Secondary Logo

Journal Logo

Reading and writing: the evolution of molecular pain genetics

Bullock, Daniel; Jesuthasan, Aaron; González-Cano, Rafael; Costigan, Michael*

doi: 10.1097/j.pain.0000000000001608
Topical Review
Editor's Choice

Anesthesia Department, Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States

*Corresponding author. Address: Anesthesia Department, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, United States. Tel.: 617 919-2310; fax: (617) 919-2772. E-mail address: (M. Costigan).

Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Web site (

D. Bullock and A. Jesuthasan contributed equally to this article.

Back to Top | Article Outline

1. Introduction

Pain is believed to affect approximately 20% of adults globally.36 The heterogeneous nature of pain-associated disease necessitates precise understanding of molecular mechanisms behind distinct disorders to improve diagnostic ability and analgesic production. Initially, analgesic identification occurred through empirical observation, as with ancient opioid use,74 or by serendipity as with acetaminophen, which is used widely as an analgesic despite its unclear mechanisms.34 Over time, a focus on experimentally targeted analgesic development has led to therapeutics including ziconotide, a voltage-gated calcium channel inhibitor,66,98 therapies created to block nerve growth factor signalling cascades,81 and new inhibitory treatments of calcitonin gene-related peptide signaling28 (Table 1). Presently, unbiased genome screens are used to agnostically decipher apex pain signalling cascades and targets, guided only by primary data. Despite this extraordinary progress, there remains much to uncover regarding molecular mechanisms in chronic pain. This review is not intended to describe each pain gene identified by association methods, as these are effectively reviewed elsewhere.56,63,80,97,114 Rather, we wish to outline developments allowing for improved gene identification and targeting, while discussing advances towards achieving a rational process for analgesic design (Fig. 1).

Table 1

Table 1

Table 1-a

Table 1-a

Figure 1.

Figure 1.

Back to Top | Article Outline

2. Gene identification

2.1. Genetic linkage

Traditionally, genetic linkage has been used to understand the role of genetic variation in disease etiology. Linkage studies compare inheritance patterns of Mendelian disease phenotypes with genetic markers across family members. Markers include microsatellites (repeated noncoding sequences) and, more recently, single-nucleotide polymorphisms (SNPs). These single genomic alterations differ from a population's “average sequence” and occur approximately every 300 nucleotides.1 Genomic regions that cosegregate with a phenotype are termed quantitative trait loci (QTL) and exist within or outside of a gene, possibly containing multiple genes.41 Linkage is evaluated using a logarithm of the odds score, with larger positive scores representing greater cosegregation between phenotype and QTL.92 On locating a disease QTL, identifying mutated genes by sequencing or determining transcript expression within the QTL by tissue screening help identify and locate causative mutants.41

Linkage techniques have helped identify affected genetic locations in pain disorders. Family studies correlate gain-of-function SCN11 mutations with chronic joint pain112 and infantile familial episodic pain,70 whereas murine models link neuropathic pain to CACNG2.68 Mogil et al.62 correlated thermal perception to calcitonin gene-related peptide, proving this connection with additional pharmacological investigation. Moreover, mechanical sensitivity is linked with Cacna2d1, Csnk1e, and Ift27.111 The TRESK potassium channel is additionally linked to migraine with aura.46

Despite notable successes, linkage studies are limited because they require pedigrees containing sufficient documentation to trace genes of interest,27 although such data permit this in countries such as Iceland.37 In mice, developing specific combinations of inbred strains has also aided trait discovery.93 Although genetic contributions often strongly affect behavior in laboratory animals, where environmental influences and background genetics are tightly controlled, calculating genetic influence in human disease is more challenging because of life experience and complex genotypes.23,62

Back to Top | Article Outline

2.2. Genome-wide association studies

Emerging in 2001, genome-wide association studies (GWAS) identify genetic differences across a wider population that produces variable phenotypes. Participants are often categorized into disease (case) or nondisease (control) phenotypes. Large sample sizes are required to prevent misidentifying genes from false positives, such as with multiple testing.38 Unlike linkage, which is useful for diseases impacted by single penetrant genes,92 GWAS permit identification of multiple loci, which may together influence complex disease phenotypes.57

Recent notable GWAS of chronic pain include analyses of the UK Biobank, containing approximately 500,000 individuals.9 These have correlated IL-10 variants with postoperative pain, associated a MAT2B SNP with hip, knee, dorsal, and neck pain,115 and associated 3343 SNPs at 28 loci with headache, including 14 loci newly associated with pain.60 The recent OPPERA project (Orofacial Pain Prospective Evaluation and Risk Assessment Study) examined genetic and environmental risk factors for temporomandibular disorder (TMD), correlating poor sleep and perceived stress with TMD incidence.85 Multiple genes have been associated with TMD,86 revealing a functional link with MRAS in males87 and EGF signaling.58 Meta-analyses of multiple GWAS cohorts have also verified data reproducibility. Such studies have identified significant associations between SOX5, DCC, and intergenic variants with chronic back pain, and potentially height, depression, and osteoarthritis.91 Another associated an SNP insertion at the 9p22.3 NFIB locus with sciatica in Finnish patients.49 In addition, GWAS association and functional analysis of FAM173B demonstrated a role in chronic pain etiology.101

Such whole-genome–based studies often produce large quantities of data, which are difficult to manage. One processing method is pathway analysis,56 where algorithms sort genes into predefined regulatory cascades. As multiple genes with related action accumulate, the likelihood of functional association increases; although owing to multiple hypothesis testing, caution must be ascribed to marginally associated data. Pathway analyses are highly useful in identifying association between genes and new or known signaling cascades.15,58 It is important to recognize these associations are a gateway to additional necessary independent experiments to further demonstrate these genetic links.

Despite the utility of GWAS, limitations remain. Large samples may produce uncoordinated phenotyping and poor association data if different study groups are combined.97 An accurate study must also thoroughly consider the mixed etiology of pain conditions, including the anatomical diversity of symptoms and risk factors for development.4,5 In addition, the inherent subjectivity in categorizing individual cases by researchers or clinicians may unintentionally confound associations. Furthermore, by only defining disease SNPs as those meeting the high level of GWAS significance, other genes influencing complex phenotypes will be missed45,108; although with the advent of machine learning, these algorithms should improve.19 Nevertheless, well-performed studies can be extremely powerful. For instance, multiple large-scale migraine studies reveal TRPM8, PRDM16, and LRP1 as strongly associated loci.3 How identified leads evolve into therapies remains to be seen, although such strategies are already shifting focus to nonopioid analgesics.24,48,80

Back to Top | Article Outline

2.3. Genomic sequencing

An individual's genetic makeup may now be determined through improved genome-reading techniques. DNA sequencing has evolved remarkably since the 1970s, supported by progressively faster and more affordable next-generation techniques.7,79 Although traditional sequencing has aided discovery in molecular genetics, more advanced deep sequencing offers new possibilities related to the high-definition maps of individual genomes. With these improvements, SNPs will likely become outdated genomic markers or may represent the first stage of a process using genome sequencing to efficiently elucidate causative mutations. Ideally, every patient's genome will be sequenced in the near future to assess genetic risk of disease or aid identification of new pharmaceutical targets. In addition, certain tissues may be targeted to determine the pathogenicity of somatic mutations.32 However, issues of patient privacy and their responses to implications of these data must be considered.76

Back to Top | Article Outline

3. Gene modification

3.1. Genetic manipulation

Once genetic association identifies potential molecular pain mechanisms, additional methods are required to verify causation. Common strategies are pharmacological targeting, gene manipulation, and mRNA knockdown.25 Tissue-specific conditional knockout and overexpression studies use the Cre recombinase enzyme, which on activation targets tagged loci for genomic recombination and expression alteration.96 Alternatively, transcript knockdown has been investigated through RNA interference (RNAi) and microRNA (miRNA) studies.55,103

We have used such methods to examine the role of tetrahydrobiopterin (BH4) in chronic pain. By extending our initial association of less ongoing pain with a GCH1 haplotype,94 we further functionally validated this association.47,48 This relatively common haplotype (present in 25% of Caucasians) results in diminished GCH1 upregulation in response to cellular stress.94 The decreased GCH1 enzyme proportionally reduces intracellular BH4 production.48 As BH4 is an essential cofactor for dopamine, serotonin, norepinephrine, epinephrine, and nitric oxide, this reduction during somatosensory stress may be expected to decrease ongoing pain. Appropriately, mice with targeted reductions in sensory and immune BH4 levels experience less chronic pain.47 In addition, recent work has defined a new functional link between BH4 and T-cell amplification.18

Diatchenko et al.21 showed that Catechol-O-methyltransferase (COMT) correlated with differential levels of experimental and chronic pain in TMD, where associated SNPs directly contributed to enzyme stability and function.22 In humans, variants coding for decreased COMT metabolism of catecholaminergic neurotransmitters resulted in increased sensitivity to noxious stimuli and chronic pain risk, which was supported in mice.78 Interestingly, the GCH1 and COMT haplotypes are consistent mechanistically, although determining the degree of this similarity requires further work.

Purinergic signaling has been highly relevant in recent pain research.8 Sorge et al. used genome-wide linkage analysis to demonstrate mutations that alter P2X7 pore size affect chronic pain sensitivity in both mice and humans.8,88 Further work demonstrated differential sex-linked chronic pain phenotypes involving P2X7 and altered microglial and T-cell responses to nerve injury.88

Back to Top | Article Outline

3.2. RNA silencing

First observed in plants and eukaryotes in the 1990s, RNAi and miRNA regulate gene signaling by reducing transcription, destabilizing mRNA, or repressing translation. In RNAi, host cell RNA-induced silencing complexes knockdown their sequence-specific gene expression using small interfering RNA (siRNA) and have antiviral capabilities.10 Meanwhile, highly conserved miRNA perform endogenous gene silencing during development and tightly regulate transcript expression.6 They can also limit inflammatory signaling in neuropathic pain.40

A full genome screen using RNAi in Drosophila determined that the α2δ calcium channel straightjacket subunit, among other genes, was necessary in thermal nociception.67 In addition, viral RNA silencing constructs enable gene silencing through extrachromosomal expression or integration into host genomes.39,43 Recent RNAi work in rodents includes targeting the N-methyl-d-aspartate receptor GluN2B subunit,104 silencing TLR4 to decrease bone cancer pain,71 and elucidating NaV1.7 as a mediator of postoperative pain sensitivity.90

Nanoparticle encapsulation of small inhibitory RNAs for precise systemic delivery112 has allowed targeting TRPV1 transcripts to reduce thermal nociception,82 p38 siRNA silencing of microglial activation,84 and reduced IRF5-mediated hyperalgesia in microglia.95 In addition, siRNA studies implicate long noncoding RNAs (lncRNAs), which regulate transcription and signaling processes,72 in diabetic neuropathic pain. Knockdown of lncRNA reduces P2X751,53 and TRPV1-mediated52 pain signaling in rat sensory neurons. Clinical trials demonstrate the therapeutic potential of RNA silencing for several diseases, although utility in pain remains unreported.11 However, antisense constructs were recently used as spinal muscular atrophy and Duchenne muscular dystrophy treatments. By removing exons harboring disease-causing mutations from pre-mRNA transcripts, this technique functionally restores nondiseased gene expression.50

Back to Top | Article Outline

3.3. CRISPR/Cas9 genome editing

The rapid development of CRISPR/Cas9 genome editing since 2012 has enhanced gene mutation and expression studies.16,33,35,73 The system uses a modified bacterial CRISPR-Associated-Protein-9 (Cas9)-endonuclease. Postulated as a prokaryotic form of acquired immunity, bacterial Cas9 cleaves and incorporates viral DNA into its genome, permitting recognition and neutralization of future attacks.33 Researchers have modified type II CRISPR/Cas9 machinery to target specific eukaryotic bases in mice,99,107 embryonic stem cells,13 and other organisms.30

The type II CRISPR/Cas9 system uses a synthesized single-guide RNA (sgRNA), directing the complex to its complementary genomic region14 (Fig. 2). A 3 base pair protospacer-adjacent-motif (PAM) sequence immediately downstream of the complementary sequence permits identification, binding, and cleavage at the target site.73 In nature, bacteria target the foreign PAM present on invading viral or plasmid DNA.64 In model organisms, algorithms search a eukaryotic genome for a PAM sequence downstream of a target sequence to obtain a complete targeting construct.102 The sgRNA then hybridizes with the host DNA target site, allowing the coupled Cas9 to cleave DNA 3 nucleotides upstream from the PAM sequence. This produces a double-stranded break and triggers endogenous DNA repair mechanisms.2

Figure 2.

Figure 2.

Repair includes non-homologous end joining, in which random base pair insertions or deletions at cleavage sites create point or frame-shift mutations that unpredictably mutate genomic sequences.75 A mutated coding region may produce premature stop codons and subsequent gene knockouts, although this process is relatively uncontrolled.113 Alternatively, homology-directed repair inserts double- or single-stranded DNA (dsDNA; ssDNA) templates at cleavage sites.44,107 A donor sequence containing a small number of intentional mismatches can be used to alter specific genomic nucleotide pairings.42 Recently, longer ssDNA oligonucleotides have been used to increase integration efficiency and reduce off-target effects.61,110 Templates may include precise loxP-flanked sites for conditional knockout, overexpression, knock-in, protein tags including fluorescence, and genetic control elements.75 Alternatively, applying catalytically inactive Cas9 to repress or activate site transcription provides another approach to selectively manipulate expression.35,54 Notably, unpredictable offsite mutations often occur with CRISPR/Cas9 editing; thus, it is necessary to limit these effects.83 A useful control is to modify a gene to demonstrate a phenotype, with subsequent CRISPR/Cas9 reversal to wild type to exhibit phenotypic normalization.12,20

Among the natural changes that can be replicated through CRISPR/Cas9 editing are point mutations that cause congenital disease, including hypersensitive and hyposensitive pain syndromes. These mutations may change one functionally relevant amino acid, cause a frame shift with premature protein termination,89,100 or inactivate pre-mRNA splice sites producing intron retention and aberrant translation.69 Ultimately, each functionally alters the gene's cellular role and creates a modified protein. Rare congenic diseases often occur in individuals because of de novo dominant negative mutations. In heterozygous individuals, dominant negative proteins override their healthy counterparts by impeding normal protein function.89 CRISPR/Cas9-mediated insertion of known patient mutations into animals allows for investigation of the phenotypic effects of modified genes.

CRISPR/Cas9 modeling of rat neurofibromatosis type I revealed increased CRMP2-mediated signaling as a regulator of hyperalgesia in this disease.65 Recently, nanoparticle administration of CRISPR/Cas9 was used to knockout NLRP3 expression in murine macrophages, reducing IL-1β cleavage in acute septic shock, peritonitis, and diabetic models.105 A removal of 8 conserved amino acids in MeCP2 increased insensitivity to noxious heat, heightened anxiety, and lowered motor and cognitive activity in male mice, mirroring symptoms of human autism and Rett syndrome also caused by MECP2 mutations.106 In addition, a CRISPR/Cas9-mediated homologous knockout of PIEZO2 in human embryonic stem cell–derived neurons demonstrated its necessity in low-threshold mechanosensation.77

We can gain insight from these studies to identify novel drug targets. A well-known example is NaV1.7, genetically linked with multiple hyposensitive and hypersensitive pain phenotypes.17,26 This has generated a concerted drug discovery effort, which has yet to unveil potent analgesics.29,109 The reasons for the misalignment of genetic phenotype with drug target efficacy remain unclear, but continued effort should produce more successful translation.29 For instance, employment of CRISPR/Cas9-edited induced pluripotent stem cell nociceptors by Bennett et al.59 has recently localized NaV1.7 expression within nociceptors, revealed trafficking of these channels to specific neuronal compartments, and verified NaV1.7's significance in modulating nociceptor excitability. In addition, with respect to novel sodium channel inhibitors, Vertex Pharmaceuticals recently announced 2 successful phase 2 trials of VX-150, a NaV1.8 blocker targeting acute and chronic pain. NaV1.8, like NaV1.7, is expressed specifically in the sensory system, and its mutations are linked with congenic pain syndromes.31 Thus, if VX-150 is truly selective, this new class of inhibitors may be highly significant clinically.

Back to Top | Article Outline

4. Conclusion

As experimental methods evolve, genomic contributions to pain disorders may be systematically studied and distinct molecular cascades effectively targeted. When coupled with efficient patient phenotyping, these methods will introduce an age of truly personalized care. Genetic linkage, GWAS, and sequencing have contributed significantly to understanding heritability and associations between genetic loci and human pain conditions. These data suggest multiple genetic mechanisms in chronic pain, an unsurprising result given its mixed etiology. As techniques, including RNA silencing and CRISPR/Cas9 editing, evolve and transition from laboratory to clinic, they will become increasingly influential. However developed these methods become, it remains crucial to consider environmental influences in chronic pain. Nevertheless, we stand at the frontier of a spectacular era in genetics, which will greatly enhance our understanding and management of chronic pain.

Back to Top | Article Outline

Conflict of interest statement

The authors have no conflicts of interest to declare.

Back to Top | Article Outline


The authors thank Mantu Bhaumik, PhD, and Alexander Davies, PhD, for advice on the content of this article. The authors thank Amgen, Inc, for financial support.

R. González-Cano was supported by Alfonso Martin Escudero Fellowship.

Back to Top | Article Outline

Supplemental video content

Video content associated with this article can be found online at

Back to Top | Article Outline


[1]. Allen-Brady K, Camp NJ. Genetic distance and markers used in linkage mapping. Methods Mol Biol 2011;713:43–53.
[2]. Anders C, Niewoehner O, Duerst A, Jinek M. Structural basis of PAM-dependent target DNA recognition by the Cas9 endonuclease. Nature 2014;513:569–73.
[3]. Anttila V, Wessman M, Kallela M, Palotie A. Genetics of migraine. Handb Clin Neurol 2018;148:493–503.
[4]. Bair E, Gaynor S, Slade GD, Ohrbach R, Fillingim RB, Greenspan JD, Dubner R, Smith SB, Diatchenko L, Maixner W. Identification of clusters of individuals relevant to temporomandibular disorders and other chronic pain conditions: the OPPERA study. PAIN 2016;157:1266–78.
[5]. Baron R, Maier C, Attal N, Binder A, Bouhassira D, Cruccu G, Finnerup NB, Haanpää M, Hansson P, Hüllemann P, Jensen TS, Freynhagen R, Kennedy JD, Magerl W, Mainka T, Reimer M, Rice AS, Segerdahl M, Serra J, Sindrup S, Sommer C, Tölle T, Vollert J, Treede RD. Peripheral neuropathic pain: a mechanism-related organizing principle based on sensory profiles. PAIN 2017;158:261–72.
[6]. Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 2004;116:281–97.
[7]. Behjati S, Tarpey PS. What is next generation sequencing? Arch Dis Child Educ Pract Ed 2013;98:236–8.
[8]. Bernier LP, Ase AR, Séguéla P. P2X receptor channels in chronic pain pathways. Br J Pharmacol 2018;175:2219–30.
[9]. Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, Motyer A, Vukcevic D, Delaneau O, O'Connell J, Cortes A, Welsh S, Young A, Effingham M, McVean G, Leslie S, Allen N, Donnelly P, Marchini J. The UK Biobank resource with deep phenotyping and genomic data. Nature 2018;562:203–9.
[10]. Carthew RW, Sontheimer EJ. Origins and Mechanisms of miRNAs and siRNAs. Cell 2009;136:642–55.
[11]. Chakraborty C, Sharma AR, Sharma G, Doss CGP, Lee SS. Therapeutic miRNA and siRNA: moving from bench to clinic as next generation medicine. Mol Ther Nucleic Acids 2017;8:132–43.
[12]. Chang CW, Lai YS, Westin E, Khodadadi-Jamayran A, Pawlik KM, Lamb LS Jr, Goldman FD, Townes TM. Modeling human severe combined immunodeficiency and correction by CRISPR/Cas9-enhanced gene targeting. Cell Rep 2015;12:1668–77.
[13]. Chen Y, Cao J, Xiong M, Petersen AJ, Dong Y, Tao Y, Huang CT, Du Z, Zhang SC. Engineering human stem cell lines with inducible gene knockout using CRISPR/Cas9. Cell Stem Cell 2015;17:233–44.
[14]. Chylinski K, Makarova KS, Charpentier E, Koonin EV. Classification and evolution of type II CRISPR-Cas systems. Nucleic Acids Res 2014;42:6091–105.
[15]. Cobos EJ, Nickerson CA, Gao F, Chandran V, Bravo-Caparros I, Gonzalez-Cano R, Riva P, Andrews NA, Latremoliere A, Seehus CR, Perazzoli G, Nieto FR, Joller N, Painter MW, Ma CHE, Omura T, Chesler EJ, Geschwind DH, Coppola G, Rangachari M, Woolf CJ, Costigan M. Mechanistic differences in neuropathic pain modalities revealed by correlating behavior with global expression profiling. Cell Rep 2018;22:1301–12.
[16]. Cong L, Ran FA, Cox D, Lin S, Barretto R, Habib N, Hsu PD, Wu X, Jiang W, Marraffini LA, Zhang F. Multiplex genome engineering using CRISPR/Cas systems. Science 2013;339:819–23.
[17]. Cox JJ, Reimann F, Nicholas AK, Thornton G, Roberts E, Springell K, Karbani G, Jafri H, Mannan J, Raashid Y, Al-Gazali L, Hamamy H, Valente EM, Gorman S, Williams R, McHale DP, Wood JN, Gribble FM, Woods CG. An SCN9A channelopathy causes congenital inability to experience pain. Nature 2006;444:894–8.
[18]. Cronin SJF, Seehus C, Weidinger A, Talbot S, Reissig S, Seifert M, Pierson Y, McNeill E, Longhi MS, Turnes BL, Kreslavsky T, Kogler M, Hoffmann D, Ticevic M, da Luz Scheffer D, Tortola L, Cikes D, Jais A, Rangachari M, Rao S, Paolino M, Novatchkova M, Aichinger M, Barrett L, Latremoliere A, Wirnsberger G, Lametschwandtner G, Busslinger M, Zicha S, Latini A, Robson SC, Waisman A, Andrews N, Costigan M, Channon KM, Weiss G, Kozlov AV, Tebbe M, Johnsson K, Woolf CJ, Penninger JM. The metabolite BH4 controls T cell proliferation in autoimmunity and cancer. Nature 2018;563:564–8.
[19]. de Los Campos G, Vazquez AI, Hsu S, Lello L. Complex-trait prediction in the era of big data. Trends Genet 2018;34:746–54.
[20]. DeWitt MA, Magis W, Bray NL, Wang T, Berman JR, Urbinati F, Heo SJ, Mitros T, Muñoz DP, Boffelli D, Kohn DB, Walters MC, Carroll D, Martin DI, Corn JE. Selection-free genome editing of the sickle mutation in human adult hematopoietic stem/progenitor cells. Sci Transl Med 2016;8:360ra134.
[21]. Diatchenko L, Slade GD, Nackley AG, Bhalang K, Sigurdsson A, Belfer I, Goldman D, Xu K, Shabalina SA, Shagin D, Max MB, Makarov SS, Maixner W. Genetic basis for individual variations in pain perception and the development of a chronic pain condition. Hum Mol Genet 2005;14:135–43.
[22]. Diatchenko L, Nackley AG, Slade GD, Bhalang K, Belfer I, Max MB, Goldman D, Maixner W. Catechol-O-methyltransferase gene polymorphisms are associated with multiple pain-evoking stimuli. PAIN 2006;125:216–24.
[23]. Diatchenko L, Nackley AG, Tchivileva IE, Shabalina SA, Maixner W. Genetic architecture of human pain perception. Trends Genet 2007;23:605–13.
[24]. Diatchenko L, Fillingim RB, Smith SB, Maixner W. The phenotypic and genetic signatures of common musculoskeletal pain conditions. Nat Rev Rheumatol 2013;9:340–50.
[25]. Dib-Hajj SD, Waxman SG. Translational pain research: lessons from genetics and genomics. Sci Transl Med 2014;6:249sr4.
[26]. Drenth JP, Waxman SG. Mutations in sodium-channel gene SCN9A cause a spectrum of human genetic pain disorders. J Clin Invest 2007;117:3603–9.
[27]. Dueker ND, Pericak-Vance MA. Analysis of genetic linkage data for Mendelian traits. Curr Protoc Hum Genet 2014;83:1–31.
[28]. Edvinsson L, Haanes KA, Warfvinge K, Krause DN. CGRP as the target of new migraine therapies—successful translation from bench to clinic. Nat Rev Neurol 2018;14:338–50.
[29]. Emery EC, Luiz AP, Wood JN. Nav1.7 and other voltage-gated sodium channels as drug targets for pain relief. Expert Opin Ther Targets 2016;20:975–83.
[30]. Esvelt KM, Smidler AL, Catteruccia F, Church GM. Concerning RNA-guided gene drives for the alteration of wild populations. Elife 2014;3e03401.
[31]. Faber CG, Lauria G, Merkies IS, Cheng X, Han C, Ahn HS, Persson AK, Hoeijmakers JG, Gerrits MM, Pierro T, Lombardi R, Kapetis D, Dib-Hajj SD, Waxman SG. Gain-of-function Nav1.8 mutations in painful neuropathy. Proc Natl Acad Sci U S A 2012;109:19444–9.
[32]. Ferrer M, Gosline SJC, Stathis M, Zhang X, Guo X, Guha R, Ryman DA, Wallace MR, Kasch-Semenza L, Hao H, Ingersoll R, Mohr D, Thomas C, Verma S, Guinney J, Blakeley JO. Pharmacological and genomic profiling of neurofibromatosis type 1 plexiform neurofibroma-derived Schwann cells. Sci Data 2018;5:180106.
[33]. Gasiunas G, Barrangou R, Horvath P, Siksnys V. Cas9-crRNA ribonucleoprotein complex mediates specific DNA cleavage for adaptive immunity in bacteria. Proc Natl Acad Sci U S A 2012;109:E2579–86.
[34]. Gerriets V, Nappe TM. Acetaminophen. Treasure Island: StatPearls, 2018.
[35]. Gilbert LA, Horlbeck MA, Adamson B, Villalta JE, Chen Y, Whitehead EH, Guimaraes C, Panning B, Ploegh HL, Bassik MC, Qi LS, Kampmann M, Weissman JS. Genome-scale CRISPR-mediated control of gene repression and activation. Cell 2014;159:647–61.
[36]. Goldberg DS, McGee SJ. Pain as a global public health priority. BMC Public Health 2011;11:770.
[37]. Hakonarson H, Gulcher JR, Stefansson K. deCODE genetics, Inc. Pharmacogenomics 2003;4:209–15.
[38]. Hayes B. Overview of statistical methods for genome-wide association studies (GWAS). Methods Mol Biol 2013;1019:149–69.
[39]. Herrera-Carrillo E, Liu YP, Berkhout B. Improving miRNA delivery by optimizing miRNA expression cassettes in diverse virus vectors. Hum Gene Ther Methods 2017;28:177–90.
[40]. Heyn J, Luchting B, Hinske LC, Hübner M, Azad SC, Kreth S. miR-124a and miR-155 enhance differentiation of regulatory T cells in patients with neuropathic pain. J Neuroinflammation 2016;13:248.
[41]. Hitzemann R, Belknap JK, McWeeney SK. Quantitative trait locus analysis: multiple cross and heterogeneous stock mapping. Alcohol Res Health 2008;31:261–5.
[42]. Hsu PD, Scott DA, Weinstein JA, Ran FA, Konermann S, Agarwala V, Li Y, Fine EJ, Wu X, Shalem O, Cradick TJ, Marraffini LA, Bao G, Zhang F. DNA targeting specificity of RNA-guided Cas9 nucleases. Nat Biotechnol 2013;31:827–32.
[43]. Hutson TH, Foster E, Moon LD, Yáñez-Muñoz RJ. Lentiviral vector-mediated RNA silencing in the central nervous system. Hum Gene Ther Methods 2014;25:14–32.
[44]. Jiang F, Doudna JA. CRISPR-Cas9 structures and mechanisms. Annu Rev Biophys 2017;46:505–29.
[45]. Korte A, Farlow A. The advantages and limitations of trait analysis with GWAS: a review. Plant Methods 2013;9:29.
[46]. Lafrenière RG, Cader MZ, Poulin JF, Andres-Enguix I, Simoneau M, Gupta N, Boisvert K, Lafrenière F, McLaughlan S, Dubé MP, Marcinkiewicz MM, Ramagopalan S, Ansorge O, Brais B, Sequeiros J, Pereira-Monteiro JM, Griffiths LR, Tucker SJ, Ebers G, Rouleau GA. A dominant-negative mutation in the TRESK potassium channel is linked to familial migraine with aura. Nat Med 2010;16:1157–60.
[47]. Latremoliere A, Latini A, Andrews N, Cronin SJ, Fujita M, Gorska K, Hovius R, Romero C, Chuaiphichai S, Painter M, Miracca G, Babaniyi O, Remor AP, Duong K, Riva P, Barrett LB, Ferreirós N, Naylor A, Penninger JM, Tegeder I, Zhong J, Blagg J, Channon KM, Johnsson K, Costigan M, Woolf CJ. Reduction of neuropathic and inflammatory pain through inhibition of the tetrahydrobiopterin pathway. Neuron 2015;86:1393–406.
[48]. Latremoliere A, Costigan M. Combining human and rodent genetics to identify new analgesics. Neurosci Bull 2018;34:143–55.
[49]. Lemmelä S, Solovieva S, Shiri R, Benner C, Heliövaara M, Kettunen J, Anttila V, Ripatti S, Perola M, Seppälä I, Juonala M, Kähönen M, Salomaa V, Viikari J, Raitakari OT, Lehtimäki T, Palotie A, Viikari-Juntura E, Husgafvel-Pursiainen K. Genome-wide meta-analysis of sciatica in Finnish population. PLoS One 2016;11:e0163877.
[50]. Li D, Mastaglia FL, Fletcher S, Wilton SD. Precision medicine through antisense oligonucleotide-mediated exon skipping. Trends Pharmacol Sci 2018;39:982–94.
[51]. Liu C, Tao J, Wu H, Yang Y, Chen Q, Deng Z, Liu J, Xu C. Effects of LncRNA BC168687 siRNA on diabetic neuropathic pain mediated by P2X7 receptor on SGCs in DRG of rats. Biomed Res Int 2017;2017:7831251.
[52]. Liu C, Li C, Deng Z, Du E, Xu C. Long non-coding RNA BC168687 is involved in TRPV1-mediated diabetic neuropathic pain in rats. Neuroscience 2018;374:214–22.
[53]. Liu S, Zou L, Xie J, Xie W, Wen S, Xie Q, Gao Y, Li G, Zhang C, Xu C, Xu H, Wu B, Lv Q, Zhang X, Wang S, Xue Y, Liang S. LncRNA NONRATT021972 siRNA regulates neuropathic pain behaviors in type 2 diabetic rats through the P2X7 receptor in dorsal root ganglia. Mol Brain 2016;9:44.
[54]. Liu SJ, Horlbeck MA, Cho SW, Birk HS, Malatesta M, He D, Attenello FJ, Villalta JE, Cho MY, Chen Y, Mandegar MA, Olvera MP, Gilbert LA, Conklin BR, Chang HY, Weissman JS, Lim DA. CRISPRi-based genome-scale identification of functional long noncoding RNA loci in human cells. Science 2017;355:6320.
[55]. Lopez-Gonzalez MJ, Landry M, Favereaux A. MicroRNA and chronic pain: from mechanisms to therapeutic potential. Pharmacol Ther 2017;180:1–15.
[56]. Lötsch J, Doehring A, Mogil JS, Arndt T, Geisslinger G, Ultsch A. Functional genomics of pain in analgesic drug development and therapy. Pharmacol Ther 2013;139:60–70.
[57]. Manolio TA. Genomewide association studies and assessment of the risk of disease. N Engl J Med 2010;363:166–76.
[58]. Martin LJ, Smith SB, Khoutorsky A, Magnussen CA, Samoshkin A, Sorge RE, Cho C, Yosefpour N, Sivaselvachandran S, Tohyama S, Cole T, Khuong TM, Mir E, Gibson DG, Wieskopf JS, Sotocinal SG, Austin JS, Meloto CB, Gitt JH, Gkogkas C, Sonenberg N, Greenspan JD, Fillingim RB, Ohrbach R, Slade GD, Knott C, Dubner R, Nackley AG, Ribeiro-da-Silva A, Neely GG, Maixner W, Zaykin DV, Mogil JS, Diatchenko L. Epiregulin and EGFR interactions are involved in pain processing. J Clin Invest 2017;127:3353–66.
[59]. McDermott LA, Weir GA, Themistocleous AC, Segerdahl AR, Blesneac I, Baskozos G, Clark AJ, Millar V, Peck LJ, Ebner D, Tracey I, Serra J, Bennett DL. Defining the functional role of NaV1.7 in human nociception. Neuron 2019;101:905–919.e8.
[60]. Meng W, Adams MJ, Hebert HL, Deary IJ, McIntosh AM, Smith BH. A genome-wide association study finds genetic associations with broadly-defined headache in UK Biobank (N=223,773). EBioMedicine 2018;28:180–6.
[61]. Miura H, Quadros RM, Gurumurthy CB, Ohtsuka M. Easi-CRISPR for creating knock-in and conditional knockout mouse models using long ssDNA donors. Nat Protoc 2018;13:195–215.
[62]. Mogil JS, Miermeister F, Seifert F, Strasburg K, Zimmermann K, Reinold H, Austin JS, Bernardini N, Chesler EJ, Hofmann HA, Hordo C, Messlinger K, Nemmani KV, Rankin AL, Ritchie J, Siegling A, Smith SB, Sotocinal S, Vater A, Lehto SG, Klussmann S, Quirion R, Michaelis M, Devor M, Reeh PW. Variable sensitivity to noxious heat is mediated by differential expression of the CGRP gene. Proc Natl Acad Sci U S A 2005;102:12938–43.
[63]. Mogil JS. Pain genetics: past, present and future. Trends Genet 2012;28:258–66.
[64]. Mojica FJM, Montoliu L. On the origin of CRISPR-cas technology: from prokaryotes to mammals. Trends Microbiol 2016;24:811–20.
[65]. Moutal A, Yang X, Li W, Gilbraith KB, Luo S, Cai S, François-Moutal L, Chew LA, Yeon SK, Bellampalli SS, Qu C, Xie JY, Ibrahim MM, Khanna M, Park KD, Porreca F, Khanna R. CRISPR/Cas9 editing of Nf1 gene identifies CRMP2 as a therapeutic target in neurofibromatosis type 1-related pain that is reversed by (S)-Lacosamide. PAIN 2017;158:2301–19.
[66]. Nair AS, Poornachand A, Kodisharapu PK. Ziconotide: indications, adverse effects, and limitations in managing refractory chronic pain. Indian J Palliat Care 2018;24:118–9.
[67]. Neely GG, Hess A, Costigan M, Keene AC, Goulas S, Langeslag M, Griffin RS, Belfer I, Dai F, Smith SB, Diatchenko L, Gupta V, Xia CP, Amann S, Kreitz S, Heindl-Erdmann C, Wolz S, Ly CV, Arora S, Sarangi R, Dan D, Novatchkova M, Rosenzweig M, Gibson DG, Truong D, Schramek D, Zoranovic T, Cronin SJ, Angjeli B, Brune K, Dietzl G, Maixner W, Meixner A, Thomas W, Pospisilik JA, Alenius M, Kress M, Subramaniam S, Garrity PA, Bellen HJ, Woolf CJ, Penninger JM. A genome-wide Drosophila screen for heat nociception identifies alpha2delta3 as an evolutionarily conserved pain gene. Cell 2010;143:628–38.
[68]. Nissenbaum J, Devor M, Seltzer Z, Gebauer M, Michaelis M, Tal M, Dorfman R, Abitbul-Yarkoni M, Lu Y, Elahipanah T, delCanho S, Minert A, Fried K, Persson AK, Shpigler H, Shabo E, Yakir B, Pisanté A, Darvasi A. Susceptibility to chronic pain following nerve injury is genetically affected by CACNG2. Genome Res 2010;20:1180–90.
[69]. Ogier JM, Arhatari BD, Carpinelli MR, McColl BK, Wilson MA, Burt RA. An intronic mutation in Chd7 creates a cryptic splice site, causing aberrant splicing in a mouse model of CHARGE syndrome. Sci Rep 2018;8:5482.
[70]. Okuda H, Noguchi A, Kobayashi H, Kondo D, Harada KH, Youssefian S, Shioi H, Kabata R, Domon Y, Kubota K, Kitano Y, Takayama Y, Hitomi T, Ohno K, Saito Y, Asano T, Tominaga M, Takahashi T, Koizumi A. Infantile pain episodes associated with novel Nav1.9 mutations in familial episodic pain syndrome in Japanese families. PLoS One 2016;11:e0154827.
[71]. Pan R, Di H, Zhang J, Huang Z, Sun Y, Yu W, Wu F. Inducible lentivirus-mediated siRNA against TLR4 reduces nociception in a rat model of bone cancer pain. Mediators Inflamm 2015;2015:523896.
[72]. Pastori C, Wahlestedt C. Involvement of long noncoding RNAs in diseases affecting the central nervous system. RNA Biol 2012;9:860–70.
[73]. Ran FA, Hsu PD, Wright J, Agarwala V, Scott DA, Zhang F. Genome engineering using the CRISPR-Cas9 system. Nat Protoc 2013;8:2281–308.
[74]. Rosenblum A, Marsch LA, Joseph H, Portenoy RK. Opioids and the treatment of chronic pain: controversies, current status, and future directions. Exp Clin Psychopharmacol 2008;16:405–16.
[75]. Salsman J, Dellaire G. Precision genome editing in the CRISPR era. Biochem Cell Biol 2017;95:187–201.
[76]. Sanderson SC, Linderman MD, Suckiel SA, Zinberg R, Wasserstein M, Kasarskis A, Diaz GA, Schadt EE. Psychological and behavioural impact of returning personal results from whole-genome sequencing: the HealthSeq project. Eur J Hum Genet 2017;25:280–92.
[77]. Schrenk-Siemens K, Wende H, Prato V, Song K, Rostock C, Loewer A, Utikal J, Lewin GR, Lechner SG, Siemens J. PIEZO2 is required for mechanotransduction in human stem cell-derived touch receptors. Nat Neurosci 2015;18:10–6.
[78]. Segall SK, Maixner W, Belfer I, Wiltshire T, Seltzer Z, Diatchenko L. Janus molecule I: dichotomous effects of COMT in neuropathic vs nociceptive pain modalities. CNS Neurol Disord Drug Targets 2012;11:222–35.
[79]. Serrati S, De Summa S, Pilato B, Petriella D, Lacalamita R, Tommasi S, Pinto R. Next-generation sequencing: advances and applications in cancer diagnosis. Onco Targets Ther 2016;9:7355–65.
[80]. Sexton JE, Cox JJ, Zhao J, Wood JN. The genetics of pain: implications for therapeutics. Annu Rev Pharmacol Toxicol 2018;58:123–42.
[81]. Shang X, Wang Z, Tao H. Mechanism and therapeutic effectiveness of nerve growth factor in osteoarthritis pain. Ther Clin Risk Manag 2017;13:951–6.
[82]. Sharma G, Chopra K, Puri S, Bishnoi M, Rishi P, Kaur IP. Topical delivery of TRPsiRNA-loaded solid lipid nanoparticles confer reduced pain sensation via TRPV1 silencing, in rats. J Drug Target 2018;26:135–49.
[83]. Shen MW, Arbab M, Hsu JY, Worstell D, Culbertson SJ, Krabbe O, Cassa CA, Liu DR, Gifford DK, Sherwood RI. Predictable and precise template-free CRISPR editing of pathogenic variants. Nature 2018;563:646–51.
[84]. Shin J, Yin Y, Park H, Park S, Triantafillu UL, Kim Y, Kim SR, Lee SY, Kim DK, Hong J, Kim DW. p38 siRNA-encapsulated PLGA nanoparticles alleviate neuropathic pain behavior in rats by inhibiting microglia activation. Nanomedicine (Lond) 2018;13:1607–21.
[85]. Slade GD, Ohrbach R, Greenspan JD, Fillingim RB, Bair E, Sanders AE, Dubner R, Diatchenko L, Meloto CB, Smith S, Maixner W. Painful temporomandibular disorder: decade of discovery from OPPERA studies. J Dent Res 2016;95:1084–92.
[86]. Smith SB, Mir E, Bair E, Slade GD, Dubner R, Fillingim RB, Greenspan JD, Ohrbach R, Knott C, Weir B, Maixner W, Diatchenko L. Genetic variants associated with development of TMD and its intermediate phenotypes: the genetic architecture of TMD in the OPPERA prospective cohort study. J Pain 2013;14(12 suppl):T91–101.e1–3.
[87]. Smith SB, Parisien M, Bair E, Belfer I, Chabot-Dore AJ, Gris P, Khoury S, Tansley S, Torosyan Y, Zaykin DV, Bernhardt O, de Oliveira Serrano P, Gracely RH, Jain D, Järvelin MR, Kaste LM, Kerr KF, Kocher T, Lähdesmäki R, Laniado N, Laurie CC, Laurie CA, Männikkö M, Meloto CB, Nackley AG, Nelson SC, Pesonen P, Ribeiro-Dasilva MC, Rizzatti-Barbosa CM, Sanders AE, Schwahn C, Sipilä K, Sofer T, Teumer A, Mogil JS, Fillingim RB, Greenspan JD, Ohrbach R, Slade GD, Maixner W, Diatchenko L. Genome-wide association reveals contribution of MRAS to painful temporomandibular disorder in males. PAIN 2019;160:579–91.
[88]. Sorge RE, Trang T, Dorfman R, Smith SB, Beggs S, Ritchie J, Austin JS, Zaykin DV, Vander Meulen H, Costigan M, Herbert TA, Yarkoni-Abitbul M, Tichauer D, Livneh J, Gershon E, Zheng M, Tan K, John SL, Slade GD, Jordan J, Woolf CJ, Peltz G, Maixner W, Diatchenko L, Seltzer Z, Salter MW, Mogil JS. Genetically determined P2X7 receptor pore formation regulates variability in chronic pain sensitivity. Nat Med 2012;18:595–9.
[89]. Spillane J, Kullmann DM, Hanna MG. Genetic neurological channelopathies: molecular genetics and clinical phenotypes. J Neurol Neurosurg Psychiatry 2016;87:37–48.
[90]. Sun J, Li N, Duan G, Liu Y, Guo S, Wang C, Zhu C, Zhang X. Increased Nav1.7 expression in the dorsal root ganglion contributes to pain hypersensitivity after plantar incision in rats. Mol Pain 2018;14:1744806918782323.
[91]. Suri P, Palmer MR, Tsepilov YA, Freidin MB, Boer CG, Yau MS, Evans DS, Gelemanovic A, Bartz TM, Nethander M, Arbeeva L, Karssen L, Neogi T, Campbell A, Mellstrom D, Ohlsson C, Marshall LM, Orwoll E, Uitterlinden A, Rotter JI, Lauc G, Psaty BM, Karlsson MK, Lane NE, Jarvik GP, Polasek O, Hochberg M, Jordan JM, Van Meurs JBJ, Jackson R, Nielson CM, Mitchell BD, Smith BH, Hayward C, Smith NL, Aulchenko YS, Williams FMK. Genome-wide meta-analysis of 158,000 individuals of European ancestry identifies three loci associated with chronic back pain. PLoS Genet 2018;14:e1007601.
[92]. Teare MD. Approaches to genetic linkage analysis. Methods Mol Biol 2011;713:55–67.
[93]. Tedeschi A, Omura T, Costigan M. CNS repair and axon regeneration: using genetic variation to determine mechanisms. Exp Neurol 2017;287:409–22.
[94]. Tegeder I, Costigan M, Griffin RS, Abele A, Belfer I, Schmidt H, Ehnert C, Nejim J, Marian C, Scholz J, Wu T, Allchorne A, Diatchenko L, Binshtok AM, Goldman D, Adolph J, Sama S, Atlas SJ, Carlezon WA, Parsegian A, Lötsch J, Fillingim RB, Maixner W, Geisslinger G, Max MB, Woolf CJ. GTP cyclohydrolase and tetrahydrobiopterin regulate pain sensitivity and persistence. Nat Med 2006;12:1269–77.
[95]. Terashima T, Ogawa N, Nakae Y, Sato T, Katagi M, Okano J, Maegawa H, Kojima H. Gene therapy for neuropathic pain through siRNA-IRF5 gene delivery with homing peptides to microglia. Mol Ther Nucleic Acids 2018;11:203–15.
[96]. Tsien JZ. Cre-lox neurogenetics: 20 Years of versatile applications in brain research and counting. Front Genet 2016;7:19.
[97]. Veluchamy A, Hébert HL, Meng W, Palmer CNA, Smith BH. Systematic review and meta-analysis of genetic risk factors for neuropathic pain. PAIN 2018;159:825–48.
[98]. Vink S, Alewood PF. Targeting voltage-gated calcium channels: developments in peptide and small-molecule inhibitors for the treatment of neuropathic pain. Br J Pharmacol 2012;167:970–89.
[99]. Wang H, Yang H, Shivalila CS, Dawlaty MM, Cheng AW, Zhang F, Jaenisch R. One-step generation of mice carrying mutations in multiple genes by CRISPR/Cas-mediated genome engineering. Cell 2013;153:910–8.
[100]. Weterman MA, Sorrentino V, Kasher PR, Jakobs ME, van Engelen BG, Fluiter K, de Wissel MB, Sizarov A, Nürnberg G, Nürnberg P, Zelcer N, Schelhaas HJ, Baas F. A frameshift mutation in LRSAM1 is responsible for a dominant hereditary polyneuropathy. Hum Mol Genet 2012;21:358–70.
[101]. Willemen HLDM, Kavelaars A, Prado J, Maas M, Versteeg S, Nellissen LJJ, Tromp J, Gonzalez Cano R, Zhou W, Jakobsson ME, Małecki J, Posthuma G, Habib AM, Heijnen CJ, Falnes PØ, Eijkelkamp N. Identification of FAM173B as a protein methyltransferase promoting chronic pain. PLoS Biol 2018;16:e2003452.
[102]. Wilson LOW, O'Brien AR, Bauer DC. The current state and future of CRISPR-cas9 gRNA design tools. Front Pharmacol 2018;9:749.
[103]. Wittrup A, Lieberman J. Knocking down disease: a progress report on siRNA therapeutics. Nat Rev Genet 2015;16:543–52.
[104]. Wu F, Pan R, Chen J, Sugita M, Chen C, Tao Y, Yu W, Sun Y. Lentivirus mediated siRNA against GluN2B subunit of NMDA receptor reduces nociception in a rat model of neuropathic pain. Biomed Res Int 2014;2014:871637.
[105]. Xu C, Lu Z, Luo Y, Liu Y, Cao Z, Shen S, Li H, Liu J, Chen K, Chen Z, Yang X, Gu Z, Wang J. Targeting of NLRP3 inflammasome with gene editing for the amelioration of inflammatory diseases. Nat Commun 2018;9:4092.
[106]. Xu M, Song P, Huang W, He R, He Y, Zhou X, Gu Y, Pan S, Hu Y. Disruption of AT-hook 1 domain in MeCP2 protein caused behavioral abnormality in mice. Biochim Biophys Acta Mol Basis Dis 2018;1864:347–58.
[107]. Yang H, Wang H, Shivalila CS, Cheng AW, Shi L, Jaenisch R. One-step generation of mice carrying reporter and conditional alleles by CRISPR/Cas-mediated genome engineering. Cell 2013;154:1370–9.
[108]. Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR, Madden PA, Heath AC, Martin NG, Montgomery GW, Goddard ME, Visscher PM. Common SNPs explain a large proportion of the heritability for human height. Nat Genet 2010;42:565–9.
[109]. Yang Y, Mis MA, Estacion M, Dib-Hajj SD, Waxman SG. NaV1.7 as a pharmacogenomic target for pain: moving toward precision medicine. Trends Pharmacol Sci 2018;39:258–75.
[110]. Yoshimi K, Kunihiro Y, Kaneko T, Nagahora H, Voigt B, Mashimo T. ssODN-mediated knock-in with CRISPR-Cas for large genomic regions in zygotes. Nat Commun 2016;7:10431.
[111]. Young EE, Bryant CD, Lee SE, Peng X, Cook B, Nair HK, Dreher KJ, Zhang X, Palmer AA, Chung JM, Mogil JS, Chesler EJ, Lariviere WR. Systems genetic and pharmacological analysis identifies candidate genes underlying mechanosensation in the von Frey test. Genes Brain Behav 2016;15:604–15.
[112]. Zatsepin TS, Kotelevtsev YV, Koteliansky V. Lipid nanoparticles for targeted siRNA delivery—going from bench to bedside. Int J Nanomedicine 2016;11:3077–86.
[113]. Zischewski J, Fischer R, Bortesi L. Detection of on-target and off-target mutations generated by CRISPR/Cas9 and other sequence-specific nucleases. Biotechnol Adv 2017;35:95–104.
[114]. Zorina-Lichtenwalter K, Meloto CB, Khoury S, Diatchenko L. Genetic predictors of human chronic pain conditions. Neuroscience 2016;338:36–62.
[115]. Zorina-Lichtenwalter K, Parisien M, Diatchenko L. Genetic studies of human neuropathic pain conditions: a review. PAIN 2018;159:583–94.

Supplemental Digital Content

Back to Top | Article Outline
© 2019 International Association for the Study of Pain