Genome-wide association studies have shown unequivocally that common complex disorders have a polygenic genetic architecture and have enabled researchers to identify genetic variants associated with ...diseases. These variants can be combined into a polygenic risk score that captures part of an individual's susceptibility to diseases. Polygenic risk scores have been widely applied in research studies, confirming the association between the scores and disease status, but their clinical utility has yet to be established. Polygenic risk scores may be used to estimate an individual's lifetime genetic risk of disease, but the current discriminative ability is low in the general population. Clinical implementation of polygenic risk score (PRS) may be useful in cohorts where there is a higher prior probability of disease, for example, in early stages of diseases to assist in diagnosis or to inform treatment choices. Important considerations are the weaker evidence base in application to non-European ancestry and the challenges in translating an individual's PRS from a percentile of a normal distribution to a lifetime disease risk. In this review, we consider how PRS may be informative at different points in the disease trajectory giving examples of progress in the field and discussing obstacles that need to be addressed before clinical implementation.
PRSice: Polygenic Risk Score software Euesden, Jack; Lewis, Cathryn M; O'Reilly, Paul F
Bioinformatics,
05/2015, Letnik:
31, Številka:
9
Journal Article
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A polygenic risk score (PRS) is a sum of trait-associated alleles across many genetic loci, typically weighted by effect sizes estimated from a genome-wide association study. The application of PRS ...has grown in recent years as their utility for detecting shared genetic aetiology among traits has become appreciated; PRS can also be used to establish the presence of a genetic signal in underpowered studies, to infer the genetic architecture of a trait, for screening in clinical trials, and can act as a biomarker for a phenotype. Here we present the first dedicated PRS software, PRSice ('precise'), for calculating, applying, evaluating and plotting the results of PRS. PRSice can calculate PRS at a large number of thresholds ("high resolution") to provide the best-fit PRS, as well as provide results calculated at broad P-value thresholds, can thin Single Nucleotide Polymorphisms (SNPs) according to linkage disequilibrium and P-value or use all SNPs, handles genotyped and imputed data, can calculate and incorporate ancestry-informative variables, and can apply PRS across multiple traits in a single run. We exemplify the use of PRSice via application to data on schizophrenia, major depressive disorder and smoking, illustrate the importance of identifying the best-fit PRS and estimate a P-value significance threshold for high-resolution PRS studies.
PRSice is written in R, including wrappers for bash data management scripts and PLINK-1.9 to minimize computational time. PRSice runs as a command-line program with a variety of user-options, and is freely available for download from http://PRSice.info
jack.euesden@kcl.ac.uk or paul.oreilly@kcl.ac.uk
Supplementary data are available at Bioinformatics online.
Genome-wide association studies have made strides in identifying common variation associated with disease. The modest effect sizes preclude risk prediction based on single genetic variants, but ...polygenic risk scores that combine thousands of variants show some predictive ability across a range of complex traits and diseases, including neuropsychiatric disorders. Here, we consider the potential for translation to clinical use.
Cannabis use has been reported to induce long-lasting psychotic disorders and a dose-response relationship has been observed. We performed a systematic review of studies that investigate the ...association between the degree of cannabis consumption and psychosis and a meta-analysis to quantify the magnitude of effect. Published studies were identified through search of electronic databases, supplemented by manual searches of bibliographies. Studies were considered if they provided data on cannabis consumption prior to the onset of psychosis using a dose criterion (frequency/amount used) and reported psychosis-related outcomes. We performed random effects meta-analysis of individual data points generated with a simulation method from the summary data of the original studies. From 571 references, 18 studies fulfilled inclusion criteria for the systematic review and 10 were inserted in the meta-analysis, enrolling a total of 66 816 individuals. Higher levels of cannabis use were associated with increased risk for psychosis in all the included studies. A logistic regression model gave an OR of 3.90 (95% CI 2.84 to 5.34) for the risk of schizophrenia and other psychosis-related outcomes among the heaviest cannabis users compared to the nonusers. Current evidence shows that high levels of cannabis use increase the risk of psychotic outcomes and confirms a dose-response relationship between the level of use and the risk for psychosis. Although a causal link cannot be unequivocally established, there is sufficient evidence to justify harm reduction prevention programs.
The predictive utility of polygenic scores is increasing, and many polygenic scoring methods are available, but it is unclear which method performs best. This study evaluates the predictive utility ...of polygenic scoring methods within a reference-standardized framework, which uses a common set of variants and reference-based estimates of linkage disequilibrium and allele frequencies to construct scores. Eight polygenic score methods were tested: p-value thresholding and clumping (pT+clump), SBLUP, lassosum, LDpred1, LDpred2, PRScs, DBSLMM and SBayesR, evaluating their performance to predict outcomes in UK Biobank and the Twins Early Development Study (TEDS). Strategies to identify optimal p-value thresholds and shrinkage parameters were compared, including 10-fold cross validation, pseudovalidation and infinitesimal models (with no validation sample), and multi-polygenic score elastic net models. LDpred2, lassosum and PRScs performed strongly using 10-fold cross-validation to identify the most predictive p-value threshold or shrinkage parameter, giving a relative improvement of 16-18% over pT+clump in the correlation between observed and predicted outcome values. Using pseudovalidation, the best methods were PRScs, DBSLMM and SBayesR. PRScs pseudovalidation was only 3% worse than the best polygenic score identified by 10-fold cross validation. Elastic net models containing polygenic scores based on a range of parameters consistently improved prediction over any single polygenic score. Within a reference-standardized framework, the best polygenic prediction was achieved using LDpred2, lassosum and PRScs, modeling multiple polygenic scores derived using multiple parameters. This study will help researchers performing polygenic score studies to select the most powerful and predictive analysis methods.
There have been several recent studies addressing the genetic architecture of depression. This review serves to take stock of what is known now about the genetics of depression, how it has increased ...our knowledge and understanding of its mechanisms, and how the information and knowledge can be leveraged to improve the care of people affected. We identify four priorities for how the field of MD genetics research may move forward in future years, namely by increasing the sample sizes available for genome-wide association studies (GWASs), greater inclusion of diverse ancestries and low-income countries, the closer integration of psychiatric genetics with electronic medical records, and the development of the neuroscience toolkit for polygenic disorders.
A review by McIntosh et al. takes stock of recent rapid progress in the genetics of depression, how it has increased our mechanistic understanding, and how this information could be used to improve patient care in future.
Depression and the autoimmune disorders are comorbid-the two classes of disorders overlap in the same individuals at a higher frequency than chance. The immune system may influence the pathological ...processes underlying depression; understanding the origins of this comorbidity may contribute to dissecting the mechanisms underlying these disorders.
We used population cohort data from the 1958 British birth cohort study (the National Child Development Study) to investigate the ages at onset of depression and 23 autoimmune disorders. We used self-report data to ascertain life-time history of depression, autoimmune disorders and their ages at onset. We modelled the effect of depression onset on subsequent autoimmune disorder onset, and vice versa, and incorporated polygenic risk scores for depression and autoimmune disorder risk.
In our analytic sample of 8174 individuals, 315 reported ever being diagnosed with an autoimmune disorder (3.9%), 1499 reported ever experiencing depression (18.3%). There was significant comorbidity between depression and the autoimmune disorders (OR = 1.66, 95% CI = 1.27-2.15). Autoimmune disorder onset associated with increased subsequent hazard of depression onset (HR = 1.39, 95% CI = 1.11-1.74, P = 0.0037), independently of depression genetic risk. Finally, depression increased subsequent hazard of autoimmune disorder onset (HR = 1.40, 95% CI = 1.09-1.80, P = 0.0095), independently of autoimmune disorder genetic risk.
Our results point to a bidirectional relationship between depression and the autoimmune disorders. This suggests that shared risk factors may contribute to this relationship, including both common environmental exposures that increase baseline inflammation levels, and shared genetic factors.
Risk stratification is an important public health priority that is central to clinical decision making and resource allocation. The aim of this study was to examine how different combinations of ...self-rated and objective health status predict all-cause mortality and leading causes of death in the UK. The UK Biobank study recruited > 500,000 participants between 2006 and 2010. Self-rated health was assessed using a single-item question and health status was derived from medical history, including data on 81 cancer and 443 non-cancer illnesses. Analyses included > 370,000 middle-aged and older adults with a median follow-up of 11.75 (IQR = 1.4) years, yielding 4,320,270 person-years of follow-up. Compared to individuals with excellent self-rated health and favourable health status, individuals with other combinations of self-rated and objective health status had a greater mortality risk, with hazard ratios ranging from HR = 1.22 (95% CI 1.15-1.29, P
< 0.001) for individuals with good self-rated health and favourable health status to HR = 7.14 (95% CI 6.70-7.60, P
< 0.001) for individuals with poor self-rated health and unfavourable health status. Our findings highlight that self-rated health captures additional health-related information and should be more widely assessed. The cross-classification between self-rated health and health status represents a straightforward metric for risk stratification, with applications to population health, clinical decision making and resource allocation.
Major depression (MD) is determined by a multitude of factors including genetic risk variants that regulate gene expression. We examined the genetic component of gene expression in MD by performing a ...transcriptome-wide association study (TWAS), inferring gene expression–trait relationships from genetic, transcriptomic, and phenotypic information.
Genes differentially expressed in depression were identified with the TWAS FUSION method, based on summary statistics from the largest genome-wide association analysis of MD (n = 135,458 cases, n = 344,901 controls) and gene expression levels from 21 tissue datasets (brain; blood; thyroid, adrenal, and pituitary glands). Follow-up analyses were performed to extensively characterize the identified associations: colocalization, conditional, and fine-mapping analyses together with TWAS-based pathway investigations.
Transcriptome-wide significant differences between cases and controls were found at 94 genes, approximately half of which were novel. Of the 94 significant genes, 6 represented strong, colocalized, and potentially causal associations with depression. Such high-confidence associations include NEGR1, CTC-467M3.3, TMEM106B, LRFN5, ESR2, and PROX2. Lastly, TWAS-based enrichment analysis highlighted dysregulation of gene sets for, among others, neuronal and synaptic processes.
This study sheds further light on the genetic component of gene expression in depression by characterizing the identified associations, unraveling novel risk genes, and determining which associations are congruent with a causal model. These findings can be used as a resource for prioritizing and designing subsequent functional studies of MD.
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