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Biomedical subjects

H P Piepho

Publications and source records attributed to H P Piepho.

8 recordsLinked to original sources
PubMed2001

A quick method for computing approximate thresholds for quantitative trait loci detection.

This article proposes a quick method for computing approximate threshold levels that control the genome-wise type I error rate of tests for quantitative trait locus (QTL) detection in interval mapping (IM) and composite interval mapping (CIM). The procedure is completely general, allowing any population structure to be handled, e.g., BC(1), advanced backcross, F(2), and advanced intercross lines. Its main advantage is applicability in complex situations where no closed form approximate thresholds are available. Extensive simulations demonstrate that the method works well over a range of situations. Moreover, the method is computationally inexpensive and may thus be used as an alternative to permutation procedures. For given values of the likelihood-ratio (LR)-profile, computations involve just a few seconds on a Pentium PC. Computations are simple to perform, requiring only the values of the LR statistics (or LOD scores) of a QTL scan across the genome as input. For CIM, the window size and the position of cofactors are also needed. For the approximation to work well, it is suggested that scans be performed with a relatively small step size between 1 and 2 cM.

Biometry↗
PubMed2001

Marker pair selection for mapping quantitative trait loci.

Mapping of quantitative trait loci (QTL) for backcross and F(2) populations may be set up as a multiple linear regression problem, where marker types are the regressor variables. It has been shown previously that flanking markers absorb all information on isolated QTL. Therefore, selection of pairs of markers flanking QTL is useful as a direct approach to QTL detection. Alternatively, selected pairs of flanking markers can be used as cofactors in composite interval mapping (CIM). Overfitting is a serious problem, especially if the number of regressor variables is large. We suggest a procedure denoted as marker pair selection (MPS) that uses model selection criteria for multiple linear regression. Markers enter the model in pairs, which reduces the number of models to be considered, thus alleviating the problem of overfitting and increasing the chances of detecting QTL. MPS entails an exhaustive search per chromosome to maximize the chance of finding the best-fitting models. A simulation study is conducted to study the merits of different model selection criteria for MPS. On the basis of our results, we recommend the Schwarz Bayesian criterion (SBC) for use in practice.

Chromosome Mapping↗
PubMed2000

Optimal marker density for interval mapping in a backcross population.

An important question in QTL mapping is the optimal choice of marker density. Using analytical results, it is shown for the case of interval mapping in a backcross population, that the power of QTL detection and the standard errors of genetic effect estimates are little affected by an increase of marker density beyond 10 cM. This finding confirms published simulation results by other authors.

Analysis of Variance↗
PubMed2000

Codominant analysis of banding data from a dominant marker system by normal mixtures.

Amplified fragment length polymorphisms (AFLPs) currently are among the most widely used marker systems. In many studies, AFLPs are analyzed on the basis of the presence or absence of a band on an electrophoretic gel. As a result, dominant homozygous individuals are not distinguished from heterozygous individuals, resulting in a considerable loss of information. This article shows how codominant information can be obtained if the amount of PCR products is quantified. Due to measurement variation, genotyping on the basis of such information is not error-free. We propose use of normal mixture distributions to determine the most likely genotype, given the data. The method is exemplified using AFLP data from sugar beet.

Analysis of Variance↗
PubMed2000

A mixed-model approach to mapping quantitative trait loci in barley on the basis of multiple environment data.

In this article, I propose a mixed-model method to detect QTL with significant mean effect across environments and to characterize the stability of effects across multiple environments. I demonstrate the method using the barley dataset by the North American Barley Genome Mapping Project. The analysis raises the need for mixed modeling in two different ways. First, it is reasonable to regard environments as a random sample from a population of target environments. Thus, environmental main effects and QTL-by-environment interaction effects are regarded as random. Second, I expect a genetic correlation among pairs of environments caused by undetected QTL. I show how random QTL-by-environment effects as well as genetic correlations are straightforwardly handled in a mixed-model framework. The main advantage of this method is the ability to assess the stability of QTL effects. Moreover, the method allows valid statistical inferences regarding average QTL effects.

Crosses, Genetic↗
PubMed1999

Fitting a regression model for genotype-by-environment data on heading dates in grasses by methods for nonlinear mixed models.

The analysis of agricultural crop variety trials is usually complicated by the presence of genotype-by-environment interaction. A number of methods and models have been proposed to tackle this problem. One of the most common methods is the regression approach due to Yates and Cochran (1938, Journal of Agricultural Science 28, 556-580), in which performances of genotypes in the environments are regressed onto environmental means. The underlying regression model contains a multiplicative term with two unknown parameters (one for genotypes and one for environments). In the present paper, the model is modified by exchanging the role of genotypes and environments. Various diagnostic plots show that this modified model is adequate for a data set on heading dates in the grass species Dactylis glomerata. If environments are considered as a random factor while genotypes are taken as fixed, the model falls into the class of nonlinear mixed models. Recently, a number of procedures have been suggested for this class of models, which are based on first-order Taylor series expansion. Alternatively, the model can be estimated by maximum likelihood. This paper discusses the application of these methods for estimating parameters of the model.

Agriculture↗