Systems Biology Guided Gene Enrichment Approaches Improve Prediction of Chronic Post-surgical Pain After Spine Fusion
ObjectivesIncorporation of genetic factors in psychosocial/perioperative models for predicting chronic postsurgical pain (CPSP) is key for personalization of analgesia. However, single variant associations with CPSP have small effect sizes, making polygenic risk assessment important. Unfortunately,...
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doaj-0a940a71eb454b52bc23a1a9a3e09a062021-03-23T05:45:47ZengFrontiers Media S.A.Frontiers in Genetics1664-80212021-03-011210.3389/fgene.2021.594250594250Systems Biology Guided Gene Enrichment Approaches Improve Prediction of Chronic Post-surgical Pain After Spine FusionVidya Chidambaran0Valentina Pilipenko1Anil G. Jegga2Anil G. Jegga3Kristie Geisler4Lisa J. Martin5Lisa J. Martin6Department of Anesthesiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United StatesDivision of Human Genetics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United StatesDepartment of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United StatesDepartment of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United StatesDepartment of Anesthesiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United StatesDivision of Human Genetics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United StatesDepartment of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United StatesObjectivesIncorporation of genetic factors in psychosocial/perioperative models for predicting chronic postsurgical pain (CPSP) is key for personalization of analgesia. However, single variant associations with CPSP have small effect sizes, making polygenic risk assessment important. Unfortunately, pediatric CPSP studies are not sufficiently powered for unbiased genome wide association (GWAS). We previously leveraged systems biology to identify candidate genes associated with CPSP. The goal of this study was to use systems biology prioritized gene enrichment to generate polygenic risk scores (PRS) for improved prediction of CPSP in a prospectively enrolled clinical cohort.MethodsIn a prospectively recruited cohort of 171 adolescents (14.5 ± 1.8 years, 75.4% female) undergoing spine fusion, we collected data about anesthesia/surgical factors, childhood anxiety sensitivity (CASI), acute pain/opioid use, pain outcomes 6–12 months post-surgery and blood (for DNA extraction/genotyping). We previously prioritized candidate genes using computational approaches based on similarity for functional annotations with a literature-derived “training set.” In this study, we tested ranked deciles of 1336 prioritized genes for increased representation of variants associated with CPSP, compared to 10,000 randomly selected control sets. Penalized regression (LASSO) was used to select final variants from enriched variant sets for calculation of PRS. PRS incorporated regression models were compared with previously published non-genetic models for predictive accuracy.ResultsIncidence of CPSP in the prospective cohort was 40.4%. 33,104 case and 252,590 control variants were included for association analyses. The smallest gene set enriched for CPSP had 80/1010 variants associated with CPSP (p < 0.05), significantly higher than in 10,000 randomly selected control sets (p = 0.0004). LASSO selected 20 variants for calculating weighted PRS. Model adjusted for covariates including PRS had AUROC of 0.96 (95% CI: 0.92–0.99) for CPSP prediction, compared to 0.70 (95% CI: 0.59–0.82) for non-genetic model (p < 0.001). Odds ratios and positive regression coefficients for the final model were internally validated using bootstrapping: PRS [OR 1.98 (95% CI: 1.21–3.22); β 0.68 (95% CI: 0.19–0.74)] and CASI [OR 1.33 (95% CI: 1.03–1.72); β 0.29 (0.03–0.38)].DiscussionSystems biology guided PRS improved predictive accuracy of CPSP risk in a pediatric cohort. They have potential to serve as biomarkers to guide risk stratification and tailored prevention. Findings highlight systems biology approaches for deriving PRS for phenotypes in cohorts less amenable to large scale GWAS.https://www.frontiersin.org/articles/10.3389/fgene.2021.594250/fullsystems biologygeneticspolygenic risk scorechronic post-surgical paingene enrichment |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Vidya Chidambaran Valentina Pilipenko Anil G. Jegga Anil G. Jegga Kristie Geisler Lisa J. Martin Lisa J. Martin |
spellingShingle |
Vidya Chidambaran Valentina Pilipenko Anil G. Jegga Anil G. Jegga Kristie Geisler Lisa J. Martin Lisa J. Martin Systems Biology Guided Gene Enrichment Approaches Improve Prediction of Chronic Post-surgical Pain After Spine Fusion Frontiers in Genetics systems biology genetics polygenic risk score chronic post-surgical pain gene enrichment |
author_facet |
Vidya Chidambaran Valentina Pilipenko Anil G. Jegga Anil G. Jegga Kristie Geisler Lisa J. Martin Lisa J. Martin |
author_sort |
Vidya Chidambaran |
title |
Systems Biology Guided Gene Enrichment Approaches Improve Prediction of Chronic Post-surgical Pain After Spine Fusion |
title_short |
Systems Biology Guided Gene Enrichment Approaches Improve Prediction of Chronic Post-surgical Pain After Spine Fusion |
title_full |
Systems Biology Guided Gene Enrichment Approaches Improve Prediction of Chronic Post-surgical Pain After Spine Fusion |
title_fullStr |
Systems Biology Guided Gene Enrichment Approaches Improve Prediction of Chronic Post-surgical Pain After Spine Fusion |
title_full_unstemmed |
Systems Biology Guided Gene Enrichment Approaches Improve Prediction of Chronic Post-surgical Pain After Spine Fusion |
title_sort |
systems biology guided gene enrichment approaches improve prediction of chronic post-surgical pain after spine fusion |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Genetics |
issn |
1664-8021 |
publishDate |
2021-03-01 |
description |
ObjectivesIncorporation of genetic factors in psychosocial/perioperative models for predicting chronic postsurgical pain (CPSP) is key for personalization of analgesia. However, single variant associations with CPSP have small effect sizes, making polygenic risk assessment important. Unfortunately, pediatric CPSP studies are not sufficiently powered for unbiased genome wide association (GWAS). We previously leveraged systems biology to identify candidate genes associated with CPSP. The goal of this study was to use systems biology prioritized gene enrichment to generate polygenic risk scores (PRS) for improved prediction of CPSP in a prospectively enrolled clinical cohort.MethodsIn a prospectively recruited cohort of 171 adolescents (14.5 ± 1.8 years, 75.4% female) undergoing spine fusion, we collected data about anesthesia/surgical factors, childhood anxiety sensitivity (CASI), acute pain/opioid use, pain outcomes 6–12 months post-surgery and blood (for DNA extraction/genotyping). We previously prioritized candidate genes using computational approaches based on similarity for functional annotations with a literature-derived “training set.” In this study, we tested ranked deciles of 1336 prioritized genes for increased representation of variants associated with CPSP, compared to 10,000 randomly selected control sets. Penalized regression (LASSO) was used to select final variants from enriched variant sets for calculation of PRS. PRS incorporated regression models were compared with previously published non-genetic models for predictive accuracy.ResultsIncidence of CPSP in the prospective cohort was 40.4%. 33,104 case and 252,590 control variants were included for association analyses. The smallest gene set enriched for CPSP had 80/1010 variants associated with CPSP (p < 0.05), significantly higher than in 10,000 randomly selected control sets (p = 0.0004). LASSO selected 20 variants for calculating weighted PRS. Model adjusted for covariates including PRS had AUROC of 0.96 (95% CI: 0.92–0.99) for CPSP prediction, compared to 0.70 (95% CI: 0.59–0.82) for non-genetic model (p < 0.001). Odds ratios and positive regression coefficients for the final model were internally validated using bootstrapping: PRS [OR 1.98 (95% CI: 1.21–3.22); β 0.68 (95% CI: 0.19–0.74)] and CASI [OR 1.33 (95% CI: 1.03–1.72); β 0.29 (0.03–0.38)].DiscussionSystems biology guided PRS improved predictive accuracy of CPSP risk in a pediatric cohort. They have potential to serve as biomarkers to guide risk stratification and tailored prevention. Findings highlight systems biology approaches for deriving PRS for phenotypes in cohorts less amenable to large scale GWAS. |
topic |
systems biology genetics polygenic risk score chronic post-surgical pain gene enrichment |
url |
https://www.frontiersin.org/articles/10.3389/fgene.2021.594250/full |
work_keys_str_mv |
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