Literature DB >> 14745835

The use of animal models in the study of complex disease: all else is never equal or why do so many human studies fail to replicate animal findings?

Scott M Williams1, Jonathan L Haines, Jason H Moore.   

Abstract

The study of the genetics of complex human disease has met with limited success. Many findings with candidate genes fail to replicate despite seemingly overwhelming physiological data implicating the genes. In contrast, animal model studies of the same genes and disease models usually have more consistent results. We propose that one important reason for this is the ability to control genetic background in animal studies. The fact that controlling genetic background can produce more consistent results suggests that the failure to replicate human findings in the same diseases is due to variation in interacting genes. Hence, the contrasting nature of the findings from the different study designs indicates the importance of non-additive genetic effects on human disease. We discuss these issues and some methodological approaches that can detect multilocus effects, using hypertension as a model disease. This article contains supplementary material, which may be viewed at the BioEssays website at http://www.interscience.wiley.com/jpages/0265-9247/suppmat/index.html. Copyright 2004 Wiley Periodicals, Inc.

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Year:  2004        PMID: 14745835     DOI: 10.1002/bies.10401

Source DB:  PubMed          Journal:  Bioessays        ISSN: 0265-9247            Impact factor:   4.345


  13 in total

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2.  A combinatorial approach to detecting gene-gene and gene-environment interactions in family studies.

Authors:  Xiang-Yang Lou; Guo-Bo Chen; Lei Yan; Jennie Z Ma; Jamie E Mangold; Jun Zhu; Robert C Elston; Ming D Li
Journal:  Am J Hum Genet       Date:  2008-10-02       Impact factor: 11.025

Review 3.  African genetic diversity: implications for human demographic history, modern human origins, and complex disease mapping.

Authors:  Michael C Campbell; Sarah A Tishkoff
Journal:  Annu Rev Genomics Hum Genet       Date:  2008       Impact factor: 8.929

4.  A genome-wide search for loci interacting with known prostate cancer risk-associated genetic variants.

Authors:  Sha Tao; Zhong Wang; Junjie Feng; Fang-Chi Hsu; Guangfu Jin; Seong-Tae Kim; Zheng Zhang; Henrik Gronberg; Lilly S Zheng; William B Isaacs; Jianfeng Xu; Jielin Sun
Journal:  Carcinogenesis       Date:  2012-01-04       Impact factor: 4.944

Review 5.  Gut microbiota in hypertension.

Authors:  Pedro A Jose; Dominic Raj
Journal:  Curr Opin Nephrol Hypertens       Date:  2015-09       Impact factor: 2.894

Review 6.  Practical issues in building risk-predicting models for complex diseases.

Authors:  Jia Kang; Judy Cho; Hongyu Zhao
Journal:  J Biopharm Stat       Date:  2010-03       Impact factor: 1.051

7.  Gene-gene interactions among CHRNA4, CHRNB2, BDNF, and NTRK2 in nicotine dependence.

Authors:  Ming D Li; Xiang-Yang Lou; Guobo Chen; Jennie Z Ma; Robert C Elston
Journal:  Biol Psychiatry       Date:  2008-06-04       Impact factor: 13.382

Review 8.  Animal models of sepsis and sepsis-induced kidney injury.

Authors:  Kent Doi; Asada Leelahavanichkul; Peter S T Yuen; Robert A Star
Journal:  J Clin Invest       Date:  2009-10-01       Impact factor: 14.808

Review 9.  Animal Models in Cardiovascular Research: Hypertension and Atherosclerosis.

Authors:  Xin-Fang Leong; Chun-Yi Ng; Kamsiah Jaarin
Journal:  Biomed Res Int       Date:  2015-05-03       Impact factor: 3.411

10.  Capturing the spectrum of interaction effects in genetic association studies by simulated evaporative cooling network analysis.

Authors:  Brett A McKinney; James E Crowe; Jingyu Guo; Dehua Tian
Journal:  PLoS Genet       Date:  2009-03-20       Impact factor: 5.917

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