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Gary A Churchill

Publications and source records attributed to Gary A Churchill.

At least 19 recordsLinked to original sources
PubMed2025

A consensus guide to preclinical indirect calorimetry experiments.

Understanding the complex factors influencing mammalian metabolism and body weight homeostasis is a long-standing challenge requiring knowledge of energy intake, absorption and expenditure. Using measurements of respiratory gas exchange, indirect calorimetry can provide non-invasive estimates of whole-body energy expenditure. However, inconsistent measurement units and flawed data normalization methods have slowed progress in this field. This guide aims to establish consensus standards to unify indirect calorimetry experiments and their analysis for more consistent, meaningful and reproducible results. By establishing community-driven standards, we hope to facilitate data comparison across research datasets. This advance will allow the creation of an in-depth, machine-readable data repository built on shared standards. This overdue initiative stands to markedly improve the accuracy and depth of efforts to interrogate mammalian metabolism. Data sharing according to established best practices will also accelerate the translation of basic findings into clinical applications for metabolic diseases afflicting global populations.

Calorimetry, Indirect↗
PubMed2007

The mouse as a model for human biology: a resource guide for complex trait analysis.

The mouse has been a powerful force in elucidating the genetic basis of human physiology and pathophysiology. From its beginnings as the model organism for cancer research and transplantation biology to the present, when dissection of the genetic basis of complex disease is at the forefront of genomics research, an enormous and remarkable mouse resource infrastructure has accumulated. This review summarizes those resources and provides practical guidelines for their use, particularly in the analysis of quantitative traits.

Animals↗
PubMed2006

The X chromosome in quantitative trait locus mapping.

The X chromosome requires special treatment in the mapping of quantitative trait loci (QTL). However, most QTL mapping methods, and most computer programs for QTL mapping, have focused exclusively on autosomal loci. We describe a method for appropriate treatment of the X chromosome for QTL mapping in experimental crosses. We address the important issue of formulating the null hypothesis of no linkage appropriately. If the X chromosome is treated like an autosome, a sex difference in the phenotype can lead to spurious linkage on the X chromosome. Further, the number of degrees of freedom for the linkage test may be different for the X chromosome than for autosomes, and so an X chromosome-specific significance threshold is required. To address this issue, we propose a general procedure to obtain chromosome-specific significance thresholds that controls the genomewide false positive rate at the desired level. We apply our methods to data on gut length in a large intercross of mice carrying the Sox10Dom mutation, a model of Hirschsprung disease. We identified QTL contributing to variation in gut length on chromosomes 5 and 18. We found suggestive evidence of linkage to the X chromosome, which would be viewed as strong evidence of linkage if the X chromosome was treated as an autosome. Our methods have been implemented in the package R/qtl.

Animals↗
PubMed2006

Multiple mechanisms limit the duration of wakefulness in Drosophila brain.

The functions of sleep and what controls it remain unanswered biological questions. According to the two-process model, a circadian process and a homeostatic process interact to regulate sleep. While progress has been made in understanding the molecular and cellular functions of the circadian process, the mechanisms of the homeostatic process remain undiscovered. We use the recently established sleep model system organism Drosophila melanogaster to examine dynamic changes in gene expression during sleep and during prolonged wakefulness in the brain. Our experimental design controls for circadian processes by killing animals at three matched time points from the beginning of the consolidated rest period [Zeitgeber time (ZT) 14)] under two conditions, sleep deprived and spontaneously sleeping. Using ANOVA at a false discovery rate of 5%, we have identified 252 genes that were differentially expressed between sleep-deprived and control groups in the Drosophila brain. Using linear trends analysis, we have separated the significant differentially expressed genes into nine temporal expression patterns relative to a common anchor point (ZT 14). The most common expression pattern is a decrease during extended wakefulness but no change during spontaneous sleep (n = 114). Genes in this category were involved in protein production (n = 47), calcium homeostasis, and membrane excitability (n = 5). Multiple mechanisms, therefore, act to limit wakefulness. In addition, by studying the effects of the mechanical stimulus used in our deprivation studies during the period when the animals are predominantly active, we provide evidence for a previously unappreciated role for the Drosophila immune system in the brain response to stress.

Animals↗
PubMed2006

Patterns and mechanisms of genome organization in the mouse.

The physical and functional organizations of a genome are correlated outcomes of evolution. Inbred strains of mice provide a unique opportunity for exploring these relationships, representing as they do, diverse genomes originally separated by millions of generations that were then scrambled in the laboratory and subjected to intense selection during inbreeding to homozygosity. Here we show that the resulting pattern of chromosome organization includes regional domains of functionally related elements that promote the co-inheritance and survival of compatible sets of alleles. There are also patterns of linkage disequilibrium between domains on separate chromosomes; these are distinctly non-random and form networks with scale-free architecture. The strong conservation of gene order among mammals suggests that the domains and networks we find likely characterize all mammals, and possibly beyond.

Animals↗
PubMed2006

Genetic analysis of the stress-responsive adrenocortical axis.

The underlying genetic components contributing to individual variability in functions of the stress-responsive hypothalamic-pituitary-adrenal (HPA) axis are poorly understood. To determine genetic loci mediating three aspects of the adrenocortical function, we conducted a quantitative trait locus (QTL) analysis in the segregating F2 generation of a Wistar Kyoto (WKY) x Fischer 344 (F344) cross, two inbred rat strains that differ in several HPA axis measures. The following three components of adrenocortical function are known to be regulated by different mechanisms that are mediated via suprahypothalamic, hypothalamic, pituitary, and intra-adrenal influences: basal plasma corticosterone (Cort) levels, plasma Cort response to a 10-min restraint stress, and adrenal weight. Genome scans identified a complex genetic architecture for the basal Cort phenotype, including sex and maternal lineage effects. Pairwise interactions were also identified for this trait. We identified three significant and two suggestive QTLs for stress Cort, along with two pairs of interacting loci for this trait. Four highly significant and two suggestive loci were identified for adrenal weight, with no interacting loci. In contrast to basal Cort, no sex- or lineage-dependent QTL were identified for stress Cort or adrenal weight, despite the large sex differences in these phenotypes. We identified three nucleotide alterations in an obvious candidate gene mapped to the most significant QTL for stress Cort, Cort-binding globulin (CBG), one of which is known to alter CBG binding. This analysis confirms that three separate traits regulated by the HPA axis are controlled by multiple, but mainly nonoverlapping, QTLs.

Adrenal Glands↗
PubMed2006

Inheritance patterns of transcript levels in F1 hybrid mice.

Genetic analysis of transcriptional regulation is a rapidly emerging field of investigation that promises to shed light on the regulatory networks that control gene expression. Although a number of such studies have been carried out, the nature and extent of the heritability of gene expression traits have not been well established. We describe the inheritance of transcript levels in liver tissue in the first filial (F1) generation of mice obtained from reciprocal crosses between the common inbred strains A/J and C57BL/6J. We obtain estimates of genetic and technical variance components from these data and demonstrate that shrinkage estimators can increase detectable heritability. Estimates of heritability vary widely from transcript to transcript, with one-third of transcripts showing essentially no heritability (<0.01) and one-quarter showing very high heritability (>0.50). Roughly half of all transcripts are differentially expressed between the two parental strains. Most transcripts show an additive pattern of inheritance. Dominance effects were observed for 20% of transcripts and a small number of transcripts were identified as showing an overdominance mode of inheritance. In addition, we identified 314 transcripts with expression levels that differ between the reciprocal F1 animals. These genes may be related to maternal effect.

Animals↗
PubMed2006

Gene expression analysis of mouse chromosome substitution strains.

An analysis of transcriptional variation in the liver using a panel of B.A chromosome substitution strains identified 4209 transcripts that are differentially expressed relative to the C57BL/6J background and 1010 transcripts that are differentially expressed between C57BL/6J and A/J strains. A subset of these strains (substituting Chromosomes 1, 6, and 15) was used to identify 386 additional differentially expressed transcripts in the kidney. Approximately 15% of differentially expressed transcripts are located on the substituted chromosome. These cis-QTL are codirectionally expressed with the donor strain A/J. By comparison, trans-regulated loci comprise 85% of differentially expressed transcripts, often show opposite direction of change compared with A/J, and can be regulated by multiple chromosome substitutions. Gene expression differences in this study provide evidence for transgressive segregation: Only 438 of 4209 QTL in liver were inside the parental range. By combining QTL data with known biological functions, we were able to identify physiologic pathways altered in multiple strains. In many cases the same pathways were altered by multiple distinct chromosome substitutions. Taken together, these results suggest that widespread epistatic background effects may result in complex and overlapping transcriptional relationships among different chromosome substitution strains. Transcriptional profiling of chromosome substitution strains reveals a complex genetic architecture of transcriptional regulation.

Animals↗
PubMed2006

Structural model analysis of multiple quantitative traits.

We introduce a method for the analysis of multilocus, multitrait genetic data that provides an intuitive and precise characterization of genetic architecture. We show that it is possible to infer the magnitude and direction of causal relationships among multiple correlated phenotypes and illustrate the technique using body composition and bone density data from mouse intercross populations. Using these techniques we are able to distinguish genetic loci that affect adiposity from those that affect overall body size and thus reveal a shortcoming of standardized measures such as body mass index that are widely used in obesity research. The identification of causal networks sheds light on the nature of genetic heterogeneity and pleiotropy in complex genetic systems.

Adipose Tissue↗
PubMed2006

QTL mapping for genetic determinants of lipoprotein cholesterol levels in combined crosses of inbred mouse strains.

To identify additional loci that influence lipoprotein cholesterol levels, we performed quantitative trait locus (QTL) mapping in offspring of PERA/EiJxI/LnJ and PERA/EiJxDBA/2J intercrosses and in a combined data set from both crosses after 8 weeks of consumption of a high fat-diet. Most QTLs identified were concordant with homologous chromosomal regions that were associated with lipoprotein levels in human studies. We detected significant new loci for HDL cholesterol levels on chromosome (Chr) 5 (Hdlq34) and for non-HDL cholesterol levels on Chrs 15 (Nhdlq9) and 16 (Nhdlq10). In addition, the analysis of combined data sets identified a QTL for HDL cholesterol on Chr 17 that was shared between both crosses; lower HDL cholesterol levels were conferred by strain PERA. This QTL colocalized with a shared QTL for cholesterol gallstone formation detected in the same crosses. Haplotype analysis narrowed this QTL, and sequencing of the candidate genes Abcg5 and Abcg8 confirmed shared alleles in strains I/LnJ and DBA/2J that differed from the alleles in strain PERA/EiJ. In conclusion, our analysis furthers the knowledge of genetic determinants of lipoprotein cholesterol levels in inbred mice and substantiates the hypothesis that polymorphisms of Abcg5/Abcg8 contribute to individual variation in both plasma HDL cholesterol levels and susceptibility to cholesterol gallstone formation.

Alleles↗
PubMed2006

Quantitative trait loci for baseline erythroid traits.

A substantial genetic contribution underlies variation in baseline peripheral blood counts. We performed quantitative trait locus/loci (QTL) analyses to identify chromosome (Chr) regions harboring genes influencing the baseline erythroid parameters in F2 intercrosses between NZW/LacJ, SM/J, and C57BLKS/J inbred mice. We identified multiple significant QTL for red blood cell (RBC) count, hemoglobin (Hgb) and hematocrit (Hct) levels, mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), and mean cell hemoglobin concentration (CHCM). We identified four RBC count QTL: Rbcq1 (Chr 1, peak LOD score at 62 cM,), Rbcq2 (Chr 4, 60 cM), Rbcq3 (Chr 11, 34 cM), and Rbcq4 (Chr 10, 60 cM). Three MCV QTL were identified: Mcvq1 (Chr 7, 30 cM), Mvcq2 (Chr 11, 6 cM), and Mcvq3 (Chr 10, 60 cM). Single significant loci for Hgb (Hgbq1, Chr 16, 32 cM), Hct (Hctq1, Chr 3, 42 cM), and MCH (Mchq1, Chr 10, 60 cM) were identified. The data support the existence of a common RBC/MCH/MCV locus on Chr 10. Two QTL for CHCM (Chcmq1, Chr 2, 48 cM; Chcmq2, Chr 9, 44 cM) and an interaction between Chcmq2 with a locus on Chr 19 were identified. These analyses emphasize the genetic complexity underlying the regulation of erythroid peripheral blood traits in normal populations and suggest that genes not previously recognized as significantly impacting normal erythropoiesis exist.

Animals↗
PubMed2006

Test- and behavior-specific genetic factors affect WKY hypoactivity in tests of emotionality.

Inbred Wistar-Kyoto rats consistently display hypoactivity in tests of emotional behavior. We used them to test the hypothesis that the genetic factors underlying the behavioral decision-making process will vary in different environmental contexts. The contexts used were the open-field test (OFT), a novel environment with no explicit threats present, and the defensive-burying test (DB), a habituated environment into which a threat has been introduced. Rearing, a voluntary behavior was measured in both tests, and our study was the first to look for genetic loci affecting grooming, a relatively automatic, stress-responsive stereotyped behavior. Quantitative trait locus analysis was performed on a population of 486 F2 animals bred from reciprocal inter-crosses. The genetic architectures of DB and OFT rearing, and of DB and OFT grooming, were compared. There were no common loci affecting grooming behavior in both tests. These different contexts produced the stereotyped behavior via different pathways, and genetic factors seem to influence the decision-making pathways and not the expression of the behavior. Three loci were found that affected rearing behavior in both tests. However, in both contexts, other loci had greater effects on the behavior. Our results imply that environmental context's effects on decision-making vary depending on the category of behavior.

Agonistic Behavior↗
PubMed2006

Quantitative trait locus analysis for obesity reveals multiple networks of interacting loci.

Obesity is a highly heritable and genetically complex trait with hundreds of potential loci identified. An intercross of 513 F2 progeny between the SM/J x NZB/BINJ inbred mouse strains was generated to identify quantitative trait loci (QTL) that are involved in the weight of four fat pads: mesenteric, inguinal, gonadal, and retroperitoneal. Sex and lean body weight were treated as covariates in the analysis of these fat pads. This analysis uncoupled genetic effects related to overall body size from those influencing the adiposity of a mouse. We identified multiple significant QTL. QTL alleles associated with increased lean body weight and individual fat pad weights are contributed by the NZB background. Adiposity loci are distinct from these body size QTLs and high-adiposity alleles are contributed by the SM background. An extended network of epistatic QTL is also observed. A QTL on Chr 19 is the center of a network of eight interacting QTL, Chr 4 is the center of six, and Chr 17 the center of four interacting QTL. We conclude that interacting networks of multiple genes characterize the regulation of fat pad depots and body weight. Haplotype patterns and a literature-driven approach were used to generate hypotheses regarding the identity of the genes and pathways underlying the QTL.

Animals↗
PubMed2006

Femur mechanical properties in the F2 progeny of an NZB/B1NJ x RF/J cross are regulated predominantly by genetic loci that regulate bone geometry.

UNLABELLED: Genetic analysis of an NZB/B1NJ x RF/J cross has identified QTLs for femur mechanical, geometric, and densitometric phenotypes. Most mechanical QTLs were associated with geometric QTLs, strongly suggesting common genetic regulation. INTRODUCTION: Previous studies have shown that bone architecture and BMD are important factors affecting bone strength, and both are genetically regulated. We conducted genetic analyses for loci regulating femur mechanical properties, geometric properties, and BMD in a cohort of F2 mice derived from intercross matings of (NZB/B1NJ x RF/J)F1 parents. MATERIALS AND METHODS: Femurs were isolated from 662 10-week-old females. Mechanical properties were determined for a femur from each animal by three-point bending. Geometric properties and volumetric BMD (vBMD) were determined by pQCT. Genotype data were obtained by PCR assays for polymorphic markers carried in the genomic DNA of each mouse. Genome-wide scans were carried out for co-segregation of genetic marker data with values from 23 different phenotypes. Quantitative trait loci (QTLs) were identified for mechanical, geometric, and mineral density phenotypes. RESULTS: QTLs for many phenotypes were significantly refined by covariate analyses using body weight and femur length. Major QTLs for mechanical and geometric phenotypes were found on chromosomes 5, 7, 9, 11, and 12. Nine chromosomal locations were identified with mechanical QTLs and 17 locations with one or more geometric QTLs. The significance of five mechanical and nine geometric QTLs was affected by the inclusion of covariates. These changes included both decreases and increases in significance. The QTLs on chromosomes 5 and 12 were decreased by inclusion of the covariates in the analysis, but QTLs on 7 and 11 were unaffected. Mechanical QTLs were almost always associated with geometric QTLs and less commonly (two of six) with vBMD QTLs. CONCLUSIONS: Genetic regulation of mechanical properties in the F(2) mice of this NZB/B1NJ x RF/J cross seems to be caused by genes regulating femur geometry.

Animals↗
PubMed2005

Quantitative trait loci for baseline white blood cell count, platelet count, and mean platelet volume.

A substantial genetic contribution to baseline peripheral blood counts has been established. We performed quantitative trait locus/loci (QTL) analyses to identify chromosome (Chr) regions harboring genes influencing the baseline white blood cell (WBC) count, platelet (Plt) count, and mean platelet volume (MPV) in F(2) intercrosses between NZW/LacJ, SM/J, and C57BLKS/J inbred mice. We identified six significant WBC QTL: Wbcq1 (peak LOD score at 38 cM, Chr 1), Wbcq2 (42 cM, Chr 3), Wbcq3 (0 cM, Chr 15), Wbcq4 (58 cM, Chr 1), Wbcq5 (82 cM, Chr 1), and Wbcq6 (8 cM, Chr 14). Three significant Plt QTL were identified: Pltq1 (24 cM, Chr 2), Pltq2 (36 cM, Chr 7), and Pltq3 (10 cM, Chr 12). Two significant MPV QTL were identified, Mpvq1 (62 cM, Chr 15) and Mpvq2 (44 cM, Chr 8). In total, the WBC QTL accounted for up to 31% of the total variance in baseline WBC count, while the Plt and MPV QTL accounted for up to 30% and 49% of the total variance, respectively. These analyses underscore the genetic complexity underlying these traits in normal populations and provide the basis for future studies to identify novel genes involved in the regulation of mammalian hematopoiesis.

Alleles↗
PubMed2005

Quantitative trait loci that determine BMD in C57BL/6J and 129S1/SvImJ inbred mice.

UNLABELLED: BMD is highly heritable; however, little is known about the genes. To identify loci controlling BMD, we conducted a QTL analysis in a (B6 x 129) F2 population of mice. We report on additional QTLs and also narrow one QTL by combining the data from multiple crosses and through haplotype analysis. INTRODUCTION: Previous studies have identified quantitative trait loci (QTL) that determine BMD in mice; however, identification of genes underlying QTLs is impeded by the large size of QTL regions. MATERIALS AND METHODS: To identify loci controlling BMD, we performed a QTL analysis of 291 (B6 x 129) F2 females. Total body and vertebral areal BMD (aBMD) were determined by peripheral DXA when mice were 20 weeks old and had consumed a high-fat diet for 14 weeks. RESULTS AND CONCLUSIONS: Two QTLs were common for both total body and vertebral aBMD: Bmd20 on chromosome (Chr) 6 (total aBMD; peak cM 26, logarithm of odds [LOD] 3.8, and vertebral aBMD; cM 32, LOD 3.6) and Bmd22 on Chr 1 (total aBMD; cM 104, LOD 2.5, and vertebral aBMD; cM 98, LOD 2.6). A QTL on Chr 10 (Bmd21, cM 68, LOD 3.0) affected total body aBMD and a QTL on Chr 7 (Bmd9, cM 44, LOD 2.7) affected vertebral aBMD. A pairwise genome-wide search did not reveal significant gene-gene interactions. Collectively, the QTLs accounted for 21.6% of total aBMD and 17.3% of vertebral aBMD of the F(2) population variances. Bmd9 was previously identified in a cross between C57BL/6J and C3H/HeJ mice, and we narrowed this QTL from 34 to 22 cM by combining the data from these crosses. By examining the Bmd9 region for conservation of ancestral alleles among the low allele strains (129S1/SvImJ and C3H/HeJ) that differed from the high allele strain (C57BL/6J), we further narrowed the region to approximately 9.9 cM, where the low allele strains share a common haplotype. Identifying the genes for these QTLs will enhance our understanding of skeletal biology.

Animals↗
PubMed2005

Bayesian model selection for genome-wide epistatic quantitative trait loci analysis.

The problem of identifying complex epistatic quantitative trait loci (QTL) across the entire genome continues to be a formidable challenge for geneticists. The complexity of genome-wide epistatic analysis results mainly from the number of QTL being unknown and the number of possible epistatic effects being huge. In this article, we use a composite model space approach to develop a Bayesian model selection framework for identifying epistatic QTL for complex traits in experimental crosses from two inbred lines. By placing a liberal constraint on the upper bound of the number of detectable QTL we restrict attention to models of fixed dimension, greatly simplifying calculations. Indicators specify which main and epistatic effects of putative QTL are included. We detail how to use prior knowledge to bound the number of detectable QTL and to specify prior distributions for indicators of genetic effects. We develop a computationally efficient Markov chain Monte Carlo (MCMC) algorithm using the Gibbs sampler and Metropolis-Hastings algorithm to explore the posterior distribution. We illustrate the proposed method by detecting new epistatic QTL for obesity in a backcross of CAST/Ei mice onto M16i.

Algorithms↗