Depressive Symptom
Introduction
Depressive symptoms are a fundamental aspect of Major Depressive Disorder (MDD), a highly prevalent and complex mental health condition that significantly impacts individuals worldwide. MDD is characterized by persistent low mood, anhedonia (loss of pleasure), changes in sleep and appetite, fatigue, feelings of worthlessness or guilt, difficulty concentrating, and recurrent thoughts of death or suicide. The severity and persistence of these symptoms define clinical depression. Understanding the biological and genetic underpinnings of depressive symptoms is crucial for advancing diagnosis, treatment, and prevention strategies.
Background
Major Depressive Disorder is a highly prevalent disorder with substantial heritability. Research indicates that heritability is even more significant in cases of recurrent MDD, where individuals experience multiple episodes of depression. [1] Despite this clear genetic component, early genome-wide association studies (GWAS) have faced challenges in consistently identifying specific genetic risk factors for MDD with substantial individual effects. [1] This suggests that the genetic architecture of depressive symptoms and MDD is likely polygenic, involving many genes with small, additive effects.
Biological Basis
The biological basis of depressive symptoms is complex, involving intricate interactions between genetic predispositions and environmental factors. Genetic studies aim to identify variations in DNA that increase susceptibility to depressive symptoms. While large-effect single nucleotide polymorphisms (SNPs) have not been readily found for MDD in initial GWAS, the cumulative effect of many common SNPs is believed to contribute to risk. For instance, one study has implicated HOMER1 in the etiology of major depression. [2] Further large-scale meta-analyses are necessary to uncover SNPs with smaller individual effects or rarer allele frequencies that collectively contribute to the risk of MDD. [1]
Clinical Relevance
The clinical relevance of understanding depressive symptoms lies in their direct impact on mental health and well-being. These symptoms are the primary criteria for diagnosing MDD and other depressive disorders, guiding treatment decisions, and monitoring patient progress. Elucidating the genetic factors influencing depressive symptoms could lead to more precise diagnostic tools, the development of targeted pharmacological or psychological interventions, and personalized medicine approaches. Ultimately, this knowledge could improve treatment efficacy and reduce the burden of depression.
Social Importance
Depressive symptoms and MDD carry immense social importance due to their widespread prevalence and profound impact on individuals, families, and society. MDD is a leading cause of disability worldwide, affecting productivity, relationships, and overall quality of life. The condition is associated with increased healthcare costs, lost economic output, and a significant societal burden. Research into the genetic and biological basis of depressive symptoms is vital for destigmatizing mental illness, fostering greater public understanding, and developing effective public health initiatives to reduce the prevalence and impact of depression globally.
Methodological and Statistical Limitations
Research into depressive symptom faces several methodological and statistical challenges that impact the interpretation of genetic findings. Many studies, particularly early genome-wide association studies (GWAS), have been underpowered to detect genetic variants with small effect sizes, which are characteristic of complex traits like depressive symptom. [1] For instance, some GWAS failed to identify any single nucleotide polymorphism (SNP) achieving genome-wide significance, suggesting that SNPs with substantial odds ratios are unlikely for common variants detected by current arrays. [1] This necessitates meta-analyses of much larger datasets to uncover variants with smaller effects or rarer allele frequencies, highlighting the ongoing need for increased sample sizes. [1] Furthermore, inconsistencies in replication across studies, where associations are found for different SNPs within the same gene or are not replicated at all, can arise from variations in study design, statistical power, or the presence of multiple causal variants within a region. [3]
The reliance on imputation methods, which infer genotypes for unassayed SNPs based on reference panels like HapMap, also introduces limitations. While imputation expands coverage, its accuracy can vary, particularly for rarer variants or in populations not well-represented in reference panels, potentially missing true associations. [4] Additionally, the scope of current GWAS arrays means that only a subset of all genetic variations is interrogated, potentially overlooking causal genes or variants not tagged by common SNPs. [5] Analyses that pool sexes may also miss sex-specific genetic effects on depressive symptom, further limiting the comprehensive understanding of its genetic architecture. [5]
Phenotypic Heterogeneity and Measurement Challenges
The complex nature of depressive symptom itself presents significant challenges for genetic research. Depressive symptom is a heterogeneous phenotype, meaning it can manifest differently across individuals, and its severity and presentation can fluctuate over time. This variability makes consistent and precise phenotyping difficult, potentially introducing misclassification bias into studies. [6] When phenotypes are averaged across multiple examinations spanning long periods or when different assessment equipment or methodologies are used, this can obscure age-dependent gene effects and introduce measurement error, making it harder to link specific genetic variants to the trait. [6] The assumption that similar genetic and environmental factors influence the trait across wide age ranges may not hold true, further complicating the interpretation of findings. [6]
Generalizability and Unaccounted Etiological Factors
A significant limitation of much of the current genetic research on depressive symptom is the restricted generalizability of findings to diverse populations. Many large-scale genetic studies have predominantly included individuals of European ancestry, often from founder populations or specific regional cohorts. [6] While efforts are made to control for population stratification within these groups using methods like genomic control or principal component analysis, the transferability of identified genetic associations to other ethnic groups remains largely unknown. [7] This demographic imbalance limits the global applicability of genetic insights and can hinder the discovery of population-specific risk factors.
Furthermore, despite substantial heritability estimates for major depressive disorder (MDD), a significant portion of the genetic variation remains unexplained, a phenomenon known as "missing heritability". [1] While genetic studies have identified some trait-specific ascertainment, a comprehensive understanding requires accounting for complex gene-environment interactions and other environmental confounders that are often not fully captured or modeled in current study designs. [4] The interplay between genetic predispositions and lifestyle factors, social determinants, or other unmeasured environmental exposures is crucial for a complete picture of depressive symptom etiology, yet these interactions are challenging to comprehensively investigate and integrate into genetic analyses.
Variants
Genetic variations play a crucial role in influencing an individual's susceptibility to various health conditions, including the complex interplay between metabolic traits and depressive symptoms. Many of the identified variants are involved in lipid metabolism, a pathway increasingly recognized for its impact on brain health and mood regulation.
Variants within genes central to cholesterol regulation, such as APOE, LDLR, APOB, and CELSR2, are significant. The APOE gene, particularly its common variants rs7412 and rs429358 that define the ε2, ε3, and ε4 alleles, profoundly influences lipid transport and brain function. The APOE gene cluster is known to affect levels of low-density lipoprotein (LDL) cholesterol. [8] The LDLR gene encodes the low-density lipoprotein receptor, which is essential for clearing LDL cholesterol from the bloodstream, and variants like rs114846969 and rs138294113 can impair this process, leading to elevated LDL levels. The LDLR gene is significantly associated with LDL cholesterol concentrations. [8] Similarly, the APOB gene, which provides instructions for making apolipoprotein B, a primary structural component of LDL particles, is strongly associated with LDL cholesterol levels. [8] Variants such as rs668948 and rs563290 can influence the stability and clearance of these particles. The CELSR2 gene, in a region linked with PSRC1 and SORT1, also contributes to cholesterol regulation; the variant rs12740374 near CELSR2 has been associated with lower LDL cholesterol [9] and the CELSR2 locus is generally linked to LDL cholesterol concentrations. [8] Dysregulation of cholesterol metabolism, often influenced by these genetic variants, can impact brain health through mechanisms like inflammation and oxidative stress, potentially contributing to the development or severity of depressive symptoms.
Other genes, including LPL, GCKR, and CETP, are pivotal in triglyceride and high-density lipoprotein (HDL) metabolism. The LPL gene encodes lipoprotein lipase, an enzyme critical for breaking down triglycerides in circulating lipoproteins. Variants such as rs12679834, rs3208305, and rs117199990 can alter its activity, affecting both triglyceride and HDL cholesterol levels. The LPL gene is significantly associated with HDL cholesterol and triglyceride concentrations. [8] The GCKR gene, encoding glucokinase regulatory protein, influences both glucose and lipid metabolism, particularly impacting triglyceride levels. Variants like rs1260326 and rs780094 are strongly associated with increased triglyceride concentrations. [8] The CETP gene, which codes for cholesteryl ester transfer protein, facilitates the transfer of lipids between lipoproteins, primarily affecting HDL cholesterol levels. Variants such as rs247617, rs247616, and rs12446515 can alter this lipid exchange, influencing HDL concentrations. The CETP gene is associated with HDL cholesterol concentrations. [8] Additionally, the variant rs964184 is strongly associated with increased triglyceride concentrations, particularly within the APOA5 gene cluster. [8] Dyslipidemia, characterized by abnormal triglyceride and HDL levels, is increasingly recognized for its contribution to systemic inflammation and metabolic dysfunction, both of which are implicated in the pathophysiology of depressive disorders.
Beyond lipid metabolism, genes involved in chromatin remodeling and retinoic acid signaling also hold relevance for mood regulation. The SMARCA4 gene encodes a core component of the SWI/SNF chromatin remodeling complex, which is critical for regulating gene expression by altering chromatin structure. Variants like rs138175288 and rs56315738 can potentially affect neurodevelopment and synaptic plasticity, processes fundamental to mood regulation. [1] The ALDH1A2 gene is crucial for the synthesis of retinoic acid, a powerful signaling molecule derived from vitamin A that is vital for brain development, neuronal differentiation, and maintaining synaptic function. Variants such as rs261291 and rs1532085 might influence retinoic acid levels, potentially impacting neural circuits and contributing to the vulnerability for depressive symptoms. [1] The ZPR1 gene, encoding a zinc finger protein, is involved in various cellular processes including cell proliferation and RNA processing. While the variant rs964184 is primarily linked to lipid metabolism via the APOA5 cluster [8] general variations in genes like ZPR1 can impact fundamental cellular pathways, which, when disrupted, may indirectly contribute to the complex etiology of mood disorders. Alterations in gene expression and cellular signaling, influenced by these variants, represent potential pathways through which genetic predispositions can manifest as depressive symptoms.
Key Variants
| RS ID | Gene | Related Traits |
|---|---|---|
| rs7412 rs429358 |
APOE | low density lipoprotein cholesterol measurement clinical and behavioural ideal cardiovascular health total cholesterol measurement reticulocyte count lipid measurement |
| rs247617 rs247616 rs12446515 |
HERPUD1 - CETP | low density lipoprotein cholesterol measurement metabolic syndrome high density lipoprotein cholesterol measurement total cholesterol measurement, hematocrit, stroke, ventricular rate measurement, body mass index, atrial fibrillation, high density lipoprotein cholesterol measurement, coronary artery disease, diastolic blood pressure, triglyceride measurement, systolic blood pressure, heart failure, diabetes mellitus, glucose measurement, mortality, cancer total cholesterol measurement, diastolic blood pressure, triglyceride measurement, systolic blood pressure, hematocrit, ventricular rate measurement, glucose measurement, body mass index, high density lipoprotein cholesterol measurement |
| rs964184 | ZPR1 | very long-chain saturated fatty acid measurement coronary artery calcification vitamin K measurement total cholesterol measurement triglyceride measurement |
| rs7528419 rs12740374 |
CELSR2 | myocardial infarction coronary artery disease total cholesterol measurement lipoprotein-associated phospholipase A(2) measurement high density lipoprotein cholesterol measurement |
| rs114846969 rs138294113 |
SMARCA4 - LDLR | lipoprotein-associated phospholipase A(2) measurement depressive symptom measurement social deprivation, low density lipoprotein cholesterol measurement low density lipoprotein cholesterol measurement, physical activity Sphingomyelin (d18:1/20:0, d16:1/22:0) measurement |
| rs1260326 rs780094 rs780093 |
GCKR | urate measurement total blood protein measurement serum albumin amount coronary artery calcification lipid measurement |
| rs138175288 rs56315738 |
SMARCA4 | depressive symptom measurement fatty acid amount omega-6 polyunsaturated fatty acid measurement saturated fatty acids measurement |
| rs12679834 rs3208305 rs117199990 |
LPL | sphingomyelin measurement triglyceride measurement diacylglycerol 34:1 measurement diacylglycerol 34:2 measurement depressive symptom measurement |
| rs668948 rs563290 |
APOB - TDRD15 | coronary artery disease anxiety measurement, low density lipoprotein cholesterol measurement depressive symptom measurement total cholesterol measurement triglyceride measurement |
| rs261291 rs1532085 |
ALDH1A2 | high density lipoprotein cholesterol measurement triglyceride measurement depressive symptom measurement anxiety measurement, non-high density lipoprotein cholesterol measurement total cholesterol measurement |
Defining Major Depressive Disorder
Depressive symptoms, particularly those reaching clinical thresholds, are often conceptualized within the framework of Major Depressive Disorder (MDD). This condition is a recurrent psychiatric disorder that has been a focus of genetic research . Genetic studies employ genome-wide association studies (GWAS) to systematically scan the entire human genome for common genetic variations, such as single nucleotide polymorphisms (SNPs), that are associated with the trait. While initial large-scale GWAS efforts faced challenges in identifying consistently significant genetic risk factors for MDD [1] subsequent research has begun to pinpoint specific genetic loci. For instance, a comprehensive genome-wide association, replication, and neuroimaging study has implicated the HOMER1 gene in the etiology of major depression. [2]
These genetic mechanisms involve not only the direct function of specific genes but also their regulatory elements and overall gene expression patterns, which can be influenced by inherited variations. The identification of such genes provides crucial insights into the molecular pathways underlying depressive symptoms, suggesting that variations in genes like HOMER1 may alter neuronal communication or other critical brain functions. The ongoing search for genetic factors continues to explore how inherited predispositions contribute to the complex manifestation of depressive disorders, moving beyond single gene effects to consider polygenic contributions and their interactions.
Molecular and Cellular Underpinnings
The biological basis of depressive symptoms extends to intricate molecular and cellular pathways, involving a network of critical biomolecules. Genetic variations can influence the production and function of key proteins, enzymes, and receptors, thereby altering fundamental cellular processes and signaling cascades. For example, studies exploring the relationship between genetics and metabolomics have shown how specific genetic variants can determine "metabotypes," which are characteristic profiles of metabolites in human serum, providing insights into potentially affected biochemical pathways. [10] This suggests that inherited differences can impact metabolic processes, such as fatty acid metabolism, by influencing enzymes like FADS1. [10]
Furthermore, these molecular insights highlight how genetic predispositions can lead to dysregulation in regulatory networks essential for neuronal health and function. Polymorphisms within the promoter region of the C-reactive protein (CRP) gene, for instance, are associated with altered plasma CRP levels, demonstrating a genetic link to inflammatory processes. [11] Such molecular changes, whether in neurotransmitter systems, inflammatory responses, or metabolic pathways, contribute to the cellular dysfunction observed in individuals experiencing depressive symptoms, affecting critical aspects like synaptic plasticity, energy metabolism, and cellular stress responses.
Systemic and Pathophysiological Contributions
Depressive symptoms arise from a complex interplay of pathophysiological processes that transcend individual cells and impact tissue and organ-level biology, leading to systemic consequences. The concept of "intermediate phenotypes on a continuous scale" is crucial here, as it allows for the investigation of specific biological pathways that, when disrupted, contribute to the broader clinical presentation of depression. [10] These disruptions can manifest as homeostatic imbalances across various physiological systems. For instance, genetic loci have been identified that influence biomarker traits such as lipid levels, liver enzyme concentrations, and inflammatory markers, indicating a systemic impact of genetic variations. [12]
While these systemic effects are not always directly linked to depressive symptoms in every study, they underscore how genetic and molecular dysregulation can perturb normal physiological functions throughout the body, including those relevant to the brain. Such widespread disruptions can affect organ-specific functions and tissue interactions, potentially contributing to the multifaceted nature of depressive disorders. The cumulative effect of these pathophysiological processes, including chronic inflammation, metabolic dysregulation, and altered neuroendocrine responses, contributes to the overall vulnerability and expression of depressive symptoms.
Cellular Signaling and Receptor-Mediated Processes
Genetic variations can significantly impact cellular communication networks, contributing to the development of depressive symptoms. For instance, the HOMER1 gene has been implicated in the etiology of major depression, suggesting its critical role in neural function and potentially synaptic organization. [2] Furthermore, the Melanocortin Type 4 Receptor (MC4R), a crucial component of receptor activation pathways, exhibits common genetic variations associated with conditions like insulin resistance, highlighting its broader systemic influence that can indirectly affect brain function and mood regulation. [13] Intracellular signaling cascades are also under genetic influence, as demonstrated by the tribbles protein family, which directly controls mitogen-activated protein kinase (MAPK) cascades, vital for diverse cellular responses including neuronal plasticity and stress responses. [8]
These signaling pathways involve intricate feedback loops and cascades where receptor activation triggers a series of molecular events, often culminating in the regulation of transcription factors. Dysregulation in these finely tuned processes, such as altered MAPK signaling or impaired receptor function, can disrupt neuronal homeostasis, impacting processes critical for mood stability and emotional processing. The collective impact of such genetic variations on signaling components can contribute to the complex pathophysiology observed in depressive states.
Metabolic Reprogramming and Bioenergetic Flux
Metabolic pathways represent a fundamental functional readout of the physiological state, where genetic variants profoundly influence the homeostasis of key lipids, carbohydrates, and amino acids. [10] For example, specific single nucleotide polymorphisms (SNPs) can lead to genetically determined metabotypes, altering the levels of essential metabolites such as sphingomyelins, phosphatidylcholines, and phosphatidylethanolamines, which are critical components of cellular membranes and signaling molecules. [10] Lipid metabolism, in particular, is subject to extensive genetic regulation, with genes like ANGPTL3 and ANGPTL4 directly regulating lipid concentrations, and the mevalonate pathway, governed by enzymes like HMG-CoA reductase (HMGCR), playing a central role in cholesterol and isoprenoid biosynthesis. [8]
Further illustrating metabolic regulation, the transcription factor SREBP-2 (Sterol Regulatory Element-Binding Protein 2) is crucial in linking isoprenoid and adenosylcobalamin metabolism, influencing the synthesis of vital compounds. [8] Variations in genes like SLC2A9 impact uric acid concentrations, while enzymes such as Glutathione S-transferase omega 1 (GSTO1) and omega 2 (GSTO2) are involved in detoxification and cellular protection, highlighting the broad impact of genetic variation on metabolic flux and cellular resilience. [14] Dysregulation in these pathways can lead to altered energy metabolism, impaired neurotransmitter synthesis, and increased oxidative stress, all of which are implicated in the mechanisms underlying depressive symptoms.
Gene Expression and Post-Translational Regulatory Control
The intricate regulation of gene expression and subsequent protein modification forms a critical layer of control over cellular function. Genetic variations can influence gene regulation at multiple levels, from transcriptional initiation to post-translational processing. For instance, genome-wide association studies have identified protein quantitative trait loci (pQTLs), demonstrating that common genetic variants can significantly affect the abundance of specific proteins, thereby altering their functional capacity and overall cellular proteome. [15] A clear example of post-transcriptional regulation is seen with the HMGCR gene, where common SNPs are known to affect the alternative splicing of exon13, leading to different protein isoforms or altered protein function, which in turn impacts the mevalonate pathway. [16]
These regulatory mechanisms extend to allosteric control and feedback loops that fine-tune enzyme activity and protein interactions. The precise control over protein levels and modifications, such as phosphorylation or glycosylation, ensures proper cellular responses. When these regulatory mechanisms are perturbed by genetic variants, it can lead to pathway dysregulation, manifesting as altered metabolic profiles or impaired signaling, ultimately contributing to the complex etiology of conditions like major depression.
Systems-Level Integration and Pathway Dysregulation
Depressive symptoms arise from complex interactions across multiple biological systems, highlighting the importance of systems-level integration and pathway crosstalk. Genetic variants often do not act in isolation but exert their effects through a network of interconnected pathways, influencing intermediate phenotypes such as metabolite profiles. [10] The interplay between signaling pathways, like those involving MC4R and its association with insulin resistance, and metabolic pathways, such as lipid homeostasis, exemplifies this crosstalk, where dysregulation in one system can propagate to others. [13] The polygenic nature of complex traits, including susceptibility to major depression, suggests that numerous genetic loci contribute to an emergent property of disease risk through a hierarchical regulation of molecular and cellular processes. [9]
Dysregulation within these integrated networks represents a key disease-relevant mechanism. For example, alterations in lipid metabolism, influenced by genes like ANGPTL3 and ANGPTL4, can impact membrane fluidity and receptor function, thereby affecting neuronal signaling. [8] Compensatory mechanisms may initially buffer the effects of genetic perturbations, but sustained or severe dysregulation can overwhelm these systems, leading to persistent imbalances in neurotransmission, energy metabolism, and cellular resilience that underpin depressive symptomatology. Understanding these intricate network interactions is crucial for identifying potential therapeutic targets that can restore systemic balance.
Clinical Relevance of Depressive Symptoms
Depressive symptoms represent a significant public health concern, often manifesting as major depressive disorder (MDD), a highly prevalent condition with substantial heritability, particularly in its recurrent forms. [1] Understanding the clinical relevance of these symptoms involves exploring their genetic underpinnings, diagnostic implications, associations with other conditions, and potential for personalized therapeutic approaches.
Genetic Basis and Diagnostic Implications
The heritable nature of recurrent major depressive disorder suggests a strong biological component influencing its onset and course. [1] However, genome-wide association studies (GWAS) conducted in European cohorts for recurrent MDD have not yet identified common single nucleotide polymorphisms (SNPs) with substantial individual effects, indicating a complex genetic architecture where many variants might contribute with small effects. [1] This complexity presents challenges for straightforward diagnostic utility based solely on common genetic markers.
Despite these challenges, specific genetic findings, such as the implication of HOMER1 in the etiology of major depression, derived from GWAS, replication, and neuroimaging studies, offer promising avenues for future clinical applications. [1] Identifying such genetic markers could enhance the diagnostic precision of depressive symptoms, enable earlier risk assessment for individuals predisposed to depression, and potentially guide the selection of more effective monitoring strategies tailored to an individual's genetic profile. Further research is essential to translate these genetic insights into actionable clinical tools.
Disease Progression and Comorbidity
The substantial heritability of recurrent major depressive disorder highlights its potential for a predictable disease progression and long-term implications for patient care. [1] Genetic insights into recurrent forms of depression are crucial for predicting disease trajectory and identifying individuals at higher risk for chronic or relapsing episodes. This prognostic value can inform early intervention strategies and enable more intensive, proactive management plans to alleviate the considerable burden associated with the illness.
Furthermore, depressive symptoms frequently overlap with other psychiatric conditions, presenting as syndromic presentations or comorbidities. For instance, major depressive disorder is often studied alongside anxiety-related diagnoses in large population cohorts, such as the Netherlands Study of Depression and Anxiety (NESDA). [12] Recognizing these common associations is vital for comprehensive patient care, allowing clinicians to address the full spectrum of a patient's mental health needs, which in turn can lead to more appropriate treatment selection and improved overall outcomes.
Risk Stratification and Personalized Medicine
Advancing the understanding of depressive symptoms is critical for refining risk stratification, with the ultimate goal of identifying high-risk individuals for targeted prevention strategies. [1] While initial GWAS have not yielded common SNPs with large effects for MDD, the continued pursuit of meta-analyses across larger datasets aims to uncover genetic variants with smaller individual effects or rarer allele frequencies. [1] Such discoveries are fundamental for developing personalized prevention strategies that account for an individual's unique genetic predispositions.
Ultimately, genetic research on depressive symptoms seeks to pave the way for personalized medicine, where an individual's genetic profile can inform treatment selection and monitoring. By identifying specific genetic markers, even those with subtle influences, clinicians could potentially predict an individual's response to particular pharmacotherapies or psychotherapies. This approach would optimize treatment pathways, minimize the current trial-and-error process, and lead to more effective, patient-specific interventions for managing depressive symptoms.
References
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