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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. There was no association between markers in FKBP5 and disease. The present study aimed at studying the associated FKBP5 markers in the ethnically diverse Sequenced Treatment Alternatives to Relieve Depression (STAR*D) sample of non-hospitalized patients treated with citalopram. Methods We used clinical data and DNA samples from 1809 outpatients with non-psychotic major depressive disorder (DSM-IV criteria), who received up to 14 weeks of citalopram. A subset of 1523 patients of White non-Hispanic or Black race was matched with 739 control subjects for a case-control analysis. The markers rs1360780 and rs4713916 were genotyped on the Illumina platform. TaqMan-assay was used for marker rs3800373. Results In the case-control analysis, marker rs1360780 was significantly associated with disease status in the White non-Hispanic sample after correction for multiple testing. A significant association was also found between rs4713916 and remission. Markers rs1360780 and rs4713916 were in strong linkage disequilibrium in the White non-Hispanic but not in the Black population. There was no significant difference in the number of previous episodes of depression between genotypes at any of the three markers. Conclusions These results indicate that FKBP5 is an important target for further studies of depression and treatment response.

The effects of the minor allele frequency of single nucleotide variants and the degree of departure from normality of a quantitative trait on type I error rates were evaluated using Genetic Analysis Workshop 17 mini-exome sequence data. Four simulated traits were generated: standard normal and gamma distributed traits and two transformations of the gamma distributed trait by log10 and rank-based inverse normal functions. Tiled regression was compared with simple linear regression. Average type I error rates were obtained for minor allele frequency classes. The distribution of the type I error rate for tiled regression analysis followed a pattern similar to that of simple linear regression analysis, but with much lower type I error.

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. We searched for genetic predictors of treatment outcome in 1,953 patients with major depressive disorder who were treated with the antidepressant citalopram in the Sequenced Treatment Alternatives for Depression (STAR*D) study and were prospectively assessed. In a split-sample design, a selection of 68 candidate genes was genotyped, with 768 single-nucleotide-polymorphism markers chosen to detect common genetic variation. We detected significant and reproducible association between treatment outcome and a marker in HTR2A (P range 1 x 10(-6) to 3.7 x 10(-5) in the total sample). Other markers in HTR2A also showed evidence of association with treatment outcome in the total sample. HTR2A encodes the serotonin 2A receptor, which is downregulated by citalopram. Participants who were homozygous for the A allele had an 18% reduction in absolute risk of having no response to treatment, compared with those homozygous for the other allele. The A allele was over six times more frequent in white than in black participants, and treatment was less effective among black participants. The A allele may contribute to racial differences in outcomes of antidepressant treatment. Taken together with prior neurobiological findings, these new genetic data make a compelling case for a key role of HTR2A in the mechanism of antidepressant action.

A novel method for joint detection of association caused by linkage disequilibrium (LD) and estimation of both recombination fraction and linkage disequilibrium parameters was compared to several existing implementations of the transmission/disequilibrium test (TDT) and modifications of the TDT in the simulated genetic isolate data from Genetic Analysis Workshop 12. The first completely genotyped trio of affected child and parents was selected from each family in each replicate so that the TDT tests are valid tests of linkage and association, rather than being only valid as tests for linkage. In general, power to detect LD using the genome-wide scan markers was inadequate in the individual replicate samples, but the power was better when analyzing several SNP markers in candidate gene 1.

The effect of inclusion of environmental risk factors on the power of sib-pair linkage methods was tested for a qualitative trait. It was found that inclusion of an environmental variable did not increase the power of the Haseman-Elston (H-E) sib-pair nonparametric linkage analysis test. However, a significant increase in power was observed for both the H-E and affected-sib-pair tests, even in small samples, when persons unexposed to the environmental risk factor were coded as unknown.

Multipoint linkage analysis was used to screen for evidence of linkage between alcoholism and five alcoholism-related quantitative traits. The results suggest that a susceptibility locus that influences monoamine oxidase activity and P300 amplitude at the Pz lead, and increases the risk of alcohol dependence may be linked to markers in the 12q24 region. Furthermore, the susceptibility for alcoholism may be associated with allele 3 (allele size 144) of D12S392.

The statistical properties of sib-pair and variance-components linkage methods were compared using the nuclear family data from Problem 2. Overall, the power to detect linkage was not high for either method. The variance-components method had better power for detection of linkage, particularly when covariates were included in the model. Type I error rates were similar to nominal error rates for both methods.

Model-free methods of testing for association of a disease with alleles at a marker locus were used to analyze simulated data for a rare disease locus and 360 marker loci distributed at 2 cM intervals along six chromosomes. After adjustment for multiple tests, there was no evidence of significant heterogeneity of parental allele frequencies between a sample of parents with at least one affected child and a sample of parents with no affected children. Several significant deviations from Hardy-Weinberg equilibrium were detected after Bonferroni correction but these were Type I errors. After adjusting for multiple tests, the model-free test for association detected significant associations of alleles at two loci with the disease. These associations were in fact part of the generating model for the simulated data. However, these methods were unable to detect two other major loci contributing to the disease since these loci were not associated with any of the marker loci. ©1995 Wiley-Liss, Inc.

The effects of different sets of critical values on type I error rates in tiled regression were investigated using genome-wide association data from the Trinity Student Study. 200 simulated null traits from the standard normal distribution were analysed using four different sets of critical values for stepwise regression within tiled regression. We observed that 1 the multicollinearity among SNPs considered and the aggregate type I error rates decreased through three levels of tiled regression; 2 the region-specific type I error rates were slightly lower than the 'nominal' critical values at the tile level; and 3 the critical value at the tile level is between the two aggregate type I error rates defined under two different assumptions about the number of tests the number of SNPs and the number of tiles. The choice of critical value at each stage of tiled regression affects the overall type I error rate.

Tiled regression is an approach designed to determine the set of independent genetic variants that contribute to the variation of a quantitative trait in the presence of many highly correlated variants. In this study, we evaluate the statistical properties of the tiled regression method using the Genetic Analysis Workshop 17 data in unrelated individuals for traits Q1, Q2, and Q4. To increase the power to detect rare variants, we use two methods to collapse rare variants and compare the results with those from the uncollapsed data. In addition, we compare the tiled regression method to traditional tests of association with and without collapsed rare variants. The results show that collapsing rare variants generally improves the power to detect associations regardless of method, although only variants with the largest allelic effects could be detected. However, for traditional simple linear regression, the average estimated type I error is dependent on the trait and varies by about three orders of magnitude. The estimated type I error rate is stable for tiled regression across traits.

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.The authors tested the association between treatment response and 768 markers that were genotyped in the full set of 1,816 eligible patients from the STAR*D cohort. In order to control for multiple testing, the subjects were divided into two study groups: discovery and replication.In addition to the previously identified marker in the HTR2A gene, a new marker (rs1954787) in the GRIK4 gene, which codes for the kainic acid-type glutamate receptor KA1, was observed. The effect size of the GRIK4 marker alone was modest, but homozygote carriers of the treatment-response-associated marker alleles of both the GRIK4 and HTR2A genes were 23% less likely to experience nonresponse to treatment relative to participants who did not carry any of these marker alleles.The findings demonstrate that genetic variation in a kainic acid-type glutamate receptor is reproducibly associated with response to the antidepressant citalopram. This finding suggests that the glutamate system plays an important role in modulating response to selective serotonin reuptake inhibitors (SSRIs).

Novel methods that could improve the power of conventional methods of gene discovery for complex diseases should be investigated. In a simulation study, we aimed to investigate the value of molecular haplotypes in the context of a family-based linkage study. The term “haplotype” (or “haploid genotype”) refers to syntenic alleles inherited on a single chromosome, and we use the term “molecular haplotype” to refer to haplotypes that have been determined directly by use of a molecular technique such as long-range allele-specific polymerase chain reaction. In our study, we simulated genotype and phenotype data and then compared the powers of analyzing these data under the assumptions that various levels of information from molecular haplotypes were available. (This information was available because of the simulation procedure.) Several conclusions can be drawn. First, as expected, when genetic homogeneity is expected or when marker data are complete, it is not efficient to generate molecular haplotyping information. However, with levels of heterogeneity and missing data patterns typical of complex diseases, we observed a 23%–77% relative increase in the power to detect linkage in the presence of heterogeneity with heterogeneity LOD scores >3.0 when all individuals are molecularly haplotyped (compared with the power when only standard genotypes are used). Furthermore, our simulations indicate that most of the increase in power can be achieved by molecularly haplotyping a single individual in each family, thereby making molecular haplotyping a valuable strategy for increasing the power of gene mapping studies of complex diseases. Maximization of power, given an existing family set, can be particularly important for late-onset, often-fatal diseases such as cancer, for which informative families are difficult to collect.

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. We explore the application of a propensity score, which is typically used in causal inference to combine multiple covariates into a single variable, as a means of allowing for multiple covariates with the addition of only one degree of freedom. In this study, binary trait data, simulated under various models involving genetic and environmental effects, were analyzed using a nonparametric linkage statistic implemented in LODPAL. Power and type I error rates were evaluated. Results suggest that the use of the propensity score to combine multiple covariates as a single covariate consistently improves the power compared to an analysis including no covariates, each covariate individually, or all covariates simultaneously. Type I error rates were inflated for analyses with covariates and increased with increasing number of covariates, but reduced to nominal rates with sample sizes of 1000 families. Therefore, we recommend using the propensity score as a single covariate in the linkage analysis of a trait suspected to be influenced by multiple covariates because of its potential to increase the power to detect linkage, while controlling for the increase in the type I error.

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., allele frequencies) are misspecified and ascertainment is through the trait, but lod-score methods are expected to be robust when ascertainment is random (as is often the case in linkage studies of quantitative traits). In previous studies, the power of lod-score linkage analysis using the "correct" generating model for the trait was found to increase when the marker allele frequencies were misspecified and parental data were missing. An investigation of Type I error rates, conducted in the absence of parental genotype data and with misspecification of marker allele frequencies, showed that an inflation in Type I error rate was the cause of at least part of this apparent increased power. To investigate whether the observed inflation in Type I error rate in model-based LOD score linkage was due to sampling variation, the trait model was estimated from each sample using REGCHUNT, an automated segregation analysis program used to fit models by maximum likelihood using many different sets of initial parameter estimates. Results The Type I error rates observed using the trait models generated by REGCHUNT were usually closer to the nominal levels than those obtained when assuming the generating trait model. Conclusion This suggests that the observed inflation of Type I error upon misspecification of marker allele frequencies is at least partially due to sampling variation. Thus, with missing parental genotype data, lod-score linkage is not as robust to misspecification of marker allele frequencies as has been commonly thought.

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. Conclusion On average, the 5 percentile critical values obtained for this study were less than the expected 0.05. The distribution of p-values does not seem to depend on chromosomal position for ROMP association analysis methods, but does in some cases for Haseman-Elston linkage analysis. Results vary with different pseudo-traits.

An extension of the traditional regression of offspring on midparent (ROMP) method was used to estimate the heritability of the trait, test for marker association, and estimate the heritability attributable to a marker locus. The fifty replicates of the Genetic Analysis Workshop (GAW) 12 simulated general population data were used to compare the ROMP method with the variance components method as implemented in SOLAR as a test for marker association, and to a standard analysis of variance (ANOVA) method. Large sample statistical properties of the ROMP and ANOVA methods were compared using 2,000 replicates resampled from the families of the original 50 replicates. Overall, the power to detect a completely associated single nucleotide polymorphism (SNP) marker was high, and the type I error rates were similar to nominal significance levels for all three methods. The standard deviations of the estimates of the heritability of the trait were large for both SOLAR and ROMP, but the estimates were, on average, close to those of the generating model for both methods. However, on average, SOLAR overestimated the heritability attributable to the associated SNP marker (by 256%) while ROMP underestimated it (by 26%).

Context: The HTTLPR, a functional polymorphism of theserotonintransportergenesolutecarrierfamily6(neurotransmitter transporter, serotonin), member 4 (SLC6A4),promoter,affectstranscriptionandmaybeinvolved 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 depressivedisorderwhoreceivedcitalopraminthefirsttreatment step. The triallelic HTTLPR locus was genotyped in 1775 samples to discriminate between long (L) and short (S) alleles, followed by the AG substitution. The low-expression S and LG alleles were grouped together compared with the high-expression LA allele. Setting: Eighteen primary care and 23 psychiatric care sites across the United States. Participants: Ages 18 to 75 years, meeting criteria for single or recurrent nonpsychotic major depression. Main Outcome Measures: Categorical response, remission, tolerance, and adverse effect burden. Results:Expression-based grouping produced a significant finding of association between the LA allele and adverse effect burden in the entire sample (P=.004 [genotype frequency];P.001 [allele frequency]). To control for bias from population stratification, a white American subsample was analyzed. A lesser adverse effect burdenwasassociatedwithLALAgenotypefrequency(P=.03) or LA allele frequency (P=.007). These findings in white patients did not hold when the L allele was undifferentiated. No association was observed between treatment outcome phenotypes and HTTLPR. Development of diarrhea and the presence of the low-expression S or LG alleles were the strongest risk factors associated with adverse effect burden. Conclusions: The HTTLPR polymorphism is associated with citalopram adverse effects. Because the LA allele confers increased SLC6A4 transcription, increased serotonintransporterlevelsinbrainandothertissuesmay lead to fewer adverse effects for antidepressant medications that target the transporter.

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. The type I error rates for each correction method and the ability of each of the methods to detect loci known to influence trait values were compared. All of the methods considered were conservative with respect to type I error, especially the Bonferroni method. The ability of these methods to detect trait loci was also low. However, this may be partially due to a limitation inherent in our binary trait definitions.