Biology subjects

Peyrot, W. J.

Publications and source records attributed to Peyrot, W. J..

5 recordsLinked to original sources

bioRxiv2023

Multimodal Data Integration Advances Longitudinal Prediction of the Naturalistic Course of Depression and Reveals a Multimodal Signature of Disease Chronicity

The ability to individually predict disease course of major depressive disorder (MDD) is essential for optimal treatment planning. Here, we use a data-driven machine learning approach to assess the predictive value of different sets of biological data (whole-blood proteomics, lipid-metabolomics, transcriptomics, genetics), both separately and added to clinical baseline variables, for the longitudinal prediction of 2-year MDD chronicity (defined as presence of MDD diagnosis after 2 years) at the individual subject level. Prediction models were trained and cross-validated in a sample of 643 patients with current MDD (2-year chronicity n = 318) and subsequently tested for performance in 161 MDD individuals (2-year chronicity n = 79). Proteomics data showed best unimodal data predictions (AUROC = 0.68). Adding proteomic to clinical data at baseline significantly improved 2-year MDD chronicity predictions (AUROC = 0.63 vs AUROC = 0.78, p = 0.013), while the addition of other -omics data to clinical data did not yield significantly increased model performance. SHAP and enrichment analysis revealed proteomic analytes involved in inflammatory response and lipid metabolism, with fibrinogen levels showing the highest variable importance, followed by symptom severity. Machine learning models outperformed psychiatrists ability to predict two-year chronicity (balanced accuracy = 71% vs 55%). This study showed the added predictive value of combining proteomic, but not other -omic data, with clinical data. Adding other -omic data to proteomics did not further improve predictions. Our results reveal a novel multimodal signature of MDD chronicity that shows clinical potential for individual MDD disease course predictions from baseline measurements.

genomics↗

bioRxiv2022

Genome-wide association studies of polygenic risk score-derived phenotypes may lead to inflated false positive rates

In a recent study, a polygenic risk score (PRS) for Alzheimers disease was used to construct a new phenotype for a subsequent genome-wide association study (GWAS). Here we show that the applied method, in which the same genetic variants are used to construct the PRS-derived phenotype as well as to assess their effect in a GWAS of the same phenotype, leads to inflated false positive rates. We illustrate this bias by simulation. We first simulate an initial discovery cohort, and run a GWAS of a disorder like Alzheimers disease. We then simulate a target cohort, in which we construct a PRS based on the initial GWAS results. Following the published study, we select the bottom and top 5% of individuals in the PRS distribution and define them as controls and cases. Lastly, we run a GWAS on the new PRS-derived phenotype using all genetic variants. We show that at a significance threshold of 5 x 10-8, false positive rates are inflated up to 0.004 (an 80,000-fold increase compared to 5 x 10-8). We also show that such inflation can be prevented by excluding all variants that were used to construct the PRS (as well as all variants in linkage disequilibrium), when a GWAS on a PRS-derived phenotype is conducted.

bioinformatics↗

bioRxiv2021

Reconsidering the validity of transcriptome-wide association studies

Transcriptome-wide association studies (TWAS) aim to detect relationships between gene expression and a phenotype, and are commonly used for secondary analysis of genome-wide association study (GWAS) results. Results from TWAS analyses are often interpreted as indicating a geneticrelationship between gene expression and a phenotype, but this interpretation is not consistent with the null hypothesis that is evaluated in the traditional TWAS framework. In this study we provide a mathematical outline of this TWAS framework, and elucidate what interpretations are warrantedgiven the null hypothesis it actually tests. We then use both simulations and real data analysis to assess the implications of misinterpreting TWAS results as indicative of a genetic relationship between gene expression and the phenotype. Our simulation results show considerably inflated type 1 error rates for TWAS when interpreted this way, with 41% of significant TWAS associations detected in the real data analysis found to have insufficient statistical evidence to infer such a relationship. This demonstrates that in current implementations, TWAS cannot reliably be used to investigate genetic relationships between gene expression and a phenotype, but that local genetic correlation analysis can serve as a potential alternative.

genetics↗

bioRxiv2020

Identifying loci with different allele frequencies among cases of eight psychiatric disorders using CC-GWAS

Psychiatric disorders are highly genetically correlated, and many studies have focused on their shared genetic components. However, little research has been conducted on the genetic differences between psychiatric disorders, because case-case comparisons of allele frequencies among cases currently require individual-level data from cases of both disorders. We developed a new method (CC-GWAS) to test for differences in allele frequency among cases of two different disorders using summary statistics from the respective case-control GWAS; CC-GWAS relies on analytical assessments of the genetic distance between cases and controls of each disorder. Simulations and analytical computations confirm that CC-GWAS is well-powered and attains effective control of type I error. In particular, CC-GWAS identifies and discards false positive associations that can arise due to differential tagging of a shared causal SNP (with the same allele frequency in cases of both disorders), e.g. due to subtle differences in ancestry between the input case-control studies. We applied CC-GWAS to publicly available summary statistics for schizophrenia, bipolar disorder and major depressive disorder, and identified 116 independent genome-wide significant loci distinguishing these three disorders, including 21 CC-GWAS-specific loci that were not genome-wide significant in the input case-control summary statistics. Two of the CC-GWAS-specific loci implicate the genes KLF6 and KLF16 from the Kruppel-like family of transcription factors; these genes have been linked to neurite outgrowth and axon regeneration. We performed a broader set of case-case comparisons by additionally analyzing ADHD, anorexia nervosa, autism, obsessive-compulsive disorder and Tourettes Syndrome, yielding a total of 196 independent loci distinguishing eight psychiatric disorders, including 72 CC-GWAS-specific loci. We confirmed that loci identified by CC-GWAS replicated convincingly in applications to data sets for which independent replication data were available. In conclusion, CC-GWAS robustly identifies loci with different allele frequencies among cases of different disorders using results from the respective case-control GWAS, providing new insights into the genetic differences between eight psychiatric disorders.

genetics↗

bioRxiv2019

Vitamin D: marker, cause or consequence of depression? An exploration using genomics

BackgroundTrials testing the effect of vitamin D or omega-3 fatty acids (n3-PUFA) supplementation on major depressive disorder (MDD) reported conflicting findings. These trials were boosted by epidemiological evidence suggesting an inverse association of circulating 25-hydroxyvitamin D (25-OH-D) and n3-PUFA levels with MDD. Observational associations may emerge from unresolved confounding, shared genetic risk, or direct causal relationships. We explored the nature of these associations exploiting data and statistical tools from genomics. MethodsResults from GWAS on 25-OH-D (N = 79366), n3-PUFA (N = 24925) and MDD (135458 cases, 344901 controls) were applied to individual-level data (>2,000 subjects with measures of genotype, DSM-IV lifetime MDD diagnoses and circulating 25-OH-D and n3-PUFA) and summary-level data analyses. Shared genetic risk between traits was tested by polygenic risk scores (PRS). Two-sample Mendelian Randomization (2SMR) analyses tested the potential bidirectional causality between traits. OutcomeIn individual-level data, PRS were associated with the phenotype of the same trait (PRS 25-OH-D p = 1.4e-20, PRS N3-PUFA p = 9.3e-6, PRS MDD p = 1.4e-4), but not with the other phenotypes, suggesting a lack of shared genetic effects. In summary-level data, 2SMR analyses provided no evidence of a causal role on MDD of 25-OH-D (p = 0.50) or n3-PUFA (p = 0.16), or for a causal role of MDD on 25-OH-D (p = 0.25) or n3-PUFA (p = 0.66). ConclusionsApplying genomics tools indicated that that shared genetic risk or direct causality between 25-OH-D, n3-PUFA and MDD is unlikely: unresolved confounding may explain the associations reported in observational studies. These findings represent a cautionary tale for testing supplementation of these compounds in preventing or treating MDD. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSMeta-analyses of trials testing the effect of vitamin D or omega-3 fatty acids (n3-PUFA) supplementation on major depressive disorder (MDD) reported conflicting findings, including small clinical effect or no effect. These trials were boosted by epidemiological evidence suggesting an inverse association of circulating 25-hydroxyvitamin D (25-OH-D) and n3-PUFA levels with MDD. However, observational associations may emerge from different scenarios, including unresolved confounding, shared genetic risk, or direct causal relationships. Added value of this studyGenomics provides unique opportunities to investigate shared risk and causality between traits applying new statistical tools and results from genome-wide association studies (GWAS). In the present study we examined the nature of the association of 25-OH-D and n3-PUFA with MDD using the latest data and tools from genomics. We found no significant evidence of shared genetic risk or direct causality between vitamin D or n-3 PUFA and MDD; at this stage, unresolved confounding should be considered the most likely explanation for the association reported by observational studies. Implications of all the available evidenceFindings from the present study, in conjunction with previous conflicting evidence from clinical studies, represent a cautionary tale for further research testing the potential therapeutic effect of vitamin D and n3-PUFA supplementation on depression, as the expectations of a direct causal effect of these compounds on mood should be substantially reconsidered. Genomic tools could be efficiently employed to examine the nature of observational associations emerging in epidemiology, providing some indications on the most promising associations to be prioritized in subsequent intervention studies.

genomics↗