Mapping quantitative trait loci for economic traits in chickens

Abstract

All rights reserved. INFORMATION TO USERSThe quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleed-through, substandard margins, and improper alignment can adversely affect reproduction.In the unlikely event that the author did not send a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. Table 1 Table 2 CHAPTER 2B Table 1 Table 2 Table 3 Significant QTL detected at the 5% chromosome-wise 58Level Table 1 False positive rates for the linear regression and Cox's 117proportional hazards models ABSTRACTIdentification of genomic regions harboring genes affecting economic traits is the primary step for the improvement of agricultural species through marker-assisted selection. Many important traits are difficult and expensive to measure, and, thus, selection can be improved by selecting directly upon genomic regions affecting these traits (QTL). White meat percentage (WM%) and Marek's disease (MD) resistance are examples of important traits in commercial chickens. One objective of the research presented herein was to identify QTL affecting WM%, other growth-related traits, and MD resistance. Another objective was to compare statistical models for identifying these QTL. Data (phenotypes and genetic markers) on an F2 broiler (meat-type chicken) cross were analyzed using half-sib, line cross, combined, and parent of origin models to identify QTL affecting WM% and other growth related traits. Sixty-eight QTL were identified at the 5% chromosome-wise level, including six QTL affecting WM% and 20 putative imprinted QTL. The use of multiple segregation and expression models proved to be beneficial for identifying QTL. A commercial egg-layer backcross was used to identify QTL affecting MD resistance based on marker genotypes of long and short survivors (selective genotyping). Seventeen markers associated with MD survival were identified at P < 0.10 using linear regression (LR) and Cox proportional hazards (CPH) models. Using simulated data reflecting the MD virus-challenged population, analyses using LR, CPH, and Weibull models were compared. Little difference in power was found between the CPH and the LR model when few individuals survived to the end of the experimental period (low censoring) and when all or selected individuals were genotyped. The simulated data did not follow a Weibull distribution, and thus the Weibull model generally resulted in less power than the other two models. The LR model was recommended for analyzing survival data when the amount of censoring is low because of the ease of implementation of the model and interpretation of estimates. Including nongenotyped individuals in the selective genotyping analysis increased power, but resulted in LR having an inflated false positive rate. The QTL identified in this research can be an integral step for the improvement...