The potential costs of accounting for genotypic errors in molecular parentage analyses

Genotypic errors, whether due to mutation or laboratory error, can cause the genotypes of parents and their offspring to appear inconsistent with Mendelian inheritance. As a result, molecular parentage analyses are expected to benefit when allowances are made for the presence of genotypic errors. However, a cost of allowing for genotypic errors might also be expected under some analytical conditions, primarily because parentage analyses that assume nonzero genotypic error rates can neither assign nor exclude parentage with certainty. The goal of this work was therefore to determine whether or not such costs might be important under conditions relevant to parentage analyses, particularly in natural populations. Simulation results indicate that the costs may often outweigh the benefits of accounting for nonzero error rates, except in situations where data are available for many marker loci. Consequently, the most powerful approach to handling genotypic errors in parentage analyses might be to apply likelihood equations with error rates set to values substantially lower than the rates at which genotypic errors occur. When applying molecular parentage analyses to natural populations, we advocate an increased consideration of optimal strategies for handling genotypic errors. Currently available software packages contain procedures that can be used for this purpose.     

Mitochondrial targeting signals and mature peptides of 3-methylcrotonyl-CoA carboxylase

Inherited deficiency of 3-methylcrotonyl-CoA carboxylase (MCC), an enzyme of leucine degradation, is an organic acidemia detectable by expanded newborn screening with a variable phenotype that ranges from asymptomatic to death in infancy. Here, we show that the two subunits of the enzyme (MCCalpha; MCCbeta) are imported into the mitochondrial matrix by the classical pathway involving cleavable amino-terminal targeting presequences. We identified the cleavage sites (Tyr41/Thr42 and Ala22/Tyr23 for MCCalpha and MCCbeta, respectively) of the targeting signals and the amino-termini of the mature polypeptides of MCC and propionyl-CoA carboxylase, a mitochondrial paralog. The amino-termini containing 39 (MCCalpha) or 20 amino acids (MCCbeta) were both necessary and sufficient for targeting. Structural requirements for mitochondrial import were defined by site-directed mutagenesis. Our studies provide the prerequisite to understand the impact of specific mutations on the clinical phenotype of MCC deficiency.     

The effect of intraspecific sample size on type I and type II error rates in comparative studies

Comparative studies have increased greatly in number in recent years due to advances in statistical and phylogenetic methodologies. For these studies, a trade-off often exists between the number of species that can be included in any given study and the number of individuals examined per species. Here, we describe a simple simulation study examining the effect of intraspecific sample size on statistical error in comparative studies. We find that ignoring measurement error has no effect on type I error of nonphylogenetic analyses, but can lead to increased type I error under some circumstances when using independent contrasts. We suggest using ANOVA to evaluate the relative amounts of within- and between-species variation when considering a phylogenetic comparative study. If within-species variance is particularly large and intraspecific sample sizes small, then either larger sample sizes or comparative methods that account for measurement error are necessary.     

A longitudinal measurement error model with a semicontinuous covariate

Covariate measurement error in regression is typically assumed to act in an additive or multiplicative manner on the true covariate value. However, such an assumption does not hold for the measurement error of sleep-disordered breathing (SDB) in the Wisconsin Sleep Cohort Study (WSCS). The true covariate is the severity of SDB, and the observed surrogate is the number of breathing pauses per unit time of sleep, which has a nonnegative semicontinuous distribution with a point mass at zero. We propose a latent variable measurement error model for the error structure in this situation and implement it in a linear mixed model. The estimation procedure is similar to regression calibration but involves a distributional assumption for the latent variable. Modeling and model-fitting strategies are explored and illustrated through an example from the WSCS.     

On the effect of misclassification on bias of perfectly measured covariates in regression

This note clarifies under what conditions a naive analysis using a misclassified predictor will induce bias for the regression coefficients of other perfectly measured predictors in the model. An apparent discrepancy between some previous results and a result for measurement error of a continuous variable in linear regression is resolved. We show that similar to the linear setting, misclassification (even when not related to the other predictors) induces bias in the coefficients of the perfectly measured predictors, unless the misclassified variable and the perfectly measured predictors are independent. Conditional and asymptotic biases are discussed in the case of linear regression, and explored numerically for an example relating birth weight to the weight and smoking status of the mother.     

MDR-like ABC transporter AtPGP4 is involved in auxin-mediated lateral root and root hair development

Previous data have suggested an involvement of MDR/PGP-like ABC transporters in transport of the plant hormone auxin and, recently, AtPGP1 has been demonstrated to catalyze the primary active export of auxin. Here we show that related isoform AtPGP4 is expressed predominantly during early root development. AtPGP4 loss-of-function plants reveal enhanced lateral root initiation and root hair lengths both known to be under the control of auxin. Further, atpgp4 plants show altered sensitivities toward auxin and the auxin transport inhibitor, NPA. Finally, mutant roots reveal elevated free auxin levels and reduced auxin transport capacities. These results together with yeast growth assays suggest a direct involvement of AtPGP4 in auxin transport processes controlling lateral root and root hair development.     

Predicting and correcting bias caused by measurement error in line transect sampling using multiplicative error models

Line transect sampling is one of the most widely used methods for animal abundance assessment. Standard estimation methods assume certain detection on the transect, no animal movement, and no measurement errors. Failure of the assumptions can cause substantial bias. In this work, the effect of error measurement on line transect estimators is investigated. Based on considerations of the process generating the errors, a multiplicative error model is presented and a simple way of correcting estimates based on knowledge of the error distribution is proposed. Using beta models for the error distribution, the effect of errors and of the proposed correction is assessed by simulation. Adequate confidence intervals for the corrected estimates are obtained using a bootstrap variance estimate for the correction and the delta method. As noted by Chen (1998, Biometrics 54, 899-908), even unbiased estimators of the distances might lead to biased density estimators, depending on the actual error distribution. In contrast with the findings of Chen, who used an additive model, unbiased estimation of distances, given a multiplicative model, lead to overestimation of density. Some error distributions result in observed distance distributions that make efficient estimation impossible, by removing the shoulder present in the original detection function. This indicates the need to improve field methods to reduce measurement error. An application of the new methods to a real data set is presented.