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. 2017 Jul 15;34(14):2280–2290. doi: 10.1089/neu.2016.4856

Applying Systems Biology Methodology To Identify Genetic Factors Possibly Associated with Recovery after Traumatic Brain Injury

Brad G Kurowski 1,✉, Amery Treble-Barna 2, Alexis J Pitzer 3, Shari L Wade 1, Lisa J Martin 1, Ranjit S Chima 1, Anil Jegga 1
PMCID: PMC5510694  PMID: 28301983

Abstract

Traumatic brain injury (TBI) is one of the leading causes of morbidity and mortality worldwide. It is linked with a number of medical, neurological, cognitive, and behavioral sequelae. The influence of genetic factors on the biology and related recovery after TBI is poorly understood. Studies that seek to elucidate the impact of genetic influences on neurorecovery after TBI will lead to better individualization of prognosis and inform development of novel treatments, which are considerably lacking. Current genetic studies related to TBI have focused on specific candidate genes. The objectives of this study were to use a system biology–based approach to identify biologic processes over-represented with genetic variants previously implicated in clinical outcomes after TBI and identify unique genes potentially related to recovery after TBI. After performing a systematic review to identify genes in the literature associated with clinical outcomes, we used the genes identified to perform a systems biology-based integrative computational analysis to ascertain the interactions between molecular components and to develop models for regulation and function of genes involved in TBI recovery. The analysis identified over-representation of genetic variants primarily in two biologic processes: response to injury (cell proliferation, cell death, inflammatory response, and cellular metabolism) and neurocognitive and behavioral reserve (brain development, cognition, and behavior). Overall, this study demonstrates the use of a systems biology–based approach to identify unique/novel genes or sets of genes important to the recovery process. Findings from this systems biology–based approach provide additional insight into the potential impact of genetic variants on the underlying complex biological processes important to TBI recovery and may inform the development of empirical genetic-related studies for TBI. Future studies that combine systems biology methodology and genomic, proteomic, and epigenetic approaches are needed in TBI.

Keywords: : genetic factors, recovery, rehabilitation, traumatic brain injury

Introduction

The Centers for Disease Control and Prevention estimates that at least 5.3 million Americans have a lifelong or long-term need as a result of a prior traumatic brain injury (TBI).1 The estimated economic cost of TBI, including direct and indirect costs, likely exceeds $76.5 billion currently.2 Childhood TBI results in approximately 2685 deaths, 37,000 hospitalizations, and 435,000 emergency department visits yearly in the United States, making it one of the leading causes of morbidity and mortality in children1,3; however, the meager amount of scientific evidence available for prognosis, management, and treatment of TBI is disproportionate to the large societal and medical impact of this condition. Recovery after injury is related to multiple factors, including age of injury, severity of injury, and time post-injury; however, there is often differential recovery in individuals with similar brain injuries.4–6 This differential recovery, in part, is likely a result of variation in individual (i.e., genetic) factors. Studies that seek to elucidate the impact of genetic influences on neurorecovery after TBI will lead to better individualization of prognosis and facilitate discovery and development of novel precision treatments, which are considerably lacking for this population.

The pathophysiology of TBI is complex and involves multiple biologic processes.7–9 After brain injuries, both focal and diffuse injury-related processes ensue, including diffuse axonal injury, cortical contusions, and hemorrhage.7,9–11 Secondary injury involves biologic processes that occur in response to injury and typically includes delayed neuronal injury, diffuse brain swelling, ischemic injury, hypoxic injury, and metabolic dysfunction.7,10,11 After the initial response to injury, biologic processes involved in repair and recovery become more critical. These processes include neuroplasticity of intact neural networks, repair of damaged neuronal circuitry, and replacement of lost neurons.7 Later in recovery, processes involved in cognitive and behavioral functioning become salient. Medical and rehabilitation interventions attempt to target several of these processes to maximize recovery. Acutely, clinical indicators (e.g., cerebral perfusion pressure) of more optimal early medical management are associated with better functional outcomes after severe TBI.12 Later after injury, optimization of medical and rehabilitation therapies that focus on neuroplasticity become important.13 Further, genetic and environmental factors likely influence these biologic processes in differing manners and at various times post-injury to affect recovery.14–24

Elucidation of the genetic contributions to recovery from TBI would transform our understanding of potential mechanisms of recovery and result in identification of novel targets for interventions to improve recovery.16,25–27 Genes related to the response to trauma (inflammatory cascade, calcium signaling, apoptosis, and vascular response), repair and plasticity, and cognitive and neurobehavioral capacity/reserve are postulated to play a role in recovery.14 To date, there is a paucity of prior work evaluating the association of genetics with recovery after TBI in general and there are even fewer studies involving pediatric samples. Various studies in adults suggest that a number of genes may affect recovery28; however, these studies are restricted primarily to one or a few candidate genes, limited by relatively small sample sizes, and lack consideration of gene–gene, gene–pathway, or gene–environment interactions.

The variety and breadth of candidate variants analyzed in prior adult studies highlight the potential for different genes to be important in recovery at various stages after TBI. For example, the interleukin genes, which are involved in cellular proliferation, seem to be important in survival and global outcomes,29–33 while dopamine-related genes may be more important for longer-term recovery of higher level cognitive and behavioral functioning.34–36 A better understanding of how genes influence biologic processes related to recovery after TBI is critically needed to advance the field of neurogenetics related to recovery after TBI.

The complex and multifactorial nature of TBI presents unique challenges to conventional, unimodal biological methodologies that integrative systems biology–based, approaches may enlighten.37 To date, genetic association studies in TBI have primarily used narrow genetic approaches where a pre-determined, single, or small set of candidate genes or variants are evaluated. These narrow approaches are often inefficient and may be misleading because other genes or variants important to the recovery process are not considered. Alternatively, an unbiased genome-wide association study (GWAS) considers all genetic possibilities but requires large sample sizes, is limited by multiple testing concerns, and there is often the risk of identifying spurious associations.

A TBI systems medicine approach—integrating systems biology of TBI with clinical information—provides a strategy for identifying diagnostic and prognostic markers for medical management of TBI. Systems biology–based computational approaches, which integrate gene-level data with biologic processes, pathways, and networks, are not only successful in extracting novel biological insights,37–40 but also facilitate biomarker discovery and identify and prioritize novel testable disease candidate genes.41–44 Prior work has described the potential use of system biology approaches for biomarker discovery for TBI,37,45,46 but an examination of the use of system biology approaches applied to genetic associations with clinical outcomes after TBI is lacking. A systems biology–based approach would inform selection of genes and require less multiple testing than a GWAS, but is more inclusive than focusing on a limited number of candidate genes or variants.

The objectives of this study were to use system biology-based approaches to identify biologic processes over-represented with genetic variants previously implicated in clinical outcomes after TBI and identify unique genes potentially related to recovery after TBI. Using these TBI-related biological processes, we identified and prioritized candidate genes in biologic processes likely important for recovery after TBI. The use of these system biology approaches provide a novel, biologically based method to better inform the evaluation of the association of genetics with recovery after TBI and has the potential to greatly advance the understanding of neurogenetics related to TBI recovery.

Methods

TBI gene list compilation

To generate a list of genes previously associated with clinical outcomes after TBI, we conducted a PubMed review using the following search terms: (“brain injury” OR “brain injuries” OR “concussion” OR “brain concussion” OR “head injuries” OR “head injury”) AND (“genetic” OR “genetics” OR “gene” OR “genes” OR “polymorphism” OR “DNA” OR “genetic polymorphism” OR “genetic variation” OR “genetic variability” OR “genotype”). In addition, the search was limited to English language articles, human studies, clinical research, and non-review articles using PubMed filters. The search was conducted from January 1, 1995, through September, 15, 2016. Inclusion criteria for articles required that each article evaluate the association of genetic variation (e.g., single nucleotide polymorphisms or other measure of genetic variation) with clinical or functional outcomes after traumatic brain injury. Genes were included only if a significant association was found. Articles evaluating the association of genetics with non-clinical outcomes (e.g., pathology post-mortem) or evaluating the association of gene expression or protein/biomarker levels with outcomes were excluded. Review articles and case reports also were excluded. Initially, 1269 articles were identified. After review of titles and abstracts, 1109 were excluded. After review of full manuscripts, 55 were excluded and the final number of studies included was 105. We then manually compiled a list of genetic loci that demonstrated a significant association with clinical or functional outcomes after TBI. Outcomes were grouped into Survival/Global functioning (e.g., Glasgow Outcome Scale), Cognitive (e.g., neuropsychological/intelligence measures), Behavioral/Emotional (e.g., Child Behavioral Checklist, anxiety, depression), and Medical Sequelae (e.g., seizures, dementia).

Functional enrichment analysis

We used ToppGene Suite47 to systematically analyze this list of compiled TBI genetic loci. ToppGene Suite is a comprehensive platform for gene set enrichment analyses and machine learning-based candidate gene ranking. It is widely used, principally for linking biological states to observations of transcriptome or exome analyses.48–53 The current backend knowledgebase for ToppGene Suite is a comprehensive collection (more than 12 million gene annotations) of diverse gene set libraries compiled from a variety of publically available gene annotation databases and resources. ToppGene detects functional similarities or enrichment of a given gene list based on ontologies, pathways, phenotypes, and drug–gene association. Although there are several enrichment analyses tools available, the strength of ToppGene is its extensive collection of functional annotations compared to other system biology tools.54,55 Biologic processes demonstrating enrichment at a p value of 0.05 (Bonferroni corrected) were considered to be significantly associated with the manually compiled gene list.

TBI candidate gene discovery and prioritization

After identifying genetically influenced biologic processes associated with TBI outcomes using information from previous genetic and TBI studies, we sought to identify additional TBI candidate genes and variants among these biologic processes likely to have the greatest impact on recovery after TBI. Using the theoretical basis that genes common among several processes would be more likely to have the greatest influence on recovery, the first approach we took was to identify genes common among these processes. Because it is possible that genes not common among several or more processes may also have a substantial impact on recovery, we took another systems biology–based approach to identify other candidate genes. In this second approach, we ranked all known genes associated with each of the enriched TBI-relevant biological processes using ToppGene47 with default parameters.56 ToppGene ranks candidate genes based on functional similarity to a user supplied training set (known TBI associated genes in this case). Functional similarity is computed using a variety of gene annotations (pathways, biological processes, phenotype, literature, protein interactions, and co-expression) in an integrative systems biology–based machine learning approach. Using this second approach, a priori, we considered genes ranked in the top 5% likely to be significantly associated with recovery from TBI.

Results

Table 129,32–36,57–135 includes the list of genes that were identified based on the search criteria and associated clinical and functional outcome categories. Thirty-three genes were identified, with apolipoprotein E (APOE) being the most commonly reported in the prior TBI literature. Figure 1 depicts the interrelationship among genes identified in Table 1 and clinical outcomes. Several genes identified were associated with multiple functional outcomes, with APOE, adenosine A1 receptor, brain-derived neurotrophic factor (BDNF), and glutamate decarboxylase 1 genes being associated with the all four outcomes. Other genes were associated with only one functional outcome category.

Table 1.

Compiled List of Genes with Genetic Variants Associated with Outcomes after TBI

Gene Citation Type of Outcome(s)
5’-Nucleotidase ecto (NT5E) 57 Medical sequelae
Adenosine kinase (ADK) 57 Medical sequelae
Adenosine A1 receptor (ADORA1) 58 Survival/global functioning; cognitive; behavioral/emotional
Angiotensin I converting enzyme (ACE) 59–61 Survival/global functioning; cognitive
Ankyrin repeat and kinase domain containing 1 (ANKK1) 35,36,62,63 Survival/global functioning; cognitive
Apolipoprotein E (APOE) 64–105 Survival/global functioning; cognitive; behavioral/emotional; medical sequelae
ATP-binding cassette B1(ABCB1) 106 Survival/global functioning
Aquaporin 4 (AQP4) 107 Survival/global functioning
ATP binding cassette subfamily B member 1 (ABCB1) 108 Survival/global functioning
B-cell lymphoma 2 (BCL2) 109 Survival/global functioning; behavioral/emotional
BMX non-receptor tyrosine kinase (BMX) 110 Behavioral/emotional; medical sequelae
Brain-derived neurotrophic factor (BDNF) 111–115 Survival/global functioning; cognitive; medical sequelae
Catechol-O-methyltransferase (COMT) 34,116,117 Cognitive
Cytochrome P450 family 19 subfamily A member 1 (CYP19A1) 118 Survival/global functioning
Dopamine beta-hydroxylase (DBH) 117 Cognitive
Fatty acid amide hydrolase (FAAH) 119 Behavioral/emotional
Glutamate decarboxylase 1 (GAD1) 117,120 Survival/global functioning; cognitive; medical sequelae
Glutamate ionotropic receptor NMDA type subunit 2A (GRIN2A) 117 Cognitive
Interleukin (IL) 29 Survival/global functioning
Interleukin 1 beta (IL-1B) 33,121 Survival/global functioning; medical sequelae
Interleukin 1 receptor antagonist (IL-1RN) 32 Survival/global functioning
Methylenetetrahydrofolate reductase (NAD(P)H) [MTHFR] 122 Medical sequelae
Monoamine oxidase A (MAO-A) 123 Behavioral/emotional
NADH: ubiquinone reductase (H+-translocating) (NADH) 124 Survival/global functioning; behavioral/emotional
Neuroglobin (NGB) 125 Survival/global functioning; behavioral/emotional
Nitric oxide synthase 3 (NOS3) 126 Survival/global functioning
Poly(ADP-ribose) polymerase 1 (PARP-1) 127 Survival/global functioning
RNA binding motif single stranded interacting protein 3 (RBMS3) 128 Medical sequelae
Solute carrier family 6 member 4 (SLC6A4) 129,130 Behavioral/emotional; survival/global functioning
Synuclein alpha (SNCA) 131,132 Medical sequelae
Tumor necrosis factor (TNFA) 133 Survival/global functioning; medical sequelae
Tumor protein P53 (TP53) 134 Survival/global functioning
WW and C2 domain containing 1 (WWC1) 135 Survival/global functioning; cognitive
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TBI, traumatic brain injury.

FIG. 1.

FIG. 1.

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Known traumatic brain injury gene network and relationship with clinical outcomes. The relationship of known traumatic brain injury genes (green circles) with clinical outcomes (orange rectangles) is depicted. Black lines depict the interrelationship of genes with each other and clinical outcomes.

Using ToppGene Suite and the manually compiled list of genes from prior TBI genetic association studies, we found significant enrichment (p value 0.05; Bonferroni correction) for biologic processes, including behavior or cognition, cell death, cell proliferation, inflammatory response, glucose metabolic process, and brain development. Figure 2 demonstrates the interrelationship of the compiled gene list, clinical outcomes, and biologic processes.

FIG. 2.

FIG. 2.

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Known traumatic brain injury gene network relationship with clinical outcomes and enriched biologic processes. The relationship of known traumatic brain injury genes (blue circles) with clinical outcomes (orange hexagons) and biological processes (green rectangle) is depicted. Black lines represent the interrelationship of genes with clinical outcomes and biologic processes.

When evaluating genes common across these processes, we identified 144 genes common to cell proliferation, cell death, and inflammatory processes (Fig. 3), and 145 genes common to brain development, cognition, and behavior (Fig. 3). When ranking the genes using ToppGene associated with biological processes relevant to TBI (cell proliferation, cell death, inflammatory response, glucose metabolic processes, brain development, and cognition and behavior) and selecting the top 5% of the ranked genes from each of the six TBI-relevant biological processes, 155 unique genes were identified. When combining the 155 ranked genes with 144 and 145 genes identified using the common biological processes approach, there were 377 candidate genes identified (Fig. 4 and Supplementary Material; see online supplementary material at www.liebertpub.com). Approximately 21% (78/377) of these genes have been associated with brain injury previously in the literature (Fig. 4 and Supplementary Material). Genes identified were over-represented primarily in two biologic processes: response to injury (cell proliferation, cell death, inflammatory response, and cellular metabolism) and neurocognitive and behavioral reserve (brain development, cognition, and behavior).

FIG. 3.

FIG. 3.

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Comparison of gene sets known to be associated with various biological processes relevant to traumatic brain injury. Venn diagram on the left shows a comparison between known gene sets related to cell death, cell proliferation, and inflammation biologic process with 144 common genes. Venn diagram on the right side displays a comparison between genes associated with behavior/cognition and brain developmental process with 145 common genes.

FIG. 4.

FIG. 4.

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Novel candidate genes for traumatic brain injury outcome identified using system biology-based approaches. Green nodes represent biologic process important to traumatic brain injury outcomes. Blue nodes represent novel candidate genes identified using system biology methodology likely to be associated with biologic process important to traumatic brain injury outcomes. The red nodes rep0resent genes previously associated with brain injury in the literature.

Discussion

Using information from the prior literature in combination with a systems biology approach, we identified 377 genes associated with biologic processes important to TBI outcomes and recovery, with 299 of these genes representing novel candidates. Many of the previous TBI and genetic studies have evaluated the association of only one or a few genetic variants with outcomes after injury.28 The findings from this study further highlight the complexity of genetic influences related to TBI recovery. There are likely multiple genetic influences on recovery and narrow genetic approaches may not be ideal to evaluate genetic influences on recovery after TBI. Using systems biology methodology to inform approaches that more broadly considers genetic variants would allow identification of genes related to biologic systems important to recovery and inform future studies. These findings provide a framework for consideration of a modified genomics approach that is a compromise between genome-wide association and candidate-gene or polymorphism studies for understanding the influence of genetics on recovery after TBI.

Genetic influences may, in part, provide an explanation as to why there is significant variability in recovery after seemingly similar TBIs. However, due to the complex nature of recovery after TBI, one gene or a few variants are unlikely to account for a significant amount of variability in outcomes. It has been postulated that candidate genes associated with various biologic processes may modulate recovery after TBI.14 Candidate genes involved in neurologic injury and inflammatory processes may be more important soon after injury, whereas candidate genes involved in cognitive and behavioral processes become more salient later during recovery, when neuroplasticity is critical. Many candidate genes have functions that span several biologic processes; therefore, instead of evaluating individual genes, it may be more informative to evaluate information about the association of candidate gene sets with outcomes after injury to determine which genetically influenced biologic processes are most critical to recovery or specific outcomes.

Gene-enrichment analyses, which focuses on evaluating the association of groups of genes that share a common biologic function, chromosomal location, or regulation with outcomes, have been used to study other disease processes,136 but have not been applied to recovery after TBI. Gene-enrichment analysis is a pathway-based statistical genetics approach to test for the over-representation of associated genetic variants.136 It reduces multiple testing burden by focusing on overall effects of genes or processes rather than individual variants. A set of genes that cluster together in biologic processes hypothesized to be associated with an outcome can be selected based on prior knowledge. “Enrichment” of genes is tested by examining whether genes associated with outcomes in a sample are significantly over-represented in biologic processes hypothesized to be associated with outcomes relative to biologic processes hypothesized not to be associated with outcomes. Gene-enrichment analysis assists in assigning biologic meaning to groups of genes that emerge from analyses. Such an approach may provide better insight into recovery than limited candidate gene approaches previously used.

Genetic factors also are likely to interact with environmental and other factors to influence recovery. It is well known that environment may moderate the effect that one particular gene has in expression of a particular phenotype.137 Consequently, it is essential to consider environmental factors in genetic studies. Genetic and environmental influences are both implicated in recovery after TBI; however, there is a lack of an understanding of how genes and environment interact to influence outcomes after TBI. Adverse effects of a given genotype may be negated by a favorable environment and/or exacerbated by a poor environment. A number of genetic variants implicated in recovery after TBI also interact with home and family environmental factors to influence outcomes in pediatric populations. For example, dopamine-receptor, dopamine pathway, serotonin transporter, and catechol-O-methyltransferase genetic variants interact with environmental factors to influence cognitive and behavioral functioning in various childhood populations.139–149 The influence brain injury and environmental factors have on genetic modifications (i.e., epigenetics) is just beginning to be explored in brain injury.150 Additionally, work within the field of pharmacogenomics has demonstrated that genetic factors may explain variability in response among individuals receiving similar treatments.151–156 This previous work suggests that there is likely a complex interplay between genetic and environmental factors that ultimately influences recovery from TBI and that to fully understand genetic influences on recovery after TBI, gene-environmental interactions need to be considered in the future.

In line with recent precision medicine initiatives,157 a better understanding of the association of variability in individual (e.g., genetic), environmental, clinical, and other factors with outcomes after TBI will critically inform prognosis and management strategies. Further, a more comprehensive understanding of the biologic processes important to recovery would allow development of biologically informed treatment strategies. Future studies that combine systems biology methodology and genomic, proteomic, and epigenetic approaches are needed.14,45,46,150 An improved understanding of the biology of recovery related to genetics in combination with an understanding of how environmental and genetic factors interact to determine recovery could transform management strategies for TBI.

Limitations

The primary limitation to the system biology approach used in this study is that the findings are biased based on previously published studies of the association of candidate genes or variants with outcomes after TBI. There may be other genes or variants important to recovery, but they are unstudied currently. As other candidate genes are identified, the system biology models should be modified to reflect this new information.

Our original gene list included genes that demonstrated an association with outcomes after brain injury; however, we did not apply a weighted approach. For instance, certain genes or variants may be more influential in recovery than others, but an accurate assessment of the relative weight was not possible; therefore, weighted associations were not included in the analysis. In addition to potentially enriching positive findings in prior literature, the systems biology approach used is also at risk for increasing or magnifying spurious findings due to limitations of the underpinning studies. Prior work is most notably limited by low sample sizes combined with multiple comparisons and paucity of independently replicated findings. Further, our approach did not account for variation in primary outcomes. For instance, certain biologic processes may be more critical for survival, whereas other processes may be more important for long-term cognitive and behavioral functioning. The feasibility of doing sub-analyses based on outcome domain is limited because of the overall limited number of genetic studies performed to date in TBI. It is unclear if the same biologic processes are equally significant for recovery after milder and more severe injuries. Further, because of the limited number of genetic studies evaluating the pediatric population, it is unclear if the biologic processes important in recovery after adult TBI are the same for pediatrics.

Conclusion

The influence of genetics on recovery after TBI is incompletely understood. Narrow genetic approaches primarily used to evaluate the association of genetics with recovery after TBI. Unbiased whole genome approaches are limited by the need for large sample sizes, multiple testing concerns, and risk of identifying spurious associations. Using system biology methodology to inform genetic association studies provides a compromise between narrow and unbiased whole genome approaches. Systems biology methodology may help identify novel genes not previously considered. Additionally, using systems biology approaches that seek to identify genes or variants over-represented in biologic processes associated with recovery after TBI may provide valuable insight into the physiology of recovery and inform the development of future precision treatments. Future studies that combine systems biology methodology and genomic, proteomic, and epigenetic approaches are needed in TBI.

Supplementary Material

Supplemental data
Supp_Data.xlsx (2.9MB, xlsx)

Acknowledgments

Funding for this work was supported, in part, by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01NS096053 and K23HD074683), National Institute of Neurological Disorders and Stroke (R01NS096053 and U01 NS081041), and Cincinnati Children's Research Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the supporting agencies.

Author Disclosure Statement

No competing financial interests exist.

References

  • 1.Faul M., Xu L., Wald M., and Coronado V. (2010). Traumatic brain injury in the United States: emergency department visits, hospitalizations and deaths 2002–2006. Centers for Disease Control and Prevention, National Center for Injury Prevention and Control: Atlanta, GA [Google Scholar]
  • 2.Finkelstein E., Corso P., and Miller T.R. (2006). The incidence and economic burden of injuries in the United States, in: The Incidence and Economic Burden of Injuries in the United States. Oxford University Press: New York, NY [Google Scholar]
  • 3.Keenan H.T. and Bratton S.L. (2006). Epidemiology and outcomes of pediatric traumatic brain injury. Dev. Neurosci. 28, 256–263 [DOI] [PubMed] [Google Scholar]
  • 4.Taylor H.G. (2004). Research on outcomes of pediatric traumatic brain injury: current advances and future directions. Dev. Neuropsychol. 25, 199–225 [DOI] [PubMed] [Google Scholar]
  • 5.Massagli T.L., Jaffe K.M., Fay G.C., Polissar N.L., Liao S., and Rivara J.B. (1996). Neurobehavioral sequelae of severe pediatric traumatic brain injury: a cohort study. Arch. Phys. Med. Rehabil. 77, 223–231 [DOI] [PubMed] [Google Scholar]
  • 6.Taylor H.G., Swartwout M.D., Yeates K.O., Walz N.C., Stancin T., and Wade S.L. (2008). Traumatic brain injury in young children: postacute effects on cognitive and school readiness skills. J. Int. Neuropsychological Soc. 14, 734–745 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Zasler N.D., Katz D.I., and Zafonte R.D. (2013). Brain Injury Medicine, 2nd ed. Demos Medial: New York, NY [Google Scholar]
  • 8.Algattas H. and Huang J.H. (2013). Neurotrauma and repair research: traumatic brain injury (TBI) and its treatments. Biomed. Eng. Comput. Biol. 5, 5–56 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Werner C. and Engelhard K. (2007). Pathophysiology of traumatic brain injury. Br. J. Anaesth. 99, 4–9 [DOI] [PubMed] [Google Scholar]
  • 10.Kochanek P.M., Clark R.S., Ruppel R.A., Adelson P.D., Bell M.J., Whalen M.J., Robertson C.L., Satchell M.A., Seidberg N.A., Marion D.W., and Jenkins L.W. (2000). Biochemical, cellular, and molecular mechanisms in the evolution of secondary damage after severe traumatic brain injury in infants and children: lessons learned from the bedside. Pediatr. Crit. Care Med. 1, 4–19 [DOI] [PubMed] [Google Scholar]
  • 11.Sahuquillo J., Poca M.A., and Amoros S. (2001). Current aspects of pathophysiology and cell dysfunction after severe head injury. Curr. Pharm. Des. 7, 1475–1503 [DOI] [PubMed] [Google Scholar]
  • 12.Vavilala M.S., Kernic M.A., Wang J., Kannan N., Mink R.B., Wainwright M.S., Groner J.I., Bell M.J., Giza C.C., Zatzick D.F., Ellenbogen R.G., Boyle L.N., Mitchell P.H., and Rivara F.P.; Pediatric Guideline Adherence and Outcomes Study. (2014). Acute care clinical indicators associated with discharge outcomes in children with severe traumatic brain injury. Crit. Care Med. 42, 2258–2266 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Forsyth R. and Basu A. (2015). The promotion of recovery through rehabilitation after acquired brain injury in children. Developmental medicine and child neurology 57, 16–22 [DOI] [PubMed] [Google Scholar]
  • 14.McAllister T.W. (2010). Genetic factors modulating outcome after neurotrauma. PM R 2, S241–S252 [DOI] [PubMed] [Google Scholar]
  • 15.Weaver S.M., Chau A., Portelli J.N., and Grafman J. (2012). Genetic polymorphisms influence recovery from traumatic brain injury. Neuroscientist 18, 631–644 [DOI] [PubMed] [Google Scholar]
  • 16.Dardiotis E., Grigoriadis S., and Hadjigeorgiou G.M. (2012). Genetic factors influencing outcome from neurotrauma. Curr. Opin. Psychiatry 25, 231–238 [DOI] [PubMed] [Google Scholar]
  • 17.Weaver S.M. and Portelli J.N. (2014). Neurobiological influences on recovery from traumatic brain injury: the role of genetic polymorphisms. Curr. Pharm. Des. 20, 4275–4283 [PubMed] [Google Scholar]
  • 18.Weaver S.M., Portelli J.N., Chau A., Cristofori I., Moretti L., and Grafman J. (2014). Genetic polymorphisms and traumatic brain injury: the contribution of individual differences to recovery. Brain Imaging Behav. 8, 420–434 [DOI] [PubMed] [Google Scholar]
  • 19.Kurowski B., Martin L.J., and Wade S.L. (2012). Genetics and outcomes after traumatic brain injury (TBI): what do we know about pediatric TBI? J. Pediatr. Rehabil. Med. 5, 217–231 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Davidson J., Cusimano M.D., and Bendena W.G. (2015). Post-traumatic brain injury: genetic susceptibility to outcome. Neuroscientist 21, 424–441 [DOI] [PubMed] [Google Scholar]
  • 21.Mathias J.L. and Wheaton P. (2015). Contribution of brain or biological reserve and cognitive or neural reserve to outcome after TBI: a meta-analysis (prior to 2015). Neurosci. Biobehav. Rev. 55, 573–593 [DOI] [PubMed] [Google Scholar]
  • 22.Bennett E.R., Reuter-Rice K., and Laskowitz D.T. (2016). Genetic influences in traumatic brain injury, in: Translational Research in Traumatic Brain Injury. Laskowitz D. and Grant G. (eds). CRC Press: Boca Raton, FL: [PubMed] [Google Scholar]
  • 23.Gerring J.P. and Wade S. (2012). The essential role of psychosocial risk and protective factors in pediatric traumatic brain injury research. J. Neurotrauma 29, 621–628 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Babikian T., Merkley T., Savage R.C., Giza C.C., and Levin H. (2015). Chronic aspects of pediatric traumatic brain injury: review of the literature. J. Neurotrauma 32, 1849–1860 [DOI] [PubMed] [Google Scholar]
  • 25.Pearson-Fuhrhop K.M., Minton B., Acevedo D., Shahbaba B., and Cramer S.C. (2013). Genetic variation in the human brain dopamine system influences motor learning and its modulation by L-Dopa. PloS One 8, e61197. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Jordan B.D. (2007). Genetic influences on outcome following traumatic brain injury. Neurochem. Res 32, 905–915 [DOI] [PubMed] [Google Scholar]
  • 27.Conley Y.P. and Alexander S. (2011). Genomic, transcriptomic, and epigenomic approaches to recovery after acquired brain injury. PM R 3, S52–S58 [DOI] [PubMed] [Google Scholar]
  • 28.Dardiotis E., Fountas K.N., Dardioti M., Xiromerisiou G., Kapsalaki E., Tasiou A., and Hadjigeorgiou G.M. (2010). Genetic association studies in patients with traumatic brain injury. Neurosurg. Focus 28, E9. [DOI] [PubMed] [Google Scholar]
  • 29.Dalla Libera A.L., Regner A., de Paoli J., Centenaro L., Martins T.T., and Simon D. (2011). IL-6 polymorphism associated with fatal outcome in patients with severe traumatic brain injury. Brain Inj. 25, 365–369 [DOI] [PubMed] [Google Scholar]
  • 30.Johnson V.E., Murray L., Raghupathi R., Stewart J., Nicoll J.A., MacKinnon M.A., McIntosh T.K., and Graham D.I. (2006). No evidence for the presence of apolipoprotein epsilon4, interleukin-lalpha allele 2 and interleukin-1beta allele 2 cause an increase in programmed cell death following traumatic brain injury in humans. Clin. Neuropathol. 25, 255–264 [PubMed] [Google Scholar]
  • 31.Tanriverdi T., Uzan M., Sanus G.Z., Baykara O., Is M., Ozkara C., and Buyra N. (2006). Lack of association between the IL1A gene (−889) polymorphism and outcome after head injury. Surg. Neurol. 65, 7–10 [DOI] [PubMed] [Google Scholar]
  • 32.Hadjigeorgiou G.M., Paterakis K., Dardiotis E., Dardioti M., Aggelakis K., Tasiou A., Xiromerisiou G., Komnos A., Zintzaras E., Scarmeas N., Papadimitriou A., and Karantanas A. (2005). IL-1RN and IL-1B gene polymorphisms and cerebral hemorrhagic events after traumatic brain injury. Neurology 65, 1077–1082 [DOI] [PubMed] [Google Scholar]
  • 33.Uzan M., Tanriverdi T., Baykara O., Kafadar A., Sanus G.Z., Tureci E., Ozkara C., Uysal O., and Buyra N. (2005). Association between interleukin-1 beta (IL-1beta) gene polymorphism and outcome after head injury: an early report. Acta Neurochir. (Wien) 147, 715–720 [DOI] [PubMed] [Google Scholar]
  • 34.Lipsky R.H., Sparling M.B., Ryan L.M., Xu K., Salazar A.M., Goldman D., and Warden D.L. (2005). Association of COMT Val158Met genotype with executive functioning following traumatic brain injury. J. Neuropsychiatry Clin. Neurosci. 17, 465–471 [DOI] [PubMed] [Google Scholar]
  • 35.McAllister T.W., Flashman L.A., Harker Rhodes C., Tyler A.L., Moore J.H., Saykin A.J., McDonald B.C., Tosteson T.D., and Tsongalis G.J. (2008). Single nucleotide polymorphisms in ANKK1 and the dopamine D2 receptor gene affect cognitive outcome shortly after traumatic brain injury: a replication and extension study. Brain Inj. 22, 705–714 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.McAllister T.W., Rhodes C.H., Flashman L.A., McDonald B.C., Belloni D., and Saykin A.J. (2005). Effect of the dopamine D2 receptor T allele on response latency after mild traumatic brain injury. Am. J. Psychiatry 162, 1749–1751 [DOI] [PubMed] [Google Scholar]
  • 37.Yu C., Boutte A., Yu X., Dutta B., Feala J.D., Schmid K., Dave J., Tawa G.J., Wallqvist A., and Reifman J. (2015). A systems biology strategy to identify molecular mechanisms of action and protein indicators of traumatic brain injury. J. Neurosci. Res. 93, 199–214 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Schadt E.E. and Lum P.Y. (2006). Thematic review series: systems biology approaches to metabolic and cardiovascular disorders. Reverse engineering gene networks to identify key drivers of complex disease phenotypes. J. Lipid Res. 47, 2601–2613 [DOI] [PubMed] [Google Scholar]
  • 39.Huan T., Zhang B., Wang Z., Joehanes R., Zhu J., Johnson A.D., Ying S., Munson P.J., Raghavachari N., Wang R., Liu P., Courchesne P., Hwang S.J., Assimes T.L., McPherson R., Samani N.J., and Schunkert H.; Coronary ARteryDIsease Genome wide Replication and Meta-analysis (CARDIoGRAM) Consortium; International Consortium for Blood Pressure GWAS (ICBP), Meng Q., Suver C., O'Donnell C.J., Derry J., Yang X., Levy D. (2013). A systems biology framework identifies molecular underpinnings of coronary heart disease. Arterioscler. Thromb. Vasc. Biol. 33, 1427–1434 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Zhang M., Zhu C., Jacomy A., Lu L.J., and Jegga A.G. (2011). The orphan disease networks. Am. J. Hum. Genet. 88, 755–766 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Chen J., Aronow B.J., and Jegga A.G. (2009). Disease candidate gene identification and prioritization using protein interaction networks. BMC Bioinform. 10, 73. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Kaimal V., Sardana D., Bardes E.E., Gudivada R.C., Chen J., and Jegga A.G. (2011). Integrative systems biology approaches to identify and prioritize disease and drug candidate genes. Methods Mol. Biol. 700, 241–259 [DOI] [PubMed] [Google Scholar]
  • 43.Jegga A.G. (2014). Candidate gene discovery and prioritization in rare diseases. Methods Mol. Biol. 1168, 295–312 [DOI] [PubMed] [Google Scholar]
  • 44.Jegga A.G., Schneider L., Ouyang X., and Zhang J. (2011). Systems biology of the autophagy-lysosomal pathway. Autophagy 7, 477–489 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Guingab-Cagmat J.D., Cagmat E.B., Hayes R.L., and Anagli J. (2013). Integration of proteomics, bioinformatics, and systems biology in traumatic brain injury biomarker discovery. Front. Neurol. 4, 61. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Feala J.D., Abdulhameed M.D., Yu C., Dutta B., Yu X., Schmid K., Dave J., Tortella F., and Reifman J. (2013). Systems biology approaches for discovering biomarkers for traumatic brain injury. J. Neurotrauma 30, 1101–1116 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Chen J., Bardes E.E., Aronow B.J., and Jegga A.G. (2009). ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 37, W305–W311 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Turkieh A., Caubere C., Barutaut M., Desmoulin F., Harmancey R., Galinier M., Berry M., Dambrin C., Polidori C., Casteilla L., Koukoui F., Rouet P., and Smih F. (2014). Apolipoprotein O is mitochondrial and promotes lipotoxicity in heart. J. Clin. Invest. 124, 2277–2286 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Zhu J., Cui L., Wang W., Hang X.Y., Xu A.X., Yang S.X., Dou J.T., Mu Y.M., Zhang X., and Gao J.P. (2013). Whole exome sequencing identifies mutation of EDNRA involved in ACTH-independent macronodular adrenal hyperplasia. Fam. Cancer 12, 657–667 [DOI] [PubMed] [Google Scholar]
  • 50.Yang J., Duan S., Zhong R., Yin J., Pu J., Ke J., Lu X., Zou L., Zhang H., Zhu Z., Wang D., Xiao H., Guo A., Xia J., Miao X., Tang S., and Wang G. (2013). Exome sequencing identified NRG3 as a novel susceptible gene of Hirschsprung's disease in a Chinese population. Mol. Neurobiol. 47, 957–966 [DOI] [PubMed] [Google Scholar]
  • 51.Boczek N.J., Best J.M., Tester D.J., Giudicessi J.R., Middha S., Evans J.M., Kamp T.J. and Ackerman M.J. (2013). Exome sequencing and systems biology converge to identify novel mutations in the L-type calcium channel, CACNA1C, linked to autosomal dominant long QT syndrome. Circulation. Cardiovasc. Genet. 6, 279–289 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Ballesta-Martinez M.J., Lopez-Gonzalez V., Dulcet L.A., Rodriguez-Santiago B., Garcia-Minaur S. and Guillen-Navarro E. (2013). Autosomal dominant oculoauriculovertebral spectrum and 14q23.1 microduplication. Am. J. Med. Genet. A 161A, 2030–2035 [DOI] [PubMed] [Google Scholar]
  • 53.Erlich Y., Edvardson S., Hodges E., Zenvirt S., Thekkat P., Shaag A., Dor T., Hannon G.J., and Elpeleg O. (2011). Exome sequencing and disease-network analysis of a single family implicate a mutation in KIF1A in hereditary spastic paraparesis. Genome Res. 21, 658–664 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Hawrylycz M., Miller J.A., Menon V., Feng D., Dolbeare T., Guillozet-Bongaarts A.L., Jegga A.G., Aronow B.J., Lee C.K., Bernard A., Glasser M.F., Dierker D.L., Menche J., Szafer A., Collman F., Grange P., Berman K.A., Mihalas S., Yao Z., Stewart L., Barabási A.L., Schulkin J., Phillips J., Ng L., Dang C., Haynor D.R., Jones A., Van Essen D.C., Koch C., and Lein E. (2015). Canonical genetic signatures of the adult human brain. Nat. Neurosci. 18, 1832–1844 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Kann M.G. (2010). Advances in translational bioinformatics: computational approaches for the hunting of disease genes. Brief Bioinform. 11, 96–110 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Chen J., Xu H., Aronow B.J., and Jegga A.G. (2007). Improved human disease candidate gene prioritization using mouse phenotype. BMC Bioinform. 8, 392. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Subramanian A., Tamayo P., Mootha V.K., Mukherjee S., Ebert B.L., Gillette M.A., Paulovich A., Pomeroy S.L., Golub T.R., Lander E.S., and Mesirov J.P. (2005). Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. U. S. A. 102, 15545–15550 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Nussbaum R., McInnes R., and Huntington W. (2007). Genetics of common disorders with complex inheritance, in: Thompson & Thompson Genetics in Medicine. Saunders Elsevier: Philadelphia, PA, pps. 151–174 [Google Scholar]
  • 59.Lavigne J.V., Herzing L.B., Cook E.H., Lebailly S.A., Gouze K.R., Hopkins J., and Bryant F.B. (2013). Gene x environment effects of serotonin transporter, dopamine receptor D4, and monoamine oxidase A genes with contextual and parenting risk factors on symptoms of oppositional defiant disorder, anxiety, and depression in a community sample of 4-year-old children. Dev. Psychopathol. 25, 555–575 [DOI] [PubMed] [Google Scholar]
  • 60.Berry D., Deater-Deckard K., McCartney K., Wang Z., and Petrill S.A. (2013). Gene-environment interaction between dopamine receptor D4 7-repeat polymorphism and early maternal sensitivity predicts inattention trajectories across middle childhood. Dev. Psychopathol. 25, 291–306 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.Berry D., McCartney K., Petrill S., Deater-Deckard K., and Blair C. (2014). Gene-environment interaction between DRD4 7-repeat VNTR and early child-care experiences predicts self-regulation abilities in prekindergarten. Dev. Psychobiol. 56, 373–391 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.Cicchetti D. and Rogosch F.A. (2012). Gene x Environment interaction and resilience: effects of child maltreatment and serotonin, corticotropin releasing hormone, dopamine, and oxytocin genes. Dev. Psychopathol. 24, 411–427 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Schellekens A.F., Franke B., Ellenbroek B., Cools A., de Jong C.A., Buitelaar J.K., and Verkes R.J. (2013). COMT Val158Met modulates the effect of childhood adverse experiences on the risk of alcohol dependence. Addict. Biol. 18, 344–356 [DOI] [PubMed] [Google Scholar]
  • 64.Sweitzer M.M., Halder I., Flory J.D., Craig A.E., Gianaros P.J., Ferrell R.E., and Manuck S.B. (2013). Polymorphic variation in the dopamine D4 receptor predicts delay discounting as a function of childhood socioeconomic status: evidence for differential susceptibility. Soc. Cogn. Affect. Neurosci. 8, 499–508 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65.Chen C., Chen C., Moyzis R., Stern H., He Q., Li H., Li J., Zhu B., and Dong Q. (2011). Contributions of dopamine-related genes and environmental factors to highly sensitive personality: a multi-step neuronal system-level approach. PloS One 6, e21636. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.van Roekel E., Goossens L., Scholte R.H., Engels R.C., and Verhagen M. (2011). The dopamine D2 receptor gene, perceived parental support, and adolescent loneliness: longitudinal evidence for gene-environment interactions. J. Child Psychol. Psychiatry 52, 1044–1051 [DOI] [PubMed] [Google Scholar]
  • 67.Das D., Tan X., and Easteal S. (2011). Effect of model choice in genetic association studies: DRD4 exon III VNTR and cigarette use in young adults. Am. J. Med. Genet. B 156B, 346–351 [DOI] [PubMed] [Google Scholar]
  • 68.Beaver K.M. and Belsky J. (2012). Gene-environment interaction and the intergenerational transmission of parenting: testing the differential-susceptibility hypothesis. Psychiatr. Q 83, 29–40 [DOI] [PubMed] [Google Scholar]
  • 69.Martel M.M., Nikolas M., Jernigan K., Friderici K., Waldman I., and Nigg J.T. (2011). The dopamine receptor D4 gene (DRD4) moderates family environmental effects on ADHD. J. Abnorm. Child Psychol. 39, 1–10 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Hayden E.P., Klein D.N., Dougherty L.R., Olino T.M., Laptook R.S., Dyson M.W., Bufferd S.J., Durbin C.E., Sheikh H.I., and Singh S.M. (2010). The dopamine D2 receptor gene and depressive and anxious symptoms in childhood: associations and evidence for gene-environment correlation and gene-environment interaction. Psychiatr. Genet. 20, 304–310 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71.Wong V.S., and Langley B. (2016). Epigenetic changes following traumatic brain injury and their implications for outcome, recovery and therapy. Neurosci. Lett. 625, 26–33 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 72.Abul-Husn N.S., Owusu Obeng A., Sanderson S.C., Gottesman O., and Scott S.A. (2014). Implementation and utilization of genetic testing in personalized medicine. Pharmgenomics Pers. Med. 7, 227–240 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 73.Fontana V., Luizon M.R., and Sandrim V.C. (2014). An update on the pharmacogenetics of treating hypertension. J. Hum. Hypertens. 29, 283–291 [DOI] [PubMed] [Google Scholar]
  • 74.Gardner K.R., Brennan F.X., Scott R., and Lombard J. (2014). The potential utility of pharmacogenetic testing in psychiatry. Psychiatry J. 2014, 730956. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 75.Krasniak A.E., Knipp G.T., Svensson C.K., and Liu W. (2014). Pharmacogenomics of acetaminophen in pediatric populations: a moving target. Front. Genet. 5, 314. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 76.Levy F. (2014). Applications of pharmacogenetics in children with attention-deficit/hyperactivity disorder. Pharmgenomics Pers. Med. 7, 349–356 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 77.Ortega V.E., Meyers D.A., and Bleecker E.R. (2015). Asthma pharmacogenetics and the development of genetic profiles for personalized medicine. Pharmgenomics Pers. Med. 8, 9–22 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 78.Schleidgen S., Klingler C., Bertram T., Rogowski W.H., and Marckmann G. (2013). What is personalized medicine: sharpening a vague term based on a systematic literature review. BMC Med. Ethics 14, 55. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 79.Diamond M.L., Ritter A.C., Jackson E.K., Conley Y.P., Kochanek P.M., Boison D., and Wagner A.K. (2015). Genetic variation in the adenosine regulatory cycle is associated with posttraumatic epilepsy development. Epilepsia 56, 1198–1206 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 80.Wagner A.K., Miller M.A., Scanlon J., Ren D., Kochanek P.M., and Conley Y.P. (2010). Adenosine A1 receptor gene variants associated with post-traumatic seizures after severe TBI. Epilepsy Res. 90, 259–272 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 81.Dardiotis E., Paterakis K., Siokas V., Tsivgoulis G., Dardioti M., Grigoriadis S., Simeonidou C., Komnos A., Kapsalaki E., Fountas K., and Hadjigeorgiou G.M. (2015). Effect of angiotensin-converting enzyme tag single nucleotide polymorphisms on the outcome of patients with traumatic brain injury. Pharmacogenet. Genomics 25, 485–490 [DOI] [PubMed] [Google Scholar]
  • 82.Kehoe A.D., Eleftheriou K.I., Heron M., Coats T.J., and Montgomery H.E. (2008). Angiotensin-converting enzyme genotype may predict survival following major trauma. Emerg. Med. J. 25, 759–761 [DOI] [PubMed] [Google Scholar]
  • 83.Ariza M., Matarin M.D., Junque C., Mataro M., Clemente I., Moral P., Antonia Poca M., Garnacho A., and Sahuquillo J. (2006). Influence of Angiotensin-converting enzyme polymorphism on neuropsychological subacute performance in moderate and severe traumatic brain injury. J. Neuropsychiatry Clin. Neurosci. 18, 39–44 [DOI] [PubMed] [Google Scholar]
  • 84.Failla M.D., Myrga J.M., Ricker J.H., Dixon C.E., Conley Y.P., and Wagner A.K. (2015). Posttraumatic Brain Injury Cognitive Performance Is Moderated by Variation Within ANKK1 and DRD2 Genes. J Head Trauma Rehabil 30, E54–E66 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 85.Yue J.K., Pronger A.M., Ferguson A.R., Temkin N.R., Sharma S., Rosand J., Sorani M.D., McAllister T.W., Barber J., Winkler E.A., Burchard E.G., Hu D., Lingsma H.F., Cooper S.R., Puccio A.M., Okonkwo D.O., Diaz-Arrastia R., and Manley G.T., COBRIT Investigators; TRACK-TBI Investigators. (2015). Association of a common genetic variant within ANKK1 with six-month cognitive performance after traumatic brain injury. Neurogenetics 16, 169–180 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 86.Alexander S., Kerr M.E., Kim Y., Kamboh M.I., Beers S.R., and Conley Y.P. (2007). Apolipoprotein E4 allele presence and functional outcome after severe traumatic brain injury. J. Neurotrauma 24, 790–797 [DOI] [PubMed] [Google Scholar]
  • 87.Anderson G.D., Temkin N.R., Dikmen S.S., Diaz-Arrastia R., Machamer J.E., Farhrenbruch C., Miller J.W., and Sadrzadeh S.M. (2009). Haptoglobin phenotype and apolipoprotein E polymorphism: relationship to posttraumatic seizures and neuropsychological functioning after traumatic brain injury. Epilepsy Behav. 16, 501–506 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 88.Ariza M., Pueyo R., Matarin Mdel M., Junque C., Mataro M., Clemente I., Moral P., Poca M.A., Garnacho A., and Sahuquillo J. (2006). Influence of APOE polymorphism on cognitive and behavioural outcome in moderate and severe traumatic brain injury. J. Neurol. Neurosurg. Psychiatry 77, 1191–1193 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 89.Brichtova E. and Kozak L. (2008). Apolipoprotein E genotype and traumatic brain injury in children–association with neurological outcome. Childs Nerv. Syst. 24, 349–356 [DOI] [PubMed] [Google Scholar]
  • 90.Bulstrode H., Nicoll J.A., Hudson G., Chinnery P.F., Di Pietro V., and Belli A. (2014). Mitochondrial DNA and traumatic brain injury. Ann. Neurol. 75, 186–195 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 91.Chiang M.F., Chang J.G., and Hu C.J. (2003). Association between apolipoprotein E genotype and outcome of traumatic brain injury. Acta Neurochir. (Wien.) 145, 649–653; discussion 653-644 [DOI] [PubMed] [Google Scholar]
  • 92.Crawford F.C., Vanderploeg R.D., Freeman M.J., Singh S., Waisman M., Michaels L., Abdullah L., Warden D., Lipsky R., Salazar A., and Mullan M.J. (2002). APOE genotype influences acquisition and recall following traumatic brain injury. Neurology 58, 1115–1118 [DOI] [PubMed] [Google Scholar]
  • 93.Diaz-Arrastia R., Gong Y., Fair S., Scott K.D., Garcia M.C., Carlile M.C., Agostini M.A., and Van Ness P.C. (2003). Increased risk of late posttraumatic seizures associated with inheritance of APOE epsilon4 allele. Arch. Neurol. 60, 818–822 [DOI] [PubMed] [Google Scholar]
  • 94.Eramudugolla R., Bielak A.A., Bunce D., Easteal S., Cherbuin N., and Anstey K.J. (2014). Long-term cognitive correlates of traumatic brain injury across adulthood and interactions with APOE genotype, sex, and age cohorts. J. Int. Neuropsychol. Soc. 20, 444–454 [DOI] [PubMed] [Google Scholar]
  • 95.Friedman G., Froom P., Sazbon L., Grinblatt I., Shochina M., Tsenter J., Babaey S., Yehuda B., and Groswasser Z. (1999). Apolipoprotein E-epsilon4 genotype predicts a poor outcome in survivors of traumatic brain injury. Neurology 52, 244–248 [DOI] [PubMed] [Google Scholar]
  • 96.Guo Z., Cupples L.A., Kurz A., Auerbach S.H., Volicer L., Chui H., Green R.C., Sadovnick A.D., Duara R., DeCarli C., Johnson K., Go R.C., Growdon J.H., Haines J.L., Kukull W.A., and Farrer L.A. (2000). Head injury and the risk of AD in the MIRAGE study. Neurology 54, 1316–1323 [DOI] [PubMed] [Google Scholar]
  • 97.Han S.D., Suzuki H., Drake A.I., Jak A.J., Houston W.S., and Bondi M.W. (2009). Clinical, cognitive, and genetic predictors of change in job status following traumatic brain injury in a military population. J. Head Trauma Rehabil. 24, 57–64 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 98.Isoniemi H., Tenovuo O., Portin R., Himanen L., and Kairisto V. (2006). Outcome of traumatic brain injury after three decades–relationship to ApoE genotype. J. Neurotrauma 23, 1600–1608 [DOI] [PubMed] [Google Scholar]
  • 99.Jellinger K.A., Paulus W., Wrocklage C., and Litvan I. (2001). Effects of closed traumatic brain injury and genetic factors on the development of Alzheimer's disease. Eur. J. Neurol. 8, 707–710 [DOI] [PubMed] [Google Scholar]
  • 100.Jiang Y., Sun X., Gui L., Xia Y., Tang W., Cao Y., and Gu Y. (2007). Correlation between APOE −491AA promoter in epsilon4 carriers and clinical deterioration in early stage of traumatic brain injury. J. Neurotrauma 24, 1802–1810 [DOI] [PubMed] [Google Scholar]
  • 101.Jiang Y., Sun X., Xia Y., Tang W., Cao Y., and Gu Y. (2006). Effect of APOE polymorphisms on early responses to traumatic brain injury. Neurosci. Lett. 408, 155–158 [DOI] [PubMed] [Google Scholar]
  • 102.Koponen S., Taiminen T., Kairisto V., Portin R., Isoniemi H., Hinkka S., and Tenovuo O. (2004). APOE-epsilon4 predicts dementia but not other psychiatric disorders after traumatic brain injury. Neurology 63, 749–750 [DOI] [PubMed] [Google Scholar]
  • 103.Lendon C.L., Harris J.M., Pritchard A.L., Nicoll J.A., Teasdale G.M., and Murray G. (2003). Genetic variation of the APOE promoter and outcome after head injury. Neurology 61, 683–685 [DOI] [PubMed] [Google Scholar]
  • 104.Liberman J.N., Stewart W.F., Wesnes K., and Troncoso J. (2002). Apolipoprotein E epsilon 4 and short-term recovery from predominantly mild brain injury. Neurology 58, 1038–1044 [DOI] [PubMed] [Google Scholar]
  • 105.Lichtman S.W., Seliger G., Tycko B., and Marder K. (2000). Apolipoprotein E and functional recovery from brain injury following postacute rehabilitation. Neurology 55, 1536–1539 [DOI] [PubMed] [Google Scholar]
  • 106.Lo T.Y., Jones P.A., Chambers I.R., Beattie T.F., Forsyth R., Mendelow A.D., and Minns R.A. (2009). Modulating effect of apolipoprotein E polymorphisms on secondary brain insult and outcome after childhood brain trauma. Childs Nerv. Syst. 25, 47–54 [DOI] [PubMed] [Google Scholar]
  • 107.Luukinen H., Viramo P., Herala M., Kervinen K., Kesaniemi Y.A., Savola O., Winqvist S., Jokelainen J., and Hillbom M. (2005). Fall-related brain injuries and the risk of dementia in elderly people: a population-based study. Eur. J. Neurol. 12, 86–92 [DOI] [PubMed] [Google Scholar]
  • 108.Mauri M., Sinforiani E., Bono G., Cittadella R., Quattrone A., Boller F., and Nappi G. (2006). Interaction between Apolipoprotein epsilon 4 and traumatic brain injury in patients with Alzheimer's disease and Mild Cognitive Impairment. Funct. Neurol. 21, 223–228 [PubMed] [Google Scholar]
  • 109.Mayeux R., Ottman R., Maestre G., Ngai C., Tang M.X., Ginsberg H., Chun M., Tycko B., and Shelanski M. (1995). Synergistic effects of traumatic head injury and apolipoprotein-epsilon 4 in patients with Alzheimer's disease. Neurology 45, 555–557 [DOI] [PubMed] [Google Scholar]
  • 110.Mehta K.M., Ott A., Kalmijn S., Slooter A.J., van Duijn C.M., Hofman A., and Breteler M.M. (1999). Head trauma and risk of dementia and Alzheimer's disease: The Rotterdam Study. Neurology 53, 1959–1962 [DOI] [PubMed] [Google Scholar]
  • 111.Millar K., Nicoll J.A., Thornhill S., Murray G.D., and Teasdale G.M. (2003). Long term neuropsychological outcome after head injury: relation to APOE genotype. J. Neurol. Neurosurg. Psychiatry 74, 1047–1052 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 112.Muller K., Ingebrigtsen T., Wilsgaard T., Wikran G., Fagerheim T., Romner B., and Waterloo K. (2009). Prediction of time trends in recovery of cognitive function after mild head injury. Neurosurgery 64, 698–704 [DOI] [PubMed] [Google Scholar]
  • 113.Nathoo N., Chetry R., van Dellen J.R., Connolly C., and Naidoo R. (2003). Apolipoprotein E polymorphism and outcome after closed traumatic brain injury: influence of ethnic and regional differences. J. Neurosurg. 98, 302–306 [DOI] [PubMed] [Google Scholar]
  • 114.Noe E., Ferri J., Colomer C., Moliner B., and Chirivella J. (2010). APOE genotype and verbal memory recovery during and after emergence from post-traumatic amnesia. Brain Inj. 24, 886–892 [DOI] [PubMed] [Google Scholar]
  • 115.Olivecrona Z. and Koskinen L.O. (2012). The release of S-100B and NSE in severe traumatic head injury is associated with APOE epsilon4. Acta Neurochir. (Wien.) 154, 675–680 [DOI] [PubMed] [Google Scholar]
  • 116.O'Meara E.S., Kukull W.A., Sheppard L., Bowen J.D., McCormick W.C., Teri L., Pfanschmidt M., Thompson J.D., Schellenberg G.D., and Larson E.B. (1997). Head injury and risk of Alzheimer's disease by apolipoprotein E genotype. Am. J. Epidemiol. 146, 373–384 [DOI] [PubMed] [Google Scholar]
  • 117.Ost M., Nylen K., Csajbok L., Blennow K., Rosengren L., and Nellgard B. (2008). Apolipoprotein E polymorphism and gender difference in outcome after severe traumatic brain injury. Acta Anaesthesiol. Scand. 52, 1364–1369 [DOI] [PubMed] [Google Scholar]
  • 118.Ponsford J., McLaren A., Schonberger M., Burke R., Rudzki D., Olver J., and Ponsford M. (2011). The association between apolipoprotein E and traumatic brain injury severity and functional outcome in a rehabilitation sample. J. Neurotrauma 28, 1683–1692 [DOI] [PubMed] [Google Scholar]
  • 119.Sundstrom A., Marklund P., Nilsson L.G., Cruts M., Adolfsson R., Van Broeckhoven C., and Nyberg L. (2004). APOE influences on neuropsychological function after mild head injury: within-person comparisons. Neurology 62, 1963–1966 [DOI] [PubMed] [Google Scholar]
  • 120.Sundstrom A., Nilsson L.G., Cruts M., Adolfsson R., Van Broeckhoven C., and Nyberg L. (2007). Fatigue before and after mild traumatic brain injury: pre-post-injury comparisons in relation to Apolipoprotein E. Brain Inj. 21, 1049–1054 [DOI] [PubMed] [Google Scholar]
  • 121.Sundstrom A., Nilsson L.G., Cruts M., Adolfsson R., Van Broeckhoven C., and Nyberg L. (2007). Increased risk of dementia following mild head injury for carriers but not for non-carriers of the APOE epsilon4 allele. Int. Psychogeriatr. 19, 159–165 [DOI] [PubMed] [Google Scholar]
  • 122.Tang M.X., Maestre G., Tsai W.Y., Liu X.H., Feng L., Chung W.Y., Chun M., Schofield P., Stern Y., Tycko B., and Mayeux R. (1996). Effect of age, ethnicity, and head injury on the association between APOE genotypes and Alzheimer's disease. Ann. N. Y. Acad. Sci. 802, 6–15 [DOI] [PubMed] [Google Scholar]
  • 123.Teasdale G.M., Murray G.D., and Nicoll J.A. (2005). The association between APOE epsilon4, age and outcome after head injury: a prospective cohort study. Brain 128, 2556–2561 [DOI] [PubMed] [Google Scholar]
  • 124.Teasdale G.M., Nicoll J.A., Murray G., and Fiddes M. (1997). Association of apolipoprotein E polymorphism with outcome after head injury. Lancet 350, 1069–1071 [DOI] [PubMed] [Google Scholar]
  • 125.Willemse-van Son A.H., Ribbers G.M., Hop W.C., van Duijn C.M., and Stam H.J. (2008). Association between apolipoprotein-epsilon4 and long-term outcome after traumatic brain injury. J. Neurol. Neurosurg. Psychiatry 79, 426–430 [DOI] [PubMed] [Google Scholar]
  • 126.Yang S.T., Hsiao I.T., Hsieh C.J., Chiang Y.H., Yen T.C., Chiu W.T., Lin K.J., and Hu C.J. (2015). Accumulation of amyloid in cognitive impairment after mild traumatic brain injury. J. Neurol. Sci. 349, 99–104 [DOI] [PubMed] [Google Scholar]
  • 127.Yousuf A., Khursheed N., Rasool I., Kundal V., Jeelani H., and Afroze D. (2015). Genetic Variation of ApoE Gene in Ethnic Kashmiri Population and Its Association with Outcome After Traumatic Brain Injury. J. Mol. Neurosci. 56, 597–601 [DOI] [PubMed] [Google Scholar]
  • 128.Wang Z.L., Xu D.S., Wang Y.X., Qin H., and Geng D. (2015). Effect of single nucleotide polymorphisms in the ATP-binding cassette B1 gene on the clinical outcome of traumatic brain injury. Genet. Mol. Res. 14, 10948–10953 [DOI] [PubMed] [Google Scholar]
  • 129.Dardiotis E., Paterakis K., Tsivgoulis G., Tsintou M., Hadjigeorgiou G.F., Dardioti M., Grigoriadis S., Simeonidou C., Komnos A., Kapsalaki E., Fountas K., and Hadjigeorgiou G.M. (2014). AQP4 tag single nucleotide polymorphisms in patients with traumatic brain injury. J. Neurotrauma 31, 1920–1926 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 130.Cousar J.L., Conley Y.P., Willyerd F.A., Sarnaik A.A., Puccio A.M., Empey P.E., Kochanek P.M., Bell M.J., Okonkwo D.O., and Clark R.S. (2013). Influence of ATP-binding cassette polymorphisms on neurological outcome after traumatic brain injury. Neurocrit. Care 19, 192–198 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 131.Hoh N.Z., Wagner A.K., Alexander S.A., Clark R.B., Beers S.R., Okonkwo D.O., Ren D., and Conley Y.P. (2010). BCL2 genotypes: functional and neurobehavioral outcomes after severe traumatic brain injury. J. Neurotrauma 27, 1413–1427 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 132.Wang Y.J., Hsu Y.W., Chang C.M., Wu C.C., Ou J.C., Tsai Y.R., Chiu W.T., Chang W.C., Chiang Y.H., and Chen K.Y. (2014). The influence of BMX gene polymorphisms on clinical symptoms after mild traumatic brain injury. Biomed. Res. Int. 2014, 293687. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 133.Failla M.D., Kumar R.G., Peitzman A.B., Conley Y.P., Ferrell R.E., and Wagner A.K. (2015). Variation in the BDNF gene interacts with age to predict mortality in a prospective, longitudinal cohort with severe TBI. Neurorehabil. Neural Repair 29, 234–246 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 134.McAllister T.W., Tyler A.L., Flashman L.A., Rhodes C.H., McDonald B.C., Saykin A.J., Tosteson T.D., Tsongalis G.J., and Moore J.H. (2012). Polymorphisms in the brain-derived neurotrophic factor gene influence memory and processing speed one month after brain injury. J. Neurotrauma 29, 1111–1118 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 135.Rostami E., Krueger F., Zoubak S., Dal Monte O., Raymont V., Pardini M., Hodgkinson C.A., Goldman D., Risling M., and Grafman J. (2011). BDNF polymorphism predicts general intelligence after penetrating traumatic brain injury. PloS One 6, e27389. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 136.Krueger F., Pardini M., Huey E.D., Raymont V., Solomon J., Lipsky R.H., Hodgkinson C.A., Goldman D., and Grafman J. (2011). The role of the Met66 brain-derived neurotrophic factor allele in the recovery of executive functioning after combat-related traumatic brain injury. J. Neurosci. 31, 598–606 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 137.Larson-Dupuis C., Chamard E., Falardeau V., Frasnelli J., Beaulieu C., Poirier J., Carrier J., Lassonde M., Theoret H., Bacon B.A., and De Beaumont L. (2015). Impact of BDNF Val66Met polymorphism on olfactory functions of female concussed athletes. Brain Inj. 29, 963–970 [DOI] [PubMed] [Google Scholar]
  • 138.Willmott C., Ponsford J., McAllister T.W., and Burke R. (2013). Effect of COMT Val158Met genotype on attention and response to methylphenidate following traumatic brain injury. Brain Inj. 27, 1281–1286 [DOI] [PubMed] [Google Scholar]
  • 139.Raymont V., Greathouse A., Reding K., Lipsky R., Salazar A., and Grafman J. (2008). Demographic, structural and genetic predictors of late cognitive decline after penetrating head injury. Brain 131, 543–558 [DOI] [PubMed] [Google Scholar]
  • 140.Garringer J.A., Niyonkuru C., McCullough E.H., Loucks T., Dixon C.E., Conley Y.P., Berga S., and Wagner A.K. (2013). Impact of aromatase genetic variation on hormone levels and global outcome after severe TBI. J. Neurotrauma 30, 1415–1425 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 141.Pardini M., Krueger F., Koenigs M., Raymont V., Hodgkinson C., Zoubak S., Goldman D., and Grafman J. (2012). Fatty-acid amide hydrolase polymorphisms and post-traumatic stress disorder after penetrating brain injury. Transl. Psychiatry 2, e75. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 142.Darrah S.D., Miller M.A., Ren D., Hoh N.Z., Scanlon J.M., Conley Y.P., and Wagner A.K. (2013). Genetic variability in glutamic acid decarboxylase genes: associations with post-traumatic seizures after severe TBI. Epilepsy Res. 103, 180–194 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 143.Diamond M.L., Ritter A.C., Failla M.D., Boles J.A., Conley Y.P., Kochanek P.M., and Wagner A.K. (2014). IL-1beta associations with posttraumatic epilepsy development: a genetics and biomarker cohort study. Epilepsia 55, 1109–1119 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 144.Scher A.I., Wu H., Tsao J.W., Blom H.J., Feit P., Nevin R.L., and Schwab K.A. (2011). MTHFR C677T genotype as a risk factor for epilepsy including post-traumatic epilepsy in a representative military cohort. J. Neurotrauma 28, 1739–1745 [DOI] [PubMed] [Google Scholar]
  • 145.Pardini M., Krueger F., Hodgkinson C., Raymont V., Ferrier C., Goldman D., Strenziok M., Guida S., and Grafman J. (2011). Prefrontal cortex lesions and MAO-A modulate aggression in penetrating traumatic brain injury. Neurology 76, 1038–1045 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 146.Conley Y.P., Okonkwo D.O., Deslouches S., Alexander S., Puccio A.M., Beers S.R., and Ren D. (2014). Mitochondrial polymorphisms impact outcomes after severe traumatic brain injury. J. Neurotrauma 31, 34–41 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 147.Chuang P.Y., Conley Y.P., Poloyac S.M., Okonkwo D.O., Ren D., Sherwood P.R., Hravnak M., and Alexander S.A. (2010). Neuroglobin genetic polymorphisms and their relationship to functional outcomes after traumatic brain injury. J. Neurotrauma 27, 999–1006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 148.Robertson C.S., Gopinath S.P., Valadka A.B., Van M., Swank P.R., and Goodman J.C. (2011). Variants of the endothelial nitric oxide gene and cerebral blood flow after severe traumatic brain injury. J. Neurotrauma 28, 727–737 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 149.Sarnaik A.A., Conley Y.P., Okonkwo D.O., Barr T.L., Fink E.L., Szabo C., Kochanek P.M., and Clark R.S. (2010). Influence of PARP-1 polymorphisms in patients after traumatic brain injury. J. Neurotrauma 27, 465–471 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 150.Gao J., Liu R., Zhao E., Huang X., Nalls M.A., Singleton A.B., and Chen H. (2015). Head injury, potential interaction with genes, and risk for Parkinson's disease. Parkinsonism Relat. Disord. 21, 292–296 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 151.Graham D.P., Helmer D.A., Harding M.J., Kosten T.R., Petersen N.J., and Nielsen D.A. (2013). Serotonin transporter genotype and mild traumatic brain injury independently influence resilience and perception of limitations in veterans. J. Psychiatr. Res. 47, 835–842 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 152.Failla M.D., Burkhardt J.N., Miller M.A., Scanlon J.M., Conley Y.P., Ferrell R.E., and Wagner A.K. (2013). Variants of SLC6A4 in depression risk following severe TBI. Brain Inj. 27, 696–706 [DOI] [PubMed] [Google Scholar]
  • 153.Lee P.C., Bordelon Y., Bronstein J., Sinsheimer J.S., Farrer M., and Ritz B. (2015). Head injury, alpha-synuclein genetic variability and Parkinson's disease. Eur. J. Neurol. 22, 874–878 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 154.Goldman S.M., Kamel F., Ross G.W., Jewell S.A., Bhudhikanok G.S., Umbach D., Marras C., Hauser R.A., Jankovic J., Factor S.A., Bressman S., Lyons K.E., Meng C., Korell M., Roucoux D.F., Hoppin J.A., Sandler D.P., Langston J.W., and Tanner C.M. (2012). Head injury, alpha-synuclein Rep1, and Parkinson's disease. Ann. Neurol. 71, 40–48 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 155.Waters R.J., Murray G.D., Teasdale G.M., Stewart J., Day I., Lee R.J., and Nicoll J.A. (2013). Cytokine gene polymorphisms and outcome after traumatic brain injury. J. Neurotrauma 30, 1710–1716 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 156.Martinez-Lucas P., Moreno-Cuesta J., Garcia-Olmo D.C., Sanchez-Sanchez F., Escribano-Martinez J., del Pozo A.C., Lizan-Garcia M., and Garcia-Olmo D. (2005). Relationship between the Arg72Pro polymorphism of p53 and outcome for patients with traumatic brain injury. Intensive Care Med. 31, 1168–1173 [DOI] [PubMed] [Google Scholar]
  • 157.Wagner A.K., Hatz L.E., Scanlon J.M., Niyonkuru C., Miller M.A., Ricker J.H., Conley Y.P., and Ferrell R.E. (2012). Association of KIBRA rs17070145 polymorphism and episodic memory in individuals with severe TBI. Brain Inj. 26, 1658–1669 [DOI] [PubMed] [Google Scholar]

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