Estimating polygenic effects using markers of the entire genome.
Molecular markers have been used to map quantitative trait loci. However, they are rarely used to evaluate effects of chromosome segments of the entire genome. The original interval-mapping approach and various modified versions of it may have limited use in evaluating the genetic effects of the entire genome because they require evaluation of multiple models and model selection. Here we present a Bayesian regression method to simultaneously estimate genetic effects associated with markers of the entire genome. With the Bayesian method, we were able to handle situations in which the number of effects is even larger than the number of observations. The key to the success is that we allow each marker effect to have its own variance parameter, which in turn has its own prior distribution so that the variance can be estimated from the data. Under this hierarchical model, we were able to handle a large number of markers and most of the markers may have negligible effects. As a result, it is possible to evaluate the distribution of the marker effects. Using data from the North American Barley Genome Mapping Project in double-haploid barley, we found that the distribution of gene effects follows closely an L-shaped Gamma distribution, which is in contrast to the bell-shaped Gamma distribution when the gene effects were estimated from interval mapping. In addition, we show that the Bayesian method serves as an alternative or even better QTL mapping method because it produces clearer signals for QTL. Similar results were found from simulated data sets of F(2) and backcross (BC) families.
Xu
Shizhong
S
Department of Botany and Plant Sciences, University of California, Riverside, California 92521, USA. xu@genetics.ucr.edu
D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D005819:Genetic Markers; D016678:Genome; D001467:Hordeum / Q000235:genetics; D020412:Multifactorial Inheritance / Q000235:genetics*; D040641:Quantitative Trait Loci; D012044:Regression Analysis
D005819:Genetic Markers