Literature DB >> 14962979

Quantitative genetic variation: a post-modern view.

Martin Farrall1.   

Abstract

It has become commonplace to map individual quantitative trait loci (QTL) in experimental organisms; the means (line-crosses and dense maps of markers) and motivation (the close relationship between continuous physiological traits and common, complex diseases) are self-evident. Progress in mapping human QTL has been more gradual, an inevitable consequence of genetic mapping in a natural population setting. The common objective of these studies has been to understand the molecular mechanisms underlying individual QTL. Recent theoretical and practical advances shift this focus to a more comprehensive or genomic perspective on quantitative variation. Fisher's infinitesimal model of adaptive evolution, which satisfied quantitative geneticists for over 50 years, has been modified in the light of data from QTL mapping experiments in plants and animals. The resulting exponential model provides a pleasing empirical fit to the distribution of QTL effect sizes, predicts that a large amount of quantitative variation will be explained by a limited number of genes and suggests a new mathematical framework for linkage mapping. Molecular analysis of QTL suggests that coding variants (e.g. allozymes) underlie a fraction of quantitative variation and that variants that affect gene expression (expression QTL, eQTL) have a substantial role. This is supported by genomic experiments that combine expression profiling with classical genetic mapping approaches to reveal a remarkable wealth of quantitative heritable variation in the transcriptome and that cis-and trans-acting regulatory factors are organized in networks reflecting pleiotropy. It is hoped that these advances will enhance our understanding of the genetic basis of complex inherited diseases.

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Year:  2004        PMID: 14962979     DOI: 10.1093/hmg/ddh084

Source DB:  PubMed          Journal:  Hum Mol Genet        ISSN: 0964-6906            Impact factor:   6.150


  35 in total

Review 1.  Systems genetics, bioinformatics and eQTL mapping.

Authors:  Hong Li; Hongwen Deng
Journal:  Genetica       Date:  2010-09-03       Impact factor: 1.082

2.  Diabetes quantitative trait locus research: from physiology to genetics and back.

Authors:  D Gauguier
Journal:  Diabetologia       Date:  2006-01-31       Impact factor: 10.122

3.  Looking for a bit of co-action?

Authors:  John D Blakey
Journal:  Thorax       Date:  2007-03       Impact factor: 9.139

4.  Accurate quantitation of allele-specific expression patterns by analysis of DNA melting.

Authors:  Sangkyun Jeong; Yoonsoo Hahn; Qi Rong; Karl Pfeifer
Journal:  Genome Res       Date:  2007-06-01       Impact factor: 9.043

5.  Translational gene mapping of cognitive decline.

Authors:  Beth Wilmot; Shannon K McWeeney; Randal R Nixon; Thomas J Montine; Jamie Laut; Christina A Harrington; Jeffrey A Kaye; Patricia L Kramer
Journal:  Neurobiol Aging       Date:  2006-12-14       Impact factor: 4.673

6.  Replication and narrowing of gene expression quantitative trait loci using inbred mice.

Authors:  Daniel M Gatti; Alison H Harrill; Fred A Wright; David W Threadgill; Ivan Rusyn
Journal:  Mamm Genome       Date:  2009-07-17       Impact factor: 2.957

7.  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

8.  Discovery of novel genetic networks associated with 19 economically important traits in beef cattle.

Authors:  Zhihua Jiang; Jennifer J Michal; Jie Chen; Tyler F Daniels; Tanja Kunej; Matthew D Garcia; Charles T Gaskins; Jan R Busboom; Leeson J Alexander; Raymond W Wright; Michael D Macneil
Journal:  Int J Biol Sci       Date:  2009-07-29       Impact factor: 6.580

9.  Genotype matrix mapping: searching for quantitative trait loci interactions in genetic variation in complex traits.

Authors:  Sachiko Isobe; Akihiro Nakaya; Satoshi Tabata
Journal:  DNA Res       Date:  2007-11-13       Impact factor: 4.458

10.  Inferring the transcriptional landscape of bovine skeletal muscle by integrating co-expression networks.

Authors:  Nicholas J Hudson; Antonio Reverter; YongHong Wang; Paul L Greenwood; Brian P Dalrymple
Journal:  PLoS One       Date:  2009-10-01       Impact factor: 3.240

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