scholarly journals Fast and accurate genotype imputation in genome-wide association studies through pre-phasing

2012 ◽  
Vol 44 (8) ◽  
pp. 955-959 ◽  
Author(s):  
Bryan Howie ◽  
Christian Fuchsberger ◽  
Matthew Stephens ◽  
Jonathan Marchini ◽  
Gonçalo R Abecasis
Keyword(s):  
Association Studies ◽  
Genotype Imputation ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

Abstract P068: A Genome-wide Association Study for Diabetic Nephropathy Genes in the Japanese Population: The J-MICC Study.

Circulation ◽  
2018 ◽  
Vol 137 (suppl_1) ◽  
Author(s):  
Yasuyuki Nakamura ◽  
Akira Narita ◽  
Seiko Ohno ◽  
Naoyuki Takashima ◽  
Kenji Wakai ◽  
...  
Keyword(s):  
Diabetic Nephropathy ◽  
Japanese Population ◽  
Association Studies ◽  
Linear Regression Analysis ◽  
Genotype Imputation ◽  
Genome Wide Association ◽  
Diabetic Patients ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
A Genome

Background: Diabetic nephropathy is the most common cause of chronic kidney disease in the developed countries. Clinical characteristics do not fully predict development of diabetic nephropathy in diabetic patients. There have been few genome-wide association studies (GWAS). Methods: We conducted a GWAS to identify common genetic variations that affected renal function in a Japanese population of 1,117 patients with type 2 diabetes mellitus (T2D) extracted from 14,091 participants appropriate for GWAS as a part of the Japan Multi-Institutional Collaborative Cohort (J-MICC) study. Genotyping was performed at a central laboratory using a HumanOmniExpressExome-8 v1.2 BeadChip array. Genotype imputation was performed using SHAPEIT and Minimac3 software based on the 1000 Genomes reference panel (phase 3). Estimated glomerular filtration rate (eGFR) was calculated according to Matsuo et al. for each patient. The association for the imputed variants with eGFR was performed by a linear regression analysis adjusted for age and sex. Results: We found that rs869312667 at NBEA (β=1.23, P=1.03E-08) and rs8523 at ELOVL2 (β=24.4, P=1.64E-08) were significantly associated with eGFR. These genes have been reported to participate in several metabolic functions and were associated with some disease conditions. However, no previous reports have implied that these genes were related to diabetic nephropathy. Conclusions: rs869312667 at NBEA and rs8523 at ELOVL2 were significantly associated with eGFR in patients with T2D in Japanese.

scholarly journals Improving the resolution of canine genome‐wide association studies using genotype imputation: A study of two breeds

2021 ◽  
Author(s):  
C. A. Jenkins ◽  
Gustavo Aguirre ◽  
Catherine André ◽  
Danika Bannasch ◽  
Doreen Becker ◽  
...  
Keyword(s):  
Association Studies ◽  
Genotype Imputation ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Canine Genome ◽  
Genome Wide

Genotype Imputation from Large Reference Panels

2018 ◽  
Vol 19 (1) ◽  
pp. 73-96 ◽  
Author(s):  
Sayantan Das ◽  
Gonçalo R. Abecasis ◽  
Brian L. Browning
Keyword(s):  
Statistical Power ◽  
Sequence Data ◽  
Single Nucleotide Polymorphism Array ◽  
Association Studies ◽  
Genotype Imputation ◽  
Genome Wide Association ◽  
Whole Genome Sequence ◽  
Computational Techniques ◽  
Genome Wide Association Studies ◽  
Genome Wide

Genotype imputation has become a standard tool in genome-wide association studies because it enables researchers to inexpensively approximate whole-genome sequence data from genome-wide single-nucleotide polymorphism array data. Genotype imputation increases statistical power, facilitates fine mapping of causal variants, and plays a key role in meta-analyses of genome-wide association studies. Only variants that were previously observed in a reference panel of sequenced individuals can be imputed. However, the rapid increase in the number of deeply sequenced individuals will soon make it possible to assemble enormous reference panels that greatly increase the number of imputable variants. In this review, we present an overview of genotype imputation and describe the computational techniques that make it possible to impute genotypes from reference panels with millions of individuals.

scholarly journals Genotype Imputation in Genome‐Wide Association Studies

2013 ◽  
Vol 78 (1) ◽  
Author(s):  
Eleonora Porcu ◽  
Serena Sanna ◽  
Christian Fuchsberger ◽  
Lars G. Fritsche
Keyword(s):  
Association Studies ◽  
Genotype Imputation ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide
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