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

Gong, Yan; Lü, Xin; Wang, Zhipeng; Hu, Fang; Luo, Y. R.; Cai, Shao-Qian; Qi, Chun-Mei; Li, Shan; Niu, Xiaofeng; Qiu, Xiaotian; Zeng, Jian; Zhang, Qin

doi:10.1186/1471-2156-11-56

Cited by 21 publications

(22 citation statements)

References 34 publications

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“…A significant QTL for HGB was also mapped to the same region, suggesting a common genetic regulation of the four traits. The QTL affecting HGB overlaps with the region reported by Zou et al (2008), whereas the three QTL for MCV, MCH and MCHC overlap with the region reported by Gong et al (2010). This study revealed a highly significant QTL affecting MCV on SSC13, which accounted for 3.3% of the phenotypic variance and overlaps with the QTL for MCV identified by Gong et al (2010).…”

Section: Resultssupporting

confidence: 59%

“…In contrast, only a limited number of QTL analyses of haematological traits (i.e. leucocyte, platelet and erythroid traits) have been performed in pigs (Wattrang et al 2005;Reiner et al 2007Reiner et al , 2008Zou et al 2008;Yang et al 2009;Gong et al 2010). Leucocyte count is a potent indicator of defence-related disorders, such as infectious diseases, because leucocytes are a component of the immune system.…”

Section: Introductionmentioning

confidence: 99%

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QTL analysis of white blood cell, platelet and red blood cell-related traits in an F2 intercross between Landrace and Korean native pigs

et al. 2011

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Haematological traits play important roles in disease resistance and defence functions. The objective of this study was to locate quantitative trait loci (QTL) and the associated positional candidate genes influencing haematological traits in an F(2) intercross between Landrace and Korean native pigs. Eight blood-related traits (six erythrocyte traits, one leucocyte trait and one platelet trait) were measured in 816 F(2) progeny. All experimental animals were genotyped with 173 informative microsatellite markers located throughout the pig genome. We report that nine chromosomes harboured QTL for the baseline blood parameters: genomic regions on SSC 1, 4, 5, 6, 8, 9, 11, 13 and 17. Eight of twenty identified QTL reached genome-wide significance. In addition, we evaluated the KIT locus, an obvious candidate gene locus affecting variation in blood-related traits. Using dense single nucleotide polymorphism marker data on SSC 8 and the marker-assisted association test, the strong association of the KIT locus with blood phenotypes was confirmed. In conclusion, our study identified both previously reported and novel QTL affecting baseline haematological parameters in pigs. Additionally, the positional candidate genes identified here could play an important role in elucidating the genetic architecture of haematological phenotype variation in swine and in humans.

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Section: Resultssupporting

confidence: 59%

Section: Introductionmentioning

confidence: 99%

QTL analysis of white blood cell, platelet and red blood cell-related traits in an F2 intercross between Landrace and Korean native pigs

et al. 2011

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“…; Gong et al . ), suggesting that alleles of QTL for WBC may vary among breeds or populations. However, the QTL for WBC on SSC2, detected in our study, is located in a similar position to a QTL for WBC identified previously in a White Duroc × Erhualian F2 resource population (Yang et al .…”

Section: Discussionmentioning

confidence: 99%

“…Only one study, Gong et al . (), has detected QTL affecting immune response in a purebred population, and this study examined haematological traits.…”

Section: Introductionmentioning

confidence: 99%

A genome‐wide scan for quantitative trait loci affecting respiratory disease and immune capacity in Landrace pigs

et al. 2012

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Respiratory disease is the most important health concern for the swine industry. Genetic improvement for disease resistance is challenging because of the difficulty in obtaining good phenotypes related with disease resistance; however, identification of genes or markers associated with disease resistance can help in the genetic improvement of pig health. The purpose of our study was to investigate whether quantitative trait loci (QTL) associated with disease resistance were segregated in a purebred population of Landrace pigs that had been selected for meat production traits and mycoplasmal pneumonia of swine (MPS) scores over five generations. We analysed 1395 pigs from the base to the fifth generation of this population. Two respiratory disease traits [MPS scores and atrophic rhinitis (AR) scores] and 11 immune-capacity traits were measured in 630-1332 animals at 7 weeks of age and when the animal's body weight reached 105 kg. Each of the pigs, except sires in the base population, was genotyped using 109 microsatellite markers, and then, QTL analysis of the full-sib family population with a multi-generational pedigree structure was performed. Variance component analysis was used to detect QTL associated with MPS or AR scores, and the logarithm of odds (LOD) score and genotypic heritability of the QTL were estimated. Five significant (LOD > 2.51) and 18 suggestive (LOD > 1.35) QTL for respiratory disease traits and immune-capacity traits were detected. The significant QTL for Log-MPS score, located on S. scrofa chromosome 2, could explain 87% of the genetic variance of this score in this analysis. This is the first report of QTL associated with respiratory disease lesions.

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“…Here, the LR test statistic is defined as LR = L ( QTL )/ L ( polygenic ) where L(QTL) is the likelihood of the full model that includes the polygenic and QTL effects. The nominal P -value was estimated assuming the LR test statistic follows a chi-square distribution with two degrees of freedom [43]. The genome-wide significance level of detected QTL was also estimated as P GenomeWide = 1 − (1 − P ) g [37] where P is the nominal P -value, and g = 365 STR loci used with LDLA analysis.…”

Section: Methodsmentioning

confidence: 99%

QTL affecting stress response to crowding in a rainbow trout broodstock population

et al. 2012

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BackgroundGenomic analyses have the potential to impact selective breeding programs by identifying markers that serve as proxies for traits which are expensive or difficult to measure. Also, identifying genes affecting traits of interest enhances our understanding of their underlying biochemical pathways. To this end we conducted genome scans of seven rainbow trout families from a single broodstock population to identify quantitative trait loci (QTL) having an effect on stress response to crowding as measured by plasma cortisol concentration. Our goal was to estimate the number of major genes having large effects on this trait in our broodstock population through the identification of QTL.ResultsA genome scan including 380 microsatellite markers representing 29 chromosomes resulted in the de novo construction of genetic maps which were in good agreement with the NCCCWA genetic map. Unique sets of QTL were detected for two traits which were defined after observing a low correlation between repeated measurements of plasma cortisol concentration in response to stress. A highly significant QTL was detected in three independent analyses on Omy16, many additional suggestive and significant QTL were also identified. With linkage-based methods of QTL analysis such as half-sib regression interval mapping and a variance component method, we determined that the significant and suggestive QTL explain about 40-43% and 13-27% of the phenotypic trait variation, respectively.ConclusionsThe cortisol response to crowding stress is a complex trait controlled in a sub-sample of our broodstock population by multiple QTL on at least 8 chromosomes. These QTL are largely different from others previously identified for a similar trait, documenting that population specific genetic variants independently affect cortisol response in ways that may result in different impacts on growth. Also, mapping QTL for multiple traits associated with stress response detected trait specific QTL which indicate the significance of the first plasma cortisol measurement in defining the trait. Fine mapping these QTL can lead towards the identification of genes affecting stress response and may influence approaches to selection for this economically important stress response trait.

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

Cited by 21 publications

References 34 publications

QTL analysis of white blood cell, platelet and red blood cell-related traits in an F2 intercross between Landrace and Korean native pigs

QTL analysis of white blood cell, platelet and red blood cell-related traits in an F2 intercross between Landrace and Korean native pigs

A genome‐wide scan for quantitative trait loci affecting respiratory disease and immune capacity in Landrace pigs

QTL affecting stress response to crowding in a rainbow trout broodstock population

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