Abstract
Genetic effect estimates for loci detected in quantitative trait locus (QTL) mapping experiments depend upon two factors. First, they are parameterizations of the genotypic values determined by the model of genetic effects. Second, they are consequently also affected by the regression method used to estimate the genotypic values from the observed marker genotypes and phenotypes. There are two common causes for marker-genotype data to be incomplete in those experiments—missing marker-genotypes and within-interval mapping. Different regression methods tend to differ in how this missing information is represented and handled. In this communication we explain why the estimates of genetic effects of QTL obtained using standard regression methods are not coherent with the model of genetic effects and indeed show intrinsic inconsistencies when there is incomeplete genotype information. We then describe the interval mapping by imputations (IMI) regression method and prove that it overcomes those problems. A numerical example is used to illustrate the use of IMI and the consequences of using current methods of choice. IMI enables researchers to obtain estimates of genetic effects that are coherent with the model of genetic effects used, despite incomplete genotype information. Furthermore, because IMI allows orthogonal estimation of genetic effects, it shows potential performance advantages for being implemented in QTL mapping tools.
Highlights
Quantitative trait locus (QTL) mapping experiments aim to detect loci that significantly contribute to the variance of a phenotype in a particular population [1]
The IMI method uses an imputation-regression approach for computing estimates of genetic effects of e.g. loci detected in a quantitative trait locus (QTL) mapping experiment
We have shown that a key difference between IMI and Haley-Knott regression (HKR) is that the latter implements the genotype probabilities into an incidence matrix (5) with multiple non-zero values per row
Summary
Quantitative trait locus (QTL) mapping experiments aim to detect loci that significantly contribute to the variance of a phenotype in a particular population [1]. In these experiments, models of genetic effects, called genotype-to-phenotype maps [2], enable researchers to evaluate the effects of allele substitutions in positions across the genome. Genotypes are missing and have to be inferred when inspecting loci within marker intervals, i.e. when performing interval mapping (IM) [8,9,10] In this communication we elaborate on how missing genotype information—regardless of its cause—distorts the estimates of genetic effects obtained in QTL mapping experiments when a standard regression method is used. We illustrate the advantages of this method using a numerical example and point out some convenient properties for its potential use in QTL mapping applications
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