-Selection for increased resistance to Salmonella colonisation and excretion could reduce the risk of foodborne Salmonella infection. In order to identify potential loci affecting resistance, differences in resistance were identified between the N and 6 1 inbred lines and two QTL research performed. In an F2 cross, the animals were inoculated at one week of age with Salmonella enteritidis and cloacal swabs were carried out 4 and 5 wk post inoculation (thereafter called CSW4F2 and CSW4F2) and caecal contamination (CAECF2) was assessed 1 week later. The animals from the (N × 6 1 ) × N backcross were inoculated at six weeks of age with Salmonella typhimurium and cloacal swabs were studied from wk 1 to 4 (thereafter called CSW1BC to CSW4BC). A total of 33 F 2 and 46 backcross progeny were selectively genotyped for 103 and 135 microsatellite markers respectively. The analysis used least-squares-based and non-parametric interval mapping. Two genome-wise significant QTL were observed on Chromosome 1 for CSW2BC and on Chromosome 2 for CSW4F2, and four suggestive QTL for CSW5F2 on Chromosome 2, for CSW5F2 and CSW2BC on chromosome 5 and for CAECF2 on chromosome 16. These results suggest new regions of interest and the putative role of SAL1.
-Selection for increased resistance to Salmonella colonisation and excretion could reduce the risk of foodborne Salmonella infection. In order to identify potential loci affecting resistance, differences in resistance were identified between the N and 6 1 inbred lines and two QTL research performed. In an F2 cross, the animals were inoculated at one week of age with Salmonella enteritidis and cloacal swabs were carried out 4 and 5 wk post inoculation (thereafter called CSW4F2 and CSW4F2) and caecal contamination (CAECF2) was assessed 1 week later. The animals from the (N × 6 1 ) × N backcross were inoculated at six weeks of age with Salmonella typhimurium and cloacal swabs were studied from wk 1 to 4 (thereafter called CSW1BC to CSW4BC). A total of 33 F 2 and 46 backcross progeny were selectively genotyped for 103 and 135 microsatellite markers respectively. The analysis used least-squares-based and non-parametric interval mapping. Two genome-wise significant QTL were observed on Chromosome 1 for CSW2BC and on Chromosome 2 for CSW4F2, and four suggestive QTL for CSW5F2 on Chromosome 2, for CSW5F2 and CSW2BC on chromosome 5 and for CAECF2 on chromosome 16. These results suggest new regions of interest and the putative role of SAL1.
Most QTL mapping methods assume that phenotypes follow a normal distribution, but many phenotypes of interest are not normally distributed, e.g. bacteria counts (or colony-forming units, CFU). Such data are extremely skewed to the right and can present a high amount of zero values, which are ties from a statistical point of view. Our objective is therefore to assess the efficiency of four QTL mapping methods applied to bacteria counts : (1) least-squares (LS) analysis, (2) maximum-likelihood (ML) analysis, (3) non-parametric (NP) mapping and (4) nested ANOVA (AN). A transformation based on quantiles is used to mimic observed distributions of bacteria counts. Single positions (1 marker, 1 QTL) as well as chromosome scans (11 markers, 1 QTL) are simulated. When compared with the analysis of a normally distributed phenotype, the analysis of raw bacteria counts leads to a strong decrease in power for parametric methods, but no decrease is observed for NP. However, when a mathematical transformation (MT) is applied to bacteria counts prior to analysis, parametric methods have the same power as NP. Furthermore, parametric methods, when coupled with MT, outperform NP when bacteria counts have a very high proportion of zeros (70n8 %). Our results show that the loss of power is mainly explained by the asymmetry of the phenotypic distribution, for parametric methods, and by the existence of ties, for the non-parametric method. Therefore, mapping of QTL for bacterial diseases, as well as for other diseases assessed by a counting process, should focus on the occurrence of ties in phenotypes before choosing the appropriate QTL mapping method.
In QTL analysis of non-normally distributed phenotypes, non-parametric approaches have been proposed as an alternative to the use of parametric tests on mathematically transformed data. The non-parametric interval mapping test uses random ranking to deal with ties. Another approach is to assign to each tied individual the average of the tied ranks (midranks). This approach is implemented and compared to the random ranking approach in terms of statistical power and accuracy of the QTL position. Non-normal phenotypes such as bacteria counts showing high numbers of zeros are simulated (0-80% zeros). We show that, for low proportions of zeros, the power estimates are similar but, for high proportions of zeros, the midrank approach is superior to the random ranking approach. For example, with a QTL accounting for 8% of the total phenotypic variance, a gain from 8% to 11% of power can be obtained. Furthermore, the accuracy of the estimated QTL location is increased when using midranks. Therefore, if non-parametric interval mapping is chosen, the midrank approach should be preferred. This test might be especially relevant for the analysis of disease resistance phenotypes such as those observed when mapping QTLs for resistance to infectious diseases.
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