Genetic Mapping with Background Control for Quantitative Trait Locus (QTL) in 8-Parental Pure-Line Populations

Multiparental advanced generation intercross (MAGIC) populations provide abundant genetic variation for use in plant genetics and breeding. In this study, we developed a method for quantitative trait locus (QTL) detection in pure-line populations derived from 8-way crosses, based on the principles of inclusive composite interval mapping (ICIM). We considered 8 parents carrying different alleles with different effects. To estimate the 8 genotypic effects, 1-locus genetic model was first built. Then, an orthogonal linear model of phenotypes against marker variables was established to explain genetic effects of the locus. The linear model was estimated by stepwise regression and finally used for phenotype adjustment and background genetic variation control in QTL mapping. Simulation studies using 3 genetic models demonstrated that the proposed method had higher detection power, lower false discovery rate (FDR), and unbiased estimation of QTL locations compared with other methods. Marginal bias was observed in the estimation of QTL effects. An 8-parental recombinant inbred line (RIL) population previously reported in cowpea and analyzed by interval mapping (IM) was reanalyzed by ICIM and genome-wide association mapping implemented in software FarmCPU. The results indicated that ICIM identified more QTLs explaining more phenotypic variation than did IM; ICIM provided more information on the detected QTL than did FarmCPU; and most QTLs identified by IM and FarmCPU were also detected by ICIM.

Comparison of alternative models for personality disorders

Background. The categorical classification system for personality disorder (PD) has been frequently criticized and several alternative dimensional models have been proposed.Method. Antecedent, concurrent and predictive markers of construct validity were examined for three models of PDs: the Five-Factor Model (FFM), the Schedule for Nonadaptive and Adaptive Personality (SNAP) model and the DSM-IV in the Collaborative Study of Personality Disorders (CLPS) sample.Results. All models showed substantial validity across a variety of marker variables over time. Dimensional models (including dimensionalized DSM-IV) consistently outperformed the conventional categorical diagnosis in predicting external variables, such as subsequent suicidal gestures and hospitalizations. FFM facets failed to improve upon the validity of higher-order factors upon cross-validation. Data demonstrated the importance of both stable trait and dynamic psychopathological influences in predicting external criteria over time.Conclusions. The results support a dimensional representation of PDs that assesses both stable traits and dynamic processes.

Dyslexia Marker Variables

Despite warnings that the field of learning disabilities (LD) must address the issue of population heterogeneity, the LD research community still lacks operational definitions of specific learning disabilities as well as a systematic approach for reporting sample characteristics. Recently, however, a definition of dyslexia has been proposed that might signal a significant advance. This article builds on this definition of dyslexia by: (a) reintroducing the concept of marker variables, (b) proposing a strategy for developing an updated marker variable system, (c) presenting a preliminary working set of dyslexia marker variables, and (d) calling for the eventual adoption of this or similar marker variable systems to facilitate future research efforts.