Uncovering the Genetic Architecture of Major Depression

McIntosh, Andrew M.; Sullivan, Patrick F.; Lewis, Cathryn M.

doi:10.1016/j.neuron.2019.03.022

Cited by 137 publications

(99 citation statements)

References 93 publications

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“…Though it is believed that the gain in statistical power from using larger sample sizes compensates for inaccuracies in minimal phenotyping 4,5,[56][57][58] , our study demonstrates that the genetic architecture of minimal phenotyping definitions of depression is enriched for non-specific effects on MDD. Using a range of definitions of MDD in UKBiobank, from self-reported help-seeking to a full assessment of the DSM-5 criteria for MDD through self-reported symptoms from the MHQ, we made five key observations.…”

Section: Discussionmentioning

confidence: 75%

“…The biological information from a minimal definition of MDD in terms of genes and pathways discovered may be informative about mental ill health in general, but not MDD in particular. One interpretation is that the characterization of genetic loci with such non-specific effects will still advance understanding of the biology of psychiatric disorders and their treatment 5,56 . A recent report on genetic analyses of subjective well-being, depressive symptoms and neuroticism identified a high degree of sharing between genetic liabilities to the three measures, and use the "quasi-replication" of GWAS loci between depressive symptoms and neuroticism as validation of their functional significance 66 .…”

Section: Discussionmentioning

confidence: 99%

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Minimal phenotyping yields GWAS hits of reduced specificity for major depression

Cai

Kendler

Flint

2018

Preprint

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Minimal phenotyping refers to the reliance on the use of a small number of self-report items for disease case identification. This strategy has been applied to genome-wide association studies (GWAS) of major depressive disorder (MDD). Here we report that the genotype derived heritability (h 2 SNP) of depression defined by minimal phenotyping (14%, SE = 0.8%) is lower than strictly defined MDD (26%, SE = 2.2%).This cannot be explained by differences in prevalence between definitions or including cases of lower liability to MDD in minimal phenotyping definitions of depression, but can be explained by misdiagnosis of those without depression or with related conditions as cases of depression. Depression defined by minimal phenotyping is as genetically correlated with strictly defined MDD (rG = 0.81, SE = 0.03) as it is with the personality trait neuroticism (rG = 0.84, SE = 0.05), a trait not defined by the cardinal symptoms of depression. While they both show similar shared genetic liability with neuroticism, a greater proportion of the genome contributes to the minimal phenotyping definitions of depression (80.2%, SE = 0.6%) than to strictly defined MDD (65.8%, SE = 0.6%). We find that GWAS loci identified in minimal phenotyping definitions of depression are not specific to MDD: they also predispose to other psychiatric conditions. Finally, while highly predictive polygenic risk scores can be generated from minimal phenotyping definitions of MDD, the predictive power can be explained entirely by the sample size used to generate the polygenic risk score, rather than specificity for MDD. Our results reveal that genetic analysis of minimal phenotyping definitions of depression identifies non-specific genetic factors shared between MDD and other psychiatric conditions. Reliance on results from minimal phenotyping for MDD may thus bias views of the genetic architecture of MDD and may impede our ability to identify pathways specific to MDD.

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Section: Discussionmentioning

confidence: 75%

Section: Discussionmentioning

confidence: 99%

Minimal phenotyping yields GWAS hits of reduced specificity for major depression

Cai

Kendler

Flint

2018

Preprint

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“…As we are increasingly able to quantify polygenic risk for depression 13 and potentially return this information to individuals in the future 14 , it becomes vital to expand knowledge of effective actionable measures for those identified at elevated risk. In addition to genetics, life history factors such as traumatic events are known to increase risk for depression 15 .…”

Section: Introductionmentioning

confidence: 99%

A two-stage approach to identifying and validating modifiable factors for the prevention of depression

Choi¹,

Stein²,

Nishimi³

et al. 2019

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Background: Although depression is recognized as the leading cause of disability worldwide, decades of research have identified few actionable preventive factors. Using phenotypic and genomic data from the UK Biobank, we took advantage of a unique opportunity to screen a wide range of potentially modifiable factors that could offset known risk factors for depression. Methods:We curated baseline data on more than 100 lifestyle and environmental factors in participants' lives, including behavioral (e.g., exercise, sleep, media use, diet), social (e.g., support, activities), and environmental (e.g., greenspace, pollution) variables. In a follow-up survey, participants reported on their traumatic life experiences and mental health, including depression. Polygenic risk scores for depression were generated based on large-scale genomewide association results. Excluding those meeting criteria for depression at baseline, we identified at-risk individuals at high predicted probability (> 90th percentile) for clinically significant depression at follow-up based on their (i) polygenic risk, or (ii) reported traumatic life events. Using a factors-wide design corrected for multiple testing and adjusted for potential confounders, we identified modifiable factors associated with follow-up depression in the full sample and among at-risk individuals. Using a two-sample Mendelian randomization (MR) design, we then examined which significant factors showed potential causal influences on depression risk, or vice versa. Results: A range of baseline modifiable factors were prospectively associated with follow-up depression, including factors related to social engagement, physical activity, media use, and diet. MR follow-up analyses provided further support for the effects of social support-seeking, TV use, and other factors on depression risk. Conclusion:As the field increasingly quantifies the role of genetic factors in complex conditions such as depression, knowledge of modifiable factors that could offset one's genetic risk has MODIFIABLE FACTORS FOR DEPRESSION 3 become highly relevant. Here, we present an approach to screening for potentially modifiable factors that may offset the risk of depression in general and among at-risk individuals. In light of the burden of disease associated with depression and the urgent need for actionable preventive strategies, this approach could help prioritize candidates for follow-up studies including clinical trials for depression prevention.

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“…Over the last few years, valuable progress has been made in understanding the underlying genetic architecture of depression [11, 13, 46]. Furthermore, stratifying depression using genetic data remains a key goal within the psychiatric genetics community [47] and should lead to improved classification of mental health conditions and more efficacious treatment for patients. Machine learning [48, 49] and polygenic risk score [6, 50] approaches offer possible methods for stratification in mental health.…”

Section: Discussionmentioning

confidence: 99%

Genetic stratification of depression in UK Biobank suggests a subgroup linked to age of natural menopause

Howard

Folkersen

Coleman

et al. 2017

Preprint

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41Depression is a common and clinically heterogeneous mental health disorder that is frequently 42 comorbid with other diseases and conditions. Stratification of depression may align sub-diagnoses 43 more closely with their underling aetiology and provide more tractable targets for research and 44 effective treatment. In the current study, we investigated whether genetic data could be used to 45 identify subgroups within people with depression using the UK Biobank. Examination of cross-locus 46correlations was used to test for evidence of subgroups by examining whether there was clustering of 47 independent genetic variants associated with eleven other complex traits and disorders in people with 48 depression. We found evidence of a subgroup within depression using age of natural menopause 49 variants (P = 1.69 × 10 -3 ) and this effect remained significant in females (P = 1.18 × 10 -3 ), but not males 50 (P = 0.186). However, no evidence for this subgroup (P > 0.05) was found in Generation Scotland, 51 iPSYCH, a UK Biobank replication cohort or the GERA cohort. In the UK Biobank, having depression was 52 also associated with a later age of menopause (beta = 0.34, standard error = 0.06, P = 9.92 × 10 -8 ). A 53 potential age of natural menopause subgroup within depression and the association between 54 depression and a later age of menopause suggests that they partially share a developmental pathway. 55 56

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Uncovering the Genetic Architecture of Major Depression

Cited by 137 publications

References 93 publications

Minimal phenotyping yields GWAS hits of reduced specificity for major depression

Minimal phenotyping yields GWAS hits of reduced specificity for major depression

A two-stage approach to identifying and validating modifiable factors for the prevention of depression

Genetic stratification of depression in UK Biobank suggests a subgroup linked to age of natural menopause

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