypervirulence and multidrug resistance in Klebsiella pneumoniae is a significant concern. Here, we report the first screen for hypermucoviscosity, a trait associated with increased virulence, using a U.S. surveillance collection of carbapenem-resistant (CR) K. pneumoniae isolates. We identified one hypermucoviscous isolate, which carried a gene encoding the KPC-3 carbapenemase, among numerous resistance genes. The strain further exhibited colistin heteroresistance undetected by diagnostics. This convergence of diverse resistance mechanisms and increased virulence underscores the need for enhanced K. pneumoniae surveillance.

Supplementary Tables 1-2 and Supplementary Figures 1-7

European and African descendants settled the continental US during the 17th-19th centuries, coming into contact with established Native American populations. The resulting admixture among these groups yielded a significant reservoir of cryptic Native American ancestry in the modern US population. We analyzed the patterns of Native American admixture seen for the three largest genetic ancestry groups in the US population: African American, European American, and Hispanic/Latino. The three groups show distinct Native American ancestry profiles, which are indicative of their historical patterns of migration and settlement across the country. Native American ancestry in the modern African American population does not coincide with local geography, instead forming a monophyletic group with origins in the southeastern US, consistent with the Great Migration of the early 20th century. European Americans show Native American ancestry that tracks their geographic origins across the US, indicative of ongoing contact during westward expansion, and Native American ancestry can resolve Hispanic/Latino individuals into distinct local groups formed by more recent migration from Mexico and Puerto Rico. We found an anomalous pattern of Native American ancestry from the US southwest, which most likely corresponds to the Nuevomexicano descendants of early Spanish settlers to the region. We addressed a number of controversies surrounding this population, including the extent of Sephardic Jewish ancestry. Nuevomexicanos are less admixed than nearby Mexican-American individuals, with more European and less Native American and African ancestry, and while they do show demonstrable Sephardic Jewish ancestry, the fraction is no greater than seen for other Hispanic/Latino populations.

Members of the Klebsiella genus promote plant growth. We report here draft whole-genome sequences for 15 Klebsiella sp. isolates from sugarcane fields in the Cauca Valley of Colombia. The genomes of these isolates were characterized as part of a broader effort to evaluate their utility as endemic plant growth-promoting biofertilizers.