Growth Hormone Receptor
Introduction
Section titled “Introduction”Background
Section titled “Background”The growth hormone receptor (GHR) is a transmembrane protein that serves as the primary mediator of the physiological actions of growth hormone (GH). It is essential for regulating linear growth, metabolic processes, and body composition throughout an individual’s life.
Biological Basis
Section titled “Biological Basis”When growth hormone binds toGHR, it triggers the dimerization of the receptor, initiating intracellular signaling pathways, predominantly the JAK/STAT pathway. This activation leads to the production and release of insulin-like growth factor 1 (IGF-1), primarily from the liver, which then acts on various target cells to stimulate cell proliferation, differentiation, and overall somatic growth. The widespread expression of GHR in different tissues underscores its broad impact on diverse physiological functions.
Clinical Relevance
Section titled “Clinical Relevance”Genetic variations and mutations within the GHR gene can significantly affect an individual’s growth and metabolic health. For instance, severe impairments in GHRfunction can result in growth hormone insensitivity, a condition characterized by pronounced short stature, exemplified by Laron syndrome. Beyond direct growth disorders, genetic factors are known to influence height; for example, a common variant ofHMGA2 has been associated with adult and childhood height in the general population. [1] The GHRsignaling pathway is also closely intertwined with metabolic regulation. Research has identified genetic influences on various metabolic traits, including loci that affect lipid concentrations and the risk of coronary artery disease.[2]Furthermore, genome-wide association studies have identified loci associated with type 2 diabetes and triglyceride levels.[3] Specific polymorphisms, such as those in HMGCR, have been linked to LDL-cholesterol levels [4] and variations in metabolic-syndrome-related pathways involving LEPR, HNF1A, IL6R, and GCKRare associated with plasma C-reactive protein.[5] The GHR pathway’s broader influence on endocrine-related traits is also a significant area of research. [6]
Social Importance
Section titled “Social Importance”Understanding the GHR and its genetic variants carries substantial social importance. It offers critical insights into the genetic underpinnings of human growth and development, which can lead to improved diagnostic tools and therapeutic interventions for growth disorders and metabolic conditions. Continued research into GHR contributes to the advancement of personalized medicine, enabling more tailored health management strategies based on an individual’s unique genetic profile.
Limitations
Section titled “Limitations”Methodological and Statistical Constraints
Section titled “Methodological and Statistical Constraints”Genetic studies, particularly genome-wide association studies (GWAS), face inherent methodological and statistical limitations that can impact the interpretation of findings related to traits like growth hormone receptor. One significant constraint is the statistical power to detect modest genetic effects, especially given the extensive multiple testing inherent in scanning thousands of single nucleotide polymorphisms (SNPs) across the genome.[7] For instance, studies performing only sex-pooled analyses may miss SNPs that exhibit associations with phenotypes exclusively in males or females. [8] Furthermore, while GWAS are designed to be unbiased, they often rely on a subset of all available SNPs, which can lead to incomplete coverage of genetic variation and potentially miss causal variants or genes. [8]
Replication of findings in independent cohorts is crucial for validating associations, but this process itself can be complex. Non-replication might occur if studies use different SNPs that are not in strong linkage disequilibrium with each other, even if both are associated with the same underlying causal variant within a gene. [9] Moreover, differences in study design, statistical power, or specific genotyping platforms can contribute to discrepancies in replication efforts. [9] The challenge of defining statistical significance in genome-wide scans, where thresholds depend on the a priori probability of association and study power, further complicates the robust identification of true genetic signals. [10]
Generalizability and Phenotypic Nuances
Section titled “Generalizability and Phenotypic Nuances”A key limitation of many genetic studies is the restricted genetic diversity of the study populations, which predominantly consist of individuals of white European ancestry. [11] This homogeneity can limit the generalizability of findings to other ethnic or ancestral groups, as genetic architectures and allele frequencies can vary significantly across populations. While some studies employ methods like principal component analysis to account for population stratification within a Caucasian cohort, the underlying issue of limited representation across global ancestries persists. [12]
Phenotypic measurements themselves can introduce variability and complexity. Traits, such as those potentially influenced by growth hormone receptor, often exhibit non-normal distributions, necessitating sophisticated statistical transformations to approximate normality for analysis. [11] While researchers apply adjustments for factors like age, sex, and other covariates, these transformations and adjustments highlight the inherent variability and potential confounding factors in the raw phenotypic data. [6] The way phenotypes are measured and processed can thus influence the observed genetic associations and their effect sizes.
Environmental Interactions and Unexplained Heritability
Section titled “Environmental Interactions and Unexplained Heritability”Genetic variants do not operate in isolation; their effects on phenotypes can be significantly modulated by environmental influences, leading to complex gene-environment interactions. [7] Many studies, however, do not undertake comprehensive investigations of these interactions, potentially overlooking crucial context-specific genetic effects. [7] For example, associations of certain genes with cardiac traits have been reported to vary based on dietary salt intake. [7]
The absence of a thorough understanding of these interactions contributes to the phenomenon of “missing heritability,” where identified genetic variants explain only a fraction of the phenotypic variation observed for complex traits. Future research needs to explore a wider range of environmental factors and their interplay with genetic predispositions to fully elucidate the genetic architecture of traits. Continued efforts with larger sample sizes and improved statistical power for gene discovery are essential to uncover additional sequence variants that collectively contribute to phenotypic variation. [13]
Variants
Section titled “Variants”Variants within the growth hormone receptor (GHR) gene, such as rs55730643 , rs4146624 , and rs116004619 , are of significant interest due to their potential to influence the body’s response to growth hormone. TheGHRgene encodes the receptor for growth hormone, a crucial protein that regulates growth, metabolism, and body composition. Polymorphisms inGHRcan alter receptor expression, binding affinity, or downstream signaling pathways, thereby affecting an individual’s growth trajectory, metabolic health, and susceptibility to conditions like short stature or metabolic syndrome. These genetic variations may contribute to the diverse phenotypic responses observed in individuals with differing growth hormone sensitivities.[14]
Genetic variations in the SELENOP gene, including rs149783633 and rs140466082 , are linked to selenoprotein P, a key protein responsible for transporting selenium throughout the body. Selenium is an essential trace element vital for the proper function of various enzymes, notably those involved in antioxidant defense and thyroid hormone metabolism. Given the intricate relationship between thyroid hormones and the growth hormone axis in regulating overall growth and metabolic homeostasis, variants inSELENOPcould indirectly impact growth hormone receptor signaling by affecting the availability of selenium for critical metabolic processes. Such variations may influence an individual’s selenium status, potentially affecting endocrine system balance and metabolic health.[3]
The long non-coding RNA LINC02996 and its associated variant rs114486789 , along with the pseudogene MTHFD2P6 and its variants rs146239168 and rs772210082 , represent elements that can exert regulatory control over gene expression. LncRNAs like LINC02996 are known to modulate the transcription and translation of protein-coding genes, potentially including GHR itself, through various mechanisms such as chromatin remodeling or mRNA stability. Similarly, pseudogenes like MTHFD2P6can act as regulatory RNAs or compete with their parental genes for microRNA binding, thereby influencing gene expression. Variants in these regions may alter their regulatory capacity, leading to subtle or significant changes in the expression levels of genes involved in growth hormone signaling or related metabolic pathways.[11], [15]Further genetic insights come from variants in PAIP1 and C5orf34-AS1, specifically rs114943096 , and genes involved in lipid and ketone metabolism such as PLCXD3, OXCT1, and HMGCS1. The PAIP1gene plays a role in regulating mRNA translation, a fundamental process for protein synthesis. Variants here could affect the efficiency of protein production, including components of the growth hormone pathway.C5orf34-AS1 is an antisense RNA, which might regulate the expression of neighboring genes. Meanwhile, PLCXD3 (Phospholipase C Like, X Domain Containing 3) is implicated in signaling, and OXCT1 (3-oxoacid CoA-transferase 1) is critical for ketone body utilization, impacting cellular energy supply. HMGCS1 (HMG-CoA synthase 1) is involved in cholesterol and ketone synthesis. Variants in these genes, including rs186149050 , rs137889806 , rs532125060 in the PLCXD3-OXCT1 region and rs144753508 in OXCT1alone, could modulate metabolic pathways that are intrinsically linked to growth hormone action and overall energy balance.[11], [13]Finally, variants in HMGCS1 and CCL28, such as rs77860766 and rs184316673 , along with the antisense RNA ANXA2R-OT1 (rs527624465 ), highlight connections between metabolism, inflammation, and gene regulation. As noted, HMGCS1 is central to lipid and ketone metabolism, processes that are closely intertwined with growth and endocrine function. CCL28 is a chemokine, a type of signaling protein involved in immune and inflammatory responses, which can influence metabolic states and potentially affect systemic growth factors. The context of other CCL genes being associated with kidney function suggests a broader role for this family in physiological regulation. ANXA2R-OT1 is an antisense RNA that may regulate the expression of ANXA2R or other genes, potentially impacting cell surface processes and signaling pathways relevant to growth factor responses. [10]
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs55730643 rs4146624 rs116004619 | GHR | growth hormone receptor measurement |
| rs149783633 rs140466082 | SELENOP | health trait growth hormone receptor measurement |
| rs114486789 | LINC02996 - GHR | growth hormone receptor measurement |
| rs146239168 rs772210082 | MTHFD2P6 - LINC02996 | growth hormone receptor measurement |
| rs114943096 | PAIP1, C5orf34-AS1 | growth hormone receptor measurement |
| rs186149050 rs137889806 | PLCXD3 - OXCT1 | growth hormone receptor measurement |
| rs77860766 rs184316673 | HMGCS1 - CCL28 | growth hormone receptor measurement |
| rs532125060 | PLCXD3 - OXCT1 | growth hormone receptor measurement |
| rs144753508 | OXCT1 | growth hormone receptor measurement |
| rs527624465 | ANXA2R-OT1 | growth hormone receptor measurement |
Classification, Definition, and Terminology
Section titled “Classification, Definition, and Terminology”Biological Background
Section titled “Biological Background”Hormonal Regulation of Growth Hormone Axis
Section titled “Hormonal Regulation of Growth Hormone Axis”The activity of the growth hormone receptor is intricately linked to the broader endocrine system, which regulates the availability of growth hormone itself. One key regulatory hormone is somatostatin, produced in the hypothalamus, which is known to inhibit the production of thyrotropin-releasing hormone (TRH) and the subsequent release of TSH. [14] The physiological roles of somatostatin and its specific receptors are subjects of ongoing research. [16]This regulatory control over upstream hormones indirectly influences the overall signaling context for the growth hormone receptor by modulating the levels of its ligand.
Downstream Signaling and Mediators
Section titled “Downstream Signaling and Mediators”Following activation by growth hormone, the receptor initiates intracellular cascades that drive various cellular functions, including growth and metabolism. A critical component of these downstream effects involvesInsulin-Like Growth Factor Binding Proteins (_IGFBP_s), whose genetic mechanisms are studied in relation to metabolic traits. [3]These proteins play a vital role in modulating the bioavailability and activity of insulin-like growth factors, which are primary mediators of growth hormone’s anabolic actions. Furthermore, theMitogen-activated protein kinase (MAPK) pathway represents another significant signaling route, with its activation observed in human skeletal muscle in response to factors like age and acute exercise.[17]This pathway is commonly involved in transmitting signals from cell surface receptors, such as the growth hormone receptor, to regulate gene expression and cellular processes.
References
Section titled “References”[1] Weedon, MN., et al. “A common variant of HMGA2 is associated with adult and childhood height in the general population.” Nat Genet, vol. 39, no. 10, 2007, pp. 1245-1250.
[2] Willer, CJ., et al. “Newly identified loci that influence lipid concentrations and risk of coronary artery disease.”Nat Genet, vol. 40, no. 2, 2008, pp. 161-169.
[3] Saxena, R. et al. “Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels.”Science, vol. 316, no. 5829, 2007, pp. 1331-36. PMID: 17463246.
[4] Burkhardt, R., et al. “Common SNPs in HMGCR in micronesians and whites associated with LDL-cholesterol levels affect alternative splicing of exon13.” Arterioscler Thromb Vasc Biol, vol. 28, 2008, pp. 2078-2086.
[5] Ridker, PM., et al. “Loci related to metabolic-syndrome pathways including LEPR, HNF1A, IL6R, and GCKR associate with plasma C-reactive protein: the Women’s Genome Health Study.”Am J Hum Genet, vol. 82, no. 5, 2008, pp. 1185-1192.
[6] Hwang, S.J. et al. “A genome-wide association for kidney function and endocrine-related traits in the NHLBI’s Framingham Heart Study.” BMC Med Genet, vol. 8, no. Suppl 1, 2007, p. S10.
[7] Vasan, Ramachandran S., et al. “Genome-wide association of echocardiographic dimensions, brachial artery endothelial function and treadmill exercise responses in the Framingham Heart Study.”BMC Med Genet, vol. 8, no. Suppl 1, 2007, p. S2.
[8] Yang, Qiong, et al. “Genome-wide association and linkage analyses of hemostatic factors and hematological phenotypes in the Framingham Heart Study.”BMC Med Genet, vol. 8, no. Suppl 1, 2007, p. S11.
[9] Sabatti, Chiara, et al. “Genome-wide association analysis of metabolic traits in a birth cohort from a founder population.”Nat Genet, vol. 41, no. 3, 2009, pp. 35-43.
[10] Wallace, C. et al. “Genome-wide association study identifies genes for biomarkers of cardiovascular disease: serum urate and dyslipidemia.”Am J Hum Genet, vol. 82, no. 1, 2008, pp. 139-49.
[11] Melzer, D. et al. “A genome-wide association study identifies protein quantitative trait loci (pQTLs).” PLoS Genet, vol. 4, no. 5, 2008, p. e1000072.
[12] Pare, Guillaume, et al. “Novel association of ABO histo-blood group antigen with soluble ICAM-1: results of a genome-wide association study of 6,578 women.” PLoS Genet, vol. 4, no. 7, 2008, p. e1000118.
[13] Kathiresan, S. et al. “Common variants at 30 loci contribute to polygenic dyslipidemia.” Nat Genet, vol. 41, no. 1, 2009, pp. 56-65.
[14] Arnaud-Lopez, L. et al. “Phosphodiesterase 8B gene variants are associated with serum TSH levels and thyroid function.”Am J Hum Genet, vol. 82, no. 6, 2008, pp. 1195-202.
[15] Benjamin, E.J. et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Med Genet, vol. 8, no. Suppl 1, 2007, p. S9.
[16] Reubi, J. C. et al. “Somatostatin and somatostatin receptor physiology.” Endocrine, vol. 20, no. 3, 2003, pp. 255-64. PMID: 12845241.
[17] Williamson, D. et al. “Mitogen-activated protein kinase (MAPK) pathway activation: effects of age and acute exercise on human skeletal muscle.”J Physiol, vol. 547, no. 3, 2003, pp. 977-87. PMID: 12626694.