Apolipoprotein E and Protection Against Hepatitis E Viral Infection in American Non-Hispanic Blacks

Hepatitis E viral (HEV) infection imposes a heavy health burden worldwide and is common in the United States. Previous investigations of risks addressed environmental and host behavioral/ lifestyle factors, but host genetic factors have not been examined. We assessed strength of associations between antibody to HEV (anti-HEV) immunoglobulin G seropositivity indicating past or recent HEV infection and human genetic variants among three major racial/ ethnic populations in the United States, involving 2434 non-Hispanic whites, 1919 non- Hispanic blacks, and 1919 Mexican Americans from the Third National Health and Nutrition Examination Survey, 1991-1994. We studied 497 single-nucleotide polymorphisms across 190 genes (particularly those associated with lipid metabolism). The genomic control method was used to adjust for potential population stratification. Non-Hispanic blacks had the lowest seroprevalence of anti-HEV immunoglobulin G (15.3%, 95% confidence interval [CI] 12.3%-19.0%) compared with non-Hispanic whites (22.3%, 95% CI 19.1%-25.7%) and Mexican Americans (21.8%, 95% CI 19.0%-25.3%; P \u3c 0.01). Non-Hispanic blacks were the only population that showed association between anti-HEV seropositivity and functional Ɛ3 and Ɛ4 alleles of the apolipoprotein E (APOE) gene, encoding the apolipoprotein E protein thatmediates lipoprotein metabolism. Seropositivity was significantly lower in participants carrying APOE Ɛ4 (odds ratio50.5, 95% CI 0.4-0.7; P50.00004) and Ɛ3 (odds ratio50.6, 95% CI 0.4-0.8; P50.001) compared to those carrying APOE Ɛ2. No significant associations were observed between other single-nucleotide polymorphisms and anti-HEV seropositivity in non-Hispanic blacks or between any single-nucleotide polymorphisms and anti-HEV seropositivity in non-Hispanic whites or Mexican Americans. Conclusion: Both APOE Ɛ3 and Ɛ4 are significantly associated with protection against HEV infection in non- Hispanic blacks; additional studies are needed to understand the basis of protection so that preventive services can be targeted to at-risk persons

Apolipoprotein E and Protection Against Hepatitis E Viral Infection in American Non-Hispanic Blacks

Hepatitis E viral (HEV) infection imposes a heavy health burden worldwide and is common in the United States. Previous investigations of risks addressed environmental and host behavioral/ lifestyle factors, but host genetic factors have not been examined. We assessed strength of associations between antibody to HEV (anti-HEV) immunoglobulin G seropositivity indicating past or recent HEV infection and human genetic variants among three major racial/ ethnic populations in the United States, involving 2434 non-Hispanic whites, 1919 non- Hispanic blacks, and 1919 Mexican Americans from the Third National Health and Nutrition Examination Survey, 1991-1994. We studied 497 single-nucleotide polymorphisms across 190 genes (particularly those associated with lipid metabolism). The genomic control method was used to adjust for potential population stratification. Non-Hispanic blacks had the lowest seroprevalence of anti-HEV immunoglobulin G (15.3%, 95% confidence interval [CI] 12.3%-19.0%) compared with non-Hispanic whites (22.3%, 95% CI 19.1%-25.7%) and Mexican Americans (21.8%, 95% CI 19.0%-25.3%; P \u3c 0.01). Non-Hispanic blacks were the only population that showed association between anti-HEV seropositivity and functional Ɛ3 and Ɛ4 alleles of the apolipoprotein E (APOE) gene, encoding the apolipoprotein E protein thatmediates lipoprotein metabolism. Seropositivity was significantly lower in participants carrying APOE Ɛ4 (odds ratio50.5, 95% CI 0.4-0.7; P50.00004) and Ɛ3 (odds ratio50.6, 95% CI 0.4-0.8; P50.001) compared to those carrying APOE Ɛ2. No significant associations were observed between other single-nucleotide polymorphisms and anti-HEV seropositivity in non-Hispanic blacks or between any single-nucleotide polymorphisms and anti-HEV seropositivity in non-Hispanic whites or Mexican Americans. Conclusion: Both APOE Ɛ3 and Ɛ4 are significantly associated with protection against HEV infection in non- Hispanic blacks; additional studies are needed to understand the basis of protection so that preventive services can be targeted to at-risk persons

Trends in Population-Based Studies of Human Genetics in Infectious Diseases

Pathogen genetics is already a mainstay of public health investigation and control efforts; now advances in technology make it possible to investigate the role of human genetic variation in the epidemiology of infectious diseases. To describe trends in this field, we analyzed articles that were published from 2001 through 2010 and indexed by the HuGE Navigator, a curated online database of PubMed abstracts in human genome epidemiology. We extracted the principal findings from all meta-analyses and genome-wide association studies (GWAS) with an infectious disease-related outcome. Finally, we compared the representation of diseases in HuGE Navigator with their contributions to morbidity worldwide. We identified 3,730 articles on infectious diseases, including 27 meta-analyses and 23 GWAS. The number published each year increased from 148 in 2001 to 543 in 2010 but remained a small fraction (about 7%) of all studies in human genome epidemiology. Most articles were by authors from developed countries, but the percentage by authors from resource-limited countries increased from 9% to 25% during the period studied. The most commonly studied diseases were HIV/AIDS, tuberculosis, hepatitis B infection, hepatitis C infection, sepsis, and malaria. As genomic research methods become more affordable and accessible, population-based research on infectious diseases will be able to examine the role of variation in human as well as pathogen genomes. This approach offers new opportunities for understanding infectious disease susceptibility, severity, treatment, control, and prevention

Influence of Familial Risk on Diabetes Risk–Reducing Behaviors Among U.S. Adults Without Diabetes

OBJECTIVE: To test the association of family history of diabetes with the adoption of diabetes risk–reducing behaviors and whether this association is strengthened by physician advice or commonly known factors associated with diabetes risk. RESEARCH DESIGN AND METHODS: We used cross-sectional data from the 2005–2008 National Health and Nutrition Examination Survey (NHANES) to examine the effects of family history of diabetes on the adoption of selected risk-reducing behaviors in 8,598 adults (aged ≥20 years) without diabetes. We used multiple logistic regression to model three risk reduction behaviors (controlling or losing weight, increasing physical activity, and reducing the amount of dietary fat or calories) with family history of diabetes. RESULTS: Overall, 36.2% of U.S. adults without diabetes had a family history of diabetes. Among them, ~39.8% reported receiving advice from a physician during the past year regarding any of the three selected behaviors compared with 29.2% of participants with no family history (P < 0.01). In univariate analysis, adults with a family history of diabetes were more likely to perform these risk-reducing behaviors compared with adults without a family history. Physician advice was strongly associated with each of the behavioral changes (P < 0.01), and this did not differ by family history of diabetes. CONCLUSIONS: Familial risk for diabetes and physician advice both independently influence the adoption of diabetes risk–reducing behaviors. However, fewer than half of participants with familial risk reported receiving physician advice for adopting these behaviors

Race-Ethnic Differences in the Association of Genetic Loci with HbA1c levels and Mortality in U.S. Adults: The Third National Health and Nutrition Examination Survey (NHANES III)

Background: Hemoglobin A1c (HbA1c) levels diagnose diabetes, predict mortality and are associated with ten single nucleotide polymorphisms (SNPs) in white individuals. Genetic associations in other race groups are not known. We tested the hypotheses that there is race-ethnic variation in 1) HbA1c-associated risk allele frequencies (RAFs) for SNPs near SPTA1, HFE, ANK1, HK1, ATP11A, FN3K, TMPRSS6, G6PC2, GCK, MTNR1B; 2) association of SNPs with HbA1c and 3) association of SNPs with mortality. Methods We studied 3,041 non-diabetic individuals in the NHANES (National Health and Nutrition Examination Survey) III. We stratified the analysis by race/ethnicity (NHW: non-Hispanic white; NHB: non-Hispanic black; MA: Mexican American) to calculate RAF, calculated a genotype score by adding risk SNPs, and tested associations with SNPs and the genotype score using an additive genetic model, with type 1 error = 0.05. Results: RAFs varied widely and at six loci race-ethnic differences in RAF were significant (p < 0.0002), with NHB usually the most divergent. For instance, at ATP11A, the SNP RAF was 54% in NHB, 18% in MA and 14% in NHW (p < .0001). The mean genotype score differed by race-ethnicity (NHW: 10.4, NHB: 11.0, MA: 10.7, p < .0001), and was associated with increase in HbA1c in NHW (β = 0.012 HbA1c increase per risk allele, p = 0.04) and MA (β = 0.021, p = 0.005) but not NHB (β = 0.007, p = 0.39). The genotype score was not associated with mortality in any group (NHW: OR (per risk allele increase in mortality) = 1.07, p = 0.09; NHB: OR = 1.04, p = 0.39; MA: OR = 1.03, p = 0.71). Conclusion: At many HbA1c loci in NHANES III there is substantial RAF race-ethnic heterogeneity. The combined impact of common HbA1c-associated variants on HbA1c levels varied by race-ethnicity, but did not influence mortality

An automatic method to generate domain-specific investigator networks using PubMed abstracts

<p>Abstract</p> <p>Background</p> <p>Collaboration among investigators has become critical to scientific research. This includes ad hoc collaboration established through personal contacts as well as formal consortia established by funding agencies. Continued growth in online resources for scientific research and communication has promoted the development of highly networked research communities. Extending these networks globally requires identifying additional investigators in a given domain, profiling their research interests, and collecting current contact information. We present a novel strategy for building investigator networks dynamically and producing detailed investigator profiles using data available in PubMed abstracts.</p> <p>Results</p> <p>We developed a novel strategy to obtain detailed investigator information by automatically parsing the affiliation string in PubMed records. We illustrated the results by using a published literature database in human genome epidemiology (HuGE Pub Lit) as a test case. Our parsing strategy extracted country information from 92.1% of the affiliation strings in a random sample of PubMed records and in 97.0% of HuGE records, with accuracies of 94.0% and 91.0%, respectively. Institution information was parsed from 91.3% of the general PubMed records (accuracy 86.8%) and from 94.2% of HuGE PubMed records (accuracy 87.0). We demonstrated the application of our approach to dynamic creation of investigator networks by creating a prototype information system containing a large database of PubMed abstracts relevant to human genome epidemiology (HuGE Pub Lit), indexed using PubMed medical subject headings converted to Unified Medical Language System concepts. Our method was able to identify 70–90% of the investigators/collaborators in three different human genetics fields; it also successfully identified 9 of 10 genetics investigators within the PREBIC network, an existing preterm birth research network.</p> <p>Conclusion</p> <p>We successfully created a web-based prototype capable of creating domain-specific investigator networks based on an application that accurately generates detailed investigator profiles from PubMed abstracts combined with robust standard vocabularies. This approach could be used for other biomedical fields to efficiently establish domain-specific investigator networks.</p

GAPscreener: An automatic tool for screening human genetic association literature in PubMed using the support vector machine technique

<p>Abstract</p> <p>Background</p> <p>Synthesis of data from published human genetic association studies is a critical step in the translation of human genome discoveries into health applications. Although genetic association studies account for a substantial proportion of the abstracts in PubMed, identifying them with standard queries is not always accurate or efficient. Further automating the literature-screening process can reduce the burden of a labor-intensive and time-consuming traditional literature search. The Support Vector Machine (SVM), a well-established machine learning technique, has been successful in classifying text, including biomedical literature. The GAPscreener, a free SVM-based software tool, can be used to assist in screening PubMed abstracts for human genetic association studies.</p> <p>Results</p> <p>The data source for this research was the HuGE Navigator, formerly known as the HuGE Pub Lit database. Weighted SVM feature selection based on a keyword list obtained by the two-way z score method demonstrated the best screening performance, achieving 97.5% recall, 98.3% specificity and 31.9% precision in performance testing. Compared with the traditional screening process based on a complex PubMed query, the SVM tool reduced by about 90% the number of abstracts requiring individual review by the database curator. The tool also ascertained 47 articles that were missed by the traditional literature screening process during the 4-week test period. We examined the literature on genetic associations with preterm birth as an example. Compared with the traditional, manual process, the GAPscreener both reduced effort and improved accuracy.</p> <p>Conclusion</p> <p>GAPscreener is the first free SVM-based application available for screening the human genetic association literature in PubMed with high recall and specificity. The user-friendly graphical user interface makes this a practical, stand-alone application. The software can be downloaded at no charge.</p

Reporting of Human Genome Epidemiology (HuGE) association studies: An empirical assessment

<p>Abstract</p> <p>Background</p> <p>Several thousand human genome epidemiology association studies are published every year investigating the relationship between common genetic variants and diverse phenotypes. Transparent reporting of study methods and results allows readers to better assess the validity of study findings. Here, we document reporting practices of human genome epidemiology studies.</p> <p>Methods</p> <p>Articles were randomly selected from a continuously updated database of human genome epidemiology association studies to be representative of genetic epidemiology literature. The main analysis evaluated 315 articles published in 2001–2003. For a comparative update, we evaluated 28 more recent articles published in 2006, focusing on issues that were poorly reported in 2001–2003.</p> <p>Results</p> <p>During both time periods, most studies comprised relatively small study populations and examined one or more genetic variants within a single gene. Articles were inconsistent in reporting the data needed to assess selection bias and the methods used to minimize misclassification (of the genotype, outcome, and environmental exposure) or to identify population stratification. Statistical power, the use of unrelated study participants, and the use of replicate samples were reported more often in articles published during 2006 when compared with the earlier sample.</p> <p>Conclusion</p> <p>We conclude that many items needed to assess error and bias in human genome epidemiology association studies are not consistently reported. Although some improvements were seen over time, reporting guidelines and online supplemental material may help enhance the transparency of this literature.</p

Gene polymorphisms in association with emerging cardiovascular risk markers in adult women

<p>Abstract</p> <p>Background</p> <p>Evidence on the associations of emerging cardiovascular disease risk factors/markers with genes may help identify intermediate pathways of disease susceptibility in the general population. This population-based study is aimed to determine the presence of associations between a wide array of genetic variants and emerging cardiovascular risk markers among adult US women.</p> <p>Methods</p> <p>The current analysis was performed among the National Health and Nutrition Examination Survey (NHANES) III phase 2 samples of adult women aged 17 years and older (sample size n = 3409). Fourteen candidate genes within <it>ADRB2, ADRB3, CAT, CRP, F2, F5, FGB, ITGB3, MTHFR, NOS3, PON1, PPARG, TLR4</it>, and <it>TNF </it>were examined for associations with emerging cardiovascular risk markers such as serum C-reactive protein, homocysteine, uric acid, and plasma fibrinogen. Linear regression models were performed using SAS-callable SUDAAN 9.0. The covariates included age, race/ethnicity, education, menopausal status, female hormone use, aspirin use, and lifestyle factors.</p> <p>Results</p> <p>In covariate-adjusted models, serum C-reactive protein concentrations were significantly (P value controlling for false-discovery rate ≤ 0.05) associated with polymorphisms in <it>CRP </it>(rs3093058, rs1205)<it>, MTHFR </it>(rs1801131)<it>, and ADRB3 </it>(rs4994). Serum homocysteine levels were significantly associated with <it>MTHFR </it>(rs1801133).</p> <p>Conclusion</p> <p>The significant associations between certain gene variants with concentration variations in serum C-reactive protein and homocysteine among adult women need to be confirmed in further genetic association studies.</p