Type I error rates of rare single nucleotide variants are inflated in tests of association with non-normally distributed traits using simple linear regression methods.

In this study, the effects of (a) the minor allele frequency of the single nucleotide variant (SNV), (b) the degree of departure from normality of the trait, and (c) the position of the SNVs on type I error rates were investigated in the Genetic Analysis Workshop (GAW) 19 whole exome sequence data. To test the distribution of the type I error rate, 5 simulated traits were considered: standard normal and gamma distributed traits; 2 transformed versions of the gamma trait (log and rank-based inverse normal transformations); and trait Q1 provided by GAW 19. Each trait was tested with 313,340 SNVs.

The FKBP5-gene in depression and treatment response--an association study in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) Cohort.

Background: In a recent study of several antidepressant drugs in hospitalized, non-Hispanic White patients, Binder et al. reported association of markers located within the FKBP5 gene with treatment response after 2 and 5 weeks. Individuals homozygous for the TT-genotype at one of the markers (rs1360780) reported more depressive episodes and responded better to antidepressant treatment.

Association of GRIK4 with outcome of antidepressant treatment in the STAR*D cohort.

Objective: An initial pharmacogenetic study of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) clinical trial reported an association between genetic variation in the HTR2A gene and outcome of citalopram treatment. By design, the study analyzed only those markers that showed reproducible association in the first wave of genotypes (comprising 1,297 patients) in the complete cohort of patients. The purpose of the present study was to utilize a second wave of genotype results, for a more powerful analysis, in the complete cohort of patients with available deoxyribonucleic acid (DNA) samples.

Association between a functional serotonin transporter promoter polymorphism and citalopram treatment in adult outpatients with major depression.

Context: The HTTLPR, a functional polymorphism of the serotonin transporter gene solute carrier family 6 (neurotransmitter transporter, serotonin), member 4 (SLC6A4), promoter, affects transcription and may be involved in antidepressant drug treatment outcome, although response rates with antidepressants can be lower in patients who experience adverse effects.

Objective: To test the hypothesis that HTTLPR is associated with treatment outcome to citalopram.

Design: A clinical effectiveness trial, Sequenced Treatment Alternatives to Relieve Depression, collected DNA samples from outpatients with nonpsychotic major depressive disorder who received citalopram in the first treatment step.

Covariate-based linkage analysis: application of a propensity score as the single covariate consistently improves power to detect linkage.

Successful identification of genetic risk loci for complex diseases has relied on the ability to minimize disease and genetic heterogeneity to increase the power to detect linkage. One means to account for disease heterogeneity is by incorporating covariate data. However, the inclusion of each covariate will add one degree of freedom to the allele sharing based linkage test, which may in fact decrease power.

Variation in the gene encoding the serotonin 2A receptor is associated with outcome of antidepressant treatment.

Depressive disorders account for a large and increasing global burden of disease. Although the condition of many patients improves with medication, only a minority experience full remission, and patients whose condition responds to one medication may not have a response to others. Individual variation in antidepressant treatment outcome is, at present, unpredictable but may have a partial genetic basis.

Allele frequency misspecification: effect on power and Type I error of model-dependent linkage analysis of quantitative traits under random ascertainment.

Background: Studies of model-based linkage analysis show that trait or marker model misspecification leads to decreasing power or increasing Type I error rate. An increase in Type I error rate is seen when marker related parameters (e.g.

Critical values and variation in type I error along chromosomes in the COGA dataset using the applied pseudo-trait method.

Background: By analyzing a "pseudo-trait," a trait not linked or associated with any of the markers tested, the distribution of the test statistic under the null hypothesis can provide the critical value for the appropriate percentile of the distribution. In addition, the anecdotal observation that p-values tend to be more significant near the telomeres was investigated.

Results: The applied pseudo-trait (APT) method was applied to the Affymetrix and Illumina SNPs in the Collaborative Study on the Genetics of Alcoholism dataset to determine appropriate critical values for regression of offspring on mid-parent (ROMP) and Haseman-Elston association and linkage analyses, investigating the occurrence of type I errors in different chromosomal locations, and the extent to which the critical values obtained depend on the type of pseudo-trait used.

Importance sampling method of correction for multiple testing in affected sib-pair linkage analysis.

Using the Genetic Analysis Workshop 13 simulated data set, we compared the technique of importance sampling to several other methods designed to adjust p-values for multiple testing: the Bonferroni correction, the method proposed by Feingold et al., and naïve Monte Carlo simulation. We performed affected sib-pair linkage analysis for each of the 100 replicates for each of five binary traits and adjusted the derived p-values using each of the correction methods.