Identification of QTL and Genes for Resistance to Marek´s Disease in Advanced Intercross Lines

Date of publication : 8/15/2008
Source : Iowa State University and USDA Poultry Science Day
An advanced intercross (F6) between two commercial layer lines was used to map genes associated with survival to Marek’s disease using candidate gene and quantitative trait locus mapping approaches. After further validation and fine-mapping, the identified genes and genomic regions can be used to select for increased resistance to Marek’s disease using marker-assisted selection approaches.


Introduction

Marek’s disease (MD) is the most serious chronic viral disease in the chicken industry. Although major advances in controlling this disease have been made through management and immunization, increasing virulence continues to pose problems. Finding genes that affect resistance to MD will enable more efficient genetic selection for resistance and give insight on disease pathways and resistance mechanisms. Genetic resistance will increase animal welfare, food safety and profitability. Objectives of this research were to utilize a unique population established by Hy-Line International to identify genomic regions associated with survival to MD. Both candidate gene and quantitative trait locus (QTL) mapping approaches were used.


Materials and Methods


Population
Two partially inbred commercial Leghorn lines that differed in resistance to MD were intercrossed within 5
families for 6 generations. Each family originated from single pair mating of a single male from Line 1 (known
susceptible to MD) and a single female from Line 2 (known resistant to MD). A total of 1615 F6 chicks and parental line controls were challenged with highly virulent (vv+) Marek’s disease virus (strain 648A) at 7 days of age and monitored for length of survival (up to 18 weeks) in two hatches.


Candidate Gene Analyses
The Rh-associated glycoprotein (RHAG) gene on GGA03, which is a candidate gene for blood group P (CPPP), and the osteopontin (OPN) gene on GGA04, were identified as potential candidate genes. One cnSNP in RHAG and three SNPs in OPN were selectively genotyped on the 20% short and long survivors within each family, and analyzed for associations with MD survival using a survival analysis model.


QTL Analyses
The F6 population was also used as a resource to map QTL conferring resistance to MD using selective DNA pooling. A total of 32 pools representing 20% birds within each family by blood group combination were formed and genotyped for a total of 217 microsatellite and 15 SNP markers. Densitometric genotyping was repeated for 35 markers that showed substantial differences in frequency between high and low survival pools. The correlation between repeated measures was 0.93, showing good accuracy. Five statistical methods were used for analysis of the data, including tests that looked for QTL with consistent effects across families and blood groups and tests that allowed for interactions between QTL, family, and blood group.


Results


Candidate Gene Analyses
The SNP in RHAG showed a significant effect on survival (P < 0.005) that was consistent across families. The three SNPs in the OPN gene did not show significant (P > 0.1) results when analyzed separately and yielded only borderline significance (P < 0.05) when analyzed as a haplotype. Thus, RHAG is associated with MD resistance but an association of OPN with MD resistance in these data is unlikely.


QTL Analyses
A total of 11 putative QTL distributed among 8 chromosomes affecting survival to MD challenge were identified at a 20% proportion of false positives significance threshold. Eight of these represent QTL that had strong main effects that came to consistent expression in the different families and blood types. Three of the putative QTL showed strong interaction effects. Of the 11 putative QTL, 10 had overall effects such that the allele derived from the more susceptible line was also the allele found at higher frequency in the susceptible pools. Thus, these results are consistent with the line differences. The one QTL that showed the opposite behavior may represent an example of “cryptic genetic variation;” such examples are often uncovered in QTL mapping studies.


Discussion

The 8 main effect QTL uncovered in this study should provide a strong platform for comparative positional cloning after confirmation of the associations by individual genotyping of the pools. Comparative functional genomics based on the complete chicken genome sequence could be used to identify candidate genes in the identified QTL containing chromosomal regions. These candidate genes can be screened further by examining their mRNA expression pattern under MDV challenge using existing data banks.


Acknowledgements

This research was financially supported by Vaadia-BARD Postdoctoral Fellowship Award No. PD FI-350-2003, a grant from the Midwest Poultry Consortium, and by Hy-Line Int. All bird rearing, trait data collection, DNA collection and pooling, and genotyping of microsatellite markers were conducted at Hy-Line Int. Genotyping of candidate genes was conducted at USDA-ARS-ADOL.


By Eli M. Heifetz (post-doctoral fellow, Animal Science); Janet E. Fulton (Hy-Line International); Neil O’Sullivan (Hy-Line International); Hans Cheng (USDA-ARS-ADOL, East Lansing); Morris Soller( professor, Hebrew University of Jerusalem); Jack C. M. Dekkers (professor of Animal Science)

A.S. Leaflet R2212 - Iowa State University and USDA Poultry Science Day Report

 
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