Quantitative trait loci for porcine baseline erythroid traits at three growth ages in a White Duroc × Erhualian F2 resource population

Zou, Zhengzhi; Ren, Jun; Yan, Xueming; Huang, Xiang; Yang, Seungho; Zhang, Zhiyan; Yang, Bin; Li, Wanbo; Huang, Lusheng

doi:10.1007/s00335-008-9142-9

Cited by 21 publications

(40 citation statements)

References 29 publications

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“…These results are generally consistent with previous results [19-21]. Significant differences ( P < 0.05) between measurements before (on day 20) and after (on day 35) inoculation with the swine fever vaccine were observed for all traits except GR%, LY% and MO%.…”

Section: Resultssupporting

confidence: 92%

“…43 QTL controlling baseline erythroid traits and erythroid traits at distinct stages of the disease model were identified on 16 chromosomes, of which twelve were significant at the genome-wide level. In a very recent study [21], a total of 101 QTL, including 46 genome-wide significant QTL, regulating baseline erythroid traits at different age stages were found on all pig chromosomes except for SSC15 and SSC18. For platelet traits, there are no reports of QTL mapping studies in swine up to now.…”

Section: Introductionmentioning

confidence: 99%

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Detection of quantitative trait loci affecting haematological traits in swine via genome scanning

et al. 2010

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BackgroundHaematological traits, which consist of mainly three components: leukocyte traits, erythrocyte traits and platelet traits, play extremely important role in animal immune function and disease resistance. But knowledge of the genetic background controlling variability of these traits is very limited, especially in swine.ResultsIn the present study, 18 haematological traits (7 leukocyte traits, 7 erythrocyte traits and 4 platelet traits) were measured in a pig resource population consisting of 368 purebred piglets of three breeds (Landrace, Large White and Songliao Black Pig), after inoculation with the swine fever vaccine when the pigs were 21 days old. A whole-genome scan of QTL for these traits was performed using 206 microsatellite markers covering all 18 autosomes and the X chromosome. Using variance component analysis based on a linear mixed model and the false discovery rate (FDR) test, 35 QTL with FDR < 0.10 were identified: 3 for the leukocyte traits, 28 for the erythrocyte traits, and 4 for the platelet traits. Of the 35 QTL, 25 were significant at FDR < 0.05 level, including 9 significant at FDR < 0.01 level.ConclusionsVery few QTL were previously identified for hematological traits of pigs and never in purebred populations. Most of the QTL detected here, in particular the QTL for the platelet traits, have not been reported before. Our results lay important foundation for identifying the causal genes underlying the hematological trait variations in pigs.

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Section: Resultssupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Detection of quantitative trait loci affecting haematological traits in swine via genome scanning

et al. 2010

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“…Genetics influences the variation of hematological parameters in multiple species (Chen and Harrison 2002; Cheung et al 2004; Ganesh et al 2009; Peters et al 2005, 2006, 2010; Uda et al 2008; Zou et al 2008). Recent human genome wide association studies have reported strong associations between specific loci and multiple hematological parameters (Ferreira et al 2009; Ganesh et al 2009; Gudbjartsson et al 2009; Kamatani et al 2010; Nalls et al 2011; Okada et al 2011; Reiner et al 2011; Soranzo et al 2009; Uda et al 2008), and in some cases the same loci are also associated with disease risk.…”

Section: Discussionmentioning

confidence: 99%

Genetic Analysis of Hematological Parameters in Incipient Lines of the Collaborative Cross

Kelada

Aylor

Peck

et al. 2012

G3 Genes|Genomes|Genetics

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Hematological parameters, including red and white blood cell counts and hemoglobin concentration, are widely used clinical indicators of health and disease. These traits are tightly regulated in healthy individuals and are under genetic control. Mutations in key genes that affect hematological parameters have important phenotypic consequences, including multiple variants that affect susceptibility to malarial disease. However, most variation in hematological traits is continuous and is presumably influenced by multiple loci and variants with small phenotypic effects. We used a newly developed mouse resource population, the Collaborative Cross (CC), to identify genetic determinants of hematological parameters. We surveyed the eight founder strains of the CC and performed a mapping study using 131 incipient lines of the CC. Genome scans identified quantitative trait loci for several hematological parameters, including mean red cell volume (Chr 7 and Chr 14), white blood cell count (Chr 18), percent neutrophils/lymphocytes (Chr 11), and monocyte number (Chr 1). We used evolutionary principles and unique bioinformatics resources to reduce the size of candidate intervals and to view functional variation in the context of phylogeny. Many quantitative trait loci regions could be narrowed sufficiently to identify a small number of promising candidate genes. This approach not only expands our knowledge about hematological traits but also demonstrates the unique ability of the CC to elucidate the genetic architecture of complex traits.

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“…The research began at the turn of the 21st century and has been continued by several groups of scientists. Until now, analyses of QTL influencing blood cells parameters have been conducted on groups of laboratory animals [1,7,12,14,16,21,26,27,39], but the same results have been obtained from examination of other mammals, such as swine [31,42] and baboons [3].…”

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confidence: 99%

Quantitative Trait Loci Analysis for Peripheral Blood Parameters in a (BALB/cW * C57BL/6J-Mpl hlb219/J) F2 Mice

et al. 2011

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Abstract:The genetic basis of the peripheral blood cell parameters is not fully elucidated. Thus, it is essential to research the correlation between blood cell counts levels and the genome in laboratory animals and subsequently in humans. In the present study, we examined 288 F 2 mice from a cross between BALB/cW and C57BL/6J-Mpl hlb219 /J. The C57BL/6J-Mpl hlb219 /J strain is a mouse model of thrombocytopenia. We found very strong correlations for PLT counts and revealed some highly significant correlations for RBC counts. On the basis of the obtained results, we presume that genetic control of erythrocyte parameters is divided into two pathways: first, the morphological determinants responsible for the red blood cell count (RBC), hematocrit (HCT), and mean corpuscular volume (MCV), and second, the functional pathway determining the hemoglobin content (HGB). The locus on Chromosome 4 is the only detected quantitative trait locus (QTL) influencing the analyzed platelets parameters. We also detected highly significant correlations for erythrocyte parameters on Chromosome 1 (RBC, MCV, MCH), Chr 7 (HGB), Chr 9 (MCHC), Chr 11 (RBC), and Chr 17 (MCH). Finally, with regards to the given correlations, using the Mouse Genome Database resource, we proposed candidate genes with possible meaning for the level of these parameters: cytokine receptor genes (e.g., Mpl), transcription factor genes (e.g., Xbp1, Ikzf1), hemoglobin chain genes (e.g., , and many others localized in the confidence intervals of found QTLs.

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Quantitative trait loci for porcine baseline erythroid traits at three growth ages in a White Duroc × Erhualian F2 resource population

Cited by 21 publications

References 29 publications

Detection of quantitative trait loci affecting haematological traits in swine via genome scanning

Detection of quantitative trait loci affecting haematological traits in swine via genome scanning

Genetic Analysis of Hematological Parameters in Incipient Lines of the Collaborative Cross

Quantitative Trait Loci Analysis for Peripheral Blood Parameters in a (BALB/cW * C57BL/6J-Mpl hlb219/J) F2 Mice

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