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      Genetic and Genomic Analysis of a Fat Mass Trait with Complex Inheritance Reveals Marked Sex Specificity

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          Abstract

          The integration of expression profiling with linkage analysis has increasingly been used to identify genes underlying complex phenotypes. The effects of gender on the regulation of many physiological traits are well documented; however, “genetical genomic” analyses have not yet addressed the degree to which their conclusions are affected by sex. We constructed and densely genotyped a large F2 intercross derived from the inbred mouse strains C57BL/6J and C3H/HeJ on an apolipoprotein E null (ApoE −/−) background. This BXH.ApoE −/− population recapitulates several “metabolic syndrome” phenotypes. The cross consists of 334 animals of both sexes, allowing us to specifically test for the dependence of linkage on sex. We detected several thousand liver gene expression quantitative trait loci, a significant proportion of which are sex-biased. We used these analyses to dissect the genetics of gonadal fat mass, a complex trait with sex-specific regulation. We present evidence for a remarkably high degree of sex-dependence on both the cis and trans regulation of gene expression. We demonstrate how these analyses can be applied to the study of the genetics underlying gonadal fat mass, a complex trait showing significantly female-biased heritability. These data have implications on the potential effects of sex on the genetic regulation of other complex traits.

          Synopsis

          Although their genomes are nearly identical, the males and females of a species exhibit striking differences in many traits, including complex traits such as obesity. This study combines genetic and genomic tools to identify in parallel quantitative trait loci (QTLs) for a measure of gonadal fat mass and for expression of transcripts in the liver. The results are used to explore the relationship between genetic variation, sexual differentiation, and obesity in the mouse model. Using over 300 intercross progeny of two inbred mouse strains, five loci in the genome were found to be highly correlated with abdominal fat mass. Four of the five loci exhibited opposite effects on obesity in the two sexes, a phenomenon known as sexual antagonism. To identify candidate genes that may be involved in obesity through their expression in the liver, global gene expression analysis was employed using microarrays. Many of these expression QTLs also show sex-specific effects on transcription. A hotspot for trans-acting QTLs regulating the expression of transcripts whose abundance is correlated with gonadal fat mass was identified on Chromosome 19. This region of the genome colocalizes with a clinical QTL for gonadal fat mass, suggesting that it harbors a good candidate gene for obesity.

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          Most cited references 33

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          Genetic dissection of transcriptional regulation in budding yeast.

          To begin to understand the genetic architecture of natural variation in gene expression, we carried out genetic linkage analysis of genomewide expression patterns in a cross between a laboratory strain and a wild strain of Saccharomyces cerevisiae. Over 1500 genes were differentially expressed between the parent strains. Expression levels of 570 genes were linked to one or more different loci, with most expression levels showing complex inheritance patterns. The loci detected by linkage fell largely into two categories: cis-acting modulators of single genes and trans-acting modulators of many genes. We found eight such trans-acting loci, each affecting the expression of a group of 7 to 94 genes of related function.
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            Functional discovery via a compendium of expression profiles.

            Ascertaining the impact of uncharacterized perturbations on the cell is a fundamental problem in biology. Here, we describe how a single assay can be used to monitor hundreds of different cellular functions simultaneously. We constructed a reference database or "compendium" of expression profiles corresponding to 300 diverse mutations and chemical treatments in S. cerevisiae, and we show that the cellular pathways affected can be determined by pattern matching, even among very subtle profiles. The utility of this approach is validated by examining profiles caused by deletions of uncharacterized genes: we identify and experimentally confirm that eight uncharacterized open reading frames encode proteins required for sterol metabolism, cell wall function, mitochondrial respiration, or protein synthesis. We also show that the compendium can be used to characterize pharmacological perturbations by identifying a novel target of the commonly used drug dyclonine.
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              Genetics of gene expression surveyed in maize, mouse and man.

              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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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Genet
                pgen
                PLoS Genetics
                Public Library of Science (San Francisco, USA )
                1553-7390
                1553-7404
                February 2006
                3 February 2006
                : 2
                : 2
                Affiliations
                [1 ] Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America
                [2 ] Rosetta Inpharmatics, Seattle, Washington, United States of America
                [3 ] Department of Statistics, College of Letters and Science, University of California Los Angeles, Los Angeles, California, United States of America
                [4 ] Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America
                [5 ] Department of Medicine, Division of Cardiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America
                [6 ] Department of Microbiology, Immunology, and Molecular Genetics, College of Letters and Science, University of California Los Angeles, Los Angeles, California, United States of America
                [7 ] Molecular Biology Institute, University of California Los Angeles, Los Angeles, California, United States of America
                North Carolina State University, United States of America
                Author notes
                * To whom correspondence should be addressed. E-mail: jlusis@ 123456mednet.ucla.edu
                Article
                05-PLGE-RA-0121R2 plge-02-02-05
                10.1371/journal.pgen.0020015
                1359071
                16462940
                Copyright: © 2006 Wang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                Page count
                Pages: 1
                Categories
                Research Article
                Genetics/Disease Models
                Genetics/Complex Traits
                Mus (Mouse)
                Custom metadata
                Wang S, Yehya N, Schadt EE, Wang H, Drake TA, et al. (2006) Genetic and genomic analysis of a fat mass trait with complex inheritance reveals marked sex specificity. PLoS Genet 2(2): e15.

                Genetics

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