Nucleotide

Introduction

Nucleotides are fundamental organic molecules that serve as the basic building blocks of nucleic acids, DNA and RNA. Each nucleotide is composed of three key components: a five-carbon sugar (deoxyribose in DNA, ribose in RNA), a phosphate group, and a nitrogenous base. The four primary nitrogenous bases found in DNA are adenine (A), guanine (G), cytosine (C), and thymine (T). In RNA, thymine is replaced by uracil (U).

Biological Basis

These molecular units link together to form the long polymeric chains of DNA and RNA, which are essential for storing, transmitting, and expressing genetic information in all known living organisms. The specific sequence of nucleotides along these chains dictates the genetic code, providing instructions for protein synthesis and regulating gene activity. Beyond their role as genetic material, nucleotides also function critically in cellular energy transfer, most notably as adenosine triphosphate (ATP), and participate in various intracellular signaling pathways.

Clinical Relevance

Variations in nucleotide sequences are a primary source of genetic diversity within populations. A common type of these variations is a Single Nucleotide Polymorphism (SNP), where a single nucleotide at a specific genomic location differs among individuals.^[1]These minute changes can have significant implications, influencing a wide array of human traits, including susceptibility to common diseases such as cardiovascular disease and diabetes, and an individual’s response to therapeutic drugs.^{[1], [2], [3]}Genome-Wide Association Studies (GWAS) are powerful tools that extensively analyze these SNPs to identify genetic regions associated with various biochemical parameters, biomarker concentrations, and disease risks. Such research provides critical insights into disease mechanisms, aids in early diagnosis, and refines risk stratification strategies.^{[1], [4]}

Social Importance

The study of nucleotides and their variations holds immense social importance, particularly in the era of personalized medicine. Understanding an individual’s unique genetic makeup, largely defined by nucleotide sequences, enables the development of tailored treatments and preventive strategies. Furthermore, this knowledge is invaluable in fields such as forensic science for identification, tracing human ancestry and migration patterns, and advancing our understanding of human evolution and population genetics.

Study Design and Statistical Constraints

Many genetic studies are limited by their sample size, which can reduce the statistical power needed to detect genetic effects of modest size, potentially leading to false negative findings.^[1] The extensive multiple testing inherent in genome-wide association studies (GWAS) requires the application of stringent significance thresholds, and despite these measures, some associations, particularly those identified in exploratory analyses, may still represent false positives.^[5] Furthermore, the reliability of identified associations ultimately requires replication in independent cohorts; a failure to replicate findings can stem from various reasons, including initial false positive discoveries, differences in study cohort characteristics, or insufficient statistical power in the replication studies themselves.^[1]

Incomplete Genetic Coverage and Imputation Accuracy

The genetic arrays used in many studies often capture only a subset of all known single nucleotide polymorphisms (SNPs), meaning that the genome-wide coverage may be insufficient to detect all genuine genetic associations.^[6] This limited coverage can result in missing causal variants or variants that are in strong linkage disequilibrium with them, thereby underestimating the true genetic contribution to a trait.^[6] While imputation methods are employed to infer ungenotyped SNPs, their accuracy depends on the quality and representativeness of the reference panels, and these processes can introduce error, with reported error rates for imputed alleles ranging from 1.46% to 2.14%.^[7] Consequently, relying on imputed data, especially for SNPs with lower imputation confidence, may compromise the detection of true associations or introduce noise into the analyses.^[8]

Generalizability and Unaccounted Confounders

A significant limitation in many genetic studies is the composition of the study cohorts, which are often largely of European descent, thereby restricting the generalizability of findings to individuals from other ethnic or racial backgrounds.^[1] Specific characteristics of a cohort, such as being predominantly middle-aged to elderly, consisting of volunteers, or involving DNA collection at later examinations, can introduce selection or survival biases that limit the broader applicability of the results.^[1] Moreover, to manage the multiple testing burden, many studies conduct sex-pooled analyses, which means that potential sex-specific genetic associations that might only be evident in males or females could remain undetected.^[6] A further knowledge gap exists in the investigation of gene-environment interactions, as genetic variants can influence phenotypes in a context-specific manner modulated by environmental factors, an area often not comprehensively explored in initial genome-wide association studies.^[5]

Variants

Several genetic variants impact diverse biological pathways, from metabolic regulation and cellular signaling to nucleotide synthesis and gene expression. These single nucleotide polymorphisms (SNPs) are located within or near genes that play fundamental roles in maintaining cellular homeostasis and influencing various physiological traits. Understanding these variants helps to elucidate genetic predispositions and the molecular mechanisms underlying complex conditions, often with implications for broader health biomarkers and cellular functions.^{[4], [9]} Variants affecting metabolic regulation include those near PHYHD1 and DOLK, such as rs17432839 . PHYHD1 is involved in the alpha-oxidation of branched-chain fatty acids, a specialized pathway important for lipid breakdown, while DOLKplays a crucial role in dolichol-phosphate biosynthesis, essential for protein glycosylation and membrane integrity. Similarly, the intergenic variantrs147535836 is located near PANCR, a long non-coding RNA implicated in cell cycle control, and ENPEP(Glutamyl Aminopeptidase), an enzyme involved in peptide metabolism. Disruptions in these genes or their regulatory regions can influence overall metabolic health, potentially impacting lipid profiles or the processing of peptides, which are vital for various physiological functions.^{[1], [10]} Central to cellular function is the UMPS gene, where the rs13146 variant is located. UMPSencodes Uridine Monophosphate Synthase, a key enzyme in the de novo synthesis of pyrimidine nucleotides, the essential building blocks of DNA and RNA. Variations inUMPScan affect the efficiency of nucleotide production, which is critical for cell division, energy metabolism, and response to cellular stress. Dysregulation of nucleotide metabolism can have downstream effects, including influencing levels of uric acid, a purine breakdown product, which is a known biomarker for various conditions.^{[2], [11]} Other variants, like rs80240706 in AKT3, rs117641309 in STK32C, rs10179520 in PRKCE, and rs72662084 near MAPK10, are associated with genes encoding kinases that are integral to diverse cell signaling pathways. AKT3 is a master regulator of cell growth, survival, and metabolism; STK32C and PRKCE are involved in signal transduction; and MAPK10(JNK3) is a stress-activated kinase crucial for neuronal function and apoptosis. These variants can modulate the activity of these kinases, thereby influencing cellular responses to stimuli, proliferation, and ultimately, cellular health and disease susceptibility ;.^[6] Further contributing to the complexity of genetic influence are variants like rs12548348 in SULF1 and rs55940287 near VIPR1 and VIPR1-AS1. SULF1 encodes a sulfatase that modifies heparan sulfate proteoglycans, impacting cell-surface receptor binding and growth factor signaling, while VIPR1is a receptor for vasoactive intestinal peptide, a neuropeptide with broad regulatory roles in immunology, neurobiology, and metabolism. These variants can alter receptor function or signaling cascade efficiency, affecting physiological responses. Additionally, long non-coding RNAs (lncRNAs) such asLINC01412 and VIPR1-AS1, associated with rs13421626 and rs55940287 respectively, play significant roles in regulating gene expression. They can influence the transcription or stability of nearby protein-coding genes like TEX41 or VIPR1, thereby modulating protein levels and overall cellular function. These regulatory variations collectively highlight the intricate genetic landscape that underpins individual differences in health and disease.^{[1], [12]}The researchs context does not contain specific information regarding the classification, definition, or terminology of ‘nucleotide’ that would allow for the creation of this section.

Key Variants

RS ID	Gene	Related Traits
rs17432839	PHYHD1 - DOLK	nucleotide measurement
rs12548348	SULF1	nucleotide measurement
rs55940287	VIPR1, VIPR1-AS1	nucleotide measurement
rs13421626	LINC01412 - TEX41	nucleotide measurement
rs147535836	PANCR, ENPEP	nucleotide measurement
rs13146	UMPS	nucleotide measurement
rs80240706	AKT3	ghrelin measurement nucleotide measurement
rs117641309	STK32C	nucleotide measurement
rs10179520	PRKCE	nucleotide measurement
rs72662084	MAPK10, MAPK10-AS1	nucleotide measurement

Biological Background

The fundamental unit of genetic information, the nucleotide, plays a critical role in determining an organism’s biological characteristics and physiological state. Variations at the single nucleotide level, known as single nucleotide polymorphisms (SNPs), can profoundly influence a wide array of molecular, cellular, and systemic processes. These genetic differences act as key determinants of individual variability in metabolic capacities, protein levels, and susceptibility to various diseases, forming the basis of genetically determined metabotypes and other intermediate phenotypes.^[9]Understanding the impact of these nucleotide variations is essential for elucidating disease pathogenesis and advancing personalized health strategies.

Genetic Variation and Molecular Regulation

Single nucleotide polymorphisms (SNPs) are fundamental genetic variations that can impact the central dogma of molecular biology, influencing the flow of information from DNA to RNA to protein.^[4] These variations can alter gene expression patterns, with some DNA variants influencing messenger RNA (mRNA) levels, thereby acting as expression quantitative trait loci (eQTLs).^[4] Beyond transcription, SNPs can affect post-transcriptional processes like alternative splicing, as observed with common SNPs in HMGCR that influence the splicing of exon13.^[13] Ultimately, these genetic differences can manifest as protein quantitative trait loci (pQTLs), where DNA variation is directly associated with altered protein levels in the blood, involving mechanisms such as altered transcription, modified receptor cleavage rates, or changes in protein secretion.^[4] Specific examples include common variants in or near genes like IL6R, CCL4, IL18, LPA, GGT1, SHBG, CRP, and IL1RN, which are associated with the circulating levels of their respective protein products.^[4]

Metabolic Homeostasis and Enzymatic Pathways

Nucleotide variations frequently exert their influence by modulating metabolic processes and the function of key enzymes. Genetic variants can lead to significant differences in an individual’s metabolic capacity, affecting the synthesis of polyunsaturated fatty acids, the beta-oxidation of short- and medium-chain fatty acids, and the breakdown of triglycerides.^[9] For instance, specific polymorphisms are linked to the activity of well-characterized enzymes involved in lipid metabolism.^[9] These genetic influences contribute to the homeostasis of vital biomolecules such as lipids, carbohydrates, and amino acids, with disruptions potentially leading to altered physiological states.^[9] The comprehensive measurement of these endogenous metabolites, known as metabolomics, provides a functional readout of the body’s metabolic status, revealing how genetic variants contribute to the intricate balance of cellular metabolic pathways.^[9]

Systemic Biomarkers and Physiological Function

The impact of nucleotide variations extends to systemic physiological functions, often reflected in the concentrations of various circulating biomarkers. Genome-wide association studies (GWAS) have identified specific SNPs that correlate with the levels of these biomarkers, which provide insights into disease pathogenesis and risk stratification.^[1]Such biomarkers include those reflecting inflammatory processes, natriuretic peptide activation, hepatic function, and vitamin metabolism.^[1] For example, specific genetic variants are associated with plasma levels of liver enzymes.^[8]and others influence hemostatic factors and hematological phenotypes like fibrinogen, factor VII, plasminogen activator inhibitor-1, von Willebrand factor, hemoglobin, mean corpuscular hemoglobin, red blood cell count, white blood cell count, and platelet aggregation.^[6] The identification of such genetic associations with intermediate phenotypes on a continuous scale offers detailed insights into potentially affected biochemical pathways and systemic consequences.^[9]

Pathophysiological Implications

Disruptions in molecular and metabolic processes due to nucleotide variations can underpin various pathophysiological conditions. Alterations in protein function or levels, stemming from genetic variants, are known to influence human diseases.^[4] For example, variations in the SLC2A9gene, which encodes a urate transporter, are associated with serum urate concentration, urate excretion, and the development of gout, exhibiting pronounced sex-specific effects.^[11]Similarly, genetic loci have been identified that influence plasma triglyceride levels and are associated with conditions like type 2 diabetes.^[14] The study of these genetically determined metabotypes provides a crucial step towards understanding the pathogenesis of common diseases and the complex interplay between genes and environmental factors, ultimately guiding personalized health care and nutrition strategies.^[9]

Nucleotide Metabolism and Homeostasis

Nucleotides are fundamental molecules involved in various metabolic processes, including the storage and transfer of energy, and as building blocks for nucleic acids. Their metabolism is tightly regulated to maintain cellular homeostasis. A key aspect of nucleotide catabolism involves the breakdown of purines, yielding uric acid. The transport of uric acid, a purine catabolite, is crucial for its excretion and maintaining serum levels. For instance, theSLC2A9 (GLUT9) gene encodes a facilitative glucose transporter family member that has been molecularly identified as a renal urate anion exchanger, regulating blood urate levels.^[15] Genetic variants in SLC2A9significantly influence serum uric acid concentrations and urate excretion, with pronounced sex-specific effects, and are associated with conditions like gout.^[11] Similarly, the SLC22A12gene, another urate anion exchanger, also plays a role in regulating serum uric acid levels, with intronic single nucleotide polymorphisms (SNPs) in this gene associated with variations in uric acid.^[16]Comprehensive metabolomics studies, which aim to measure all endogenous metabolites in body fluids, provide a functional readout of the physiological state, helping to identify genetic variants that associate with changes in the homeostasis of key metabolites, including those related to nucleotide pathways.^[9]

Nucleotide-Dependent Signaling Pathways

Nucleotides and their derivatives serve as critical signaling molecules, mediating cellular responses through intricate cascades. Cyclic nucleotides like cAMP and cGMP are well-established second messengers in various intracellular signaling pathways. For example, cAMP-dependent Cl-transport, involving proteins like CFTR, plays a role in the mechanical properties of cells.^[17]Furthermore, the regulation of cGMP signaling is influenced by factors such as Angiotensin II, which can increase the expression of phosphodiesterase 5A in vascular smooth muscle cells, thereby antagonizing cGMP signaling.^[18] Intracellular signaling cascades, including the mitogen-activated protein kinase (MAPK) pathway, are also tightly controlled, with protein families like human tribbles regulating these cascades.^[19]These pathways often involve rapid changes in nucleotide levels or the activity of nucleotide-binding proteins, leading to diverse cellular outcomes.

Regulatory Mechanisms Involving Nucleotides and Nucleic Acids

Nucleotides are integral to various regulatory mechanisms at both genetic and post-translational levels. Gene regulation is influenced by nucleic acid modifications and processing, such as the adenosine-to-inosine editing of microRNAs (miRNAs), a process that can redirect the silencing targets of these regulatory RNA molecules.^[20]This RNA editing fundamentally alters the genetic information carried by miRNAs, influencing gene expression. Beyond RNA modification, alternative splicing of pre-mRNA is another critical regulatory mechanism that determines the final protein product. For instance, common single nucleotide polymorphisms in theHMGCR gene, which is involved in lipid metabolism, have been shown to affect the alternative splicing of exon 13, thereby impacting the enzyme’s function.^[13]Additionally, post-translational modifications of proteins, often ATP-dependent, are crucial for regulating protein activity and stability. ThePJA1gene, encoding a RING-H2 finger ubiquitin ligase, highlights the role of ubiquitination in protein degradation and regulation, a process that relies on ATP hydrolysis for ubiquitin activation and transfer.^[21]

Systems Integration and Disease Relevance of Nucleotide Pathways

The intricate interplay of nucleotide pathways is crucial for overall physiological function, and their dysregulation is frequently implicated in various diseases. Uric acid, a product of purine catabolism, exemplifies a nucleotide-related metabolite whose aberrant levels are linked to several systemic disorders. Hyperuricemia, often influenced by genetic variants in transporters likeSLC2A9, is not merely a marker but a potential pathogenic factor in cardiovascular disease, metabolic syndrome, and type 2 diabetes mellitus.^[15]The broader field of metabolomics, by comprehensively measuring metabolite profiles, helps to bridge the gap between genotypes and phenotypes, revealing how genetic variants affect intermediate metabolic pathways, including those involving nucleotides.^[9] This systems-level approach allows for the identification of pathway crosstalk, such as the associations between genetic variants influencing lipid concentrations (e.g., ANGPTL3, ANGPTL4) and the risk of coronary artery disease, which can be indirectly linked to overall metabolic health influenced by nucleotide status.^[22]Understanding these integrated networks and their dysregulation offers critical insights for identifying therapeutic targets and developing compensatory mechanisms against disease.

Clinical Relevance

Nucleotide variations, particularly single nucleotide polymorphisms (SNPs), represent fundamental genetic differences that significantly influence an individual’s health trajectory and response to interventions. The clinical relevance of these variations stems from their ability to impact various biological processes, ranging from biomarker concentrations to complex disease susceptibility, offering valuable insights for diagnostic, prognostic, and therapeutic strategies.

Genetic Insights into Disease Risk and Prevention

Nucleotide variations, specifically single nucleotide polymorphisms (SNPs), are pivotal in understanding an individual’s predisposition to various diseases and for refining prevention strategies. Genome-wide association studies (GWAS) have demonstrated that these genetic differences contribute significantly to the variability of systemic biomarker concentrations, which are fundamental to disease pathogenesis, diagnosis, and risk stratification.^[1]For instance, genetic risk scores, which aggregate the effects of multiple lipid-associated loci, have shown improved discriminative accuracy in predicting dyslipidemia and overall cardiovascular risk compared to models based solely on age, sex, and body mass index.^[23] This enhanced risk assessment can identify high-risk individuals earlier, allowing for the implementation of targeted preventive strategies.

The ability of these genetic profiles to predict conditions like hypercholesterolemia and to differentiate individuals exceeding clinical thresholds for lipid levels underscores their diagnostic utility.^[23] Such insights enable clinicians to move towards personalized medicine approaches, where prevention strategies are tailored to an individual’s unique genetic makeup. By incorporating genetic data into routine clinical care, healthcare providers can refine risk stratification models, potentially leading to earlier interventions and improved patient outcomes.^[2]

Prognostic Value and Monitoring Strategies

The prognostic importance of systemic biomarker concentrations, which are influenced by genetic factors, is a significant area of clinical relevance for nucleotide variations. Variations at specific nucleotide positions can predict disease outcomes and progression by affecting the levels of key biomarkers involved in inflammation, natriuretic peptide activation, and hepatic function.^[1]For example, genetic profiles have been shown to improve the classification of coronary heart disease (CHD) risk when combined with traditional clinical factors, offering a more comprehensive assessment of an individual’s long-term cardiovascular outlook.^[23]Beyond initial risk assessment, specific genetic markers may serve as valuable tools for monitoring disease progression or treatment response. The association of genetic risk scores with subclinical atherosclerosis markers, such as intima media thickness (IMT), highlights their potential in tracking disease activity and evaluating the efficacy of interventions.^[23] Understanding the genetic contributions to biomarker variability allows for more refined monitoring strategies, potentially leading to adjustments in patient management to optimize therapeutic benefits and mitigate adverse long-term implications.

Molecular Associations and Comorbidity Understanding

Nucleotide variations provide critical insights into the underlying molecular mechanisms and genetic architectures of complex diseases, often revealing associations with various comorbidities and overlapping phenotypes. Genome-wide association studies have identified numerous single nucleotide polymorphisms (SNPs) linked to a wide array of systemic biomarker concentrations, including those related to inflammation, oxidative stress, liver function, and vitamin metabolism.^[1] For instance, specific polymorphisms in the HNF1Agene, which is part of metabolic-syndrome pathways, have been consistently associated with C-reactive protein (CRP) levels, indicating a genetic predisposition that can link metabolic disturbances with inflammatory states.^[12]The concept of pleiotropy, where a single nucleotide variation influences multiple traits or biological domains, is frequently observed and helps in understanding complex disease presentations.^[1]Research has identified common variants at numerous loci that collectively contribute to polygenic dyslipidemia, highlighting the intricate genetic basis of lipid disorders and their potential connections to other cardiovascular complications.^[10]Furthermore, associations between SNPs and other biomarkers like gamma glutamyl transferase (GGT) or components of renal function, such as urine albumin and sodium, further illustrate how genetic insights can deepen the understanding of related conditions and potential syndromic presentations.^[2]

Frequently Asked Questions About Nucleotide Measurement

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

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1. Why are my cholesterol levels different from my sibling’s, even if we eat similar things?

Your genes play a significant role! Common genetic variations strongly influence biochemical parameters like HDL, LDL, and triglyceride concentrations. Even with similar lifestyles, these genetic differences lead to interindividual variability in how your body processes and maintains these levels. So, your sibling might have different genetic variants impacting their cholesterol.

2. Why are my liver enzyme levels high, even if I feel fine?

It’s possible your genetics are at play. Common genetic variations can significantly influence biochemical parameters, including liver enzymes like gamma glutamyl transferase (GGT). These variations can affect your hepatic function, leading to differences in GGT levels even without obvious symptoms. Further studies are needed to pinpoint the exact functional variants.

3. Does my ancestry affect my risk for certain health problems?

Yes, your ancestral background can definitely influence your health risks. Many genetic studies have focused primarily on populations of European ancestry, and findings from these groups might not apply directly or with the same effect size to other ancestral groups. This means your unique genetic background could impact how common genetic variations affect your health markers.

4. Why do I sometimes retain water easily, even if I’m active?

Your genes can influence how your body handles water and sodium. For instance, specific genetic variations, like an SNP identified near thePDYNgene, are linked to urinary sodium levels. This gene is involved in producing opioid peptides that regulate sodium and water excretion, explaining why some individuals have different fluid balance tendencies.

5. Can knowing my genes help me manage my unique health risks better?

Absolutely. Understanding your genetic profile can provide valuable insights into your individual health risks. By identifying how common genetic variations influence your specific biochemical parameters and protein levels, doctors can move towards more personalized medicine approaches, tailoring prevention and treatment strategies specifically for you.

6. Is it true that my body handles salt differently than others?

Yes, it’s very true! There’s significant interindividual variability in how bodies handle salt, and genetics are a key factor. For example, a specific genetic variation near the PDYNgene has been identified that influences urinary sodium levels, affecting how your body excretes salt and water.

7. Could my genes explain why my red blood cell count is sometimes low?

Yes, your genes can certainly play a role in your blood cell counts. Common genetic variations are known to significantly influence biochemical parameters like hemoglobin (Hgb), mean corpuscular hemoglobin (MCH), and red blood cell count (RBCC). These variations contribute to the natural differences observed in these critical health indicators among individuals.

8. Will my children inherit my specific health tendencies like high cholesterol?

There’s a good chance they will inherit some of those tendencies. Systemic biomarker concentrations, including those related to cholesterol (HDL, LDL, triglycerides), are known to be heritable. This means common genetic variations you carry can be passed down, influencing your children’s predisposition to similar health profiles.

9. Why do some health studies say one thing, and then another says something different?

It’s a common challenge in genetic research. Different studies might identify different genetic variations (SNPs) within the same region that are linked to a trait, often because they’re in strong linkage disequilibrium with an unknown causal variant. Variations in study design, sample size, and the possibility of multiple causal variants within a single gene also contribute to these replication challenges.

10. Will eating certain foods affect my body’s inflammation more than my friend’s?

It’s very likely. Your individual genetic makeup influences how your body processes various physiological processes, including inflammatory responses. Common genetic variations can affect your systemic biomarker concentrations related to inflammation, meaning your body might react differently to certain dietary inputs compared to someone else.

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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

[1] Benjamin, E. J., et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Medical Genetics, vol. 8 Suppl 1, 2007, p. S10.

[2] Wallace, C. “Genome-wide association study identifies genes for biomarkers of cardiovascular disease: serum urate and dyslipidemia.”American Journal of Human Genetics, vol. 82, no. 1, 2008, pp. 139-49.

[3] Meigs, J. B., et al. “Genome-wide association with diabetes-related traits in the Framingham Heart Study.” BMC Medical Genetics, vol. 8 Suppl 1, 2007, p. S15.

[4] Melzer, D., et al. “A genome-wide association study identifies protein quantitative trait loci (pQTLs).” PLoS Genetics, vol. 4, no. 5, 2008, e1000072.

[5] 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 Medical Genetics, vol. 8, no. 1, 2007, p. 69.

[6] Yang, Q., 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. S12.

[7] Kathiresan, Sekar, et al. “Common variants at 30 loci contribute to polygenic dyslipidemia.” Nature Genetics, vol. 40, no. 12, 2008, pp. 1419-1427.

[8] Yuan, X., et al. “Population-based genome-wide association studies reveal six loci influencing plasma levels of liver enzymes.” Am J Hum Genet, vol. 83, no. 4, 2008, pp. 520-528.

[9] Gieger, C. et al. “Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum.”PLoS Genet, vol. 4, no. 11, 2008, e1000282.

[10] Kathiresan, Sekar, et al. “Common variants at 30 loci contribute to polygenic dyslipidemia.” Nature Genetics, vol. 41, no. 1, 2009, pp. 56-65.

[11] Vitart, V. et al. “SLC2A9 is a newly identified urate transporter influencing serum urate concentration, urate excretion and gout.”Nat Genet, vol. 40, no. 4, 2008, pp. 432-437.

[12] Reiner, Alex P., et al. “Polymorphisms of the HNF1A gene encoding hepatocyte nuclear factor-1 alpha are associated with C-reactive protein.”American Journal of Human Genetics, vol. 82, no. 5, 2008, pp. 1193-1200.

[13] 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, no. 11, 2008, pp. 2078-2085.

[14] Kooner, J. S., et al. “Genome-wide scan identifies variation in MLXIPL associated with plasma triglycerides.” Nat Genet, vol. 40, no. 2, 2008, pp. 149-151.

[15] Li, S. et al. “The GLUT9 gene is associated with serum uric acid levels in Sardinia and Chianti cohorts.”PLoS Genet, vol. 3, no. 11, 2007, e194.

[16] Shima, Y. et al. “Association between intronic SNP in urate-anion exchanger gene, SLC22A12, and serum uric acid levels in Japanese.”Life Sci, vol. 79, no. 24, 2006, pp. 2234-2237.

[17] Robert, R. et al. “Disruption of CFTR chloride channel alters mechanical properties and cAMP-dependent Cl-transport of mouse aortic smooth muscle cells.”J Physiol (Lond), vol. 568, no. 2, 2005, pp. 483-495.

[18] Kim, D. et al. “Angiotensin II increases phosphodiesterase 5A expression in vascular smooth muscle cells: a mechanism by which angiotensin II antagonizes cGMP signaling.”J Mol Cell Cardiol, vol. 38, no. 1, 2005, pp. 175-184.

[19] Kiss-Toth, E. et al. “Human tribbles, a protein family controlling mitogen-activated protein kinase cascades.” J Biol Chem, vol. 279, no. 41, 2004, pp. 42703-42708.

[20] Kawahara, Y. et al. “Redirection of silencing targets by adenosine-to-inosine editing of miRNAs.”Science, vol. 315, no. 5815, 2007, pp. 1137-1140.

[21] Yu, P. et al. “PJA1, encoding a RING-H2 finger ubiquitin ligase, is a novel human X chromosome gene abundantly expressed in brain.” Genomics, vol. 79, no. 6, 2002, pp. 869-874.

[22] Willer, C. J. 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.

[23] Aulchenko, Yurii S., et al. “Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts.”Nature Genetics, vol. 41, no. 1, 2009, pp. 18-24.