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The clinical field of depression and other mood disorders is characterised by the vast heterogeneity between those who present for care, and the highly variable degree of response to the range of psychological, pharmacological and physical treatments currently provided. These individual differences likely have a genetic component, and leveraging genetic risk is appealing because genetic risk factors point to causality. The possibility that individual genotyping at entry to health care may be a key way forward is worthy of discussion (Torkamani et al., 2018).
The schizophrenia polygenic risk score (SCZ-PRS) is an emerging tool in psychiatry.
We aimed to evaluate the utility of SCZ-PRS in a young, transdiagnostic, clinical cohort.
SCZ-PRSs were calculated for young people who presented to early-intervention youth mental health clinics, including 158 patients of European ancestry, 113 of whom had longitudinal outcome data. We examined associations between SCZ-PRS and diagnosis, clinical stage and functioning at initial assessment, and new-onset psychotic disorder, clinical stage transition and functional course over time in contact with services.
Compared with a control group, patients had elevated PRSs for schizophrenia, bipolar disorder and depression, but not for any non-psychiatric phenotype (for example cardiovascular disease). Higher SCZ-PRSs were elevated in participants with psychotic, bipolar, depressive, anxiety and other disorders. At initial assessment, overall SCZ-PRSs were associated with psychotic disorder (odds ratio (OR) per s.d. increase in SCZ-PRS was 1.68, 95% CI 1.08–2.59, P = 0.020), but not assignment as clinical stage 2+ (i.e. discrete, persistent or recurrent disorder) (OR = 0.90, 95% CI 0.64–1.26, P = 0.53) or functioning (R = 0.03, P = 0.76). Longitudinally, overall SCZ-PRSs were not significantly associated with new-onset psychotic disorder (OR = 0.84, 95% CI 0.34–2.03, P = 0.69), clinical stage transition (OR = 1.02, 95% CI 0.70–1.48, P = 0.92) or persistent functional impairment (OR = 0.84, 95% CI 0.52–1.38, P = 0.50).
In this preliminary study, SCZ-PRSs were associated with psychotic disorder at initial assessment in a young, transdiagnostic, clinical cohort accessing early-intervention services. Larger clinical studies are needed to further evaluate the clinical utility of SCZ-PRSs, especially among individuals with high SCZ-PRS burden.
Nick Martin is a pioneer in recognizing the need for large sample size to study the complex, heterogeneous and polygenic disorders of common mental disorders. In the predigital era, questionnaires were mailed to thousands of twin pairs around Australia. Always quick to adopt new technology, Nick’s studies progressed to phone interviews and then online. Moreover, Nick was early to recognize the value of collecting DNA samples. As genotyping technologies improved over the years, these twin and family cohorts were used for linkage, candidate gene and genome-wide association studies. These cohorts have underpinned many analyses to disentangle the complex web of genetic and lifestyle factors associated with mental health. With characteristic foresight, Nick is chief investigator of our Australian Genetics of Depression Study, which has recruited 16,000 people with self-reported depression (plus DNA samples) over a time frame of a few months — analyses are currently ongoing. The mantra of sample size, sample size, sample size has guided Nick’s research over the last 30 years and continues to do so.
We sought to investigate the risk of incident major depressive disorder (MDD) attributable to a range of sleep disorders in the Danish population. Data were obtained by linking longitudinal Danish population-based registers. A total of 65,739 individuals who had first onset of depression between 1995 and 2013 were selected as cases. For each case, a set of 20 controls of the same sex, birth month and year and who had not had depression by the date that the case was diagnosed were selected at random form the population (N = 1,307,580 in total). We examined whether there was an increased rate of prior sleep disorders in MDD cases compared to controls using conditional logistic regression. An increased risk of incident depression in cases was found for all sleep disorders analyzed. Highest incidence rate ratios (IRRs) were found for circadian rhythm disorders (IRR = 7.06 [2.78–17.91]) and insomnia of inorganic origin (IRR = 6.76 [4.37–10.46]). The lowest estimated IRR was for narcolepsy (IRR = 2.00 [1.26–3.17]). Those diagnosed with a sleep disorder in the last 6 months were at highest risk of developing depression compared to those with at least 1 year since diagnosis (3.10 vs. 2.36). Our results suggest that having any sleep disorder is a risk factor for incident depression. Depression screening should be considered for patients with sleep disorders, and where possible, long-term follow-up for mental health problems is advisable.
Despite established clinical associations among major depression (MD), alcohol dependence (AD), and alcohol consumption (AC), the nature of the causal relationship between them is not completely understood. We leveraged genome-wide data from the Psychiatric Genomics Consortium (PGC) and UK Biobank to test for the presence of shared genetic mechanisms and causal relationships among MD, AD, and AC.
Linkage disequilibrium score regression and Mendelian randomization (MR) were performed using genome-wide data from the PGC (MD: 135 458 cases and 344 901 controls; AD: 10 206 cases and 28 480 controls) and UK Biobank (AC-frequency: 438 308 individuals; AC-quantity: 307 098 individuals).
Positive genetic correlation was observed between MD and AD (rgMD−AD = + 0.47, P = 6.6 × 10−10). AC-quantity showed positive genetic correlation with both AD (rgAD−AC quantity = + 0.75, P = 1.8 × 10−14) and MD (rgMD−AC quantity = + 0.14, P = 2.9 × 10−7), while there was negative correlation of AC-frequency with MD (rgMD−AC frequency = −0.17, P = 1.5 × 10−10) and a non-significant result with AD. MR analyses confirmed the presence of pleiotropy among these four traits. However, the MD-AD results reflect a mediated-pleiotropy mechanism (i.e. causal relationship) with an effect of MD on AD (beta = 0.28, P = 1.29 × 10−6). There was no evidence for reverse causation.
This study supports a causal role for genetic liability of MD on AD based on genetic datasets including thousands of individuals. Understanding mechanisms underlying MD-AD comorbidity addresses important public health concerns and has the potential to facilitate prevention and intervention efforts.
Cytokines and vitamin D both have a role in modulating the immune system, and are also potentially useful biomarkers in mental illnesses such as major depressive disorder (MDD) and schizophrenia. Studying the variability of cytokines and vitamin D in a healthy population sample may add to understanding the association between these biomarkers and mental illness. To assess genetic and environmental contributions to variation in circulating levels of cytokines and vitamin D (25-hydroxy vitamin D: 25(OH)D3), we analyzed data from a healthy adolescent twin cohort (mean age 16.2 years; standard deviation 0.25). Plasma cytokine measures were available for 400 individuals (85 MZ, 115 DZ pairs), dried blood spot sample vitamin D measures were available for 378 individuals (70 MZ, 118 DZ pairs). Heritability estimates were moderate but significant for the cytokines transforming growth factor-β1 (TGF-β1), 0.57 (95% CI 0.26–0.80) and tumor necrosis factor-receptor type 1 (TNFR1), 0.50 (95% CI 0.11–0.63) respectively. Measures of 25(OH)D3 were within normal range and heritability was estimated to be high (0.86, 95% CI 0.61–0.94). Assays of other cytokines did not generate meaningful results. These potential biomarkers may be useful in mental illness, with further research warranted in larger sample sizes. They may be particularly important in adolescents with mental illness where diagnostic uncertainty poses a significant clinical challenge.
Diagnosis of a major depressive episode by the Diagnostic and Statistical Manual of Mental Disorders of the American Psychiatric Association requires 5 out of 9 symptoms to be present. Therefore, individuals may differ in the specific symptoms they experience and reach a diagnosis of depression via different pathways. It has been suggested that depressed women more often report symptoms of sleep disturbance, appetite or weight disturbance, fatigue, feelings of guilt/worthlessness and psychomotor retardation than depressed men. In the current study, we investigate whether depressed men and women differ in the symptoms they report. Two samples were selected from a sample of Dutch and Australian twins and siblings. First, Dutch and Australian unrelated depressed individuals were selected. Second, a matched epidemiological sample was created consisting of opposite-sex twin and sibling pairs in which both members were depressed. No sex differences in prevalence rates for symptoms were found, with the exception of decreased weight in women in the sample of unrelated individuals. In general, the similarities in symptoms seem to far outweigh the differences in symptoms between men and women. This signifies that men and women are alike in their symptom profiles for major depression and genes for depression are probably expressed in the same way in the two sexes.
The design and interpretation of genetic association studies depends on the relationship between the genotyped variants and the underlying functional variant, often parameterized as the squared correlation or r2 measure of linkage disequilibrium between two loci. While it has long been recognized that placing a constraint on the r2 between two loci also places a constraint on the difference in frequencies between the coupled alleles, this constraint has not been quantified. Here, quantification of this severe constraint is presented. For example, for r2 ≥ .8, the maximum difference in allele frequency is ± .06 which occurs when one locus has allele frequency .5. For r2 ≥ .8 and allele frequency at one locus of .1, the maximum difference in allele frequency at the second locus is only ± .02. The impact on the design and interpretation of association studies is discussed.
The associations between social support and depression, and between stress and depression have been the subject of considerable research, and although this has included longitudinal designs, these have rarely controlled for genetic effects that mediate these associations. The sample comprised 7,356 female and 4,882 male participants aged 18–95 from the Australian NHMRC Twin Registry (ATR). Of these, between 100 and 324 female pairs and between 41 and 169 male pairs, depending on the measure, were monozygotic (MZ) pairs discordant for depression. We use the co-twin control design in combination with prospective analyses to explore the association between a composite of predictors (perceived social support, stress, and support × stress) and depression. With familial effects included, both perceived support and stress were antecedents to, and sequelae of, depression, but no stress-buffering occurred. With familial effects controlled, stress was a sequela of a prior depressive episode, and neither lack of support nor stress were antecedents to depression, though their interaction approached significance for males. The male twin who later became depressed had previously reported lower perceived support in the face of multiple stressors compared to his co-twin who did not become depressed. We show that associations commonly observed with prospective designs are partly due to familial factors.
People meeting diagnostic criteria for anxiety or depressive disorders tend to score high on the personality scale of neuroticism. Studying this dimension of personality can therefore give insights into the etiology of important psychiatric disorders. Neuroticism can be assessed easily via self-report questionnaires in large population samples. We have examined the genetic and phenotypic stability of neuroticism, measured up to 4 times over 22 years, on different scales, on a data set of 4999 families with over 20,000 individuals completing at least 1 neuroticism questionnaire. The neuroticism scales used were the Eysenck Personality Questionnaire revised (EPQ-R), the EPQ-R shortened form, and the NEO 5 factor inventory personality questionnaire. The estimates of heritability of the individual measures ranged from .26 ± .04 to .36 ± .03. Genetic, environmental, and phenotypic correlations averaged .91, .42, and .57 respectively. Despite the range in heritabilities, a more parsimonious ‘repeatability model’ of equal additive genetic variances and genetic correlations of unity could not be rejected. Use of multiple measures increases the effective heritability from .33 for a single measure to .43 for mean score because of the reduction in the estimate of the environmental variance, and this will increase power in genetic linkage or association studies of neuroticism.
One way to achieve the large sample sizes required for genetic studies of complex traits is to combine samples collected by different groups. It is not often clear, however, whether this practice is reasonable from a genetic perspective. To assess the comparability of samples from the Australian and the Netherlands twin studies, we estimated Fst (the proportion of total genetic variability attributable to genetic differences between cohorts) based on 359 short tandem repeat polymorphisms in 1068 individuals. Fst was estimated to be 0.30% between the Australian and the Netherlands cohorts, a smaller value than between many European groups. We conclude that it is reasonable to combine the Australian and the Netherlands samples for joint genetic analyses.
A method is presented for the prediction of rate of inbreeding for populations with discrete generations. The matrix of Wright's numerator relationships is partitioned into ‘contribution’ matrices which describe the contribution of the Mendelian sampling of genes of ancestors in a given generation to the relationship between individuals in later generations. These contributions stabilize with time and the value to which they stabilize is shown to be related to the asymptotic rate of inbreeding and therefore also the effective population size, where N is the number of individuals per generation and μr and are the mean and variance of long-term relationships or long-term contributions. These stabilized values are then predicted using a recursive equation via the concept of selective advantage for populations with hierarchical mating structures undergoing mass selection. Account is taken of the change in genetic parameters as a consequence of selection and also the increasing ‘competitiveness’ of contemporaries as selection proceeds. Examples are given and predicted rates of inbreeding are compared to those calculated in simulations. For populations of 20 males and 20, 40, 100 or 200 females the rate of inbreeding was found to increase by as much as 75% over the rate of inbreeding in an unselected population depending on mating ratio, selection intensity and heritability of the selected trait. The prediction presented here estimated the rate of inbreeding usually within 5% of that calculated from simulation.
One of the goals of human genetics research is to understand genetic variation between people in their susceptibility to disease. From twin and family studies, and the study of Mendelian disease, it is clear that some traits and diseases “run in families” and that the reason for the increased disease risk of relatives of affected individuals is, at least in part, because of their genetic predisposition. Genetic variation in populations is caused by mutations that cause differences in DNA sequence and by other genome events in the germline, for example insertions, deletions, duplications and translocations of stretches of DNA. If these mutation events have an effect on a phenotype of the carrier, for example an increased risk of disease or an effect on a continuously varying phenotype (such as blood pressure or body mass index), then there will be an association between the genotype and the phenotype. Gene mapping aims to identify locations on the genome that are responsible for genetic variation and, ultimately, to identify which specific variants cause the observed effect. Gene mapping is useful because it leads to an understanding of the nature of genetic variation and the identification of variants and biological pathways that cause or predispose to disease. This knowledge can be used to develop drug targets or other treatments and in the future may be used for disease diagnosis or the assessment of susceptibility to disease.
The reduction in additive genetic variance due to selection is investigated when index selection using family records is practised. A population of infinite size with no accumulation of inbreeding, an infinitesimal model and discrete generations are assumed. After several generations of selection, the additive genetic variance and the rate of response to selection reach an asymptote. A prediction of the asymptotic rate of response is considered to be more appropriate for comparing response from alternative breeding programmes and for comparing predicted and realized response than the response following the first generation of selection that is classically used. Algorithms to calculate asymptotic response rate are presented for selection based on indices which include some or all of the records of an individual, its full- and half-sibs and its parental estimated breeding values. An index using all this information is used to predict response when selection is based on breeding values estimated by using a Best Linear Unbiased Prediction (BLUP) animal model, and predictions agree well with simulation results. The predictions are extended to multiple trait selection.
Asymptotic responses are compared with one-generation responses for a variety of alternative breeding schemes differing in population structure, selection intensity and heritability of the trait. Asymptotic responses can be up to one-quarter less than one-generation responses, the difference increasing with selection intensity and accuracy of the index. Between family variance is reduced considerably by selection, perhaps to less than half its original value, so selection indices which do not account for this tend to place too much emphasis on family information. Asymptotic rates of response to selection, using indices including family information for traits not measurable on the individuals available for selection, such as sex limited or post-slaughter traits, are found to be as much as two-fifths less than their expected one-generation responses. Despite this, the ranking of the breeding schemes is not greatly altered when compared by one-generation rather than asymptotic responses, so the one-generation prediction is usually likely to be adequate for determining optimum breeding structure.
Best Linear Unbiased Prediction (BLUP) is now the method of choice for the estimation of breeding values in dairy and beef populations. The advantages of this mixed model methodology over traditional methods are well documented and include the simultaneous estimation of fixed effects and prediction of random effects and the utilization of records from all relatives to predict an individuals breeding value. In addition, account is taken of genetic trend and of reduction in genetic variance due to selection. In Canada, BLUP is now used for breeding value estimation of pigs but the structure of the Canadian pig industry is one of many herds practising selection with the herds linked by a widespread use of artificial insemination. The advantages of BLUP have not been investigated for the situation of the UK pig industry where most selection is performed within closed nucleus herds.
The objectives of this study were to use computer simulation to determine rates of response, accuracy of prediction and accummulation of inbreeding for pigs in closed nucleus herds when selection decisions were based on estimated breeding values (EBVs) derived from BLUP compared to more traditional methods of phenotypic selection and index selection.
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