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

PGA: power calculator for case-control genetic association analyses

<p>Abstract</p> <p>Background</p> <p>Statistical power calculations inform the design and interpretation of genetic association studies, but few programs are tailored to case-control studies of single nucleotide polymorphisms (SNPs) in unrelated subjects.</p> <p>Results</p> <p>We have developed the "Power for Genetic Association analyses" (PGA) package which comprises algorithms and graphical user interfaces for sample size and minimum detectable risk calculations using SNP or haplotype effects under different genetic models and study constrains. The software accounts for linkage disequilibrium and statistical multiple comparisons. The results are presented in graphs or tables and can be printed or exported in standard file formats.</p> <p>Conclusion</p> <p>PGA is user friendly software that can facilitate decision making for association studies of candidate genes, fine-mapping studies, and whole-genome scans. Stand-alone executable files and a Matlab toolbox are available for download at: <url>http://dceg.cancer.gov/bb/tools/pga</url></p

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

Power to identify a genetic predictor of antihypertensive drug response using different methods to measure blood pressure response

<p>Abstract</p> <p>Background</p> <p>To determine whether office, home, ambulatory daytime and nighttime blood pressure (BP) responses to antihypertensive drug therapy measure the same signal and which method provides greatest power to identify genetic predictors of BP response.</p> <p>Methods</p> <p>We analyzed office, home, ambulatory daytime and nighttime BP responses in hypertensive adults randomized to atenolol (N = 242) or hydrochlorothiazide (N = 257) in the Pharmacogenomic Evaluation of Antihypertensive Responses Study. Since different measured BP responses may have different predictors, we tested the "same signal" model by using linear regression methods to determine whether known predictors of BP response depend on the method of BP measurement. We estimated signal-to-noise ratios and compared power to identify a genetic polymorphism predicting BP response measured by each method separately and by weighted averages of multiple methods.</p> <p>Results</p> <p>After adjustment for pretreatment BP level, known predictors of BP response including plasma renin activity, race, and sex were independent of the method of BP measurement. Signal-to-noise ratios were more than 2-fold greater for home and ambulatory daytime BP responses than for office and ambulatory nighttime BP responses and up to 11-fold greater for weighted averages of all four methods. Power to identify a genetic polymorphism predicting BP response was directly related to the signal-to-noise ratio and, therefore, greatest with the weighted averages.</p> <p>Conclusion</p> <p>Since different methods of measuring BP response to antihypertensive drug therapy measure the same signal, weighted averages of the BP responses measured by multiple methods minimize measurement error and optimize power to identify genetic predictors of BP response.</p

Telephone-administered psychotherapy for depression in MS patients: moderating role of social support

Depression is common in individuals with multiple sclerosis (MS). While psychotherapy is an effective treatment for depression, not all individuals benefit. We examined whether baseline social support might differentially affect treatment outcome in 127 participants with MS and depression randomized to either Telephone-administered Cognitive-Behavioral Therapy (T-CBT) or Telephone-administered Emotion-Focused Therapy (T-EFT). We predicted that those with low social support would improve more in T-EFT, since this approach emphasizes the therapeutic relationship, while participants with strong social networks and presumably more emotional resources might fare better in the more structured and demanding T-CBT. We found that both level of received support and satisfaction with that support at baseline did moderate treatment outcome. Individuals with high social support showed a greater reduction in depressive symptoms in the T-CBT as predicted, but participants with low social support showed a similar reduction in both treatments. This suggests that for participants with high social support, CBT may be a more beneficial treatment for depression compared with EFT

The contribution of genetic variants to disease depends on the ruler

Our understanding of the genetic basis of disease has evolved from descriptions of overall heritability or familiality to the identification of large numbers of risk loci. One can quantify the impact of such loci on disease using a plethora of measures, which can guide future research decisions. However, different measures can attribute varying degrees of importance to a variant. In this Analysis, we consider and contrast the most commonly used measures-specifically, the heritability of disease liability, approximate heritability, sibling recurrence risk, overall genetic variance using a logarithmic relative risk scale, the area under the receiver-operating curve for risk prediction and the population attributable fraction-and give guidelines for their use that should be explicitly considered when assessing the contribution of genetic variants to disease

Don't Fall Off the Adaptation Cliff: When Asymmetrical Fitness Selects for Suboptimal Traits

The cliff-edge hypothesis introduces the counterintuitive idea that the trait value associated with the maximum of an asymmetrical fitness function is not necessarily the value that is selected for if the trait shows variability in its phenotypic expression. We develop a model of population dynamics to show that, in such a system, the evolutionary stable strategy depends on both the shape of the fitness function around its maximum and the amount of phenotypic variance. The model provides quantitative predictions of the expected trait value distribution and provides an alternative quantity that should be maximized (“genotype fitness”) instead of the classical fitness function (“phenotype fitness”). We test the model's predictions on three examples: (1) litter size in guinea pigs, (2) sexual selection in damselflies, and (3) the geometry of the human lung. In all three cases, the model's predictions give a closer match to empirical data than traditional optimization theory models. Our model can be extended to most ecological situations, and the evolutionary conditions for its application are expected to be common in nature

Search for intracranial aneurysm susceptibility gene(s) using Finnish families

BACKGROUND: Cerebrovascular disease is the third leading cause of death in the United States, and about one-fourth of cerebrovascular deaths are attributed to ruptured intracranial aneurysms (IA). Epidemiological evidence suggests that IAs cluster in families, and are therefore probably genetic. Identification of individuals at risk for developing IAs by genetic tests will allow concentration of diagnostic imaging on high-risk individuals. We used model-free linkage analysis based on allele sharing with a two-stage design for a genome-wide scan to identify chromosomal regions that may harbor IA loci. METHODS: We previously estimated sibling relative risk in the Finnish population at between 9 and 16, and proceeded with a genome-wide scan for loci predisposing to IA. In 85 Finnish families with two or more affected members, 48 affected sibling pairs (ASPs) were available for our genetic study. Power calculations indicated that 48 ASPs were adequate to identify chromosomal regions likely to harbor predisposing genes and that a liberal stage I lod score threshold of 0.8 provided a reasonable balance between detection of false positive regions and failure to detect real loci with moderate effect. RESULTS: Seven chromosomal regions exceeded the stage I lod score threshold of 0.8 and five exceeded 1.0. The most significant region, on chromosome 19q, had a maximum multipoint lod score (MLS) of 2.6. CONCLUSIONS: Our study provides evidence for the locations of genes predisposing to IA. Further studies are necessary to elucidate the genes and their role in the pathophysiology of IA, and to design genetic tests

Novel germline variants identified in the inner mitochondrial membrane transporter TIMM44 and their role in predisposition to oncocytic thyroid carcinomas

Familial Non-Medullary Thyroid Carcinoma (fNMTC) represents 3–7% of all thyroid tumours and is associated with some of the highest familial risks among all cancers, with an inheritance pattern compatible with an autosomal dominant model with reduced penetrance. We previously mapped a predisposing locus, TCO (Thyroid tumour with Cell Oxyphilia) on chromosome 19p13.2, for a particular form of thyroid tumour characterised by cells with an abnormal proliferation of mitochondria (oxyphilic or oncocytic cells). In the present work, we report the systematic screening of 14 candidate genes mapping to the region of linkage in affected TCO members, that led us to identify two novel variants respectively in exon 9 and exon 13 of TIMM44, a mitochondrial inner membrane translocase for the import in the mitochondria of nuclear-encoded proteins. These variants were co-segregating with the TCO phenotype, were not present in a large group of controls and were predicted to negatively affect the protein (exon 9 change) or the transcript (exon 13 change). Functional analysis was performed in vitro for both changes and although no dramatic loss of function effects were identified for the mutant alleles, subtler effects might still be present that could alter Timm44 function and thus promote oncocytic tumour development. Thus we suggest that TIMM44 should be considered for further studies in independent samples of affected individuals with TCO

Relationship between cyclooxygenase 8473T>C polymorphism and the risk of lung cancer: a case-control study

BACKGROUND: Cyclooxygenase-2 (COX-2) plays an important role in the development of lung cancer. DNA sequence variations in the COX-2 gene may lead to altered COX-2 production and/or activity, and so they cause inter-individual differences in the susceptibility to lung cancer. To test this hypothesis, we investigated the association between the 8473T>C polymorphism in the 3'-untranslated region of the COX-2 gene and the risk of lung cancer in a Korean population. METHODS: The COX-2 genotypes were determined using PCR-based primer-introduced restriction analysis in 582 lung cancer patients and in 582 healthy controls that were frequency-matched for age and gender. RESULTS: The distribution of the COX-2 8473T>C genotypes was not significantly different between the overall lung cancer cases and the controls. However, when the cases were categorized by the tumor histology, the combined 8473 TC + CC genotype was associated with a significantly decreased risk of adenocarcinoma as compared with the 8473 TT genotype (adjusted OR = 0.64; 95% CI = 0.46–0.90, P = 0.01). On the stratification analysis, the protective effect of the combined 8473 TC + CC genotype against adenocarcinoma was statistically significant in the males, older individuals and ever-smokers (adjusted OR = 0.59; 95% CI = 0.39–0.91, P = 0.02; adjusted OR = 0.55; 95% CI = 0.33–0.93, P = 0.03; and adjusted OR = 0.57; 95% CI = 0.37–0.87, P = 0.01, respectively). CONCLUSION: These findings suggest that the COX-2 8473T>C polymorphism could be used as a marker for the genetic susceptibility to adenocarcinoma of the lung