Homa Ir
Background
Section titled “Background”The Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) is a widely used method for quantifying insulin resistance and beta-cell function from fasting glucose and insulin (or C-peptide) levels. Developed as a simple, non-invasive alternative to more complex methods like the euglycemic-hyperinsulinemic clamp, HOMA-IR provides an estimate of insulin sensitivity and pancreatic beta-cell activity. It is calculated using a specific formula that incorporates fasting plasma glucose and fasting plasma insulin concentrations. A higher HOMA-IR value generally indicates greater insulin resistance.
Biological Basis
Section titled “Biological Basis”Insulin resistance is a physiological condition in which cells fail to respond normally to the hormone insulin. Insulin, produced by the beta cells of the pancreas, plays a crucial role in regulating blood glucose levels by facilitating glucose uptake into cells for energy or storage. When cells become resistant to insulin, the pancreas must produce more insulin to maintain normal blood glucose. Over time, the beta cells may become exhausted, leading to persistently high blood glucose levels, a hallmark of type 2 diabetes. HOMA-IR directly reflects this interplay by assessing how much insulin is required to maintain a given fasting glucose level.
Clinical Relevance
Section titled “Clinical Relevance”HOMA-IR is a valuable tool in clinical and research settings for identifying individuals at risk for various metabolic disorders. Elevated HOMA-IR values are strongly associated with an increased risk of developing type 2 diabetes, metabolic syndrome, non-alcoholic fatty liver disease (NAFLD), and cardiovascular disease. It can be used to monitor changes in insulin sensitivity over time in response to lifestyle interventions, medications, or disease progression. Its ease of calculation from routine blood tests makes it a practical marker for assessing metabolic health.
Social Importance
Section titled “Social Importance”The prevalence of insulin resistance and related conditions like type 2 diabetes and metabolic syndrome represents a significant global public health challenge. These conditions contribute to a substantial burden of morbidity and mortality, impacting individual quality of life and imposing considerable costs on healthcare systems. Understanding and identifying insulin resistance through markers like HOMA-IR can facilitate early intervention strategies, including dietary changes, increased physical activity, and pharmacological treatments, potentially preventing or delaying the onset of severe complications. This has broad social implications for promoting healthier populations and reducing the societal impact of chronic metabolic diseases.
Limitations
Section titled “Limitations”The interpretation of findings from the research on homa ir is subject to several important methodological and conceptual limitations. Acknowledging these limitations is crucial for a balanced understanding of the study’s scope and the generalizability of its conclusions.
Methodological and Statistical Constraints
Section titled “Methodological and Statistical Constraints”The study’s design and statistical approaches present certain constraints that impact the robustness and replicability of its findings. Despite efforts to conduct large-scale analyses, sample sizes may still offer limited power for detecting genetic variants with very small effect sizes or those with low minor allele frequencies (MAFs).[1]Replication efforts, for instance, have shown that variants with low MAF are less likely to be successfully replicated, indicating a potential for effect-size inflation in initial discovery phases or challenges in consistently identifying less common genetic influences.[2] Furthermore, while permutation tests help establish appropriate P-value thresholds, the fundamental statistical power remains a factor, particularly for complex traits influenced by numerous genetic and environmental factors.[2]Meta-analyses, while increasing power, often rely on fixed-effect models that assume a common effect size across different populations.[3] This assumption might not hold true if there is significant biological heterogeneity, which could lead to an underestimation of population-specific genetic effects or mask true associations. Imputation quality is another critical aspect, as the accuracy and coverage of imputed variants are dependent on the reference panels used and the applied quality thresholds.[3] If reference panels are not ethnically matched to the study population, or if strict MAF filters are applied (e.g., MAF > 0.05 or > 0.1%), the study may miss associations with rarer variants that could have substantial biological impact.[1]
Ancestry and Generalizability
Section titled “Ancestry and Generalizability”A significant limitation arises from the specific ancestry of the study population, which is predominantly Japanese.[2] While this focus provides valuable insights into the genetic architecture of traits within this specific group, the generalizability of these findings to other populations is not guaranteed. Genetic architecture, including allele frequencies and linkage disequilibrium patterns, can vary considerably across different ancestral groups, meaning that variants identified in one population may not have the same effect or even exist in others.[4] This underscores the necessity of multi-ancestry studies to differentiate between universal and population-specific genetic effects.[5] Moreover, phenotype definition and can introduce variability. For instance, reliance on clinically defined phenotypes, especially for diseases that are rare or diagnostically challenging, can lead to potential misclassification or heterogeneity within the study cohorts.[1] Differences in genotyping platforms and arrays across various contributing cohorts can also introduce technical biases or inconsistencies in marker coverage, necessitating rigorous quality control and harmonization protocols to ensure comparability of genetic data.[5]
Unaccounted Environmental Factors and Heritability Gaps
Section titled “Unaccounted Environmental Factors and Heritability Gaps”The genetic associations identified in this research predominantly focus on common genetic variants, often without fully integrating the complex influence of environmental factors or gene-environment interactions. Lifestyle, dietary habits, socioeconomic status, and other environmental exposures are known to significantly modulate genetic predispositions and disease risk, yet these are frequently difficult to capture comprehensively in large-scale genetic studies. Consequently, the observed genetic effects may represent only a partial view of the true biological mechanisms underlying the trait, potentially overlooking crucial synergistic or antagonistic interactions with the environment.
Despite the identification of numerous genetic loci, a substantial portion of the heritability for complex traits often remains unexplained by common variants, a phenomenon known as “missing heritability.” While methods like LD score regression are employed to estimate heritability and correct for confounding.[2] these estimates can be influenced by the choice of reference panels and the exclusion of specific genomic regions, such as the HLA region.[2] This remaining knowledge gap suggests that other genetic factors, including rare variants, structural variations, epigenetic modifications, or more intricate gene-gene interactions, contribute significantly to the trait but are not fully elucidated by current GWAS methodologies, thus limiting a complete understanding of the genetic landscape.
Variants
Section titled “Variants”Genetic variations play a crucial role in an individual’s predisposition to metabolic conditions, including insulin resistance, often quantified by the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR). Several genes are involved in glucose and lipid metabolism, insulin signaling, and inflammatory processes, all of which can influence HOMA-IR levels. Understanding these variants helps to unravel the complex genetic architecture underlying metabolic health.[1]Single nucleotide polymorphisms (SNPs) within or near these genes can alter protein function, expression levels, or regulatory mechanisms, thereby impacting metabolic pathways.[1]Variants in genes directly involved in glucose sensing and insulin action, such asGCKR and IRS1, are particularly relevant. The gene GCKR(Glucokinase Regulatory Protein) encodes a protein that regulates glucokinase, a key enzyme in the liver responsible for glucose phosphorylation and sensing. Thers780094 variant in GCKRis often associated with altered glucokinase activity, potentially leading to increased hepatic glucose output and higher fasting insulin levels, thereby contributing to insulin resistance and elevated HOMA-IR. Similarly,IRS1(Insulin Receptor Substrate 1) is a central component of the insulin signaling cascade; its product acts as an adapter protein that relays signals from the insulin receptor to downstream pathways. Thers77723860 variant in IRS1could impair this critical signaling, reducing cellular responsiveness to insulin and directly contributing to insulin resistance.[1] Another gene, CAMK1D(Calcium/Calmodulin Dependent Protein Kinase ID), is involved in various cellular processes, including insulin secretion from pancreatic beta cells and potentially overall glucose homeostasis; thers10906203 variant may influence these functions, affecting blood glucose regulation and HOMA-IR.[1]Lipid metabolism and growth factor signaling also play significant roles in insulin sensitivity. TheAPOA5gene (Apolipoprotein A5) is a major regulator of plasma triglyceride levels, and itsrs662799 variant is widely recognized for its association with higher triglyceride concentrations. Elevated triglycerides are a common feature of metabolic syndrome and are often linked to increased HOMA-IR, indicating a close relationship between lipid dysregulation and insulin resistance. Thers115567901 variant, located in a region encompassing DYNC1I2 and SLC25A12, could impact mitochondrial function, particularly through SLC25A12(Solute Carrier Family 25 Member 12), which is crucial for transporting metabolites into mitochondria. Mitochondrial dysfunction is a recognized contributor to insulin resistance, suggesting that this variant might affect cellular energy metabolism and insulin sensitivity.[1] Furthermore, IGF1(Insulin-like Growth Factor 1) is a hormone with structural similarity to insulin, involved in growth and metabolism; thers35767 variant in this gene or its regulatory region (LINC00485) might influence IGF1levels or signaling, thereby indirectly modulating insulin sensitivity and HOMA-IR.[1]Other variants may exert their influence on HOMA-IR through more indirect mechanisms, such as inflammation or cellular trafficking. Thers13079202 variant, found near IL20RB (Interleukin 20 Receptor Subunit Beta) and RNA5SP142, could affect inflammatory pathways, as IL20RBis part of a receptor for cytokines involved in immune responses. Chronic low-grade inflammation is a known driver of insulin resistance, suggesting a potential link between this variant and HOMA-IR.[1] The CHST11(Carbohydrate Sulfotransferase 11) gene, with itsrs703672 variant, is involved in the biosynthesis of chondroitin sulfate, a component of the extracellular matrix. Changes in extracellular matrix composition can affect tissue structure and cellular signaling, potentially influencing insulin sensitivity in various tissues. Lastly, variants likers10766579 in NAV2 (Neuron Navigator 2) and rs184772418 in LYST(Lysosomal Trafficking Regulator) are associated with genes involved in broad cellular functions like neuronal development and lysosomal trafficking, respectively. While their direct links to HOMA-IR are less established, disruptions in fundamental cellular processes can have widespread metabolic consequences, contributing to a complex interplay of genetic factors influencing insulin resistance.[1]
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs780094 | GCKR | urate alcohol consumption quality gout low density lipoprotein cholesterol triglyceride |
| rs703672 | CHST11 | HOMA-IR |
| rs77723860 | IRS1 | HOMA-IR |
| rs662799 | APOA5 - LNC-RHL1 | high density lipoprotein cholesterol triglyceride metabolic syndrome platelet count level of phosphatidylcholine |
| rs10906203 | CAMK1D | Headache, HOMA-B Headache, HOMA-IR |
| rs13079202 | IL20RB - RNA5SP142 | Headache, HOMA-IR |
| rs10766579 | NAV2 | Headache, HOMA-IR |
| rs184772418 | LYST | HOMA-IR |
| rs115567901 | DYNC1I2 - SLC25A12 | HOMA-IR |
| rs35767 | IGF1 - LINC00485 | blood insulin amount HOMA-IR |
Causes of Intracranial Aneurysm
Section titled “Causes of Intracranial Aneurysm”The etiology of Intracranial Aneurysm (IA) is complex, involving a combination of genetic predispositions, environmental exposures, and other contributing factors that collectively influence an individual’s susceptibility. Research, particularly through genome-wide association studies (GWAS), has illuminated various causal pathways.
Genetic Susceptibility
Section titled “Genetic Susceptibility”Multiple studies highlight the significant role of genetic factors in the development of Intracranial Aneurysm. Genome-wide association studies have identified several susceptibility loci across the human genome. For instance, strong associations have been found on chromosome 7, with independent neighboring regions suggesting distinct susceptibility factors within this chromosomal area. One such associated single nucleotide polymorphism (SNP) isrs10230207 . . Beyond Insulin signaling, other interconnected pathways, such as those involving MAP2K1/2, Mapk, Mek, P38 MAPK, and Jnk, also play roles in cellular responses to metabolic cues, stress, and growth factors, further influencing cellular sensitivity to Insulin.[6]Furthermore, metabolic processes like carbohydrate metabolism, orchestrated by enzymes such as D-xylose 1-dehydrogenase (NADP), DHDH, and trans-1,2-dihydrobenzene-1,2-diol dehydrogenase, are intricately linked toInsulin action and energy balance, underscoring the complex interplay of biomolecules in maintaining metabolic health.[6]
Genetic Regulation and Cellular Function
Section titled “Genetic Regulation and Cellular Function”The precise control of gene expression is essential for maintaining cellular functions and adapting to physiological changes, with genetic mechanisms profoundly influencing metabolic health. Transcription factors like JUN, Creb, STAT, and Ap1, in conjunction with RNA polymerase II, are key biomolecules that orchestrate the transcription of genes vital for cell growth, differentiation, and metabolic regulation.[6]Genetic variations, including single nucleotide polymorphisms, can alter gene function or expression patterns, thereby impacting the production or activity of critical proteins such asHsp90 and Hsp27, which are vital chaperones involved in protein folding and cellular stress responses.[6]These genetic and regulatory elements, along with epigenetic modifications, determine the cellular phenotype and its responsiveness to metabolic signals, ultimately contributing to an individual’s susceptibility to conditions like insulin resistance.[7]
Immune System and Inflammatory Processes
Section titled “Immune System and Inflammatory Processes”The immune system is a crucial modulator of systemic health, and its dysregulation is a significant contributor to pathophysiological processes, including chronic inflammation associated with insulin resistance. Pro-inflammatory cytokines, such asIL1, IFN alpha/beta, IFNG, and members of the Tnf (family), are key signaling molecules that mediate inflammatory responses, often activating pathways involving the NFkB (complex) and IKK (complex).[6] Persistent activation of these inflammatory cascades can lead to a state of low-grade chronic inflammation, which is known to impair Insulin signaling and contribute to the development and progression of metabolic disorders and vascular complications. Immune cell trafficking, facilitated by adhesion molecules like alpha4 integrins and influenced by factors such as ABO histo-blood group antigens and soluble ICAM-1, is critical for immune surveillance but can also exacerbate inflammatory pathology within tissues.[8] Dysregulation of immune-related genes such as CLEC16A and IFIH1 further highlights the genetic predisposition to immune-mediated conditions that can indirectly impact metabolic homeostasis.[9]
Tissue Interactions and Pathophysiological Consequences
Section titled “Tissue Interactions and Pathophysiological Consequences”Maintaining the structural integrity and functional capacity of tissues and organs is paramount for overall health, with disruptions often leading to systemic consequences. Structural components, including Collagen type I and other collagens, are fundamental for the extracellular matrix, providing essential support and influencing cellular behavior, while cell adhesion molecules like Integrin and CLDN11 mediate vital cell-cell and cell-matrix interactions that maintain tissue architecture.[6]Pathophysiological processes, such as uncontrolled cell proliferation observed in cancer or the cellular changes in dermatological diseases, frequently involve the dysregulation of cell cycle components like Cyclin A, Cyclin E,CCND1, and the tumor suppressor Rb.[6]These cellular and tissue-level disruptions, alongside metabolic imbalances, can contribute to the development of complex conditions, including cardiovascular complications like myocardial infarction and angina pectoris, illustrating the profound systemic interconnections within biological systems.[10]
Genetic Regulation and Gene Expression
Section titled “Genetic Regulation and Gene Expression”Genomic modulators play a crucial role in regulating gene expression, with studies identifying specific genetic variations influencing the expression levels of various genes.[7] This regulation can be cell type-specific, as seen in primary immune cells where master regulators and HLA alleles govern gene expression, thereby impacting cellular identity and function.[11] Furthermore, gene regulation extends to distal regulatory regions, such as the HBS1L-MYBintergenic interval, which is associated with elevated fetal hemoglobin (HbF) levels in erythroid cells.[12] Transcription factors like GATA-binding proteins are essential components of this regulatory machinery, controlling gene transcription in specific cell types like human eosinophils and basophils.[13] Genetic variants, such as those in HMGA2, can also directly influence complex traits like adult and childhood height through their impact on gene function.[14]
Metabolic Homeostasis and Regulation
Section titled “Metabolic Homeostasis and Regulation”Metabolic pathways are central to maintaining cellular and organismal homeostasis, encompassing processes like energy metabolism, biosynthesis, and catabolism. Metabolomic quantitative trait loci (mQTL) mapping reveals how genetic variations influence the profiles of diverse metabolites in human serum, providing insights into the genetic architecture of metabolic phenotypes.[15] This approach has been instrumental in understanding the genetic determinants of conditions such as diabetes, where specific loci are mapped to metabolic phenotypes.[16] Regulation of metabolic flux is critical, and genetic factors can significantly impact the concentrations of key metabolites. For instance, the SLC2A9gene influences uric acid levels with notable sex-specific effects, highlighting precise regulatory control over metabolic pathways.[17]Similarly, genome-wide association studies have shed light on intergenic regions associated with high-density lipoprotein cholesterol, indicating complex regulatory mechanisms that govern lipid metabolism and overall metabolic health.[18]
Cellular Signaling and Protein Dynamics
Section titled “Cellular Signaling and Protein Dynamics”Cellular communication relies on intricate signaling pathways initiated by receptor activation, which then propagate through intracellular cascades. Alpha4 integrins, for example, function as crucial receptors involved in mediating the immune response by facilitating cell adhesion and migration.[19] Downstream of receptor activation, complex protein-protein interactions, such as the assembly of SNARE complexes regulated by proteins like Amisyn, are fundamental for processes like vesicle fusion and neurotransmission.[20]Post-translational regulation, including mechanisms like the ubiquitin-proteasome system, is vital for controlling protein abundance, activity, and localization. This system tags proteins for degradation, a process that is often dysregulated in disease states, such as its increased activity in symptomatic carotid disease, which contributes to inflammation and atherosclerotic plaque destabilization.[15] Furthermore, cells respond to various stressors, like endoplasmic reticulum (ER) stress, by activating specific gene expression programs that involve genetic variation, demonstrating adaptive regulatory mechanisms to maintain cellular integrity.[15]
Systems-Level Integration and Disease Pathogenesis
Section titled “Systems-Level Integration and Disease Pathogenesis”Biological systems operate through highly integrated networks where various pathways exhibit significant crosstalk and hierarchical regulation, leading to emergent properties that define complex phenotypes. The interplay between immune cell gene expression, metabolic profiles, and protein degradation pathways exemplifies this systems-level integration, influencing overall physiological responses and disease susceptibility.[11]Molecular biosignatures, derived from such integrated analyses, are increasingly used to reclassify cardiovascular risk, underscoring the importance of understanding network interactions in health and disease.[15]Dysregulation within these intricate pathways is a hallmark of many diseases, often triggering compensatory mechanisms that attempt to restore homeostasis but can also contribute to pathology. For instance, the dysregulation of the ubiquitin-proteasome system in carotid atherosclerosis contributes to inflammation and plaque instability, representing a critical disease-relevant mechanism.[15]Identifying such points of pathway dysregulation offers potential therapeutic targets, as demonstrated by treatments like rosiglitazone affecting ubiquitin-proteasome system activity in carotid disease, providing avenues for intervention.[15]
Frequently Asked Questions About Homa Ir
Section titled “Frequently Asked Questions About Homa Ir”These questions address the most important and specific aspects of homa ir based on current genetic research.
1. My family has diabetes. Am I more likely to have high HOMA-IR?
Section titled “1. My family has diabetes. Am I more likely to have high HOMA-IR?”Yes, if your family has a history of type 2 diabetes, you are more likely to develop insulin resistance, which HOMA-IR measures. Genetics play a significant role in predisposing individuals to this condition. While it doesn’t mean you’ll definitely get it, understanding your family history helps assess your personal risk. Early monitoring and lifestyle interventions can be especially important for you.
2. I eat well and exercise. Why is my HOMA-IR still high?
Section titled “2. I eat well and exercise. Why is my HOMA-IR still high?”Even with healthy habits, genetic predispositions can influence your HOMA-IR. Some people have genetic variants that make them more prone to insulin resistance, regardless of their lifestyle efforts. Additionally, other unmeasured environmental factors or even rarer genetic variations not fully captured by current studies could be at play. It’s a complex interplay between your genes and environment.
3. I’m not Japanese. Does HOMA-IR apply to me the same way?
Section titled “3. I’m not Japanese. Does HOMA-IR apply to me the same way?”HOMA-IR as a measure of insulin resistance is a universal concept applicable across all populations. However, the specific genetic risk factors identified in studies focusing predominantly on Japanese populations might not be identical for you. Genetic architecture, including specific gene variants and how common they are, can vary significantly between different ancestral groups. Therefore, while theconcept applies, your specific genetic predispositions might differ.
4. My friend eats anything, but her HOMA-IR is fine. Why is mine high?
Section titled “4. My friend eats anything, but her HOMA-IR is fine. Why is mine high?”This often comes down to individual genetic differences. Some people are genetically more resilient to the effects of certain foods or lifestyle choices on their insulin sensitivity. You might have genetic predispositions that make you more susceptible to insulin resistance, even with similar or better habits than your friend. This highlights the complex interplay of genetics and environment in metabolic health.
5. If my HOMA-IR is high, can I really lower it with lifestyle changes?
Section titled “5. If my HOMA-IR is high, can I really lower it with lifestyle changes?”Yes, absolutely! Lifestyle interventions like dietary changes and increased physical activity are crucial and often very effective in improving insulin sensitivity and lowering HOMA-IR. Even with a genetic predisposition, these changes can significantly prevent or delay the progression to more severe metabolic conditions. It’s a powerful tool for managing your metabolic health.
6. Is getting my HOMA-IR tested actually useful for me?
Section titled “6. Is getting my HOMA-IR tested actually useful for me?”Yes, it can be very useful! HOMA-IR is a practical and non-invasive way to assess your insulin resistance and beta-cell function from routine blood tests. It can help identify if you’re at risk for conditions like type 2 diabetes, metabolic syndrome, or fatty liver disease, even before symptoms appear. This early insight allows for proactive lifestyle changes or medical interventions.
7. I feel healthy. Can my HOMA-IR still be high without symptoms?
Section titled “7. I feel healthy. Can my HOMA-IR still be high without symptoms?”Yes, definitely. Insulin resistance often develops silently over many years without noticeable symptoms. Your body might be producing extra insulin to compensate and keep your blood glucose normal. A high HOMA-IR can indicate this underlying resistance before it progresses to persistently high blood glucose or other overt health issues. It’s a valuable early warning sign.
8. Does my HOMA-IR naturally get worse as I get older?
Section titled “8. Does my HOMA-IR naturally get worse as I get older?”While not explicitly stated as a universal rule, insulin resistance can progress over time, and the risk of related metabolic disorders generally increases with age. Monitoring HOMA-IR over years can track changes in your insulin sensitivity. Lifestyle factors and disease progression can certainly influence how your HOMA-IR changes as you age.
9. Does stress or lack of sleep affect my HOMA-IR score?
Section titled “9. Does stress or lack of sleep affect my HOMA-IR score?”Yes, environmental factors like chronic stress and insufficient sleep are known to significantly impact your metabolic health and can worsen insulin resistance. These factors can modulate your genetic predispositions, making it harder for your body to manage blood glucose effectively. Addressing sleep and stress can be an important part of improving your HOMA-IR.
10. Does eating a lot of sugar specifically make my HOMA-IR worse?
Section titled “10. Does eating a lot of sugar specifically make my HOMA-IR worse?”Yes, consistently consuming high amounts of sugar and refined carbohydrates can contribute to insulin resistance and elevate your HOMA-IR. Your pancreas has to work harder to produce more insulin to handle the glucose spikes. Over time, this can lead to cells becoming less responsive to insulin, increasing your risk for metabolic issues.
This FAQ was automatically generated based on current genetic research and may be updated as new information becomes available.
Disclaimer: This information is for educational purposes only and should not be used as a substitute for professional medical advice. Always consult with a healthcare provider for personalized medical guidance.
References
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[3] Rafnar T, et al. “Variants associating with uterine leiomyoma highlight genetic background shared by various cancers and hormone-related traits.”Nat Commun, vol. 9, no. 1, 2018, p. 3636. PMID: 30194396.
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[9] Li, J., et al. “Association of CLEC16A with human common variable immunodeficiency disorder and role in murine B cells.” Nat Commun, 2015.
[10] Pare, G., 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, 2008.
[11] Fairfax, B. P., et al. “Genetics of gene expression in primary immune cells identifies cell type-specific master regulators and roles of HLA alleles.” Nature Genetics, vol. 44, 2012, pp. 502–510.
[12] Wahlberg, K., et al. “The HBS1L-MYB intergenic interval associated with elevated HbF levels shows characteristics of a distal regulatory region in erythroid cells.” Blood, vol. 114, 2009, pp. 1254–1262.
[13] Zon, L. I., et al. “Expression of mRNA for the GATA-binding proteins in human eosinophils and basophils: potential role in gene transcription.” Blood, vol. 81, 1993, pp. 3234–3241.
[14] Weedon, M. N., et al. “A common variant of HMGA2 is associated with adult and childhood height in the general population.” Nature Genetics, vol. 39, 2007, pp. 1245–1250.
[15] Kraus, W. E. “Metabolomic Quantitative Trait Loci (mQTL) Mapping Implicates the Ubiquitin Proteasome System in Cardiovascular Disease Pathogenesis.”PLoS Genetics, vol. 11, no. 11, 2015, p. e1005612.
[16] Dumas, M. E., et al. “Direct quantitative trait locus mapping of mammalian metabolic phenotypes in diabetic and normoglycemic rat models.” Nature Genetics, vol. 39, 2007, pp. 666–672.
[17] Döring, A., et al. “SLC2A9 influences uric acid concentrations with pronounced sex-specific effects.”Journal of Medical Genetics, vol. 45, no. 11, 2008, pp. 707-712.
[18] Heid, I. M., et al. “Genome-wide association analysis of high-density lipoprotein cholesterol in the population-based KORA Study sheds new light on intergenic regions.”Circulation: Cardiovascular Genetics, vol. 1, 2008, pp. 10–20.
[19] Rose, D. M., et al. “Alpha4 integrins and the immune response.” Immunological Reviews, vol. 186, 2002, pp. 118–124.
[20] Scales, S. J., et al. “Amisyn, a novel syntaxin-binding protein that may regulate SNARE complex assembly.” Journal of Biological Chemistry, vol. 277, 2002, pp. 28271–28279.