Interactions in hypoxic and hypercapnic breathing are genetically linked to mouse chromosomes 1 and 5

2004 ◽  
Vol 97 (1) ◽  
pp. 77-84 ◽  
Author(s):  
Clarke G. Tankersley ◽  
Karl W. Broman
Keyword(s):  
Quantitative Trait Loci ◽  
Mouse Chromosome ◽  
Quantitative Trait ◽  
Odds Ratio ◽  
Genetic Model ◽  
Strain Differences ◽  
Interactive Effects ◽  
Chromosome 1 ◽  
Whole Body ◽  
Trait Loci

The genetic basis for differences in the regulation of breathing is certainly multigenic. The present paper builds on a well-established genetic model of differences in breathing using inbred mouse strains. We tested the interactive effects of hypoxia and hypercapnia in two strains of mice known for variation in hypercapnic ventilatory sensitivity (HCVS); i.e., high gain in C57BL/6J (B6) and low gain in C3H/HeJ (C3) mice. Strain differences in the magnitude and pattern of breathing were measured during normoxia [inspired O2 fraction (FiO2) = 0.21] and hypoxia (FiO2 = 0.10) with mild or severe hypercapnia (inspired CO2 fraction = 0.03 or 0.08) using whole body plethysmography. At each level of FiO2, the change in minute ventilation (V̇e) from 3 to 8% CO2 was computed, and the strain differences between B6 and C3 mice in HCVS were maintained. Inheritance patterns showed potentiation effects of hypoxia on HCVS (i.e., CO2 potentiation) unique to the B6C3F1/J offspring of B6 and C3 progenitors; i.e., the change in V̇e from 3 to 8% CO2 was significantly greater ( P < 0.01) with hypoxia relative to normoxia in F1 mice. Linkage analysis using intercross progeny (F2; n = 52) of B6 and C3 progenitors revealed two significant quantitative trait loci associated with variable HCVS phenotypes. After normalization for body weight, variation in V̇e responses during 8% CO2 in hypoxia was linked to mouse chromosome 1 (logarithm of the odds ratio = 4.4) in an interval between 68 and 89 cM (i.e., between D1Mit14 and D1Mit291). The second quantitative trait loci linked differences in CO2 potentiation to mouse chromosome 5 (logarithm of the odds ratio = 3.7) in a region between 7 and 29 cM (i.e., centered at D5Mit66). In conclusion, these results support the hypothesis that a minimum of two significant genes modulate the interactive effects of hypoxia and hypercapnia in this genetic model.

Empirical Nonparametric Bootstrap Strategies in Quantitative Trait Loci Mapping: Conditioning on the Genetic Model

Genetics ◽  
1998 ◽  
Vol 148 (1) ◽  
pp. 525-535
Author(s):  
Claude M Lebreton ◽  
Peter M Visscher
Keyword(s):  
Quantitative Trait Loci ◽  
Confidence Interval ◽  
Confidence Intervals ◽  
Quantitative Trait ◽  
Genetic Factors ◽  
Genetic Model ◽  
Original Data ◽  
Nonparametric Bootstrap ◽  
Test Protocol ◽  
Trait Loci

AbstractSeveral nonparametric bootstrap methods are tested to obtain better confidence intervals for the quantitative trait loci (QTL) positions, i.e., with minimal width and unbiased coverage probability. Two selective resampling schemes are proposed as a means of conditioning the bootstrap on the number of genetic factors in our model inferred from the original data. The selection is based on criteria related to the estimated number of genetic factors, and only the retained bootstrapped samples will contribute a value to the empirically estimated distribution of the QTL position estimate. These schemes are compared with a nonselective scheme across a range of simple configurations of one QTL on a one-chromosome genome. In particular, the effect of the chromosome length and the relative position of the QTL are examined for a given experimental power, which determines the confidence interval size. With the test protocol used, it appears that the selective resampling schemes are either unbiased or least biased when the QTL is situated near the middle of the chromosome. When the QTL is closer to one end, the likelihood curve of its position along the chromosome becomes truncated, and the nonselective scheme then performs better inasmuch as the percentage of estimated confidence intervals that actually contain the real QTL's position is closer to expectation. The nonselective method, however, produces larger confidence intervals. Hence, we advocate use of the selective methods, regardless of the QTL position along the chromosome (to reduce confidence interval sizes), but we leave the problem open as to how the method should be altered to take into account the bias of the original estimate of the QTL's position.

scholarly journals Identification of Quantitative Trait Loci Influencing Traits Related to Energy Balance in Selection and Inbred Lines of Mice

Genetics ◽  
1999 ◽  
Vol 152 (2) ◽  
pp. 699-711 ◽  
Author(s):  
D E Moody ◽  
D Pomp ◽  
M K Nielsen ◽  
L D Van Vleck
Keyword(s):  
Quantitative Trait Loci ◽  
Energy Balance ◽  
Heat Loss ◽  
Quantitative Trait ◽  
Subcutaneous Fat ◽  
High Heat ◽  
Chromosome 1 ◽  
Direct Calorimetry ◽  
Resource Population ◽  
Trait Loci

Abstract Energy balance is a complex trait with relevance to the study of human obesity and maintenance energy requirements of livestock. The objective of this study was to identify, using unique mouse models, quantitative trait loci (QTL) influencing traits that contribute to variation in energy balance. Two F2 resource populations were created from lines of mice differing in heat loss measured by direct calorimetry as an indicator of energy expenditure. The HB F2 resource population originated from a cross between a noninbred line selected for high heat loss and an inbred line with low heat loss. Evidence for significant QTL influencing heat loss was found on chromosomes 1, 2, 3, and 7. Significant QTL influencing body weight and percentage gonadal fat, brown fat, liver, and heart were also identified. The LH F2 resource population originated from noninbred lines of mice that had undergone divergent selection for heat loss. Chromosomes 1 and 3 were evaluated. The QTL for heat loss identified on chromosome 1 in the HB population was confirmed in the LH population, although the effect was smaller. The presence of a QTL influencing 6-wk weight was also confirmed. Suggestive evidence for additional QTL influencing heat loss, percentage subcutaneous fat, and percentage heart was found for chromosome 1.

Congenic strains confirm aerobic running capacity quantitative trait loci on rat chromosome 16 and identify possible intermediate phenotypes

2007 ◽  
Vol 29 (1) ◽  
pp. 91-97 ◽  
Author(s):  
Justin A. Ways ◽  
Brian M. Smith ◽  
John C. Barbato ◽  
Ramona S. Ramdath ◽  
Krista M. Pettee ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Strain Differences ◽  
Proximal Portion ◽  
Chromosome 16 ◽  
Rat Strains ◽  
High Performing ◽  
Intermediate Phenotypes ◽  
Trait Loci ◽  
Inbred Rat

We previously identified two inbred rat strains divergent for treadmill aerobic running capacity (ARC), the low-performing Copenhagen (COP) and the high-performing DA rats, and used an F2(COP×DA) population to identify ARC quantitative trait loci (QTLs) on rat chromosome 16 (RNO16) and the proximal portion of rat chromosome 3 (RNO3). Two congenic rat strains were bred to further investigate these ARC QTLs by introgressing RNO16 and the proximal portion of RNO3 from DA rats into the genetic background of COP rats and were named COP.DA(chr 16) and COP.DA(chr 3), respectively. COP.DA(chr 16) rats had significantly greater ARC compared with COP rats (696.7 ± 38.2 m vs. 571.9 ± 27.5 m, P = 0.03). COP.DA(chr 3) rats had increased, although not significant, ARC compared with COP rats (643.6 ± 40.9 m vs. 571.9 ± 27.5 m). COP.DA(chr 16) rats had significantly greater subcutaneous abdominal fat, as well as decreased fasting triglyceride levels, compared with COP rats ( P < 0.05), indicating that genes responsible for strain differences in fat metabolism are also located on RNO16. While this colocalization of QTLs may be coincidental, it is also possible that these differences in energy balance may be associated with the superior running performance of COP.DA(chr 16) consomic rats.

Bayesian Model Choice and Search Strategies for Mapping Interacting Quantitative Trait Loci

Genetics ◽  
2003 ◽  
Vol 165 (2) ◽  
pp. 867-883 ◽  
Author(s):  
Nengjun Yi ◽  
Shizhong Xu ◽  
David B Allison
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Bayesian Model ◽  
Genetic Model ◽  
Genetic Effects ◽  
Monte Carlo Algorithm ◽  
Data Set ◽  
Epistatic Effects ◽  
Trait Loci

AbstractMost complex traits of animals, plants, and humans are influenced by multiple genetic and environmental factors. Interactions among multiple genes play fundamental roles in the genetic control and evolution of complex traits. Statistical modeling of interaction effects in quantitative trait loci (QTL) analysis must accommodate a very large number of potential genetic effects, which presents a major challenge to determining the genetic model with respect to the number of QTL, their positions, and their genetic effects. In this study, we use the methodology of Bayesian model and variable selection to develop strategies for identifying multiple QTL with complex epistatic patterns in experimental designs with two segregating genotypes. Specifically, we develop a reversible jump Markov chain Monte Carlo algorithm to determine the number of QTL and to select main and epistatic effects. With the proposed method, we can jointly infer the genetic model of a complex trait and the associated genetic parameters, including the number, positions, and main and epistatic effects of the identified QTL. Our method can map a large number of QTL with any combination of main and epistatic effects. Utility and flexibility of the method are demonstrated using both simulated data and a real data set. Sensitivity of posterior inference to prior specifications of the number and genetic effects of QTL is investigated.

Detection of quantitative trait loci for locomotion and osteochondrosis-related traits in Large White ✕ Meishan pigs

2003 ◽  
Vol 76 (2) ◽  
pp. 155-165 ◽  
Author(s):  
G.J. Lee ◽  
A.L. Archibald ◽  
G.B. Garth ◽  
A.S. Law ◽  
D. Nicholson ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Chromosome 1 ◽  
Chromosome 15 ◽  
Large White ◽  
Chromosome 13 ◽  
Genome Wide ◽  
A Genome ◽  
Trait Loci ◽  
Gait Score

AbstractData from the F2 generation of a Large White (LW) ✕ Meishan (MS) crossbred population were analysed to detect quantitative trait loci (QTL) for leg and gait scores, osteochondrosis and physis scores. Legs, feet and gait score were assessed in 308 F2 animals at 85 ( + 5) kg and osteochondrosis and physis scores were recorded for the right foreleg after slaughter. A genome scan was performed using 111 genetic markers chosen to span the genome that were genotyped on the F2 animals and their F1 parents and purebred grandparents. A QTL on chromosome 1 affecting gait score was significant at the genome-wide significance level. Additional QTL significant at the chromosome-wide 5% threshold level (approx. equivalent to the genome-wide suggestive level) were detected on chromosome 1 for front feet and back legs scores, on chromosome 13 for front legs and front feet scores, on chromosome 14 for front legs, front feet and back legs scores and on chromosome 15 for back feet score. None of the QTL for osteochondrosis score exceeded the chromosome-wide suggestive level, but one chromosome-wide QTL for physis score was found on chromosome 7. On chromosome 1, gait and front feet scores mapped to the middle of the chromosome and showed additive effects in favour of the LW alleles and no dominance effects. The QTL for back legs score mapped to the distal end of the chromosome and showed a dominant effect and no additive effect. On chromosomes 14 and 15, the LW allele was again superior to the MS allele. On chromosome 13, there were both additive and dominance effects in favour of the MS allele. The MS alleles on chromosome 13 may have potential for introgression into a commercial LW population. The other putative QTLs identified may have value in marker-assisted selection in LW or MS-synthetic populations.

Quantitative trait loci for insulin-like growth factor I, leptin, thyroxine, and corticosterone in genetically heterogeneous mice

2003 ◽  
Vol 15 (1) ◽  
pp. 44-51 ◽  
Author(s):  
James M. Harper ◽  
Andrzej T. Galecki ◽  
David T. Burke ◽  
Stephen L. Pinkosky ◽  
Richard A. Miller
Keyword(s):  
Quantitative Trait Loci ◽  
Growth Factor ◽  
Quantitative Trait ◽  
Chromosome 1 ◽  
Insulin Like Growth Factor ◽  
Factor I ◽  
Genotype Information ◽  
Igf I ◽  
Trait Loci ◽  
Growth Factor I

Genotype information was collected at 87 loci in a group of 1,108 UM-HET3 mice bred as the progeny of [BALB/cJ × C57BL/6J]F1 mothers and [C3H/HeJ × DBA/2J]F1 fathers, for which thyroxine (T4), insulin-like growth factor I (IGF-I), and leptin levels had been measured at 4 and 15 mo of age. The data provided significant evidence for quantitative trait loci (QTL) modulating IGF-I levels on chromosomes 1, 3, 8, 10, and 17; for loci affecting T4 on chromosomes 4, 15, and 17; and for leptin on chromosome 3. Fecal levels of corticosterone at 17 mo of age were influenced by a QTL on chromosome 1. Nine other gene/hormone associations reached a nominal P < 0.01, providing suggestive but not statistical evidence for additional QTL. QTL with an influence on a given hormone were in nearly all cases additive, with little or no evidence for epistasis. Of the 12 strongest QTL, 5 had effects that were age dependent, having more effect in 15-mo-old than in 4-mo-old mice in all but one case; the other QTL had effects that were apparently age-independent. These results show that the genetic controls over late-life hormone levels are complex and dependent on effects of genes that act both early and late in the life course.

In silico mapping of quantitative trait loci (QTL) regulating the milk ionome in mice identifies a milk iron locus on chromosome 1

2018 ◽  
Vol 29 (9-10) ◽  
pp. 632-655 ◽  
Author(s):  
Darryl L. Hadsell ◽  
Louise A. Hadsell ◽  
Monique Rijnkels ◽  
Yareli Carcamo-Bahena ◽  
Jerry Wei ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
In Silico ◽  
Chromosome 1 ◽  
In Silico Mapping ◽  
Trait Loci

Mouse chromosome 9 quantitative trait loci for soft tissue regeneration: Congenic analysis and fine mapping

2007 ◽  
Vol 15 (6) ◽  
pp. 922-927 ◽  
Author(s):  
Hongrun Yu ◽  
David J. Baylink ◽  
Godfred L. Masinde ◽  
Runzhi Li ◽  
Bay Nguyen ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Soft Tissue ◽  
Mouse Chromosome ◽  
Tissue Regeneration ◽  
Fine Mapping ◽  
Quantitative Trait ◽  
Chromosome 9 ◽  
Soft Tissue Regeneration ◽  
Trait Loci

scholarly journals Genetic mapping of two blood pressure quantitative trait loci on rat chromosome 1.

1996 ◽  
Vol 97 (3) ◽  
pp. 777-788 ◽  
Author(s):  
L Gu ◽  
H Dene ◽  
A Y Deng ◽  
B Hoebee ◽  
M T Bihoreau ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Quantitative Trait Loci ◽  
Genetic Mapping ◽  
Quantitative Trait ◽  
Chromosome 1 ◽  
Trait Loci

scholarly journals Genetic Mapping Identifies Consistent Quantitative Trait Loci for Yield Traits of Rice under Greenhouse Drought Conditions

Genes ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 62
Author(s):  
Niranjan Baisakh ◽  
Jonalyn Yabes ◽  
Andres Gutierrez ◽  
Venkata Mangu ◽  
Peiyong Ma ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Grain Yield ◽  
Drought Resistance ◽  
Quantitative Trait ◽  
Chromosome 1 ◽  
Phenotypic Variance ◽  
Yield Traits ◽  
Rice Varieties ◽  
Drought Tolerant ◽  
Trait Loci

Improving drought resistance in crops is imperative under the prevailing erratic rainfall patterns. Drought affects the growth and yield of most modern rice varieties. Recent breeding efforts aim to incorporate drought resistance traits in rice varieties that can be suitable under alternative irrigation schemes, such as in a (semi)aerobic system, as row (furrow-irrigated) rice. The identification of quantitative trait loci (QTLs) controlling grain yield, the most important trait with high selection efficiency, can lead to the identification of markers to facilitate marker-assisted breeding of drought-resistant rice. Here, we report grain yield QTLs under greenhouse drought using an F2:3 population derived from Cocodrie (drought sensitive) × Nagina 22 (N22) (drought tolerant). Eight QTLs were identified for yield traits under drought. Grain yield QTL under drought on chromosome 1 (phenotypic variance explained (PVE) = 11.15%) co-localized with the only QTL for panicle number (PVE = 37.7%). The drought-tolerant parent N22 contributed the favorable alleles for all QTLs except qGN3.2 and qGN5.1 for grain number per panicle. Stress-responsive transcription factors, such as ethylene response factor, WD40 domain protein, zinc finger protein, and genes involved in lipid/sugar metabolism were linked to the QTLs, suggesting their possible role in drought tolerance mechanism of N22 in the background of Cocodrie, contributing to higher yield under drought.

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