Genome-wide association studies with metabolomics

Adamski, Jerzy

doi:10.1186/gm333

Cited by 64 publications

(31 citation statements)

References 51 publications

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“…On the one hand, metabolome data can be related to genetic polymorphisms in GWAS, allowing for the identification of genetic factors underlying specific metabolic traits. The specific advantages of metabolomics for GWAS were recently summarized in a review by Adamski (2012). On the other hand, metabolic traits can be related to specific phenotypes, resulting in the identification of metabolome signatures caused by lifestyle factors.…”

Section: Technological Prerequisites and General Significancementioning

confidence: 99%

A description of large-scale metabolomics studies: increasing value by combining metabolomics with genome-wide SNP genotyping and transcriptional profiling

Homuth¹,

Teumer²,

Völker³

et al. 2012

Journal of Endocrinology

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The metabolome, defined as the reflection of metabolic dynamics derived from parameters measured primarily in easily accessible body fluids such as serum, plasma, and urine, can be considered as the omics data pool that is closest to the phenotype because it integrates genetic influences as well as nongenetic factors. Metabolic traits can be related to genetic polymorphisms in genome-wide association studies, enabling the identification of underlying genetic factors, as well as to specific phenotypes, resulting in the identification of metabolome signatures primarily caused by nongenetic factors. Similarly, correlation of metabolome data with transcriptional or/and proteome profiles of blood cells also produces valuable data, by revealing associations between metabolic changes and mRNA and protein levels. In the last years, the progress in correlating genetic variation and metabolome profiles was most impressive. This review will therefore try to summarize the most important of these studies and give an outlook on future developments.

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Section: Technological Prerequisites and General Significancementioning

confidence: 99%

A description of large-scale metabolomics studies: increasing value by combining metabolomics with genome-wide SNP genotyping and transcriptional profiling

Homuth¹,

Teumer²,

Völker³

et al. 2012

Journal of Endocrinology

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“…Loss of skeletal muscle function and mass is a major disorder in geriatric medicine and is related to diseases such as sarcopenia, osteoporotic fracture, and frailty susceptibility loci [99][100][101][102][103][104].…”

Section: Gwas Related To Sarcopenia and Lean Massmentioning

confidence: 99%

Recent genetic discoveries in osteoporosis, sarcopenia and obesity [Review]

Urano

Inoue

2015

Endocr J

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Abstract. Osteoporosis is a skeletal disorder characterized by low bone mineral density (BMD) and an increased susceptibility to fractures. Evidence from genetic studies indicates that BMD, a complex quantitative trait with a normal distribution, is genetically controlled. Genome-wide association studies (GWAS) as well as studies using candidate gene approaches have identified single-nucleotide polymorphisms (SNPs) that are associated with BMD, osteoporosis and osteoporotic fractures. These SNPs have been mapped close to or within genes including those encoding WNT/β-catenin signaling proteins. Understanding the genetics of osteoporosis will help to identify novel candidates for diagnostic and therapeutic targets. Genetic factors are also important for the development of sarcopenia, which is characterized by a loss of lean body mass, and obesity, which is characterized by high fat mass. Hence, in this review, we discuss the genetic factors, identified by genetic studies, which regulate the body components related to osteoporosis, sarcopenia, and obesity.

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“…Recent studies identified gene-metabolite dependencies by integrating genome-wide association studies (GWAS) with metabolomics data sets (mGWAS) supporting the cross-omics strategy (101–104). Combining these studies in a systems approach (mGWAS reviewed in Adamski [105] and Adamski and Suhre [106]) synergistically expands insight into disease pathogenesis and strengthens the associations with disease phenotype. One such study applied targeted metabolomics, transcriptome analysis, and whole genome sequencing to liver samples from a diabetes-resistant C57BL/6 leptin ob/ob and diabetes-susceptible BTBR leptin ob/ob mouse strain (101).…”

Section: Metabolomics and Diabetes Researchmentioning

confidence: 99%

Metabolomics and Diabetes: Analytical and Computational Approaches

et al. 2015

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Diabetes is characterized by altered metabolism of key molecules and regulatory pathways. The phenotypic expression of diabetes and associated complications encompasses complex interactions between genetic, environmental, and tissue-specific factors that require an integrated understanding of perturbations in the network of genes, proteins, and metabolites. Metabolomics attempts to systematically identify and quantitate small molecule metabolites from biological systems. The recent rapid development of a variety of analytical platforms based on mass spectrometry and nuclear magnetic resonance have enabled identification of complex metabolic phenotypes. Continued development of bioinformatics and analytical strategies has facilitated the discovery of causal links in understanding the pathophysiology of diabetes and its complications. Here, we summarize the metabolomics workflow, including analytical, statistical, and computational tools, highlight recent applications of metabolomics in diabetes research, and discuss the challenges in the field.

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Genome-wide association studies with metabolomics

Cited by 64 publications

References 51 publications

A description of large-scale metabolomics studies: increasing value by combining metabolomics with genome-wide SNP genotyping and transcriptional profiling

A description of large-scale metabolomics studies: increasing value by combining metabolomics with genome-wide SNP genotyping and transcriptional profiling

Recent genetic discoveries in osteoporosis, sarcopenia and obesity [Review]

Metabolomics and Diabetes: Analytical and Computational Approaches

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