scholarly journals New quantitative trait loci for carotid atherosclerosis identified in an intercross derived from apolipoprotein E-deficient mouse strains

2013 ◽  
Vol 45 (8) ◽  
pp. 332-342 ◽  
Author(s):  
Jessica S. Rowlan ◽  
Zhimin Zhang ◽  
Qian Wang ◽  
Yan Fang ◽  
Weibin Shi
Keyword(s):  
Apolipoprotein E ◽  
Quantitative Trait ◽  
Carotid Atherosclerosis ◽  
Plasma Lipid ◽  
Hdl Cholesterol ◽  
Carotid Bifurcation ◽  
Mouse Strains ◽  
Entire Genome ◽  
Significant Qtls ◽  
Trait Locus

Carotid atherosclerosis is the primary cause of ischemic stroke. To identify genetic factors contributing to carotid atherosclerosis, we performed quantitative trait locus (QTL) analysis using female mice derived from an intercross between C57BL/6J (B6) and BALB/cJ (BALB) apolipoprotein E ( Apoe−/−) mice. We started 266 F2 mice on a Western diet at 6 wk of age and fed them the diet for 12 wk. Atherosclerotic lesions in the left carotid bifurcation and plasma lipid levels were measured. We genotyped 130 microsatellite markers across the entire genome. Three significant QTLs, Cath1 on chromosome (Chr) 12, Cath2 on Chr5, and Cath3 on Chr13, and four suggestive QTLs on Chr6, Chr9, Chr17, and Chr18 were identified for carotid lesions. The Chr6 locus replicated a suggestive QTL and was named Cath4. Six QTLs for HDL, three QTLs for non-HDL cholesterol, and three QTLs for triglyceride were found. Of these, a significant QTL for non-HDL on Chr1 at 60.3 cM, named Nhdl13, and a suggestive QTL for HDL on ChrX were new. A significant locus for HDL ( Hdlq5) was overlapping with a suggestive locus for carotid lesions on Chr9. A significant correlation between carotid lesion sizes and HDL cholesterol levels was observed in the F2 population ( R = −0.153, P = 0.0133). Thus, we have identified several new QTLs for carotid atherosclerosis and the locus on Chr9 may exert effect through interactions with HDL.

scholarly journals Genetic contributors to lipoprotein cholesterol levels in an intercross of 129S1/SvImJ and RIIIS/J inbred mice

2004 ◽  
Vol 17 (2) ◽  
pp. 114-121 ◽  
Author(s):  
Malcolm A. Lyons ◽  
Ron Korstanje ◽  
Renhua Li ◽  
Kenneth A. Walsh ◽  
Gary A. Churchill ◽  
...  
Keyword(s):  
Total Cholesterol ◽  
Inbred Mice ◽  
Density Lipoprotein ◽  
Hdl Cholesterol ◽  
Lipoprotein Cholesterol ◽  
Mouse Strains ◽  
High Cholesterol ◽  
Significant Qtls ◽  
Cholesterol Levels ◽  
Trait Locus

To determine the genetic contribution to variation among lipoprotein cholesterol levels, we performed quantitative trait locus (QTL) analyses on an intercross between mouse strains RIIIS/J and 129S1/SvImJ. Male mice of the parental strains and the reciprocal F1 and F2 populations were fed a high-cholesterol, cholic acid-containing diet for 8–12 wk. At the end of the feeding period, plasma total, high-density lipoprotein (HDL), and non-HDL cholesterol were determined. For HDL cholesterol, we identified three significant QTLs on chromosomes (Chrs) 1 ( D1Mit507, 88 cM, 72–105 cM, 4.8 LOD), 9 ( D11Mit149, 14 cM, 10–25 cM, 9.4 LOD), and 12 ( D12Mit60, 20 cM, 0–50 cM, 5.0 LOD). These QTLs were considered identical to QTLs previously named Hdlq5, Hdlq17, and Hdlq18, respectively, in crosses sharing strain 129. For total cholesterol, we identified two significant QTLs on Chrs 1 and 9, which were named Chol10 ( D1Mit507, 88 cM, 10–105 cM, 3.9 LOD) and Chol11 ( D11Mit149, 14 cM, 0–30 cM, 4.4 LOD), respectively. In addition, for total cholesterol, we identified two suggestive QTLs on Chrs 12 (distal) and 17, which remain unnamed. For non-HDL cholesterol, we identified and named one new QTL on Chr 17, Nhdlq3 ( D17Mit221, 58 cM, 45–60 cM, 3.4 LOD). Nhdlq3 colocalized with orthologous human QTLs for lipoprotein phenotypes, and with Abcg5 and Abcg8. Overall, we detected eight QTLs for lipoprotein cholesterol concentrations on Chrs 1, 9, 12, and 17 (each two per chromosome), including a new QTL for non-HDL cholesterol, Nhdlq3, on Chr 17.

Quantitative Trait Loci on Chromosomes 10 and 11 Influencing Mandible Size of SMXA RI Mouse Strains

2002 ◽  
Vol 81 (7) ◽  
pp. 501-504 ◽  
Author(s):  
A. Dohmoto ◽  
K. Shimizu ◽  
Y. Asada ◽  
T. Maeda
Keyword(s):  
Quantitative Trait Locus ◽  
Quantitative Trait ◽  
Major Gene ◽  
Recombinant Inbred Strain ◽  
Mouse Strains ◽  
Chromosome 11 ◽  
Significant Qtls ◽  
Mandible Length ◽  
Trait Locus ◽  
Mandible Size

Predicting the mandible size before the termination of growth of the maxillofacial bones is essential in pedodontics as well as for the predictions needed for genetic analysis. Here, Quantitative Trait Locus (QTL) analysis was used to detect the chromosomal regions responsible for the mandible length between the menton and gonion in an SMXA recombinant inbred strain of mice. Around the region 60 cM from the centromere in chromosome 10, the logarithm of the odds score showed a higher than suggestive level. Around the regions 13 cM and 16 cM in chromosome 11, two significant QTLs were detected. Analysis of genotypes from loci corresponding to those QTLs revealed a large mandible when the region between the markers Hba and D11Mit163 and D10Mit70 and D10Mit136 indicated the genotype from the A/J and SM/J alleles, respectively. These results suggest that the major gene(s) responsible for mandible length are located in these regions.

scholarly journals Quantitative Trait Locus Analysis of Carotid Atherosclerosis in an Intercross Between C57BL/6 and C3H Apolipoprotein E–Deficient Mice

Stroke ◽  
2008 ◽  
Vol 39 (1) ◽  
pp. 166-173 ◽  
Author(s):  
Qiongzhen Li ◽  
Yuhua Li ◽  
Zhimin Zhang ◽  
Timothy R. Gilbert ◽  
Alan H. Matsumoto ◽  
...  
Keyword(s):  
Quantitative Trait Locus ◽  
Apolipoprotein E ◽  
Quantitative Trait Locus Analysis ◽  
Quantitative Trait ◽  
Carotid Atherosclerosis ◽  
Trait Locus ◽  
Deficient Mice

scholarly journals Quantitative trait loci associated with susceptibility to therapy-related acute murine promyelocytic leukemia in hCG-PML/RARA transgenic mice

Blood ◽  
2008 ◽  
Vol 112 (4) ◽  
pp. 1434-1442 ◽  
Author(s):  
Ryan K. Funk ◽  
Taylor J. Maxwell ◽  
Masayo Izumi ◽  
Deepa Edwin ◽  
Friederike Kreisel ◽  
...  
Keyword(s):  
Quantitative Trait ◽  
Complex Trait ◽  
Myelogenous Leukemia ◽  
Mouse Strains ◽  
Spleen Weight ◽  
Nucleotide Polymorphisms ◽  
Treatment Regimens ◽  
Multiple Loci ◽  
Acute Myelogenous ◽  
Trait Locus

Abstract Therapy-related acute myelogenous leukemia (t-AML) is an important late adverse effect of alkylator chemotherapy. Susceptibility to t-AML has a genetic component, yet specific genetic variants that influence susceptibility are poorly understood. We analyzed an F2 intercross (n = 282 mice) between mouse strains resistant or susceptible to t-AML induced by the alkylator ethyl-N-nitrosourea (ENU) to identify genes that regulate t-AML susceptibility. Each mouse carried the hCG-PML/RARA transgene, a well-characterized initiator of myeloid leukemia. In the absence of ENU treatment, transgenic F2 mice developed leukemia with higher incidence (79.4% vs 12.5%) and at earlier time points (108 days vs 234 days) than mice in the resistant background. ENU treatment of F2 mice further increased incidence (90.4%) and shortened median survival (171 vs 254 days). We genotyped F2 mice at 384 informative single nucleotide polymorphisms across the genome and performed quantitative trait locus (QTL) analysis. Thirteen QTLs significantly associated with leukemia-free survival, spleen weight, or white blood cell count were identified on 8 chromosomes. These results suggest that susceptibility to ENU-induced leukemia in mice is a complex trait governed by genes at multiple loci. Improved understanding of genetic risk factors should lead to tailored treatment regimens that reduce risk for patients predisposed to t-AML.

scholarly journals Regional Variation in Genetic Control of Atherosclerosis in Hyperlipidemic Mice

2020 ◽  
Vol 10 (12) ◽  
pp. 4679-4689
Author(s):  
Michael B. Jones ◽  
Alexander An ◽  
Lisa J. Shi ◽  
Weibin Shi
Keyword(s):  
Genetic Control ◽  
Aortic Root ◽  
Subclinical Atherosclerosis ◽  
Hdl Cholesterol ◽  
Carotid Bifurcation ◽  
Mouse Strains ◽  
Aortic Atherosclerosis ◽  
Bioinformatics Analyses ◽  
Aortic Lesion ◽  
Glucose Levels

Atherosclerosis is a polygenic disorder that often affects multiple arteries. Carotid arteries are common sites for evaluating subclinical atherosclerosis, and aortic root is the standard site for quantifying atherosclerosis in mice. We compared genetic control of atherosclerosis between the two sites in the same cohort derived from two phenotypically divergent Apoe-null (Apoe−/−) mouse strains. Female F2 mice were generated from C57BL/6 (B6) and C3H/He (C3H) Apoe−/− mice and fed 12 weeks of Western diet. Atherosclerotic lesions in carotid bifurcation and aortic root and plasma levels of fasting lipids and glucose were measured. 153 genetic markers across the genome were typed. All F2 mice developed aortic atherosclerosis, while 1/5 formed no or little carotid lesions. Genome-wide scans revealed 3 significant loci on chromosome (Chr) 1, Chr15, 6 suggestive loci for aortic atherosclerosis, 2 significant loci on Chr6, Chr12, and 6 suggestive loci for carotid atherosclerosis. Only 2 loci for aortic lesions showed colocalization with loci for carotid lesions. Carotid lesion sizes were moderately correlated with aortic lesion sizes (r = 0.303; P = 4.6E-6), but they showed slight or no association with plasma HDL, non-HDL cholesterol, triglyceride, or glucose levels among F2 mice. Bioinformatics analyses prioritized Cryge as a likely causal gene for Ath30, Cdh6 and Dnah5 as causal genes for Ath22. Our data demonstrate vascular site-specific effects of genetic factors on atherosclerosis in the same animals and highlight the need to extend studies of atherosclerosis to sites beyond aortas of mice.

scholarly journals Human Pedigree-Based Quantitative-Trait–Locus Mapping: Localization of Two Genes Influencing HDL-Cholesterol Metabolism

1999 ◽  
Vol 64 (6) ◽  
pp. 1686-1693 ◽  
Author(s):  
Laura Almasy ◽  
James E. Hixson ◽  
David L. Rainwater ◽  
Shelley Cole ◽  
Jeff T. Williams ◽  
...  
Keyword(s):  
Quantitative Trait Locus ◽  
Quantitative Trait Locus Mapping ◽  
Quantitative Trait ◽  
Cholesterol Metabolism ◽  
Hdl Cholesterol ◽  
Trait Locus ◽  
Locus Mapping

scholarly journals Genomic Locus Modulating IOP in the BXD RI Mouse Strains

2017 ◽  
Author(s):  
Rebecca King ◽  
Ying Li ◽  
Jiaxing Wang ◽  
Felix L. Struebing ◽  
Eldon E. Geisert
Keyword(s):  
Candidate Genes ◽  
Quantitative Trait ◽  
Significant Quantitative Trait Locus ◽  
Interval Mapping ◽  
Mouse Strains ◽  
Genomic Locus ◽  
Quantitative Trait Analysis ◽  
Significant Locus ◽  
Trait Locus ◽  
Different Strains

AbstractPurposeIntraocular pressure (IOP) is the primary risk factor for developing glaucoma. The present study examines genomic contribution to the normal regulation of IOP in the mouse.MethodsThe BXD recombinant inbred (RI) strain set was used to identify genomic loci modulating IOP. We measured the IOP from 532 eyes from 33 different strains. The IOP data will be subjected to conventional quantitative trait analysis using simple and composite interval mapping along with epistatic interactions to define genomic loci modulating normal IOP.ResultsThe analysis defined one significant quantitative trait locus (QTL) on Chr.8 (100 to 106 Mb). The significant locus was further examined to define candidate genes that modulate normal IOP. There are only two good candidate genes within the 6 Mb over the peak, Cdh8 (Cadherin 8) and Cdh11 (Cadherin 11). Expression analysis on gene expression and immunohistochemistry indicate that Cdh11 is the best candidate for modulating the normal levels of IOP.ConclusionsWe have examined the genomic regulation of IOP in the BXD RI strain set and found one significant QTL on Chr. 8. Within this QTL that are two potential candidates for modulating IOP with the most likely gene being Cdh11.

scholarly journals Genomic Locus Modulating IOP in the BXD RI Mouse Strains

2017 ◽  
Author(s):  
Rebecca King ◽  
Ying Li ◽  
Jiaxing Wang ◽  
Felix L. Struebing ◽  
Eldon E. Geisert
Keyword(s):  
Candidate Genes ◽  
Quantitative Trait ◽  
Significant Quantitative Trait Locus ◽  
Interval Mapping ◽  
Mouse Strains ◽  
Genomic Locus ◽  
Quantitative Trait Analysis ◽  
Significant Locus ◽  
Trait Locus ◽  
Different Strains

AbstractPurposeIntraocular pressure (IOP) is the primary risk factor for developing glaucoma. The present study examines genomic contribution to the normal regulation of IOP in the mouse.MethodsThe BXD recombinant inbred (RI) strain set was used to identify genomic loci modulating IOP. We measured the IOP from 532 eyes from 34 different strains. The IOP data will be subjected to conventional quantitative trait analysis using simple and composite interval mapping along with epistatic interactions to define genomic loci modulating normal IOP.ResultsThe analysis defined one significant quantitative trait locus (QTL) on Chr.8 (100 to 106 Mb). The significant locus was further examined to define candidate genes that modulate normal IOP. There are only two good candidate genes within the 6 Mb over the peak, Cdh8 (Cadherin 8) and Cdh11 (Cadherin 11). Expression analysis on gene expression and immunohistochemistry indicate that Cdh11 is the best candidate for modulating the normal levels of IOP.ConclusionsWe have examined the genomic regulation of IOP in the BXD RI strain set and found one significant QTL on Chr. 8. Within this QTL that are two potential candidates for modulating IOP with the most likely gene being Cdh11.

scholarly journals Genetic Mapping by Bulk Segregant Analysis in Drosophila: Experimental Design and Simulation-Based Inference

2016 ◽  
Author(s):  
John E Pool
Keyword(s):  
Quantitative Trait Locus ◽  
Quantitative Trait ◽  
Bulk Segregant Analysis ◽  
Experimental Population ◽  
Modern Biology ◽  
Significant Qtls ◽  
Trait Mapping ◽  
Wide Range ◽  
Simulation Based ◽  
Trait Locus

AbstractIdentifying the genomic regions that underlie complex phenotypic variation is a key challenge in modern biology. Many approaches to quantitative trait locus mapping in animal and plant species suffer from limited power and genomic resolution. Here, I investigate whether bulk segregant analysis (BSA), which has been successfully applied for yeast, may have utility in the genomic era for trait mapping in Drosophila (and other organisms that can be experimentally bred in similar numbers). I perform simulations to investigate the statistical signal of a quantitative trait locus (QTL) in a wide range of BSA and introgression mapping (IM) experiments. BSA consistently provides more accurate mapping signals than IM (in addition to allowing the mapping of multiple traits from the same experimental population). The performance of BSA and IM is maximized by having multiple independent crosses, more generations of interbreeding, larger numbers of breeding individuals, and greater genotyping effort, but is less affected by the proportion of individuals selected for phenotypic extreme pools. I also introduce a prototype analysis method for Simulation-based Inference for BSA Mapping (SIBSAM). This method identifies significant QTLs and estimates their genomic confidence intervals and relative effect sizes. Importantly, it also tests whether overlapping peaks should be considered as two distinct QTLs. This approach will facilitate improved trait mapping in Drosophila and other species for which hundreds or thousands of offspring (but not millions) can be studied.

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