Abstract
Identification of QTL affecting a phenotype which is measured multiple times on the same experimental unit is not a trivial task because the repeated measures are not independent and in most cases show a trend in time. A complicating factor is that in most cases the mean increases non-linear with time as well as the variance. A two- step approach was used to analyze a simulated data set containing 1000 individuals with 5 measurements each. First the measurements were summarized in latent variables and subsequently a genome wide analysis was performed of these latent variables to identify segregating QTL using a Bayesian algorithm. For each individual a logistic growth curve was fitted and three latent variables: asymptote (ASYM), inflection point (XMID) and scaling factor (SCAL) were estimated per individual. Applying an 'animal' model showed heritabilities of approximately 48% for ASYM and SCAL while the heritability for XMID was approximately 24%. The genome wide scan revealed four QTLs affecting ASYM, one QTL affecting XMID and four QTLs affecting SCAL. The size of the QTL differed. QTL with a larger effect could be more precisely located compared to QTL with small effect. The locations of the QTLs for separate parameters were very close in some cases and probably caused the genetic correlation observed between ASYM and XMID and SCAL respectively. None of the QTL appeared on chromosome five. Repeated observations on individuals were affected by at least nine QTLs. For most QTL a precise location could be determined. The QTL for the inflection point (XMID) was difficult to pinpoint and might actually exist of two closely linked QTL on chromosome one.
Highlights
Identification of QTL affecting a phenotype which is measured multiple times on the same experimental unit is not a trivial task because the repeated measures are not independent and in most cases show a trend in time
Yang and Xu [2] applied functional mapping using a Bayesian shrinkage analyses with Legendre polynomials, which has the advantage that it will fit any trend in time but can be harder to interpret biologically
The objective of this study was to identify segregating QTL affecting a simulated phenotype that was repeatedly measured on each individual, using a Bayesian algorithm
Summary
Identification of QTL affecting a phenotype which is measured multiple times on the same experimental unit is not a trivial task because the repeated measures are not independent and in most cases show a trend in time. A complicating factor is that in most cases the mean increases non-linear with time as well as the variance. A complicating factor is that in most cases the mean increases non-linear with time as well as the variance, e.g. growth or yield. Another example is behavior, where a questionnaire involving many items is used to describe the phenotype, e.g. aggression. In this case multiple measurements have to be combined in order to detect QTL affecting such a trait. Yang and Xu [2] applied functional mapping using a Bayesian shrinkage analyses with Legendre polynomials, which has the advantage that it will fit any trend in time but can be harder to interpret biologically
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