Fine mapping and detection of the causative mutation underlying Quantitative Trait Loci

The effect on power and precision of including the causative SNP amongst the investigated markers in Quantitative Trait Loci (QTL) mapping experiments was investigated. Three fine mapping methods were tested to see which was most efficient in finding the causative mutation: combined linkage and linkage disequilibrium mapping (LLD); association mapping (MARK); a combination of LLD and association mapping (LLDMARK). Two simulated data sets were analysed: in one set, the causative SNP was included amongst the markers, while in the other set the causative SNP was masked between markers. Including the causative SNP amongst the markers increased both precision and power in the analyses. For the LLD method the number of correctly positioned QTL increased from 17 for the analysis without the causative SNP to 77 for the analysis including the causative SNP. The likelihood of the data analysis increased from 3.4 to 13.3 likelihood units for the MARK method when the causative SNP was included. When the causative SNP was masked between the analysed markers, the LLD method was most efficient in detecting the correct QTL position, while the MARK method was most efficient when the causative SNP was included as a marker in the analysis. The LLDMARK method, combining association mapping and LLD, assumes a QTL as the null hypothesis (using LLD method) and tests whether the ‘putative causative SNP’ explains significantly more variance than a QTL in the region. Thus, if the putative causative SNP does not only give an Identical‐By‐Descent (IBD) signal, but also an Alike‐In‐State (AIS) signal, LLDMARK gives a positive likelihood ratio. LLDMARK detected less than half as many causative SNPs as the other methods, and also had a relatively high false discovery rate when the QTL effect was large. LLDMARK may however be more robust against spurious associations, because the regional IBD is largely corrected for by fitting a QTL effect in the null hypothesis model.