49 research outputs found
Disrupted Human–Pathogen Co-Evolution: A Model for Disease
Get PDFA major goal in infectious disease research is to identify the human and pathogenic genetic variants that explain differences in microbial pathogenesis. However, neither pathogenic strain nor human genetic variation in isolation has proven adequate to explain the heterogeneity of disease pathology. We suggest that disrupted co-evolution between a pathogen and its human host can explain variation in disease outcomes, and that genome-by-genome interactions should therefore be incorporated into genetic models of disease caused by infectious agents. Genetic epidemiological studies that fail to take both the pathogen and host into account can lead to false and misleading conclusions about disease etiology. We discuss our model in the context of three pathogens, Helicobacter pylori, Mycobacterium tuberculosis and human papillomavirus, and generalize the conditions under which it may be applicable
Human and Helicobacter Pylori Coevolution Shapes the Risk of Gastric Disease
Get PDFHelicobacter pylori is the principal cause of gastric cancer, the second leading cause of cancer mortality worldwide. However, H. pylori prevalence generally does not predict cancer incidence. To determine whether coevolution between host and pathogen influences disease risk, we examined the association between the severity of gastric lesions and patterns of genomic variation in matched human and H. pylori samples. Patients were recruited from two geographically distinct Colombian populations with significantly different incidences of gastric cancer, but virtually identical prevalence of H. pylori infection. All H. pylori isolates contained the genetic signatures of multiple ancestries, with an ancestral African cluster predominating in a low-risk, coastal population and a European cluster in a high-risk, mountain population. The human ancestry of the biopsied individuals also varied with geography, with mostly African ancestry in the coastal region (58%), and mostly Amerindian ancestry in the mountain region (67%). The interaction between the host and pathogen ancestries completely accounted for the difference in the severity of gastric lesions in the two regions of Colombia. In particular, African H. pylori ancestry was relatively benign in humans of African ancestry but was deleterious in individuals with substantial Amerindian ancestry. Thus, coevolution likely modulated disease risk, and the disruption of coevolved human and H. pylori genomes can explain the high incidence of gastric disease in the mountain population
Disrupted Human–Pathogen Co-Evolution: A Model for Disease
Get PDFA major goal in infectious disease research is to identify the human and pathogenic genetic variants that explain differences in microbial pathogenesis. However, neither pathogenic strain nor human genetic variation in isolation has proven adequate to explain the heterogeneity of disease pathology. We suggest that disrupted co-evolution between a pathogen and its human host can explain variation in disease outcomes, and that genome-by-genome interactions should therefore be incorporated into genetic models of disease caused by infectious agents. Genetic epidemiological studies that fail to take both the pathogen and host into account can lead to false and misleading conclusions about disease etiology. We discuss our model in the context of three pathogens, Helicobacter pylori, Mycobacterium tuberculosis and human papillomavirus, and generalize the conditions under which it may be applicable
Гарри Поттер как современный миф
Get PDFPlasminogen activator inhibitor 1 (PAI-1), a major modulator of the fibrinolytic system, is an important factor in cardiovascular disease (CVD) susceptibility and severity. PAI-1 is highly heritable, but the few genes associated with it explain only a small portion of its variation. Studies of PAI-1 typically employ linear regression to estimate the effects of genetic variants on PAI-1 levels, but PAI-1 is not normally distributed, even after transformation. Therefore, alternative statistical methods may provide greater power to identify important genetic variants. Additionally, most genetic studies of PAI-1 have been performed on populations of European descent, limiting the generalizability of their results. We analyzed >30,000 variants for association with PAI-1 in a Ghanaian population, using median regression, a non-parametric alternative to linear regression. Three variants associated with median PAI-1, the most significant of which was in the gene arylsulfatase B (ARSB) (p = 1.09 x 10(-7)). We also analyzed the upper quartile of PAI-1, the most clinically relevant part of the distribution, and found 19 SNPs significantly associated in this quartile. Of note an association was found in period circadian clock 3 (PER3). Our results reveal novel associations with median and elevated PAI-1 in an understudied population. The lack of overlap between the two analyses indicates that the genetic effects on PAI-1 are not uniform across its distribution. They also provide evidence of the generalizability of the circadian pathway's effect on PAI-1, as a recent meta-analysis performed in Caucasian populations identified another circadian clock gene (ARNTL)
Diverse Convergent Evidence in the Genetic Analysis of Complex Disease: Coordinating Omic, Informatic, and Experimental Evidence to Better Identify and Validate Risk Factors
Get PDFIn omic research, such as genome wide association studies, researchers seek to repeat their results in other datasets to reduce false positive findings and thus provide evidence for the existence of true associations. Unfortunately this standard validation approach cannot completely eliminate false positive conclusions, and it can also mask many true associations that might otherwise advance our understanding of pathology. These issues beg the question: How can we increase the amount of knowledge gained from high throughput genetic data? To address this challenge, we present an approach that complements standard statistical validation methods by drawing attention to both potential false negative and false positive conclusions, as well as providing broad information for directing future research. The Diverse Convergent Evidence approach (DiCE) we propose integrates information from multiple sources (omics, informatics, and laboratory experiments) to estimate the strength of the available corroborating evidence supporting a given association. This process is designed to yield an evidence metric that has utility when etiologic heterogeneity, variable risk factor frequencies, and a variety of observational data imperfections might lead to false conclusions. We provide proof of principle examples in which DiCE identified strong evidence for associations that have established biological importance, when standard validation methods alone did not provide support. If used as an adjunct to standard validation methods this approach can leverage multiple distinct data types to improve genetic risk factor discovery/validation, promote effective science communication, and guide future research directions
Evolutionary Triangulation: Informing Genetic Association Studies with Evolutionary Evidence
Get PDFGenetic studies of human diseases have identified many variants associated with pathogenesis and severity. However, most studies have used only statistical association to assess putative relationships to disease, and ignored other factors for evaluation. For example, evolution is a factor that has shaped disease risk, changing allele frequencies as human populations migrated into and inhabited new environments. Since many common variants differ among populations in frequency, as does disease prevalence, we hypothesized that patterns of disease and population structure, taken together, will inform association studies. Thus, the population distributions of allelic risk variants should reflect the distributions of their associated diseases. Evolutionary Triangulation (ET) exploits this evolutionary differentiation by comparing population structure among three populations with variable patterns of disease prevalence. By selecting populations based on patterns where two have similar rates of disease that differ substantially from a third, we performed a proof of principle analysis for this method. We examined three disease phenotypes, lactase persistence, melanoma, and Type 2 diabetes mellitus. We show that for lactase persistence, a phenotype with a simple genetic architecture, ET identifies the key gene, lactase. For melanoma, ET identifies several genes associated with this disease and/or phenotypes related to it, such as skin color genes. ET was less obviously successful for Type 2 diabetes mellitus, perhaps because of the small effect sizes in known risk loci and recent environmental changes that have altered disease risk. Alternatively, ET may have revealed new genes involved in conferring disease risk for diabetes that did not meet nominal GWAS significance thresholds. We also compared ET to another method used to filter for phenotype associated genes, population branch statistic (PBS), and show that ET performs better in identifying genes known to associate with diseases appropriately distributed among populations. Our results indicate that ET can filter association results to improve our ability to discover disease loci
Plasminogen Activator Inhibitor-1 and Diagnosis of the Metabolic Syndrome in a West African Population
Get PDFBACKGROUND: Metabolic syndrome (MetS) is diagnosed by the presence of at least 3 of the following: obesity, hypertension, hyperglycemia, hypertriglyceridemia, and low high-density lipoprotein. Individuals with MetS also typically have elevated plasma levels of the antifibrinolytic factor, plasminogen activator inhibitor-1 (PAI-1), but the relationships between PAI-1 and MetS diagnostic criteria are not clear. Understanding these relationships can elucidate the relevance of MetS to cardiovascular disease risk, because PAI-1 is associated with ischemic events and directly involved in thrombosis. METHODS AND RESULTS: In a cross-sectional analysis of 2220 Ghanaian men and women from urban and rural locales, we found the age-standardized prevalence of MetS to be as high as 21.4% (urban women). PAI-1 level increased exponentially as the number of diagnostic criteria increased linearly (P<10(-13)), supporting the conclusion that MetS components have a joint effect that is stronger than their additive contributions. Body mass index, triglycerides, and fasting glucose were more strongly correlated with PAI-1 than with canonical MetS criteria, and this pattern did not change when pair-wise correlations were conditioned on all other risk factors, supporting an independent role for PAI-1 in MetS. Finally, whereas the correlations between conventional risk factors did not vary significantly by sex or across urban and rural environments, correlations with PAI-1 were generally stronger among urban participants. CONCLUSIONS: MetS prevalence in the West African population we studied was comparable to that of the industrialized West. PAI-1 may serve as a key link between MetS, as currently defined, and the endpoints with which it is associated. Whether this association is generalizable will require follow-up
Plasminogen Activator Inhibitor-1 and Diagnosis of the Metabolic Syndrome in a West African Population
Get PDFBACKGROUND: Metabolic syndrome (MetS) is diagnosed by the presence of at least 3 of the following: obesity, hypertension, hyperglycemia, hypertriglyceridemia, and low high-density lipoprotein. Individuals with MetS also typically have elevated plasma levels of the antifibrinolytic factor, plasminogen activator inhibitor-1 (PAI-1), but the relationships between PAI-1 and MetS diagnostic criteria are not clear. Understanding these relationships can elucidate the relevance of MetS to cardiovascular disease risk, because PAI-1 is associated with ischemic events and directly involved in thrombosis. METHODS AND RESULTS: In a cross-sectional analysis of 2220 Ghanaian men and women from urban and rural locales, we found the age-standardized prevalence of MetS to be as high as 21.4% (urban women). PAI-1 level increased exponentially as the number of diagnostic criteria increased linearly (P<10(-13)), supporting the conclusion that MetS components have a joint effect that is stronger than their additive contributions. Body mass index, triglycerides, and fasting glucose were more strongly correlated with PAI-1 than with canonical MetS criteria, and this pattern did not change when pair-wise correlations were conditioned on all other risk factors, supporting an independent role for PAI-1 in MetS. Finally, whereas the correlations between conventional risk factors did not vary significantly by sex or across urban and rural environments, correlations with PAI-1 were generally stronger among urban participants. CONCLUSIONS: MetS prevalence in the West African population we studied was comparable to that of the industrialized West. PAI-1 may serve as a key link between MetS, as currently defined, and the endpoints with which it is associated. Whether this association is generalizable will require follow-up
Data from: Cardiovascular disease risk factors in Ghana during the rural-to-urban transition: a cross-sectional study
No full textPopulations in sub-Saharan Africa are shifting from rural to increasingly urban. Although the burden of cardiovascular disease is expected to increase with this changing landscape, few large studies have assessed a wide range of risk factors in urban and rural populations, particularly in West Africa. We conducted a cross-sectional, population-based survey of 3317 participants from Ghana (≥18 years old), of whom 2265 (57% female) were from a mid-sized city (Sunyani, population ~250,000) and 1052 (55% female) were from surrounding villages (populations <5000). We measured canonical cardiovascular disease risk factors (BMI, blood pressure, fasting glucose, lipids) and fibrinolytic markers (PAI-1 and t-PA), and assessed how their distributions and related clinical outcomes (including obesity, hypertension and diabetes) varied with urban residence and sex. Urban residence was strongly associated with obesity (OR: 7.8, 95% CI: 5.3–11.3), diabetes (OR 3.6, 95% CI: 2.3–5.7), and hypertension (OR 3.2, 95% CI: 2.6–4.0). Among the quantitative measures, most affected were total cholesterol (+0.81 standard deviations, 95% CI 0.73–0.88), LDL cholesterol (+0.89, 95% CI: 0.79–0.99), and t-PA (+0.56, 95% CI: 0.48–0.63). Triglycerides and HDL cholesterol profiles were similarly poor in both urban and rural environments, but significantly worse among rural participants after BMI-adjustment. For most of the risk factors, the strength of the association with urban residence did not vary with sex. Obesity was a major exception, with urban women at particularly high risk (26% age-standardized prevalence) compared to urban men (7%). Overall, urban residents had substantially worse cardiovascular risk profiles, with some risk factors at levels typically seen in the developed world
