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

Neurochemical Changes in the Mouse Hippocampus Underlying the Antidepressant Effect of Genetic Deletion of P2X7 Receptors.

Recent investigations have revealed that the genetic deletion of P2X7 receptors (P2rx7) results in an antidepressant phenotype in mice. However, the link between the deficiency of P2rx7 and changes in behavior has not yet been explored. In the present study, we studied the effect of genetic deletion of P2rx7 on neurochemical changes in the hippocampus that might underlie the antidepressant phenotype. P2X7 receptor deficient mice (P2rx7-/-) displayed decreased immobility in the tail suspension test (TST) and an attenuated anhedonia response in the sucrose preference test (SPT) following bacterial endotoxin (LPS) challenge. The attenuated anhedonia was reproduced through systemic treatments with P2rx7 antagonists. The activation of P2rx7 resulted in the concentration-dependent release of [3H]glutamate in P2rx7+/+ but not P2rx7-/- mice, and the NR2B subunit mRNA and protein was upregulated in the hippocampus of P2rx7-/- mice. The brain-derived neurotrophic factor (BDNF) expression was higher in saline but not LPS-treated P2rx7-/- mice; the P2rx7 antagonist Brilliant blue G elevated and the P2rx7 agonist benzoylbenzoyl ATP (BzATP) reduced BDNF level. This effect was dependent on the activation of NMDA and non-NMDA receptors but not on Group I metabotropic glutamate receptors (mGluR1,5). An increased 5-bromo-2-deoxyuridine (BrdU) incorporation was also observed in the dentate gyrus derived from P2rx7-/- mice. Basal level of 5-HT was increased, whereas the 5HIAA/5-HT ratio was lower in the hippocampus of P2rx7-/- mice, which accompanied the increased uptake of [3H]5-HT and an elevated number of [3H]citalopram binding sites. The LPS-induced elevation of 5-HT level was absent in P2rx7-/- mice. In conclusion there are several potential mechanisms for the antidepressant phenotype of P2rx7-/- mice, such as the absence of P2rx7-mediated glutamate release, elevated basal BDNF production, enhanced neurogenesis and increased 5-HT bioavailability in the hippocampus

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 multi-center population-based case–control study of ovarian cancer in African-American women: the African American Cancer Epidemiology Study (AACES)

Abstract: Background: Ovarian cancer (OVCA) is the leading cause of death from gynecological cancer, with poorer survival for African American (AA) women compared to whites. However, little is known about risk factors for OVCA in AA. To study the epidemiology of OVCA in this population, we started a collaborative effort in 10 sites in the US. Here we describe the study and highlight the challenges of conducting a study of a lethal disease in a minority population. Methods: The African American Cancer Epidemiology Study (AACES) is an ongoing, population-based case–control study of OVCA in AA in 10 geographic locations, aiming to recruit 850 women with invasive epithelial OVCA and 850 controls age- and geographically-matched to cases. Rapid case ascertainment and random-digit-dialing systems are in place to ascertain cases and controls, respectively. A telephone survey focuses on risk factors as well as factors of particular relevance for AAs. Food-frequency questionnaires, follow-up surveys, biospecimens and medical records are also obtained. Results: Current accrual of 403 AA OVCA cases and 639 controls exceeds that of any existing study to date. We observed a high proportion (15%) of deceased non-responders among the cases that in part is explained by advanced stage at diagnosis. A logistic regression model did not support that socio-economic status was a factor in advanced stage at diagnosis. Most risk factor associations were in the expected direction and magnitude. High BMI was associated with ovarian cancer risk, with multivariable adjusted ORs and 95% CIs of 1.50 (0.99-2.27) for obese and 1.27 (0.85- 1.91) for morbidly obese women compared to normal/underweight women. Conclusions: AACES targets a rare tumor in AAs and addresses issues most relevant to this population. The importance of the study is accentuated by the high proportion of OVCA cases ascertained as deceased. Our analyses indicated that obesity, highly prevalent in this population (>60% of the cases), was associated with increased OVCA risk. While these findings need to be replicated, they suggest the potential for an effective intervention on the risk in AAs. Upon completion of enrollment, AACES will be the largest epidemiologic study of OVCA in AA women

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

Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma

Therapy development for adult diffuse glioma is hindered by incomplete knowledge of somatic glioma driving alterations and suboptimal disease classification. We defined the complete set of genes associated with 1,122 diffuse grade II-III-IV gliomas from The Cancer Genome Atlas and used molecular profiles to improve disease classification, identify molecular correlations, and provide insights into the progression from low- to high-grade disease. Whole-genome sequencing data analysis determined that ATRX but not TERT promoter mutations are associated with increased telomere length. Recent advances in glioma classification based on IDH mutation and 1p/19q co-deletion status were recapitulated through analysis of DNA methylation profiles, which identified clinically relevant molecular subsets. A subtype of IDH mutant glioma was associated with DNA demethylation and poor outcome; a group of IDH-wild-type diffuse glioma showed molecular similarity to pilocytic astrocytoma and relatively favorable survival. Understanding of cohesive disease groups may aid improved clinical outcomes

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

Accounting for Population Stratification in Practice: A Comparison of the Main Strategies Dedicated to Genome-Wide Association Studies

Genome-Wide Association Studies are powerful tools to detect genetic variants associated with diseases. Their results have, however, been questioned, in part because of the bias induced by population stratification. This is a consequence of systematic differences in allele frequencies due to the difference in sample ancestries that can lead to both false positive or false negative findings. Many strategies are available to account for stratification but their performances differ, for instance according to the type of population structure, the disease susceptibility locus minor allele frequency, the degree of sampling imbalanced, or the sample size. We focus on the type of population structure and propose a comparison of the most commonly used methods to deal with stratification that are the Genomic Control, Principal Component based methods such as implemented in Eigenstrat, adjusted Regressions and Meta-Analyses strategies. Our assessment of the methods is based on a large simulation study, involving several scenarios corresponding to many types of population structures. We focused on both false positive rate and power to determine which methods perform the best. Our analysis showed that if there is no population structure, none of the tests led to a bias nor decreased the power except for the Meta-Analyses. When the population is stratified, adjusted Logistic Regressions and Eigenstrat are the best solutions to account for stratification even though only the Logistic Regressions are able to constantly maintain correct false positive rates. This study provides more details about these methods. Their advantages and limitations in different stratification scenarios are highlighted in order to propose practical guidelines to account for population stratification in Genome-Wide Association Studies