Imputed gene expression risk scores: a functionally informed component of polygenic risk

Susceptibility gene identification and risk evaluation model construction by transcriptome-wide association analysis for salt sensitivity of blood pressure

BACKGROUND

Salt sensitivity of blood pressure (SSBP) is an intermediate phenotype of hypertension and is a predictor of long-term cardiovascular events and death. However, the genetic structures of SSBP are uncertain, and it is difficult to precisely diagnose SSBP in population. So, we aimed to identify genes related to susceptibility to the SSBP, construct a risk evaluation model, and explore the potential functions of these genes.

METHODS AND RESULTS

A genome-wide association study of the systemic epidemiology of salt sensitivity (EpiSS) cohort was performed to obtain summary statistics for SSBP. Then, we conducted a transcriptome-wide association study (TWAS) of 12 tissues using FUSION software to predict the genes associated with SSBP and verified the genes with an mRNA microarray. The potential roles of the genes were explored. Risk evaluation models of SSBP were constructed based on the serial P value thresholds of polygenetic risk scores (PRSs), polygenic transcriptome risk scores (PTRSs) and their combinations of the identified genes and genetic variants from the TWAS. The TWAS revealed that 2605 genes were significantly associated with SSBP. Among these genes, 69 were differentially expressed according to the microarray analysis. The functional analysis showed that the genes identified in the TWAS were enriched in metabolic process pathways. The PRSs were correlated with PTRSs in the heart atrial appendage, adrenal gland, EBV-transformed lymphocytes, pituitary, artery coronary, artery tibial and whole blood. Multiple logistic regression models revealed that a PRS of P < 0.05 had the best predictive ability compared with other PRSs and PTRSs. The combinations of PRSs and PTRSs did not significantly increase the prediction accuracy of SSBP in the training and validation datasets.

CONCLUSIONS

Several known and novel susceptibility genes for SSBP were identified via multitissue TWAS analysis. The risk evaluation model constructed with the PRS of susceptibility genes showed better diagnostic performance than the transcript levels, which could be applied to screen for SSBP high-risk individuals.

Integrating human endogenous retroviruses into transcriptome-wide association studies highlights novel risk factors for major psychiatric conditions

Human endogenous retroviruses (HERVs) are repetitive elements previously implicated in major psychiatric conditions, but their role in aetiology remains unclear. Here, we perform specialised transcriptome-wide association studies that consider HERV expression quantified to precise genomic locations, using RNA sequencing and genetic data from 792 post-mortem brain samples. In Europeans, we identify 1238 HERVs with expression regulated in cis, of which 26 represent expression signals associated with psychiatric disorders, with ten being conditionally independent from neighbouring expression signals. Of these, five are additionally significant in fine-mapping analyses and thus are considered high confidence risk HERVs. These include two HERV expression signatures specific to schizophrenia risk, one shared between schizophrenia and bipolar disorder, and one specific to major depressive disorder. No robust signatures are identified for autism spectrum conditions or attention deficit hyperactivity disorder in Europeans, or for any psychiatric trait in other ancestries, although this is likely a result of relatively limited statistical power. Ultimately, our study highlights extensive HERV expression and regulation in the adult cortex, including in association with psychiatric disorder risk, therefore providing a rationale for exploring neurological HERV expression in complex neuropsychiatric traits.

Gene expression imputation provides clinical and biological insights into treatment-resistant schizophrenia polygenic risk

Genome-wide association studies (GWAS) have revealed the polygenic nature of treatment-resistant schizophrenia TRS. Gene expression imputation allowed the translation of GWAS results into regulatory mechanisms and the construction of gene expression (GReX) risk scores (GReX-RS).  In the present study we computed GReX-RS from the largest GWAS of TRS to assess its association with clinical features. We perform transcriptome imputation in the largest GWAS of TRS to find GReX associated with TRS using brain tissues. Then, for each tissue, we constructed a GReX-RS of the identified genes in a sample of 254 genotyped first episode of psychosis (FEP) patients to test its association with clinical phenotypes, including clinical symptomatology, global functioning and cognitive performance. Our analysis provides evidence that the polygenic basis of TRS includes genetic variants that modulate the expression of certain genes in certain brain areas (substantia nigra, hippocampus, amygdala and frontal cortex), which at the same time are related to clinical features in FEP patients, mainly persistence of negative symptoms and cognitive alterations in sustained attention, which have also been suggested as clinical predictors of TRS. Our results provide a clinical explanation of the polygenic architecture of TRS and give more insight into the biological mechanisms underlying TRS.

Metabolomic Investigation of Major Depressive Disorder Identifies a Potentially Causal Association With Polyunsaturated Fatty Acids

BACKGROUND

Metabolic differences have been reported between individuals with and without major depressive disorder (MDD), but their consistency and causal relevance have been unclear.

METHODS

We conducted a metabolome-wide association study of MDD with 249 metabolomic measures available in the UK Biobank (n = 29,757). We then applied two-sample bidirectional Mendelian randomization and colocalization analysis to identify potentially causal relationships between each metabolite and MDD.

RESULTS

A total of 191 metabolites tested were significantly associated with MDD (false discovery rate-corrected p < .05), which decreased to 129 after adjustment for likely confounders. Lower abundance of omega-3 fatty acid measures and a higher omega-6 to omega-3 ratio showed potentially causal effects on liability to MDD. There was no evidence of a causal effect of MDD on metabolite levels. Furthermore, genetic signals associated with docosahexaenoic acid colocalized with loci associated with MDD within the fatty acid desaturase gene cluster. Post hoc Mendelian randomization of gene-transcript abundance within the fatty acid desaturase cluster demonstrated a potentially causal association with MDD. In contrast, colocalization analysis did not suggest a single causal variant for both transcript abundance and MDD liability, but rather the likely existence of two variants in linkage disequilibrium with one another.

CONCLUSIONS

Our findings suggest that decreased docosahexaenoic acid and increased omega-6 to omega-3 fatty acids ratio may be causally related to MDD. These findings provide further support for the causal involvement of fatty acids in MDD.

Evaluating the Classification Accuracy of Expression Quantitative Trait Loci Calculated Polygenic Risk Scores in Alzheimer's Disease

Polygenic risk scores (PRS) hold promise for the early identification of those at risk from neurodegenerative disorders such as Alzheimer's Disease (AD), allowing for intervention to occur prior to neuronal damage. The current selection of informative single nucleotide polymorphisms (SNPs) to generate the risk scores is based on the modelling of large genome-wide association data using significance thresholds. However, the biological relevance of these SNPs is largely unknown. This study, in contrast, aims to identify SNPs with biological relevance to AD and then assess them for their ability to accurately classify cases and controls. Samples selected from the Brains for Dementia Research (BDR) were used to produce gene expression data to identify potential expression quantitative trait loci (eQTLs) relevant to AD. These SNPs were then incorporated into a PRS model to classify AD and controls in the full BDR cohort. Models derived from these eQTLs demonstrate modest classification potential with an accuracy between 61% and 67%. Although the model accuracy is not as high as some values in the literature based on significance thresholds from genome-wide association studies, these models may reflect a more biologically relevant model, which may provide novel targets for therapeutic intervention.

Transcriptomic risk scores for attention deficit/hyperactivity disorder

Attention deficit/hyperactivity disorder (ADHD) is a highly heritable neurodevelopmental disorder. We performed a transcriptome-wide association study (TWAS) using the latest genome-wide association study (GWAS) meta-analysis, in 38,691 individuals with ADHD and 186,843 controls, and 14 gene-expression reference panels across multiple brain tissues and whole blood. Based on TWAS results, we selected subsets of genes and constructed transcriptomic risk scores (TRSs) for the disorder in peripheral blood mononuclear cells of individuals with ADHD and controls. We found evidence of association between ADHD and TRSs constructed using expression profiles from multiple brain areas, with individuals with ADHD carrying a higher burden of TRSs than controls. TRSs were uncorrelated with the polygenic risk score (PRS) for ADHD and, in combination with PRS, improved significantly the proportion of variance explained over the PRS-only model. These results support the complementary predictive potential of genetic and transcriptomic profiles in blood and underscore the potential utility of gene expression for risk prediction and deeper insight in molecular mechanisms underlying ADHD.

Imputed Gene Expression versus Single Nucleotide Polymorphism in Predicting Gray Matter Phenotypes

Genetics plays an important role in psychiatric disorders. A clinically relevant question is whether we can predict psychiatric traits from genetics, which holds promise for early detection and tailored intervention. Imputed gene expression, also known as genetically-regulated expression (GRE), reflects the tissue-specific regulatory effects of multiple single nucleotide polymorphisms (SNPs) on genes. In this work, we explored the utility of GRE for trait association studies and how the GRE-based polygenic risk score (gPRS) compared with SNP-based PRS (sPRS) in predicting psychiatric traits. A total of 13 schizophrenia-related gray matter networks identified in another study served as the target brain phenotypes for assessing genetic associations and prediction accuracies in 34,149 individuals from the UK Biobank cohort. GRE was computed leveraging MetaXcan and GTEx tools for 56,348 genes across 13 available brain tissues. We then estimated the effects of individual SNPs and genes separately on each tested brain phenotype in the training set. The effect sizes were then used to compute gPRS and sPRS in the testing set, whose correlations with the brain phenotypes were used to assess the prediction accuracy. The results showed that, with the testing sample size set to 1,138, for training sample sizes from 1,138 up to 33,011, overall both gPRS and sPRS successfully predicted the brain phenotypes with significant correlations observed in the testing set, and higher accuracies noted for larger training sets. In addition, gPRS outperformed sPRS by showing significantly higher prediction accuracies across 13 brain phenotypes, with greater improvement noted for training sample sizes below ∼15,000. These findings support that GRE may serve as the primary genetic variable in brain phenotype association and prediction studies. Future imaging genetic studies may consider GRE as an option depending on the available sample size.

A new polygenic score for refractive error improves detection of children at risk of high myopia but not the prediction of those at risk of myopic macular degeneration

BACKGROUND

High myopia (HM), defined as a spherical equivalent refractive error (SER) ≤ -6.00 diopters (D), is a leading cause of sight impairment, through myopic macular degeneration (MMD). We aimed to derive an improved polygenic score (PGS) for predicting children at risk of HM and to test if a PGS is predictive of MMD after accounting for SER.

METHODS

The PGS was derived from genome-wide association studies in participants of UK Biobank, CREAM Consortium, and Genetic Epidemiology Research on Adult Health and Aging. MMD severity was quantified by a deep learning algorithm. Prediction of HM was quantified as the area under the receiver operating curve (AUROC). Prediction of severe MMD was assessed by logistic regression.

FINDINGS

In independent samples of European, African, South Asian and East Asian ancestry, the PGS explained 19% (95% confidence interval 17-21%), 2% (1-3%), 8% (7-10%) and 6% (3-9%) of the variation in SER, respectively. The AUROC for HM in these samples was 0.78 (0.75-0.81), 0.58 (0.53-0.64), 0.71 (0.69-0.74) and 0.67 (0.62-0.72), respectively. The PGS was not associated with the risk of MMD after accounting for SER: OR = 1.07 (0.92-1.24).

INTERPRETATION

Performance of the PGS approached the level required for clinical utility in Europeans but not in other ancestries. A PGS for refractive error was not predictive of MMD risk once SER was accounted for.

FUNDING

Supported by the Welsh Government and Fight for Sight (24WG201).

Examining the biological mechanisms of human mental disorders resulting from gene-environment interdependence using novel functional genomic approaches

We explore how functional genomics approaches that integrate datasets from human and non-human model systems can improve our understanding of the effect of gene-environment interplay on the risk for mental disorders. We start by briefly defining the G-E paradigm and its challenges and then discuss the different levels of regulation of gene expression and the corresponding data existing in humans (genome wide genotyping, transcriptomics, DNA methylation, chromatin modifications, chromosome conformational changes, non-coding RNAs, proteomics and metabolomics), discussing novel approaches to the application of these data in the study of the origins of mental health. Finally, we discuss the multilevel integration of diverse types of data. Advance in the use of functional genomics in the context of a G-E perspective improves the detection of vulnerabilities, informing the development of preventive and therapeutic interventions.

Polygenic transcriptome risk scores (PTRS) can improve portability of polygenic risk scores across ancestries

BACKGROUND

Polygenic risk scores (PRS) are valuable to translate the results of genome-wide association studies (GWAS) into clinical practice. To date, most GWAS have been based on individuals of European-ancestry leading to poor performance in populations of non-European ancestry.

RESULTS

We introduce the polygenic transcriptome risk score (PTRS), which is based on predicted transcript levels (rather than SNPs), and explore the portability of PTRS across populations using UK Biobank data.

CONCLUSIONS

We show that PTRS has a significantly higher portability (Wilcoxon p=0.013) in the African-descent samples where the loss of performance is most acute with better performance than PRS when used in combination.

Bench Research Informed by GWAS Results

Scientifically interesting as well as practically important phenotypes often belong to the realm of complex traits. To the extent that these traits are hereditary, they are usually 'highly polygenic'. The study of such traits presents a challenge for researchers, as the complex genetic architecture of such traits makes it nearly impossible to utilise many of the usual methods of reverse genetics, which often focus on specific genes. In recent years, thousands of genome-wide association studies (GWAS) were undertaken to explore the relationships between complex traits and a large number of genetic factors, most of which are characterised by tiny effects. In this review, we aim to familiarise 'wet biologists' with approaches for the interpretation of GWAS results, to clarify some issues that may seem counterintuitive and to assess the possibility of using GWAS results in experiments on various complex traits.