Literature DB >> 19763931

Candidate gene association analysis.

Jonathan B Singer1.   

Abstract

Candidate gene association study is the most common method for associating human genetic variations with the phenotypes they produce, due to the relative simplicity of acquiring patient samples and genotype data. The study design begins with identifying appropriate DNA samples and an appropriate phenotype for analysis. The candidate genes and polymorphisms must then be chosen. After genotyping the candidate genes in the DNA samples, the results are checked to ensure appropriate quality and association analysis is performed. The raw results are interpreted and placed into context and follow-up analysis is carried out to validate and refine the findings. A wide range of software packages are available for both the association analysis and other steps in the study. This chapter describes the use of PLINK as the analysis tool in an example, as that suite has emerged as the most popular option for genetic association testing.

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Year:  2009        PMID: 19763931     DOI: 10.1007/978-1-60761-247-6_13

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  8 in total

1.  Zinc transporter genes and urological cancers: integrated analysis suggests a role for ZIP11 in bladder cancer.

Authors:  Lang Wu; Kari G Chaffee; Alexander S Parker; Hugues Sicotte; Gloria M Petersen
Journal:  Tumour Biol       Date:  2015-04-23

2.  Fine mapping by composite genome-wide association analysis.

Authors:  Joaquim Casellas; Jhon Jacobo Cañas-Álvarez; Marta Fina; Jesús Piedrafita; Alessio Cecchinato
Journal:  Genet Res (Camb)       Date:  2017-06-06       Impact factor: 1.588

3.  Pilot study demonstrating potential association between breast cancer image-based risk phenotypes and genomic biomarkers.

Authors:  Hui Li; Maryellen L Giger; Chang Sun; Umnouy Ponsukcharoen; Dezheng Huo; Li Lan; Olufunmilayo I Olopade; Andrew R Jamieson; Jeremy Bancroft Brown; Anna Di Rienzo
Journal:  Med Phys       Date:  2014-03       Impact factor: 4.071

4.  Is GSN significant for hip BMD in female Caucasians?

Authors:  Fei-Yan Deng; Wei Zhu; Yong Zeng; Ji-Gang Zhang; Na Yu; Yao-Zhong Liu; Yong-Jun Liu; Qing Tian; Hong-Wen Deng
Journal:  Bone       Date:  2014-03-04       Impact factor: 4.398

Review 5.  Application of Genetic, Genomic and Biological Pathways in Improvement of Swine Feed Efficiency.

Authors:  Pourya Davoudi; Duy Ngoc Do; Stefanie M Colombo; Bruce Rathgeber; Younes Miar
Journal:  Front Genet       Date:  2022-06-09       Impact factor: 4.772

6.  Pan-genomic analysis of bovine monocyte-derived macrophage gene expression in response to in vitro infection with Mycobacterium avium subspecies paratuberculosis.

Authors:  David E Machugh; Maria Taraktsoglou; Kate E Killick; Nicolas C Nalpas; John A Browne; Stephen DE Park; Karsten Hokamp; Eamonn Gormley; David A Magee
Journal:  Vet Res       Date:  2012-03-28       Impact factor: 3.683

7.  A Transcriptome Analysis Reveals that Hepatic Glycolysis and Lipid Synthesis Are Negatively Associated with Feed Efficiency in DLY Pigs.

Authors:  Cineng Xu; Xingwang Wang; Zhanwei Zhuang; Jie Wu; Shenping Zhou; Jianping Quan; Rongrong Ding; Yong Ye; Longlong Peng; Zhenfang Wu; Enqin Zheng; Jie Yang
Journal:  Sci Rep       Date:  2020-06-18       Impact factor: 4.379

Review 8.  Genetic and epigenetic analyses of panic disorder in the post-GWAS era.

Authors:  Yoshiro Morimoto; Shinji Ono; Naohiro Kurotaki; Akira Imamura; Hiroki Ozawa
Journal:  J Neural Transm (Vienna)       Date:  2020-05-09       Impact factor: 3.575

  8 in total

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