Mitigating type 1 error inflation and power loss in GxE PRS: Genotype-environment interaction in polygenic risk score models.

The use of polygenic risk score (PRS) models has transformed the field of genetics by enabling the prediction of complex traits and diseases based on an individual's genetic profile. However, the impact of genotype-environment interaction (GxE) on the performance and applicability of PRS models remains a crucial aspect to be explored. Currently, existing genotype-environment interaction polygenic risk score (GxE PRS) models are often inappropriately used, which can result in inflated type 1 error rates and compromised results. In this study, we propose novel GxE PRS models that jointly incorporate additive and interaction genetic effects although also including an additional quadratic term for nongenetic covariates, enhancing their robustness against model misspecification. Through extensive simulations, we demonstrate that our proposed models outperform existing models in terms of controlling type 1 error rates and enhancing statistical power. Furthermore, we apply the proposed models to real data, and report significant GxE effects. Specifically, we highlight the impact of our models on both quantitative and binary traits. For quantitative traits, we uncover the GxE modulation of genetic effects on body mass index by alcohol intake frequency. In the case of binary traits, we identify the GxE modulation of genetic effects on hypertension by waist-to-hip ratio. These findings underscore the importance of employing a robust model that effectively controls type 1 error rates, thus preventing the occurrence of spurious GxE signals. To facilitate the implementation of our approach, we have developed an innovative R software package called GxEprs, specifically designed to detect and estimate GxE effects. Overall, our study highlights the importance of accurate GxE modeling and its implications for genetic risk prediction, although providing a practical tool to support further research in this area.

Cross-ancestry genetic architecture and prediction for cholesterol traits.

While cholesterol is essential, a high level of cholesterol is associated with the risk of cardiovascular diseases. Genome-wide association studies (GWASs) have proven successful in identifying genetic variants that are linked to cholesterol levels, predominantly in white European populations. However, the extent to which genetic effects on cholesterol vary across different ancestries remains largely unexplored. Here, we estimate cross-ancestry genetic correlation to address questions on how genetic effects are shared across ancestries. We find significant genetic heterogeneity between ancestries for cholesterol traits. Furthermore, we demonstrate that single nucleotide polymorphisms (SNPs) with concordant effects across ancestries for cholesterol are more frequently found in regulatory regions compared to other genomic regions. Indeed, the positive genetic covariance between ancestries is mostly driven by the effects of the concordant SNPs, whereas the genetic heterogeneity is attributed to the discordant SNPs. We also show that the predictive ability of the concordant SNPs is significantly higher than the discordant SNPs in the cross-ancestry polygenic prediction. The list of concordant SNPs for cholesterol is available in GWAS Catalog. These findings have relevance for the understanding of shared genetic architecture across ancestries, contributing to the development of clinical strategies for polygenic prediction of cholesterol in cross-ancestral settings.

A Metabolome-Wide Mendelian Randomization Study Identifies Dysregulated Arachidonic Acid Synthesis as a Potential Causal Risk Factor for Bipolar Disorder.

BACKGROUND: Bipolar disorder (BPD) is a debilitating mood disorder with an unclear etiology. A better understanding of the underlying pathophysiological mechanisms will help to identify novel targets for improved treatment options and prevention strategies. In this metabolome-wide Mendelian randomization study, we screened for metabolites that may have a causal role in BPD. METHODS: We tested a total of 913 circulating metabolite exposures assessed in 14,296 Europeans using a mass spectrometry-based platform. For the BPD outcome, we used summary data from the largest and most recent genome-wide association study reported to date, including 41,917 BPD cases. RESULTS: We identified 33 metabolites associated with BPD (padjusted < 5.48 × 10-5). Most of them were lipids, including arachidonic acid (ß = -0.154, SE = 0.023, p = 3.30 × 10-11), a polyunsaturated omega-6 fatty acid, along with several complex lipids containing either an arachidonic or a linoleic fatty acid side chain. These associations did not extend to other closely related psychiatric disorders like schizophrenia or depression, although they may be involved in the regulation of lithium response. These lipid associations were driven by genetic variants within the FADS1/2/3 gene cluster, which is a robust BPD risk locus encoding a family of fatty acid desaturase enzymes that are responsible for catalyzing the conversion of linoleic acid into arachidonic acid. Statistical colocalization analyses indicated that 27 of the 33 metabolites shared the same genetic etiology with BPD at the FADS1/2/3 cluster, demonstrating that our findings are not confounded by linkage disequilibrium. CONCLUSIONS: Overall, our findings support the notion that arachidonic acid and other polyunsaturated fatty acids may represent potential targets for BPD.

In-silico functional analyses identify TMPRSS15-mediated intestinal absorption of lithium as a modulator of lithium response in bipolar disorder.

BACKGROUND: The therapeutic response to lithium in patients with bipolar disorder is highly variable and has a polygenic basis. Genome-wide association studies investigating lithium response have identified several relevant loci, though the precise mechanisms driving these associations are poorly understood. We aimed to prioritise the most likely effector gene and determine the mechanisms underlying an intergenic lithium response locus on chromosome 21 identified by the International Consortium on Lithium Genetics (ConLi+Gen). METHODS: We conducted in-silico functional analyses by integrating and synthesising information from several publicly available functional genetic datasets and databases including the Genotype-Tissue Expression (GTEx) project and HaploReg. RESULTS: The findings from this study highlighted TMPRSS15 as the most likely effector gene at the ConLi+Gen lithium response locus. TMPRSS15 encodes enterokinase, a gastrointestinal enzyme responsible for converting trypsinogen into trypsin and thus aiding digestion. Convergent findings from gene-based lookups in human and mouse databases as well as co-expression network analyses of small intestinal RNA-seq data (GTEx) implicated TMPRSS15 in the regulation of intestinal nutrient absorption, including ions like sodium and potassium, which may extend to lithium. LIMITATIONS: Although the findings from this study indicated that TMPRSS15 was the most likely effector gene at the ConLi+Gen lithium response locus, the evidence was circumstantial. Thus, the conclusions from this study need to be validated in appropriately designed wet-lab studies. CONCLUSIONS: The findings from this study are consistent with a model whereby TMPRSS15 impacts the efficacy of lithium treatment in patients with bipolar disorder by modulating intestinal lithium absorption.

IDeRare: a lightweight and extensible open-source phenotype and exome analysis pipeline for germline rare disease diagnosis.

Objectives: Diagnosing rare diseases is an arduous and challenging process in clinical settings, resulting in the late discovery of novel variants and referral loops. To help clinicians, we built IDeRare pipelines to accelerate phenotype-genotype analysis for patients with suspected rare diseases. Materials and Methods: IDeRare pipeline is separated into phenotype and genotype parts. The phenotype utilizes our handmade Python library, while the genotype part utilizes command line (bash) and Python script to combine bioinformatics executable and Docker image. Results: We described various implementations of IDeRare phenotype and genotype parts with real-world clinical and exome data using IDeRare, accelerating the terminology conversion process and giving insight on the diagnostic pathway based on disease linkage analysis until exome analysis and HTML-based reporting for clinicians. Conclusion: IDeRare is freely available under the BSD-3 license, obtainable via GitHub. The portability of IDeRare pipeline could be easily implemented for semi-technical users and extensible for advanced users.

Development and validation of an intrinsic capacity score in the UK Biobank study.

BACKGROUND: In 2015, the World Health Organization introduced the concept of intrinsic capacity (IC) to define the individual-level characteristics that enable an older person to be and do the things they value. This study developed an intrinsic capacity score for UK Biobank study participants and validated its use as a tool for health outcome prediction, understanding healthy aging trajectories, and genetic research. METHODS: Our analysis included data from 45,208 UK biobank participants who had a complete record of the ten variables included in the analysis. Factor adequacy was tested using Kaiser-Meyer-Olkin, Barthelt's, and the determinant of matrix tests, and the number of factors was determined by the parallel analysis method. Exploratory and confirmatory factor analyses were employed to determine the structure and dimensionality of indicators. Finally, the intrinsic capacity score was generated, and its construct and predictive validities as well as reliability were assessed. RESULTS: The factor analysis identified a multidimensional construct comprising one general factor (intrinsic capacity) and five specific factors (locomotor, vitality, cognitive, psychological, and sensory). The bifactor structure showed a better fit (comparative fit index = 0.995, Tucker Lewis index = 0.976, root mean square error of approximation = 0.025, root mean square residual = 0.009) than the conventional five-factor structure. The intrinsic capacity score generated using the bifactor confirmatory factor analysis has good construct validity, as demonstrated by an inverse association with age (lower intrinsic capacity in older age; (ß) =-0.035 (95%CI: -0.036, -0.034)), frailty (lower intrinsic capacity score in prefrail participants, ß = -0.104 (95%CI: (-0.114, -0.094)) and frail participants, ß = -0.227 (95%CI: -0.267, -0.186) than robust participants), and comorbidity (a lower intrinsic capacity score associated with increased Charlson's comorbidity index, ß =-0.019 (95%CI: -0.022, -0.015)). The intrinsic capacity score also predicted comorbidity (a one-unit increase in baseline intrinsic capacity score led to a lower Charlson's comorbidity index, ß = 0.147 (95%CI: -0.173, -0.121)) and mortality (a one-unit increase in baseline intrinsic capacity score led to 25 % lower risk of death, odds ratio = 0.75(95%CI: 0.663, 0.848)). CONCLUSION: The bifactor structure showed a better fit in all goodness of fit tests. The intrinsic capacity construct has strong structural, construct, and predictive validities and is a promising tool for monitoring aging trajectories.