絞り込み

16642

広告

e-GRASP: an integrated evolutionary and GRASP resource for exploring disease associations.

著者 Karim S , NourEldin HF , Abusamra H , Salem N , Alhathli E , Dudley J , Sanderford M , Scheinfeldt LB , Kumar S
BMC Genomics.2016 Oct 17 ; 17(Suppl 9):770.
この記事をPubMed上で見るPubMedで表示
この記事をGoogle翻訳上で見る Google翻訳で開く

スターを付ける スターを付ける     (1view , 0users)

Full Text Sources

Genome-wide association studies (GWAS) have become a mainstay of biological research concerned with discovering genetic variation linked to phenotypic traits and diseases. Both discrete and continuous traits can be analyzed in GWAS to discover associations between single nucleotide polymorphisms (SNPs) and traits of interest. Associations are typically determined by estimating the significance of the statistical relationship between genetic loci and the given trait. However, the prioritization of bona fide, reproducible genetic associations from GWAS results remains a central challenge in identifying genomic loci underlying common complex diseases. Evolutionary-aware meta-analysis of the growing GWAS literature is one way to address this challenge and to advance from association to causation in the discovery of genotype-phenotype relationships.
PMID: 27766955 [PubMed - in process]
印刷用ページを開く Endnote用テキストダウンロード