In some patients with major depressive disorder (MDD), individual illness characteristics appear consistent with those of a neuroprogressive illness. Features of neuroprogression include poorer ...symptomatic, treatment and functional outcomes in patients with earlier disease onset and increased number and length of depressive episodes. In such patients, longer and more frequent depressive episodes appear to increase vulnerability for further episodes, precipitating an accelerating and progressive illness course leading to functional decline. Evidence from clinical, biochemical and neuroimaging studies appear to support this model and are informing novel therapeutic approaches. This paper reviews current knowledge of the neuroprogressive processes that may occur in MDD, including structural brain consequences and potential molecular mechanisms including the role of neurotransmitter systems, inflammatory, oxidative and nitrosative stress pathways, neurotrophins and regulation of neurogenesis, cortisol and the hypothalamic-pituitary-adrenal axis modulation, mitochondrial dysfunction and epigenetic and dietary influences. Evidence-based novel treatments informed by this knowledge are discussed.
Genetic correlations are the genome-wide aggregate effects of causal variants affecting multiple traits. Traditionally, genetic correlations between complex traits are estimated from pedigree ...studies, but such estimates can be confounded by shared environmental factors. Moreover, for diseases, low prevalence rates imply that even if the true genetic correlation between disorders was high, co-aggregation of disorders in families might not occur or could not be distinguished from chance. We have developed and implemented statistical methods based on linear mixed models to obtain unbiased estimates of the genetic correlation between pairs of quantitative traits or pairs of binary traits of complex diseases using population-based case-control studies with genome-wide single-nucleotide polymorphism data. The method is validated in a simulation study and applied to estimate genetic correlation between various diseases from Wellcome Trust Case Control Consortium data in a series of bivariate analyses. We estimate a significant positive genetic correlation between risk of Type 2 diabetes and hypertension of ~0.31 (SE 0.14, P = 0.024).
Our methods, appropriate for both quantitative and binary traits, are implemented in the freely available software GCTA (http://www.complextraitgenomics.com/software/gcta/reml_bivar.html).
hong.lee@uq.edu.au
Supplementary data are available at Bioinformatics online.
Abstract
Summary
Genome-wide association study (GWAS) analyses, at sufficient sample sizes and power, have successfully revealed biological insights for several complex traits. RICOPILI, an ...open-sourced Perl-based pipeline was developed to address the challenges of rapidly processing large-scale multi-cohort GWAS studies including quality control (QC), imputation and downstream analyses. The pipeline is computationally efficient with portability to a wide range of high-performance computing environments. RICOPILI was created as the Psychiatric Genomics Consortium pipeline for GWAS and adopted by other users. The pipeline features (i) technical and genomic QC in case-control and trio cohorts, (ii) genome-wide phasing and imputation, (iv) association analysis, (v) meta-analysis, (vi) polygenic risk scoring and (vii) replication analysis. Notably, a major differentiator from other GWAS pipelines, RICOPILI leverages on automated parallelization and cluster job management approaches for rapid production of imputed genome-wide data. A comprehensive meta-analysis of simulated GWAS data has been incorporated demonstrating each step of the pipeline. This includes all the associated visualization plots, to allow ease of data interpretation and manuscript preparation. Simulated GWAS datasets are also packaged with the pipeline for user training tutorials and developer work.
Availability and implementation
RICOPILI has a flexible architecture to allow for ongoing development and incorporation of newer available algorithms and is adaptable to various HPC environments (QSUB, BSUB, SLURM and others). Specific links for genomic resources are either directly provided in this paper or via tutorials and external links. The central location hosting scripts and tutorials is found at this URL: https://sites.google.com/a/broadinstitute.org/RICOPILI/home
Supplementary information
Supplementary data are available at Bioinformatics online.
In this article, we review some of the data that contribute to our understanding of the genetic architecture of psychiatric disorders. These include results from evolutionary modelling (hence no ...data), the observed recurrence risk to relatives and data from molecular markers. We briefly discuss the common-disease common-variant hypothesis, the success (or otherwise) of genome-wide association studies, the evidence for polygenic variance and the likely success of exome and whole-genome sequencing studies. We conclude that the perceived dichotomy between 'common' and 'rare' variants is not only false, but unhelpful in making progress towards increasing our understanding of the genetic basis of psychiatric disorders. Strong evidence has been accumulated that is consistent with the contribution of many genes to risk of disease, across a wide range of allele frequencies and with a substantial proportion of genetic variation in the population in linkage disequilibrium with single-nucleotide polymorphisms (SNPs) on commercial genotyping arrays. At the same time, most causal variants that segregate in the population are likely to be rare and in total these variants also explain a significant proportion of genetic variation. It is the combination of allele frequency, effect size and functional characteristics that will determine the success of new experimental paradigms such as whole exome/genome sequencing to detect such loci. Empirical results suggest that roughly half the genetic variance is tagged by SNPs on commercial genome-wide chips, but that individual causal variants have a small effect size, on average. We conclude that larger experimental sample sizes are essential to further our understanding of the biology underlying psychiatric disorders.
Brain age predicts mortality Cole, J H; Ritchie, S J; Bastin, M E ...
Molecular psychiatry,
05/2018, Letnik:
23, Številka:
5
Journal Article
Recenzirano
Odprti dostop
Age-associated disease and disability are placing a growing burden on society. However, ageing does not affect people uniformly. Hence, markers of the underlying biological ageing process are needed ...to help identify people at increased risk of age-associated physical and cognitive impairments and ultimately, death. Here, we present such a biomarker, 'brain-predicted age', derived using structural neuroimaging. Brain-predicted age was calculated using machine-learning analysis, trained on neuroimaging data from a large healthy reference sample (N=2001), then tested in the Lothian Birth Cohort 1936 (N=669), to determine relationships with age-associated functional measures and mortality. Having a brain-predicted age indicative of an older-appearing brain was associated with: weaker grip strength, poorer lung function, slower walking speed, lower fluid intelligence, higher allostatic load and increased mortality risk. Furthermore, while combining brain-predicted age with grey matter and cerebrospinal fluid volumes (themselves strong predictors) not did improve mortality risk prediction, the combination of brain-predicted age and DNA-methylation-predicted age did. This indicates that neuroimaging and epigenetics measures of ageing can provide complementary data regarding health outcomes. Our study introduces a clinically-relevant neuroimaging ageing biomarker and demonstrates that combining distinct measurements of biological ageing further helps to determine risk of age-related deterioration and death.
As custom arrays are cheaper than generic GWAS arrays, larger sample size is achievable for gene discovery. Custom arrays can tag more variants through denser genotyping of SNPs at associated loci, ...but at the cost of losing genome-wide coverage. Balancing this trade-off is important for maximizing experimental designs. We quantified both the gain in captured SNP-heritability at known candidate regions and the loss due to imperfect genome-wide coverage for inflammatory bowel disease using immunochip (iChip) and imputed GWAS data on 61,251 and 38.550 samples, respectively. For Crohn's disease (CD), the iChip and GWAS data explained 19 and 26% of variation in liability, respectively, and SNPs in the densely genotyped iChip regions explained 13% of the SNP-heritability for both the iChip and GWAS data. For ulcerative colitis (UC), the iChip and GWAS data explained 15 and 19% of variation in liability, respectively, and the dense iChip regions explained 10 and 9% of the SNP-heritability in the iChip and the GWAS data. From bivariate analyses, estimates of the genetic correlation in risk between CD and UC were 0.75 (SE 0.017) and 0.62 (SE 0.042) for the iChip and GWAS data, respectively. We also quantified the SNP-heritability of genomic regions that did or did not contain the previous 163 GWAS hits for CD and UC, and SNP-heritability of the overlapping loci between the densely genotyped iChip regions and the 163 GWAS hits. For both diseases, over different genomic partitioning, the densely genotyped regions on the iChip tagged at least as much variation in liability as in the corresponding regions in the GWAS data, however a certain amount of tagged SNP-heritability in the GWAS data was lost using the iChip due to the low coverage at unselected regions. These results imply that custom arrays with a GWAS backbone will facilitate more gene discovery, both at associated and novel loci.
Common diseases such as endometriosis (ED), Alzheimer's disease (AD) and multiple sclerosis (MS) account for a significant proportion of the health care burden in many countries. Genome-wide ...association studies (GWASs) for these diseases have identified a number of individual genetic variants contributing to the risk of those diseases. However, the effect size for most variants is small and collectively the known variants explain only a small proportion of the estimated heritability. We used a linear mixed model to fit all single nucleotide polymorphisms (SNPs) simultaneously, and estimated genetic variances on the liability scale using SNPs from GWASs in unrelated individuals for these three diseases. For each of the three diseases, case and control samples were not all genotyped in the same laboratory. We demonstrate that a careful analysis can obtain robust estimates, but also that insufficient quality control (QC) of SNPs can lead to spurious results and that too stringent QC is likely to remove real genetic signals. Our estimates show that common SNPs on commercially available genotyping chips capture significant variation contributing to liability for all three diseases. The estimated proportion of total variation tagged by all SNPs was 0.26 (SE 0.04) for ED, 0.24 (SE 0.03) for AD and 0.30 (SE 0.03) for MS. Further, we partitioned the genetic variance explained into five categories by a minor allele frequency (MAF), by chromosomes and gene annotation. We provide strong evidence that a substantial proportion of variation in liability is explained by common SNPs, and thereby give insights into the genetic architecture of the diseases.
The availability of genome-wide genetic data on hundreds of thousands of people has led to an equally rapid growth in methodologies available to analyse these data. While the motivation for ...undertaking genome-wide association studies (GWAS) is identification of genetic markers associated with complex traits, once generated these data can be used for many other analyses. GWAS have demonstrated that complex traits exhibit a highly polygenic genetic architecture, often with shared genetic risk factors across traits. New methods to analyse data from GWAS are increasingly being used to address a diverse set of questions about the aetiology of complex traits and diseases, including psychiatric disorders. Here, we give an overview of some of these methods and present examples of how they have contributed to our understanding of psychiatric disorders. We consider: (i) estimation of the extent of genetic influence on traits, (ii) uncovering of shared genetic control between traits, (iii) predictions of genetic risk for individuals, (iv) uncovering of causal relationships between traits, (v) identifying causal single-nucleotide polymorphisms and genes or (vi) the detection of genetic heterogeneity. This classification helps organise the large number of recently developed methods, although some could be placed in more than one category. While some methods require GWAS data on individual people, others simply use GWAS summary statistics data, allowing novel well-powered analyses to be conducted at a low computational burden.
Genetic studies in adults indicate that genes influencing the personality trait of neuroticism account for substantial genetic variance in anxiety and depression and in somatic health. Here, we ...examine for the first time the factors underlying the relationship between neuroticism and anxiety/depressive and somatic symptoms during adolescence.
The Somatic and Psychological Health Report (SPHERE) assessed symptoms of anxiety/depression (PSYCH-14) and somatic distress (SOMA-10) in 2459 adolescent and young adult twins 1168 complete pairs (35.4% monozygotic, 53% female) aged 12-25 years (mean=15.5 ± 2.9). Differences between boys and girls across adolescence were explored for neuroticism, SPHERE-34, and the subscales PSYCH-14 and SOMA-10. Trivariate analyses partitioned sources of covariance in neuroticism, PSYCH-14 and SOMA-10.
Girls scored higher than boys on both neuroticism and SPHERE, with SPHERE scores for girls increasing slightly over time, whereas scores for boys decreased or were unchanged. Neuroticism and SPHERE scores were strongly influenced by genetic factors heritability (h(2)) = 40-52%. A common genetic source influenced neuroticism, PSYCH-14 and SOMA-10 (impacting PSYCH-14 more than SOMA-10). A further genetic source, independent of neuroticism, accounted for covariation specific to PSYCH-14 and SOMA-10. Environmental influences were largely specific to each measure.
In adolescence, genetic risk factors indexed by neuroticism contribute substantially to anxiety/depression and, to a lesser extent, perceived somatic health. Additional genetic covariation between anxiety/depressive and somatic symptoms, independent of neuroticism, had greatest influence on somatic distress, where it was equal in influence to the factor shared with neuroticism.
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