High Dimensional Endophenotype Ranking in the Search for Major Depression Risk Genes

Glahn, David C.; Curran, Joanne E.; Winkler, Anderson M.; Carless, Melanie A.; Kent, Jack W.; Charlesworth, Jac; Johnson, Matthew P.; Göring, Harald H.H.; Cole, Shelley A.; Dyer, Thomas D.; Moses, Eric K.; Olvera, Rene L.; Kochunov, Peter; Duggirala, Ravi; Fox, Peter T.; Almasy, Laura; Blangero, John

doi:10.1016/j.biopsych.2011.08.022

Cited by 174 publications

(168 citation statements)

References 70 publications

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“…More than 3,500 native Englishspeaking adults with normal or corrected-to-normal vision were recruited from Harvard University, MGH, and the surrounding Boston communities. To avoid spurious effects resulting from population stratification, we restricted our analyses to 1,320 young adults (18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35) Imaging Data Processing. We used FreeSurfer (freesurfer.net) (49), version 4.5.0, a freely available, widely used, and extensively validated brain MRI analysis software package, to process the structural brain MRI scans and compute global and regional morphological measurements.…”

Section: Methodsmentioning

confidence: 99%

“…Although, in principle, any measure computable from different brain imaging modalities can be used as phenotypes in genetic studies, ideal candidate imaging traits should be heritable intermediate (or endo-) phenotypes (22)(23)(24), to uncover the genetic underpinnings of various neuropsychiatric disorders or biological processes of interest (25). However, due to the computational complexity and inordinate options of brain imaging measurements, few tools exist to enable efficient heritability-based screening of these phenotypes (26,27), and the exploration of their genetic basis has been limited to a small subset of the search space. All high-dimensional (whole-brain, voxel-/vertex-wise) heritability maps computed to date have relied on twin or pedigree data (28)(29)(30)(31)(32).…”

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confidence: 99%

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Massively expedited genome-wide heritability analysis (MEGHA)

Nichols

Lee

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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The discovery and prioritization of heritable phenotypes is a computational challenge in a variety of settings, including neuroimaging genetics and analyses of the vast phenotypic repositories in electronic health record systems and population-based biobanks. Classical estimates of heritability require twin or pedigree data, which can be costly and difficult to acquire. Genome-wide complex trait analysis is an alternative tool to compute heritability estimates from unrelated individuals, using genome-wide data that are increasingly ubiquitous, but is computationally demanding and becomes difficult to apply in evaluating very large numbers of phenotypes. Here we present a fast and accurate statistical method for high-dimensional heritability analysis using genome-wide SNP data from unrelated individuals, termed massively expedited genome-wide heritability analysis (MEGHA) and accompanying nonparametric sampling techniques that enable flexible inferences for arbitrary statistics of interest. MEGHA produces estimates and significance measures of heritability with several orders of magnitude less computational time than existing methods, making heritability-based prioritization of millions of phenotypes based on data from unrelated individuals tractable for the first time to our knowledge. As a demonstration of application, we conducted heritability analyses on global and local morphometric measurements derived from brain structural MRI scans, using genome-wide SNP data from 1,320 unrelated young healthy adults of non-Hispanic European ancestry. We also computed surface maps of heritability for cortical thickness measures and empirically localized cortical regions where thickness measures were significantly heritable. Our analyses demonstrate the unique capability of MEGHA for largescale heritability-based screening and high-dimensional heritability profile construction.heritability | genome-wide complex trait analysis | imaging genetics | endophenotype | phenomics

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Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Massively expedited genome-wide heritability analysis (MEGHA)

Nichols

Lee

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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“…A significant positive association between the number of past 6 month stressful life events (SLEs) and MDD was found (β =0. 21 To test the heritability of SLE only personal life events were included. These are events that should be unique to an individual.…”

Section: Resultsmentioning

confidence: 99%

“…A co-twin control study, on the other hand, found that neuroticism and depression are related to a higher risk of experiencing SLEs, but this didn't appear to be due to shared genetic risk factors 18 . As neuroticism is highly correlated with depression, both phenotypically 19 and genetically 20,21 , it is also possible that personality traits associated with MDD increase the sensitivity to and/or the reporting of SLEs amongst depressed individuals.…”

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confidence: 99%

Genetic and environmental determinants of stressful life events and their overlap with depression and neuroticism

Clarke¹,

Zeng²,

Navrady³

et al. 2018

Wellcome Open Res

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Background: Stressful life events (SLEs) and neuroticism are risk factors for major depressive disorder (MDD). However, SLEs and neuroticism are heritable and genetic risk for SLEs is correlated with risk for MDD. We sought to investigate the genetic and environmental contributions to SLEs in a family-based sample, and quantify genetic overlap with MDD and neuroticism. Methods: A subset of Generation Scotland: the Scottish Family Health Study (GS), consisting of 9618 individuals with information on MDD, past 6 month SLEs, neuroticism and genome-wide genotype data was used in the present study. We estimated the heritability of SLEs using GCTA software. The environmental contribution to SLEs was assessed by modelling familial, couple and sibling components. Using polygenic risk scores (PRS) and LD score regression (LDSC) we analysed the genetic overlap between MDD, neuroticism and SLEs. Results: Past 6-month life events were positively correlated with lifetime MDD status (β=0.21, r2=1.1%, p=2.5 x 10-25) and neuroticism (β =0.13, r2=1.9%, p=1.04 x 10-37) at the phenotypic level. Common SNPs explained 8% of the phenotypic variance in personal life events (those directly affecting the individual) (S.E.=0.03, p= 9 x 10-4). A significant effect of couple environment was detected accounting for 13% (S.E.=0.03, p=0.016) of the phenotypic variation in SLEs. PRS analyses found that reporting more SLEs was associated with a higher polygenic risk for MDD (β =0.05, r2=0.3%, p=3 x 10-5), but not a higher polygenic risk for neuroticism. LDSC showed a significant genetic correlation between SLEs and both MDD (rG=0.33, S.E.=0.08 ) and neuroticism (rG=0.15, S.E.=0.07). Conclusions: These findings suggest that SLEs should not be regarded solely as environmental risk factors for MDD as they are partially heritable and this heritability is shared with risk for MDD and neuroticism. Further work is needed to determine the causal direction and source of these associations.

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“…The concept has known a great popularity [395], however only a few endophenotypes have been identified for affective disorders [394,396,397]. These include: neuroticism [394], personal life events [9] and perceived social support [7,8].…”

Section: Introductionmentioning

confidence: 99%

Breaking down the genetics of depression using brain endophenotypes

Couvy‐Duchesne¹

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The Queensland Brain Institute 2 Thesis AbstractThe objective of the work presented in this thesis was to extend our understanding of depression aetiology and to identify brain MRI endophenotypes of depression. Our work builds on previous research that highlighted the genetic relationships between stress, sex and depression. In addition, MRI measurements are heritable and do not relying on selfreport. As such, they represent promising endophenotypes that could help break down further the genetics of depression.Preliminary work involved reporting depression prevalence in a young adult Australian twin sample (N=2,773) as well as comorbidity with affective and substance use disorders (Chapter 1). In addition, we demonstrated the good psychometric properties of the SPHERE questionnaire, which provides self-reported measures of anxiety-depression and chronic fatigue. Properties of the scores include: i) stochastic ordering on the sum score, ii) limited sex and age bias through adolescence, iii) high internal consistency, iv) good 3 months test-retest, v) moderate heritability, vi) significant association with lifetime MDD, social anxiety and alcohol dependence diagnoses (Chapter 2).In an older sample of Australian twins (N=5,221, Chapter 3), we performed a direct test of the diathesis-stress hypothesis in depression, which states that stressors can mediate the effect of genetic predisposition (diathesis) on the disease risk. We used Polygenic Risk Scores as a measure of diathesis and report a significant interaction with personal stressful life events, supporting the idea that stress and predisposition have a multiplicative effect on the depression liability scale. This interaction may be different between sexes as suggested by the stratified analysis.We then performed 3 analyses of resting-state fMRI from which we showed that: 1) Head motion (HM) is reliable and heritable in addition to being a major confound in fMRI analyses. However, this is unlikely to greatly confound twin studies of resting-state functional connectivity (FC). As a by-product of the analysis we showed that FC in the Broca's areas networks exhibited low to moderate test-retest and heritability (Chapter 4).2) HM is phenotypically and genetically associated with ADHD scores of Inattention and Hyperactivity/Impulsivity. This leads to most head motion image processing options to reduce power and/or to induce a sampling bias in RS-fMRI studies of ADHD. We discussed ways of circumventing this problem, via volume pruning or multivariate analysis (Chapter 5). 33) Nuisance regression (global signal regression, CompCor and HM regression) options in RS-fMRI processing have a moderate impact on the test-retest of regional homogeneity. Independently of the processing options, the low to moderate intra-class coefficients estimated in our sample may be due to insufficient scanning time (5mins 20s) and would impair our statistical power to detect RS-fMRI endophenotypes of depression (Chapter 6).Rather, we focused on structural brain images and contributed sample...

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High Dimensional Endophenotype Ranking in the Search for Major Depression Risk Genes

Cited by 174 publications

References 70 publications

Massively expedited genome-wide heritability analysis (MEGHA)

Massively expedited genome-wide heritability analysis (MEGHA)

Genetic and environmental determinants of stressful life events and their overlap with depression and neuroticism

Breaking down the genetics of depression using brain endophenotypes

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