Systems Biology Guided Gene Enrichment Approaches Improve Prediction of Chronic Post-surgical Pain After Spine Fusion.
Systems Biology Guided Gene Enrichment Approaches Improve Prediction of Chronic Post-surgical Pain After Spine Fusion.
复制标题
DOI：
10.3389/fgene.2021.594250
复制
发表时间：
2021
影响因子：
3.7
通讯作者：
Martin LJ
中科院分区：
文献类型：
--
作者：
Chidambaran V;Pilipenko V;Jegga AG;Geisler K;Martin LJ
将遗传因素纳入预测慢性术后疼痛（CPSP）的心理/围手术期模型是个性化镇痛的关键。然而，与CPSP的单变异关联具有小的效应大小，使得多基因风险评估变得重要。不幸的是，儿科CPSP研究没有足够的力量来进行无偏的全基因组关联（GWAS）。我们以前利用系统生物学来识别与CPSP相关的候选基因。本研究的目的是使用系统生物学优先基因富集来生成多基因风险评分（PRS），以改善前瞻性招募临床队列中CPSP的预测。在前瞻性招募的171名接受脊柱融合术的青少年（14.5 ± 1.8岁，75.4%为女性）队列中，我们收集了有关麻醉/手术因素、儿童焦虑敏感性（CASI）、急性疼痛/阿片类药物使用、术后6-12个月疼痛结局和血液（用于DNA提取/基因分型）的数据。我们先前使用基于与文献衍生的“训练集”的功能注释相似性的计算方法对候选基因进行优先排序。在这项研究中，我们测试了1336个优先级基因的排名十分位数，与10，000个随机选择的对照组相比，增加了与CPSP相关的变异的代表性。使用惩罚回归（LASSO）从富集的变体集中选择最终变体用于计算PRS。PRS纳入回归模型进行了比较，与以前发表的非遗传模型的预测准确性。前瞻性队列中CPSP的发生率为40.4%。33，104例病例和252，590例对照变异被纳入关联分析。富集CPSP的最小基因组具有80/1010个与CPSP相关的变体（p < 0.05），显著高于10，000个随机选择的对照组（p = 0.0004）。LASSO选择了20个变量用于计算加权PRS。针对包括PRS在内的协变量调整的模型对于CPSP预测的AUROC为0.96（95%CI：0.92-0.99），相比之下，非遗传模型的AUROC为0.70（95%CI：0.59-0.82）（p < 0.001）。使用自举法对最终模型的比值比和正回归系数进行内部验证：PRS [OR 1.98（95% CI：1.21-3.22）; β 0.68（95% CI：0.19-0.74）]和CASI [OR 1.33（95% CI：1.03-1.72）; β 0.29（0.03-0.38）]。系统生物学指导的PRS提高了儿科队列中CPSP风险的预测准确性。它们有潜力作为生物标志物来指导风险分层和量身定制的预防。研究结果强调了系统生物学方法，用于推导不太适合大规模GWAS的队列中表型的PRS。
Incorporation 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. In 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. Incidence 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)]. Systems 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.
登录
查看更多内容
影响因子：
9.8
作者：
Gerbershagen, H. J.;Rothaug, J.;Meissner, W.
通讯作者：
Meissner, W.
影响因子：
6.7
作者：
Chidambaran, Vidya;Ashton, Maria;Jegga, Anil G.
通讯作者：
Jegga, Anil G.
影响因子：
25.8
作者：
Andersen, Allan M.;Pietrzak, Robert H.;Han, Shizhong
通讯作者：
Han, Shizhong
影响因子：
4.2
作者：
Escott-Price, Valentina;Shoai, Maryam;Hardy, John
通讯作者：
Hardy, John
影响因子：
14.9
作者：
Chen J;Bardes EE;Aronow BJ;Jegga AG
通讯作者：
Jegga AG