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Conference abstract2013

Genetic analysis and replication of QTL effects on chicken body weight using a multi-locus introgression line

Ahsan, Muhammad; Ek, Weronika; Marklund, Stefan; Pettersson, Mats; Ragavendran, Ashok; Siegel, Paul B.; Muir, William; Carlborg, Örjan

Abstract

Quantitative Trait Loci (QTL) mapping is a useful initial step to dissect the geneticarchitecture of complex biological traits. Intercrosses between divergently selected lines ofanimals is a powerful approach to map loci that affect such traits individually or throughinteractions. The drawback with the approach is a low mapping resolution and consequentlyfurther fine mapping is needed for detailed studies of the mapped loci. We have earliermapped a network of four interacting loci that through reciprocal epistatic capacitationexplains nearly half of the difference in body weight between the Virginia High WeightSelected (HWS) and Low Weight Selected (LWS) chicken lines that after 50 generationsdisplay a 12-fold difference in weight at 56 days of age. The network has been replicated andfine-mapped in an eight-generation Advance Intercross Line (AIL) and its large effect onweight confirmed. A three-locus introgression line has been bred, where the LWS haplotypesat the three loci in the network with the strongest effects on weight have been simultaneouslyintrogressed into the HWS background. Here we describe the first results from a haplotypebasedassociation analysis in this multi-locus introgression line that again replicate the effectsof the introgressed loci on body-weight in the Virginia chicken lines, but also indicate anallelic heterogeneity at the loci within the founder-lines. Further analyses are in progress toevaluate the epistatic effects of the loci in this population as well as the functionalcontribution of the allelic heterogeneity.

Published in

Journal of Data Mining in Genomics and Proteomics
2013, Volume: 4, number: 5, pages: 83-83
Publisher: Omics group Inc.

Conference

International Conference on Functional and Comparative Genomics & Pharmacogenomics