Literature DB >> 12646919

Genetics of gene expression surveyed in maize, mouse and man.

Eric E Schadt1, Stephanie A Monks, Thomas A Drake, Aldons J Lusis, Nam Che, Veronica Colinayo, Thomas G Ruff, Stephen B Milligan, John R Lamb, Guy Cavet, Peter S Linsley, Mao Mao, Roland B Stoughton, Stephen H Friend.   

Abstract

Treating messenger RNA transcript abundances as quantitative traits and mapping gene expression quantitative trait loci for these traits has been pursued in gene-specific ways. Transcript abundances often serve as a surrogate for classical quantitative traits in that the levels of expression are significantly correlated with the classical traits across members of a segregating population. The correlation structure between transcript abundances and classical traits has been used to identify susceptibility loci for complex diseases such as diabetes and allergic asthma. One study recently completed the first comprehensive dissection of transcriptional regulation in budding yeast, giving a detailed glimpse of a genome-wide survey of the genetics of gene expression. Unlike classical quantitative traits, which often represent gross clinical measurements that may be far removed from the biological processes giving rise to them, the genetic linkages associated with transcript abundance affords a closer look at cellular biochemical processes. Here we describe comprehensive genetic screens of mouse, plant and human transcriptomes by considering gene expression values as quantitative traits. We identify a gene expression pattern strongly associated with obesity in a murine cross, and observe two distinct obesity subtypes. Furthermore, we find that these obesity subtypes are under the control of different loci.

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Year:  2003        PMID: 12646919     DOI: 10.1038/nature01434

Source DB:  PubMed          Journal:  Nature        ISSN: 0028-0836            Impact factor:   49.962


  630 in total

1.  A family-based test for correlation between gene expression and trait values.

Authors:  Peter Kraft; Eric Schadt; Jason Aten; Steve Horvath
Journal:  Am J Hum Genet       Date:  2003-04-08       Impact factor: 11.025

Review 2.  Functional genomics in rodent models of hypertension.

Authors:  Martin W McBride; Fadi J Charchar; Delyth Graham; William H Miller; Pamela Strahorn; Fiona J Carr; Anna F Dominiczak
Journal:  J Physiol       Date:  2004-01-01       Impact factor: 5.182

3.  Rapid evolution of male-biased gene expression in Drosophila.

Authors:  Colin D Meiklejohn; John Parsch; José M Ranz; Daniel L Hartl
Journal:  Proc Natl Acad Sci U S A       Date:  2003-08-07       Impact factor: 11.205

4.  Combining gene expression and molecular marker information for mapping complex trait genes: a simulation study.

Authors:  Miguel Pérez-Enciso; Miguel A Toro; Michel Tenenhaus; Daniel Gianola
Journal:  Genetics       Date:  2003-08       Impact factor: 4.562

5.  Gene expression from the aneuploid chromosome in a trisomy mouse model of down syndrome.

Authors:  Robert Lyle; Corinne Gehrig; Charlotte Neergaard-Henrichsen; Samuel Deutsch; Stylianos E Antonarakis
Journal:  Genome Res       Date:  2004-07       Impact factor: 9.043

6.  Quantifying the relationship between gene expressions and trait values in general pedigrees.

Authors:  Yan Lu; Peng-Yuan Liu; Yong-Jun Liu; Fu-Hua Xu; Hong-Wen Deng
Journal:  Genetics       Date:  2004-09-15       Impact factor: 4.562

7.  Identification of the Bile Acid Transporter Slco1a6 as a Candidate Gene That Broadly Affects Gene Expression in Mouse Pancreatic Islets.

Authors:  Jianan Tian; Mark P Keller; Angie T Oler; Mary E Rabaglia; Kathryn L Schueler; Donald S Stapleton; Aimee Teo Broman; Wen Zhao; Christina Kendziorski; Brian S Yandell; Bruno Hagenbuch; Karl W Broman; Alan D Attie
Journal:  Genetics       Date:  2015-09-18       Impact factor: 4.562

8.  Genetic variation within the Chrna7 gene modulates nicotine reward-like phenotypes in mice.

Authors:  J L Harenza; P P Muldoon; M De Biasi; M I Damaj; M F Miles
Journal:  Genes Brain Behav       Date:  2013-12-26       Impact factor: 3.449

9.  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

10.  Genetic factors involved in risk for methamphetamine intake and sensitization.

Authors:  John K Belknap; Shannon McWeeney; Cheryl Reed; Sue Burkhart-Kasch; Carrie S McKinnon; Na Li; Harue Baba; Angela C Scibelli; Robert Hitzemann; Tamara J Phillips
Journal:  Mamm Genome       Date:  2013-11-13       Impact factor: 2.957

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