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Sökning: WFRF:(Wang Jingye)

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1.
  • Deng, Min, et al. (författare)
  • Genome-wide association analyses in Han Chinese identify two new susceptibility loci for amyotrophic lateral sclerosis
  • 2013
  • Ingår i: Nature Genetics. - : Nature Publishing Group. - 1061-4036 .- 1546-1718. ; 45:6, s. 697-
  • Tidskriftsartikel (refereegranskat)abstract
    • To identify susceptibility genes for amyotrophic lateral sclerosis (ALS), we conducted a genome-wide association study (GWAS) in 506 individuals with sporadic ALS and 1,859 controls of Han Chinese ancestry. Ninety top SNPs suggested by the current GWAS and 6 SNPs identified by previous GWAS were analyzed in an independent cohort of 706 individuals with ALS and 1,777 controls of Han Chinese ancestry. We discovered two new susceptibility loci for ALS at 1q32 (CAMK1G, rs6703183, P-combined = 2.92 x 10(-8), odds ratio (OR) = 1.31) and 22p11 (CABIN1 and SUSD2, rs8141797, P-combined = 2.35 x 10(-9), OR = 1.52). These two loci explain 12.48% of the overall variance in disease risk in the Han Chinese population. We found no association evidence for the previously reported loci in the Han Chinese population, suggesting genetic heterogeneity of disease susceptibility for ALS between ancestry groups. Our study identifies two new susceptibility loci and suggests new pathogenic mechanisms of ALS.
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2.
  • Wei, Runmin, et al. (författare)
  • Metabotypes Related to Meat and Vegetable Intake Reflect Microbial, Lipid and Amino Acid Metabolism in Healthy People
  • 2018
  • Ingår i: Molecular Nutrition and Food Research. - : Wiley. - 1613-4125 .- 1613-4133. ; 62:21
  • Tidskriftsartikel (refereegranskat)abstract
    • Scope: The objective of this study is to develop a new methodology to identify the relationship between dietary patterns and metabolites indicative of food intake and metabolism. Methods and results: Plasma and urine samples from healthy Swiss subjects (n = 89) collected over two time points are analyzed for a panel of host–microbial metabolites using GC– and LC–MS. Dietary intake is evaluated using a validated food frequency questionnaire. Dietary pattern clusters and relationships with metabolites are determined using Non-Negative Matrix Factorization (NNMF) and Sparse Generalized Canonical Correlation Analysis (SGCCA). Use of NNMF allows detection of latent diet clusters in this population, which describes a high intake of meat or vegetables. SGCCA associates these clusters to i) diet-host microbial and lipid associated bile acid metabolism, and ii) essential amino acid metabolism. Conclusion: This novel application of NNMF and SGCCA allows detection of distinct metabotypes for meat and vegetable dietary patterns in a heterogeneous population. As many of the metabolites associated with meat or vegetable intake are the result of host–microbiota interactions, the findings support a role for microbiota mediating the metabolic imprinting of different dietary choices.
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