| 1. |
|
|
| 2. |
- Berndt, Sonja I., et al.
(författare)
-
Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture
- 2013
-
Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 45:5, s. 501-U69
-
Tidskriftsartikel (refereegranskat)abstract
- Approaches exploiting trait distribution extremes may be used to identify loci associated with common traits, but it is unknown whether these loci are generalizable to the broader population. In a genome-wide search for loci associated with the upper versus the lower 5th percentiles of body mass index, height and waist-to-hip ratio, as well as clinical classes of obesity, including up to 263,407 individuals of European ancestry, we identified 4 new loci (IGFBP4, H6PD, RSRC1 and PPP2R2A) influencing height detected in the distribution tails and 7 new loci (HNF4G, RPTOR, GNAT2, MRPS33P4, ADCY9, HS6ST3 and ZZZ3) for clinical classes of obesity. Further, we find a large overlap in genetic structure and the distribution of variants between traits based on extremes and the general population and little etiological heterogeneity between obesity subgroups.
|
|
| 3. |
- Cedeno, Martha, et al.
(författare)
-
Serum metabolomic profiling identifies potential biomarkers in arthritis in older adults: an exploratory study
- 2023
-
Ingår i: Metabolomics : Official journal of the Metabolomic Society. - 1573-3890. ; 19
-
Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Seronegative elderly-onset rheumatoid arthritis (EORA)neg and polymyalgia rheumatica (PMR) have similar clinical characteristics making them difficult to distinguish based on clinical features. We hypothesized that the study of serum metabolome could identify potential biomarkers of PMR vs. EORAneg. METHODS: Arthritis in older adults (ARTIEL) is an observational prospective cohort with patients older than 60 years of age with newly diagnosed arthritis. Patients' blood samples were compared at baseline with 18 controls. A thorough clinical examination was conducted. A Bruker Avance 600MHz spectrometer was used to acquire Nuclear Magnetic Resonance (NMR) spectra of serum samples. Chenomx NMR suite 8.5 was used for metabolite identification and quantification.Student t-test, one-way ANOVA, binary linear regression and ROC curve, Pearson's correlation along with pathway analyses were conducted. RESULTS: Twenty-eight patients were diagnosed with EORAneg and 20 with PMR. EORAneg patients had a mean disease activity score (DAS)-Erythrocyte Sedimentation Rate (ESR) of 6.21±1.00. All PMR patients reported shoulder pain, and 90% reported pelvic pain. Fifty-eight polar metabolites were identified. Of these, 3-hydroxybutyrate, acetate, glucose, glycine, lactate, and o-acetylcholine (o-ACh), were significantly different between groups. Of interest, IL-6 correlated with different metabolites in PMR and EORAneg suggesting different inflammatory activated pathways. Finally, lactate, o-ACh, taurine, and sex (female) were identified as distinguishable factors of PMR from EORAneg with a sensitivity of 90%, specificity of 92.3%, and an AUC of 0.925 (p<0.001). CONCLUSION: These results suggest that EORAneg and PMR have different serum metabolomic profiles that might be related to their pathobiology and can be used as biomarker to discriminate between both diseases.
|
|
| 4. |
- Ried, Janina S., et al.
(författare)
-
A principal component meta-analysis on multiple anthropometric traits identifies novel loci for body shape
- 2016
-
Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 7
-
Tidskriftsartikel (refereegranskat)abstract
- Large consortia have revealed hundreds of genetic loci associated with anthropometric traits, one trait at a time. We examined whether genetic variants affect body shape as a composite phenotype that is represented by a combination of anthropometric traits. We developed an approach that calculates averaged PCs (AvPCs) representing body shape derived from six anthropometric traits (body mass index, height, weight, waist and hip circumference, waist-to-hip ratio). The first four AvPCs explain >99% of the variability, are heritable, and associate with cardiometabolic outcomes. We performed genome-wide association analyses for each body shape composite phenotype across 65 studies and meta-analysed summary statistics. We identify six novel loci: LEMD2 and CD47 for AvPC1, RPS6KA5/C14orf159 and GANAB for AvPC3, and ARL15 and ANP32 for AvPC4. Our findings highlight the value of using multiple traits to define complex phenotypes for discovery, which are not captured by single-trait analyses, and may shed light onto new pathways.
|
|
| 5. |
- Serra-Musach, Jordi, et al.
(författare)
-
Cancer network activity associated with therapeutic response and synergism
- 2016
-
Ingår i: Genome Medicine. - : BIOMED CENTRAL LTD. - 1756-994X. ; 8:88
-
Tidskriftsartikel (refereegranskat)abstract
- Background: Cancer patients often show no or only modest benefit from a given therapy. This major problem in oncology is generally attributed to the lack of specific predictive biomarkers, yet a global measure of cancer cell activity may support a comprehensive mechanistic understanding of therapy efficacy. We reasoned that network analysis of omic data could help to achieve this goal. Methods: A measure of "cancer network activity" (CNA) was implemented based on a previously defined network feature of communicability. The network nodes and edges corresponded to human proteins and experimentally identified interactions, respectively. The edges were weighted proportionally to the expression of the genes encoding for the corresponding proteins and relative to the number of direct interactors. The gene expression data corresponded to the basal conditions of 595 human cancer cell lines. Therapeutic responses corresponded to the impairment of cell viability measured by the half maximal inhibitory concentration (IC50) of 130 drugs approved or under clinical development. Gene ontology, signaling pathway, and transcription factor-binding annotations were taken from public repositories. Predicted synergies were assessed by determining the viability of four breast cancer cell lines and by applying two different analytical methods. Results: The effects of drug classes were associated with CNAs formed by different cell lines. CNAs also differentiate target families and effector pathways. Proteins that occupy a central position in the network largely contribute to CNA. Known key cancer-associated biological processes, signaling pathways, and master regulators also contribute to CNA. Moreover, the major cancer drivers frequently mediate CNA and therapeutic differences. Cell-based assays centered on these differences and using uncorrelated drug effects reveals novel synergistic combinations for the treatment of breast cancer dependent on PI3K-mTOR signaling. Conclusions: Cancer therapeutic responses can be predicted on the basis of a systems-level analysis of molecular interactions and gene expression. Fundamental cancer processes, pathways, and drivers contribute to this feature, which can also be exploited to predict precise synergistic drug combinations.
|
|
