Genetic Ancestry, Self-Reported Race and Ethnicity in African Americans and European Americans in the PCaP Cohort

Family history and African-American race are important risk factors for both prostate cancer (CaP) incidence and aggressiveness. When studying complex diseases such as CaP that have a heritable component, chances of finding true disease susceptibility alleles can be increased by accounting for genetic ancestry within the population investigated. Race, ethnicity and ancestry were studied in a geographically diverse cohort of men with newly diagnosed CaP.Individual ancestry (IA) was estimated in the population-based North Carolina and Louisiana Prostate Cancer Project (PCaP), a cohort of 2,106 incident CaP cases (2063 with complete ethnicity information) comprising roughly equal numbers of research subjects reporting as Black/African American (AA) or European American/Caucasian/Caucasian American/White (EA) from North Carolina or Louisiana. Mean genome wide individual ancestry estimates of percent African, European and Asian were obtained and tested for differences by state and ethnicity (Cajun and/or Creole and Hispanic/Latino) using multivariate analysis of variance models. Principal components (PC) were compared to assess differences in genetic composition by self-reported race and ethnicity between and within states.Mean individual ancestries differed by state for self-reporting AA (p = 0.03) and EA (p = 0.001). This geographic difference attenuated for AAs who answered "no" to all ethnicity membership questions (non-ethnic research subjects; p = 0.78) but not EA research subjects, p = 0.002. Mean ancestry estimates of self-identified AA Louisiana research subjects for each ethnic group; Cajun only, Creole only and both Cajun and Creole differed significantly from self-identified non-ethnic AA Louisiana research subjects. These ethnicity differences were not seen in those who self-identified as EA.Mean IA differed by race between states, elucidating a potential contributing factor to these differences in AA research participants: self-reported ethnicity. Accurately accounting for genetic admixture in this cohort is essential for future analyses of the genetic and environmental contributions to CaP

A Bayesian method for evaluating and discovering disease loci associations

Background: A genome-wide association study (GWAS) typically involves examining representative SNPs in individuals from some population. A GWAS data set can concern a million SNPs and may soon concern billions. Researchers investigate the association of each SNP individually with a disease, and it is becoming increasingly commonplace to also analyze multi-SNP associations. Techniques for handling so many hypotheses include the Bonferroni correction and recently developed Bayesian methods. These methods can encounter problems. Most importantly, they are not applicable to a complex multi-locus hypothesis which has several competing hypotheses rather than only a null hypothesis. A method that computes the posterior probability of complex hypotheses is a pressing need. Methodology/Findings: We introduce the Bayesian network posterior probability (BNPP) method which addresses the difficulties. The method represents the relationship between a disease and SNPs using a directed acyclic graph (DAG) model, and computes the likelihood of such models using a Bayesian network scoring criterion. The posterior probability of a hypothesis is computed based on the likelihoods of all competing hypotheses. The BNPP can not only be used to evaluate a hypothesis that has previously been discovered or suspected, but also to discover new disease loci associations. The results of experiments using simulated and real data sets are presented. Our results concerning simulated data sets indicate that the BNPP exhibits both better evaluation and discovery performance than does a p-value based method. For the real data sets, previous findings in the literature are confirmed and additional findings are found. Conclusions/Significance: We conclude that the BNPP resolves a pressing problem by providing a way to compute the posterior probability of complex multi-locus hypotheses. A researcher can use the BNPP to determine the expected utility of investigating a hypothesis further. Furthermore, we conclude that the BNPP is a promising method for discovering disease loci associations. © 2011 Jiang et al

European American Stratification in Ovarian Cancer Case Control Data: The Utility of Genome-Wide Data for Inferring Ancestry

We investigated the ability of several principal components analysis (PCA)-based strategies to detect and control for population stratification using data from a multi-center study of epithelial ovarian cancer among women of European-American ethnicity. These include a correction based on an ancestry informative markers (AIMs) panel designed to capture European ancestral variation and corrections utilizing un-thinned genome-wide SNP data; case-control samples were drawn from four geographically distinct North-American sites. The AIMs-only and genome-wide first principal components (PC1) both corresponded to the previously described North or Northwest-Southeast axis of European variation. We found that the genome-wide PCA captured this primary dimension of variation more precisely and identified additional axes of genome-wide variation of relevance to epithelial ovarian cancer. Associations evident between the genome-wide PCs and study site corroborate North American immigration history and suggest that undiscovered dimensions of variation lie within Northern Europe. The structure captured by the genome-wide PCA was also found within control individuals and did not reflect the case-control variation present in the data. The genome-wide PCA highlighted three regions of local LD, corresponding to the lactase (LCT) gene on chromosome 2, the human leukocyte antigen system (HLA) on chromosome 6 and to a common inversion polymorphism on chromosome 8. These features did not compromise the efficacy of PCs from this analysis for ancestry control. This study concludes that although AIMs panels are a cost-effective way of capturing population structure, genome-wide data should preferably be used when available

Characterizing Mutational Heterogeneity in a Glioblastoma Patient with Double Recurrence

Human cancers are driven by the acquisition of somatic mutations. Separating the driving mutations from those that are random consequences of general genomic instability remains a challenge. New sequencing technology makes it possible to detect mutations that are present in only a minority of cells in a heterogeneous tumor population. We sought to leverage the power of ultra-deep sequencing to study various levels of tumor heterogeneity in the serial recurrences of a single glioblastoma multiforme patient. Our goal was to gain insight into the temporal succession of DNA base-level lesions by querying intra- and inter-tumoral cell populations in the same patient over time. We performed targeted “next-generation" sequencing on seven samples from the same patient: two foci within the primary tumor, two foci within an initial recurrence, two foci within a second recurrence, and normal blood. Our study reveals multiple levels of mutational heterogeneity. We found variable frequencies of specific EGFR, PIK3CA, PTEN, and TP53 base substitutions within individual tumor regions and across distinct regions within the same tumor. In addition, specific mutations emerge and disappear along the temporal spectrum from tumor at the time of diagnosis to second recurrence, demonstrating evolution during tumor progression. Our results shed light on the spatial and temporal complexity of brain tumors. As sequencing costs continue to decline and deep sequencing technology eventually moves into the clinic, this approach may provide guidance for treatment choices as we embark on the path to personalized cancer medicine

Lack of association between modifiable exposures and glioma risk: a Mendelian randomization analysis.

Background The etiological basis of glioma is poorly understood. We have used genetic markers in a Mendelian randomization (MR) framework to examine if lifestyle, cardiometabolic, and inflammatory factors influence the risk of glioma. This methodology reduces bias from confounding and is not affected by reverse causation.Methods We identified genetic instruments for 37 potentially modifiable risk factors and evaluated their association with glioma risk using data from a genome-wide association study of 12 488 glioma patients and 18 169 controls. We used the estimated odds ratio of glioma associated with each of the genetically defined traits to infer evidence for a causal relationship with the following exposures:Lifestyle and dietary factors-height, plasma insulin-like growth factor 1, blood carnitine, blood methionine, blood selenium, blood zinc, circulating adiponectin, circulating carotenoids, iron status, serum calcium, vitamins (A1, B12, B6, E, and 25-hydroxyvitamin D), fatty acid levels (monounsaturated, omega-3, and omega-6) and circulating fetuin-A;Cardiometabolic factors-birth weight, high density lipoprotein cholesterol, low density lipoprotein cholesterol, total cholesterol, total triglycerides, basal metabolic rate, body fat percentage, body mass index, fasting glucose, fasting proinsulin, glycated hemoglobin levels, diastolic and systolic blood pressure, waist circumference, waist-to-hip ratio; andInflammatory factors- C-reactive protein, plasma interleukin-6 receptor subunit alpha and serum immunoglobulin E.Results After correction for the testing of multiple potential risk factors and excluding associations driven by one single nucleotide polymorphism, no significant association with glioma risk was observed (ie, PCorrected > 0.05).Conclusions This study did not provide evidence supporting any of the 37 factors examined as having a significant influence on glioma risk

Dissecting the Within-Africa Ancestry of Populations of African Descent in the Americas

The ancestry of African-descended Americans is known to be drawn from three distinct populations: African, European, and Native American. While many studies consider this continental admixture, few account for the genetically distinct sources of ancestry within Africa--the continent with the highest genetic variation. Here, we dissect the within-Africa genetic ancestry of various populations of the Americas self-identified as having primarily African ancestry using uniparentally inherited mitochondrial DNA.We first confirmed that our results obtained using uniparentally-derived group admixture estimates are correlated with the average autosomal-derived individual admixture estimates (hence are relevant to genomic ancestry) by assessing continental admixture using both types of markers (mtDNA and Y-chromosome vs. ancestry informative markers). We then focused on the within-Africa maternal ancestry, mining our comprehensive database of published mtDNA variation (∼5800 individuals from 143 African populations) that helped us thoroughly dissect the African mtDNA pool. Using this well-defined African mtDNA variation, we quantified the relative contributions of maternal genetic ancestry from multiple W/WC/SW/SE (West to South East) African populations to the different pools of today's African-descended Americans of North and South America and the Caribbean.Our analysis revealed that both continental admixture and within-Africa admixture may be critical to achieving an adequate understanding of the ancestry of African-descended Americans. While continental ancestry reflects gender-specific admixture processes influenced by different socio-historical practices in the Americas, the within-Africa maternal ancestry reflects the diverse colonial histories of the slave trade. We have confirmed that there is a genetic thread connecting Africa and the Americas, where each colonial system supplied their colonies in the Americas with slaves from African colonies they controlled or that were available for them at the time. This historical connection is reflected in different relative contributions from populations of W/WC/SW/SE Africa to geographically distinct Africa-derived populations of the Americas, adding to the complexity of genomic ancestry in groups ostensibly united by the same demographic label

The treatment and outcomes of early-stage epithelial ovarian cancer: have we made any progress?

The objective of this study is to determine the progress and trends in the treatment and survival of women with early-stage (I–II) epithelial ovarian cancer. Data were obtained from the SEER database between 1988 and 2001. Kaplan–Meier and Cox regressions methods were employed for statistical analyses. Of the 8372 patients, the median age was 57 years (range: 12–99 years). A total of 6152 patients (73.4%) presented with stage I and 2220 (26.5%) with stage II disease. Over the periods 1988–1992, 1993–1997, and 1998–2001, 3-year disease-specific survivals increased from 86.1 to 87.2 to 88.8% (P=0.076). The number of patients that underwent lymphadenectomy has increased significantly from 26.2 to 38.7 to 54.2% over the study period (P<0.001). Of those patients who underwent staging procedures with lymphadenectomy, there was no improvement in survival over the three study periods (from 93.2 to 93.5 to 93.1%; P=0.978). On multivariate analysis, younger age, nonclear cell histology, earlier stage, lower grade, surgery, and lymphadenectomy were significant independent prognostic factors for improved survival. After adjusting for surgical staging with lymphadenectomy, the year of diagnosis was no longer an important prognostic factor. In conclusion, the use of lymphadenectomy during surgery for early-stage ovarian cancer has doubled over the last 14 years. The marginal improvement in survival demonstrated over time is potentially attributed to the increased use of staging procedures with lymphadenectomy