Power of quantitative trait locus mapping for polygenic binary traits using generalized and regression interval mapping in multi-family half-sib designs

A generalized interval mapping (GIM) method to map quantitative trait loci (QTL) for binary polygenic traits in a multi-family half-sib design is deve loped based on threshold theory and implemented using a Newton-Raphson algo rithm. Statistical power and bias of QTL mapping for binary traits by GIM i s compared with linear regression interval mapping (RIM) using simulation. Data on 20 paternal half-sib families were simulated with two genetic marke rs that bracketed an additive QTL. Data simulated and analysed were: (1) da ta on the underlying normally distributed liability (NDL) scale, (2) binary data created by truncating NDL data based on three thresholds yielding dat a sets with three different incidences, and (3) NDL data with polygenic and QTL. effects reduced by a proportion equal to the ratio of the heritabilit ies on the binary versus NDL scale (reduced-NDL). Binary data were simulate d with and without systematic environmental (herd) effects in an unbalanced design. GIM and RIM gave similar pourer to detect the QTL and similar esti mates of QTL location, effects and variances. Presence of fixed effects cau sed differences in bias between RIM and GIM, where GIM showed smaller bias which was affected less by incidence. The original NDL data had higher powe r and lower bias in QTL parameter estimates than binary and reduced-NDL dat a, RIM for reduced-NDL and binary data gave similar power and estimates of QTL parameters, indicating that the impact of the binary nature of data on QTL analysis is equivalent to its impact on heritability.