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Journal of the American Academy of Orthopaedic Surgeons:
doi: 10.5435/JAAOS-20-08-536
Review Article

Orthogenomics: An Update

Matzko, Michelle Elizabeth PhD; Bowen, Thomas R. MD; Smith, Wade R. MD

Free Access
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Author Information

From the Department of Orthopaedic Surgery, Geisinger Clinic, Danville, PA (Dr. Matzko and Dr. Bowen) and the Mountain Orthopedic Trauma Surgeons at Swedish, Englewood, CO (Dr. Smith).

Dr. Smith or an immediate family member is a member of a speakers' bureau or has made paid presentations on behalf of, serves as a paid consultant to or is an employee of, and has received research or institutional support from Synthes. Neither of the following authors nor any immediate family member has received anything of value from or holds stock or stock options in a commercial company or institution related directly or indirectly to the subject of this article: Dr. Matzko and Dr. Bowen.

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The study of genomics in orthopaedics has considerably lagged behind such study in other medical disciplines. Seminal work from other lines of medical research demonstrates the importance of genomic information in the evolution of personalized medicine. Common techniques for studying genome‐phenotype associations include single nucleotide polymorphism, haplotype, and quantitative trait loci analysis. The few genome‐based studies in major orthopaedic and related conditions have focused on osteoporosis, osteoarthritis, neuropathy and nerve compression, spinal deformity, trauma and inflammatory response, and pain and analgesia. The nascent field of orthogenomics, newly defined here as the application of genomic study to orthopaedic practice, has produced findings that could affect the practice of orthopaedics. However, more work is required, and the findings must be distilled and harnessed into applicable and achievable steps to improve clinical orthopaedic practice.

The study of genomics is widely acknowledged as being the foundation for the evolution of personalized medicine. In the past decade, predictions of the use of medical genomics included technologies that would allow physicians to tailor treatment based on individual risk. The current director of the National Institutes of Health, Francis Collins, MD, PhD, forecast the use of clinically administered genetic tests to calculate a person's risk for common conditions.1 Puzas et al2 predicted the use of gene cards—credit card‐type items that could be scanned in the clinical setting to communicate to providers each patient's genetic predisposition to disease.

Although such ideas have not come to fruition, progress has been made with regard to identifying and managing conditions resulting from single‐gene mutations, classifying tumors and developing tumor‐specific oncologic treatments, identifying risk groups, and cataloging patient responses to standard therapies based on genotype. Medical disciplines such as oncology and cardiology are advanced in their use of genomics, but other specialties, including orthopaedics, lag behind. Initial research demonstrates possible pathways to individualized care in patients with osteoporosis, osteoarthritis, neuropathy and nerve compression, spinal deformity and inflammatory response, and pain and analgesia.

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Evolution of Medical Genetics/Genomics

Historically, genetic discovery focused on the identification of the approximately 2,000 known monogenic or mendelian conditions (ie, single gene linked to one or many disease phenotypes). With advances in genetic techniques, the discovery rate for mutated genes in commonly described monogenic conditions (eg, cystic fibrosis, sickle cell anemia) burgeoned in the mid to late 20th century. Although the discovery rate has slowed in recent decades, rare conditions are still being described using the newest generation of sequencing techniques. More recently, genomic research has been focused on understanding diseases with multiple genetic influences, identifying patients at risk for particular diseases, assigning treatment based on genetic and molecular disease signatures, and intervening early in persons at high risk for complications (Figure 1). Several concurrent lines of research are emerging.

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The most robust bodies of genomic work illustrate disease processes. Genes that influence multifactorial disorders such as cancer, cardiovascular disease, asthma, and diabetes were among the first to be described. Literature now exists citing genetic associations to most common conditions known to have both genetic and environmental influences. The second major thrust in medical genomics has been to identify populations vulnerable to or presenting heterogeneity in clinical conditions. Some medical disciplines create distinct subgroups of patients based on specific genomic influences. For example, patients with rheumatoid arthritis can be tested for expression of innate immune defense genes on peripheral blood mononuclear cells.3 Through these tests, a more discriminatory classification of rheumatoid arthritis subtypes can refine the recommended treatment (a third application). Finally, work is emerging that evaluates genetic variability in the effectiveness of treatments and risk for treatment‐related complications. For example, identifying genetic variation in individual responses to warfarin and clopidogrel has opened the door for examining the effectiveness of other medications through pharmacogenomic investigations, a growing area of treatmentrelated genomic research.

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Genomics in Orthopaedics

A handful of reviews were published at the conception of the orthogenomic discipline in the early 2000s.2,4,5 Each mentioned the importance of genomics in future orthopaedic practice, but implementation has been slow. Primers on genetic architecture were included, and then‐current technologies for performing genomic analysis were well‐described (Figure 2). Strategies were suggested to identify diseases of interest, such as those with a significant genetic component (eg, osteoarthritis [OA]), those with underdeveloped surgical or medical treatments (eg, disk degeneration), and those affecting a large population (eg, infection). A recent review on genomic studies published for sports medicine specialists described the application of single nucleotide polymorphism (SNP) analysis in orthopaedics and discussed dosage effects between mutant collagen genes and Achilles tendinopathy or Ehlers‐Danlos syndrome.6,7 However, orthogenomic research has progressed little since those efforts to explain techniques and provide examples of useful application.

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Few published studies have investigated the genomic influences on primarily orthopaedic conditions. Most of the existing work is published in nonorthopaedic literature, particularly rheumatology and oncology periodicals. For example, information regarding bone‐related cancers focuses on pathologic identification and chemotherapeutic management rather than on surgical management, yet the genomics of orthopaedic oncology may be the best‐studied orthogenomic discipline currently. Identification of tumor cell markers has led to better pathologic diagnosis and staging as well as more refined treatment options. For instance, pediatric osteosarcomas and Ewing sarcomas commonly express platelet‐derived growth factor (PDGF) ligand and receptor and/or KIT kinase. Medicines designed to target PDGF or KIT kinase (eg, imatinib mesylate) have demonstrated unique effectiveness against gastrointestinal stromal tumors and chronic myeloid lymphomas expressing these factors. Although one phase II trial did not support use of this treatment in managing pediatric orthopaedic tumors,8 PDGF or KIT signatures may one day allow investigation into the application of related drugs in orthopaedic oncology.

Genotype predicts risk for osteosarcoma and Paget disease as well as prognosis following diagnosis of soft‐tissue sarcoma and chrondrosarcoma.9–13 A mutation dosing effect has been observed in chondrosarcoma. More mutations, including those associated with multiple cancer types (eg, p53 gene), occur regularly and nearly exclusively in high‐grade disease but not low‐grade disease. Some persons progress from low‐ to high‐grade disease, which suggests that mutations are not initially present but that they may evolve simultaneously with progression. These mutations are referred to as somatic mutations, which are acquired over time, in contrast to germline mutations, which are inherited. Additionally, SNPs in genes associated with osteosarcoma have stretched across the orthopaedic and oncologic domains, linking multiple biologic processes (eg, bone metabolism, endocrine, carcinogenic, growth) with this cancer type.14 Although oncologic research has modestly progressed the field of orthogenomics through the discovery of molecular mechanisms regulating oncogenesis and new therapies, orthopaedic contribution to orthogenomics is in the early stages.

Even less genetic evaluation has been done of nononcologic orthopaedic conditions. Animal models provide information on genes and pathways related to broad orthopaedic outcomes. Knockout mice (ie, those lacking a specific gene) have been used to assess genes involved with changes in, for example, bone mass, fracture healing, tendon properties, and disk degeneration. These models have led to descriptions of molecular mechanisms for therapeutic targets; however, physiologic variation between species is high, and animal models are not a human equivalent. There is interest in genomic research in human populations. Mutations in the low‐density lipoprotein receptor‐related protein 5 gene, LRP5, are linked to decreased osteoblast proliferation and bone mass through Lrp5 receptor changes in the Wnt signaling pathway (a signaling pathway common to many biological processes, including bone resorption and building). Identification of this mutation led to an association between adult bone remodeling and cell differentiation and migration functions of the Wnt pathway.15 Altered regulation of the Wnt pathway is a predictor of OA because of the involvement of Wnt in chondrocyte differentiation and bone‐cartilage integrity.16 Multiple proteins of the Wnt pathway may be targets for treatments of OA, thus providing an example of the applicability of genomics to human disease.

Genomics in orthopaedics may also be applicable in patient followup. Many surgical complications are difficult to predict and may be related to an individual's genetic risk. Nonunions after fracture, aseptic loosening after arthroplasty, pulmonary emboli, and many other postoperative complications occur in a seemingly random manner and cannot be predicted by clinical factors alone. Outcome variability without predictable cause implies remaining unidentified etiologic factors. Select genes may be predictive of a treatment complication or failure. Early identification of an individual patient's propensity for complications through genomic testing offers an opportunity for tailored follow‐up and earlier postoperative intervention.

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Technique Update

Population‐based studies have historically informed the level of genetic contribution to a phenotype, and familial studies have confirmed heritable patterns and penetrance. However, the approaches currently used in the fields of pharmacogenomics, surgicogenomics, and personalized medicine are based on individual analysis. SNP, haplotype, and quantitative trait loci approaches are emphasized below.

One current technique with clinical application involves analysis of single base changes that can affect the action of a gene. These mutations are termed SNPs when they occur in >1% of the population. Their location in relation to a gene may underlie differential functional effects. SNPs in promoter regions can affect transcription factor binding, thereby potentially altering the quantity of transcribed messenger RNA (mRNA) and translated protein. Polymorphisms in noncoding regions do not directly modify the nucleotide sequence of the protein but can influence its function in other ways. For instance, both epigenetic modifications or transcription factor binding may alter the translation rate of a protein, but they do not necessarily affect its structure. Base changes in exons can lead to nonsynonymous changes in which a new amino acid is substituted or synonymous changes in which the same amino acid is coded but ribosomal processing is potentially slowed. These processes may modify the shape, size, or signaling effectiveness of the resulting protein. Functional analysis of SNP actions often provides new insights into pathways of interest for therapeutic targeting, especially when a prior association was unknown between a gene and an outcome.

The incidences and functions of SNPs vary between ethnic cohorts. A risk allele in one cohort may be protective or nonexistent in another; thus, a homogeneous population is required for allelic studies. SNPs must have a minor allele frequency >10% to 15% in an ethnic group to be helpful for linking a disease outcome to a particular genotype. The International HapMap Project website provides frequencies of individual SNPs in several populations, including Han Chinese, Tokyo Japanese, Ibadan Nigerians, and white Americans.17 There is variation by ethnicity and sex in the prevalence of common orthopaedic conditions and in genes that influence these conditions. Although ethnic differences in genomic associations are more related to genotype, sex differences within an ethnicity are likely physiologic.

Large‐scale analysis techniques are used to identify regions of the genome that are suggestive of variation in a particular trait and about which there is no prior knowledge of an association. Quantitative trait loci analysis first identifies genomic hotspots of activity related to an outcome, after which relevant genes and SNPs therein are identified. Use of this method has led to findings that seem to support a relationship between the genes receptor activator of nuclear factor‐κ B ligand (RANKL), osteoprotegerin (OPG), estrogen receptor 2 (ESR2) and osteoporotic fractures.18 With SNP analysis, new areas of the genome have been associated with disease, as with osteoporosis and genes in the major histocompatibility complex and zinc fingers of BTB domain 40 (necessary for cell programming).18,19 The discovery of new physiologic associations is the foundation for developing new medications.

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Genomics of Major Orthopaedic Conditions


Genetic contributions to the etiology and progression of common orthopaedic diseases are well‐studied in comparison with genetic contributions to treatments and outcomes. Genes affecting osteoporosis have been identified in the vicinity of the genes OPG; vitamin D receptor, VDR; and LRP5, among others.20,21 The known function of these genes and proteins relates to inhibition of osteoclast production and Wnt signaling, which affects osteoclast and osteoblast activity, leading to decreased bone mineral density (BMD) and osteoporosis. For example, polymorphisms in VDR have been associated with a 15% to 48% increased risk of fracture, an impressive contribution from a single base change.21 Fang et al21 found that combinations of risk haplotype alleles at two loci were associated with 48% additional risk beyond clinical risk factors (ie, age, sex, body mass index, BMD) for fractures in elderly whites. Haplotype risk group status was associated with a 15% reduction in VDR mRNA expression and a 30% increase in VDR mRNA decay in an osteoblastic cell line, suggesting VDR signaling impairment as one explanation of the correlation between the risk genotype and clinically observed fractures. SNPs near OPG and Lrp5 increase the risk for osteoporotic fracture independent of decreased BMD and similar to the degree of risk for osteoporotic fracture attributed to glucocorticoid use. More importantly, as shown in a different study, the prevalence of OPG‐related risk alleles in approximately 8,500 white women was 10‐fold higher than the prevalence of glucocorticoid use.20 This suggests that the genomic profile of a population of orthopaedic patients is more relevant to population outcomes than are some well‐established environmental risk factors (Figure 3, B).

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BMD has been estimated to have a heritability >70%.22 Because of the strong genetic influence and many contributing factors that affect BMD, genes in several physiologic pathways have been implicated. Polymorphisms in the “a disintegrin and metalloproteinase domain with thrombospondin motifs” 18 (ADAMTS18) and transforming growth factor‐β receptor type 3 (TGFBR3) genes have been associated with BMD; proteins encoded from ADAMTS18 have antiangiogenic properties, and TGFBR3 regulates TGF‐β signaling and extracellular matrix assembly. Associations between cortical BMD and SNPs near the OPG, RANK, and RANKL genes have been discovered both in adolescents and the elderly.22,23 The risk alleles of one RANKL SNP were determined to be additive; each copy led to an additional 0.14 standard deviation loss of BMD.23 In addition, polymorphisms in ESR1 and ESR2 are known risk factors for low BMD in postmenopausal women of multiple ethnicities.24 Other aberrant aspects of bone remodeling demonstrate a genetic etiology. Studies identifying risk genotypes for heterotopic ossification revealed that SNPs in genes in the adrenergic system (β2‐adrenergic receptor), immune system (toll‐like receptor 4), and alternative complement system (complement factor H) play a role.25 Furthermore, SNPs linked to both ossification and osteoporosis may identify a previously unknown pathophysiology.26

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The contribution of genetics to OA has been estimated to be 65% for the knee (Figure 3, C), 60% for the hip, and 39% for the hand.27 Large association studies have detected only two loci that reach statistical significance using conservative techniques in repeat populations. The involved genes are growth differentiation factor‐5 (GDF5) and component of oligometric Golgi complex‐5 (COG5), which are associated with bone and cartilage development and maintenance (GDF5 functions in a manner similar to bone morphogenetic protein) and Golgi body function, respectively.16,28 Only two loci were detected; their minor alleles are common (occurring in >20% of the population) but have small effect sizes (odds ratio [OR], ∽1.15 for each association).16,28 Thus, a complex etiology with no potent genetic cause for OA is inferred.

Less robust individual SNP analysis has shown that several genes with myriad functions are linked to jointspecific OA. These genes affect Wntassociated bone mass, involvement of chondrogenic cells in joint remodeling, bone changes in response to mechanical compression, cartilage turnover, chondrogenic processes mediated by TGF‐β1, and the development of type II cartilage.29 In generalized OA, genomic “hot spots” lie close to genes involved in cartilage loss, matrix synthesis, or adaptive changes in bone in response to cartilage degeneration.30 However, the effect sizes of these loci are very small, and many more contributing factors and interactions between these factors are likely necessary to yield clinical OA.

The mechanism by which OA SNPs affect OA risk is not established; however, some OA SNPs are risk factors for knee and hip OA in both sexes and in select ethnic populations. For example, positive associations between calmodulin‐1 (CALM1) and asporin (ASPN) SNPs and OA have been identified in Japanese patients with OA but not white patients with OA.29,31,32 A haplotype containing the ASPN allele was also associated with OA in whites.29 The same study reported sex differences in the association of various SNPs and OA. Frizzled‐related protein‐2 (FRZB2) was associated with OA only in females and collagen type II alpha‐1 (COL2A1) only in males; cartilage oligomeric matrix protein (COMP) demonstrated differential effects for males and females.29 Some SNPs are associated with only hip, hand, or knee OA specifically, which is suggestive of diverse etiologies. These results support the belief in complex factors underpinning OA, differences by sex and ethnicity, and differential influences to the affected joint. Nevertheless, even relatively small contributions from several genes are important to investigate in developing new therapies.

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Neuropathy and Nerve Compression

Charcot‐Marie‐Tooth (CMT) disease encompasses several peripheral neuropathies, and it has autosomal dominant, recessive, and X‐linked inheritance patterns. Because of its disparate clinical features (ie, age of onset, clinical severity, subtypes), genetic explanations for its various presentations have been explored. Alterations of genes related to nerve development and function result in susceptibility to CMT; these alterations were the first evidence of a genetic contribution.

Recently, two SNPs involved in myelination and axon‐glial cell communication were identified as being causative for various presentations of peripheral neuropathy.33 Homozygous inheritance of these risk alleles resulted in CMT, whereas heterozygous mutations were associated with less severe axonal neuropathy and carpal tunnel syndrome. Additionally, a SNP in LRSAM1 (leucine rich repeat and sterile alpha motif 1) is associated with a version of axonal CMT.34 The risk genotype gives rise to a truncated version of its complementary protein, which is involved in protein degradation, proper adhesion of neuronal cells, and neurodegenerative conditions.

Whole‐exome sequencing in a family of clinically evident but not subtyped CMT confirmed a causative mutation in the gene GJB1 (gap junction β‐1 protein), a locus previously associated with CMT characteristics.35 The results provided a specific diagnosis of CMT1X and allowed cataloguing of phenotypes associated with this genotype (ie, axonal degradation, decreased nerve conduction velocities, upper extremity involvement), as determined from clinical observations.

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Spinal Deformity

Genetic studies describe factors that contribute to disk degeneration. Twin studies demonstrate that degeneration in adults may be explained up to 74% by genes alone.36 Two polymorphisms in VDR have been frequently linked to early predictors of disk disease,37,38 in addition to cervical spondylotic myelopathy.39 SNPs in cartilage intermediate layer protein 1 (CILP), COL9A2, and COL9A3 underlie bone and collagen remodeling pathways, affecting both risk for and severity of disk degeneration.40,41 In comparison, suspected environmental factors were found to contribute little (eg, physical loading, cigarette smoking, age [2% to 7% variation])42,43 or not at all (eg, whole‐body vibration associated with exposure to motor vehicle use)44 (Figure 3, F). There is some variation with respect to the level of the spine, but the effects are small compared with the ability to predict degenerative change based on family data.

Adolescent idiopathic scoliosis (AIS) presents as a range of manifestations, with high variability in curve severity even within families. Unlike infantile idiopathic scoliosis, about which little is known of the involved genetics, several genes are postulated to be associated with AIS based on candidate‐gene analysis, the physiologic functions of the proteins encoded from the genes, association with other bone or cartilage disorders, or involvement with chromosomal abnormalities in patients with AIS. Multiple mutations in the chromodomain‐helicase‐DNA‐binding protein 7 gene, CHD7, cause the congenital deformity CHARGE (coloboma of the eye, heart defects, atresia of the choanae, retardation of growth and/or development, genital and/or urinary abnormalities, and ear abnormalities and deafness) syndrome. A single polymorphism in CHD7 is linked to AIS,45 although this relationship has not been confirmed in other studies. Similarly, no causation has been established between polymorphisms in genes encoding VDRs, ESRs, or enzymes involved in steroid metabolism and AIS.

In the best example of applied orthogenomics, it is now possible to analyze a combination of 53 genetic markers together with the presenting Cobb angle in order to stratify patients based on predicted risk of progression46 (Figure 3, A). Using this clinical risk model, physicians can ease the treatment regimen for patients at low risk of progression and enroll higher‐risk patients in innovative but potentially riskier treatment protocols.

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Trauma and Inflammatory Response

Orthopaedic trauma patients are at risk of multiple complications, including acute respiratory distress syndrome (ARDS), multiple organ dysfunction syndrome, septic shock, deep vein thrombosis (DVT), and death. Genetic associations with ARDS have been identified for proteins that underpin vascular and neuroprotective processes during acute traumatic injury. The associations for single SNPs range from limited (OR = 1.28 for angiopoietin‐2 SNP and risk for ARDS)47 to clinically substantial (OR = 4.29 for a single vascular endothelial growth factor polymorphism and the risk of mortality from ARDS).48 Similarly, an interleukin (IL)‐10 polymorphism, along with shock index and severity of head injury, emerged as the only significant independent risk factors for multiple organ dysfunction syndrome following major trauma49 (Figure 3, E). In a different study, this same IL‐10 locus was linked to reduced blood levels of IL‐10 and increased mortality of critically ill patients.50 Toll‐like receptor and tumor necrosis factor (TNF)‐α SNPs and haplotypes are significant risk factors for trauma‐associated complicated sepsis.51,52

DVT risk is associated with polymorphisms in factor V Leiden, prothrombin, antithrombin‐III, thrombin‐activatable fibrinolysis inhibitor (TAFI), and protein S genes. In one study, mutations in factor V Leiden, antithrombin‐III, and prothrombin genes were better predictors of DVT than were standard coagulation or thrombophilic measures.53 Currently, some of this knowledge is used by surgeons in choosing the most appropriate DVT prophylaxis and in preoperative patient counseling. The identification of more risk genotypes may lead to a time when treatment can be individualized based on each patient's cumulative genetic and environmental risk for posttraumatic complications.

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Pain and Analgesia

No best treatment is known for postoperative and chronic pain in orthopaedic patients. There are significant interindividual differences in the appreciation of acute and chronic pain and among individuals whose pain persists after tissue injury. Current therapies have limited effectiveness, with one study showing up to 80% of patients reporting insufficient pain control in the postoperative period.54

Our understanding of the genetic basis of nociception is improving. The gene that underlies the classic rare pain disorders congenital insensitivity to pain, paroxysmal extreme pain disorder, and primary erythermalgia55–57 also influences general pain perception. More subtle variation in this gene, SCN9, which encodes the voltage‐gated sodium channel Nav1.7 α‐subunit, is also associated with disparities between radiographic determinations of OA severity and individual pain scores58 (Figure 3, D). This relationship was further confirmed by linking higher reported pain with the presence of an SCN9 risk allele in 1,227 patients with several conditions: sciatica, phantom pain after amputation, pain following lumbar diskectomy, and pancreatitis.

Pharmacogenomic research demonstrates that mutations in several genes affect patient responses to analgesics. Multiple copies of the gene CYP2D6, which encodes cytochrome P450, lead to enhanced conversion of codeine to morphine, thereby increasing the risk of toxicity.59,60 Conversely, alterations yielding diminished or absent cytochrome P450 functioning leave codeine and tramadol ineffective.60,61 Similarly, common haplotypes in COMT, the gene for catechol‐O‐methyltransferase, affect sensitivity to management of chronic pain. In one study, persons with haplotypes that yielded high activity of the enzyme and who thereby experienced less pain responded poorly to the β‐adrenergic antagonist propranalol.62 In other genotypes, this is an effective analgesic.

Gene‐sex‐environment interactions have been noted. Two minor alleles in the melanocortin‐1 receptor gene, Mc1r, which presents in humans as a red hair and fair skin phenotype, was shown to increase the actions of pentazocine in women and, in general, increase the effectiveness of κ‐opioid analgesics in women and female mice.63 In men and male mice, variants in the arginine vasopressin receptor gene AVPR1A were linked with reduced pain levels and greater analgesic effects, but only in stressed subjects in which pain relief pathways were already engaged. In a different study, the COMT mutation leading to highest pain sensitization predicted a 3‐ to 5‐month postoperative pain score of ≥4 out of 10 in patients with chronic shoulder pain and who additionally catastrophize pain (ie, characterize their pain as extreme or unbearable).64

These findings highlight the vast individual and genotypic characteristics that underlie pain perception and analgesic efficacy. Currently, individualized treatment recommendations cannot be made. Future genomic research will likely better inform the treatment approach for patients who experience inadequate pain control.

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Little work has examined the orthogenomics related to treatment outcomes. Several SNPs in genes related to bone remodeling, inflammatory, or cellular turnover pathways have been implicated for osteolysis in revision total hip and knee arthroplasties. All demonstrate significant predictive value for osteolysis‐related surgical revision, with ORs ranging from 1.5 for IL‐1R to 8.23 for TGF‐ β1.65–68 Time to failure has also been measured. Kolundzić et al68 found IL‐6, TGF‐β1, and TNF‐α SNPs to be predictive of time to onset of aseptic instability following total hip arthroplasty. SNPs in the promoter regions of IL‐6 and TGF‐β1 were found to affect prosthesis survival by hazard ratios of 4.9 and 5.7, respectively; that is, the odds of failure of the risk genotype were five to six times higher at any time compared with those of the protective genotype (Figure 3, G).

Infection is the most thoroughly evaluated complication related to orthopaedic surgery. Numerous studies have searched for the genetic underpinnings of predisposition to infection. However, genetic studies predicting infection are difficult to design and interpret, primarily because of the complexity of the immune system. There is little research on the genomics of orthopaedicrelated infections or inflammatory response, although several cytokine pathways (eg, TNF‐α, IL‐1α, IL‐4, IL‐6) demonstrate significant involvement (Figure 3, H). Further research is needed to determine the genetic predisposition to infection and other complications following orthopaedic treatment.

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Despite the slow pace of change regarding genomic exploration and discovery in orthopaedics, the groundwork has been laid for advancement in a variety of conditions. Orthopaedic researchers can benefit from the clinical refinement of genomic analysis techniques made available by other medical researchers and can harness those techniques for our own questions. Several lines of orthogenomic research will likely benefit from increased use of next‐generation methods such as whole‐genome and ‐exome sequencing. Potential examples of clinical application of genomics research include the assessment of trauma patients, greater precision in subclassification of orthopaedic oncologic conditions, and early identification of patients at increased risk of common complications. Each of these offers scholarly, patient care‐centered, and fundable opportunities for the orthopaedic researcher.

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Evidence‐based Medicine: Levels of evidence are described in the table of contents. In this article, reference 62 is a level I study. Reference 8 is a level II study. References 3, 6, 9‐14, 16, 18‐41, 45, 47‐53, 55‐61, 63, 64, and 68 are level III studies. Reference 54 is a level IV study. References 1, 2, 4, 5, 7, and 15 are level V expert opinion.

References printed in bold type are those published within the past 5 years.

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2. Puzas JE, O'Keefe RJ, Lieberman JR: The orthopaedic genome: What does the future hold and are we ready? J Bone Joint Surg Am 2002;84(1):133-141.

3. van der Pouw Kraan TC, Wijbrandts CA, van Baarsen LG, et al: Rheumatoid arthritis subtypes identified by genomic profiling of peripheral blood cells: Assignment of a type I interferon signature in a subpopulation of patients. Ann Rheum Dis 2007;66(8):1008-1014.

4. Bayat A, Barton A, Ollier WE: Dissection of complex genetic disease: Implications for orthopaedics. Clin Orthop Relat Res 2004;(419):297-305.

5. Evans CH, Rosier RN: Molecular biology in orthopaedics: The advent of molecular orthopaedics. J Bone Joint Surg Am 2005;87(11):2550-2564.

6. September AV, Cook J, Handley CJ, van der Merwe L, Schwellnus MP, Collins M: Variants within the COL5A1 gene are associated with Achilles tendinopathy in two populations. Br J Sports Med 2009;43(5):357-365.

7. Gibson WT: Genetic association studies for complex traits: Relevance for the sports medicine practitioner. Br J Sports Med 2009;43(5):314-316.

8. Bond M, Bernstein ML, Pappo A, et al: A phase II study of imatinib mesylate in children with refractory or relapsed solid tumors: A Children's Oncology Group study. Pediatr Blood Cancer 2008;50(2): 254-258.

9. Daroszewska A, Hocking LJ, McGuigan FE, et al: Susceptibility to Paget's disease of bone is influenced by a common polymorphic variant of osteoprotegerin. J Bone Miner Res 2004;19(9):1506-1511.

10. Morimoto Y, Ozaki T, Ouchida M, et al: Single nucleotide polymorphism in fibroblast growth factor receptor 4 at codon 388 is associated with prognosis in high-grade soft tissue sarcoma. Cancer 2003;98(10):2245-2250.

11. Mirabello L, Berndt SI, Seratti GF, et al: Genetic variation at chromosome 8q24 in osteosarcoma cases and controls. Carcinogenesis 2010;31(8):1400-1404.

12. Rozeman LB, Hameetman L, Cleton-Jansen AM, Taminiau AH, Hogendoorn PC, Bovée JV: Absence of IHH and retention of PTHrP signalling in enchondromas and central chondrosarcomas. J Pathol 2005;205(4):476-482.

13. Albagha OM, Visconti MR, Alonso N, et al: Genome-wide association study identifies variants at CSF1, OPTN and TNFRSF11A as genetic risk factors for Paget's disease of bone. Nat Genet 2010; 42(6):520-524.

14. Savage SA, Mirabello L: Using epidemiology and genomics to understand osteosarcoma etiology. Sarcoma 2011;2011:548151.

15. Krishnan V, Bryant HU, Macdougald OA: Regulation of bone mass by Wnt signaling. J Clin Invest 2006;116(5): 1202-1209.

16. Evangelou E, Chapman K, Meulenbelt I, et al: Large-scale analysis of association between GDF5 and FRZB variants and osteoarthritis of the hip, knee, and hand. Arthritis Rheum 2009;60(6):1710-1721.

17. International HapMap Project. Available at: Accessed June 26, 2012.

18. Styrkarsdottir U, Halldorsson BV, Gretarsdottir S, et al: Multiple genetic loci for bone mineral density and fractures. N Engl J Med 2008;358(22): 2355-2365.

19. Zollman S, Godt D, Privé GG, Couderc JL, Laski FA: The BTB domain, found primarily in zinc finger proteins, defines an evolutionarily conserved family that includes several developmentally regulated genes in Drosophila. Proc Natl Acad Sci U S A 1994;91(22):10717-10721.

20. Richards JB, Rivadeneira F, Inouye M, et al: Bone mineral density, osteoporosis, and osteoporotic fractures: A genomewide association study. Lancet 2008; 371(9623):1505-1512.

21. Fang Y, van Meurs JB, d'Alesio A, et al: Promoter and 3′-untranslated-region haplotypes in the vitamin d receptor gene predispose to osteoporotic fracture: The Rotterdam study. Am J Hum Genet 2005;77(5):807-823.

22. Arden NK, Baker J, Hogg C, Baan K, Spector TD: The heritability of bone mineral density, ultrasound of the calcaneus and hip axis length: A study of postmenopausal twins. J Bone Miner Res 1996;11(4):530-534.

23. Paternoster L, Lorentzon M, Vandenput L, et al: Genome-wide association metaanalysis of cortical bone mineral density unravels allelic heterogeneity at the RANKL locus and potential pleiotropic effects on bone. PLoS Genet 2010;6(11): e1001217.

24. Greendale GA, Chu J, Ferrell R, Randolph JF Jr, Johnston JM, Sowers MR: The association of bone mineral density with estrogen receptor gene polymorphisms. Am J Med 2006;119(9 suppl 1):S79-S86.

25. Mitchell EJ, Canter J, Norris P, Jenkins J, Morris J: The genetics of heterotopic ossification: Insight into the bone remodeling pathway. J Orthop Trauma 2010;24(9):530-533.

26. Yan L, Zhao WG, Li JJ, Yang H, Wang H, Lin X: Linkage of three polymorphisms on chromosome 20p12 to ossification of the posterior longitudinal ligament of spine and its severity in Han Chinese patients. Chin Med J (Engl) 2010;123(17):2341-2346.

27. Spector TD, MacGregor AJ: Risk factors for osteoarthritis: Genetics. Osteoarthritis Cartilage 2004;12 suppl A:S39-S44.

28. Kerkhof HJ, Lories RJ, Meulenbelt I, et al: A genome-wide association study identifies an osteoarthritis susceptibility locus on chromosome 7q22. Arthritis Rheum 2010;62(2):499-510.

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