Genome‐wide association studies (GWAS) have been important for characterizing the genetic component and enhancing our understanding of the biological aetiology of both neuropsychiatric disorders and ...sleep‐related phenotypes such as chronotype, which is our preference for morning or evening time. Mendelian randomization (MR) is a post‐GWAS analysis that is used to infer causal relationships between potential risk factors and outcomes. MR uses genetic variants as instrumental variants for exposures to study the effect on outcomes. This review details the main results from GWAS of neuropsychiatric disorders and sleep‐related phenotypes, and the application of MR to investigate their bidirectional relationship. The main results from MR studies of neuropsychiatric disorders and sleep‐related phenotypes are summarized. These MR studies have identified 37 causal relationships between neuropsychiatric disorders and sleep‐related phenotypes. MR studies identified evidence of a causal role for five neuropsychiatric disorders and symptoms (attention deficit hyperactivity disorder, bipolar disorder, depressive symptoms, major depressive disorder and schizophrenia) on sleep‐related phenotypes and evidence of a causal role for five sleep‐related phenotypes (daytime napping, insomnia, morning person, long sleep duration and sleep duration) on risk for neuropsychiatric disorders. These MR results show a bidirectional relationship between neuropsychiatric disorders and sleep‐related phenotypes and identify potential risk factors for follow‐up studies.
Mendelian randomization (MR) uses genetic variants as instrumental variants for exposures e.g., chronotype (preference for morning or evening time) to investigate their causal relationship with an outcome e.g., schizophrenia (SCZ), under a number of assumptions. This review of MR studies summarises the bidirectional relationship between neuropsychiatric disorders and sleep‐related phenotypes.
Understanding the determinants of healthy mental ageing is a priority for society today. So far, we know that intelligence differences show high stability from childhood to old age and there are ...estimates of the genetic contribution to intelligence at different ages. However, attempts to discover whether genetic causes contribute to differences in cognitive ageing have been relatively uninformative. Here we provide an estimate of the genetic and environmental contributions to stability and change in intelligence across most of the human lifetime. We used genome-wide single nucleotide polymorphism (SNP) data from 1,940 unrelated individuals whose intelligence was measured in childhood (age 11 years) and again in old age (age 65, 70 or 79 years). We use a statistical method that allows genetic (co)variance to be estimated from SNP data on unrelated individuals. We estimate that causal genetic variants in linkage disequilibrium with common SNPs account for 0.24 of the variation in cognitive ability change from childhood to old age. Using bivariate analysis, we estimate a genetic correlation between intelligence at age 11 years and in old age of 0.62. These estimates, derived from rarely available data on lifetime cognitive measures, warrant the search for genetic causes of cognitive stability and change.
Genomic technologies have accelerated research progress in autism spectrum disorder (ASD) genomics and promises to further transform our understanding of the genetic basis of this neurodevelopmental ...disorder. Here we review the current evidence for the genetic basis of ASD, present the progress of large-scale studies to date and highlight the potential of genomic technologies. In particular, we discuss evidence for the importance of identifying rare genetic variants in family-based studies. Genomics is a key feature of future healthcare and our review illustrates it's potential to improve our biological understanding of neurodevelopmental disorders, and to ultimately aid ASD diagnosis, inform medical decision making and establish genomics as central to personalised medicine.
•Application of genomic technologies can detect rare ASD genetic variants.•Increased sequencing coverage of the human genome will increase variant identification.•Prioritising large-scale studies and family-based design are key to gene variant discovery.•Potential of genomics to aid ASD diagnosis and inform medical decision making.
Rare copy-number variants (CNVs) associated with neurodevelopmental disorders (NDDs), i.e., ND-CNVs, provide an insight into the neurobiology of NDDs and, potentially, a link between biology and ...clinical outcomes. However, ND-CNVs are characterised by incomplete penetrance resulting in heterogeneous carrier phenotypes, ranging from non-affected to multimorbid psychiatric, neurological, and physical phenotypes. Recent evidence indicates that other variants in the genome, or ‘other hits’, may partially explain the variable expressivity of ND-CNVs. These may be other rare variants or the aggregated effects of common variants that modify NDD risk. Here we discuss the recent findings, current questions, and future challenges relating to other hits research in the context of ND-CNVs and their potential for improved clinical diagnostics and therapeutics for ND-CNV carriers.
Several large-scale studies report additional variants that exacerbate NDD risk in patients carrying pathogenic ND-CNVs.GWAS-derived polygenic risk scores (PRSs) for cognitive and psychiatric phenotypes may influence clinical outcomes by increasing the risk for a given NDD in ND-CNV carriers.Exome sequencing studies of NDD patient cohorts demonstrated the presence of additional, rare, pathogenic single-nucleotide variants (SNVs) in the genomes of ND-CNV carriers.A variety of analytical approaches to integrate genomic variants, including burden testing, variance-component testing, and machine learning, can be applied to interrogate ND-CNV pathogenicity and variable phenotype expressivity in carriers.
Chronotype is a proxy sleep measure that has been associated with neuropsychiatric disorders. By investigating how chronotype influences risk for neuropsychiatric disorders and vice versa, we may ...identify modifiable risk factors for each phenotype. Here we used Mendelian randomization (MR), to explore causal effects by (1) studying the causal relationships between neuropsychiatric disorders and chronotype and (2) characterizing the genetic components of these phenotypes. Firstly, we investigated if a causal role exists between five neuropsychiatric disorders and chronotype using the largest genome-wide association studies (GWAS) available. Secondly, we integrated data from expression quantitative trait loci (eQTLs) to investigate the role of gene expression alterations on these phenotypes. Evening chronotype was causal for increased risk of schizophrenia and autism spectrum disorder and schizophrenia was causal for a tendency toward evening chronotype. We identified 12 eQTLs where gene expression changes in brain or blood were causal for one of the phenotypes, including two eQTLs for SNX19 in hippocampus and hypothalamus that were causal for schizophrenia. These findings provide important evidence for the complex, bidirectional relationship that exists between a sleep-based phenotype and neuropsychiatric disorders, and use gene expression data to identify causal roles for genes at associated loci.
Autism is a prevalent neurodevelopmental condition, highly heterogenous in both genotype and phenotype. This communication adds to existing discussion of the heterogeneity of clinical sequencing ...tests, “gene panels”, marketed for application in autism. We evaluate the clinical utility of available gene panels based on existing genetic evidence. We determine that diagnostic yields of these gene panels range from 0.22% to 10.02% and gene selection for the panels is variable in relevance, here measured as percentage overlap with SFARI Gene and ranging from 15.15% to 100%. We conclude that gene panels marketed for use in autism are currently of limited clinical utility, and that sequencing with greater coverage may be more appropriate.
Molecular timing mechanisms known as circadian clocks drive endogenous 24-h rhythmicity in most physiological functions, including innate and adaptive immunity. Consequently, the response to immune ...challenge such as vaccination might depend on the time of day of exposure. This study assessed whether the time of day of vaccination (TODV) is associated with the subsequent immune and clinical response by conducting a systematic review of previous studies. The Cochrane Library, PubMed, Google, Medline, and Embase were searched for studies that reported TODV and immune and clinical outcomes, yielding 3114 studies, 23 of which met the inclusion criteria. The global severe acute respiratory syndrome coronavirus 2 vaccination program facilitated investigation of TODV and almost half of the studies included reported data collected during the COVID-19 pandemic. There was considerable heterogeneity in the demography of participants and type of vaccine, and most studies were biased by failure to account for immune status prior to vaccination, self-selection of vaccination time, or confounding factors such as sleep, chronotype, and shiftwork. The optimum TODV was concluded to be afternoon (5 studies), morning (5 studies), morning and afternoon (1 study), midday (1 study), and morning or late afternoon (1 study), with the remaining 10 studies reporting no effect. Further research is required to understand the relationship between TODV and subsequent immune outcome and whether any clinical benefit outweighs the potential effect of this intervention on vaccine uptake.
The identification of early biological changes associated with the psychotic disorder (PD) is important as it may provide clues to the underlying pathophysiological mechanisms. We undertook the first ...proteomic profiling of blood plasma samples of children who later develop a PD. Participants were recruited from the UK Avon Longitudinal Study of Parents and Children (ALSPAC) cohort who also participated in psychiatric assessment interviews at age 18. Protein expression levels at age 11 were compared between individuals who developed PD at age 18 (n = 37) with population-based age-matched controls (n = 38). Sixty out of 181 plasma proteins profiled were found to be differentially expressed (P < .05) in children with an outcome of the PD. Thirty-four of these proteins were found to be differentially expressed following correction for multiple comparisons. Pathway analysis implicated the complement and coagulation cascade. A second, targeted proteomic approach was used to verify these findings in age 11 plasma from subjects who reported psychotic experiences at age 18 (n = 40) in comparison to age-matched controls (n = 66). Our findings indicate that the complement and coagulation system is dysregulated in the blood during childhood before the development of the PD.
Activated partial thromboplastin time (aPTT) and prothrombin time (PT) are clinical tests commonly used to screen for coagulation-factor deficiencies. One genome-wide association study (GWAS) has ...been reported previously for aPTT, but no GWAS has been reported for PT. We conducted a GWAS and meta-analysis to identify genetic loci for aPTT and PT. The GWAS for aPTT was conducted in 9,240 individuals of European ancestry from the Atherosclerosis Risk in Communities (ARIC) study, and the GWAS for PT was conducted in 2,583 participants from the Genetic Study of Three Population Microisolates in South Tyrol (MICROS) and the Lothian Birth Cohorts (LBC) of 1921 and 1936. Replication was assessed in 1,041 to 3,467 individuals. For aPTT, previously reported associations with KNG1, HRG, F11, F12, and ABO were confirmed. A second independent association in ABO was identified and replicated (rs8176704, p = 4.26 × 10−24). Pooling the ARIC and replication data yielded two additional loci in F5 (rs6028, p = 3.22 × 10−9) and AGBL1 (rs2469184, p = 3.61 × 10−8). For PT, significant associations were identified and confirmed in F7 (rs561241, p = 3.71 × 10−56) and PROCR/EDEM2 (rs2295888, p = 5.25 × 10−13). Assessment of existing gene expression and coronary artery disease (CAD) databases identified associations of five of the GWAS loci with altered gene expression and two with CAD. In summary, eight genetic loci that account for ∼29% of the variance in aPTT and two loci that account for ∼14% of the variance in PT were detected and supported by functional data.
Combining datasets across independent studies can boost statistical power by increasing the numbers of observations and can achieve more accurate estimates of effect sizes. This is especially ...important for genetic studies where a large number of observations are required to obtain sufficient power to detect and replicate genetic effects. There is a need to develop and evaluate methods for joint-analytical analyses of rich datasets collected in imaging genetics studies. The ENIGMA-DTI consortium is developing and evaluating approaches for obtaining pooled estimates of heritability through meta-and mega-genetic analytical approaches, to estimate the general additive genetic contributions to the intersubject variance in fractional anisotropy (FA) measured from diffusion tensor imaging (DTI). We used the ENIGMA-DTI data harmonization protocol for uniform processing of DTI data from multiple sites. We evaluated this protocol in five family-based cohorts providing data from a total of 2248 children and adults (ages: 9–85) collected with various imaging protocols. We used the imaging genetics analysis tool, SOLAR-Eclipse, to combine twin and family data from Dutch, Australian and Mexican-American cohorts into one large “mega-family”. We showed that heritability estimates may vary from one cohort to another. We used two meta-analytical (the sample-size and standard-error weighted) approaches and a mega-genetic analysis to calculate heritability estimates across-population. We performed leave-one-out analysis of the joint estimates of heritability, removing a different cohort each time to understand the estimate variability. Overall, meta- and mega-genetic analyses of heritability produced robust estimates of heritability.
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•Data pooling using analytical approaches leads to improved power of genetic analyses.•In the largest DTI study to date we evaluated three metaanalytical methods.•Data from 2248 subjects were normalized using ENIGMA-DTI protocol.•Heritability estimates varied substantially for five cohort that contributed subjects.•Meta-analyses boosted power and improved stability of heritability estimates.
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