Abstract
Meta and/or combined QTL analysis from multiple studies can improve quantitative trait loci (QTL) position estimates compared to the individual experiments. Hereby we present results of a meta-analysis of QTL on chicken chromosome 9, 14 and 18 using data from three separate experiments and joint QTL analysis for chromosome 14 and 18. Meta QTL analysis uses information from multiple QTLs studies. Joint QTL analysis is based on combining raw data from different QTL experimental populations. QTLs under the study were related to specific antibody response to keyhole lymphet hemocyanin (KLH), and natural antibodies to environmental antigens, lipopolisaccharide (LPS) and lipoteichoic acid (LTA). Meta QTL analysis resulted in narrowing down the confidence interval for two QTLs on GGA14. The first one for natural antibodies against LTA and the second one for specific antibody response toward KLH. Also, a confidence interval of a QTL for natural antibodies against LPS located on GGA18 was narrowed down. Combined QTL analysis was successful for two QTLs: for specific antibody response toward KLH on GGA14, and for natural antibodies against LPS on GGA18. The greatest statistical power for QTL detection in joint analysis was achieved when raw data from segregating half–sib families from different populations under the study was used.
Highlights
The major goal of quantitative trait locus (QTL) analysis is to describe the genetic basis for a trait of interest
Meta quantitative trait loci (QTL) analysis was performed for QTLs on three chicken chromosomes: GGA9, GGA14 and GGA18 (Fig. 1)
The second meta QTL associated with LPS natural antibodies is located on GGA18 in marker bracket: ADL0304 – ADL0290
Summary
The major goal of quantitative trait locus (QTL) analysis is to describe the genetic basis for a trait of interest. The other alternative, joint QTL analysis, is based on combining individuals from several experimental populations, and increasing the number of individuals and the size of resource population. This approach has already been successfully used for joint QTL analysis in pigs (Rückert and Bennewitz 2010; Walling et al 2000) and dairy cattle (Bennewitz et al 2003). The goal of this study was to investigate the possibility to increase statistical power of QTLs and narrow down QTL confidence intervals by applying two statistical approaches: meta QTL and combined QTL analysis to QTLs for immune traits on three chicken
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