ASSESSING THE PERFORMANCE OF CLOSED-FORM APPROXIMATIONS TO THE REML ESTIMATOR OF HERITABILITY

For many researchers the restricted maximum likelihood (REML) method of estimation is the procedure of choice for estimating heritability. In most applications the REML estimate can only be obtained via an iterative method. In some cases the algorithm used to compute the REML estimate may be slow or fail to converge. These predicaments have provided the motivation to develop closed-form approximations to the REML estimator of heritability in mixed linear models having two variance components. These estimators are compared to the REML estimator by considering their large and small sample properties. We provide guidance on how to select the closed-form estimator that provides the best approximation to the REML estimator. A simple one-way random effects model and an animal breeding model with correlated genetic effects are presented

THE PROBABILITY OF PREPONDERANCY: AN ALTERNATIVE TO THE INTRACLASS CORRELATION

We propose a new parameter for measuring the influence of a random effect in a mixed linear model. This is the probability of preponderance of the random effect under study over the other random effects. In a one-way random effects model, this is simply the probability the group random effect is larger in absolute size than the individual random effect (or error). We discuss the meaning of the parameter and relate it to the more familiar intraclass correlation coefficient. The new parameter has the appealing property that it is applicable for any distribution, whereas the intraclass correlation has its origins in normally distributed random effects. Furthermore, the new parameter directly measures the random effect\u27s impact on the observations whereas the intraclass correlation relies on the variances (second moments) of the random effects. We suggest parametric and nonparametric estimators of the parameter, and demonstrate the applicability of the results using real data. We also indicate how to extend the ideas to models with more than two sources of variation

POINT ESTIMATORS OF HERITABILITY BASED ON CONFIDENCE INTERVALS: A CLOSED-FORM APPROXIMATION TO THE REML ESTIMATOR

Estimating heritability, the proportion of variation in phenotypic values due to (additive) genetic effects, is an important subject matter to plant and animal breeders alike. In most applications there is not an analytic expression for the restricted maximum likelihood (REML) estimator of heritability since it is obtained via an iterative procedure. The focus of this paper is to find a closed-form approximation to the REML estimator of heritability for those scenarios in which mixed linear models having two variance components are appropriate. This procedure is equivalent to constructing approximate pivotal quantities and thus confidence intervals for heritability. See Burch and Iyer (1997) and Harris and Burch (2000) for more details concerning this approach. The closed-form estimator is compared to the REML estimator by evaluating their asymptotic standard errors. An application involving yearling bulls from a Red Angus seed stock herd suggests that the closed-form estimator mimics the REML estimator and is a viable candidate for investigators seeking a non-iterative method to estimate heritability

USING CONFIDENCE INTERVALS TO OBTAIN A FAMILY OF ESTIMATORS OF THE INTRACLASS CORRELATION COEFFICIENT (OR HERITABILITY)

A family of point estimators is presented for the intraclass correlation coefficient (or heritability) in the balanced one-way random effects model. The family is obtained by equating a pivotal quantity to different values of the pivoting distribution, and includes the familiar ML and REML estimators. In terms of mean-squared error, most members of the family of estimators are admissible within the family. A sire model is used to illustrate the estimation of heritability. The authors provide guidance concerning the choice of an individual member of the family for estimation purposes and indicate how the method can be extended to unbalanced designs

ESTIMATING INTRACLASS CORRELATION: OPTIMAL RESULTS USING LIMITED RESOURCES

From plant and animal breeding studies to industrial applications, the intraclass correlation coefficient (p) is used to measure the proportion of the total variation in the responses that may be attributed to a particular source. Confidence intervals for p are used to determine the optimal allocation of experimental material in one-way random effects models. Assuming the sample size is fixed, the authors investigate the number of groups and the number of observations per group required to minimize the expected length of confidence intervals. Examples are used to illustrate the selection of the best design. Both asymptotic and exact results suggest that practitioners should allocate no more than four experimental units per group

AN IMPROVED ESTIMATOR FOR ASSESSING THE MEASURE OF AGREEMENT WITH A GOLD STANDARD

St. Laurent (1998, Biometrics 54, 537-545) developed a measure of agreement for method comparison studies in which an approximate method of measurement is compared to a gold standard method of measurement. The measure of agreement proposed was shown to be related to a population intraclass correlation coefficient. This paper develops a family of estimators for the measure of agreement based on pivotal quantities. A blend of two particular members of the family is suggested as an estimator itself. In general, this estimator outperforms the maximum likelihood estimator in terms of bias and mean-squared error

Properties of the mechanosensitive channel MscS pore revealed by tryptophan scanning mutagenesis

Funding This work was supported by a Wellcome Trust Programme grant [092552/A/10/Z awarded to I.R.B., S.M., J. H. Naismith (University of St Andrews, St Andrews, U.K.), and S. J. Conway (University of Oxford, Oxford, U.K.)] (T.R. and M.D.E.), by a BBSRC grant (A.R.) [BB/H017917/1 awarded to I.R.B., J. H. Naismith, and O. Schiemann (University of St Andrews)], by a Leverhulme Emeritus Fellowship (EM-2012-060\2), and by a CEMI grant to I.R.B. from the California Institute of Technology. The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013 FP7/2007-2011) under Grant PITN-GA-2011-289384 (FP7-PEOPLE-2011-ITN NICHE) (H.G.) (awarded to S.M.).Peer reviewedPublisher PD

Age- and Lesion-Related Comorbidity Burden Among US Adults With Congenital Heart Disease: A Population-Based Study.

Background As patients with congenital heart disease (CHD) are living longer, understanding the comorbidities they develop as they age is increasingly important. However, there are no published population-based estimates of the comorbidity burden among the US adult patients with CHD. Methods and Results Using the IBM MarketScan commercial claims database from 2010 to 2016, we identified adults aged ≥18 years with CHD and 2 full years of continuous enrollment. These were frequency matched with adults without CHD within categories jointly defined by age, sex, and dates of enrollment in the database. A total of 40 127 patients with CHD met the inclusion criteria (mean [SD] age, 36.8 [14.6] years; and 48.2% were women). Adults with CHD were nearly twice as likely to have any comorbidity than those without CHD (P<0.001). After adjusting for covariates, patients with CHD had a higher prevalence risk ratio for "previously recognized to be common in CHD" (risk ratio, 9.41; 95% CI, 7.99-11.1), "other cardiovascular" (risk ratio, 1.73; 95% CI, 1.66-1.80), and "noncardiovascular" (risk ratio, 1.47; 95% CI, 1.41-1.52) comorbidities. After adjusting for covariates and considering interaction with age, patients with severe CHD had higher risks of previously recognized to be common in CHD and lower risks of other cardiovascular comorbidities than age-stratified patients with nonsevere CHD. For noncardiovascular comorbidities, the risk was higher among patients with severe than nonsevere CHD before, but not after, the age of 40 years. Conclusions Our data underscore the unique clinical needs of adults with CHD compared with their peers. Clinicians caring for CHD may want to use a multidisciplinary approach, including building close collaborations with internists and specialists, to help provide appropriate care for the highly prevalent noncardiovascular comorbidities

Herschel-ATLAS: A Binary HyLIRG Pinpointing a Cluster of Starbursting Protoellipticals

Panchromatic observations of the best candidate hyperluminous infrared galaxies from the widest Herschel extragalactic imaging survey have led to the discovery of at least four intrinsically luminous z = 2.41 galaxies across an 100 kpc region—a cluster of starbursting protoellipticals. Via subarcsecond interferometric imaging we have measured accurate gas and star formation surface densities. The two brightest galaxies span ~3 kpc FWHM in submillimeter/radio continuum and CO J = 4-3, and double that in CO J = 1-0. The broad CO line is due partly to the multitude of constituent galaxies and partly to large rotational velocities in two counter-rotating gas disks—a scenario predicted to lead to the most intense starbursts, which will therefore come in pairs. The disks have M dyn of several × 1011 M ☉, and gas fractions of ~40%. Velocity dispersions are modest so the disks are unstable, potentially on scales commensurate with their radii: these galaxies are undergoing extreme bursts of star formation, not confined to their nuclei, at close to the Eddington limit. Their specific star formation rates place them 5 × above the main sequence, which supposedly comprises large gas disks like these. Their high star formation efficiencies are difficult to reconcile with a simple volumetric star formation law. N-body and dark matter simulations suggest that this system is the progenitor of a B(inary)-type 1014.6-M ☉ cluster

Does self-control improve with practice? Evidence from a 6-week training program

Can self-control be improved through practice? Several studies have found that repeated practice of tasks involving self-control improves performance on other tasks relevant to self-control. However, in many of these studies, improvements after training could be attributable to methodological factors (e.g., passive control conditions). Moreover, the extent to which the effects of training transfer to real-life settings is not yet clear. In the present research, participants (N = 174) completed a 6-week training program of either cognitive or behavioral self-control tasks. We then tested the effects of practice on a range of measures of self-control, including lab-based and real-world tasks. Training was compared to both active and no-contact control conditions. Despite high levels of adherence to the training tasks, there was no effect of training on any measure of self-control. Trained participants did not, for example, show reduced ego depletion effects, become better at overcoming their habits, or report exerting more self-control in everyday life. Moderation analyses found no evidence that training was effective only among particular groups of participants. Bayesian analyses suggested that the data was more consistent with a null effect of training on self-control than with previous estimates of the effect of practice. The implication is that training self-control through repeated practice does not result in generalized improvements in self-control