Dataset Information

The impact of self-identified race on epidemiologic studies of gene expression.

ABSTRACT: Although population differences in gene expression have been established, the impact on differential gene expression studies in large populations is not well understood. We describe the effect of self-reported race on a gene expression study of lung function in asthma. We generated gene expression profiles for 254 young adults (205 non-Hispanic whites and 49 African Americans) with asthma on whom concurrent total RNA derived from peripheral blood CD4(+) lymphocytes and lung function measurements were obtained. We identified four principal components that explained 62% of the variance in gene expression. The dominant principal component, which explained 29% of the total variance in gene expression, was strongly associated with self-identified race (P<10(-16)). The impact of these racial differences was observed when we performed differential gene expression analysis of lung function. Using multivariate linear models, we tested whether gene expression was associated with a quantitative measure of lung function: pre-bronchodilator forced expiratory volume in one second (FEV(1)). Though unadjusted linear models of FEV(1) identified several genes strongly correlated with lung function, these correlations were due to racial differences in the distribution of both FEV(1) and gene expression, and were no longer statistically significant following adjustment for self-identified race. These results suggest that self-identified race is a critical confounding covariate in epidemiologic studies of gene expression and that, similar to genetic studies, careful consideration of self-identified race in gene expression profiling studies is needed to avoid spurious association.

SUBMITTER: Sharma S

PROVIDER: S-EPMC3718033 | biostudies-literature | 2011 Feb

REPOSITORIES: biostudies-literature

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The impact of self-identified race on epidemiologic studies of gene expression.

Sharma Sunita S Murphy Amy A Howrylak Judie J Himes Blanca B Cho Michael H MH Chu Jen-Hwa JH Hunninghake Gary M GM Fuhlbrigge Anne A Klanderman Barbara B Ziniti John J Senter-Sylvia Jody J Liu Andy A Szefler Stanley J SJ Strunk Robert R Castro Mario M Hansel Nadia N NN Diette Gregory B GB Vonakis Becky M BM Adkinson N Franklin NF Carey Vincent J VJ Raby Benjamin A BA

Genetic epidemiology 20110201 2

Although population differences in gene expression have been established, the impact on differential gene expression studies in large populations is not well understood. We describe the effect of self-reported race on a gene expression study of lung function in asthma. We generated gene expression profiles for 254 young adults (205 non-Hispanic whites and 49 African Americans) with asthma on whom concurrent total RNA derived from peripheral blood CD4(+) lymphocytes and lung function measurements ...[more]

PMID: 21254216

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Project description:BackgroundSocioeconomic and health care utilization factors are major drivers of prostate cancer (PC) mortality disparities in the USA; however, tumor molecular heterogeneity may also contribute to the higher mortality among Black men.ObjectiveTo compare differences in PC subtype frequency and genomic aggressiveness by self-identified race.Design setting and participantsFive molecular subtype classifiers were applied for 426 Black and 762 White PC patients in the Decipher Genomics Resource Information Database (GRID).Outcome measurements and statistical analysisDifferences in subtype frequency and tumor genomic risk (Decipher score >0.6) by race were evaluated using χ2 tests and multivariable-adjusted logistic regression models.Results and limitationsSubtype frequencies differed by race for four classifiers. Subtypes characterized by the presence of SPOP mutations, SPINK1 overexpression, and neuroendocrine differentiation were more common among Black men. ERG and ETS fusion-positive subtypes were more frequent among White men, with no clear differences for subtypes reflecting luminal versus basal lineage. The hypothesized low-risk Kamoun S2 subtype was associated with a lower Decipher score among White men only (p = 0.01 for heterogeneity), while the aggressive You PCS1 subtype was associated with a higher Decipher score among White men only (p = 0.001 for heterogeneity). The Tomlins ERG+ subtype was associated with a higher Decipher score relative to all other subtypes among Black men, with no association among White men (p = 0.007 for heterogeneity).ConclusionsThe frequency of PC molecular subtypes differed by self-identified race. Additional studies are required to evaluate whether our observations suggest differences in the tumor genomic risk of progression by self-identified race.Patient summaryWe studied five classifiers that identify subtypes of prostate tumors and found that subtypes differed in frequency between Black and White patients. Further research is warranted to evaluate how differences in tumor subtypes may contribute to disparities in prostate cancer mortality.

Project description:BackgroundStudies have suggested that agreement between administrative health data and self-report for asthma status ranges from fair to good, but few studies benefited from administrative health data over a long period. We aimed to (1) evaluate agreement between asthma status ascertained in administrative health data covering a period of 30 years and from self-report, and (2) identify determinants of agreement between the two sources.MethodsWe used administrative health data (1983-2012) from the Quebec Birth Cohort on Immunity and Health, which included 81,496 individuals born in the province of Quebec, Canada, in 1974. Additional information, including self-reported asthma, was collected by telephone interview with 1643 participants in 2012. By design, half of them had childhood asthma based on health services utilization. Results were weighted according to the inverse of the sampling probabilities. Five algorithms were applied to administrative health data (having ≥ 2 physician claims over a 1-, 2-, 3-, 5-, or 30-year interval or ≥ 1 hospitalization), to enable comparisons with previous studies. We estimated the proportion of overall agreement and Kappa, between asthma status derived from algorithms and self-reports. We used logistic regression to identify factors associated with agreement.ResultsApplying the five algorithms, the prevalence of asthma ranged from 49 to 55% among the 1643 participants. At interview (mean age = 37 years), 49% and 47% of participants respectively reported ever having asthma and asthma diagnosed by a physician. Proportions of agreement between administrative health data and self-report ranged from 88 to 91%, with Kappas ranging from 0.57 (95% CI: 0.52-0.63) to 0.67 (95% CI: 0.62-0.72); the highest values were obtained with the [≥ 2 physician claims over a 30-year interval or ≥ 1 hospitalization] algorithm. Having sought health services for allergic diseases other than asthma was related to lower agreement (Odds ratio = 0.41; 95% CI: 0.25-0.65 comparing ≥ 1 health services to none).ConclusionsThese findings indicate good agreement between asthma status defined from administrative health data and self-report. Agreement was higher than previously observed, which may be due to the 30-year lookback window in administrative data. Our findings support using both administrative health data and self-report in population-based epidemiological studies.

Dataset Information

The impact of self-identified race on epidemiologic studies of gene expression.

Publications

The impact of self-identified race on epidemiologic studies of gene expression.

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