Additive genetic contribution to symptom dimensions in major depressive disorder.

Pearson, Rahel; Palmer, Rohan; Brick, Leslie A.; McGeary, John E.; Knopik, Valerie S.; Beevers, Christopher G.

doi:10.1037/abn0000161

Cited by 10 publications

(6 citation statements)

References 31 publications

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“…However, one recent study conducted GCTAs of depression symptom clusters, which included insomnia. The SNP-based heritability of insomnia symptoms was 30% in this sample, with estimates for other depression symptoms ranging from 5% (anxiety) to 30% (appetite) [86]. This does align with current insomnia heritability estimates, but should be interpreted with caution given that insomnia is presented in the context of depression.…”

Section: Discussionsupporting

confidence: 62%

Genetic Pathways to Insomnia

Lind

Gehrman

2016

Brain Sciences

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This review summarizes current research on the genetics of insomnia, as genetic contributions are thought to be important for insomnia etiology. We begin by providing an overview of genetic methods (both quantitative and measured gene), followed by a discussion of the insomnia genetics literature with regard to each of the following common methodologies: twin and family studies, candidate gene studies, and genome-wide association studies (GWAS). Next, we summarize the most recent gene identification efforts (primarily GWAS results) and propose several potential mechanisms through which identified genes may contribute to the disorder. Finally, we discuss new genetic approaches and how these may prove useful for insomnia, proposing an agenda for future insomnia genetics research.

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Section: Discussionsupporting

confidence: 62%

Genetic Pathways to Insomnia

Lind

Gehrman

2016

Brain Sciences

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“…For instance, efforts to identify the genetic etiology of depression may be undermined to a degree by searching for genetic variants that are associated with a diagnosis that involves heterogeneous depression symptoms that can potentially have more than a thousand different symptom configurations (Fried & Nesse, 2015a) rather than searching for variants associated with specific depression symptoms (Flint & Kendler, 2014). Indeed, there is preliminary evidence that depression symptoms may differ in heritability (Pearson et al, 2016). Future work on the etiology and maintenance of depression could potentially enhance model specificity by examining whether or not theoretical models are consistent across different symptom dimensions.…”

Section: Discussionmentioning

confidence: 99%

Association between negative cognitive bias and depression: A symptom-level approach.

Beevers

Mullarkey

Dainer-Best

et al. 2019

Journal of Abnormal Psychology

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Cognitive models of depression posit that negatively biased self-referent processing and attention have important roles in the disorder. However, depression is a heterogeneous collection of symptoms and all symptoms are unlikely to be associated with these negative cognitive biases. The current study involved 218 community adults whose depression ranged from no symptoms to clinical levels of depression. Random forest machine learning was used to identify the most important depression symptom predictors of each negative cognitive bias. Depression symptoms were measured with the Beck Depression Inventory—II. Model performance was evaluated using predictive R-squared (Rpred2), the expected variance explained in data not used to train the algorithm, estimated by 10 repetitions of 10-fold cross-validation. Using the self-referent encoding task (SRET), depression symptoms explained 34% to 45% of the variance in negative self-referent processing. The symptoms of sadness, self-dislike, pessimism, feelings of punishment, and indecision were most important. Notably, many depression symptoms made virtually no contribution to this prediction. In contrast, for attention bias for sad stimuli, measured with the dot-probe task using behavioral reaction time (RT) and eye gaze metrics, no reliable symptom predictors were identified. Findings indicate that a symptom-level approach may provide new insights into which symptoms, if any, are associated with negative cognitive biases in depression.

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“… 12 Furthermore, in 2017, Power et al 17 utilised additional phenotypic data to stratify cases and thereby reduce heterogeneity, which enabled the identification of a genetic risk locus associated with MDD onset in adults aged over 27 years. Stratification of MDD cases based on symptom dimensions represents an alternative method of utilising phenotypic data to reduce heterogeneity within GWA studies, with Pearson et al 46 showing common SNPs explain varying proportions of the variation in the depression symptom dimensions of core depression symptoms, insomnia, appetite and anxiety symptoms (SNP-based heritability=14.3%, 30.3%, 29.6% and 4.7%, respectively). Meanwhile, a complementary approach is to obtain larger sample sizes, which are more representative of the general population.…”

Section: Discussionmentioning

confidence: 99%

A continuum of genetic liability for minor and major depression

et al. 2017

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The recent success of a large genome-wide association (GWA) study—analysing 130 620 major depression cases and 347 620 controls—in identifying the first single-nucleotide polymorphism (SNP) loci robustly associated with major depression in Europeans confirms that immense sample sizes are required to identify risk loci for depression. Given the phenotypic similarity between major depressive disorder (MDD) and the less severe minor depressive disorder (MiDD), we hypothesised that broadening the case definition to include MiDD may be an efficient approach to increase sample sizes in GWA studies of depression. By analysing two large twin pair cohorts, we show that minor depression and major depression lie on a single genetic continuum, with major depression being more severe but not aetiologically distinct from minor depression. Furthermore, we estimate heritabilities of 37% for minor depression, 46% for major depression and 48% for minor or major depression in a cohort of older adults (aged 50–92). However, the heritability of minor or major depression was estimated at 40% in a cohort of younger adults (aged 23–38). Moreover, two robust major depression-risk SNPs nominally associated with major depression in our Australian GWA data set produced more significant evidence for association with minor or major depression. Hence, broadening the case phenotype in GWA studies to include subthreshold definitions, such as MiDD, should facilitate the identification of additional genetic risk loci for depression.

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Additive genetic contribution to symptom dimensions in major depressive disorder.

Cited by 10 publications

References 31 publications

Genetic Pathways to Insomnia

Genetic Pathways to Insomnia

Association between negative cognitive bias and depression: A symptom-level approach.

A continuum of genetic liability for minor and major depression

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