Population structure in Daphnia obtusa: quantitative genetic and allozymic variation

Quantitative genetic analyses for body size and for life history characters within and among populations of Daphnia obtusa reveal substantial genetic variance at both hierarchical levels for all traits measured. Simultaneous allozymic analysis on the same population samples indicate a moderate degree of differentiation: GST = 0.28. No associations between electrophoretic genotype and phenotypic characters were found, providing support for the null hypothesis that the allozymic variants are effectively neutral. Therefore, GST can be used as the null hypothesis that neutral phenotypic evolution within populations led to the observed differentiation for the quantitative traits, which I call QST. The results of this study provide evidence that natural selection has promoted diversification for body size among populations, and has impeded diversification for relative fitness. Analyses of population differentiation for clutch size, age at reproduction, and growth rate indicate that neutral phenotypic evolution cannot be excluded as the cause.     

A simulation study of the effects of assignment of prior identity-by-descent probabilities to unselected sib pairs, in covariance-structure modeling of a quantitative-trait locus

Sib pair-selection strategies, designed to identify the most informative sib pairs in order to detect a quantitative-trait locus (QTL), give rise to a missing-data problem in genetic covariance-structure modeling of QTL effects. After selection, phenotypic data are available for all sibs, but marker data-and, consequently, the identity-by-descent (IBD) probabilities-are available only in selected sib pairs. One possible solution to this missing-data problem is to assign prior IBD probabilities (i.e., expected values) to the unselected sib pairs. The effect of this assignment in genetic covariance-structure modeling is investigated in the present paper. Two maximum-likelihood approaches to estimation are considered, the pi-hat approach and the IBD-mixture approach. In the simulations, sample size, selection criteria, QTL-increaser allele frequency, and gene action are manipulated. The results indicate that the assignment of prior IBD probabilities results in serious estimation bias in the pi-hat approach. Bias is also present in the IBD-mixture approach, although here the bias is generally much smaller. The null distribution of the log-likelihood ratio (i.e., in absence of any QTL effect) does not follow the expected null distribution in the pi-hat approach after selection. In the IBD-mixture approach, the null distribution does agree with expectation.     

A nonparametric bootstrap method for testing close linkage vs. pleiotropy of coincident quantitative trait loci

A novel method using the nonparametric bootstrap is proposed for testing whether a quantitative trait locus (QTL) at one chromosomal position could explain effects on two separate traits. If the single-QTL hypothesis is accepted, pleiotropy could explain the effect on two traits. If it is rejected, then the effects on two traits are due to linked QTLs. The method can be used in conjunction with several QTL mapping methods as long as they provide a straightforward estimate of the number of QTLs detectable from the data set. A selection step was introduced in the bootstrap procedure to reduce the conservativeness of the test of close linkage vs. pleiotropy, so that the erroneous rejection of the null hypothesis of pleiotropy only happens at a frequency equal to the nominal type I error risk specified by the user. The approach was assessed using computer simulations and proved to be relatively unbiased and robust over the range of genetic situations tested. An example of its application on a real data set from a saline stress experiment performed on a recombinant population of wheat (Triticum aestivum L. ) doubled haploid lines is also provided.     

Least squares interval mapping of quantitative trait loci under the infinitesimal genetic model in outbred populations

Genetic marker and phenotypic data for a quantitative trait were simulated on 20 paternal half-sib families with 100 progeny to investigate properties of within-family-regression interval mapping of a postulated single quantitative trait locus (QTL) in a marker interval under the infinitesimal genetic model, which has been the basis of the application of quantitative genetics to genetic improvement programs, and to investigate use of the infinitesimal model as null hypothesis in testing for presence of a major QTL. Genetic effects on the marked chromosome were generated based on a major gene model, which simulated a central biallelic QTL, or based on 101 biallelic QTL of equal effect, which approximated the infinitesimal model. The marked chromosome contained 0, 3.3%, 13.3%, or 33.3% of genetic variance and heritability was 0.25 or 0.70. Under the polygenic model with 3.3% of genetic variance on the marked chromosome, which corresponds to the infinitesimal model for the bovine, significant QTL effects were found for individual families. Correlations between estimates of QTL effects and true chromosome substitution effects were 0.29 and 0.47 for heritabilities of 0.25 and 0.70 but up to 0.85 with 33.3% of polygenic variance on the marked chromosome. These results illustrate the potential of marker-assisted selection even under the infinitesimal genetic model. Power of tests for presence of QTL was substantially reduced when the polygenic model with 3.3% of genetic variance on the chromosome was used as a null hypothesis. The ability to determine whether genetic variance on a chromosome was contributed by a single QTL of major effect or a large number of QTL with minor effects, corresponding to the infinitesimal model, was limited.     

Analytical and clinical performance of an automated immunoassay system (Immulite) for estradiol in serum

Mouse strains congenic for individual quantitative trait loci (QTLs) conferring hypnotic sensitivity to ethanol were constructed by backcrossing genotypically selected ILS x ISS N2 individuals to either inbred Long Sleep (ILS) or inbred Short Sleep (ISS) mice. We used a novel "speed congenic" approach in which N2 mice were genotyped for markers flanking each of the five originally identified QTLs. Genotypic selection for ISS regions at four of the five QTLs, and for ILS/ISS at the fifth QTL, allowed rapid fixation of the genetic background. We call this strategy "QTL-Marker-Assisted Counter Selection" or QMACS. By the N4 generation, phenotypic assessments showed that in some sublines the QTL had not been captured; these sublines were discarded and positive lines split to create new replicate sublines. One QTL, on Chromosome (Chr) 8, was not confirmed. At the N8, virtually all sublines on the remaining QTLs retained the phenotypic difference between heterozygotes and ISS homozygotes. Small numbers of interim congenics were produced at the N6 and later generations in which the ILS QTL was made homozygous on the ISS background; as expected, these congenic mice showed an increased sleep time. For later backcrosses (after the N4), the parents were selected on the basis of phenotype as well as genotype. The parent-offspring correlation over all QTLs was significant, supporting the use of phenotypic selection in congenic construction.     

The error of accepting the "theoretical" null hypothesis: The rise, fall, and resurrection of commonsense hypotheses in psychology.

When psychologists test a commonsense (CS) hypothesis and obtain no support, they tend to erroneously conclude that the CS belief is wrong. In many such cases it appears, after many years, that the CS hypothesis was valid after all. It is argued that this error of accepting the "theoretical" null hypothesis reflects confusion between the operationalized hypothesis and the theory or generalization that it is designed to test. That is, on the basis of reliable null data one can accept the operationalized null hypothesis (e.g., "A measure of attitude x is not correlated with a measure of behavior y"). In contrast, one cannot generalize from the findings and accept the abstract or theoretical null (e.g., "We know that attitudes do not predict behavior"). The practice of accepting the theoretical null hypothesis hampers research and reduces the trust of the public in psychological research. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Testing differences between nested covariance structure models: Power analysis and null hypotheses.

For comparing nested covariance structure models, the standard procedure is the likelihood ratio test of the difference in fit, where the null hypothesis is that the models fit identically in the population. A procedure for determining statistical power of this test is presented where effect size is based on a specified difference in overall fit of the models. A modification of the standard null hypothesis of zero difference in fit is proposed allowing for testing an interval hypothesis that the difference in fit between models is small, rather than zero. These developments are combined yielding a procedure for estimating power of a test of a null hypothesis of small difference in fit versus an alternative hypothesis of larger difference. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Genetic response from marker assisted selection in an outbred population for differing marker bracket sizes and with two identified quantitative trait loci

Effect of flanking quantitative trait loci (QTL)-marker bracket size on genetic response to marker assisted selection in an outbred population was studied by simulation of a nucleus breeding scheme. In addition, genetic response with marker assisted selection (MAS) from two quantitative trait loci on the same and different chromosome(s) was investigated. QTL that explained either 5% or 10% of phenotypic variance were simulated. A polygenic component was simulated in addition to the quantitative trait loci. In total, 35% of the phenotypic variance was due to genetic factors. The trait was measured on females only. Having smaller marker brackets flanking the QTL increased the genetic response from MAS selection. This was due to the greater ability to trace the QTL transmission from one generation to the next with the smaller flanking QTL-marker bracket, which increased the accuracy of estimation of the QTL allelic effects. Greater negative covariance between effects at both QTL was observed when two QTL were located on the same chromosome compared to different chromosomes. Genetic response with MAS was greater when the QTL were on the same chromosome in the early generations and greater when they were on different chromosomes in the later generations of MAS.     

On the probability of making Type I errors.

A statistical test leads to a Type I error whenever it leads to the rejection of a null hypothesis that is in fact true. The probability of making a Type I error can be characterized in the following 3 ways: the conditional prior probability, the overall prior probability, and the conditional posterior probability. In this article, we show (a) that the alpha level can be equated with the 1st of these and (b) that it provides an upper bound for the second but (c) that it does not provide an estimate of the third, although it is commonly assumed to do so. We trace the source of this erroneous assumption first to statistical texts used by psychologists, which are generally ambiguous about which of the 3 interpretations is intended at any point in their discussions of Type I errors and which typically confound the conditional prior and posterior probabilities. Underlying this, however, is a more general fallacy in reasoning about probabilities, and we suggest that this may be the result of erroneous inferences about probabilistic conditional statements. Finally, we consider the possibility of estimating the (posterior) probability of a Type I error in situations in which the null hypothesis is rejected and, hence, the proportion of statistically significant results that may be Type I errors. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Hemoglobin concentration of high-altitude Tibetans and Bolivian Aymara

Elevated hemoglobin concentrations have been reported for high-altitude sojourners and Andean high-altitude natives since early in the 20th century. Thus, reports that have appeared since the 1970s describing relatively low hemoglobin concentration among Tibetan high-altitude natives were unexpected. These suggested a hypothesis of population differences in hematological response to high-altitude hypoxia. A case of quantitatively different responses to one environmental stress would offer an opportunity to study the broad evolutionary question of the origin of adaptations. However, many factors may confound population comparisons. The present study was designed to test the null hypothesis of no difference in mean hemoglobin concentration of Tibetan and Aymara native residents at 3,800-4,065 meters by using healthy samples that were screened for iron deficiency, abnormal hemoglobins, and thalassemias, recruited and assessed using the same techniques. The hypothesis was rejected, because Tibetan males had a significantly lower mean hemoglobin concentration of 15.6 gm/dl compared with 19.2 gm/dl for Aymara males, and Tibetan females had a mean hemoglobin concentration of 14.2 gm/dl compared with 17.8 gm/dl for Aymara females. The Tibetan hemoglobin distribution closely resembled that from a comparable, sea-level sample from the United States, whereas the Aymara distribution was shifted toward 3-4 gm/dl higher values. Genetic factors accounted for a very high proportion of the phenotypic variance in hemoglobin concentration in both samples (0.86 in the Tibetan sample and 0.87 in the Aymara sample). The presence of significant genetic variance means that there is the potential for natural selection and genetic adaptation of hemoglobin concentration in Tibetan and Aymara high-altitude populations.     

Multiplicity, directional (Type III) errors, and the Null Hypothesis.

L. V. Jones and J. W. Tukey (2000) pointed out that the usual 2-sided, equal-tails null hypothesis test at level ot can be reinterpreted as simultaneous tests of 2 directional inequality hypotheses, each at level α/2, and that the maximum probability of a Type I error is α/2 if the truth of the null hypothesis is considered impossible. This article points out that in multiple testing with familywise error rate controlled at ot, the directional error rate (assuming all null hypotheses are false) is greater than α/2 and can be arbitrarily close to α. Single-step, step-down, and step-up procedures are analyzed, and other error rates, including the false discovery rate, are discussed. Implications for confidence interval estimation and hypothesis testing practices are considered. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Weighing the evidence for hypotheses with small samples of right-censored exponential data

The p-value evidence for an alternative to a null hypothesis regarding the mean lifetime can be unreliable if based on asymptotic approximations when there is only a small sample of right-censored exponential data. However, a guarded weight of evidence for the alternative can always be obtained without approximation, no matter how small the sample, and has some other advantages over p-values. Weights of evidence are defined as estimators of 0 when the null hypothesis is true and 1 when the alternative is true, and they are judged on the basis of the ensuing risks, where risk is mean squared error of estimation. The evidence is guarded in that a pre-assigned bound is placed on the risk under the hypothesis. Practical suggestions are given for choosing the bound and for interpreting the magnitude of the weight of evidence. Acceptability profiles are obtained by inversion of a family of guarded weights of evidence for two-sided alternatives to point hypotheses, just as confidence intervals are obtained from tests; these profiles are arguably more informative than confidence intervals, and are easily determined for any level and any sample size, however small. They can help understand the effects of different amounts of censoring. They are found for several small size data sets, including a sample of size 12 for post-operative cancer patients. Both singly Type I and Type II censored examples are included. An examination of the risk functions of these guarded weights of evidence suggests that if the censoring time is of the same magnitude as the mean lifetime, or larger, then the risks in using a guarded weight of evidence based on a likelihood ratio are not much larger than they would be if the parameter were known.     

Quantitative trait locus (QTL) mapping using different testers and independent population samples in maize reveals low power of QTL detection and large bias in estimates of QTL effects

The efficiency of marker-assisted selection (MAS) depends on the power of quantitative trait locus (QTL) detection and unbiased estimation of QTL effects. Two independent samples N = 344 and 107 of F2 plants were genotyped for 89 RFLP markers. For each sample, testcross (TC) progenies of the corresponding F3 lines with two testers were evaluated in four environments. QTL for grain yield and other agronomically important traits were mapped in both samples. QTL effects were estimated from the same data as used for detection and mapping of QTL (calibration) and, based on QTL positions from calibration, from the second, independent sample (validation). For all traits and both testers we detected a total of 107 QTL with N = 344, and 39 QTL with N = 107, of which only 20 were in common. Consistency of QTL effects across testers was in agreement with corresponding genotypic correlations between the two TC series. Most QTL displayed no significant QTL x environment nor epistatic interactions. Estimates of the proportion of the phenotypic and genetic variance explained by QTL were considerably reduced when derived from the independent validation sample as opposed to estimates from the calibration sample. We conclude that, unless QTL effects are estimated from an independent sample, they can be inflated, resulting in an overly optimistic assessment of the efficiency of MAS.     

Testing for substantive significance in applied research by specifying nonzero effect null hypotheses.

Contends that researchers using ANOVA, who wish to qualify the reporting of a statistically significant but nonsubstantive treatment effect, may test a null hypothesis that specifies the minimum effects size considered important and then reject or accept that hypothesis with the same confidence ensuing from a test of traditional null hypothesis of no difference in population means. A method is proposed that uses a simplified approximate solution, since appropriate noncentral F distributions are not generally available. (19 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

More powerful randomization-based p-values in double-blind trials with non-compliance

Standard randomization-based tests of sharp null hypotheses in randomized clinical trials, that is, intent-to-treat analyses, are valid without extraneous assumptions, but generally can be appropriately powerful only with alternative hypotheses that involve treatment assignment having an effect on outcome. In the context of clinical trials with non-compliance, other alternative hypotheses can be more natural. In particular, when a trial is double-blind, it is often reasonable for the alternative hypothesis to exclude any effect of treatment assignment on outcome for a unit unless the assignment affected which treatment that unit actually received. Bayesian analysis under this alternative 'exclusion' hypothesis leads to new estimates of the effect of receipt of treatment, and to a new randomization-based procedure that has frequentist validity yet can be substantially more powerful than the standard intent-to-treat procedure. The key idea is to obtain a p-value using a posterior predictive check distribution, which includes a model for non-compliance behaviour, although only under the standard sharp null hypothesis of no effect of assignment (or receipt) of treatment on outcome. It is important to note that these new procedures are distinctly different from 'as treated' and 'per protocol' analyses, which are not only badly biased in general, but generally have very low power.     

Comparison of four sib-pair linkage methods for analyzing sibships with more than two affecteds: interest of the binomial maximum likelihood approach

Family samples collected for sib-pair linkage studies usually include some sibships with more than two affecteds (multiplex sibships). Several methods have been proposed to take into account these multiplex sibships, and four of them are discussed in this work. Two methods, which are the most widely used, are based on the number of alleles shared by the sib-pairs constitutive of the multiplex sibship, with the first using the total number of these shared alleles ("all possible pairs" method) and the second considering a weighted number of these alleles (weighted method). The two other approaches considered the sibship as a whole, with in particular a likelihood method based on a binomial distribution of parental alleles among affected offspring. We theoretically show that, in the analysis of sibships with two affecteds, this likelihood method is expected to be more powerful than the classical mean test when a common asymptotic type I error is used. The variation of the sibship informativeness (assessed by the proportion of heterozygous parents) according to the number of affected sibs is investigated under various genetic models. Simulations under the null hypothesis of no linkage indicate that the "all possible pairs" is anticonservative, especially for type I errors < or = 0.001, whereas the weighted method generally provides satisfactory results. The likelihood method shows very consistent results in terms of type I errors, whatever the sample size, and provides power levels similar to those of the other methods. This binomial likelihood approach, which accounts in a natural way for multiplex sibships and provides a simple likelihood-ratio test for linkage involving a single parameter, appears to be a quite interesting alternative to analyze sib-pair studies.     

A probability matrix for the identification of campylobacters, helicobacters and allied taxa

A probabilistic identification matrix for campylobacteria, comprising 76 phenotypic characters and 37 taxa, is described. The accuracy and integrity of the matrix was evaluated using established computer-assisted methods. Certain taxa (for example, Campylobacter concisus and Camp. gracilis) demonstrated significant phenotypic diversity; previous data corroborated these findings. Differentiation between a few pairs of taxa proved difficult, although discriminatory characteristics were noted in each of these cases. The results indicate that most campylobacteria can be identified accurately and objectively with phenotypic tests when probabilistic methods of data assessment are employed.     

Shuttling of glucokinase between the nucleus and the cytoplasm in primary cultures of rat hepatocytes: possible involvement in the regulation of the glucose metabolism

Kin selection coefficients are used in two distinct ways. First, these coefficients measure phenotypic correlations that affect the marginal costs and benefits of behaviors. For example, the phenotypic correlation in sex ratio produced by two females in an isolated patch influences the favoured sex ratio. Second, kin selection coefficients describe genotypic correlations that measure fidelity of transmission. For example, a female values daughters vs. nieces according to genotypic correlations. It is widely known that kin selection coefficients may be interpreted as phenotypic or genotypic correlations in different contexts. However, these different interpretations have never been fully separated, and their different role have not been clearly explained. I provide proofs of a generic analytical approach. The technique automatically separates phenotypic correlations among social partners from genotypic components of transmission. The result is a general method that can be derived from first principles and applied to multivariate problems in social evolution. I emphasize a simple, practical maximization method that can be used to calculate equilibrium conditions for complex social interactions.     

Directional statistical hypotheses and comparisons among means.

Presents a reformulation of the rationale for the t test of the difference between 2 means, similar to but distinct from that proposed by H. Kaiser. Basically, the traditional 2-sided formulation, with the null hypothesis MU1 = MU2 vs. the alternative hypothesis MU1 > MU2, is replaced by a simultaneous test of 2 1-sided hypotheses: MU1 ± MU2 vs. MU1 > MU2; and MU1 . MU2 vs. MU1