Genetic Liability for Depression, Social Factors and Their Interaction Effect in Depressive Symptoms and Depression Over Time in Older Adults

Stringa, Najada; Milaneschi, Yuri; Schoor, Natasja M. van; Suanet, Bianca; Lee, Sven J. van der; Holstege, Henne; Reinders, Marcel J. T.; Beekman, Aartjan; Huisman, Martijn

doi:10.1016/j.jagp.2020.02.011

Cited by 15 publications

(15 citation statements)

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“…To account for population structure, PCs were calculated on genetic data prior to imputation. In the replication cohort, cases were genotyped on the GSA array and the controls on the Axiom-NL array from Affymetrix (Avera Institute for Human Genetics, Sioux Falls, SD) [ 26 , 27 ]. Quality control was performed in the same way as described for the discovery dataset for cases and controls independently and frequencies of the variants were compared after imputation.…”

Section: Methodsmentioning

confidence: 99%

Genome-wide association study of frontotemporal dementia identifies a C9ORF72 haplotype with a median of 12-G4C2 repeats that predisposes to pathological repeat expansions

et al. 2021

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Genetic factors play a major role in frontotemporal dementia (FTD). The majority of FTD cannot be genetically explained yet and it is likely that there are still FTD risk loci to be discovered. Common variants have been identified with genome-wide association studies (GWAS), but these studies have not systematically searched for rare variants. To identify rare and new common variant FTD risk loci and provide more insight into the heritability of C9ORF72-related FTD, we performed a GWAS consisting of 354 FTD patients (including and excluding N = 28 pathological repeat carriers) and 4209 control subjects. The Haplotype Reference Consortium was used as reference panel, allowing for the imputation of rare genetic variants. Two rare genetic variants nearby C9ORF72 were strongly associated with FTD in the discovery (rs147211831: OR = 4.8, P = 9.2 × 10−9, rs117204439: OR = 4.9, P = 6.0 × 10−9) and replication analysis (P < 1.1 × 10−3). These variants also significantly associated with amyotrophic lateral sclerosis in a publicly available dataset. Using haplotype analyses in 1200 individuals, we showed that these variants tag a sub-haplotype of the founder haplotype of the repeat expansion that was previously found to be present in virtually all pathological C9ORF72 G4C2 repeat lengths. This new risk haplotype was 10 times more likely to contain a C9ORF72 pathological repeat length compared to founder haplotypes without one of the two risk variants (~22% versus ~2%; P = 7.70 × 10−58). In haplotypes without a pathologic expansion, the founder risk haplotype had a higher number of repeats (median = 12 repeats) compared to the founder haplotype without the risk variants (median = 8 repeats) (P = 2.05 × 10−260). In conclusion, the identified risk haplotype, which is carried by ~4% of all individuals, is a major risk factor for pathological repeat lengths of C9ORF72 G4C2. These findings strongly indicate that longer C9ORF72 repeats are unstable and more likely to convert to germline pathological C9ORF72 repeat expansions.

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Section: Methodsmentioning

confidence: 99%

Genome-wide association study of frontotemporal dementia identifies a C9ORF72 haplotype with a median of 12-G4C2 repeats that predisposes to pathological repeat expansions

et al. 2021

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“…Another study showed with increase in the subject age, greater depressive symptoms were present (Rippon & Steptoe, 2018). Other factors mentioned concerning older age depression were genetic, brain anatomical, and cytokine levels (Derry et al, 2021;Klinedinst et al, 2015;Laird et al, 2019;Stringa et al, 2020;Wang et al, 2018).…”

Section: Biological Factorsmentioning

confidence: 95%

“…;Horita et al, 2013;Jovanova et al, 2016;Karczewska & Młynarska, 2021;Leggett et al, 2012;Meesters et al, 2014;Nicolosi et al, 2011; Olsen, Schirmer, Wilsgaard, Bønaa, & Hanssen, 2018;Popescu et al, 2012;Pusswald et al, 2012; Rhee, Jang, Kim, & Chang, 2021;Rzewuska et al, 2015;Sadler, McLaren, & Jenkins, 2013; Sanders, Comijs, Bremmer, Deeg, & Beekman, 2015;Wang et al, 2016; Ylli et al, 2016), older age(Olsen et al, 2018;Rippon & Steptoe, 2018;Rzewuska et al, 2015), genetics(Klinedinst, Resnick, Yerges-Armstrong, & Dorsey, 2015;Stringa et al, 2020), brain anatomical(Laird et al, 2019;Wang et al, 2018), cytokine level(Derry et al, 2021), taking drugs(Bond et al, 2020), low activity(Davison, McCabe, Knight, & Mellor, 2012; Kobyla nska et al, 2019; …”

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confidence: 99%

Biopsychosocial factors associated with depression in the elderly: A systematic literature review

Hadiwiardjo

Damayanti

2022

F1000Res

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Background: One of the most common mental illnesses worldwide and that has significant negative effects on well-being and physical health, is depression. Many factors that affect the occurrence of depression are biopsychosocial. Elderly people have high risk factors for depression. This study aims to review and find a gap in current evidence of biopsychosocial factors affecting depression in the elderly. Methods: We conducted an advanced search on PubMed database between October and November 2021. We screened the bibliographies of the articles from the database search using a set of inclusion criteria: studies with quantitative design; elderly population sample; exploration of at least one biopsychosocial factor; depression as the outcome or dependent variable; written in English; and published between 2011 and 2021. Results: We found 54 studies that met the inclusion criteria. Of these, five papers discussed biopsychosocial factors, 18 discussed biological and psychological/social factors, five discussed psychosocial factors, and the rest of the studies discussed one component of biopsychosocial factors. Biological factors influencing elderly depression include being female, having health problems or low health status, older age, genetics, brain structure, and biomarkers, taking certain drugs, and lack of or inability to carry out daily activities. Psychosocial factors include lack of social support, low education level, financial problems, single/unmarried, lack of contact with family/friends, living alone, low self-perceived health, stressful life events, loneliness, and losing hobbies. From the literature search, it was also found that there are biopsychosocial factors that have not been studied and are expected to be able to make references for future research. From this literature review, we found gaps in the availability of evidence about biopsychosocial factors that require further research. Conclusions: Knowing the biopsychosocial factors that affect them can help to prevent depression in the elderly.

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“…Suppose y i is a binary outcome; this is, of course, a common analytic scenario (Ancelin et al, 2017;Culverhouse et al, 2018;Stringa et al, 2020). We will discuss this scenario in the context of Figure 1.…”

Section: Binary Outcomesmentioning

confidence: 99%

Ubiquitous bias & false discovery due to model misspecification in analysis of statistical interactions: The role of the outcome's distribution and metric properties

Domingue¹,

Kanopka²,

Trejo³

et al. 2021

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Lived experience suggests substantial heterogeneity in how people react to a common stimuli. Previous work has considered several potential sources of bias and confusion in studying interactions but relatively less attention has been devoted to the nature of the outcome variable in such studies. Here, we consider power and false discovery associated with estimates of interaction parameters as a function of the distributional and metric properties of the outcome. Focusing on a variety of models for non-continuously distributed outcomes (binary, count, and ordinal outcomes), we show that power analyses need to carefully attend to specific details of a given situation and that attempts to use the linear model for recovery can be catastrophic in some settings. Focusing on transformations of normally distributed variables (i.e., censoring and departures from interval scaling) we show that linear models produce spurious interaction effects when such effects are absent from the generating model. We also provide illustrations offering geometric intuition as to why interactions can be a challenge for these incorrectly specified linear models. In light of these findings, we make two specific suggestions. First, a careful consideration of the distributional properties of the outcome variable should be a standard component in interaction studies. Second, hand-tailored power calculations should be provided; to that end, we have written software to help researchers scrutinize their ability to study interactions in given data contexts.

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Genetic Liability for Depression, Social Factors and Their Interaction Effect in Depressive Symptoms and Depression Over Time in Older Adults

Cited by 15 publications

References 32 publications

Genome-wide association study of frontotemporal dementia identifies a C9ORF72 haplotype with a median of 12-G4C2 repeats that predisposes to pathological repeat expansions

Genome-wide association study of frontotemporal dementia identifies a C9ORF72 haplotype with a median of 12-G4C2 repeats that predisposes to pathological repeat expansions

Biopsychosocial factors associated with depression in the elderly: A systematic literature review

Ubiquitous bias & false discovery due to model misspecification in analysis of statistical interactions: The role of the outcome's distribution and metric properties

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Genetic Liability for Depression, Social Factors and Their Interaction Effect in Depressive Symptoms and Depression Over Time in Older Adults

Cited by 15 publications

References 32 publications

Genome-wide association study of frontotemporal dementia identifies a C9ORF72 haplotype with a median of 12-G4C2 repeats that predisposes to pathological repeat expansions

Genome-wide association study of frontotemporal dementia identifies a C9ORF72 haplotype with a median of 12-G4C2 repeats that predisposes to pathological repeat expansions

Biopsychosocial factors associated with depression in the elderly: A systematic literature review

Ubiquitous bias &amp; false discovery due to model misspecification in analysis of statistical interactions: The role of the outcome's distribution and metric properties

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Product

Resources

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Ubiquitous bias & false discovery due to model misspecification in analysis of statistical interactions: The role of the outcome's distribution and metric properties