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O3‐03‐06: CROSS‐TISSUE TRANSCRIPTOME‐WIDE ASSOCIATION META‐ANALYSIS IDENTIFIES NOVEL RISK GENES FOR LATE‐ONSET ALZHEIMER'S DISEASE
Author(s) -
Lu Qiongshi,
Hu Yiming,
Li Mo,
Weng Haoyi,
Wang Jiawei,
Zekavat Seyedeh M.,
Yu Zhaolong,
Li Boyang,
Muchnik Sydney,
Shi Yu,
Kunkle Brian W.,
Mukherjee Shubhabrata,
Natarajan Pradeep,
Crane Paul K.,
Zhao Hongyu
Publication year - 2018
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2018.06.2787
Subject(s) - genome wide association study , imputation (statistics) , genetic architecture , genetic association , transcriptome , computational biology , biology , bonferroni correction , expression quantitative trait loci , genomics , quantitative trait locus , gene , genetics , computer science , genome , gene expression , single nucleotide polymorphism , statistics , missing data , genotype , machine learning , mathematics
relationships between metabolomics and genomics. Community detection was used to identify the best partitions of variables. Results: We identified 85,835 significant correlations involving 10,602 unique variables. This consisted primarily of metabolitemetabolite (47,345) and gene-gene (37,585) correlations. Our network had 424 nodes and 679 edges, which included 135 metabolite-gene and 529 metabolite-AD risk factor correlations (Figure 1). Notably, no AD risk factors were directly linked to genes. However, most of the larger communities, such as the communities centered on insulin resistance and bodymass index (BMI), included genes that were indirectly linked to AD risk factors through metab-