Haplotype reconstruction for scnp DNA: a consensus vote approach with extensive sequence data from populations of the migratory locust (Locusta migratoria)

Single copy nuclear polymorphic (scnp) DNA is potentially a powerful molecular marker for evolutionary studies of populations. However, a practical obstacle to its employment is the general problem of haplotype determination due to the common occurrence of heterozygosity in diploid organisms. We explore here a 'consensus vote' (CV) approach to this question, combining statistical haplotype reconstruction and experimental verification using as an example an indel-free scnp DNA marker from the flanking region of a microsatellite locus of the migratory locust. The raw data comprise 251-bp sequences from 526 locust individuals (1052 chromosomes), with 71 (28.3%) polymorphic nucleotide sites (including seven triallelic sites) and 141 distinct genotypes (with frequencies ranging from 0.2 to 25.5%). Six representative statistical haplotype reconstruction algorithms are employed in our CV approach, including one parsimony method, two expectation-maximization (EM) methods and three Bayesian methods. The phases of 116 ambiguous individuals inferred by this approach are verified by molecular cloning experiments. We demonstrate the effectiveness of the CV approach compared to inferences based on individual statistical algorithms. First, it has the unique power to partition the inferrals into a reliable group and an uncertain group, thereby allowing the identification of the inferrals with greater uncertainty (12.7% of the total sample in this case). This considerably reduces subsequent efforts of experimental verification. Second, this approach is capable of handling genotype data pooled from many geographical populations, thus tolerating heterogeneity of genetic diversity among populations. Third, the performance of the CV approach is not influenced by the number of heterozygous sites in the ambiguous genotypes. Therefore, the CV approach is potentially a reliable strategy for effective haplotype determination of nuclear DNA markers. Our results also show that rare variations and rare inferrals tend to be more vulnerable to inference error, and hence deserve extra surveillance.     

A Bayesian framework for comparative quantitative genetics

Bayesian approaches have been extensively used in animal breeding sciences, but similar approaches in the context of evolutionary quantitative genetics have been rare. We compared the performance of Bayesian and frequentist approaches in estimation of quantitative genetic parameters (viz. matrices of additive and dominance variances) in datasets typical of evolutionary studies and traits differing in their genetic architecture. Our results illustrate that it is difficult to disentangle the relative roles of different genetic components from small datasets, and that ignoring, e.g. dominance is likely to lead to biased estimates of additive variance. We suggest that a natural summary statistic for G-matrix comparisons can be obtained by examining how different the underlying multinormal probability distributions are, and illustrate our approach with data on the common frog (Rana temporaria). Furthermore, we derive a simple Monte Carlo method for computation of fraternity coefficients needed for the estimation of dominance variance, and use the pedigree of a natural Siberian jay (Perisoreus infaustus) population to illustrate that the commonly used approximate values can be substantially biased.     

Estimating quality of template-based protein models by alignment stability

The error in protein tertiary structure prediction is unavoidable, but it is not explicitly shown in most of the current prediction algorithms. Estimated error of a predicted structure is crucial information for experimental biologists to use the prediction model for design and interpretation of experiments. Here, we propose a method to estimate errors in predicted structures based on the stability of the optimal target-template alignment when compared with a set of suboptimal alignments. The stability of the optimal alignment is quantified by an index named the SuboPtimal Alignment Diversity (SPAD). We implemented SPAD in a profile-based threading algorithm and investigated how well SPAD can indicate errors in threading models using a large benchmark dataset of 5232 alignments. SPAD shows a very good correlation not only to alignment shift errors but also structure-level errors, the root mean square deviation (RMSD) of predicted structure models to the native structures (i.e. global errors), and local errors at each residue position. We have further compared SPAD with seven other quality measures, six from sequence alignment-based measures and one atomic statistical potential, discrete optimized protein energy (DOPE), in terms of the correlation coefficient to the global and local structure-level errors. In terms of the correlation to the RMSD of structure models, when a target and a template are in the same SCOP family, the sequence identity showed a best correlation to the RMSD; in the superfamily level, SPAD was the best; and in the fold level, DOPE was best. However, in a head-to-head comparison, SPAD wins over the other measures. Next, SPAD is compared with three other measures of local errors. In this comparison, SPAD was best in all of the family, the superfamily and the fold levels. Using the discovered correlation, we have also predicted the global and local error of our predicted structures of CASP7 targets by the SPAD. Finally, we proposed a sausage representation of predicted tertiary structures which intuitively indicate the predicted structure and the estimated error range of the structure simultaneously.     

Development of a simple molecular marker specific for detecting the self-compatible<Emphasis Type="Italic">S4′</Emphasis> haplotype in sweet cherry (<Emphasis Type="Italic">Prunus avium</Emphasis> L.)

In sweet cherry (Prunus avium L.), theS4′ haplotype, characterized by a self-incompatibility (SI) defect in pollen, is self-compatible and is derived from the self-incompatibleS4 haplotype by x-ray mutagenesis.SFBs (S haplotype-specific F-box protein genes) have been found to associate with pollen determinant of SI. This report identified theSFB4′ of the self-compatibleS4′ haplotype. The alignment of the sequences ofSFB4′ andSFB4 by the BLAST program revealed a 4-bp deletion inSFB4′, which is TTTA. The sequence polymorphism generated by the TTTA deletion inSFB4′ was exploited to develop a simple molecular marker specific for detecting theS4′ but not theS4 haplotype. The simple marker specific to theS4′ haplotype can be visualized directly on an agarose gel, so it can be immediately applied to a marker-assistant cherry-breeding program. Thus, this work provides a practical molecular marker for cherry breeding. Principal author. An erratum to this article is available at .     

Plasmid-encoded enzymatic degradation of dimethoate

Dimethoate-degrading enzymatic activity in Bacillus licheniformis, Pseudomonas aeruginosa, Aeromonas hydrophila, Proteus mirabilis and Bacillus pumilus was found to be 6.4, 1.760, 4.09, 1.196 and 0.505 units/mg protein, respectively. The Escherichia coli C600 transconjugants of the isolated bacterial strains also exhibited dimethoate-degrading enzymatic activities. The cured derivatives did not show any decrease in the amount of dimethoate substrate and did not harbour plasmid as found in the original and transconjugant strains. Thus, the ability of enzymatic degradation of dimethoate was plasmid-mediated in B. licheniformis, Ps. aeruginosa, A. hydrophila, P. mirabilis and B. pumilus.     

Monitoring crayfish using a mark-recapture method: potentials,recommendations, and limitations

Crayfish are regarded as useful indicators of environmental quality and freshwater biodiversity. However, reliable methods for monitoring their populations are needed so that this potential can be fully utilised. We report and discuss methodological aspects of the white-clawed crayfish (Austropotamobius pallipes complex) survey conducted in Piedmont, Italy, with the use of mark-recapture. The results suggest that the method can serve as a convenient tool for estimating the size of crayfish populations and inferring their temporal trends. The two populations investigated appeared closed except for wintertime and July. Consequently, the Robust Design, which is regarded as the most reliable mark-recapture approach, can be easily applied. The minimum effective sampling plan for monitoring purposes should comprise one primary period per year, conducted in the summer–autumn season, and consisting of three capture sessions. If gaining insight into the ecology of the investigated species is the prime objective and sufficient resources are available, the optimal plan should include two primary periods (in spring and the summer–autumn season) of five capture sessions each. Capture sessions need to be separated by roughly 2-week intervals in order to avoid the strong, but short-term, negative effect of capturing crayfish on their recapture chances. As the model without heterogeneity in capture probabilities ensures better estimate precision we recommend that data collected for both sexes are analysed separately. Taking into consideration higher male catchabilities and sex ratio being invariably 1:1, it also seems beneficial to estimate only male numbers and double them to achieve total population sizes.     

A bias in ML estimates of branch lengths in the presence of multiple signals

Sequence data often have competing signals that are detected by network programs or Lento plots. Such data can be formed by generating sequences on more than one tree, and combining the results, a mixture model. We report that with such mixture models, the estimates of edge (branch) lengths from maximum likelihood (ML) methods that assume a single tree are biased. Based on the observed number of competing signals in real data, such a bias of ML is expected to occur frequently. Because network methods can recover competing signals more accurately, there is a need for ML methods allowing a network. A fundamental problem is that mixture models can have more parameters than can be recovered from the data, so that some mixtures are not, in principle, identifiable. We recommend that network programs be incorporated into best practice analysis, along with ML and Bayesian trees.     

Balancing bias and precision in capture-recapture estimates of abundance

Capture‐recapture estimates of abundance using photographic identification data are sensitive to the quality of photographs used and distinctiveness of individuals in the population. Here analyses are presented for examining the effects of photographic quality and individual animal distinctiveness scores and for objectively selecting a subset of data to use for capture‐recapture analyses using humpback whale (Megaptera novaeangliae) data from a 2‐year study in the North Atlantic. Photographs were evaluated for their level of quality and whales for their level of individual distinctiveness. Photographic quality scores had a 0.21 probability of changing by a single‐quality level, and there were no changes by two or more levels. Individual distinctiveness scores were not independent of photographic quality scores. Estimates of abundance decreased as poor‐quality photographs were removed. An appropriate balance between precision and bias in abundance estimates was achieved by removing the lowest‐quality photographs and those of incompletely photographed flukes given our assumptions about the true population abundance. A simulation of the selection process implied that, if the estimates are negatively biased by heterogeneity, the increase in bias produced by decreasing the sample size is not more than 2%. Capture frequencies were independent of individual distinctiveness scores.     

Assessing model accuracy using the homology modeling automatically software

Homology modeling is a powerful technique that greatly increases the value of experimental structure determination by using the structural information of one protein to predict the structures of homologous proteins. We have previously described a method of homology modeling by satisfaction of spatial restraints (Li et al., Protein Sci 1997;6:956-970). The Homology Modeling Automatically (HOMA) web site, , is a new tool, using this method to predict 3D structure of a target protein based on the sequence alignment of the target protein to a template protein and the structure coordinates of the template. The user is presented with the resulting models, together with an extensive structure validation report providing critical assessments of the quality of the resulting homology models. The homology modeling method employed by HOMA was assessed and validated using twenty-four groups of homologous proteins. Using HOMA, homology models were generated for 510 proteins, including 264 proteins modeled with correct folds and 246 modeled with incorrect folds. Accuracies of these models were assessed by superimposition on the corresponding experimentally determined structures. A subset of these results was compared with parallel studies of modeling accuracy using several other automated homology modeling approaches. Overall, HOMA provides prediction accuracies similar to other state-of-the-art homology modeling methods. We also provide an evaluation of several structure quality validation tools in assessing the accuracy of homology models generated with HOMA. This study demonstrates that Verify3D (Luthy et al., Nature 1992;356:83-85) and ProsaII (Sippl, Proteins 1993;17:355-362) are most sensitive in distinguishing between homology models with correct or incorrect folds. For homology models that have the correct fold, the steric conformational energy (including primarily the Van der Waals energy), MolProbity clashscore (Word et al., Protein Sci 2000;9:2251-2259), and the PROCHECK G-factors (Laskowski et al., J Biomol NMR 1996;8:477-486) provide sensitive and consistent methods for assessing accuracy and can distinguish between homology models of higher and lower accuracy. As demonstrated in the accompanying paper (Bhattacharya et al., accompanying paper), combinations of these scores for models generated with HOMA provide a basis for distinguishing low from high accuracy models.     

Unbiased estimation of selected treatment means in two-stage trials

Straightforward estimation of a treatment's effect in an adaptive clinical trial can be severely hindered when it has been chosen from a larger group of potential candidates. This is because selection mechanisms that condition on the rank order of treatment statistics introduce bias. Nevertheless, designs of this sort are seen as a practical and efficient way to fast track the most promising compounds in drug development. In this paper we extend the method of Cohen and Sackrowitz (1989) who proposed a two-stage unbiased estimate for the best performing treatment at interim. This enables their estimate to work for unequal stage one and two sample sizes, and also when the quantity of interest is the best, second best, or j -th best treatment out of k. The implications of this new flexibility are explored via simulation.