PMC:1359071 / 27692-33163 JSONTXT

{"project":"2_test","denotations":[{"id":"16462940-15467983-86014495","span":{"begin":550,"end":552},"obj":"15467983"},{"id":"16462940-15457344-86014495","span":{"begin":550,"end":552},"obj":"15457344"},{"id":"16462940-14993241-86014495","span":{"begin":550,"end":552},"obj":"14993241"},{"id":"16462940-15722954-86014495","span":{"begin":550,"end":552},"obj":"15722954"},{"id":"16462940-8770600-86014496","span":{"begin":1367,"end":1369},"obj":"8770600"},{"id":"16462940-10353911-86014496","span":{"begin":1367,"end":1369},"obj":"10353911"},{"id":"16462940-12855751-86014496","span":{"begin":1367,"end":1369},"obj":"12855751"},{"id":"16462940-8770600-86014497","span":{"begin":1378,"end":1380},"obj":"8770600"},{"id":"16462940-15044658-86014498","span":{"begin":1381,"end":1383},"obj":"15044658"},{"id":"16462940-9725853-86014499","span":{"begin":1394,"end":1396},"obj":"9725853"},{"id":"16462940-15044658-86014500","span":{"begin":1397,"end":1399},"obj":"15044658"},{"id":"16462940-12646919-86014501","span":{"begin":1553,"end":1554},"obj":"12646919"},{"id":"16462940-12646919-86014502","span":{"begin":3013,"end":3014},"obj":"12646919"},{"id":"16462940-15711544-86014503","span":{"begin":3015,"end":3017},"obj":"15711544"},{"id":"16462940-15837804-86014504","span":{"begin":3814,"end":3816},"obj":"15837804"},{"id":"T88409","span":{"begin":550,"end":552},"obj":"15467983"},{"id":"T81197","span":{"begin":550,"end":552},"obj":"15457344"},{"id":"T66925","span":{"begin":550,"end":552},"obj":"14993241"},{"id":"T32941","span":{"begin":550,"end":552},"obj":"15722954"},{"id":"T36625","span":{"begin":1367,"end":1369},"obj":"8770600"},{"id":"T12720","span":{"begin":1367,"end":1369},"obj":"10353911"},{"id":"T28858","span":{"begin":1367,"end":1369},"obj":"12855751"},{"id":"T75575","span":{"begin":1378,"end":1380},"obj":"8770600"},{"id":"T39830","span":{"begin":1381,"end":1383},"obj":"15044658"},{"id":"T91815","span":{"begin":1394,"end":1396},"obj":"9725853"},{"id":"T84579","span":{"begin":1397,"end":1399},"obj":"15044658"},{"id":"T38280","span":{"begin":1553,"end":1554},"obj":"12646919"},{"id":"T99391","span":{"begin":3013,"end":3014},"obj":"12646919"},{"id":"T24477","span":{"begin":3015,"end":3017},"obj":"15711544"},{"id":"T78375","span":{"begin":3814,"end":3816},"obj":"15837804"}],"text":"Significant Sex Bias in the Regulation of Both Complex Traits and Gene Expression\nGiven the known dichotomy between females and males in the susceptibility and control of obesity, this study was designed to sufficiently power the detection of significant QTLs for this and other traits with sex-dependent effects. Note, however, that these effects can extend to traits without overall mean differences between the sexes. Previous studies have described the advantages of performing QTL analysis both with and without sex as an interactive covariate [22–25]. Analyzing the sexes separately is suboptimal since it reduces sample size in both groups, thus reducing power to detect main QTL effects, as demonstrated by our genome scan of Chromosome 5 (Figure 1B). Furthermore, separate analyses would not allow for the detection of QTLs that have opposing, or sex-antagonistic, effects in females and males and would hinder the detection of QTLs specific to one sex.\nAccordingly, we detected five cQTLs for the gonadal fat mass trait on Chromosomes 1, 3, 5, 11, and 19. The detection of all five cQTLs was “driven” by the larger effect in females, with significant improvement by the incorporation of sex*additive and sex*dominant parameters. QTLs associated with obesity, gonadal fat, and abdominal fat have been reported before overlapping with cQTLs on Chromosomes 1 [26–28], 5 [26,29], and 11 [19,29] reported here, whereas the cQTL on Chromosome 3 represents a novel QTL for this trait. The Chromosome 19 cQTL for fat mass was recently reported by us [5] in the BXD intercross F2 progeny from the strains B6 and DBA (which shares the same haplotype at this region as the C3H strain used in this study). Interestingly, significant heritability and genetic regulation was seen in this F2 population despite the hyperlipidemic, proinflammatory ApoE−/− background and the high-fat Western diet. This background possesses several advantages, such as allowing the modeling of human-like disease states. The predominantly female-driven effects of the five cQTLs likely reflect the significant effect of differential gonadal hormone secretions on the genetic regulation of this complex trait.\nThe identification of genes underlying cQTLs remains a challenge. The widespread availability of genome-wide expression analysis has begun to address this by providing a snapshot of transcription in relevant organs and thus providing initial information for which genes can differentiate a given trait. Furthermore, by treating transcript levels as quantitative traits, we can map the genetic regulation underlying differential gene expression (eQTLs). Those eQTLs that have cis-acting variations affecting their transcription are potential candidate genes for the trait. At a single trait, genome-wide significance level of 0.05, we detected 6,676 eQTLs representing 4,998 genes, of which 2,118 were cis-acting. At increased thresholds, the proportion of cis-eQTLs increased, which is in good agreement with previous studies [5,15] and likely reflects the increased power to detect cis-acting variations affecting transcription. Of all 6,676 significant eQTLs, 1,166 possessed significant sex interactions. Of these, 304 were cis and 852 were trans, suggesting that only a minority of the sex-specific effects on the regulation of gene expression occur through polymorphisms within the gene itself. Rather, underlying genetic regulation of most transcripts is the result of interactions between trans loci and sex-specific factors (e.g., hormones). As with cQTLs, sex bias in the predominantly trans genetic regulation of gene expression is likely secondary to different sex hormone profiles.\nRecently, using a similar dataset, our group demonstrated that significant cis-eQTLs (p \u003c 5 × 10−5) largely represent true positives [30] and are enriched for highly polymorphic regions over the mouse genome. The cis-eQTLs presented in Table 5 overlap with one of the gonadal fat mass cQTLs and should be considered potential candidates. Given the sex effects in the gonadal fat mass cQTLs, we reasoned that the cis-eQTLs with significant sex*additive and sex*dominant effects should receive priority consideration. The use of eQTLs to dissect cQTLs is a method still in its infancy, with uncertain efficacy and applicability. Nevertheless, application of this analysis to this dataset provides some tantalizingly attractive candidate genes.\nOne shortcoming of this approach, however, is that candidate genes are limited to those whose transcript expression levels vary in association with a nearby polymorphism that differs between the parental strains—in other words, genes with significant and detectable cis-eQTLs. However, it is not strictly necessary for candidate genes to have evidence of such linkage: polymorphisms underlying a trait cQTL can affect gene function or posttranslational modifications. Nevertheless, several phenotypes are known to be regulated, at least partly, at the level of transcription or mRNA stability, which is exactly what our methods are designed to detect. A separate problem is that organ-specific gene expression differences may preclude one from detecting the relevant causative gene if the tissue arrayed is not the tissue where the control is exerted. This is particularly relevant for a trait such as adipose tissue mass, which is controlled by multiple tissues. We propose that analysis of correlated genes can provide guidance as discussed below."}

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Sex Bias in the Regulation of Both Complex Traits and Gene Expression\nGiven the known dichotomy between females and males in the susceptibility and control of obesity, this study was designed to sufficiently power the detection of significant QTLs for this and other traits with sex-dependent effects. Note, however, that these effects can extend to traits without overall mean differences between the sexes. Previous studies have described the advantages of performing QTL analysis both with and without sex as an interactive covariate [22–25]. Analyzing the sexes separately is suboptimal since it reduces sample size in both groups, thus reducing power to detect main QTL effects, as demonstrated by our genome scan of Chromosome 5 (Figure 1B). Furthermore, separate analyses would not allow for the detection of QTLs that have opposing, or sex-antagonistic, effects in females and males and would hinder the detection of QTLs specific to one sex.\nAccordingly, we detected five cQTLs for the gonadal fat mass trait on Chromosomes 1, 3, 5, 11, and 19. The detection of all five cQTLs was “driven” by the larger effect in females, with significant improvement by the incorporation of sex*additive and sex*dominant parameters. QTLs associated with obesity, gonadal fat, and abdominal fat have been reported before overlapping with cQTLs on Chromosomes 1 [26–28], 5 [26,29], and 11 [19,29] reported here, whereas the cQTL on Chromosome 3 represents a novel QTL for this trait. The Chromosome 19 cQTL for fat mass was recently reported by us [5] in the BXD intercross F2 progeny from the strains B6 and DBA (which shares the same haplotype at this region as the C3H strain used in this study). Interestingly, significant heritability and genetic regulation was seen in this F2 population despite the hyperlipidemic, proinflammatory ApoE−/− background and the high-fat Western diet. This background possesses several advantages, such as allowing the modeling of human-like disease states. The predominantly female-driven effects of the five cQTLs likely reflect the significant effect of differential gonadal hormone secretions on the genetic regulation of this complex trait.\nThe identification of genes underlying cQTLs remains a challenge. The widespread availability of genome-wide expression analysis has begun to address this by providing a snapshot of transcription in relevant organs and thus providing initial information for which genes can differentiate a given trait. Furthermore, by treating transcript levels as quantitative traits, we can map the genetic regulation underlying differential gene expression (eQTLs). Those eQTLs that have cis-acting variations affecting their transcription are potential candidate genes for the trait. At a single trait, genome-wide significance level of 0.05, we detected 6,676 eQTLs representing 4,998 genes, of which 2,118 were cis-acting. At increased thresholds, the proportion of cis-eQTLs increased, which is in good agreement with previous studies [5,15] and likely reflects the increased power to detect cis-acting variations affecting transcription. Of all 6,676 significant eQTLs, 1,166 possessed significant sex interactions. Of these, 304 were cis and 852 were trans, suggesting that only a minority of the sex-specific effects on the regulation of gene expression occur through polymorphisms within the gene itself. Rather, underlying genetic regulation of most transcripts is the result of interactions between trans loci and sex-specific factors (e.g., hormones). As with cQTLs, sex bias in the predominantly trans genetic regulation of gene expression is likely secondary to different sex hormone profiles.\nRecently, using a similar dataset, our group demonstrated that significant cis-eQTLs (p \u003c 5 × 10−5) largely represent true positives [30] and are enriched for highly polymorphic regions over the mouse genome. The cis-eQTLs presented in Table 5 overlap with one of the gonadal fat mass cQTLs and should be considered potential candidates. Given the sex effects in the gonadal fat mass cQTLs, we reasoned that the cis-eQTLs with significant sex*additive and sex*dominant effects should receive priority consideration. The use of eQTLs to dissect cQTLs is a method still in its infancy, with uncertain efficacy and applicability. Nevertheless, application of this analysis to this dataset provides some tantalizingly attractive candidate genes.\nOne shortcoming of this approach, however, is that candidate genes are limited to those whose transcript expression levels vary in association with a nearby polymorphism that differs between the parental strains—in other words, genes with significant and detectable cis-eQTLs. However, it is not strictly necessary for candidate genes to have evidence of such linkage: polymorphisms underlying a trait cQTL can affect gene function or posttranslational modifications. Nevertheless, several phenotypes are known to be regulated, at least partly, at the level of transcription or mRNA stability, which is exactly what our methods are designed to detect. A separate problem is that organ-specific gene expression differences may preclude one from detecting the relevant causative gene if the tissue arrayed is not the tissue where the control is exerted. This is particularly relevant for a trait such as adipose tissue mass, which is controlled by multiple tissues. We propose that analysis of correlated genes can provide guidance as discussed below."}

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Sex Bias in the Regulation of Both Complex Traits and Gene Expression\nGiven the known dichotomy between females and males in the susceptibility and control of obesity, this study was designed to sufficiently power the detection of significant QTLs for this and other traits with sex-dependent effects. Note, however, that these effects can extend to traits without overall mean differences between the sexes. Previous studies have described the advantages of performing QTL analysis both with and without sex as an interactive covariate [22–25]. Analyzing the sexes separately is suboptimal since it reduces sample size in both groups, thus reducing power to detect main QTL effects, as demonstrated by our genome scan of Chromosome 5 (Figure 1B). Furthermore, separate analyses would not allow for the detection of QTLs that have opposing, or sex-antagonistic, effects in females and males and would hinder the detection of QTLs specific to one sex.\nAccordingly, we detected five cQTLs for the gonadal fat mass trait on Chromosomes 1, 3, 5, 11, and 19. The detection of all five cQTLs was “driven” by the larger effect in females, with significant improvement by the incorporation of sex*additive and sex*dominant parameters. QTLs associated with obesity, gonadal fat, and abdominal fat have been reported before overlapping with cQTLs on Chromosomes 1 [26–28], 5 [26,29], and 11 [19,29] reported here, whereas the cQTL on Chromosome 3 represents a novel QTL for this trait. The Chromosome 19 cQTL for fat mass was recently reported by us [5] in the BXD intercross F2 progeny from the strains B6 and DBA (which shares the same haplotype at this region as the C3H strain used in this study). Interestingly, significant heritability and genetic regulation was seen in this F2 population despite the hyperlipidemic, proinflammatory ApoE−/− background and the high-fat Western diet. This background possesses several advantages, such as allowing the modeling of human-like disease states. The predominantly female-driven effects of the five cQTLs likely reflect the significant effect of differential gonadal hormone secretions on the genetic regulation of this complex trait.\nThe identification of genes underlying cQTLs remains a challenge. The widespread availability of genome-wide expression analysis has begun to address this by providing a snapshot of transcription in relevant organs and thus providing initial information for which genes can differentiate a given trait. Furthermore, by treating transcript levels as quantitative traits, we can map the genetic regulation underlying differential gene expression (eQTLs). Those eQTLs that have cis-acting variations affecting their transcription are potential candidate genes for the trait. At a single trait, genome-wide significance level of 0.05, we detected 6,676 eQTLs representing 4,998 genes, of which 2,118 were cis-acting. At increased thresholds, the proportion of cis-eQTLs increased, which is in good agreement with previous studies [5,15] and likely reflects the increased power to detect cis-acting variations affecting transcription. Of all 6,676 significant eQTLs, 1,166 possessed significant sex interactions. Of these, 304 were cis and 852 were trans, suggesting that only a minority of the sex-specific effects on the regulation of gene expression occur through polymorphisms within the gene itself. Rather, underlying genetic regulation of most transcripts is the result of interactions between trans loci and sex-specific factors (e.g., hormones). As with cQTLs, sex bias in the predominantly trans genetic regulation of gene expression is likely secondary to different sex hormone profiles.\nRecently, using a similar dataset, our group demonstrated that significant cis-eQTLs (p \u003c 5 × 10−5) largely represent true positives [30] and are enriched for highly polymorphic regions over the mouse genome. The cis-eQTLs presented in Table 5 overlap with one of the gonadal fat mass cQTLs and should be considered potential candidates. Given the sex effects in the gonadal fat mass cQTLs, we reasoned that the cis-eQTLs with significant sex*additive and sex*dominant effects should receive priority consideration. The use of eQTLs to dissect cQTLs is a method still in its infancy, with uncertain efficacy and applicability. Nevertheless, application of this analysis to this dataset provides some tantalizingly attractive candidate genes.\nOne shortcoming of this approach, however, is that candidate genes are limited to those whose transcript expression levels vary in association with a nearby polymorphism that differs between the parental strains—in other words, genes with significant and detectable cis-eQTLs. 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Sex Bias in the Regulation of Both Complex Traits and Gene Expression\nGiven the known dichotomy between females and males in the susceptibility and control of obesity, this study was designed to sufficiently power the detection of significant QTLs for this and other traits with sex-dependent effects. Note, however, that these effects can extend to traits without overall mean differences between the sexes. Previous studies have described the advantages of performing QTL analysis both with and without sex as an interactive covariate [22–25]. Analyzing the sexes separately is suboptimal since it reduces sample size in both groups, thus reducing power to detect main QTL effects, as demonstrated by our genome scan of Chromosome 5 (Figure 1B). Furthermore, separate analyses would not allow for the detection of QTLs that have opposing, or sex-antagonistic, effects in females and males and would hinder the detection of QTLs specific to one sex.\nAccordingly, we detected five cQTLs for the gonadal fat mass trait on Chromosomes 1, 3, 5, 11, and 19. The detection of all five cQTLs was “driven” by the larger effect in females, with significant improvement by the incorporation of sex*additive and sex*dominant parameters. QTLs associated with obesity, gonadal fat, and abdominal fat have been reported before overlapping with cQTLs on Chromosomes 1 [26–28], 5 [26,29], and 11 [19,29] reported here, whereas the cQTL on Chromosome 3 represents a novel QTL for this trait. The Chromosome 19 cQTL for fat mass was recently reported by us [5] in the BXD intercross F2 progeny from the strains B6 and DBA (which shares the same haplotype at this region as the C3H strain used in this study). Interestingly, significant heritability and genetic regulation was seen in this F2 population despite the hyperlipidemic, proinflammatory ApoE−/− background and the high-fat Western diet. This background possesses several advantages, such as allowing the modeling of human-like disease states. The predominantly female-driven effects of the five cQTLs likely reflect the significant effect of differential gonadal hormone secretions on the genetic regulation of this complex trait.\nThe identification of genes underlying cQTLs remains a challenge. The widespread availability of genome-wide expression analysis has begun to address this by providing a snapshot of transcription in relevant organs and thus providing initial information for which genes can differentiate a given trait. Furthermore, by treating transcript levels as quantitative traits, we can map the genetic regulation underlying differential gene expression (eQTLs). Those eQTLs that have cis-acting variations affecting their transcription are potential candidate genes for the trait. At a single trait, genome-wide significance level of 0.05, we detected 6,676 eQTLs representing 4,998 genes, of which 2,118 were cis-acting. At increased thresholds, the proportion of cis-eQTLs increased, which is in good agreement with previous studies [5,15] and likely reflects the increased power to detect cis-acting variations affecting transcription. Of all 6,676 significant eQTLs, 1,166 possessed significant sex interactions. Of these, 304 were cis and 852 were trans, suggesting that only a minority of the sex-specific effects on the regulation of gene expression occur through polymorphisms within the gene itself. Rather, underlying genetic regulation of most transcripts is the result of interactions between trans loci and sex-specific factors (e.g., hormones). As with cQTLs, sex bias in the predominantly trans genetic regulation of gene expression is likely secondary to different sex hormone profiles.\nRecently, using a similar dataset, our group demonstrated that significant cis-eQTLs (p \u003c 5 × 10−5) largely represent true positives [30] and are enriched for highly polymorphic regions over the mouse genome. The cis-eQTLs presented in Table 5 overlap with one of the gonadal fat mass cQTLs and should be considered potential candidates. Given the sex effects in the gonadal fat mass cQTLs, we reasoned that the cis-eQTLs with significant sex*additive and sex*dominant effects should receive priority consideration. The use of eQTLs to dissect cQTLs is a method still in its infancy, with uncertain efficacy and applicability. Nevertheless, application of this analysis to this dataset provides some tantalizingly attractive candidate genes.\nOne shortcoming of this approach, however, is that candidate genes are limited to those whose transcript expression levels vary in association with a nearby polymorphism that differs between the parental strains—in other words, genes with significant and detectable cis-eQTLs. However, it is not strictly necessary for candidate genes to have evidence of such linkage: polymorphisms underlying a trait cQTL can affect gene function or posttranslational modifications. Nevertheless, several phenotypes are known to be regulated, at least partly, at the level of transcription or mRNA stability, which is exactly what our methods are designed to detect. A separate problem is that organ-specific gene expression differences may preclude one from detecting the relevant causative gene if the tissue arrayed is not the tissue where the control is exerted. This is particularly relevant for a trait such as adipose tissue mass, which is controlled by multiple tissues. We propose that analysis of correlated genes can provide guidance as discussed below."}