Research on how interactions between candidate genes and environmental factors influence psychiatric illnesses has generated enthusiasm but not many replicable findings. The authors discovered that ...the more closely a replication study matched the original research, the less likely it was to have similar results. Publication bias toward positive findings was apparent both in reports of novel findings and in replication studies. Another contributor to false discoveries in many studies of candidate genes is low statistical power due to small study groups or other design factors.
Objective:Gene-by-environment interaction (G×E) studies in psychiatry have typically been conducted using a candidate G×E (cG×E) approach, analogous to the candidate gene association approach used to test genetic main effects. Such cG×E research has received widespread attention and acclaim, yet cG×E findings remain controversial. The authors examined whether the many positive cG×E findings reported in the psychiatric literature were robust or if, in aggregate, cG×E findings were consistent with the existence of publication bias, low statistical power, and a high false discovery rate.
Method:The authors conducted analyses on data extracted from all published studies (103 studies) from the first decade (2000–2009) of cG×E research in psychiatry.
Results:Ninety-six percent of novel cG×E studies were significant compared with 27% of replication attempts. These findings are consistent with the existence of publication bias among novel cG×E studies, making cG×E hypotheses appear more robust than they actually are. There also appears to be publication bias among replication attempts because positive replication attempts had smaller average sample sizes than negative ones. Power calculations using observed sample sizes suggest that cG×E studies are underpowered. Low power along with the likely low prior probability of a given cG×E hypothesis being true suggests that most or even all positive cG×E findings represent type I errors.
Conclusions:In this new era of big data and small effects, a recalibration of views about groundbreaking findings is necessary. Well-powered direct replications deserve more attention than novel cG×E findings and indirect replications.
Mind the Gap Duncan, Laramie E.; Pollastri, Alisha R.; Smoller, Jordan W.
The American psychologist,
04/2014, Letnik:
69, Številka:
3
Journal Article
Recenzirano
Odprti dostop
As our field seeks to elucidate the biopsychosocial etiologies of mental health disorders, many traditional psychological and social science researchers have added, or plan to add, genetic components ...to their programs of research. An understanding of the history, methods, and perspectives of the psychiatric genetics community is useful in this pursuit. In this article we provide a brief overview of psychiatric genetic methods and findings. This overview lays the groundwork for a more thorough review of gene-environment interaction (G×E) research and the candidate gene approach to G×E research that remains popular among many psychologists and social scientists. We describe the differences in perspective between psychiatric geneticists and psychological scientists that have contributed to a growing divide between the research cited and conducted by these two related disciplines. Finally, we outline a strategy for the future of research on gene-environment interactions that capitalizes on the relative strengths of each discipline.
ABSTRACT
Prioritizing missense variants for further experimental investigation is a key challenge in current sequencing studies for exploring complex and Mendelian diseases. A large number of in ...silico tools have been employed for the task of pathogenicity prediction, including PolyPhen‐2, SIFT, FatHMM, MutationTaster‐2, MutationAssessor, Combined Annotation Dependent Depletion, LRT, phyloP, and GERP++, as well as optimized methods of combining tool scores, such as Condel and Logit. Due to the wealth of these methods, an important practical question to answer is which of these tools generalize best, that is, correctly predict the pathogenic character of new variants. We here demonstrate in a study of 10 tools on five datasets that such a comparative evaluation of these tools is hindered by two types of circularity: they arise due to (1) the same variants or (2) different variants from the same protein occurring both in the datasets used for training and for evaluation of these tools, which may lead to overly optimistic results. We show that comparative evaluations of predictors that do not address these types of circularity may erroneously conclude that circularity confounded tools are most accurate among all tools, and may even outperform optimized combinations of tools.
In a study of ten in silico pathogenicity prediction tools on five datasets we demonstrate that two types of circularity hinder a comparative evaluation of these prediction tools. We further show that comparative evaluations of predictors that do not address these types of circularity may erroneously conclude that circularity confounded tools are most accurate among all tools, and may even outperform optimized combinations of tools.
Women experience major depression and post-traumatic stress disorder (PTSD) approximately twice as often as men. Estrogen is thought to contribute to sex differences in these disorders, and reduced ...estrogen is also known to be a key driver of menopause symptoms such as hot flashes. Moreover, estrogen is used to treat menopause symptoms. In order to test for potential shared genetic influences between menopause symptoms and psychiatric disorders, we conducted a genome-wide association study (GWAS) of estrogen medication use (as a proxy for menopause symptoms) in the UK Biobank.
The analysis included 232 993 women aged 39-71 in the UK Biobank. The outcome variable for genetic analyses was estrogen medication use, excluding women using hormonal contraceptives. Trans-ancestry GWAS meta-analyses were conducted along with genetic correlation analyses on the European ancestry GWAS results. Hormone usage was also tested for association with depression and PTSD.
GWAS of estrogen medication use (compared to non-use) identified a locus in the
gene, which was previously linked to hot flashes in menopause top rs77322567, odds ratio (OR) = 0.78,
= 7.7 × 10
. Genetic correlation analyses revealed shared genetic influences on menopause symptoms and depression (
= 0.231, s.e.
0.055,
= 2.8 × 10
). Non-genetic analyses revealed higher psychiatric symptoms scores among women using estrogen medications.
These results suggest that menopause symptoms have a complex genetic etiology which is partially shared with genetic influences on depression. Moreover, the
gene identified here has direct clinical relevance; antagonists for the neurokinin 3 receptor (coded for by
) are effective treatments for hot flashes.
Genome-wide approaches including polygenic risk scores (PRSs) are now widely used in medical research; however, few studies have been conducted in low- and middle-income countries (LMICs), especially ...in South America. This study was designed to test the transferability of psychiatric PRSs to individuals with different ancestral and cultural backgrounds and to provide genome-wide association study (GWAS) results for psychiatric outcomes in this sample. The PrOMIS cohort (N = 3308) was recruited from prenatal care clinics at the Instituto Nacional Materno Perinatal (INMP) in Lima, Peru. Three major psychiatric outcomes (depression, PTSD, and suicidal ideation and/or self-harm) were scored by interviewers using valid Spanish questionnaires. Illumina Multi-Ethnic Global chip was used for genotyping. Standard procedures for PRSs and GWAS were used along with extra steps to rule out confounding due to ancestry. Depression PRSs significantly predicted depression, PTSD, and suicidal ideation/self-harm and explained up to 0.6% of phenotypic variation (minimum p = 3.9 × 10
). The associations were robust to sensitivity analyses using more homogeneous subgroups of participants and alternative choices of principal components. Successful polygenic prediction of three psychiatric phenotypes in this Peruvian cohort suggests that genetic influences on depression, PTSD, and suicidal ideation/self-harm are at least partially shared across global populations. These PRS and GWAS results from this large Peruvian cohort advance genetic research (and the potential for improved treatments) for diverse global populations.
Purpose of Review
This review highlights recent research on sex- and gender-related factors in the prevalence, symptom expression, and treatment of PTSD. Further discoveries about the underlying ...mechanisms of sex and gender effects have the potential to shape innovative directions for research.
Recent Findings
The prevalence of PTSD is substantially higher among women, but women show a modest advantage with respect to treatment response. There is evidence of greater heritability among females. Women are more likely to experience sexual and intimate violence, childhood trauma exposure, and repeated trauma exposures. Specific characteristics of social contexts act as gender-linked risks for PTSD. Among individuals diagnosed with PTSD, men and women are similar in phenotypic expression.
Summary
Though research has yet to fully account for the factors that explain sex- and gender- related effects on PTSD, emerging research suggests these effects occur across multiple levels. Shared risk factors for trauma exposure and PTSD merit further investigation. Both social and biological contexts merit investigation to understand sex-linked differences in heritability.
Genetic influences on testosterone and PTSD Cusack, Shannon E.; Maihofer, Adam X.; Bustamante, Daniel ...
Journal of psychiatric research,
June 2024, 2024-Jun, 2024-06-00, 20240601, Letnik:
174
Journal Article
Recenzirano
Females are twice as likely to experience PTSD as compared to males. Although sex differences in prevalence are well-established, little is known about why such sex differences occur. Biological ...factors that vary with sex, including sex hormone production, may contribute to these differences. Considerable evidence links sex hormones, such as testosterone, to PTSD risk though less is known about the shared genetic underpinnings. The objective of the present study was to test for genetic relationships between testosterone and PTSD. To do so, we used summary statistics from large, publicly available genetic consortia to conduct linkage disequilibrium score regression to estimate the genetic correlations between PTSD and testosterone in males and females, and two-sample, bi-directional Mendelian randomization to examine potential causal relationships of testosterone on PTSD and the reverse. Heritability estimates of testosterone were significantly higher in males (0.17, SE = 0.02) than females (0.11, SE = 0.01; z = 2.46, p = 00.01). The correlation between testosterone and PTSD was negative in males (rg = −0.11, SE = 0.02, p = 6.7 x 10-6), but not significant in females (rg = 0.002, SE = 0.03, p = 0.95). MR analyses found no evidence of a causal effect of testosterone on PTSD or the reverse. Findings are consistent with phenotypic literature suggesting a relationship between testosterone and PTSD that may be sex-specific. This work provides early evidence of a relationship between testosterone and PTSD genotypically and suggests an avenue for future research that will enable a better understanding of disparities in PTSD.
The classical twin design (CTD) uses observed covariances from monozygotic and dizygotic twin pairs to infer the relative magnitudes of genetic and environmental causes of phenotypic variation. ...Despite its wide use, it is well known that the CTD can produce biased estimates if its stringent assumptions are not met. By modeling observed covariances of twins’ relatives in addition to twins themselves, extended twin family designs (ETFDs) require less stringent assumptions, can estimate many more parameters of interest, and should produce less biased estimates than the CTD. However, ETFDs are more complicated to use and interpret, and by attempting to estimate a large number of parameters, the precision of parameter estimates may suffer. This paper is a formal investigation into a simple question: Is it worthwhile to use more complex models such as ETFDs in behavioral genetics? In particular, we compare the bias, precision, and accuracy of estimates from the CTD and three increasingly complex ETFDs. We find the CTD does a decent job of estimating broad sense heritability, but CTD estimates of shared environmental effects and the relative importance of additive versus non-additive genetic variance can be biased, sometimes wildly so. Increasingly complex ETFDs, on the other hand, are more accurate and less sensitive to assumptions than simpler models. We conclude that researchers interested in characterizing the environment or the makeup of genetic variation should use ETFDs when possible.
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