Archival ReportHigh Dimensional Endophenotype Ranking in the Search for Major Depression Risk Genes
Section snippets
Participants
A total of 1,122 Mexican American individuals from extended pedigrees (71 families, average size 14.9 [1–87] people) were included in the analysis. Participants were 64% female and ranged in age from 18 to 97 (mean ± SD 47.11 ± 14.2) years. Individuals in this cohort have actively participated in research for over 18 years and were randomly selected from the community with the constraints that they are of Mexican American ancestry, part of a large family, and live within the San Antonio region
Heritability of Recurrent Major Depression
Two hundred fifteen individuals met criteria for lifetime rMDD (19% of the sample; 73% female subjects). Eighty-six individuals were clinically depressed at the time of the assessment. The estimated heritability for lifetime rMDD was h2 = .463 (standard error ± .12), p = 4.0 × 10−6. We previously demonstrated that this heritability estimate is not significantly influenced by common environmental factors as indexed by shared household (21). Additionally, there was no evidence for dominance (p =
Discussion
Our results demonstrate the utility of the ERV approach for formally identifying endophenotypes in high-dimensional data and provide a novel genome-wide significant QTL for recurrent major depression. Bivariate genetic analyses including a quantitative endophenotype and disease risk significantly improved QTL detection over that observed utilizing diagnosis alone. These results may reflect the improved statistical sensitivity of quantitative over qualitative traits or that endophenotypes index
References (71)
- et al.
Novel family-based approaches to genetic risk in thrombosis
J Thromb Haemost
(2003) - et al.
Future of genetics of mood disorders research
Biol Psychiatry
(2002) - et al.
Endophenotypes: Bridging genomic complexity and disorder heterogeneity
Biol Psychiatry
(2009) - et al.
Cortical surface-based analysisI. Segmentation and surface reconstruction
Neuroimage
(1999) - et al.
Cortical surface-based analysisII: Inflation, flattening, and a surface-based coordinate system
Neuroimage
(1999) - et al.
Cortical thickness or grey matter volume?The importance of selecting the phenotype for imaging genetics studies
Neuroimage
(2010) - et al.
Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data
Neuroimage
(2006) - et al.
Multipoint quantitative-trait linkage analysis in general pedigrees
Am J Hum Genet
(1998) - et al.
Joint multipoint linkage analysis of multivariate qualitative and quantitative traitsI. Likelihood formulation and simulation results
Am J Hum Genet
(1999) - et al.
Joint multipoint linkage analysis of multivariate qualitative and quantitative traitsII. Alcoholism and event-related potentials
Am J Hum Genet
(1999)
Detection and integration of genotyping errors in statistical genetics
Am J Hum Genet
Markov chain Monte Carlo segregation and linkage analysis for oligogenic models
Am J Hum Genet
Neurobiology of depression
Neuron
Hepatic lipase maturation: A partial proteome of interacting factors
J Lipid Res
An E3 ubiquitin ligase, Really Interesting New Gene (RING) Finger 41, is a candidate gene for anxiety-like behavior and beta-carboline-induced seizures
Biol Psychiatry
PAR-1 kinase plays an initiator role in a temporally ordered phosphorylation process that confers tau toxicity in Drosophila
Cell
A comprehensive profile of brain enzymes that hydrolyze the endocannabinoid 2-arachidonoylglycerol
Chem Biol
Localization and identification of human quantitative trait loci: King harvest has surely come
Curr Opin Genet Dev
Human pedigree-based quantitative-trait-locus mapping: Localization of two genes influencing HDL-cholesterol metabolism
Am J Hum Genet
Quantitative trait locus determining dietary macronutrient intakes is located on human chromosome 2p22
Am J Clin Nutr
Major depressive disorder
N Engl J Med
The epidemiology of major depressive disorder: Results from the National Comorbidity Survey Replication (NCS-R)
JAMA
A hospital-based twin register of the heritability of DSM-IV unipolar depression
Arch Gen Psychiatry
Genetic epidemiology of major depression: Review and meta-analysis
Am J Psychiatry
Genome-wide association for major depressive disorder: A possible role for the presynaptic protein piccolo
Mol Psychiatry
Genome-wide association study of recurrent early-onset major depressive disorder
Mol Psychiatry
Genome-wide association study of recurrent major depressive disorder in two European case-control cohorts
Mol Psychiatry
Genome-wide pharmacogenetics of antidepressant response in the GENDEP Project
Am J Psychiatry
Genome-wide association study of major recurrent depression in the U.K. population
Am J Psychiatry
A genome-wide significant linkage for severe depression on chromosome 3: The Depression Network Study
Am J Psychiatry
A 3p26-3p25 genetic linkage finding for DSM-IV major depression in heavy smoking families
Am J Psychiatry
The endophenotype concept in psychiatry: Etymology and strategic intentions
Am J Psychiatry
Discovering endophenotypes for major depression
Neuropsychopharmacology
Common variants at 30 loci contribute to polygenic dyslipidemia
Nat Genet
A major quantitative trait locus determining serum leptin levels and fat mass is located on human chromosome 2
Nat Genet
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2022, Biomarkers in Bipolar DisordersKetamine specifically reduces cognitive symptoms in depressed patients: An investigation of associated neural activation patterns
2021, Journal of Psychiatric ResearchCitation Excerpt :The specific effect on cognitive function might be related to enhanced prefrontal control after ketamine (Gärtner et al., 2019) mediated by rapid synaptogenesis (Duman et al., 2016). Deficits in working memory (WM) have been discussed as potential endophentype candidates for recurrent MDD (Glahn et al., 2012). Using a multivoxel pattern classification approach to investigate brain activity, we showed in a recent study (Gärtner et al., 2018) that MDD patients can be distinguished from healthy controls with good classification accuracy based on functional activation patterns during a WM task.