Estimations of the efficacy and reliability of paternity assignments from DNA microsatellite analysis of multiple-sire matings

It is important for bovine DNA testing laboratories to provide the cattle industry with accurate estimates of the efficacy and reliability of DNA tests offered so that end users of this technology can adequately assess the cost-benefits of testing. To address these issues for bovine paternity testing, paternity exclusion probability estimates were obtained from breed panel data and were predictive of the efficacy of the DNA tests used in 39 multiple-sire mating groups, involving 5960 calves and 505 bulls. Paternity testing of these mating groups has demonstrated that the majority involve a variable proportion of unknown sires and this impacts on the reliability of sire allocation. Mathematical models based on binomial or beta-binomial probability distributions were used to estimate the reliability of single-sire allocations from multiple-sire matings involving unknown sires. Reliability of 98-99% is achieved when the exclusion probability is 0.99 or greater, after allowing for up to 20% unknown sires. When the exclusion probability drops below 0.90 and there are 20% unknown sires, the reliability is poor, bringing into question the benefits of testing. This highlights the need for DNA testing laboratories to offer paternity tests with an exclusion power of at least 99%.     

Exclusion probabilities of 22 bovine microsatellite markers in fluorescent multiplexes for semiautomated parentage testing

Six multiplexes developed for semiautomated fluorescence genotyping were evaluated for parentage testing. These multiplexes contained primer pairs for the amplification of 22 microsatellites on 17 bovine autosomes. Exclusion probabilities were determined from genotypes of 1022 Holstein cattle and 311 beef cattle belonging to five breeds. Two cases were considered: case 1, genotypes are known for an alleged parent and an offspring but genotypes of a confirmed parent are unknown; and case 2, genotypes are known for an alleged parent, a confirmed parent and an offspring. If the alleged parent is not the true parent, then the 22 markers will exclude the alleged parent with a probability of >0·9986 for case 1 and with a probability of >0·99999 for case 2. On the basis of these exclusion probabilities, the probability that an alleged parent will be falsely included as a parent is in the range of 1/716 to 1/2845 for case 1 and 1/1·2 million to 1/159753 for case 2. In addition to these results, a rapid and efficient non-organic method for extraction of DNA from semen is described.     

A comparison of alternative methods to compute conditional genotype probabilities for genetic evaluation with finite locus models

An increased availability of genotypes at marker loci has prompted the development of models that include the effect of individual genes. Selection based on these models is known as marker-assisted selection (MAS). MAS is known to be efficient especially for traits that have low heritability and non-additive gene action. BLUP methodology under non-additive gene action is not feasible for large inbred or crossbred pedigrees. It is easy to incorporate non-additive gene action in a finite locus model. Under such a model, the unobservable genotypic values can be predicted using the conditional mean of the genotypic values given the data. To compute this conditional mean, conditional genotype probabilities must be computed. In this study these probabilities were computed using iterative peeling, and three Markov chain Monte Carlo (MCMC) methods – scalar Gibbs, blocking Gibbs, and a sampler that combines the Elston Stewart algorithm with iterative peeling (ESIP). The performance of these four methods was assessed using simulated data. For pedigrees with loops, iterative peeling fails to provide accurate genotype probability estimates for some pedigree members. Also, computing time is exponentially related to the number of loci in the model. For MCMC methods, a linear relationship can be maintained by sampling genotypes one locus at a time. Out of the three MCMC methods considered, ESIP, performed the best while scalar Gibbs performed the worst.     

Polymorphic microsatellite loci for paternity analysis in the Madagascar paradise flycatcher (Terpsiphone mutata: Aves)

Nine polymorphic microsatellite loci from the Madagascar paradise flycatcher Terpsiphone mutata were isolated using nonradioactive polymerase chain reaction (PCR)‐based techniques to screen an enriched genomic library. Seven polymorphic loci showed no evidence of null alleles and exhibited high levels of variation in 18 unrelated individuals (mean diversity = 0.80, mean number of alleles = 13.6). These loci are therefore suitable for analysis of population structure and paternity (exclusion probability for six unlinked loci = 0.9998).     

Eleven polymorphic microsatellite markers for paternity analysis in the pectoral sandpiper,Calidris melanotos

Eleven polymorphic microsatellite markers were isolated for the pectoral sandpiper, Calidris melanotos. The number of alleles observed in a sample of 149 presumably unrelated adults ranged from nine to 23 with an observed heterozygosity ranging from 0.79 to 0.92. The set of markers described here will prove useful for accurately determining paternity and therefore elucidate the hitherto unknown mating system of this species. We also report on cross‐species amplification of these markers in the semipalmated sandpiper, Calidris pusilla.     

Microsatellite loci for paternity analysis in the fathead minnow,Pimephales promelas (Teleostei: Cyprinidae)

Many recent studies have employed molecular markers to uncover important aspects of mating systems in teleost fishes. The fathead minnow (Pimephales promelas) is a nest‐building North American cyprinid that spawns multiply and exhibits exclusive male parental care. A battery of microsatellite markers was developed to analyse paternity in this species. The seven characterized loci possess four to 31 alleles and expected heterozygosities of 0.455–0.974. In combination, they elicit an exclusion probability of 0.999, a desirable level for paternity analysis. In addition, cross‐amplifications were conducted to test primer efficacy in 13 other taxa, including two congeners.     

Ten polymorphic microsatellite DNA loci for paternity and population genetics analysis in the fen raft spider (Dolomedes plantarius)

Ten polymorphic di‐ and trinucleotide microsatellite loci were developed in the fen raft spider Dolomedes plantarius from a partial phagemid genomic library enriched for microsatellite inserts. The expected heterozygosity at these loci ranges from 0.62 to 0.9, with the observed allele numbers varying from four to 15 in the 22 individuals tested. Average paternity exclusion probabilities ranged between 0.290 and 0.686. In combination, the 10 polymorphic loci elicit an exclusion probability of 0.999. The high level of polymorphism of these microsatellite loci makes them ideal genetic markers for paternity and population genetics analysis in this endangered species.     

Quantitative polymerase chain reaction analysis of DNA from noninvasive samples for accurate microsatellite genotyping of wild chimpanzees (Pan troglodytes verus)

Noninvasive samples are useful for molecular genetic analyses of wild animal populations. However, the low DNA content of such samples makes DNA amplification difficult, and there is the potential for erroneous results when one of two alleles at heterozygous microsatellite loci fails to be amplified. In this study we describe an assay designed to measure the amount of amplifiable nuclear DNA in low DNA concentration extracts from noninvasive samples. We describe the range of DNA amounts obtained from chimpanzee faeces and shed hair samples and formulate a new efficient approach for accurate microsatellite genotyping. Prescreening of extracts for DNA quantity is recommended for sorting of samples for likely success and reliability. Repetition of results remains extensive for analysis of microsatellite amplifications beginning from low starting amounts of DNA, but is reduced for those with higher DNA content.     

A simple approach to guide factor retention decisions when applying principal component analysis to biomechanical data

The use of principal component analysis (PCA) as a multivariate statistical approach to reduce complex biomechanical data-sets is growing. With its increased application in biomechanics, there has been a concurrent divergence in the use of criteria to determine how much the data is reduced (i.e. how many principal factors are retained). This short communication presents power equations to support the use of a parallel analysis (PA) criterion as a quantitative and transparent method for determining how many factors to retain when conducting a PCA. Monte Carlo simulation was used to carry out PCA on random data-sets of varying dimension. This process mimicked the PA procedure that would be required to determine principal component (PC) retention for any independent study in which the data-set dimensions fell within the range tested here. A surface was plotted for each of the first eight PCs, expressing the expected outcome of a PA as a function of the dimensions of a data-set. A power relationship was used to fit the surface, facilitating the prediction of the expected outcome of a PA as a function of the dimensions of a data-set. Coefficients used to fit the surface and facilitate prediction are reported. These equations enable the PA to be freely adopted as a criterion to inform PC retention. A transparent and quantifiable criterion to determine how many PCs to retain will enhance the ability to compare and contrast between studies.     

Pedigree-based assignment tests for reversing coyote (Canis latrans) introgression into the wild red wolf (Canis rufus) population

The principal threat to the persistence of the endangered red wolf (Canis rufus) in the wild is hybridization with the coyote (Canis latrans). To facilitate idengification and removal of hybrids, assignment tests are developed which use genotype data to estimate identity as coyote, 1/4, 1/2, 3/4 or full red wolf. The tests use genotypes from the red wolves that founded the surviving population and the resulting pedigree, rather than a contemporary red wolf sample. The tests are evaluated by analysing both captive red wolves at 18 microsatellite loci, and data simulated under a highly parameterized, biologically reasonable model. The accuracy of assignment rates are generally high, with over 95% of known red wolves idengified correctly. There are, however, tradeoffs between ambiguous assignments and misassignments, and between misidengifying red wolves as hybrids and hybrids as red wolves. These result in a compromise between limiting introgression and avoiding demographic losses. The management priorities and level of introgression determine the combination of test and removal strategy that best balances these tradeoffs. Ultimately, we conclude that the use of the assignment tests has the capacity to arrest and reverse introgression. To our knowledge, the presented approach is novel in that it accounts for genetic drift when the genotypes under analysis are temporally separated from the reference populations to which they are being assigned. These methods may be valuable in cases where reference databases for small populations have aged substantially, pedigree information is available or data are generated from historical samples.     

Quasi-multiparameter sensitivity measure for robustness analysis of complex biochemical networks

Robustness is the ability to resume reliable operation in the face of different types of perturbations. Analysis of how network structure achieves robustness enables one to understand and design cellular systems. It is typically true that all parameters simultaneously differ from their nominal values in vivo, but there have been few intelligible measures to estimate the robustness of a system's function to the uncertainty of all parameters.We propose a numerical and fast measure of a robust property to the uncertainty of all kinetic parameters, named quasi-multiparameter sensitivity (QMPS), which is defined as the sum of the squared magnitudes of single-parameter sensitivities. Despite its plain idea, it has hardly been employed in analysis of biological models. While QMPS is theoretically derived as a linear model, QMPS can be consistent with the expected variance simulated by the widely used Monte Carlo method in nonlinear biological models, when relatively small perturbations are given. To demonstrate the feasibility of QMPS, it is employed for numerical comparison to analyze the mechanism of how specific regulations generate robustness in typical biological models.QMPS characterizes the robustness much faster than the Monte Carlo method, thereby enabling the extensive search of a large parameter space to perform the numerical comparison between alternative or competing models. It provides a theoretical or quantitative insight to an understanding of how specific network structures are related to robustness. In circadian oscillators, a negative feedback loop with multiple phosphorylations is demonstrated to play a critical role in generating robust cycles to the uncertainty of multiple parameters.     

A comparison of five distance‐based methods for spatial pattern analysis

Abstract. The behaviour of five statistics (extensions of Pielou's, Clark and Evansapos;, Pollard's, Johnson & Zimmer's, and Eberhardt's statistics, which are denoted as P_i, C_e, P_o, J_z and E_b respectively) that involve the distance from a random point to its jth nearest neighbour were examined against several alternative patterns (lattice‐based regular, inhomogeneous random, and Poisson cluster patterns) through Monte Carlo simulation to test their powers to detect patterns. The powers of all the five statistics increase as distance order j increases against inhomogeneous random pattern. They decrease for P_i and C_e and increase for P_o, J_z, and E_b against regular and Poisson cluster patterns. P_o, J_z, and E_b can reach high powers with the third or higher order distances in most cases. However, P_o is recommended because no extra information is needed, it can reach a high power with the second or third distance even though the sample size is not large in most cases, and the test can be performed with an approximate χ² distribution associated with it. When a regular pattern is expected, J_z is recommended because it is more sensitive to lattice‐based regular pattern than P_o and E_b, especially for the first distance. However, simulation tests should be used because the speed of convergence of J_z to normal distribution is very slow.     

Comparison of statistical methods for the analysis of limiting dilution assays

Summary This study reports the results of a critical comparison of five statistical methods for estimating the density of viable cells in a limiting dilution assay (LDA). Artificial data were generated using Monte Carlo simulation. The performance of each statistical method was examined with respect to the accuracy of its estimator and, most importantly, the accuracy of its associated estimated standard error (SE). The regression method was found to perform at a level that is unacceptable for scientific research, due primarily to gross underestimation of the SE. The maximum likelihood method exhibited the best overall performance. A corrected version of Taswell's weighted-mean method, which provides the best performance among all noniterative methods examined, is also presented.     

On the use of split moving window analysis for boundary detection in ordered dataseries from benthic communities

The capacity of Split Moving Window (SMW) boundary analysis was tested to detect the depth of the transition of a littoral to a profundal invertebrate community in two datasets that were ordered along a depth gradient. The littoral community was dominated byPolypedilum nubeculosum agg.,Potamopyrgus jenkinsi andGlyptotendipes cf.pallens. The species richness of the profundal community was much lower. Its fauna was dominated byChironomus cf.plumosus andChaoborus flavicans. The transition could be detected with small window sizes. The discontinuities are located at the same depth as the oxygen stratification. We conclude that SMW can be used to study artificial transects. Noisy datasets should be transformed to reduce the variation. The scale dependence of SMW is an advantage. Information about the nature of a discontinuity (transition, trend or local discontinuity) can be obtained by increasing the window size. The location of discontinuities that are detected at the edge of a series, with large windows, should be interpreted carefully due to the existence of a blind zone.     

Proper multivariate conditional autoregressive models for spatial data analysis

In the past decade conditional autoregressive modelling specifications have found considerable application for the analysis of spatial data. Nearly all of this work is done in the univariate case and employs an improper specification. Our contribution here is to move to multivariate conditional autoregressive models and to provide rich, flexible classes which yield proper distributions. Our approach is to introduce spatial autoregression parameters. We first clarify what classes can be developed from the family of Mardia (1988) and contrast with recent work of Kim et al. (2000). We then present a novel parametric linear transformation which provides an extension with attractive interpretation. We propose to employ these models as specifications for second-stage spatial effects in hierarchical models. Two applications are discussed; one for the two-dimensional case modelling spatial patterns of child growth, the other for a four-dimensional situation modelling spatial variation in HLA-B allele frequencies. In each case, full Bayesian inference is carried out using Markov chain Monte Carlo simulation.     

Testing for genetic evidence of population expansion and contraction: an empirical analysis of microsatellite DNA variation using a hierarchical Bayesian model

The role of past climatic change in shaping the distributions of tropical rain forest vertebrates is central to long-standing hypotheses about the legacy of the Quaternary ice ages. One approach to testing such hypotheses is to use genetic data to infer the demographic history of codistributed species. Population genetic theory that relates the structure of allelic genealogies to historical changes in effective population size can be used to detect a past history of demographic expansion or contraction. The fruit bats Cynopterus sphinx and C. brachyotis (Chiroptera: Pteropodidae) exhibit markedly different distribution patterns across the Indomalayan region and therefore represent an exemplary species pair to use for such tests. The purpose of this study was to test alternative hypotheses about historical patterns of demographic expansion and contraction in C. sphinx and C. brachyotis using a coalescent-based analysis of microsatellite variation. Specifically, we used a hierarchical Bayesian model based on Markov chain Monte Carlo simulations to estimate the posterior distribution of genealogical and demographic parameters. The results revealed strong evidence for population contraction in both species. Evidence for a population contraction in C. brachyotis was expected on the basis of biogeographic considerations. However, similar evidence for population contraction in C. sphinx does not support the hypothesis that this species underwent a pronounced range expansion during the late Quaternary. Genetic evidence for population decline may reflect the consequences of habitat destruction on a more recent time scale.     

A new mixed‐effects regression model for the analysis of zero‐modified hierarchical count data

Count data sets are traditionally analyzed using the ordinary Poisson distribution. However, such a model has its applicability limited as it can be somewhat restrictive to handle specific data structures. In this case, it arises the need for obtaining alternative models that accommodate, for example, (a) zero‐modification (inflation or deflation at the frequency of zeros), (b) overdispersion, and (c) individual heterogeneity arising from clustering or repeated (correlated) measurements made on the same subject. Cases (a)–(b) and (b)–(c) are often treated together in the statistical literature with several practical applications, but models supporting all at once are less common. Hence, this paper's primary goal was to jointly address these issues by deriving a mixed‐effects regression model based on the hurdle version of the Poisson–Lindley distribution. In this framework, the zero‐modification is incorporated by assuming that a binary probability model determines which outcomes are zero‐valued, and a zero‐truncated process is responsible for generating positive observations. Approximate posterior inferences for the model parameters were obtained from a fully Bayesian approach based on the Adaptive Metropolis algorithm. Intensive Monte Carlo simulation studies were performed to assess the empirical properties of the Bayesian estimators. The proposed model was considered for the analysis of a real data set, and its competitiveness regarding some well‐established mixed‐effects models for count data was evaluated. A sensitivity analysis to detect observations that may impact parameter estimates was performed based on standard divergence measures. The Bayesian ‐value and the randomized quantile residuals were considered for model diagnostics.     

单木生物量模型估计区域尺度生物量的不确定性

采用系统抽样体系江西省固定样地杉木连续观测数据和生物量数据,通过Monte Carlo法反复模拟由单木生物量模型推算区域尺度地上生物量的过程,估计了江西省杉木地上总生物量。基于不同水平建模样本量n及不同决定系数R~2的设计,分别研究了单木生物量模型参数变异性及模型残差变异性对区域尺度生物量估计不确定性的影响。研究结果表明:2009年江西省杉木地上生物量估计值为(19.84±1.27)t/hm~2,不确定性占生物量估计值约6.41%。生物量估计值和不确定性值达到平稳状态所需的运算时间随建模样本量及决定系数R~2的增大而减小;相对于模型参数变异性,残差变异性对不确定性的影响更小。