Asymmetric Dimethylarginine

Asymmetric dimethylarginine (ADMA) is a naturally occurring chemical compound found in blood plasma, derived from the methylation of L-arginine residues within proteins. It is released into the circulation following the proteolytic degradation of these methylated proteins.

Biological Basis

ADMA functions as an endogenous inhibitor of nitric oxide synthase (NOS), a family of enzymes responsible for producing nitric oxide (NO) from L-arginine. Nitric oxide is a crucial signaling molecule involved in a wide range of physiological processes, including the regulation of vascular tone, blood pressure, neurotransmission, and immune responses. By competitively inhibiting NOS, ADMA reduces the bioavailability of NO, leading to impaired endothelial function and potentially contributing to vasoconstriction and increased vascular stiffness. The primary enzyme responsible for metabolizing ADMA is dimethylarginine dimethylaminohydrolase (DDAH), which converts ADMA into L-citrulline and dimethylamine.

Clinical Relevance

Elevated plasma levels of ADMA have been consistently associated with various cardiovascular and metabolic diseases. These conditions include hypertension, atherosclerosis, coronary artery disease, heart failure, chronic kidney disease, and diabetes mellitus. As a marker of endothelial dysfunction, ADMA levels are studied for their potential utility in assessing cardiovascular risk, predicting disease progression, and monitoring therapeutic responses. Its role in inhibiting NO production makes it a significant factor in the health of the vascular system.

The investigation into ADMA’s role in human health carries significant social importance. Research continues to explore its potential as a diagnostic biomarker for the early identification of individuals at risk for cardiovascular disease, even before overt symptoms appear. Furthermore, understanding the pathways regulated by ADMA opens avenues for developing novel therapeutic strategies aimed at improving endothelial function, restoring NO balance, and ultimately reducing the global burden of cardiovascular and metabolic disorders.

Limitations

The study of asymmetric dimethylarginine (ADMA) levels through genetic association studies faces several methodological and interpretative challenges that warrant consideration. These limitations impact the robustness, generalizability, and mechanistic understanding of identified genetic influences on ADMA.

Methodological and Statistical Constraints

Genetic association studies, including those investigating ADMA, often require very large sample sizes to achieve sufficient statistical power for detecting genetic variants with small effect sizes.^[1] Inadequate sample sizes can lead to missed associations or potentially inflated effect sizes for those that do reach statistical significance, thereby impacting the reliability and replicability of findings across different cohorts. Initial genetic associations for ADMA require rigorous replication in independent populations to confirm their validity.^[2] Without such external validation, findings remain exploratory, highlighting a critical gap in confirming true genetic effects.^[2] Furthermore, the extensive number of statistical tests performed in genome-wide analyses necessitates stringent corrections for multiple comparisons, such as Bonferroni correction, to minimize false positives, which can inadvertently increase the risk of overlooking genuine, but less statistically significant, associations.^[1]Current genome-wide association studies typically use a subset of all available single nucleotide polymorphisms (SNPs), which may lead to missing some genes or variants due to incomplete genomic coverage.^[3] While imputation methods can infer missing genotypes and facilitate meta-analysis across studies utilizing different marker sets, the accuracy of imputation can vary, particularly for rare variants or in ethnically diverse populations, potentially introducing errors or reducing confidence in reported associations.^[4] Additionally, despite efforts to correct for population stratification through methods like genomic control or principal component analysis, residual effects of population substructure might still influence results, particularly in multi-ethnic cohorts.^[5]

Generalizability and Phenotype Heterogeneity

A significant limitation in many genetic studies, including those that might investigate ADMA, is the predominant focus on populations of European ancestry.^[6] This lack of diversity severely restricts the generalizability of findings to other ethnic groups, as genetic architectures and allele frequencies can differ substantially across populations, potentially leading to missed associations or misinterpretations in non-European cohorts. The precise definition and of complex traits like ADMA can also vary across studies, affecting comparability and the ability to conduct robust meta-analyses. Different analytical platforms, assay sensitivities, and pre-analytical sample handling procedures can introduce variability in ADMA measurements, potentially confounding genetic associations.^[7] Moreover, the adjustment for covariates such as age, sex, and clinical status, as well as the handling of outliers or individuals on specific medications, may differ across studies, further contributing to heterogeneity in phenotype definition and impacting the consistency of observed genetic effects.^[6] Many studies perform sex-pooled analyses to increase statistical power, which may inadvertently mask important sex-specific genetic associations that influence ADMA levels or its related pathways.^[3] Traits can exhibit different genetic influences or magnitudes of effect between males and females, and without dedicated sex-stratified analyses, such nuances remain undetected, limiting a comprehensive understanding of the trait’s genetic architecture.

Unexplained Variation and Biological Interpretation

While genetic association studies have identified numerous loci, they often explain only a fraction of the total heritable variation for complex traits, a phenomenon known as “missing heritability”.^[8] For ADMA, this implies that a substantial portion of its genetic determinants remains undiscovered, possibly due to the combined effects of many common variants with individually very small effects, rare variants, structural variants, or complex gene-gene and gene-environment interactions not fully captured by current study designs. This unexplained variation underscores the incompleteness of current genetic models for ADMA.

Furthermore, current association studies primarily identify statistical links between genetic variants and phenotypes, providing limited direct insight into the underlying biological mechanisms.^[1] Translating these associations into a functional understanding requires extensive follow-up research, including functional genomics, proteomics, and metabolomics approaches, to elucidate precisely how associated variants influence protein expression, enzyme activity, or metabolic pathways relevant to ADMA metabolism and its physiological roles. Without this deeper mechanistic understanding, the clinical relevance and therapeutic potential of identified genetic associations with ADMA remain largely speculative.

Variants

Genetic variations play a crucial role in influencing diverse biological pathways, including those that may indirectly affect asymmetric dimethylarginine (ADMA) levels, a key biomarker for endothelial function and cardiovascular risk. Thers6584192 variant is located within the PYROXD2 gene, which encodes a protein involved in cellular redox regulation and maintaining oxidative balance. Alterations in PYROXD2 activity due to this variant could impact cellular oxidative stress, a known factor in ADMA metabolism and nitric oxide bioavailability.^[9] Similarly, the rs2068906 variant is associated with HACL1, a gene critical for the alpha-oxidation pathway of branched-chain fatty acids. Variations in HACL1 may alter lipid metabolism, potentially affecting overall metabolic health and contributing to systemic inflammation, which can influence ADMA levels.^[9] Further impacting cellular metabolism and signaling are variants such as rs586023 , located within the MTHFS gene and its readthrough transcript ST20-MTHFS. MTHFS is essential in the folate pathway, converting specific folate derivatives, which are vital for one-carbon metabolism, a process intrinsically linked to homoc The ZZEF1 gene, where rs2176324 is found, functions as a guanine nucleotide exchange factor for Rho GTPases, proteins that regulate cell motility, adhesion, and proliferation. Variants inZZEF1 could subtly alter these fundamental cellular processes, which are important for maintaining vascular integrity and function, thereby indirectly affecting ADMA-related pathways.^[9] Other variants highlight diverse cellular functions with potential systemic implications. The rs17647667 variant is within the PRKN gene, known for its role in mitochondrial quality control and autophagy, processes vital for cellular health and stress response. Mitochondrial dysfunction, which can be influenced by PRKN variants, contributes to oxidative stress and inflammation, factors that are closely associated with elevated ADMA levels and impaired nitric oxide production.^[9] The rs1020812 variant in VAV3 also relates to cell signaling, as VAV3acts as a guanine nucleotide exchange factor for Rho family GTPases, participating in cytoskeletal reorganization and immune cell signaling. AlteredVAV3 function could impact endothelial cell responses to stress or inflammation, influencing ADMA-mediated vascular effects. Moreover, non-coding RNA variations, such as rs6045682 (associated with SCP2D1-AS1 and SLC24A3) and rs3762222 (linked to LCDR and SCP2D1-AS1), may regulate the expression of neighboring genes like SLC24A3, a calcium exchanger. Disruptions in calcium homeostasis, influenced by SLC24A3 activity, can affect vascular tone and endothelial function, thereby relevant to ADMA.^[9] Finally, the rs1188568 variant near OR11P1P - OR11G2 and rs12349140 in MAP1LC3BP1 further underscore the broad genetic landscape influencing health. While olfactory receptors (like OR11G2) are primarily involved in smell, some have documented roles in non-olfactory tissues, potentially influencing broader physiological processes, while MAP1LC3BP1plays a role in autophagy, a process critical for cellular waste removal and adaptation to stress, with implications for metabolic and cardiovascular health.

Key Variants

RS ID	Gene	Related Traits
rs6584192	PYROXD2	asymmetric dimethylarginine metabolite N6,N6-dimethyllysine N6-methyllysine
rs2068906	HACL1	asymmetric dimethylarginine
rs6045682	SCP2D1-AS1 - SLC24A3	asymmetric dimethylarginine
rs3762222	LCDR - SCP2D1-AS1	asymmetric dimethylarginine
rs586023	MTHFS, ST20-MTHFS	asymmetric dimethylarginine NG-monomethyl-arginine
rs2176324	ZZEF1	asymmetric dimethylarginine
rs17647667	PRKN	asymmetric dimethylarginine
rs1020812	VAV3	asymmetric dimethylarginine
rs1188568	OR11P1P - OR11G2	asymmetric dimethylarginine
rs12349140	MAP1LC3BP1	asymmetric dimethylarginine

Causes of Asymmetric Dimethylarginine

The concentration of metabolites in the human body, including asymmetric dimethylarginine, is influenced by a complex interplay of genetic predispositions, environmental factors, and physiological states. Research into metabolomics has revealed that specific genetic variants can significantly alter the homeostasis of various metabolites, while external factors such as diet, lifestyle, and medications also contribute to an individual’s metabolic profile.

Genetic Predisposition and Metabolite Homeostasis

Genetic factors play a fundamental role in shaping an individual’s metabolic landscape, influencing the levels and ratios of various metabolites. Genome-wide association studies (GWAS) have identified numerous common genetic variants, often single nucleotide polymorphisms (SNPs), that are associated with alterations in metabolite concentrations.^[1] For instance, specific genotypes within genes like FADS1, which codes for fatty acid desaturase, have been linked to changes in the levels of various phosphatidylcholines, sphingomyelins, and phosphatidylethanolamines, thereby modifying the efficiency of fatty acid metabolism.^[1] Similarly, variants in genes encoding enzymes such as short-chain acyl-Coenzyme A dehydrogenase (SCAD) and medium-chain acyl-Coenzyme A dehydrogenase (MCAD) are strongly associated with the ratios of short-chain and medium-chain acylcarnitines, respectively, highlighting the direct impact of inherited variants on metabolic pathways.^[1]These findings underscore how polygenic risk, characterized by the cumulative effect of multiple genetic variants, dictates an individual’s propensity for certain metabolite profiles, with an additive genetic model often employed to assess the impact of these variants.^[1]

Environmental and Lifestyle Influences

Beyond genetic blueprints, an individual’s environment and lifestyle choices exert considerable influence over their metabolic status. Factors such as diet, exposure to certain substances, and socioeconomic conditions can significantly modulate metabolite levels. For example, studies investigating metabolic traits frequently collect data on participants’ medical history, medication use, and habits such as tobacco and alcohol consumption, recognizing their potential impact on physiological parameters.^[10] The presence of specific conditions like diabetes, or the use of certain medications, including lipid-lowering therapies, are often considered critical variables that can alter metabolite concentrations, necessitating their adjustment or exclusion in analyses of metabolic profiles.^[6]These environmental and lifestyle elements can interact with an individual’s genetic makeup, further shaping their unique metabolic signature.

Interplay of Genes and Environment

The intricate relationship between an individual’s genetic background and their environment is a crucial determinant of complex metabolic traits. Research in metabolomics aims to unravel how genetic predispositions interact with environmental triggers to influence metabolite homeostasis, thereby contributing to the etiology of complex diseases.^[1]While specific examples for all metabolites may vary, the general principle suggests that a genetic variant might only manifest its full effect under particular environmental conditions, or conversely, an environmental exposure might have a differential impact depending on an individual’s genotype. For instance, researchers often investigate potential gene-environment interaction effects, such as how genetic variations might modify the impact of conditions like dyslipidemia on metabolic parameters, demonstrating the ongoing effort to understand this complex interplay.^[10]

Physiological and Clinical Modulators

A variety of physiological and clinical factors, including comorbidities, medication effects, and age-related changes, can also significantly modulate metabolite levels. The presence of comorbid conditions such as atherogenic dyslipidemia, coronary artery disease, various forms of diabetes mellitus, hypertension, or inflammatory diseases like Crohn’s disease or rheumatoid arthritis, are known to be associated with distinct metabolic profiles and can influence metabolite concentrations.^[1] Furthermore, pharmacological interventions, including general medication use, lipid-lowering agents, or anti-diabetic medications, directly impact metabolic pathways and can alter circulating metabolite levels.^[10] Age is another critical physiological factor, as metabolic processes naturally change over the lifespan, with studies frequently accounting for age and age-squared as covariates to normalize for these chronological variations in metabolite concentrations.^[6]

Metabolomics as a Window into Physiological States

Metabolomics represents a rapidly evolving field aimed at comprehensively measuring endogenous metabolites within a cell or body fluid, providing a functional readout of an individual’s physiological state.^[1] This approach allows for the identification of genetic variants that influence the homeostasis of key lipids, carbohydrates, or amino acids, offering insights into the underlying molecular mechanisms of various diseases.^[1]By quantifying a wide array of naturally occurring organic compounds, including sugar molecules, biogenic amines, prostaglandins, acylcarnitines, and amino acids, metabolomics can reveal detailed information about potentially affected biochemical pathways.^[1]The concentrations of these metabolites serve as crucial intermediate phenotypes that are often more directly related to disease etiology than clinical outcomes alone.

Genetic Regulation of Metabolic Pathways

Genetic mechanisms play a significant role in shaping an individual’s metabolic profile, with specific gene functions and regulatory elements directly impacting the efficiency of enzymatic conversions. When a genetic polymorphism, such as a single nucleotide polymorphism (SNP), affects the activity or abundance of an enzyme, it can lead to observable changes in the concentrations of its substrates and products.^[1] For instance, variations in the FADS1 gene, which encodes the delta-5 desaturase, can alter the efficiency of fatty acid desaturation, influencing the balance of polyunsaturated fatty acids.^[1] Such genetic influences on metabolic reactions result in distinct patterns of metabolite concentrations, providing strong indicators of pathway modifications.

Fatty Acid Metabolism and Energy Homeostasis

Fatty acid metabolism is a critical biological process for energy homeostasis, involving various enzymes that process fatty acids of different chain lengths. The delta-5 desaturase, encoded by FADS1, is crucial for converting eicosatrienoyl-CoA (C20:3) to arachidonyl-CoA (C20:4), which are then incorporated into complex lipids like glycerophospholipids.^[1] A reduction in the catalytic activity of delta-5 desaturase due to a genetic polymorphism can lead to increased concentrations of its substrate-derived lipids (e.g., PC aa C36:3) and reduced concentrations of its product-derived lipids (e.g., PC aa C36:4).^[1] Similarly, short-chain acyl-Coenzyme A dehydrogenase (SCAD) and medium-chain acyl-Coenzyme A dehydrogenase (MCAD) initiate the beta-oxidation of fatty acids in mitochondria, with genetic variants in these genes strongly associating with ratios of corresponding acylcarnitines (e.g., C3/C4 for SCAD and C8/C10 for MCAD).^[1] These enzyme activities are critical for the proper breakdown and utilization of fatty acids, highlighting how genetic variations can disrupt fundamental energy pathways.

Lipid Remodeling and Regulatory Networks

Beyond primary metabolic conversions, genetic variations can have broader systemic consequences, influencing lipid remodeling and interconnected regulatory networks. For example, changes in glycerophospholipid metabolism due toFADS1 polymorphisms can impact the homeostasis of other lipids, such as sphingomyelins, which can be produced from phosphatidylcholine.^[1] This indicates a complex interplay where alterations in one pathway propagate throughout the cellular lipid landscape. Furthermore, enzymes like HMGCR (3-hydroxy-3-methylglutaryl-CoA reductase), a key enzyme in cholesterol biosynthesis, also exhibit genetic variations, such as SNPs affecting alternative splicing of its exon13, which can influence its activity and degradation rate.^[11] These intricate regulatory networks and tissue interactions underscore how genetic predispositions can lead to widespread metabolic disruptions, potentially contributing to various pathophysiological processes.

Genetic and Enzymatic Regulation of Metabolic Flux

The homeostasis of circulating metabolites, including lipids and amino acids, is fundamentally governed by a complex interplay of genetic factors and enzymatic reactions. Genome-wide association studies (GWAS) coupled with metabolomics reveal that common genetic variants can significantly alter the concentrations of key endogenous metabolites, acting as intermediate phenotypes that directly reflect physiological states.^[1]For instance, single nucleotide polymorphisms (SNPs) within theFADS1 gene cluster are strongly associated with the composition of polyunsaturated fatty acids, directly influencing the efficiency of the delta-5 desaturase reaction that converts eicosatrienoyl-CoA (C20:3) to arachidonyl-CoA (C20:4).^[1] Similarly, variants in genes like SCAD (short-chain acyl-Coenzyme A dehydrogenase) and MCAD (medium-chain acyl-Coenzyme A dehydrogenase) impact the initial steps of fatty acid beta-oxidation, leading to altered ratios of specific acylcarnitines (e.g., C3/C4 for SCAD and C10/C12 for MCAD).^[1] These examples highlight how genetic variations can modulate enzyme activity, thereby controlling metabolic flux through critical biosynthetic and catabolic pathways.

Post-Translational Control and Alternative Splicing in Metabolic Regulation

Beyond direct genetic influence on enzyme expression, the fine-tuning of metabolic pathways involves intricate regulatory mechanisms such as post-translational modifications and alternative splicing. The enzyme 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR), a key regulator of the mevalonate pathway and cholesterol biosynthesis, provides a clear illustration of such control.^[12] Common genetic variants in HMGCR have been shown to affect alternative splicing of its exon 13, which can impact the enzyme’s structure and function, consequently influencing LDL-cholesterol levels.^[11] Alternative splicing, a process allowing a single gene to produce multiple protein isoforms, is a widespread mechanism for regulating gene expression and protein diversity, playing a crucial role in diverse biological processes, including metabolic pathways.^[13] Furthermore, the oligomerization state of HMGCR itself influences its degradation rate, demonstrating another layer of post-translational control over enzyme abundance and activity.^[14]

Systems-Level Integration and Pathway Crosstalk

Metabolic pathways do not operate in isolation but are interconnected through complex networks, exhibiting extensive crosstalk and hierarchical regulation. The comprehensive analysis of metabolite profiles in human serum reveals a functional readout of the entire metabolic network, where changes in one pathway can ripple through others.^[1] The observation that analyzing ratios of closely related metabolites (substrate/product pairs) can dramatically enhance the statistical power of genetic associations underscores the interconnected nature of these systems, providing direct insights into affected enzymatic reactions and underlying biological processes.^[1]For instance, the regulation of adiponectin levels, influenced by theADIPOQgene, is critical for metabolic health, impacting traits like insulin resistance and coronary artery disease, demonstrating the systemic implications of individual metabolic regulators.^[10] These integrated networks ensure metabolic adaptability, but also mean that dysregulation in one component can have broad systemic consequences.

Disease-Relevant Mechanisms and Therapeutic Implications

Dysregulation within these finely balanced metabolic pathways is frequently implicated in the etiology of complex human diseases. Genetic variants that perturb metabolite homeostasis are not merely markers but often represent direct molecular mechanisms underlying disease pathogenesis, offering functional insights beyond simple genotype-phenotype associations.^[1] The association of SLC2A9with uric acid concentrations, for example, highlights a genetic determinant of a metabolic trait linked to conditions like gout.^[15] Understanding these specific genetic-metabolite links can reveal compensatory mechanisms the body employs to maintain metabolic balance and, when these fail, identify potential therapeutic targets. By providing access to functionally relevant endpoints, metabolomics combined with GWAS opens new avenues for investigating gene-environment interactions and developing individualized medication strategies for complex diseases.^[1]

Frequently Asked Questions About Asymmetric Dimethylarginine

These questions address the most important and specific aspects of asymmetric dimethylarginine based on current genetic research.

1. My family has heart issues; am I at higher risk?

Yes, if your family has a history of cardiovascular diseases like hypertension or heart failure, you might have a higher predisposition. Studies show that ADMA levels, which are linked to these conditions, can be influenced by inherited factors. While specific genes for ADMA aren’t fully known, understanding your family history is crucial for assessing your overall risk.

2. Does my ethnic background affect my heart risk?

Yes, your ethnic background can play a role. Many genetic studies, including those looking at compounds like ADMA and heart disease risk, have focused mainly on people of European descent. This means genetic risk factors and their frequencies might be different in other ethnic groups, so findings may not fully apply to you.

3. Do men and women have different heart risks from this?

It’s possible. While many studies combine data from men and women to increase statistical power, this can sometimes hide specific genetic influences that might affect ADMA levels or heart disease risk differently in males versus females. More research is needed to fully understand these potential sex-specific differences.

4. Could a blood test show my heart risk really early?

Yes, measuring compounds like ADMA in your blood is being investigated as a way to identify your risk for cardiovascular disease earlier, even before you show obvious symptoms. Elevated ADMA levels are linked to issues like impaired endothelial function, which is an early sign of heart problems. This research aims to help doctors intervene sooner.

5. Can my healthy lifestyle overcome bad family heart history?

While genetics certainly play a role in your predisposition to heart conditions, a healthy lifestyle is incredibly powerful. Even if your family has a history of heart disease, maintaining good habits like diet and exercise can significantly mitigate your risk by improving your vascular health and potentially influencing factors like ADMA levels. It’s a balance between your inherited tendencies and your daily choices.

6. Why do some people get heart disease but others don’t?

The reasons are complex, involving a mix of genetic predispositions and environmental factors. Individual differences in compounds like ADMA, which are linked to heart disease, are partly genetic, but a significant portion of what makes us different is still not fully understood. Unique interactions between your genes and your lifestyle contribute to your individual risk profile.

7. Is my doctor already checking for this kind of risk?

While ADMA is a promising marker for cardiovascular risk, it’s not yet a standard part of routine doctor’s check-ups. Research is ongoing to determine its full clinical utility and how best to integrate it into patient care. Your doctor will typically assess your risk using more established factors like blood pressure, cholesterol, and family history.

8. Does my chronic illness make my heart risk worse?

Yes, if you have chronic conditions like diabetes or chronic kidney disease, your risk for heart problems is generally higher. These conditions are often associated with elevated levels of ADMA, which can contribute to impaired vascular function and increase your overall cardiovascular burden. Managing your chronic illness well is key to reducing this risk.

9. Could stress or sleep affect my ADMA levels?

While specific genetic links between stress, sleep, and ADMA levels are still being investigated, your overall physiological state significantly influences vascular health. Since ADMA plays a crucial role in regulating nitric oxide, which is impacted by stress and sleep, it’s plausible that these daily factors could affect your ADMA levels and contribute to your cardiovascular risk. Maintaining good sleep and managing stress are important for your general health.

10. If I’m taking heart meds, can this help track if they’re working?

Yes, ADMA levels are being studied for their potential to help monitor how well treatments for cardiovascular disease are working. By tracking changes in ADMA, doctors might someday gain insights into whether a therapy is effectively improving endothelial function and reducing your risk. This could offer a more personalized approach to managing your heart health.

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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[2] Benjamin, E. J., et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Medical Genetics, vol. 8, no. 1, 2007, p. 55.

[3] Yang, Q., et al. “Genome-wide association and linkage analyses of hemostatic factors and hematological phenotypes in the Framingham Heart Study.”BMC Medical Genetics, vol. 8, no. 1, 2007, p. 54.

[4] Yuan, X., et al. “Population-based genome-wide association studies reveal six loci influencing plasma levels of liver enzymes.” American Journal of Human Genetics, vol. 83, no. 6, 2008, pp. 675-684.

[5] Dehghan, A., et al. “Association of three genetic loci with uric acid concentration and risk of gout: a genome-wide association study.”Lancet, vol. 372, no. 9654, 2008, pp. 1959-1965.

[6] Kathiresan, S., et al. “Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans.”Nature Genetics, vol. 40, no. 2, 2008, pp. 189-197.

[7] Pare, G., et al. “Novel association of HK1with glycated hemoglobin in a non-diabetic population: a genome-wide evaluation of 14,618 participants in the Women’s Genome Health Study.”PLoS Genetics, vol. 4, no. 12, 2008, e1000308.

[8] Benyamin, B., et al. “Variants in TF and HFEexplain approximately 40% of genetic variation in serum-transferrin levels.”American Journal of Human Genetics, vol. 84, no. 1, 2009, pp. 60-65.

[9] Aulchenko, Y. S., et al. “Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts.”Nature Genetics, vol. 41, no. 11, 2009, pp. 1110-1116.

[10] Ling, H., et al. “Genome-wide linkage and association analyses to identify genes influencing adiponectin levels: the GEMS Study.”Obesity (Silver Spring, Md.), 2009.

[11] 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. 29, no. 1, 2009, pp. 10–16.

[12] Goldstein, JL., and Brown, MS. “Regulation of the mevalonate pathway.” Nature, 1990.

[13] Matlin, AJ., Clark, F., and Smith, CW. “Understanding alternative splicing: towards a cellular code.” Nature Reviews Molecular Cell Biology, 2005.

[14] Cheng, HH., et al. “Oligomerization state influences the degradation rate of 3-hydroxy-3-methylglutaryl-CoA reductase.” Journal of Biological Chemistry, 1999.

[15] Do¨ring, A., et al. “SLC2A9 influences uric acid concentrations with pronounced sex-specific effects.”Nature Genetics, 2008.