Search

Abstract Background Identification of rare variants involved in complex, polygenic diseases like Crohn’s disease (CD) has accelerated with the introduction of whole exome/genome sequencing association studies. Rare variants can be used in both diagnostic and therapeutic assessments; however, since they are likely to be restricted to specific ancestry groups, their contributions to risk assessment need to be evaluated outside the discovery population. Prior studies implied that the three known rare variants in NOD2 are absent in West African and Asian populations and only contribute in African Americans via admixture. Methods Whole genome sequencing (WGS) data from 3418 African American individuals, 1774 inflammatory bowel disease (IBD) cases, and 1644 controls were used to assess odds ratios and allele frequencies (AF), as well as haplotype-specific ancestral origins of European-derived CD variants discovered in a large exome-wide association study. Local and global ancestry was performed to assess the contribution of admixture to IBD contrasting European and African American cohorts. Results Twenty-five rare variants associated with CD in European discovery cohorts are typically five-fold lower frequency in African Americans. Correspondingly, where comparisons could be made, the rare variants were found to have a predicted four-fold reduced burden for IBD in African Americans, when compared to European individuals. Almost all of the rare CD European variants were found on European haplotypes in the African American cohort, implying that they contribute to disease risk in African Americans primarily due to recent admixture. In addition, proportion of European ancestry correlates the number of rare CD European variants each African American individual carry, as well as their polygenic risk of disease. Similar findings were observed for 23 mutations affecting 10 other common complex diseases for which the rare variants were discovered in European cohorts. Conclusions European-derived Crohn’s disease rare variants are even more rare in African Americans and contribute to disease risk mainly due to admixture, which needs to be accounted for when performing cross-ancestry genetic assessments.

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.

Abstract 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.

Health equity means the opportunity for all people and populations to attain optimal health, and it requires intentional efforts to promote fairness in patient treatments and outcomes. Pharmacogenomic variants are genetic differences associated with how patients respond to medications, and their presence can inform treatment decisions. In this perspective, we contend that the study of pharmacogenomic variation within and between human populations—population pharmacogenomics—can and should be leveraged in support of health equity. The key observation in support of this contention is that racial and ethnic groups exhibit pronounced differences in the frequencies of numerous pharmacogenomic variants, with direct implications for clinical practice. The use of race and ethnicity to stratify pharmacogenomic risk provides a means to avoid potential harm caused by biases introduced when treatment regimens do not consider genetic differences between population groups, particularly when majority group genetic profiles are assumed to hold for minority groups. We focus on the mitigation of adverse drug reactions as an area where population pharmacogenomics can have a direct and immediate impact on public health.

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 relevance of race and ethnicity to genetics and medicine has long been a matter of debate. An emerging consensus holds that race and ethnicity are social constructs and thus poor proxies for genetic diversity. The goal of this study was to evaluate the relationship between race, ethnicity, and clinically relevant pharmacogenomic variation in cosmopolitan populations. We studied racially and ethnically diverse cohorts of 65,120 participants from the United States All of Us Research Program (All of Us) and 31,396 participants from the United Kingdom Biobank (UKB). Genome-wide patterns of pharmacogenomic variation—6311 drug response-associated variants for All of Us and 5966 variants for UKB—were analyzed with machine learning classifiers to predict participants’ self-identified race and ethnicity. Pharmacogenomic variation predicts race/ethnicity with averages of 92.1% accuracy for All of Us and 94.3% accuracy for UKB. Group-specific prediction accuracies range from 99.0% for the White group in UKB to 92.9% for the Hispanic group in All of Us. Prediction accuracies are substantially lower for individuals who identified with more than one group in All of Us (16.7%) or as Mixed in UKB (70.7%). There are numerous individual pharmacogenomic variants with large allele frequency differences between race/ethnicity groups in both cohorts. Frequency differences for toxicity-associated variants predict hundreds of adverse drug reactions per 1000 treated participants for minority groups in All of Us. Our results indicate that race and ethnicity can be used to stratify pharmacogenomic risk in the US and UK populations and should not be discounted when making treatment decisions. We resolve the contradiction between the results reported here and the orthodoxy of race and ethnicity as non-genetic, social constructs by emphasizing the distinction between global and local patterns of human genetic diversity, and we stress the current and future limitations of race and ethnicity as proxies for pharmacogenomic variation.

Abstract Previous studies have shown the sugarcane microbiome harbors diverse plant growth promoting microorganisms, including nitrogen-fixing bacteria (diazotrophs), which can serve as biofertilizers. The genomes of 22 diazotrophs from Colombian sugarcane fields were sequenced to investigate potential biofertilizers. A genome-enabled computational phenotyping approach was developed to prioritize sugarcane associated diazotrophs according to their potential as biofertilizers. This method selects isolates that have potential for nitrogen fixation and other plant growth promoting (PGP) phenotypes while showing low risk for virulence and antibiotic resistance. Intact nitrogenase (nif) genes and operons were found in 18 of the isolates. Isolates also encode phosphate solubilization and siderophore production operons, and other PGP genes. The majority of sugarcane isolates showed uniformly low predicted virulence and antibiotic resistance compared to clinical isolates. Six strains with the highest overall genotype scores were experimentally evaluated for nitrogen fixation, phosphate solubilization, and the production of siderophores, gibberellic acid, and indole acetic acid. Results from the biochemical assays were consistent and validated computational phenotype predictions. A genotypic and phenotypic threshold was observed that separated strains by their potential for PGP versus predicted pathogenicity. Our results indicate that computational phenotyping is a promising tool for the assessment of bacteria detected in agricultural ecosystems.

Abstract Background Pharmacogenomic (PGx) variants mediate how individuals respond to medication, and response differences among racial/ethnic groups have been attributed to patterns of PGx diversity. We hypothesized that genetic ancestry (GA) would provide higher resolution for stratifying PGx risk, since it serves as a more reliable surrogate for genetic diversity than self-identified race/ethnicity (SIRE), which includes a substantial social component. We analyzed a cohort of 8628 individuals from the United States (US), for whom we had both SIRE information and whole genome genotypes, with a focus on the three largest SIRE groups in the US: White, Black (African-American), and Hispanic (Latino). Our approach to the question of PGx risk stratification entailed the integration of two distinct methodologies: population genetics and evidence-based medicine. This integrated approach allowed us to consider the clinical implications for the observed patterns of PGx variation found within and between population groups. Results Whole genome genotypes were used to characterize individuals’ continental ancestry fractions—European, African, and Native American—and individuals were grouped according to their GA profiles. SIRE and GA groups were found to be highly concordant. Continental ancestry predicts individuals’ SIRE with > 96% accuracy, and accordingly, GA provides only a marginal increase in resolution for PGx risk stratification. In light of the concordance between SIRE and GA, taken together with the fact that information on SIRE is readily available to clinicians, we evaluated PGx variation between SIRE groups to explore the potential clinical utility of race and ethnicity. PGx variants are highly diverged compared to the genomic background; 82 variants show significant frequency differences among SIRE groups, and genome-wide patterns of PGx variation are almost entirely concordant with SIRE. The vast majority of PGx variation is found within rather than between groups, a well-established fact for almost all genetic variants, which is often taken to argue against the clinical utility of population stratification. Nevertheless, analysis of highly differentiated PGx variants illustrates how SIRE partitions PGx variation based on groups’ characteristic ancestry patterns. These cases underscore the extent to which SIRE carries clinically valuable information for stratifying PGx risk among populations, albeit with less utility for predicting individual-level PGx alleles (genotypes), supporting the concept of population pharmacogenomics. Conclusions Perhaps most interestingly, we show that individuals who identify as Black or Hispanic stand to gain far more from the consideration of race/ethnicity in treatment decisions than individuals from the majority White population.

Abstract 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.

Abstract Background Admixture occurs when previously isolated populations come together and exchange genetic material. We hypothesize that admixture can enable rapid adaptive evolution in human populations by introducing novel genetic variants (haplotypes) at intermediate frequencies, and we test this hypothesis through the analysis of whole genome sequences sampled from admixed Latin American populations in Colombia, Mexico, Peru, and Puerto Rico. Results Our 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 employ a combined evidence approach to evaluate levels of ancestry enrichment at single loci across multiple populations and multiple loci that function together to encode polygenic traits. We find 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, such as HLA-A, HLA-DRB51, and HLA-DRB5, show 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 show evidence of admixture-enabled polygenic selection in Latin American populations. Conclusions The 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.

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.

Summary: We investigated the ancestral origins of four Ecuadorian ethnic groups—Afro-Ecuadorian, Mestizo, Montubio, and the Indigenous Tsáchila—in an effort to gain insight on the relationship between ancestry, culture, and the formation of ethnic identities in Latin America. The observed patterns of genetic ancestry are largely concordant with ethnic identities and historical records of conquest and colonization in Ecuador. Nevertheless, a number of exceptional findings highlight the complex relationship between genetic ancestry and ethnicity in Ecuador. Afro-Ecuadorians show far less African ancestry, and the highest levels of Native American ancestry, seen for any Afro-descendant population in the Americas. Mestizos in Ecuador show high levels of Native American ancestry, with substantially less European ancestry, despite the relatively low Indigenous population in the country. The recently recognized Montubio ethnic group is highly admixed, with substantial contributions from all three continental ancestries. The Tsáchila show two distinct ancestry subgroups, with most individuals showing almost exclusively Native American ancestry and a smaller group showing a Mestizo characteristic pattern. Considered together with historical data and sociological studies, our results indicate the extent to which ancestry and culture interact, often in unexpected ways, to shape ethnic identity in Ecuador.

C-reactive protein (CRP) is a routinely measured blood biomarker for inflammation. Elevated levels of circulating CRP are associated with response to infection, risk for a number of complex common diseases, and psychosocial stress. The objective of this study was to compare the contributions of genetic ancestry, socioenvironmental factors, and inflammation-related health conditions to ethnic differences in C-reactive protein levels. We used multivariable regression to compare CRP blood serum levels between Black and White ethnic groups from the United Kingdom Biobank (UKBB) prospective cohort study. CRP serum levels are significantly associated with ethnicity in an age and sex adjusted model. Study participants who identify as Black have higher average CRP than those who identify as White, CRP increases with age, and females have higher average CRP than males. Ethnicity and sex show a significant interaction effect on CRP. Black females have higher average CRP levels than White females, whereas White males have higher average CRP than Black males. Significant associations between CRP, ethnicity, and genetic ancestry are almost completely attenuated in a fully adjusted model that includes socioenvironmental factors and inflammation-related health conditions. BMI, smoking, and socioeconomic deprivation all have high relative effects on CRP. These results indicate that socioenvironmental factors contribute more to CRP ethnic differences than genetics. Differences in CRP are associated with ethnic disparities for a number of chronic diseases, including type 2 diabetes, essential hypertension, sarcoidosis, and lupus erythematosus. Our results indicate that ethnic differences in CRP are linked to both socioenvironmental factors and numerous ethnic health disparities.

Currently, the vast majority of genomic research cohorts are made up of participants with European ancestry. Genomic medicine will only reach its full potential when genomic studies become more broadly representative of global populations. We are working to support the establishment of genomic medicine in developing countries in Latin America via studies of ethnically and ancestrally diverse Colombian populations. The goal of this study was to analyze the effect of ethnicity and genetic ancestry on observed disease prevalence and predicted disease risk in Colombia. Population distributions of Colombia’s three major ethnic groups – Mestizo, Afro-Colombian, and Indigenous – were compared to disease prevalence and socioeconomic indicators. Indigenous and Mestizo ethnicity show the highest correlations with disease prevalence, whereas the effect of Afro-Colombian ethnicity is substantially lower. Mestizo ethnicity is mostly negatively correlated with six high-impact health conditions and positively correlated with seven of eight common cancers; Indigenous ethnicity shows the opposite effect. Malaria prevalence in particular is strongly correlated with ethnicity. Disease prevalence co-varies across geographic regions, consistent with the regional distribution of ethnic groups. Ethnicity is also correlated with regional variation in human development, partially explaining the observed differences in disease prevalence. Patterns of genetic ancestry and admixture for a cohort of 624 individuals from Medellín were compared to disease risk inferred via polygenic risk scores (PRS). African genetic ancestry is most strongly correlated with predicted disease risk, whereas European and Native American ancestry show weaker effects. African ancestry is mostly positively correlated with disease risk, and European ancestry is mostly negatively correlated. The relationships between ethnicity and disease prevalence do not show an overall correspondence with the relationships between ancestry and disease risk. We discuss possible reasons for the divergent health effects of ethnicity and ancestry as well as the implication of our results for the development of precision medicine in Colombia.

ABSTRACT Azole resistance in pathogenic Aspergillus fumigatus has become a global public health issue threatening the use of medical azoles. The environmentally occurring resistance mutations, TR34/L98H (TR34) and TR46/Y121F/T289A (TR46), are widespread across multiple continents and emerging in the United States. We used whole-genome single nucleotide polymorphism (SNP) analysis on 179 nationally represented clinical and environmental A. fumigatus genomes from the United States along with 18 non-U.S. genomes to evaluate the genetic diversity and foundation of the emergence of azole resistance in the United States. We demonstrated the presence of clades of A. fumigatus isolates: clade A (17%) comprised a global collection of clinical and environmental azole-resistant strains, including all strains with the TR34/L98H allele from India, The Netherlands, the United Kingdom, and the United States, and clade B (83%) consisted of isolates without this marker mainly from the United States. The TR34/L98H polymorphism was shared among azole-resistant A. fumigatus strains from India, The Netherlands, the United Kingdom, and the United States, suggesting the common origin of this resistance mechanism. Six percent of azole-resistant A. fumigatus isolates from the United States with the TR34 resistance marker had a mixture of clade A and clade B alleles, suggestive of recombination. Additionally, the presence of equal proportions of both mating types further suggests the ongoing presence of recombination. This study demonstrates the genetic background for the emergence of azole resistance in the United States, supporting a single introduction and subsequent propagation, possibly through recombination of environmentally driven resistance mutations. IMPORTANCE Aspergillus fumigatus is one of the most common causes of invasive mold infections in patients with immune deficiencies and has also been reported in patients with severe influenza and severe acute respiratory syndrome coronavirus 2 (SARs-CoV-2). Triazole drugs are the first line of therapy for this infection; however, their efficacy has been compromised by the emergence of azole resistance in A. fumigatus, which was proposed to be selected for by exposure to azole fungicides in the environment [P. E. Verweij, E. Snelders, G. H. J. Kema, E. Mellado, et al., Lancet Infect Dis 9:789–795, 2009, https://doi.org/10.1016/S1473-3099(09)70265-8]. Isolates with environmentally driven resistance mutations, TR34/L98H (TR34) and TR46/Y121F/T289A (TR46), have been reported worldwide. Here, we used genomic analysis of a large sample of resistant and susceptible A. fumigatus isolates to demonstrate a single introduction of TR34 in the United States and suggest its ability to spread into the susceptible population is through recombination between resistant and susceptible isolates.

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.

Abstract Background Like many bacteria, Vibrio cholerae 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. Results We sequence two dozen V. cholerae strain genomes from diverse sources and develop a novel and adaptable bioinformatics tool based on hidden Markov models. We identify two new T6SS auxiliary gene clusters and describe Aux 5 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 indicates it is a lipase, which we name TleV1 (type VI lipase effector Vibrio). Ectopic TleV1 expression induces toxicity in Escherichia coli, which is rescued by co-expression of the TliV1a immunity factor. A clinical V. cholerae reference strain expressing the Aux 5 cluster uses TleV1 to lyse its parental strain upon contact via its T6SS but is unable to kill parental cells expressing the TliV1a immunity factor. Conclusion We develop a novel bioinformatics method and identify new T6SS gene clusters in V. cholerae. We also show the TleV1 toxin is delivered in a T6SS manner by V. cholerae and can lyse other bacterial cells. Our web-based tool can be modified to identify additional novel T6SS genomic loci in diverse bacterial species.

Abstract Background Modern Latin American populations were formed via genetic admixture among ancestral source populations from Africa, the Americas and Europe. We are interested in studying how combinations of genetic ancestry in admixed Latin American populations may impact genomic determinants of health and disease. For this study, we characterized the impact of ancestry and admixture on genetic variants that underlie health- and disease-related phenotypes in population genomic samples from Colombia, Mexico, Peru, and Puerto Rico. Results We analyzed a total of 347 admixed Latin American genomes along with 1102 putative ancestral source genomes from Africans, Europeans, and Native Americans. We characterized the genetic ancestry, relatedness, and admixture patterns for each of the admixed Latin American genomes, finding a spectrum of ancestry proportions within and between populations. We then identified single nucleotide polymorphisms (SNPs) with anomalous ancestry-enrichment patterns, i.e. SNPs that exist in any given Latin American population at a higher frequency than expected based on the population’s genetic ancestry profile. For this set of ancestry-enriched SNPs, we inspected their phenotypic impact on disease, metabolism, and the immune system. All four of the Latin American populations show ancestry-enrichment for a number of shared pathways, yielding evidence of similar selection pressures on these populations during their evolution. For example, all four populations show ancestry-enriched SNPs in multiple genes from immune system pathways, such as the cytokine receptor interaction, T cell receptor signaling, and antigen presentation pathways. We also found SNPs with excess African or European ancestry that are associated with ancestry-specific gene expression patterns and play crucial roles in the immune system and infectious disease responses. Genes from both the innate and adaptive immune system were found to be regulated by ancestry-enriched SNPs with population-specific regulatory effects. Conclusions Ancestry-enriched SNPs in Latin American populations have a substantial effect on health- and disease-related phenotypes. The concordant impact observed for same phenotypes across populations points to a process of adaptive introgression, whereby ancestry-enriched SNPs with specific functional utility appear to have been retained in modern populations by virtue of their effects on health and fitness.

Heteroresistance is an underappreciated phenomenon that may be the cause of some unexplained antibiotic treatment failures. Misclassification of heteroresistant isolates as susceptible may lead to inappropriate therapy.

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.

At least 20% of Colombians identify as having African ancestry, yielding the second largest population of Afro-descendants in Latin America. To date, there have been relatively few studies focused on the genetic ancestry of Afro-Latino populations. We report a comparative analysis of the genetic ancestry of Chocó, a state located on Colombia’s Pacific coast with a population that is >80% Afro-Colombian. We compared genome-wide patterns of genetic ancestry and admixture for Chocó to six other admixed American populations, with an emphasis on a Mestizo population from the nearby Colombian city of Medellín. One hundred sample donors from Chocó were genotyped across 610,545 genomic sites and compared with 94 publicly available whole genome sequences from Medellín. At the continental level, Chocó shows mostly African genetic ancestry (76%) with a nearly even split between European (13%) and Native American (11%) fractions, whereas Medellín has primarily European ancestry (75%), followed by Native American (18%) and African (7%). Sample donors from Chocó self-identify as having more African ancestry, and conversely less European and Native American ancestry, than can be genetically inferred, as opposed to what we previously found for Medellín, where individuals tend to overestimate levels of European ancestry. We developed a novel approach for subcontinental ancestry assignment, which allowed us to characterize subcontinental source populations for each of the three distinct continental ancestry fractions separately. Despite the clear differences between Chocó and Medellín at the level of continental ancestry, the two populations show overall patterns of subcontinental ancestry that are highly similar. Their African subcontinental ancestries are only slightly different, with Chocó showing more exclusive shared ancestry with the modern Yoruba (Nigerian) population, and Medellín having relatively more shared ancestry with West African populations in Sierra Leone and Gambia. Both populations show very similar Spanish ancestry within Europe and virtually identical patterns of Native American ancestry, with main contributions from the Embera and Waunana tribes. When the three subcontinental ancestry components are considered jointly, the populations of Chocó and Medellín are shown to be most closely related, to the exclusion of the other admixed American populations that we analyzed. We consider the implications of the existence of shared subcontinental ancestries for Colombian populations that appear, at first glance, to be clearly distinct with respect to competing notions of national identity that emphasize ethnic mixing (mestizaje) vs. group-specific identities (multiculturalism).

Stem cells aging: balancing self-renewal halt with complexity Mounting evidence suggests a link between cellular senescence and human adult stem cell function upon aging. Senescence is often viewed as an intrinsic program to prevent oncogenic transformation. However, the collaborative research lead by Aelan Cell Technologies suggests that replicative senescence might be more dynamic than previously anticipated. Integrated genomic and proteomic analyses of human adult stem cells revealed that subset of senescence-associated miRNAs (SA-miRNA) functionally required for establishing senescence, act to provide an intricate balance between driving and restraining cancerous events upon senescence. It is commonly believed that cancer arises as a consequence of an imbalance in cellular homeostasis. These new results shed light on the fundamental role of these SA-miRNA as functionally antagonistic regulators of gene networks, future exploration of which can help us understand the etiology underlying age-related disease and cancer.

Assortative mating is a universal feature of human societies, and individuals from ethnically diverse populations are known to mate assortatively based on similarities in genetic ancestry. However, little is currently known regarding the exact phenotypic cues, or their underlying genetic architecture, which inform ancestry-based assortative mating. We developed a novel approach, using genome-wide analysis of ancestry-specific haplotypes, to evaluate ancestry-based assortative mating on traits whose expression varies among the three continental population groups – African, European, and Native American – that admixed to form modern Latin American populations. Application of this method to genome sequences sampled from Colombia, Mexico, Peru, and Puerto Rico revealed widespread ancestry-based assortative mating. We discovered a number of anthropometric traits (body mass, height, and facial development) and neurological attributes (educational attainment and schizophrenia) that serve as phenotypic cues for ancestry-based assortative mating. Major histocompatibility complex (MHC) loci show population-specific patterns of both assortative and disassortative mating in Latin America. Ancestry-based assortative mating in the populations analyzed here appears to be driven primarily by African ancestry. This study serves as an example of how population genomic analyses can yield novel insights into human behavior.

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.

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.

Human transposable element (TE) activity in somatic tissues causes mutations that can contribute to tumorigenesis. Indeed, TE insertion mutations have been implicated in the etiology of a number of different cancer types. Nevertheless, the full extent of somatic TE activity, along with its relationship to tumorigenesis, have yet to be fully explored. Recent developments in bioinformatics software make it possible to analyze TE expression levels and TE insertional activity directly from transcriptome (RNA-seq) and whole genome (DNA-seq) next-generation sequence data. We applied these new sequence analysis techniques to matched normal and primary tumor patient samples from the Cancer Genome Atlas (TCGA) in order to analyze the patterns of TE expression and insertion for three cancer types: breast invasive carcinoma, head and neck squamous cell carcinoma, and lung adenocarcinoma. Our analysis focused on the three most abundant families of active human TEs: Alu, SVA and L1. We found evidence for high levels of somatic TE activity for these three families in normal and cancer samples across diverse tissue types. Abundant transcripts for all three TE families were detected in both normal and cancer tissues along with an average of ~80 unique TE insertions per individual patient/tissue. We observed an increase in L1 transcript expression and L1 insertional activity in primary tumor samples for all three cancer types. Tumor-specific TE insertions are enriched for private mutations, consistent with a potentially causal role in tumorigenesis. We used genome feature analysis to investigate two specific cases of putative cancer-causing TE mutations in further detail. An Alu insertion in an upstream enhancer of the CBL tumor suppressor gene is associated with down-regulation of the gene in a single breast cancer patient, and an L1 insertion in the first exon of the BAALC gene also disrupts its expression in head and neck squamous cell carcinoma. Our results are consistent with widespread somatic activity of human TEs leading to numerous insertion mutations that can contribute to tumorigenesis in a variety of tissues.

ABSTRACT The increased prevalence of vancomycin-intermediate Staphylococcus aureus (VISA) is an emerging health care threat. Genome-based comparative methods hold great promise to uncover the genetic basis of the VISA phenotype, which remains obscure. S. aureus isolates were collected from a single individual that presented with recurrent staphylococcal bacteremia at three time points, and the isolates showed successively reduced levels of vancomycin susceptibility. A population genomic approach was taken to compare patient S. aureus isolates with decreasing vancomycin susceptibility across the three time points. To do this, patient isolates were sequenced to high coverage (~500×), and sequence reads were used to model site-specific allelic variation within and between isolate populations. Population genetic methods were then applied to evaluate the overall levels of variation across the three time points and to identify individual variants that show anomalous levels of allelic change between populations. A successive reduction in the overall levels of population genomic variation was observed across the three time points, consistent with a population bottleneck resulting from antibiotic treatment. Despite this overall reduction in variation, a number of individual mutations were swept to high frequency in the VISA population. These mutations were implicated as potentially involved in the VISA phenotype and interrogated with respect to their functional roles. This approach allowed us to identify a number of mutations previously implicated in VISA along with allelic changes within a novel class of genes, encoding LPXTG motif-containing cell-wall-anchoring proteins, which shed light on a novel mechanistic aspect of vancomycin resistance. IMPORTANCE The emergence and spread of antibiotic resistance among bacterial pathogens are two of the gravest threats to public health facing the world today. We report the development and application of a novel population genomic technique aimed at uncovering the evolutionary dynamics and genetic determinants of antibiotic resistance in Staphylococcus aureus. This method was applied to S. aureus cultures isolated from a single patient who showed decreased susceptibility to the vancomycin antibiotic over time. Our approach relies on the increased resolution afforded by next-generation genome-sequencing technology, and it allowed us to discover a number of S. aureus mutations, in both known and novel gene targets, which appear to have evolved under adaptive pressure to evade vancomycin mechanisms of action. The approach we lay out in this work can be applied to resistance to any number of antibiotics across numerous species of bacterial pathogens.

ABSTRACT Unraveling the drivers controlling the response and adaptation of biological communities to environmental change, especially anthropogenic activities, is a central but poorly understood issue in ecology and evolution. Comparative genomics studies suggest that lateral gene transfer (LGT) is a major force driving microbial genome evolution, but its role in the evolution of microbial communities remains elusive. To delineate the importance of LGT in mediating the response of a groundwater microbial community to heavy metal contamination, representative Rhodanobacter reference genomes were sequenced and compared to shotgun metagenome sequences. 16S rRNA gene-based amplicon sequence analysis indicated that Rhodanobacter populations were highly abundant in contaminated wells with low pHs and high levels of nitrate and heavy metals but remained rare in the uncontaminated wells. Sequence comparisons revealed that multiple geochemically important genes, including genes encoding Fe2+/Pb2+ permeases, most denitrification enzymes, and cytochrome c553, were native to Rhodanobacter and not subjected to LGT. In contrast, the Rhodanobacter pangenome contained a recombinational hot spot in which numerous metal resistance genes were subjected to LGT and/or duplication. In particular, Co2+/Zn2+/Cd2+ efflux and mercuric resistance operon genes appeared to be highly mobile within Rhodanobacter populations. Evidence of multiple duplications of a mercuric resistance operon common to most Rhodanobacter strains was also observed. Collectively, our analyses indicated the importance of LGT during the evolution of groundwater microbial communities in response to heavy metal contamination, and a conceptual model was developed to display such adaptive evolutionary processes for explaining the extreme dominance of Rhodanobacter populations in the contaminated groundwater microbiome. IMPORTANCE Lateral gene transfer (LGT), along with positive selection and gene duplication, are the three main mechanisms that drive adaptive evolution of microbial genomes and communities, but their relative importance is unclear. Some recent studies suggested that LGT is a major adaptive mechanism for microbial populations in response to changing environments, and hence, it could also be critical in shaping microbial community structure. However, direct evidence of LGT and its rates in extant natural microbial communities in response to changing environments is still lacking. Our results presented in this study provide explicit evidence that LGT played a crucial role in driving the evolution of a groundwater microbial community in response to extreme heavy metal contamination. It appears that acquisition of genes critical for survival, growth, and reproduction via LGT is the most rapid and effective way to enable microorganisms and associated microbial communities to quickly adapt to abrupt harsh environmental stresses.

Sphingolipids are structurally diverse and their metabolic pathways highly complex, which makes it difficult to follow all of the subspecies in a biological system, even using “lipidomic” approaches. This report describes a method to use transcriptomic data to visualize and predict potential differences in sphingolipid composition, and it illustrates its use with published data for cancer cell lines and tumors. In addition, several novel sphingolipids that were predicted to differ between MDA-MB-231 and MCF7 cells based on published microarray data for these breast cancer cell lines were confirmed by mass spectrometry. For the data that we were able to find for these comparisons, there was a significant match between the gene expression data and sphingolipid composition (P < 0.001 by Fisher's exact test). Upon considering the large number of gene expression datasets produced in recent years, this simple integration of two types of “omic” technologies (“transcriptomics” to direct “sphingolipidomics”) might facilitate the discovery of useful relationships between sphingolipid metabolism and disease, such as the identification of new biomarkers.

Evolutionary knowledge is often used to facilitate computational attempts at gene function prediction. One rich source of evolutionary information is the relative rates of gene sequence divergence, and in this report we explore the connection between gene evolutionary rates and function. We performed a genome-scale evaluation of the relationship between evolutionary rates and functional annotations for the yeast Saccharomyces cerevisiae. Non-synonymous (dN) and synonymous (dS) substitution rates were calculated for 1,095 orthologous gene sets common to S. cerevisiae and six other closely related yeast species. Differences in evolutionary rates between pairs of genes (ΔdN & ΔdS) were then compared to their functional similarities (sGO), which were measured using Gene Ontology (GO) annotations. Substantial and statistically signifi cant correlations were found between ΔdN and sGO, whereas there is no apparent relationship between ΔdS and sGO. These results are consistent with a mode of action for natural selection that is based on similar rates of elimination of deleterious protein coding sequence variants for functionally related genes. The connection between gene evolutionary rates and function was stronger than seen for phylogenetic profiles, which have previously been employed to inform functional inference. The co-evolution of functionally related yeast genes points to the relevance of specific function for the efficacy of natural selection and underscores the utility of gene evolutionary rates for functional predictions.

Evolutionary knowledge is often used to facilitate computational attempts at gene function prediction. One rich source of evolutionary information is the relative rates of gene sequence divergence, and in this report we explore the connection between gene evolutionary rates and function. We performed a genome-scale evaluation of the relationship between evolutionary rates and functional annotations for the yeast Saccharomyces cerevisiae. Non-synonymous ( dN ) and synonymous ( dS ) substitution rates were calculated for 1,095 orthologous gene sets common to S. cerevisiae and six other closely related yeast species. Differences in evolutionary rates between pairs of genes (Δ dN & Δ dS ) were then compared to their functional similarities ( sGO ), which were measured using Gene Ontology (GO) annotations. Substantial and statistically significant correlations were found between Δ dN and sGO , whereas there is no apparent relationship between Δ dS and sGO . These results are consistent with a mode of action for natural selection that is based on similar rates of elimination of deleterious protein coding sequence variants for functionally related genes. The connection between gene evolutionary rates and function was stronger than seen for phylogenetic profiles, which have previously been employed to inform functional inference. The co-evolution of functionally related yeast genes points to the relevance of specific function for the efficacy of natural selection and underscores the utility of gene evolutionary rates for functional predictions.

Introduction The Rose hypothesis predicts that since genetic variation is greater within than between populations, genetic risk factors will be associated with individuals’ risk of disease but not population disparities, and since socioenvironmental variation is greater between than within populations, socioenvironmental risk factors will be associated with population disparities but not individuals’ disease risk.Methods We used the UK Biobank to test the Rose hypothesis for type 2 diabetes (T2D) ethnic disparities in the UK. Our cohort consists of 26 912 participants from Asian, black and white ethnic groups. Participants were characterised as T2D cases or controls based on the presence or absence of T2D diagnosis codes in electronic health records. T2D genetic risk was measured using a polygenic risk score (PRS), and socioeconomic deprivation was measured with the Townsend Index (TI). The variation of genetic (PRS) and socioeconomic (TI) risk factors within and between ethnic groups was calculated using analysis of variance. Multivariable logistic regression was used to associate PRS and TI with T2D cases, and mediation analysis was used to analyse the effect of PRS and TI on T2D ethnic group disparities.Results T2D prevalence differs for Asian 23.34% (OR=5.14, CI=4.68 to 5.65), black 16.64% (OR=3.81, CI=3.44 to 4.22) and white 7.35% (reference) ethnic groups in the UK. Both genetic and socioenvironmental T2D risk factors show greater within (w) than between (b) ethnic group variation: PRS w=64.60%, b=35.40%; TI w=71.18%, b=28.19%. Nevertheless, both genetic risk (PRS OR=1.96, CI=1.87 to 2.07) and socioeconomic deprivation (TI OR=1.09, CI=1.08 to 1.10) are associated with T2D individual risk and mediate T2D ethnic disparities (Asian PRS=22.5%, TI=9.8%; black PRS=32.0%, TI=25.3%).Conclusion A relative excess of within-group versus between-group variation does not preclude T2D risk factors from contributing to T2D ethnic disparities. Our results support an integrative approach to health disparities research that includes both genetic and socioenvironmental risk factors.