Genetic contributions to self-reported tiredness

Discrimination between healthy participants and people with panic disorder based on polygenic scores for psychiatric disorders and for intermediate phenotypes using machine learning

OBJECTIVE

Panic disorder is a modestly heritable condition. Currently, diagnosis is based only on clinical symptoms; identifying objective biomarkers and a more reliable diagnostic procedure is desirable. We investigated whether people with panic disorder can be reliably diagnosed utilizing combinations of multiple polygenic scores for psychiatric disorders and their intermediate phenotypes, compared with single polygenic score approaches, by applying specific machine learning techniques.

METHODS

Polygenic scores for 48 psychiatric disorders and intermediate phenotypes based on large-scale genome-wide association studies (n = 7556-1,131,881) were calculated for people with panic disorder (n = 718) and healthy controls (n = 1717). Discrimination between people with panic disorder and healthy controls was based on the 48 polygenic scores using five methods for classification: logistic regression, neural networks, quadratic discriminant analysis, random forests and a support vector machine. Differences in discrimination accuracy (area under the curve) due to an increased number of polygenic score combinations and differences in the accuracy across five classifiers were investigated.

RESULTS

All five classifiers performed relatively well for distinguishing people with panic disorder from healthy controls by increasing the number of polygenic scores. Of the 48 polygenic scores, the polygenic score for anxiety UK Biobank was the most useful for discrimination by the classifiers. In combinations of two or three polygenic scores, the polygenic score for anxiety UK Biobank was included as one of polygenic scores in all classifiers. When all 48 polygenic scores were used in combination, the greatest areas under the curve significantly differed among the five classifiers. Support vector machine and logistic regression had higher accuracy than quadratic discriminant analysis and random forests. For each classifier, the greatest area under the curve was 0.600 ± 0.030 for logistic regression (polygenic score combinations N = 14), 0.591 ± 0.039 for neural networks (N = 9), 0.603 ± 0.033 for quadratic discriminant analysis (N = 10), 0.572 ± 0.039 for random forests (N = 25) and 0.617 ± 0.041 for support vector machine (N = 11). The greatest areas under the curve at the best polygenic score combination significantly differed among the five classifiers. Random forests had the lowest accuracy among classifiers. Support vector machine had higher accuracy than neural networks.

CONCLUSIONS

These findings suggest that increasing the number of polygenic score combinations up to approximately 10 effectively improved the discrimination accuracy and that support vector machine exhibited greater accuracy among classifiers. However, the discrimination accuracy for panic disorder, when based solely on polygenic score combinations, was found to be modest.

A genome-wide association study identifies a locus associated with knee extension strength in older Japanese individuals

Sarcopenia is a common skeletal muscle disease in older people. Lower limb muscle strength is a good predictive value for sarcopenia; however, little is known about its genetic components. Here, we conducted a genome-wide association study (GWAS) for knee extension strength in a total of 3452 Japanese aged 60 years or older from two independent cohorts. We identified a significant locus, rs10749438 which is an intronic variant in TACC2 (transforming acidic coiled-coil-containing 2) (P = 4.2 × 10-8). TACC2, encoding a cytoskeleton-related protein, is highly expressed in skeletal muscle, and is reported as a target of myotonic dystrophy 1-associated splicing alterations. These suggest that changes in TACC2 expression are associated with variations in muscle strength in older people. The association was consistently observed in young and middle-aged subjects. Our findings would shed light on genetic components of lower limb muscle strength and indicate TACC2 as a potential therapeutic target for sarcopenia.

Why does malaise/fatigue occur? Underlying mechanisms and potential relevance to treatments in rheumatoid arthritis

INTRODUCTION

Fatigue and malaise are commonly associated with a wide range of medical conditions, including rheumatoid arthritis (RA). Evidence suggests that fatigue and malaise can be overwhelming for patients, yet these symptoms remain inadequately-managed, largely due to an incomplete elucidation of the underlying causes.

AREAS COVERED

In this assessment of the published literature relating to the pathogenesis of fatigue or malaise in chronic conditions, four key mechanistic themes were identified. Each theme (inflammation, hypothalamic-pituitary-adrenal axis, dysautonomia, and monoamines) is discussed, as well as the complex network of interconnections between themes which suggests a key role for inflammatory cytokines in the development and persistence of fatigue.

EXPERT OPINION

Fatigue is multifaceted, poorly defined, and imperfectly comprehended. Moreover, the cause and severity of fatigue may change over time, as a consequence of the natural disease course or pharmacologic treatment. This detailed synthesis of available evidence permits us to identify avenues for current treatment optimization and future research, to improve the management of fatigue and malaise in RA. Within the development pipeline, several new anti-inflammatory therapies are currently under investigation, and we anticipate that the next five years will herald much-needed progress to reduce the debilitating nature of fatigue in patients with RA.

COLOCdb: a comprehensive resource for multi-model colocalization of complex traits

Large-scale genome-wide association studies (GWAS) have provided profound insights into complex traits and diseases. Yet, deciphering the fine-scale molecular mechanisms of how genetic variants manifest to cause the phenotypes remains a daunting task. Here, we present COLOCdb (https://ngdc.cncb.ac.cn/colocdb), a comprehensive genetic colocalization database by integrating more than 3000 GWAS summary statistics and 13 types of xQTL to date. By employing two representative approaches for the colocalization analysis, COLOCdb deposits results from three key components: (i) GWAS-xQTL, pair-wise colocalization between GWAS loci and different types of xQTL, (ii) GWAS-GWAS, pair-wise colocalization between the trait-associated genetic loci from GWASs and (iii) xQTL-xQTL, pair-wise colocalization between the genetic loci associated with molecular phenotypes in xQTLs. These results together represent the most comprehensive colocalization analysis, which also greatly expands the list of shared variants with genetic pleiotropy. We expect that COLOCdb can serve as a unique and useful resource in advancing the discovery of new biological mechanisms and benefit future functional studies.

Causal association between subtypes of osteoarthritis and common comorbidities: A Mendelian randomisation study

Objective

To investigate the causal association between Osteoarthritis (OA) and five comorbidities: depression, tiredness, multisite chronic pain, irritable bowel syndrome (IBS) and gout.

Design

This study used two-sample Mendelian Randomisation (MR). To select the OA genetic instruments, we used data from the largest recent genome-wide association study (GWAS) of OA (GO Consortium), with a focus on OA of the knee (62,497 cases, 333,557 controls), hip (35,445 cases, 316,943 controls) and hand (20,901 cases, 282,881 controls). Genetic associations for comorbidities were selected from GWAS for depression (246,363 cases, 561,190 controls), tiredness (449,019 participants), multisite chronic pain (387,649 participants), IBS (53,400 cases, 433,201 controls) and gout (6543 cases, 456,390 controls). We performed a bidirectional MR analysis using the inverse variance weighted method, for both joint specific and overall OA.

Results

Hip OA had a causal effect on multisite chronic pain (per unit change 0.02, 95% CI 0.01 to 0.04). Multisite chronic pain had a causal effect on knee (odd ratio (OR) 2.74, 95% CI 2.20 to 3.41), hip (OR 2.12, 95% CI 1.54 to 2.92), hand (OR 2.24, 95% CI 1.59 to 3.16) and overall OA (OR 2.44, 95% CI, 2.06 to 2.86). In addition, depression and tiredness had causal effects on knee and hand, but not hip, OA.

Conclusions

Apart from Hip OA to multisite chronic pain, other joint OA did not have causal effects on these comorbidities. In contrast, multisite chronic pain had a causal effect on any painful OA.

Causal association between self-reported fatigue and coronary artery disease: a bidirectional two-sample Mendelian randomization analysis

Background

Observational studies have reported the association between fatigue and coronary artery disease (CAD), but the causal association between fatigue and CAD is unclear.

Method

We conducted a bidirectional Mendelian randomization (MR) study using publicly available genome-wide association studies (GWAS) data. The inverse-variance weighted (IVW) method was used as the primary analysis. We performed three complementary methods, including weighted median, MR-Egger regression, and MR pleiotropy residual sum and outlier (MR-PRESSO) to evaluate the sensitivity and horizontal pleiotropy of the results.

Result

Self-reported fatigue had a causal effect on coronary artery atherosclerosis (CAA) (OR 1.047, 95%CI 1.033-1.062), myocardial infarction (MI) (OR 1.027 95%CI 1.014-1.039) and coronary heart disease (CHD) (OR 1.037, 95%CI 1.021-1.053). We did not find a significant reverse causality between self-reported fatigue and CAD. Given the heterogeneity revealed by MR-Egger regression, we employed the IVW random effect model. For the examination of fatigue on CHD and the reverse analysis of CAA, and MI on fatigue, the MR-PRESSO test found horizontal pleiotropy. No significant outliers were found.

Conclusion

The MR analysis reveals a causal relationship between self-reported fatigue and CAD. The results should be interpreted with caution due to horizontal pleiotropy.

Exploring causal effects of smoking and alcohol related lifestyle factors on self-report tiredness: A Mendelian randomization study

Self-reported tiredness or low energy, often referred to as fatigue, has been linked to lifestyle factors, although data from randomized-controlled trials are lacking. We investigate whether modifiable lifestyle factors including smoking and alcohol intake related exposures (SAIEs) are causal factors for fatigue using Mendelian randomization (MR). A two-sample MR study was performed by using genome-wide association summary results from UK Biobank (UKBB), and each of the sample size is more than 100,000. We used the inverse variance weighted method, and sensitivity analyses (MR Egger, weighted median, penalized median estimators, and multivariable MR) to account for pleiotropy. The two-sample MR analyses showed inverse causal effect of never-smoking status and positive effect of current smoking status on the risk of fatigue. Similarly, genetically predicted alcoholic intake was positively associated with fatigue. The results were consistent across the different MR methods. Our Mendelian randomization analyses do support that the cessation of smoking and alcohol can decrease the risk of fatigue, and limit alcohol intake frequency can also reduce the risk.

Interindividual Variability in Mental Fatigue-Related Impairments in Endurance Performance: A Systematic Review and Multiple Meta-regression

BACKGROUND

The negative effect of mental fatigue (MF) on physical performance has recently been questioned. One reason behind this could lie in the interindividual differences in MF-susceptibility and the individual features influencing them. However, the range of individual differences in mental fatigue-susceptibility is not known, and there is no clear consensus on which individual features could be responsible for these differences.

OBJECTIVE

To give an overview of interindividual differences in the effects of MF on whole-body endurance performance, and individual features influencing this effect.

METHODS

The review was registered on the PROSPERO database (CRD42022293242). PubMed, Web of Science, SPORTDiscus and PsycINFO were searched until the 16th of June 2022 for studies detailing the effect of MF on dynamic maximal whole-body endurance performance. Studies needed to include healthy participants, describe at least one individual feature in participant characteristics, and apply at least one manipulation check. The Cochrane crossover risk of bias tool was used to assess risk of bias. The meta-analysis and regression were conducted in R.

RESULTS

Twenty-eight studies were included, with 23 added to the meta-analysis. Overall risk of bias of the included studies was high, with only three presenting an unclear or low rating. The meta-analysis shows the effect of MF on endurance performance was on average slightly negative (g = - 0.32, [95% CI - 0.46; - 0.18], p < 0.001). The multiple meta-regression showed no significant influences of the included features (i.e. age, sex, body mass index and physical fitness level) on MF-susceptibility.

CONCLUSIONS

The present review confirmed the negative impact of MF on endurance performance. However, no individual features influencing MF-susceptibility were identified. This can partially be explained by the multiple methodological limitations such as underreporting of participant characteristics, lack of standardization across studies, and the restricted inclusion of potentially relevant variables. Future research should include a rigorous description of multiple different individual features (e.g., performance level, diet, etc.) to further elucidate MF mechanisms.

Brain Catalog: a comprehensive resource for the genetic landscape of brain-related traits

A broad range of complex phenotypes are related to dysfunctions in brain (hereafter referred to as brain-related traits), including various mental and behavioral disorders and diseases of the nervous system. These traits in general share overlapping symptoms, pathogenesis, and genetic components. Here, we present Brain Catalog (https://ngdc.cncb.ac.cn/braincatalog), a comprehensive database aiming to delineate the genetic components of more than 500 GWAS summary statistics datasets for brain-related traits from multiple aspects. First, Brain Catalog provides results of candidate causal variants, causal genes, and functional tissues and cell types for each trait identified by multiple methods using comprehensive annotation datasets (58 QTL datasets spanning 6 types of QTLs). Second, Brain Catalog estimates the SNP-based heritability, the partitioning heritability based on functional annotations, and genetic correlations among traits. Finally, through bidirectional Mendelian randomization analyses, Brain Catalog presents inference of risk factors that are likely causal to each trait. In conclusion, Brain Catalog presents a one-stop shop for the genetic components of brain-related traits, potentially serving as a valuable resource for worldwide researchers to advance the understanding of how GWAS signals may contribute to the biological etiology of brain-related traits.

Identification of shared and differentiating genetic architecture for autism spectrum disorder, attention-deficit hyperactivity disorder and case subgroups

Attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are highly heritable neurodevelopmental conditions, with considerable overlap in their genetic etiology. We dissected their shared and distinct genetic etiology by cross-disorder analyses of large datasets. We identified seven loci shared by the disorders and five loci differentiating them. All five differentiating loci showed opposite allelic directions in the two disorders and significant associations with other traits, including educational attainment, neuroticism and regional brain volume. Integration with brain transcriptome data enabled us to identify and prioritize several significantly associated genes. The shared genomic fraction contributing to both disorders was strongly correlated with other psychiatric phenotypes, whereas the differentiating portion was correlated most strongly with cognitive traits. Additional analyses revealed that individuals diagnosed with both ASD and ADHD were double-loaded with genetic predispositions for both disorders and showed distinctive patterns of genetic association with other traits compared with the ASD-only and ADHD-only subgroups. These results provide insights into the biological foundation of the development of one or both conditions and of the factors driving psychopathology discriminatively toward either ADHD or ASD.

Determining the stability of genome-wide factors in BMI between ages 40 to 69 years

Genome-wide association studies (GWAS) have successfully identified common variants associated with BMI. However, the stability of aggregate genetic variation influencing BMI from midlife and beyond is unknown. By analysing 165,717 men and 193,073 women from the UKBiobank, we performed BMI GWAS on six independent five-year age intervals between 40 and 72 years. We then applied genomic structural equation modeling to test competing hypotheses regarding the stability of genetic effects for BMI. LDSR genetic correlations between BMI assessed between ages 40 to 73 were all very high and ranged 0.89 to 1.00. Genomic structural equation modeling revealed that molecular genetic variance in BMI at each age interval could not be explained by the accumulation of any age-specific genetic influences or autoregressive processes. Instead, a common set of stable genetic influences appears to underpin genome-wide variation in BMI from middle to early old age in men and women alike.

Personality and fatigue: meta-analysis of seven prospective studies

The present study examined the cross-sectional and longitudinal associations between the five major personality traits and fatigue. Participants were adults aged 16-104 years old (N > 40,000 at baseline) from the Health and Retirement Study, the National Social Life, Health, and Aging Project, the Wisconsin Longitudinal Study graduate and sibling samples, the National Health and Aging Trends Survey, the Longitudinal Internet Studies for the Social Sciences and the English Longitudinal Study of Ageing. Personality traits, fatigue, demographic factors, and other covariates were assessed at baseline, and fatigue was assessed again 5-20 years later. Across all samples, higher neuroticism was related to a higher risk of concurrent (meta-analytic OR = 1.73, 95% CI 1.62-1.86) and incident (OR = 1.38, 95% CI 1.29-1.48) fatigue. Higher extraversion, openness, agreeableness, and conscientiousness were associated with a lower likelihood of concurrent (meta-analytic OR range 0.67-0.86) and incident (meta-analytic OR range 0.80-0.92) fatigue. Self-rated health and physical inactivity partially accounted for these associations. There was little evidence that age or gender moderated these associations. This study provides consistent evidence that personality is related to fatigue. Higher neuroticism and lower extraversion, openness, agreeableness, and conscientiousness are risk factors for fatigue.

ANO10 Function in Health and Disease

Anoctamin 10 (ANO10), also known as TMEM16K, is a transmembrane protein and member of the anoctamin family characterized by functional duality. Anoctamins manifest ion channel and phospholipid scrambling activities and are involved in many physiological processes such as cell division, migration, apoptosis, cell signalling, and developmental processes. Several diseases, including neurological, muscle, blood disorders, and cancer, have been associated with the anoctamin family proteins. ANO10, which is the main focus of the present review, exhibits both scrambling and chloride channel activity; calcium availability is necessary for protein activation in either case. Additional processes implicating ANO10 include endosomal sorting, spindle assembly, and calcium signalling. Dysregulation of calcium signalling in Purkinje cells due to ANO10 defects is proposed as the main mechanism leading to spinocerebellar ataxia autosomal recessive type 10 (SCAR10), a rare, slowly progressive spinocerebellar ataxia. Regulation of the endolysosomal pathway is an additional ANO10 function linked to SCAR10 aetiology. Further functional investigation is essential to unravel the ANO10 mechanism of action and involvement in disease development.

A New Approach to Understanding Cancer-Related Fatigue: Leveraging the 3P Model to Facilitate Risk Prediction and Clinical Care

Simple Summary

For the growing number of cancer survivors worldwide, fatigue presents a major hurdle to function and quality of life. Treatment options for cancer-related fatigue are still emerging, and our current understanding of its etiology is limited. In this paper, we describe a new application of a comprehensive model for cancer-related fatigue: the predisposing, precipitating, and perpetuating (3P) factors model. We propose that the 3P model may be leveraged—particularly using metabolomics, the microbiome, and inflammation in conjunction with behavioral science—to better understand the pathophysiology of cancer-related fatigue.

Abstract

A major gap impeding development of new treatments for cancer-related fatigue is an inadequate understanding of the complex biological, clinical, demographic, and lifestyle mechanisms underlying fatigue. In this paper, we describe a new application of a comprehensive model for cancer-related fatigue: the predisposing, precipitating, and perpetuating (3P) factors model. This model framework outlined herein, which incorporates the emerging field of metabolomics, may help to frame a more in-depth analysis of the etiology of cancer-related fatigue as well as a broader and more personalized set of approaches to the clinical treatment of fatigue in oncology care. Included within this review paper is an in-depth description of the proposed biological mechanisms of cancer-related fatigue, as well as a presentation of the 3P model’s application to this phenomenon. We conclude that a clinical focus on organization risk stratification and treatment around the 3P model may be warranted, and future research may benefit from expanding the 3P model to understand fatigue not only in oncology, but also across a variety of chronic conditions.

Early brain injury and cognitive impairment after aneurysmal subarachnoid haemorrhage

The first 72 h following aneurysm rupture play a key role in determining clinical and cognitive outcomes after subarachnoid haemorrhage (SAH). Yet, very little is known about the impact of so called “early brain injury” on patents with clinically good grade SAH (as defined as World Federation of Neurosurgeons Grade 1 and 2). 27 patients with good grade SAH underwent MRI scanning were prospectively recruited at three time-points after SAH: within the first 72 h (acute phase), at 5–10 days and at 3 months. Patients underwent additional, comprehensive cognitive assessment 3 months post-SAH. 27 paired healthy controls were also recruited for comparison. In the first 72 h post-SAH, patients had significantly higher global and regional brain volume than controls. This change was accompanied by restricted water diffusion in patients. Persisting abnormalities in the volume of the posterior cerebellum at 3 months post-SAH were present to those patients with worse cognitive outcome. When using this residual abnormal brain area as a region of interest in the acute-phase scans, we could predict with an accuracy of 84% (sensitivity 82%, specificity 86%) which patients would develop cognitive impairment 3 months later, despite initially appearing clinically indistinguishable from those making full recovery. In an exploratory sample of good clinical grade SAH patients compared to healthy controls, we identified a region of the posterior cerebellum for which acute changes on MRI were associated with cognitive impairment. Whilst further investigation will be required to confirm causality, use of this finding as a risk stratification biomarker is promising.

An atlas of gene regulatory elements in adult mouse cerebrum

The mammalian cerebrum performs high-level sensory perception, motor control and cognitive functions through highly specialized cortical and subcortical structures1. Recent surveys of mouse and human brains with single-cell transcriptomics2-6 and high-throughput imaging technologies7,8 have uncovered hundreds of neural cell types distributed in different brain regions, but the transcriptional regulatory programs that are responsible for the unique identity and function of each cell type remain unknown. Here we probe the accessible chromatin in more than 800,000 individual nuclei from 45 regions that span the adult mouse isocortex, olfactory bulb, hippocampus and cerebral nuclei, and use the resulting data to map the state of 491,818 candidate cis-regulatory DNA elements in 160 distinct cell types. We find high specificity of spatial distribution for not only excitatory neurons, but also most classes of inhibitory neurons and a subset of glial cell types. We characterize the gene regulatory sequences associated with the regional specificity within these cell types. We further link a considerable fraction of the cis-regulatory elements to putative target genes expressed in diverse cerebral cell types and predict transcriptional regulators that are involved in a broad spectrum of molecular and cellular pathways in different neuronal and glial cell populations. Our results provide a foundation for comprehensive analysis of gene regulatory programs of the mammalian brain and assist in the interpretation of noncoding risk variants associated with various neurological diseases and traits in humans.

Shared transethnic genetic basis of panic disorder and psychiatric and related intermediate phenotypes

Panic disorder (PD), a common anxiety disorder, is modestly heritable. The genetic basis of anxiety disorders overlaps with that of other psychiatric disorders and their intermediate phenotypes in individuals of European ancestry. Here, we investigated the transethnic polygenetic features shared between Japanese PD patients and European patients with psychiatric disorders and their intermediate phenotypes by conducting polygenic risk score (PRS) analyses. Large-scale European genome-wide association study (GWAS) datasets (n = 7,556-1,131,881) for ten psychiatric disorders and seven intermediate phenotypes were utilized as discovery samples. PRSs derived from these GWASs were calculated for Japanese target subjects [718 PD patients and 1,717 healthy controls (HCs)]. The effects of these PRSs from European GWASs on the risk of PD in Japanese patients were investigated. The PRSs from European studies of anxiety disorders were marginally higher in Japanese PD patients than in HCs (p = 0.013). Regarding other psychiatric disorders, the PRSs for depression in European patients were significantly higher in Japanese PD patients than in HCs (p = 2.31×10^-4), while the PRSs for attention-deficit/hyperactivity disorder in European patients were nominally lower in Japanese PD patients than in HCs (p = 0.024). Regarding health-related, personality-based and cognitive intermediate phenotypes, the PRSs for loneliness (especially isolation) in European individuals were significantly higher in Japanese PD patients than in HCs (p = 9.02×10^-4). Furthermore, Japanese PD patients scored nominally higher than HCs in PRSs for neuroticism in European people (p = 3.37×10^-3), while Japanese PD patients scored nominally lower than HCs in PRSs for tiredness (p = 0.025), educational attainment (p = 0.035) and cognitive function (p = 9.63×10^-3). Our findings suggest that PD shares transethnic genetic etiologies with other psychiatric disorders and related intermediate phenotypes.

The Role of Genetic Variation of BMI, Body Composition, and Fat Distribution for Mental Traits and Disorders: A Look-Up and Mendelian Randomization Study

Anthropometric traits and mental disorders or traits are known to be associated clinically and to show genetic overlap. We aimed to identify genetic variants with relevance for mental disorders/traits and either (i) body mass index (or obesity), (ii) body composition, (and/or) (iii) body fat distribution. We performed a look-up analysis of 1,005 genome-wide significant SNPs for BMI, body composition, and body fat distribution in 15 mental disorders/traits. We identified 40 independent loci with one or more SNPs fulfilling our threshold significance criterion (P < 4.98 × 10^–5) for the mental phenotypes. The majority of loci was associated with schizophrenia, educational attainment, and/or intelligence. Fewer associations were found for bipolar disorder, neuroticism, attention deficit/hyperactivity disorder, major depressive disorder, depressive symptoms, and well-being. Unique associations with measures of body fat distribution adjusted for BMI were identified at five loci only. To investigate the potential causality between body fat distribution and schizophrenia, we performed two-sample Mendelian randomization analyses. We found no causal effect of body fat distribution on schizophrenia and vice versa. In conclusion, we identified 40 loci which may contribute to genetic overlaps between mental disorders/traits and BMI and/or shape related phenotypes. The majority of loci identified for body composition overlapped with BMI loci, thus suggesting pleiotropic effects.

Brain aging comprises many modes of structural and functional change with distinct genetic and biophysical associations

Brain imaging can be used to study how individuals' brains are aging, compared against population norms. This can inform on aspects of brain health; for example, smoking and blood pressure can be seen to accelerate brain aging. Typically, a single 'brain age' is estimated per subject, whereas here we identified 62 modes of subject variability, from 21,407 subjects' multimodal brain imaging data in UK Biobank. The modes represent different aspects of brain aging, showing distinct patterns of functional and structural brain change, and distinct patterns of association with genetics, lifestyle, cognition, physical measures and disease. While conventional brain-age modelling found no genetic associations, 34 modes had genetic associations. We suggest that it is important not to treat brain aging as a single homogeneous process, and that modelling of distinct patterns of structural and functional change will reveal more biologically meaningful markers of brain aging in health and disease.

Potential influence of socioeconomic status on genetic correlations between alcohol consumption measures and mental health

BACKGROUND

Frequency and quantity of alcohol consumption are metrics commonly used to measure alcohol consumption behaviors. Epidemiological studies indicate that these alcohol consumption measures are differentially associated with (mental) health outcomes and socioeconomic status (SES). The current study aims to elucidate to what extent genetic risk factors are shared between frequency and quantity of alcohol consumption, and how these alcohol consumption measures are genetically associated with four broad phenotypic categories: (i) SES; (ii) substance use disorders; (iii) other psychiatric disorders; and (iv) psychological/personality traits.

METHODS

Genome-Wide Association analyses were conducted to test genetic associations with alcohol consumption frequency (N = 438 308) and alcohol consumption quantity (N = 307 098 regular alcohol drinkers) within UK Biobank. For the other phenotypes, we used genome-wide association studies summary statistics. Genetic correlations (rg) between the alcohol measures and other phenotypes were estimated using LD score regression.

RESULTS

We found a substantial genetic correlation between the frequency and quantity of alcohol consumption (rg = 0.52). Nevertheless, both measures consistently showed opposite genetic correlations with SES traits, and many substance use, psychiatric, and psychological/personality traits. High alcohol consumption frequency was genetically associated with high SES and low risk of substance use disorders and other psychiatric disorders, whereas the opposite applies for high alcohol consumption quantity.

CONCLUSIONS

Although the frequency and quantity of alcohol consumption show substantial genetic overlap, they consistently show opposite patterns of genetic associations with SES-related phenotypes. Future studies should carefully consider the potential influence of SES on the shared genetic etiology between alcohol and adverse (mental) health outcomes.

Social and non-social autism symptoms and trait domains are genetically dissociable

The core diagnostic criteria for autism comprise two symptom domains - social and communication difficulties, and unusually repetitive and restricted behaviour, interests and activities. There is some evidence to suggest that these two domains are dissociable, though this hypothesis has not yet been tested using molecular genetics. We test this using a genome-wide association study (N = 51,564) of a non-social trait related to autism, systemising, defined as the drive to analyse and build systems. We demonstrate that systemising is heritable and genetically correlated with autism. In contrast, we do not identify significant genetic correlations between social autistic traits and systemising. Supporting this, polygenic scores for systemising are significantly and positively associated with restricted and repetitive behaviour but not with social difficulties in autistic individuals. These findings strongly suggest that the two core domains of autism are genetically dissociable, and point at how to fractionate the genetics of autism.

Heritability estimates of individual psychological distress symptoms from genetic variation

BACKGROUND

Psychological distress symptoms are associated with an increased risk of psychiatric disorders and medical illness. Although psychological distress is influenced by environmental factors, such as socioeconomic status, lifetime events, or interpersonal relationships, substantial interindividual variation also exists. However, heritability and genetic determinants of distress are poorly understood.

METHODS

In the Korean Genome and Epidemiology Study sample (n = 12,680), we estimated the heritability of individual psychological distress symptoms using the GCTA-REML method and carried out a genome-wide association study of individual psychological distress symptoms showing significant heritability.

RESULTS

We found three psychological distress items showing significant heritability: subjective well-being (9%), fatigue and appetite (11%), and enjoying daily life (8%). Additionally, we found genome-wide significant associations of rs6735649 located between STEAP3 and C1QL2 on chromosome 2 with subjective well-being (P = 1.32 × 10^-8, odds ratio [OR] = 1.18, 95% confidence interval [CI]: 1.12-1.25) and rs35543418 located between SYT16 and KCNH5 on chromosome 14 with enjoying daily life (P = 1.33 × 10^-8, OR = 1.59, 95% CI: 1.35-1.86).

LIMITATIONS

The lack of replication in independent cohorts and longitudinal assessment of distress may limit generalizability.

CONCLUSIONS

Our results indicate that distress symptoms are partly heritable. Further analysis in larger cohorts investigating gene-environment interactions is required to identify genetic variants that explain the proportion of variation in distress.

Identification of common genetic risk variants for autism spectrum disorder

Autism spectrum disorder (ASD) is a highly heritable and heterogeneous group of neurodevelopmental phenotypes diagnosed in more than 1% of children. Common genetic variants contribute substantially to ASD susceptibility, but to date no individual variants have been robustly associated with ASD. With a marked sample-size increase from a unique Danish population resource, we report a genome-wide association meta-analysis of 18,381 individuals with ASD and 27,969 controls that identified five genome-wide-significant loci. Leveraging GWAS results from three phenotypes with significantly overlapping genetic architectures (schizophrenia, major depression, and educational attainment), we identified seven additional loci shared with other traits at equally strict significance levels. Dissecting the polygenic architecture, we found both quantitative and qualitative polygenic heterogeneity across ASD subtypes. These results highlight biological insights, particularly relating to neuronal function and corticogenesis, and establish that GWAS performed at scale will be much more productive in the near term in ASD.

Fatigue symptoms in relation to neuroticism, anxiety-depression, and musculoskeletal pain. A longitudinal twin study

BACKGROUND

The nature of the relationship between fatigue and its risk factors is poorly understood. In the present study the genetic and environmental association between anxiety-depression, musculoskeletal (MS) pain and fatigue was examined, and the role of neuroticism as a shared risk factor that may possibly explain the co-occurrence between these phenotypes was investigated in a combined cross-sectional and longitudinal twin design.

METHODS

The sample consisted of 746 monozygotic (MZ) and 770 dizygotic (DZ) twins in the age group of 50-65 (mean = 57.11 years, SD = 4.5). Continuous measures of fatigue symptoms and the other phenotypes were employed. Using Cholesky modeling, genetic and environmental influences on the phenotypes, and the associations among them, were determined. Analyses were performed using measures of neuroticism obtained concurrently and 13-19 years earlier.

RESULTS

Results from multiple regression analyses showed that neuroticism, anxiety-depression symptoms, and MS pain were all significantly associated with fatigue, controlling for sex, education, and general health indices. The best-fitting biometric models included additive genetic and individual-specific environmental effects. Heritabilities in the 0.40-0.53 range were demonstrated. Furthermore, while there was a considerable overlap in genetic risk factors between the four phenotypes, a substantial proportion of the genetic risk shared between anxiety-depression and fatigue, and between MS pain and fatigue, was independent of neuroticism.

CONCLUSION

Evidence for a common underlying susceptibility to report fatigue symptoms, genetically linked to neuroticism, anxiety-depression, and MS pain, was found. Both unique and pleiotropic effects appear to be involved in the genetic architecture of the phenotypes.

Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression

Major depressive disorder (MDD) is a common illness accompanied by considerable morbidity, mortality, costs, and heightened risk of suicide. We conducted a genome-wide association meta-analysis based in 135,458 cases and 344,901 controls and identified 44 independent and significant loci. The genetic findings were associated with clinical features of major depression and implicated brain regions exhibiting anatomical differences in cases. Targets of antidepressant medications and genes involved in gene splicing were enriched for smaller association signal. We found important relationships of genetic risk for major depression with educational attainment, body mass, and schizophrenia: lower educational attainment and higher body mass were putatively causal, whereas major depression and schizophrenia reflected a partly shared biological etiology. All humans carry lesser or greater numbers of genetic risk factors for major depression. These findings help refine the basis of major depression and imply that a continuous measure of risk underlies the clinical phenotype.

Polygenic risk for schizophrenia and measured domains of cognition in individuals with psychosis and controls

Psychotic disorders including schizophrenia are commonly accompanied by cognitive deficits. Recent studies have reported negative genetic correlations between schizophrenia and indicators of cognitive ability such as general intelligence and processing speed. Here we compare the effect of polygenetic risk for schizophrenia (PRS_SCZ) on measures that differ in their relationships with psychosis onset: a measure of current cognitive abilities (the Brief Assessment of Cognition in Schizophrenia, BACS) that is greatly reduced in psychotic disorder patients, a measure of premorbid intelligence that is minimally affected by psychosis onset (the Wide-Range Achievement Test, WRAT); and educational attainment (EY), which covaries with both BACS and WRAT. Using genome-wide single nucleotide polymorphism (SNP) data from 314 psychotic and 423 healthy research participants in the Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) Consortium, we investigated the association of PRS_SCZ with BACS, WRAT, and EY. Among apparently healthy individuals, greater genetic risk for schizophrenia (PRS_SCZ) was significantly associated with lower BACS scores (r = -0.17, p = 6.6 × 10^-4 at P_T = 1 × 10^-4), but not with WRAT or EY. Among individuals with psychosis, PRS_SCZ did not associate with variations in any of these three phenotypes. We further investigated the association between PRS_SCZ and WRAT in more than 4500 healthy subjects from the Philadelphia Neurodevelopmental Cohort. The association was again null (p > 0.3, N = 4511), suggesting that different cognitive phenotypes vary in their etiologic relationship with schizophrenia.