Candidate-gene association analysis of response to risperidone in African-American and white patients with schizophrenia.

Clinical trial data were evaluated for the association between 22 single-nucleotide polymorphisms (SNPs) and response in acutely ill patients diagnosed with schizophrenia, schizoaffective disorder or schizophreniform disorder, who were treated with oral risperidone. All patients in the exploratory (78 African Americans) and validation (65 whites) data sets received risperidone 2-6 mg per day over 2-12 weeks. Two SNPs were found to have significant associations with response to risperidone over 2-12 weeks in both African-American and white patients and had a consistent direction of effect in both cohorts. Metabotropic glutamate receptor (GRM3) SNP, rs724226, was associated with a change in the positive and negative syndrome scale (PANSS) total response. Catechol-O-methyltransferase (COMT) SNP, rs165599, was moderately associated with a change in the PANSS Negative score. The greater prevalence of poor-responder GRM3 and COMT alleles in white versus African-American patients might have a clinical significance in evaluating the ethnic-specific response to risperidone.

Evaluating the performance of a genetic test using information from previous studies in place of a gold standard.

OBJECTIVES: To evaluate the performance of a diagnostic test, a researcher usually must classify study subjects with respect to (1) whether the test result was positive or negative and (2) whether the test result should have been positive or negative. To classify the subjects in the second manner, the researcher needs to have access to a gold standard (ie, a test that classifies the subjects with 100% accuracy). The authors show here how to evaluate the performance of a diagnostic test that allows researchers to determine whether a disease that is occurring within a family is attributable to one of two newly discovered genes without the use of a gold standard. METHODS: By taking advantage of well-known genetic phenomena and their statistical implications, the behavior of the diagnostic test is mathematically modeled, and its performance with respect to various criteria is shown to be functions of genetic parameters. RESULTS: The performance of the test over a wide range of values of the genetic parameters was evaluated, and cutoff points that would allow the test to perform very well or well with respect to all criteria were found for almost all of the situations examined. CONCLUSIONS: This test can be used effectively under a wide range of conditions. In addition, because the genetic parameters have been estimated in previous studies, the effectiveness of the test for the specific conditions the researcher may need to run the study under can be evaluated before the study is performed.

Clinical trials in the genomic era: effects of protective genotypes on sample size and duration of trial.

It is well known that individuals can vary widely in their disease susceptibilities. One potential source of this variation is the genetic makeup of individuals, which can confer either protection or susceptibility to disease. Here we examine the effects of protective genotypes on the sample sizes and time required to detect differences between clinical trial arms. We show that including individuals with protective genotypes in a clinical trial can increase required sample sizes and trial duration. One can deal with this issue by pregenotyping subjects and selectively enrolling them based on their genotype. Thus we also calculate the number of individuals that must be recruited and pregenotyped to fulfill sample size requirements. The benefits of genotypically screening study subjects will depend on numerous factors, including ease of patient recruitment, cost of genotyping, long-term costs of study (or long-term cost per subject), and the strength of the protective effect. We present several examples that show the potential value of incorporating information about protective genotypes into a clinical trial.

Hierarchical modeling of the relation between sequence variants and a quantitative trait: addressing multiple comparison and population stratification issues.

When analyzing the relation between genetic sequence information and disease traits, false-positive associations can arise due to multiple comparisons and population stratification. In an attempt to address these issues, we incorporate into a conventional analytic model higher-level--or "prior"--models that use additional information to improve estimates while allowing for differing population structures. We apply this hierarchical model to simulated data from the Genetic Analysis Workshop 12. We focus on the effects of common candidate gene sequence variants on quantitative risk factor 5 (Q5) levels. In particular, we compare the regression coefficients (and 95% confidence intervals) obtained from conventional (one-stage) analyses versus the corresponding results from the hierarchical analyses. When examining either the marry-ins or all subjects in the general and isolate populations, the conventional model detected numerous sites in candidate genes 1-5 and 7 that had statistically significant regression coefficients (alpha level = 0.05). In contrast, our hierarchical model primarily only detected associations for variants in candidate gene 2, which is the casual gene for Q5.

Predicting quantitative trait levels by modeling SNP interaction.

Predicting phenotype from genotype is difficult when the phenotype is affected by a gene with numerous weakly penetrant alleles that differ only in the pattern of their single nucleotide polymorphisms (SNPs). While it is probable that SNP interactions affect phenotype, to our knowledge no one has determined the most effective way of evaluating whether SNPs interact and of modeling the interaction. Therefore, to explore this issue, we investigate here three methods of modeling SNP interaction using data from Genetic Analysis Workshop 12. Since major gene 5 (MG5) has sequence information and explains 37% of the variation in quantitative trait 5 (Q5), we focus on using SNPs within MG5 to predict Q5 among individuals who married into the pedigree. As a preliminary screening step, we reduced the number of SNPs from 269 to 34 based on their association with Q5. In our first models we assumed that SNPs affected Q5 in a simple additive manner. These models explained 34% and 15% of the variation in Q5 in women and men, respectively. Our second model was a linear model, which used individual SNPs and simple interaction terms as predictors. These models explained 36% and 16% of the variation in Q5 levels for women and men, respectively. Our last model was a "hit"-based model which was motivated by the hypothesis that disequilibrium between SNPs may reflect the fact that SNPs affect phenotype by acting in concert with other SNPs within their "disequilibrium set." Thus, the number of hits within the disequilibrium sets were used as predictors. These models explained 35% and 19% of the variation in Q5 for women and men, respectively. Our results suggest that phenotype can be predicted from complex patterns of weakly penetrant SNPs using relatively simple models. We concluded that SNP interaction either was not included in the simulation model, or had only a weak impact on Q5 levels.

Lifetime prevalence of uveal melanoma in white patients with oculo(dermal) melanocytosis.

OBJECTIVE: In the white population, an association between oculo(dermal) melanocytosis (ODM) and uveal melanoma is well recognized. However, the lifetime prevalence of uveal melanoma in the ODM population is not known. This study was designed to determine the lifetime prevalence of uveal melanoma among patients with ocular melanocytosis. DESIGN: Fifty-six white patients manifesting ODM with uveal melanoma formed the basis of the study. MAIN OUTCOME MEASURES: Published prevalence rates of ODM and uveal melanoma were used for calculations using Bayes' theorem. RESULTS: The lifetime prevalence of uveal melanoma in white patients with ODM is estimated to be 2.6 x 10(-3). The median age at diagnosis of uveal melanoma in the ODM population was similar to a randomly selected population (60.5 years and 62.5 years, respectively). In the vast majority of patients (90%) with ODM-associated uveal melanoma, the uveal melanoma was diagnosed between the ages of 31 years and 80 years. CONCLUSIONS: One of about 400 patients with ODM followed for life is estimated to develop uveal melanoma. Excessive melanocytes in the uveal tract in ODM may provide the biologic basis for susceptibility to the development of uveal melanoma. Patients with ODM should be monitored ophthalmoscopically, especially during the susceptible period, for the development of uveal melanoma. The authors suggest that a national registry of ODM patients be created and prospective data collected to better assess the risk of developing uveal melanoma.

Association between CYP2D6 genotype and tardive dyskinesia in Korean schizophrenics.

The CYP2D6 gene codes for human cytochrome P450 2D6 enzyme, which is responsible for the metabolism of many psychiatric drugs. In schizophrenic patients treated with neuroleptics, decreased or loss of function CYP2D6 alleles may contribute to the development of tardive dyskinesia (TD), a movement disorder that frequently occurs with chronic neuroleptic treatment. The goal of this study was to determine whether the occurrence of TD is associated with CYP2D6 genotype in a cohort of Korean schizophrenics by employing a CYP450 GeneChip((R)) oligonucleotide microarray and PCR assays to screen for 19 CYP2D6 alleles. Our results revealed that males with at least one decreased or loss of function allele have a moderately greater chance of developing TD than males with only wild-type alleles. Female schizophrenics did not have a significantly greater chance of developing TD. Our results demonstrate the utility of CYP2D6 microarrays to assess genotype status in this Korean cohort.