Literature DB >> 14717541

Detection of different quantitative trait loci for antibody responses to keyhole lympet hemocyanin and Mycobacterium butyricum in two unrelated populations of laying hens.

M Siwek1, A J Buitenhuis, S J B Cornelissen, M G B Nieuwland, H Bovenhuis, R P M A Crooijmans, M A M Groenen, G de Vries-Reilingh, H K Parmentier, J J van der Poel.   

Abstract

Quantitative trait loci involved in the primary antibody response to keyhole lympet hemocyanin (KLH) and Mycobacterium butyricum were detected in two independent populations of laying hens. The first population was an F2 cross (H/L) of lines divergently selected for either high or low primary antibody responses to SRBC, and the second population was an F2 cross between 2 commercial layer lines displaying differences in feather pecking behavior (FP). Both populations were typed with microsatellite markers widely distributed over the genome with similar intervals between markers. Titers of antibodies binding KLH and M. butyricum were measured for all individuals by ELISA. Two genetic models were applied to detect QTL involved in the humoral immune response: a half-sib model and a line-cross model, both using the regression interval method. In the half-sib analysis, 2 QTL (on GGA14 and GGA27) were detected for the antibody response to KLH for the H/L population, and 2 QTL (on GGA14 and GGA18) were detected for the FP population. Only 1 QTL was detected for M. butyricum on GGA14 in the FP population using the half-sib analysis model. Two QTL were detected for the FP population on GGA2 and GGA3 using the line-cross analysis model. A QTL for the primary antibody response to KLH detected on GGA14 was validated in both populations under the half-sib analysis model. The present data suggest differences in the genetic regulation of antibody responses to two different T-cell dependent antigens.

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Year:  2003        PMID: 14717541     DOI: 10.1093/ps/82.12.1845

Source DB:  PubMed          Journal:  Poult Sci        ISSN: 0032-5791            Impact factor:   3.352


  8 in total

1.  New QTL for resistance to Salmonella carrier-state identified on fowl microchromosomes.

Authors:  Fanny Calenge; Alain Vignal; Julie Demars; Katia Fève; Pierrette Menanteau; Philippe Velge; Catherine Beaumont
Journal:  Mol Genet Genomics       Date:  2011-01-30       Impact factor: 3.291

2.  The growing feather as a dermal test site: Comparison of leukocyte profiles during the response to Mycobacterium butyricum in growing feathers, wattles, and wing webs.

Authors:  G F Erf; I R Ramachandran
Journal:  Poult Sci       Date:  2016-04-14       Impact factor: 3.352

3.  Detection of two QTL on chicken chromosome 14 for keyhole lymphet haemocyanin.

Authors:  Maria Siwek; Joanna Szyda; Anna Sławińska; Marek Bednarczyk
Journal:  J Appl Genet       Date:  2011-11-03       Impact factor: 3.240

4.  The identification of loci for immune traits in chickens using a genome-wide association study.

Authors:  Lei Zhang; Peng Li; Ranran Liu; Maiqing Zheng; Yan Sun; Dan Wu; Yaodong Hu; Jie Wen; Guiping Zhao
Journal:  PLoS One       Date:  2015-03-30       Impact factor: 3.240

5.  Meta - and combined - QTL analysis of different experiments on immune traits in chickens.

Authors:  Anna Slawinska; Maria Siwek
Journal:  J Appl Genet       Date:  2013-10-11       Impact factor: 3.240

6.  A genome-wide association study identifies major loci affecting the immune response against infectious bronchitis virus in chicken.

Authors:  Chenglong Luo; Hao Qu; Jie Ma; Jie Wang; Xiaoxiang Hu; Ning Li; Dingming Shu
Journal:  Infect Genet Evol       Date:  2013-12-11       Impact factor: 3.342

7.  Correlated effects of selection for immunity in White Leghorn chicken lines on natural antibodies and specific antibody responses to KLH and M. butyricum.

Authors:  Giulietta Minozzi; Henk K Parmentier; Sandrine Mignon-Grasteau; Mike Gb Nieuwland; Bertrand Bed'hom; David Gourichon; Francis Minvielle; Marie-Helen Pinard-van der Laan
Journal:  BMC Genet       Date:  2008-01-14       Impact factor: 2.797

8.  Sensitivity analysis based on the random forest machine learning algorithm identifies candidate genes for regulation of innate and adaptive immune response of chicken.

Authors:  Aneta Polewko-Klim; Wojciech Lesiński; Agnieszka Kitlas Golińska; Krzysztof Mnich; Maria Siwek; Witold R Rudnicki
Journal:  Poult Sci       Date:  2020-09-12       Impact factor: 3.352

  8 in total

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