| 1. |
- Speliotes, Elizabeth K., et al.
(författare)
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Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index
- 2010
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Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 42:11, s. 937-948
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Tidskriftsartikel (refereegranskat)abstract
- Obesity is globally prevalent and highly heritable, but its underlying genetic factors remain largely elusive. To identify genetic loci for obesity susceptibility, we examined associations between body mass index and ~2.8 million SNPs in up to 123,865 individuals with targeted follow up of 42 SNPs in up to 125,931 additional individuals. We confirmed 14 known obesity susceptibility loci and identified 18 new loci associated with body mass index (P < 5 × 10−8), one of which includes a copy number variant near GPRC5B. Some loci (at MC4R, POMC, SH2B1 and BDNF) map near key hypothalamic regulators of energy balance, and one of these loci is near GIPR, an incretin receptor. Furthermore, genes in other newly associated loci may provide new insights into human body weight regulation.
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| 2. |
- Hong, Mun-Gwan, et al.
(författare)
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A genome-wide assessment of variability in human serum metabolism
- 2013
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Ingår i: Human Mutation. - : Hindawi Limited. - 1059-7794 .- 1098-1004. ; 34:3, s. 515-524
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Tidskriftsartikel (refereegranskat)abstract
- The study of the genetic regulation of metabolism in human serum samples can contribute to a better understanding of the intermediate biological steps that lead from polymorphism to disease. Here, we conducted a genome-wide association study (GWAS) to discover metabolic quantitative trait loci (mQTLs) utilizing samples from a study of prostate cancer in Swedish men, consisting of 402 individuals (214 cases and 188 controls) in a discovery set and 489 case-only samples in a replication set. A global nontargeted metabolite profiling approach was utilized resulting in the detection of 6,138 molecular features followed by targeted identification of associated metabolites. Seven replicating loci were identified (PYROXD2, FADS1, PON1, CYP4F2, UGT1A8, ACADL, and LIPC) with associated sequence variants contributing significantly to trait variance for one or more metabolites (P = 10(-13) -10(-91)). Regional mQTL enrichment analyses implicated two loci that included FADS1 and a novel locus near PDGFC. Biological pathway analysis implicated ACADM, ACADS, ACAD8, ACAD10, ACAD11, and ACOXL, reflecting significant enrichment of genes with acyl-CoA dehydrogenase activity. mQTL SNPs and mQTL-harboring genes were over-represented across GWASs conducted to date, suggesting that these data may have utility in tracing the molecular basis of some complex disease associations.
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| 3. |
- Kumar, Jitender, et al.
(författare)
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Associations of Body Mass Index and Obesity-Related Genetic Variants with Serum Metabolites
- 2014
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Ingår i: Current Metabolomics. - 2213-235X. ; 2:1, s. 27-36-
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: Body mass index (BMI) is one of the most important risk factors for different metabolic and cardiovascular disorders. Previously, both genetic and environmental agents associated with BMI have been described. The main focus of this exploratory study was to find the circulating metabolites associated with BMI utilizing an untargeted metabolomics approach. Additionally, significant metabolites identified were studied for their relation with BMIassociated single nucleotide polymorphisms (SNPs). Materials and Methods: A total of 971 individuals from the Cancer of the Prostate in Sweden study (discovery sample- 275 prostate cancers patients and 182 controls; replication sample- 514 prostate cancer patients) were utilized. Blood samples were collected and serum metabolic profiling was obtained using ultra-performance liquid chromatography followed by mass spectrometry. Genotyping data was available for 26 out of 32 SNPs (21 genotyped and 5 proxies) previously robustly associated with BMI in individuals of European descent. Weighted genetic risk score was generated using these SNPs and studied for its association with metabolites. Results: A total of 6138 and 5209 metabolite features were detected in discovery and replication samples, respectively. Out of 6138 metabolite features in discovery sample, 201 were found to be significantly associated with BMI (p<8.15*10-6) after multiple testing correction. These 201 features were further investigated in the replication samples and 16 were found to be significantly associated with BMI (p<2.49*10-4). Seven of these significant features were isotopes for four of the primary metabolites. Four metabolites were putatively identified: monoacylglyceride (18:1), diacylglyrcerol (32:1) and two phosphatidylcholines (34:0 and 36:0). Weighted genetic score of BMI-associated SNPs was not associated with these four metabolites. Conclusion: Four identifiable metabolites (monoacylglyceride, diacyclglyrcerol and two phosphatidylcholines) were found to be significantly associated with BMI in both discovery and replication samples. Common variants associated with BMI did not show association with these four metabolites. - See more at: http://www.eurekaselect.com/120422/article#sthash.PgqffHqv.dpuf
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| 4. |
- Kumar, Jitender, et al.
(författare)
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Influence of Biological and Technical Covariates on Non-targeted Metabolite Profiling in a Large-scale Epidemiological Study
- 2013
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Ingår i: Current Metabolomics. - 2213-235X. ; 1:3, s. 220-226-
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Tidskriftsartikel (refereegranskat)abstract
- Non-targeted metabolite profiling using ultra performance liquid chromatography-mass spectrometry (UPLCMS) was performed as part of a large-scale epidemiological study involving biobanked serum samples. The influence of both biological (age and body mass index) and technical (season of sample collection, fasting time, handling time, and storage time) covariates on the analysis was assessed. Statistical models including different sets of these covariates were compared and the results illustrate that variation in which covariates were included did not have an appreciable effect on the number or composition of biologically significant metabolite features associated with body mass index or age. Furthermore, when all covariates were included in the model, there was little overlap of metabolite features significantly associated with the different covariates. Thus, the results of this study illustrate that while some of the observed quantitative variance of metabolite features can be explained by biological and technical covariates, the use of non-targeted metabolite profiling of serum by UPLC-MS is valid for studies of biological outcomes in biobanked clinical samples from large-scale studies. - See more at: http://www.eurekaselect.com/115259/article#sthash.BOvtwWe7.dpuf
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| 5. |
- Lango Allen, Hana, et al.
(författare)
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Hundreds of variants clustered in genomic loci and biological pathways affect human height.
- 2010
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Ingår i: Nature. - : Springer Science and Business Media LLC. - 1476-4687 .- 0028-0836. ; 467:7317, s. 832-8
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Tidskriftsartikel (refereegranskat)abstract
- Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P<0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.
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