QTLs for murine red blood cell parameters in LG/J and SM/J F2 and advanced intercross lines

Bartnikas, Thomas B.; Parker, Clarissa C.; Cheng, Riyan; Campagna, Dean R.; Lim, Jackie E.; Palmer, Abraham A.; Fleming, Mark D.

doi:10.1007/s00335-012-9393-3

Cited by 7 publications

(10 citation statements)

References 47 publications

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“…The median size of a QTL region was 4.3 Mb. The QTL were slightly narrower than QTL intervals we identified in this population for other traits, such as red blood cell parameters [median 1.5-LOD support interval = 4.7 Mb (Bartnikas et al 2012)] and body weight [median 1.5-LOD support interval = 5.5 Mb .…”

Section: Discussionmentioning

confidence: 58%

“…This is a slightly smaller interval than the suggested 1.8-LOD interval based on simulations in two intercrosses, although in practice the best interval for each QTL depends on a number of factors, including the QTL effect size (Manichaikul et al 2006). The main reason we chose this interval was to be consistent with our previous studies using AIL mice (e.g., Bartnikas et al 2012).…”

Section: Qtl Regionsmentioning

confidence: 94%

“…AILs have been employed in several previous studies to successfully map QTL for complex traits in mice, including locomotor activity , muscle weight (Lionikas et al 2010), red blood cell characteristics (Bartnikas et al 2012), body weight , methamphetamine sensitivity (Parker et al 2012a), prepulse inhibition , and the conditioned fear phenotypes studied in this article (Parker et al 2012b). This study makes several key contributions over previous work in this area.…”

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confidence: 99%

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High-Resolution Genetic Mapping of Complex Traits from a Combined Analysis of F2 and Advanced Intercross Mice

et al. 2014

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Genetic influences on anxiety disorders are well documented; however, the specific genes underlying these disorders remain largely unknown. To identify quantitative trait loci (QTL) for conditioned fear and open field behavior, we used an F 2 intercross (n = 490) and a 34th-generation advanced intercross line (AIL) (n = 687) from the LG/J and SM/J inbred mouse strains. The F 2 provided strong support for several QTL, but within wide chromosomal regions. The AIL yielded much narrower QTL, but the results were less statistically significant, despite the larger number of mice. Simultaneous analysis of the F 2 and AIL provided strong support for QTL and within much narrower regions. We used a linear mixed-model approach, implemented in the program QTLRel, to correct for possible confounding due to familial relatedness. Because we recorded the full pedigree, we were able to empirically compare two ways of accounting for relatedness: using the pedigree to estimate kinship coefficients and using genetic marker estimates of "realized relatedness." QTL mapping using the marker-based estimates yielded more support for QTL, but only when we excluded the chromosome being scanned from the marker-based relatedness estimates. We used a forward model selection procedure to assess evidence for multiple QTL on the same chromosome. Overall, we identified 12 significant loci for behaviors in the open field and 12 significant loci for conditioned fear behaviors. Our approach implements multiple advances to integrated analysis of F 2 and AILs that provide both power and precision, while maintaining the advantages of using only two inbred strains to map QTL.

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Section: Discussionmentioning

confidence: 58%

Section: Qtl Regionsmentioning

confidence: 94%

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confidence: 99%

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High-Resolution Genetic Mapping of Complex Traits from a Combined Analysis of F2 and Advanced Intercross Mice

et al. 2014

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“…QTLRel was designed specifically for analysis in multigenerational crosses among model organisms and has been used to map QTLs for a variety of behavioral (Cheng et al 2010; Samocha et al 2010; Yoshizawa et al 2012; Parker et al 2012; Weber et al 2013; Logan et al 2013) and physiological (Lionikas et al 2010; Parker et al 2011; Heydemann et al 2012; Bartnikas et al 2012; Svenson et al 2012; Leamy et al 2012; Leamy et al 2013a) traits in mouse AIL and HS populations. QTLRel estimates kinship using a rapid approach that can accommodate deep, complex pedigrees (Cheng et al 2011).…”

Section: Mixed Model Association Softwarementioning

confidence: 99%

Fine-mapping QTLs in advanced intercross lines and other outbred populations

Gonzales

Palmer

2014

Mamm Genome

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Quantitative genetic studies in model organisms, particularly in mice, have been extremely successful in identifying chromosomal regions that are associated with a wide variety of behavioral and other traits. However, it is now widely understood that identification of the underlying genes will be far more challenging. In the last few years, a variety of populations have been utilized in an effort to more finely map these chromosomal regions with the goal of identifying specific genes. The common property of these newer populations is that linkage disequilibrium spans relatively short distances, which permits fine-scale mapping resolution. This review focuses on advanced intercross lines (AILs) which are the simplest such population. As originally proposed in 1995 by Darvasi and Soller, an AIL is the product of intercrossing two inbred strains beyond the F2 generation. Unlike recombinant inbred strains, AILs are maintained as outbred populations; brother-sister matings are specifically avoided. Each generation of intercrossing beyond the F2 further degrades linkage disequilibrium between adjacent makers, which allows for fine scale mapping of quantitative trait loci (QTLs). Advances in genotyping technology and techniques for the statistical analysis of AILs have permitted rapid advances in the application of AILs. We review some of the analytical issues and available software, including QTLRel, EMMA, EMMAX, GEMMA, TASSEL, GRAMMAR, WOMBAT, Mendel and others.

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“…Genome-wide quantitative trait loci (QTL) analysis and forward genetic approaches have been successfully used to identify the genetic loci and specific genes that control iron-related quantitative traits such as tissue iron status and red blood cell parameters [8], [9]. Previous studies showed marked differences in the concentrations of liver and spleen non-heme iron content among inbred mouse strains [10].…”

Section: Introductionmentioning

confidence: 99%

Fine-Mapping and Genetic Analysis of the Loci Affecting Hepatic Iron Overload in Mice

Guo

Zhang

et al. 2013

PLoS ONE

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The liver, as the major organ for iron storage and production of hepcidin, plays pivotal roles in maintaining mammalian iron homeostasis. A previous study showed that Quantitative Trait Loci (QTLs) on chromosome 7 (Chr7) and 16 (Chr16) may control hepatic non-heme iron overload in an F2 intercross derived from C57BL/6J (B6) and SWR/J (SWR) mice. In this study, we aimed to validate the existence of these loci and identify the genes responsible for the phenotypic variations by generating congenic mice carrying SWR chromosome segments expanding these QTLs (D7Mit68-D7Mit71 and D16Mit125-D16Mit185, respectively). We excluded involvement of Chr7 based on the lack of iron accumulation in congenic mice. In contrast, liver iron accumulation was observed in Chr16 congenic mice. Through use of a series of subcongenic murine lines the interval on Chr16 was further fine-mapped to a 0.8 Mb segment spanning 11 genes. We found that the mRNA expression pattern in the liver remained unchanged for all 11 genes tested. Most importantly, we detected 4 missense mutations in 3 candidate genes including Sidt1 (P172R), Spice1(R708S), Boc (Q1051R) and Boc (S450-insertion in B6 allele) in the liver of SWR homozygous congenic mice. To further delineate potential modifier gene(s), we reconstituted seven candidate genes, Sidt1, Boc, Zdhhc23, Gramd1c, Atp6v1a, Naa50 and Gtpbp8, in mouse liver through hydrodynamic transfection. However, we were unable to detect significant changes in liver iron levels upon reconstitution of these candidate genes. Taken together, our work provides strong genetic evidence of the existence of iron modifiers on Chr16. Moreover, we were able to delineate the phenotypically responsible region to a 0.8 Mb region containing 11 coding genes, 3 of which harbor missense mutations, using a series of congenic mice.

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QTLs for murine red blood cell parameters in LG/J and SM/J F2 and advanced intercross lines

Cited by 7 publications

References 47 publications

High-Resolution Genetic Mapping of Complex Traits from a Combined Analysis of F2 and Advanced Intercross Mice

High-Resolution Genetic Mapping of Complex Traits from a Combined Analysis of F2 and Advanced Intercross Mice

Fine-mapping QTLs in advanced intercross lines and other outbred populations

Fine-Mapping and Genetic Analysis of the Loci Affecting Hepatic Iron Overload in Mice

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