Literature DB >> 15471811

Quantitative genomics: exploring the genetic architecture of complex trait predisposition.

D Pomp1, M F Allan, S R Wesolowski.   

Abstract

Most phenotypes with agricultural or biomedical relevance are multifactorial traits controlled by complex contributions of genetics and environment. Genetic predisposition results from combinations of relatively small effects due to variations within a large number of genes, known as QTL. Well over 200 QTL have been reported for growth and body composition traits in the mouse, which likely represent at least 50 to 100 distinct genes. Molecular biology has yielded significant advances in understanding these traits at the metabolic and physiological levels; however, little has been learned regarding the identity and nature of the underlying polygenes. In addition to the significantly poor precision inherent to QTL localization, it is very difficult to differentiate between co-localization and coincidence when comparing QTL with other QTL and with potential candidate genes. The wide gap between our knowledge of physiological mechanisms underlying complex traits and the nature of genetic predisposition significantly impairs discovery of genes underlying QTL. Identification and genetic mapping of key transcriptional, proteomic, metabolomic, and endocrine events will uncover large lists of significant positional candidate genes for growth and body composition. However, integration of experimental approaches to jointly evaluate predisposition and physiology will increase success of QTL identification by merging the power of recombination with functional analysis. Measuring physiologically relevant subphenotypes within a structured QTL mapping population will not only facilitate pathway-specific prioritization among candidate genes, but may also directly identify genes underlying QTL. This would advance our understanding of the genetic architecture of complex traits by testing the central hypothesis that genes controlling predisposition to a quantitative trait are primarily involved in trans-regulation of the primary physiological pathways that regulate the trait.

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Mesh:

Year:  2004        PMID: 15471811     DOI: 10.2527/2004.8213_supplE300x

Source DB:  PubMed          Journal:  J Anim Sci        ISSN: 0021-8812            Impact factor:   3.159


  16 in total

1.  Individuality in gut microbiota composition is a complex polygenic trait shaped by multiple environmental and host genetic factors.

Authors:  Andrew K Benson; Scott A Kelly; Ryan Legge; Fangrui Ma; Soo Jen Low; Jaehyoung Kim; Min Zhang; Phaik Lyn Oh; Derrick Nehrenberg; Kunjie Hua; Stephen D Kachman; Etsuko N Moriyama; Jens Walter; Daniel A Peterson; Daniel Pomp
Journal:  Proc Natl Acad Sci U S A       Date:  2010-10-11       Impact factor: 11.205

2.  Genomic mapping of direct and correlated responses to long-term selection for rapid growth rate in mice.

Authors:  Mark F Allan; Eugene J Eisen; Daniel Pomp
Journal:  Genetics       Date:  2005-06-08       Impact factor: 4.562

Review 3.  Expression genetics and the phenotype revolution.

Authors:  Robert W Williams
Journal:  Mamm Genome       Date:  2006-06-12       Impact factor: 2.957

4.  Bayesian analyses of multiple epistatic QTL models for body weight and body composition in mice.

Authors:  Nengjun Yi; Denise K Zinniel; Kyoungmi Kim; Eugene J Eisen; Alfred Bartolucci; David B Allison; Daniel Pomp
Journal:  Genet Res       Date:  2006-02       Impact factor: 1.588

5.  Parent-of-origin effects on voluntary exercise levels and body composition in mice.

Authors:  Scott A Kelly; Derrick L Nehrenberg; Kunjie Hua; Ryan R Gordon; Theodore Garland; Daniel Pomp
Journal:  Physiol Genomics       Date:  2009-11-10       Impact factor: 3.107

Review 6.  How to get the most bang for your buck: the evolution and physiology of nutrition-dependent resource allocation strategies.

Authors:  Enoch Ng'oma; Anna M Perinchery; Elizabeth G King
Journal:  Proc Biol Sci       Date:  2017-06-28       Impact factor: 5.349

7.  Microarray profiling for differential gene expression in ovaries and ovarian follicles of pigs selected for increased ovulation rate.

Authors:  Alexandre Rodrigues Caetano; Rodger K Johnson; J Joe Ford; Daniel Pomp
Journal:  Genetics       Date:  2004-11       Impact factor: 4.562

8.  Serious limitations of the QTL/microarray approach for QTL gene discovery.

Authors:  Ricardo A Verdugo; Charles R Farber; Craig H Warden; Juan F Medrano
Journal:  BMC Biol       Date:  2010-07-12       Impact factor: 7.431

9.  Differentially expressed genes in adipose tissues of high body weight-selected (obese) and unselected (lean) mouse lines.

Authors:  Soner Aksu; Dirk Koczan; Ulla Renne; Hans-Jurgen Thiesen; Gudrun A Brockmann
Journal:  J Appl Genet       Date:  2007       Impact factor: 3.240

Review 10.  Complex genetics of obesity in mouse models.

Authors:  Daniel Pomp; Derrick Nehrenberg; Daria Estrada-Smith
Journal:  Annu Rev Nutr       Date:  2008       Impact factor: 11.848

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