Structural Model Analysis of Multiple Quantitative Traits

Li, Renhau; Tsaih, Shirng‐Wern; Shockley, Keith R.; Stylianou, Ioannis M.; Wergedal, Jon E.; Paigen, Bev; Churchill, Gary A.

doi:10.1371/journal.pgen.0020114.eor

Cited by 46 publications

(66 citation statements)

References 14 publications

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“…By contrast, the food volume QTL on Chr 17 strengthened considerably (LOD increased from 4.6 to 7.6) (Fig. 1B), suggesting that this locus has effects on both food intake and body weight but with effects in opposite direction (29). No other significant QTLs for food volume were detected, suggesting a unique contribution of the Chr 17 locus.…”

Section: Identification Of Qtl For Total Food Volumementioning

confidence: 93%

Genetic variation inGlp1rexpression influences the rate of gastric emptying in mice

Kumar¹,

Byerley²,

Vólaufová³

et al. 2008

American Journal of Physiology-Regulatory, Integrative and Comp

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We demonstrated previously that food intake traits map to a quantitative trait locus (QTL) on proximal chromosome 17, which encompasses Glp1r (glucagon-like peptide 1 receptor), encoding an important modulator of gastric emptying. We then confirmed this QTL in a B6.CAST-17 congenic strain that consumed 27% more carbohydrate and 17% more total calories, yet similar fat calories, per body weight compared with the recipient C57BL/6J. The congenic strain also consumed greater food volume. The current aims were to 1) identify genetic linkage for total food volume in F 2 mice, 2) perform gene expression profiling in stomach of B6.CAST-17 congenic mice using oligonucleotide arrays, 3) test for allelic imbalance in Glp1r expression, 4) evaluate gastric emptying rate in parental and congenic mice, and 5) investigate a possible effect of genetic variation in Glp1r on gastric emptying. A genome scan revealed a single QTL for total food volume (Tfv1) (log of the odds ratio ϭ 7.6), which was confirmed in B6.CAST-17 congenic mice. Glp1r exhibited allelic imbalance in stomach, which correlated with accelerated gastric emptying in parental CAST and congenic B6.CAST-17 mice. Moreover, congenic mice displayed an impaired gastric emptying response to exendin-(9-39). These results suggest that genetic variation in Glp1r contributes to the strain differences in gastric emptying rate.quantitative trait locus; glucagon-like peptide 1 receptor THE GUT HORMONE GLUCAGON-LIKE PEPTIDE 1 (GLP-1) inhibits gastric emptying, reduces postprandial gastric secretion, and may play a physiological role in regulating appetite and energy intake. Systemic administration of GLP-1 in physiological amounts in both humans and animals potently inhibits gastric emptying (9,13,15,31,59,61), and this effect can be blocked by administration of the GLP-1 receptor antagonist exendin (9-39) (21). The mechanism by which GLP-1 inhibits gastric emptying is thought to involve receptors located either in the central nervous system or associated with afferent pathways to the brain stem (9,21,60). In addition to the deceleration of gastric emptying, GLP-1 is involved in regulating satiety, as shown in normal-weight and obese men infused with 33,34). GLP-1 also decreases food intake; by contrast, central administration of the GLP-1 receptor (GLP-1R) antagonist exendin-(9-39) stimulates food intake (14, 54).The GLP-1R is a seven-transmembrane domain receptor belonging to the B family of G-protein-coupled receptors (6, 52). The GLP-1R recognizes GLP-1 specifically and does not show demonstrable binding by related peptides (Ref. 52; for review, see Ref. 6). In rodents, GLP-1 receptor mRNA and high-affinity GLP-1 binding sites have been identified in pancreatic islets, in gastrointestinal tract, in lung, in kidney, in heart, and throughout the brain (7,8,16). The satiety-promoting effects of GLP-1 implicate Glp1r as a physiological gene candidate for ingestive behavior phenotypes (1).Glp1r maps to the peak of a genetic region containing overlapping quantitative trait loci (Q...

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Section: Identification Of Qtl For Total Food Volumementioning

confidence: 93%

Genetic variation inGlp1rexpression influences the rate of gastric emptying in mice

Kumar¹,

Byerley²,

Vólaufová³

et al. 2008

American Journal of Physiology-Regulatory, Integrative and Comp

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“…Ignoring one or more of these factors may substantially affect the accuracy and the generality of the conclusions that can be drawn from the model (Li et al 2006;Hartley et al 2012;Alimi et al 2013), both in the context of genome-wide association studies (GWAS) and genomic selection (GS). Indeed a lot of attention has been devoted in recent literature to improving traditional additive genetic models, which were originally defined using only allele counts (e.g., Meuwissen et al 2001), by supplementing them with additional information.…”

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confidence: 99%

Multiple Quantitative Trait Analysis Using Bayesian Networks

et al. 2014

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Models for genome-wide prediction and association studies usually target a single phenotypic trait. However, in animal and plant genetics it is common to record information on multiple phenotypes for each individual that will be genotyped. Modeling traits individually disregards the fact that they are most likely associated due to pleiotropy and shared biological basis, thus providing only a partial, confounded view of genetic effects and phenotypic interactions. In this article we use data from a Multiparent Advanced Generation Inter-Cross (MAGIC) winter wheat population to explore Bayesian networks as a convenient and interpretable framework for the simultaneous modeling of multiple quantitative traits. We show that they are equivalent to multivariate genetic best linear unbiased prediction (GBLUP) and that they are competitive with single-trait elastic net and single-trait GBLUP in predictive performance. Finally, we discuss their relationship with other additive-effects models and their advantages in inference and interpretation. MAGIC populations provide an ideal setting for this kind of investigation because the very low population structure and large sample size result in predictive models with good power and limited confounding due to relatedness.

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“…In addition, it would be possible to map quantitative trait loci (QTLs) and test whether these loci affect one or more regulatory processes and whether they affect lifespan. QTLs of particular interest can be pursued using a combination of genetic, gene expression, and bioinformatic approaches to identify the underlying molecular pathways and genes (DiPetrillo et al 2005;Drake et al 2006;Li et al 2006;Churchill 2007). This system genetics approach is developing rapidly and promises to be a powerful means of identifying the molecular mechanisms underlying many complex traits.…”

Section: Discussionmentioning

confidence: 99%

Thermoregulation in mice exhibits genetic variability early in senescence

Gonzales

Rikke

2009

AGE

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Aging leads to a loss of thermoregulation that can be readily monitored in laboratory mice. However, it is unclear from previous studies-we provide a tabular summary of 15 articles-whether significant loss occurs by midlife (∼15 months of age). In this study, we examined 34 females from 22 LSXSS strains starting at 4 and 8 months of age (17 mice per age group). We used transponders inserted just under the loose skin of the pelt and calibrated against rectal body temperature to measure temperatures quickly without restraint. We found that the mean body temperatures measured 5 months later (9 and 13 months of age) had dropped significantly below normal in both groups: 0.6ºC lower in the younger cohort and 1.0ºC lower in the older cohort. These drops were not associated with weight loss or signs of pathology. Notably, the loss of thermoregulation between 8 and 13 months of age also exhibited genetic variation that was highly significant (P=0.004). Such variation is potentially a powerful tool for determining the cause of thermoregulatory loss with age and whether this loss predicts senescence changes later in life, including the force of mortality.

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Structural Model Analysis of Multiple Quantitative Traits

Cited by 46 publications

References 14 publications

Genetic variation inGlp1rexpression influences the rate of gastric emptying in mice

Genetic variation inGlp1rexpression influences the rate of gastric emptying in mice

Multiple Quantitative Trait Analysis Using Bayesian Networks

Thermoregulation in mice exhibits genetic variability early in senescence

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