Alexa J.M. Sorant

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. 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.

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. 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.

Equivalence of Single- and Multilocus Markers: Power to Detect Linkage with Composite Markers Derived from Biallelic Loci

The reintroduction of biallelic markers, now in the form of single-nucleotide polymorphisms (SNPs), has again raised concerns about the practicality of the use of markers with low heterozygosity for genomic screening for complex traits, even if thousands of such markers are available. Like the early blood-group markers (e.g., Rh and MNS), tightly linked biallelic SNPs can be combined into composite markers with heterozygosity similar to that of short-tandem-repeat polymorphisms. The assumptions that underlie the equivalence between single-locus multiallelic and composite markers are presented. We used computer simulation to determine the power of the Haseman-Elston test for linkage with composite markers when not all of these assumptions hold. The Genometric Analysis Simulation Program was used to simulate continuous and discrete traits, one single-locus four-allele marker, and six biallelic markers. We studied composite markers created from pairs, trios, and quartets of biallelic markers in nuclear families and in independent sib pairs. The power to detect linkage with a two-point approach for composite markers and with a multipoint approach that incorporated all six biallelic markers was compared with that for a single-locus, four-allele reference marker. Although the power to detect linkage with a single biallelic marker was considerably less than that of the reference marker, the power to detect linkage with two- and three-locus composite markers was quite similar to that of the reference marker. The power to detect linkage with four-locus composite markers was similar to that of a multipoint approach.

Comparison of sib-pair and variance-components methods for genomic screening

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.

Comparison of results from tests of association in unrelated individuals with uncollapsed and collapsed sequence variants using tiled regression

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.

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. 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.

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. 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.

Comparison of variance components, ANOVA and regression of offspring on midparent (ROMP) methods for SNP markers.

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%).

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 (log10 and rank-based inverse normal transformations); and trait Q1 provided by GAW 19. Each trait was tested with 313,340 SNVs. Tests of association were performed with simple linear regression and average type I error rates were determined for minor allele frequency classes. Rare SNVs (minor allele frequency < 0.05) showed inflated type I error rates for non–normally distributed traits that increased as the minor allele frequency decreased. The inflation of average type I error rates increased as the significance threshold decreased. Normally distributed traits did not show inflated type I error rates with respect to the minor allele frequency for rare SNVs. There was no consistent effect of transformation on the uniformity of the distribution of the location of SNVs with a type I error.

Tiled regression reduces type I error rates in tests of association of rare single nucleotide variants with non-normally distributed traits, compared with simple linear regression

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.