TITLE: Study Protocol: The Genetic Markers of Chronic Postsurgical Pain
AUTHORS: Stephan Frangakis

[DESCRIPTION]
This study will investigate the association of genetic variation with chronic postsurgical pain. Patient data will come from two single-center prospective observational cohorts, the Michigan Genomics Initiative (MGI) and the Analgesic Outcome Study (AOS). We will employ a discovery genome-wide association study (GWAS), genome-wide pathway analysis (GWPA), and a targeted replication analysis in our investigation. The primary outcome will be the change in pain scores from baseline to 3 months postoperatively. Secondary outcome will be a pain score ≤4 at 3 months postoperatively (yes/no).

[STEPS]
SECTION: Abstract
1. This study will investigate the association of genetic variation with chronic postsurgical pain. Patient data will come from two single-center prospective observational cohorts, the Michigan Genomics
Initiative (MGI)1 and the Analgesic Outcome Study (AOS). We will employ a discovery genome-wide association study (GWAS), genome-wide pathway analysis (GWPA), and a targeted replication analysis in our investigation. The primary outcome will be the change in pain scores from baseline to 3 months postoperatively. Secondary outcome will be a pain score ≤4 at 3 months postoperatively (yes/no).
SECTION: Introduction
2. Although there are multiple factors that have been shown to influence pain after surgery, genetic factors have been identified as potential risk factors in the development of postoperative pain. Furthermore, genetic factors have been associated with the development of chronic post-surgical pain (CPSP), affecting up to 30% of postsurgical patients, with estimates of the heritability of CPSP ranging from 30 to 70% 2. Yet attempts to predict patients that are genetically predisposed to severe postoperative pain or who will progress to develop CPSP, have been largely ineffective. The goal of this work is to identify genetic variants that are associated with the development of acute postoperative pain and/or CPSP. Identifying variants associated with post-surgical pain will allow for targeted studies of other pain cohorts, provide insight into the pathophysiology of pain, and serve as a basis for future investigation into personalized pharmacologic therapies for the prevention and treatment of postoperative pain.
SECTION: Aims
3. Aim 1a: To test individual genetic variants for association with variability in acute and chronic postoperative pain after surgery throughout the entirety of the human genome using genome-wide association studies (GWAS). The GWAS will be performed in a surgical patient population: >3500 patients for the MGI and AOS datasets. The primary endpoint will be the change in pain scores from baseline to 3 months postoperatively. Secondary endpoint will be pain scores ≤4 at 3 months postoperatively (yes/no).

Aim 1b: Using the GWAS data obtained in Aim 1a, but not dependent on its success, this aim will use genome-wide pathway analysis (GWPA) to provide further insight into the genetics of postoperative pain by surveying the cumulative effect of variation in genetic pathways that associate with variability in postoperative pain. GWPA is an agnostic approach that harnesses the wealth of information from GWAS data to identify the additive effects of single variants aggregating in particular gene sets or pathways. We will interpret our GWAS results in the context of gene function and molecular pathways/functions, grouping multiple SNPs into clusters to functionally annotate the observed findings in an integrated manner. This will provide key biological and functional context to the proposed genetic analysis.
SECTION: Methods
5. Study Population
Data for this study come from two single-center prospective observational cohorts, the Michigan Genomics Initiative (MGI) and the Analgesic Outcome Study (AOS). All participants completed a full written informed consent, and the use of the data for this study was approved by the Institutional Review Board (University of Michigan, Ann Arbor, MI). The consent specifically allowed for future uses of phenotypic and genotypic data after IRB approval, hence additional participant approval was not required. 

Patients were recruited prior to surgery and completed a brief battery validated, self-report measures, including pain severity using the 0-10 Likert scale pain questions from the Brief Pain Inventory, which has been validated in various pain disorders.3 DNA was extracted from a blood sample collected at consent. Patients that underwent surgical intervention and had baseline and 3-month data pain outcomes data were included in the analysis. Patients were excluded from analysis if they had one or more datapoints that were missing (age, gender, baseline pain, pain at 3 months, and self-reported race). Data for Aims 1 and 2 will be derived from this portion of MGI/AOS with existing prospectively collected patient-reported outcomes and pain data at baseline and 3 months after surgery (n >3500). The use of the data for this study was approved by the Institutional Review Board (IRB No. HUM00071298, University of Michigan, Ann Arbor, MI).

In addition to the above eligibility criteria, individuals had to meet the broad inclusion criteria of MGI and AOS. Included participants were 18 years of age or older and had a procedure done with an IV for blood draw at a University of Michigan surgical site (University Hospital, Mott Children’s Hospital, Cardiovascular Center, Cardiac Procedures Unit, or East Ann Arbor). Exclusion criteria were: current pregnancy, history of allogenic bone marrow transplant, enrollment in another study, language barrier, cognitive impairment, and lack of consent.
SECTION: Results
12. 

Genetic and survey data from a total of 3,137patients from the AOS study were included in our analyses. From the MGI study, 447 patients had pre- and 3-month postoperative pain surveys and were also included in our analyses, for a combined total of 3,584 patients. 

We anticipate that novel genes and variants that are associated with CPSP will be identified through our genome-wide association analysis (Aim 1a). We also anticipate that several variants previously reported to be associated with CPSP will be replicated in our cohort through targeted gene analysis (Aim 2).

We anticipate that GWPA (Aim 1b) will show a significant enrichment of SNPs in several genetic pathways, specifically signal transduction, neurotransmission, inflammatory response and signaling, and drug metabolism. Using three different gene pathway datasets, each with its own unique advantages, we expect to find multiple pathways associated with the development of chronic postsurgical pain. Pathways identified with more than one dataset will reinforce the validity of our results.



SECTION: Aims
4. Aim 2: A replication analysis of specific variants of interest based on previously reported associations with postoperative pain. These specific variants/genes of interest have been compiled through a systematic review of the literature of all genetic variation reported to be associated with postoperative pain. The primary endpoint will be the change in pain scores from baseline to 3 months postoperatively. Secondary endpoint will be a postoperative pain score of 4 or greater at 3 months postoperatively.



SECTION: Methods
6. Patient Reported Outcomes
Participants completed validated patient-reported outcomes prior to surgery and again after surgery at set time points, as previously described. These surveys were designed to identify changes from baseline psychological and physical health characteristics. Patients were initially contacted via mail followed by two follow-up attempts via phone and one via email. Subjects were able to complete either a paper form of the survey or an electronic form via Qualtrics survey.
SECTION: Methods
7. Data Collection
After enrollment, participants completed a brief battery of validated self-report measures of pain, mood, function, and medication use, along with demographics and medical history. Preoperative survey data for this analysis will include: pain score at (1) the site of surgery and (2) overall body pain in the preceding week using the 11-point numeric rating scale from the Brief Pain Inventory (average pain score and pain score at its worst, on a 10-point scale with 0 = no pain and 10 = worst pain imaginable); demographics including age, sex, and self-reported race/ethnicity.
SECTION: Methods
8. Genetic Data


600K variants were directly genotyped on either the Illumina Infinium CoreExome array or the Global Screening Array depending on time of recruitment. Participant genotypes underwent rigorous QC and were subsequently imputed to > 51M variants passing standard imputation quality metrics using the Michigan Imputation Server4 with the Trans-Omics for Precision Medicine (TopMED) reference panel.5
SECTION: Methods
9. Outcomes
The primary outcome for Aims 1 and 2 is change in patient-reported surgical site pain (scaled from 0 to 10, with 0 equal to no pain) at baseline and 3 months postoperatively (Δ = postsurgical – baseline). This CPSP outcome definition is the same as the A2CPS: Acute to Chronic Pain Signatures program, which is the largest ever NIH funded study of postsurgical pain.6 We define our secondary outcome as a postoperative pain score of 4 or greater (TRUE IF postop ≥ 4).

11. 
SECTION: Methods
10. Statistical Analyses
For GWAS (Aim 1a), we will perform genome-wide association analyses using SAIGE software.7 We will control for the first 10 principal components (PC) and chip and/or study version. Our primary outcome is the difference in pre- and postsurgical pain scores (Difference). Since our primary outcome is quantitative, we will perform its association analysis using a linear mixed model. Prior to inputting our phenotype into SAIGE, we will perform a linear regression on our Difference data in the R statistical software, and using the covariates of age, gender, patient reported race, and preoperative (baseline) pain score. The post-regression residual values will be normalized using the ordered quantile normalization method in R, and subsequently utilized for the GWAS.

For GWPA (Aim 1b), we will use several different methods to capture an array of potential associated genetic signals and obtain robust pathway results, based on the recommendation of Mooney and Wilmot (2015),8. Pathway-level assessment can be competitive or self-contained depending on our null hypothesis. If multiple significant SNPs are found in the GWAS analysis in Aim 1a, the SNPs will be mapped to corresponding gene loci and we will utilize overrepresentation analysis to assess for an enrichment of significant SNPs in specific pathways. As this compares a single pathway with all others, it is a competitive assessment with a null hypothesis that the enrichment of said pathway is not more than the average of all pathways. We will additionally use set-based methods (regardless
of the results of the GWAS analysis in Aim 1a) that combine data from all SNPs and genes within each pathway into one aggregated value. Each value will be analyzed as to whether it is larger than what would be expected under the null hypothesis that there is no association between genes in the specified pathway and our measured outcome. We will use software such as IPA,9 MetaCore,10 PathVisio 3,11and to identify and visualize potential genetic pathways contributing to our primary and secondary outcomes. We will use three gene-set references that each group genes based on functional-annotation (as opposed to disorder-based or high-throughput-data-based): Gene Ontology (GO),12 Kyoto Encyclopedia of Genes and Genomes (KEGG),13 and Synapse Gene Ontology (SynGO).14

For the replication analysis (Aim 2), we will perform gene-based association analyses using SAIGE-GENE+,15 or similar software. Our analysis will focus on detecting associations in our dataset with genes previously identified as being associated with postoperative pain. Our completed literature review of genetic variation associated with post-operative pain suggests targeted gene testing of
156 variants in 79 genes (full list provided in Appendix). Threshold for significance will be calculated using a Bonferroni correction based on the number of variants (p< 3.2 x 10-4 [0.05/156]) or genes (p<6.3 x 10-4 [0.05/79]).

SECTION: Appendix
14.   Previously reported genes/alleles with significant association with postsurgical pain
        Gene  allele/variant    ABCB1  rs1045642    ABCB1  rs1128503    ABCB1  rs2032582    ADIPOR1  rs12045862    ADRA2A  rs1800035    ADRA2A  rs201376588    ADRA2A  rs775887911    ADRB1  rs1801253    ADRB2  rs1042713    ASTN2  rs7858836    ASTN2  rs958804    ATXN1  rs179997    BDNF  rs1491850    BDNF  rs6265    CACNA1E  rs3845446    CALCA  rs145837941     CALCA  rs3781719    CCL2  rs4586    CHRNA6  rs7828365    CNR1  (AAT)n    COMT  rs165656    COMT  rs4633    COMT  rs4680    COMT  rs4818    COMT  rs6269    COMT  rs740603    CREB1  rs2952768    CRP  rs2794521    CRTC3  rs117119665    CTSG  rs2070697    CTSG  rs2236742    CX3CL1  rs614230    CXCL8  rs4073    CYP2C9  rs1799853    CYP2D6  rs1065852    CYP2D6  rs28371725    CYP2D6  rs35742686    CYP2D6  rs3892097    CYP2D6  rs5030655    CYP2D6  rs5030865    CYP2D6  rs5030867    CYP3A4  rs2242480    CYP3A4  rs28371759    CYP3A5  rs10264272    CYP3A5  rs776746    DQB1  DQB1 hla    DRB1  DRB1 hla    DRD2  rs12364283    DRD2  rs4648317    DRD4  DRD4 VNTR    FAAH  rs324420    FKBP5  rs3800373    GCH1  rs10483639    GCH1  rs3783641    GCH1  rs4411417    GCH1  rs8007267    GDF5  rs143384    GRIN2A  rs3219790    HCRTR2  rs2653349    HTR1B  rs6296    HTR2A  rs1923886    HTR2A  rs2770298    HTR2A  rs6313    HTR2A  rs7330636    HTR2A  rs9534511    HTR3A  rs1985242    IL17A  rs2275913    IL1B  rs1143634    IL1R2  rs11674595    IL1RN  IL1RN VNTR    IL2  rs2069762    IL6  rs1800795    IL6  rs2069840    IL6R  rs2228145    IQGAP1  rs1145324    KCNA1  rs4766311    KCND2  rs1072198    KCND2  rs17376373    KCNJ3  rs12995382    KCNJ3  rs17641121    KCNJ6  rs1543754    KCNJ6  rs1787337    KCNJ6  rs2070995    KCNJ6  rs2211843    KCNJ6  rs2835859    KCNJ6  rs2835925    KCNJ6  rs2835930    KCNJ6  rs858003    KCNJ6  rs858035    KCNJ6  rs928723    KCNJ6  rs9981629    KCNK4  rs2286614    KCNK9  rs2014712    KCNK9  rs2542424    KCNK9  rs2545457    KCNS1  rs734784    LAMB3  rs2076222    MAOA  rs1800659    MAOA  rs2064070    MAOA  rs2283724    MAOA  rs3788862    MAOA  rs979605    MAOB  rs1799836    NAV3  rs118184265    NFKB1A  rs696    NFKBIA  rs8904    OPRK1  rs6985606    OPRM1  rs1323040    OPRM1  rs1799971    OPRM1  rs2075572    OPRM1  rs499796    OPRM1  rs548646    OPRM1  rs634479    OPRM1  rs679987    OPRM1  rs9322447    OPRM1  rs9384179    P2RX7  rs1718125    P2RX7  rs208294    P2RX7  rs208296    P2RX7  rs7958311    P2RY12  rs3732765    PENK  rs3138832    PRKCA  rs887797    PTGS2  rs20417    PTGS2  rs2206593    RETN  rs3745367    SCN10A  rs6795970    SCN11A  rs11709492    SCN11A  rs13080116    SCN11A  rs33985936    SCN9A  rs12619987    SCN9A  rs12994338    SCN9A  rs16851799    SCN9A  rs4286289    SCN9A  rs4369876    SCN9A  rs4387806    SCN9A  rs6724623    SCN9A  rs6739404    SCN9A  rs6746030    SCN9A  rs6754031    SCN9A  rs9646772    SLC22A1  rs12208357    SLC22A1  rs34059508    SLC22A1  rs34130495    SLC22A1  rs55918055    SLC22A1  rs72552763    SLC6A4  44 bp in/del    TGFB1  rs1800469    TH  rs2070762    TLR2  rs3804100    TLR4  rs4986790    TNF  rs1800610    TNF  rs1800629    TRPC3  rs1465040     UGT2B7  rs7439366    ZNF429  rs2562456  
 
 

 
 
SECTION: Appendix
13. References

1. Zawistowski, M., et al., The Michigan Genomics Initiative: A biobank linking genotypes and electronic clinical records in Michigan Medicine patients. Cell Genomics, 2023. 3(2): p. 100257.
2. Clarke, H., et al., Genetics of chronic post-surgical pain: a crucial step toward personal pain medicine. Canadian Journal of Anesthesia/Journal canadien d'anesthésie, 2015. 62(3): p. 294-303.
3.  Keller, S., et al., Validity of the brief pain inventory for use in documenting the outcomes of patients with noncancer pain.Clin J Pain, 2004. 20(5): p. 309-18.
4. Das, S., et al., Next-generation genotype imputation service and methods. Nature Genetics, 2016. 48(10): p. 1284-1287.
5. Taliun, D., et al., Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature, 2021. 590(7845): p. 290-299.
6. Berardi, G., et al., Multi-Site Observational Study to Assess Biomarkers for Susceptibility or Resilience to Chronic Pain: The Acute to Chronic Pain Signatures (A2CPS) Study Protocol. Front Med
(Lausanne), 2022. 9: p. 849214.
7. Zhou, W., et al., Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies. Nature Genetics, 2018. 50(9): p. 1335-1341.
8. Mooney, M.A. and B. Wilmot, Gene set analysis: A step-by-step guide. Am J Med Genet B Neuropsychiatr Genet, 2015. 168(7): p. 517-27.
9. Analysis, I.o.Q.s.I.P., Calculating and Interpreting the p-values for Functions, Pathways and Lists in IPA. 2016.
10. Song, G.G. and Y.H. Lee, Pathway analysis of genome-wide association studies for Parkinson's disease. Mol Biol Rep, 2013. 40(3): p. 2599-607.
11. Kutmon, M., et al., PathVisio 3: an extendable pathway analysis toolbox. PLoS Comput Biol, 2015. 11(2): p. e1004085.
12. The Gene Ontology resource: enriching a GOldmine. Nucleic Acids Res, 2021. 49(D1): p. D325-d334.
13. Kanehisa, M. and S. Goto, KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res, 2000. 28(1): p. 27-30.
14. Koopmans, F., et al., SynGO: An Evidence-Based, Expert-Curated Knowledge Base for the Synapse. Neuron, 2019. 103(2): p. 217-234.e4.
15. Zhou, W., et al., Set-based rare variant association tests for biobank scale sequencing data sets. medRxiv, 2021: p. 2021.07.12.21260400.