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Multivariate permutation analysis associates multiple polymorphisms with subphenotypes of major depression.

ABSTRACT: Unipolar major depressive disorder (MDD) is a prevalent, disabling condition with multiple genetic and environmental factors impacting disease risk. The diagnosis of MDD relies on a cumulative measure derived from multiple trait dimensions and alone is limited in elucidating MDD genetic determinants. We and others have proposed that MDD may be better dissected using paradigms that assess how specific genes associate with component features of MDD. This within-disease design requires both a well-phenotyped cohort and a robust statistical approach that retains power with multiple tests of genetic association. In the present study, common polymorphic variants of genes related to central monoaminergic and cholinergic pathways that previous studies align with functional change in vitro or depression associations in vivo were genotyped in 110 individuals with unipolar MDD. Subphenotypic characteristics were examined using responses to individual items assessed with the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders (DSM IV), the 17-item Hamilton Rating Scale for Depression (HAM-D) and the NEO Five Factor Inventory. Multivariate Permutation Testing (MPT) was used to infer genotype-phenotype relationships underlying dimensional findings within clinical categories. MPT analyses show significant associations of the norepinephrine transporter (NET, SLC6A2) -182 T/C (rs2242446) with recurrent depression [odds ratio, OR = 4.15 (1.91-9.02)], NET -3081 A/T (rs28386840) with increase in appetite [OR = 3.58 (1.53-8.39)] and the presynaptic choline transporter (CHT, SLC5A7) Ile89Val (rs1013940) with HAM-D-17 total score {i.e. overall depression severity [OR = 2.74 (1.05-7.18)]}. These relationships illustrate an approach to the elucidation of gene influences on trait components of MDD and with replication, may help identify MDD subpopulations that can benefit from more targeted pharmacotherapy.

SUBMITTER: Hahn MK

PROVIDER: S-EPMC2670227 | biostudies-literature | 2008 Jun

REPOSITORIES: biostudies-literature

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Multivariate permutation analysis associates multiple polymorphisms with subphenotypes of major depression.

Hahn M K MK Blackford J U JU Haman K K Mazei-Robison M M English B A BA Prasad H C HC Steele A A Hazelwood L L Fentress H M HM Myers R R Blakely R D RD Sanders-Bush E E Shelton R R

Genes, brain, and behavior 20071206 4

Unipolar major depressive disorder (MDD) is a prevalent, disabling condition with multiple genetic and environmental factors impacting disease risk. The diagnosis of MDD relies on a cumulative measure derived from multiple trait dimensions and alone is limited in elucidating MDD genetic determinants. We and others have proposed that MDD may be better dissected using paradigms that assess how specific genes associate with component features of MDD. This within-disease design requires both a well- ...[more]

PMID: 18081710

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Project description:BACKGROUND:Inbreeding is caused by mating between related individuals and its most common consequence is inbreeding depression. Several studies have detected heterogeneity in inbreeding depression among founder individuals, and recently a procedure for predicting hidden inbreeding depression loads associated with founders and the Mendelian sampling of non-founders has been developed. The objectives of our study were to expand this model to predict the inbreeding loads for all individuals in the pedigree and to estimate the covariance between the inbreeding loads and the additive genetic effects for the trait of interest. We tested the proposed approach with simulated data and with two datasets of records on weaning weight from the Spanish Pirenaica and Rubia Gallega beef cattle breeds. RESULTS:The posterior estimates of the variance components with the simulated datasets did not differ significantly from the simulation parameters. In addition, the correlation between the predicted and simulated inbreeding loads were always positive and ranged from 0.27 to 0.82. The beef cattle datasets comprised 35,126 and 75,194 records on weights between 170 and 250 days of age, and pedigrees of 308,836 and 384,434 individual-sire-dam entries for the Pirenaica and Rubia Gallega breeds, respectively. The posterior mean estimates of the variance of inbreeding depression loads were 29,967.8 and 28,222.4 for the Pirenaica and Rubia Gallega breeds, respectively. They were larger than those of the additive variance (695.0 and 439.8 for Pirenaica and Rubia Gallega, respectively), because they should be understood as the variance of the inbreeding depression achieved by a fully inbred (100%) descendant. Therefore, the inbreeding loads have to be rescaled for smaller inbreeding coefficients. In addition, a strong negative correlation (-?0.43?±?0.10) between additive effects and inbreeding loads was detected in the Pirenaica, but not in the Rubia Gallega breed. CONCLUSIONS:The results of the simulation study confirmed the ability of the proposed procedure to predict inbreeding depression loads for all individuals in the populations. Furthermore, the results obtained from the two real datasets confirmed the variability in the inbreeding depression loads in both breeds and suggested a negative correlation of the inbreeding loads with the additive genetic effects in the Pirenaica breed.

Project description:Multivariate decoding models are increasingly being applied to functional magnetic imaging (fMRI) data to interpret the distributed neural activity in the human brain. These models are typically formulated to optimize an objective function that maximizes decoding accuracy. For decoding models trained on full-brain data, this can result in multiple models that yield the same classification accuracy, though some may be more reproducible than others--i.e. small changes to the training set may result in very different voxels being selected. This issue of reproducibility can be partially controlled by regularizing the decoding model. Regularization, along with the cross-validation used to estimate decoding accuracy, typically requires retraining many (often on the order of thousands) of related decoding models. In this paper we describe an approach that uses a combination of bootstrapping and permutation testing to construct both a measure of cross-validated prediction accuracy and model reproducibility of the learned brain maps. This requires re-training our classification method on many re-sampled versions of the fMRI data. Given the size of fMRI datasets, this is normally a time-consuming process. Our approach leverages an algorithm called fast simultaneous training of generalized linear models (FaSTGLZ) to create a family of classifiers in the space of accuracy vs. reproducibility. The convex hull of this family of classifiers can be used to identify a subset of Pareto optimal classifiers, with a single-optimal classifier selectable based on the relative cost of accuracy vs. reproducibility. We demonstrate our approach using full-brain analysis of elastic-net classifiers trained to discriminate stimulus type in an auditory and visual oddball event-related fMRI design. Our approach and results argue for a computational approach to fMRI decoding models in which the value of the interpretation of the decoding model ultimately depends upon optimizing a joint space of accuracy and reproducibility.

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Multivariate permutation analysis associates multiple polymorphisms with subphenotypes of major depression.

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Multivariate permutation analysis associates multiple polymorphisms with subphenotypes of major depression.

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