Regression and data mining methods for analyses of multiple rare variants in the Genetic Analysis Workshop 17 mini-exome data.

Group 14 of Genetic Analysis Workshop 17 examined several issues related to analysis of complex traits using DNA sequence data. These issues included novel methods for analyzing rare genetic variants in an aggregated manner (often termed collapsing rare variants), evaluation of various study designs to increase power to detect effects of rare variants, and the use of machine learning approaches to model highly complex heterogeneous traits. Various published and novel methods for analyzing traits with extreme locus and allelic heterogeneity were applied to the simulated quantitative and disease phenotypes. Overall, we conclude that power is (as expected) dependent on locus-specific heritability or contribution to disease risk, large samples will be required to detect rare causal variants with small effect sizes, extreme phenotype sampling designs may increase power for smaller laboratory costs, methods that allow joint analysis of multiple variants per gene or pathway are more powerful in general than analyses of individual rare variants, population-specific analyses can be optimal when different subpopulations harbor private causal mutations, and machine learning methods may be useful for selecting subsets of predictors for follow-up in the presence of extreme locus heterogeneity and large numbers of potential predictors.

Trust in primary care providers and antiretroviral adherence in an urban HIV clinic.

Interpersonal trust is an integral component of the patient-provider relationship and has been associated with patient adherence to medications. Studies suggest African Americans may have lower levels of trust in their health care providers than non-Hispanic Whites. This study examines the association between trust in one's primary care provider (PCP) and antiretroviral (ARV) adherence among 175 patients at an urban HIV clinic. Interviews elicited participants' level of trust in their current PCP using a multiple-item trust scale and assessed ARV adherence with a seven-day recall questionnaire. Logistic regression was used to ascertain the effect of trust in PCP on ARV adherence. High trust in PCP was significantly associated with increased odds of ARV adherence compared with low trust (adjusted odds ratio, 2.67; 95% confidence interval, 1.24 to 5.76; p=.01). Enhancing trust in PCPs may be a good target for interventions to improve ARV adherence, particularly among African American patients.

Role of isotope selection in long-term outcomes in patients with intermediate-risk prostate cancer treated with a combination of external beam radiotherapy and low-dose-rate interstitial brachytherapy.

OBJECTIVE: To examine the rates of long-term biochemical recurrence-free survival (BRFS) with respect to isotope in intermediate-risk prostate cancer treated with external beam radiotherapy (EBRT) and brachytherapy. METHODS: A total of 242 consecutive patients with intermediate-risk prostate cancer were treated with iodine-125 ((125)I) or palladium-103 ((103)Pd) implants after EBRT (range 45.0-50.4 Gy) from 1996 to 2002. Of the 242 patients, 119 (49.2%) were treated with (125)I and 123 (50.8%) with (103)Pd. Multivariate Cox regression analysis was used to analyze BRFS, defined according to the Phoenix definition (prostate-specific antigen nadir plus 2 ng/mL) with respect to Gleason score, stage, pretreatment prostate-specific antigen level, and source selection. Late genitourinary/gastrointestinal toxicities were assessed using the Radiation Therapy Oncology Group/European Organization for Research and Treatment of Cancer scale. RESULTS: At a median follow-up of 10 years, the BRFS rate was 77.3%. A statistically significant difference was found in the 10-year BRFS rate between the (125)I- and (103)Pd-treated groups (82.7% and 70.6%, respectively; P = .001). The addition of hormonal therapy did not improve the 10-year BRFS rate (77.6%) compared with RT alone (77.1%; P = .22). However, a statistically significant difference in the BRFS rate was found with the addition of hormonal therapy to (103)Pd, improving the 10-year BRFS rate for (73.8%) compared with (103)Pd alone (69.1%; P = .008). On multivariate analysis, isotope type ((103)Pd vs (125)I), pretreatment prostate-specific antigen level >10 ng/mL, and greater tumor stage increased the risk of recurrence by 2.6-fold (P = .007), 5.9-fold (P < .0001), and 1.7-fold (P = .14), respectively. CONCLUSION: (125)I renders a superior rate of BRFS compared with (103)Pd when used with EBRT. Hormonal therapy does not provide additional benefit in patients with intermediate-risk prostate cancer treated with a combination of EBRT and brachytherapy, except for the addition of hormonal therapy to (103)Pd.

A LASSO-based approach to analyzing rare variants in genetic association studies.

Genetic markers with rare variants are spread out in the genome, making it necessary and difficult to consider them in genetic association studies. Consequently, wisely combining rare variants into "composite" markers may facilitate meaningful analyses. In this paper, we propose a novel approach of analyzing rare variant data by incorporating the least absolute shrinkage and selection operator technique. We applied this method to the Genetic Analysis Workshop 17 data, and our results suggest that this new approach is promising. In addition, we took advantage of having 200 phenotype replications and assessed the performance of our approach by means of repeated classification tree analyses. Our method and analyses were performed without knowledge of the underlying simulating model. Our method identified 38 markers (in 65 genes) that are significantly associated with the phenotype Affected and correctly identified two causal genes, SIRT1 and PDGFD.

Novel tree-based method to generate markers from rare variant data.

Existing methods for analyzing rare variant data focus on collapsing a group of rare variants into a single common variant; collapsing is based on an intuitive function of the rare variant genotype information, such as an indicator function or a weighted sum. It is more natural, however, to take into account the single-nucleotide polymorphism (SNP) interactions informed directly by the data. We propose a novel tree-based method that automatically detects SNP interactions and generates candidate markers from the original pool of rare variants. In addition, we utilize the advantage of having 200 phenotype replications in the Genetic Analysis Workshop 17 data to assess the candidate markers by means of repeated logistic regressions. This new approach shows potential in the rare variant analysis. We correctly identify the association between gene FLT1 and phenotype Affect, although there exist other false positives in our results. Our analyses are performed without knowledge of the underlying simulating model.

Clinical estimation of fetal weight and the Hawthorne effect.

OBJECTIVE: The Hawthorne effect refers to improvement in performance solely due to the subject's knowledge that he or she is being studied. We sought to determine if an obstetrician's clinical estimation of fetal weight (EFW) is influenced by the Hawthorne effect seen in some clinical trials. STUDY DESIGN: We compared obstetricians' clinical EFW's obtained during a study period to those obtained prior to the study period in one institution. We included any patient presenting at > or =37 weeks gestation. We excluded multiple pregnancies and patients with a recent sonographic EFW. RESULTS: There was no difference in regards to the proportion of EFW's within 10% of the birthweight (67.9% vs. 68.5%, p=.91), the mean absolute difference of EFW-birthweight (282+/-227 g vs. 285+/-232 g, p=.88), or the mean absolute percent error (8.5+/-7.4% vs. 8.6+/-7.2%, p=.96). We also could not find any Hawthorne effect when we excluded resident physicians' EFW's and when we analyzed the subgroup of newborns with a birth weight > or =4000 g. CONCLUSION: An obstetrician's knowledge that he or she is being studied is unlikely to improve clinical EFW accuracy. Published clinical EFW accuracies are likely to be similar to those obtained in clinical practice.