Analysis of Genome-Wide Association Data

2016 ◽  
pp. 161-173 ◽  
Author(s):  
Allan F. McRae
Keyword(s):  
Genome Wide Association ◽  
Genome Wide ◽  
Association Data

scholarly journals Meta-Analyses of Genome-Wide Association Data Hold New Promise for Addiction Genetics

2016 ◽  
Vol 77 (5) ◽  
pp. 676-680 ◽  
Author(s):  
Arpana Agrawal ◽  
Howard J. Edenberg ◽  
Joel Gelernter
Keyword(s):  
Genome Wide Association ◽  
Genome Wide ◽  
Association Data ◽  
Meta Analyses

scholarly journals Network-based Analysis of Genome Wide Association Data Provides Novel Candidate Genes for Lipid and Lipoprotein Traits

2013 ◽  
Vol 12 (11) ◽  
pp. 3398-3408 ◽  
Author(s):  
Amitabh Sharma ◽  
Natali Gulbahce ◽  
Samuel J. Pevzner ◽  
Jörg Menche ◽  
Claes Ladenvall ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Genome Wide Association ◽  
Genome Wide ◽  
Association Data

GENOMIZER: an integrated analysis system for genome-wide association data

2006 ◽  
Vol 27 (6) ◽  
pp. 583-588 ◽  
Author(s):  
Andre Franke ◽  
Andreas Wollstein ◽  
Markus Teuber ◽  
Michael Wittig ◽  
Tim Lu ◽  
...  
Keyword(s):  
Genome Wide Association ◽  
Integrated Analysis ◽  
Genome Wide ◽  
Association Data ◽  
Analysis System

scholarly journals A gene-based recessive diplotype exome scan discovers FGF6, a novel hepcidin-regulating iron-metabolism gene

Blood ◽  
2019 ◽  
Vol 133 (17) ◽  
pp. 1888-1898 ◽  
Author(s):  
Shicheng Guo ◽  
Shuai Jiang ◽  
Narendranath Epperla ◽  
Yanyun Ma ◽  
Mehdi Maadooliat ◽  
...  
Keyword(s):  
Growth Factor ◽  
Fibroblast Growth Factor ◽  
Iron Metabolism ◽  
Iron Homeostasis ◽  
Genome Wide Association Study ◽  
Strong Association ◽  
Genome Wide Association ◽  
Fibroblast Growth ◽  
Genome Wide ◽  
Association Data

Abstract Standard analyses applied to genome-wide association data are well designed to detect additive effects of moderate strength. However, the power for standard genome-wide association study (GWAS) analyses to identify effects from recessive diplotypes is not typically high. We proposed and conducted a gene-based compound heterozygosity test to reveal additional genes underlying complex diseases. With this approach applied to iron overload, a strong association signal was identified between the fibroblast growth factor–encoding gene, FGF6, and hemochromatosis in the central Wisconsin population. Functional validation showed that fibroblast growth factor 6 protein (FGF-6) regulates iron homeostasis and induces transcriptional regulation of hepcidin. Moreover, specific identified FGF6 variants differentially impact iron metabolism. In addition, FGF6 downregulation correlated with iron-metabolism dysfunction in systemic sclerosis and cancer cells. Using the recessive diplotype approach revealed a novel susceptibility hemochromatosis gene and has extended our understanding of the mechanisms involved in iron metabolism.

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