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To identify genetic risk loci for major depressive disorder (MDD), two broad study design approaches have been applied: (1) to maximize sample size by combining data from different phenotype assessment modalities (e.g. clinical interview, self-report questionnaires) and (2) to reduce phenotypic heterogeneity through selecting more homogenous MDD subtypes. The value of these strategies has been debated. In this review, we summarize the most recent findings of large genomic studies that applied these approaches, and we highlight the merits and pitfalls of both approaches with particular attention to methodological and psychometric issues. We also discuss the results of analyses that investigated the heterogeneity of MDD. We conclude that both study designs are essential for further research. So far, increasing sample size has led to the identification of a relatively high number of genomic loci linked to depression. However, part of the identified variants may be related to a phenotype common to internalizing disorders and related traits. As such, samples containing detailed clinical information are needed to dissect depression heterogeneity and enable the potential identification of variants specific to a more restricted MDD phenotype. A balanced portfolio reconciling both study design approaches is the optimal approach to progress further in unraveling the genetic architecture of depression.
Epoch of Reionisation (EoR) data analysis requires unprecedented levels of accuracy in radio interferometer pipelines. We have developed an imaging power spectrum analysis to meet these requirements and generate robust 21 cm EoR measurements. In this work, we build a signal path framework to mathematically describe each step in the analysis, from data reduction in the Fast Holographic Deconvolution (FHD) package to power spectrum generation in the εppsilon package. In particular, we focus on the distinguishing characteristics of FHD/εppsilon: highly accurate spectral calibration, extensive data verification products, and end-to-end error propagation. We present our key data analysis products in detail to facilitate understanding of the prominent systematics in image-based power spectrum analyses. As a verification to our analysis, we also highlight a full-pipeline analysis simulation to demonstrate signal preservation and lack of signal loss. This careful treatment ensures that the FHD/εppsilon power spectrum pipeline can reduce radio interferometric data to produce credible 21 cm EoR measurements.
Most studies underline the contribution of heritable factors for psychiatric disorders. However, heritability estimates depend on the population under study, diagnostic instruments, and study designs that each has its inherent assumptions, strengths, and biases. We aim to test the homogeneity in heritability estimates between two powerful, and state of the art study designs for eight psychiatric disorders.
We assessed heritability based on data of Swedish siblings (N = 4 408 646 full and maternal half-siblings), and based on summary data of eight samples with measured genotypes (N = 125 533 cases and 208 215 controls). All data were based on standard diagnostic criteria. Eight psychiatric disorders were studied: (1) alcohol dependence (AD), (2) anorexia nervosa, (3) attention deficit/hyperactivity disorder (ADHD), (4) autism spectrum disorder, (5) bipolar disorder, (6) major depressive disorder, (7) obsessive-compulsive disorder (OCD), and (8) schizophrenia.
Heritability estimates from sibling data varied from 0.30 for Major Depression to 0.80 for ADHD. The estimates based on the measured genotypes were lower, ranging from 0.10 for AD to 0.28 for OCD, but were significant, and correlated positively (0.19) with national sibling-based estimates. When removing OCD from the data the correlation increased to 0.50.
Given the unique character of each study design, the convergent findings for these eight psychiatric conditions suggest that heritability estimates are robust across different methods. The findings also highlight large differences in genetic and environmental influences between psychiatric disorders, providing future directions for etiological psychiatric research.
Race, psychiatric history, and adverse life events have all been independently associated with postpartum depression (PPD). However, the role these play together in Black and Latina women remains inadequately studied. Therefore, we performed a case–control study of PPD, including comprehensive assessments of symptoms and biomarkers, while examining the effects of genetic ancestry.
We recruited our sample (549 cases, 968 controls) at 6 weeks postpartum from obstetrical clinics in North Carolina. PPD status was determined using the MINI-plus. Psychiatric history was extracted from medical records. Participants were administered self-report instruments to assess depression (Edinburgh Postnatal Depression Scale) and adverse life events. Levels of estradiol, progesterone, brain-derived neurotrophic factor, oxytocin, and allopregnanalone were assayed. Principal components from genotype data were used to estimate genetic ancestry and logistic regression was used to identify predictors of PPD.
This population was racially diverse (68% Black, 13% Latina, 18% European). Genetic ancestry was not a predictor of PPD. Case status was predicted by a history of major depression (p = 4.01E-14), lifetime anxiety disorder diagnosis (p = 1.25E-34), and adverse life events (p = 6.06E-06). There were no significant differences between groups in any hormones or neurosteroids.
Psychiatric history and multiple exposures to adverse life events were significant predictors of PPD in a population of minority and low-income women. Genetic ancestry and hormone levels were not predictive of case status. Increased genetic vulnerability in conjunction with risk factors may predict the onset of PPD, whereas genetic ancestry does not appear predictive.
Family history is a long-standing and readily obtainable risk factor for schizophrenia (SCZ). Low-cost genotyping technologies have enabled large genetic studies of SCZ, and the results suggest the utility of genetic risk scores (GRS, direct assessments of inherited common variant risk). Few studies have evaluated family history and GRS simultaneously to ask whether one can explain away the other.
We studied 5959 SCZ cases and 8717 controls from four Nordic countries. All subjects had family history data from national registers and genome-wide genotypes that were processed through the quality control procedures used by the Psychiatric Genomics Consortium. Using external training data, GRS were estimated for SCZ, bipolar disorder (BIP), major depression, autism, educational attainment, and body mass index. Multivariable modeling was used to estimate effect sizes.
Using harmonized genomic and national register data from Denmark, Estonia, Norway, and Sweden, we confirmed that family history of SCZ and GRS for SCZ and BIP were risk factors for SCZ. In a joint model, the effects of GRS for SCZ and BIP were essentially unchanged, and the effect of family history was attenuated but remained significant. The predictive capacity of a model including GRS and family history neared the minimum for clinical utility.
Combining national register data with measured genetic risk factors represents an important investigative approach for psychotic disorders. Our findings suggest the potential clinical utility of combining GRS and family history for early prediction and diagnostic improvements.
Universal screening for postpartum depression is recommended in many countries. Knowledge of whether the disclosure of depressive symptoms in the postpartum period differs across cultures could improve detection and provide new insights into the pathogenesis. Moreover, it is a necessary step to evaluate the universal use of screening instruments in research and clinical practice. In the current study we sought to assess whether the Edinburgh Postnatal Depression Scale (EPDS), the most widely used screening tool for postpartum depression, measures the same underlying construct across cultural groups in a large international dataset.
Ordinal regression and measurement invariance were used to explore the association between culture, operationalized as education, ethnicity/race and continent, and endorsement of depressive symptoms using the EPDS on 8209 new mothers from Europe and the USA.
Education, but not ethnicity/race, influenced the reporting of postpartum depression [difference between robust comparative fit indexes (∆*CFI) < 0.01]. The structure of EPDS responses significantly differed between Europe and the USA (∆*CFI > 0.01), but not between European countries (∆*CFI < 0.01).
Investigators and clinicians should be aware of the potential differences in expression of phenotype of postpartum depression that women of different educational backgrounds may manifest. The increasing cultural heterogeneity of societies together with the tendency towards globalization requires a culturally sensitive approach to patients, research and policies, that takes into account, beyond rhetoric, the context of a person's experiences and the context in which the research is conducted.
The Murchison Widefield Array is a new low-frequency interferometric radio telescope built in Western Australia at one of the locations of the future Square Kilometre Array. We describe the automated radio-frequency interference detection strategy implemented for the Murchison Widefield Array, which is based on the aoflagger platform, and present 72–231 MHz radio-frequency interference statistics from 10 observing nights. Radio-frequency interference detection removes 1.1% of the data. Radio-frequency interference from digital TV is observed 3% of the time due to occasional ionospheric or atmospheric propagation. After radio-frequency interference detection and excision, almost all data can be calibrated and imaged without further radio-frequency interference mitigation efforts, including observations within the FM and digital TV bands. The results are compared to a previously published Low-Frequency Array radio-frequency interference survey. The remote location of the Murchison Widefield Array results in a substantially cleaner radio-frequency interference environment compared to Low-Frequency Array’s radio environment, but adequate detection of radio-frequency interference is still required before data can be analysed. We include specific recommendations designed to make the Square Kilometre Array more robust to radio-frequency interference, including: the availability of sufficient computing power for radio-frequency interference detection; accounting for radio-frequency interference in the receiver design; a smooth band-pass response; and the capability of radio-frequency interference detection at high time and frequency resolution (second and kHz-scale respectively).
Attention deficit hyperactivity disorder (ADHD) symptoms and autistic traits often occur together. The pattern and etiology of co-occurrence are largely unknown, particularly in adults. This study investigated the co-occurrence between both traits in detail, and subsequently examined the etiology of the co-occurrence, using two independent adult population samples.
Data on ADHD traits (Inattention and Hyperactivity/Impulsivity) were collected in a population sample (S1, n = 559) of unrelated individuals. Data on Attention Problems (AP) were collected in a population-based family sample of twins and siblings (S2, n = 560). In both samples five dimensions of autistic traits were assessed (social skills, routine, attentional switching, imagination, patterns).
Hyperactive traits (S1) did not correlate substantially with the autistic trait dimensions. For Inattention (S1) and AP (S2), the correlations with the autistic trait dimensions were low, apart from a prominent correlation with the attentional switching scale (0.47 and 0.32 respectively). Analyses in the genetically informative S2 revealed that this association could be explained by a shared genetic factor.
Our findings suggest that the co-occurrence of ADHD traits and autistic traits in adults is not determined by problems with hyperactivity, social skills, imagination or routine preferences. Instead, the association between those traits is due primarily to shared attention-related problems (inattention and attentional switching capacity). As the etiology of this association is purely genetic, biological pathways involving attentional control could be a promising focus of future studies aimed at unraveling the genetic causes of these disorders.
Autism spectrum disorder (ASD) is a neurodevelopmental disorder of complex etiology. Although strong evidence supports the causal role of genetic factors, environmental risk factors have also been implicated. This study used a co-twin–control design to investigate low birth weight as a risk factor for ASD.
We studied a population-based sample of 3715 same-sex twin pairs participating in the Child and Adolescent Twin Study of Sweden (CATSS). ASD was assessed using a structured parent interview for screening of ASD and related developmental disorders, based on DSM-IV criteria. Birth weight was obtained from medical birth records maintained by the Swedish Medical Birth Registry.
Twins lower in birth weight in ASD-discordant twin pairs (n=34) were more than three times more likely to meet criteria for ASD than heavier twins [odds ratio (OR) 3.25]. Analyses of birth weight as a continuous risk factor showed a 13% reduction in risk of ASD for every 100 g increase in birth weight (n=78). Analysis of the effect of birth weight on ASD symptoms in the entire population (most of whom did not have ASD) showed a modest association. That is, for every 100 g increase in birth weight, a 2% decrease in severity of ASD indexed by scores on the Autism – Tics, attention-deficit hyperactivity disorder (AD/HD), and other Comorbidities (A-TAC) inventory would be expected in the sample as a whole.
The data were consistent with the hypothesis that low birth weight confers risk to ASD. Thus, although genetic effects are of major importance, a non-genetic influence associated with birth weight may contribute to the development of ASD.
Candidate gene studies have been a key approach to the genetics of schizophrenia (SCZ). However, the results of these studies are confusing and no genes have been unequivocally implicated. The hypothesis-driven candidate gene literature can be appraised by comparison with the results of genome-wide association studies (GWAS).
We describe the characteristics of hypothesis-driven candidate gene studies from the SZGene database, and use pathway analysis to compare hypothesis-driven candidate genes with GWAS results from the International Schizophrenia Consortium (ISC).
SZGene contained 732 autosomal genes evaluated in 1374 studies. These genes had poor statistical power to detect genetic effects typical for human diseases, assessed only 3.7% of genes in the genome, and had low marker densities per gene. Most genes were assessed once or twice (76.9%), providing minimal ability to evaluate consensus across studies. The ISC studies had 89% power to detect a genetic effect typical for common human diseases and assessed 79% of known autosomal common genetic variation. Pathway analyses did not reveal enrichment of smaller ISC p values in hypothesis-driven candidate genes, nor did a comprehensive evaluation of meta-hypotheses driving candidate gene selection (SCZ as a disease of the synapse or neurodevelopment). The most studied hypothesis-driven candidate genes (COMT, DRD3, DRD2, HTR2A, NRG1, BDNF, DTNBP1 and SLC6A4) had no notable ISC results.
We did not find support for the idea that the hypothesis-driven candidate genes studied in the literature are enriched for the common genetic variation involved in the etiology of SCZ. Larger samples are required to evaluate this conclusion definitively.
There have been nearly 400 genome-wide association studies (GWAS) published since 2005. The GWAS approach has been exceptionally successful in identifying common genetic variants that predispose to a variety of complex human diseases and biochemical and anthropometric traits. Although this approach is relatively new, there are many excellent reviews of different aspects of the GWAS method. Here, we provide a primer, an annotated overview of the GWAS method with particular reference to psychiatric genetics. We dissect the GWAS methodology into its components and provide a brief description with citations and links to reviews that cover the topic in detail.
The mechanisms underlying the co-occurrence of the functional somatic syndromes are largely unknown. No empirical study has explicitly examined how genetic and environmental factors influence the co-morbidity of these syndromes. We aimed to examine how the co-morbidity of functional somatic syndromes is influenced by genetic and environmental factors that are in common to the syndromes.
A total of 31318 twins in the Swedish Twin Registry aged 41–64 years underwent screening interviews via a computer-assisted telephone system from 1998 to 2002. Four functional somatic syndromes (chronic widespread pain, chronic fatigue, irritable bowel syndrome, and recurrent headache) and two psychiatric disorders (major depression and generalized anxiety disorder) were assessed using structured questions based on standard criteria for each illness in a blinded manner.
Multivariate twin analyses revealed that a common pathway model with two latent traits that were shared by the six illnesses fit best to the women's data. One of the two latent traits loaded heavily on the psychiatric disorders, whereas the other trait loaded on all four of the functional somatic syndromes, particularly chronic widespread pain, but not on the psychiatric disorders. All illnesses except the psychiatric disorders were also affected by genetic influences that were specific to each.
The co-occurrence of functional somatic syndromes in women can be best explained by affective and sensory components in common to all these syndromes, as well as by unique influences specific to each of them. The findings clearly suggest a complex view of the multifactorial pathogenesis of these illnesses.