Residual Variance Research Articles

Modelling lactation curves for fat-to-protein ratio of milk in the first three lactations of Polish Holstein-Friesian cows

Among milk traits, fat-to-protein ratio (FPR) is considered a potential measure of a cow’s energy status and is one of the selection criteria necessary to improve metabolic stability. Further genetic analyses require an appropriate model that describes the pattern of FPR changes throughout lactation. The objective of the study was to examine five mathematical functions to describe the lactation curve for FPR in the first three lactations of Polish Holstein-Friesians. The dataset contained FPR records for 5690 cows in the first lactation, 4081 cows in the second, and 2636 cows in the third lactation based on 48908, 34706, and 22097 test-day (TD) records, respectively. Using the MIXED procedure of SAS statistical analytics software, ten linear models (five with fixed effects only, and five with the additional random effect of cow) were fitted to the TD records. The goodness of fit was tested with Akaike's information criterion, residual variances and the correlation coefficient between the actual and estimated values. The model proposed by Ali and Schaeffer (1987) had the best fit to FPR in the first three parities, and the model of Wilmink (1987) provided the worst fit. The correlation coefficient between the actual and the estimated values of FPR was higher for models that included the random cow effect compared with models without this effect.

A Lasso and a Regression Tree Mixed-Effect Model with Random Effects for the Level, the Residual Variance, and the Autocorrelation

Research in psychology is experiencing a rapid increase in the availability of intensive longitudinal data. To use such data for predicting feelings, beliefs, and behavior, recent methodological work suggested combinations of the longitudinal mixed-effect model with Lasso regression or with regression trees. The present article adds to this literature by suggesting an extension of these models that—in addition to a random effect for the mean level—also includes a random effect for the within-subject variance and a random effect for the autocorrelation. After introducing the extended mixed-effect location scale (E-MELS), the extended mixed-effect location-scale Lasso model (Lasso E-MELS), and the extended mixed-effect location-scale tree model (E-MELS trees), we show how its parameters can be estimated using a marginal maximum likelihood approach. Using real and simulated example data, we illustrate how to use E-MELS, Lasso E-MELS, and E-MELS trees for building prediction models to forecast individuals’ daily nervousness. The article is accompanied by an R package (called mels) and functions that support users in the application of the suggested models.

An IBD-based mixed model approach for QTL mapping in multiparental populations

Key messageThe identity-by-descent (IBD)-based mixed model approach introduced in this study can detect quantitative trait loci (QTLs) referring to the parental origin and simultaneously account for multilevel relatedness of individuals within and across families. This unified approach is proved to be a powerful approach for all kinds of multiparental population (MPP) designs.Multiparental populations (MPPs) have become popular for quantitative trait loci (QTL) detection. Tools for QTL mapping in MPPs are mostly developed for specific MPPs and do not generalize well to other MPPs. We present an IBD-based mixed model approach for QTL mapping in all kinds of MPP designs, e.g., diallel, Nested Association Mapping (NAM), and Multiparental Advanced Generation Intercross (MAGIC) designs. The first step is to compute identity-by-descent (IBD) probabilities using a general Hidden Markov model framework, called reconstructing ancestry blocks bit by bit (RABBIT). Next, functions of IBD information are used as design matrices, or genetic predictors, in a mixed model approach to estimate variance components for multiallelic genetic effects associated with parents. Family-specific residual genetic effects are added, and a polygenic effect is structured by kinship relations between individuals. Case studies of simulated diallel, NAM, and MAGIC designs proved that the advanced IBD-based multi-QTL mixed model approach incorporating both kinship relations and family-specific residual variances (IBD.MQMkin_F) is robust across a variety of MPP designs and allele segregation patterns in comparison to a widely used benchmark association mapping method, and in most cases, outperformed or behaved at least as well as other tools developed for specific MPP designs in terms of mapping power and resolution. Successful analyses of real data cases confirmed the wide applicability of our IBD-based mixed model methodology.

Estimation of genetic parameters for sexual dimorphism in body weight of Baluchi sheep

The objective of this study was to estimate genetic parameters for sexual dimorphism (SD) in birth weights (BW), weaning weights (WW), 6-month weight (W6), 9-month weight (W9) and 12-month weight (W12) of Baluchi sheep. Data were collected during 1981-2015 from Baluchi sheep flock (flock no 1) of Abbas Abad sheep breeding station located in Mashhad, Iran. Data were included 7339, 7303, 5884, 5162 and 4691 records of birth weight, weaning weight, 6-month weight, 9-month weight and 12-month weight, respectively. Six bivariate animal models were used to estimate variance components and corresponding genetic parameters in male and female lambs separately. The SD levels (ratio of Male/Female) increased from 1.06 at birth to 1.13 at yearling age. Except for W9 in which additive genetic variance and direct heritability (h_a^2) were greater in males, for the other studied traits, females experienced higher additive genetic variance and h_a^2. In both sex, maternal effects contributed significantly in phenotypic variation of body weights with greater contribution to body weights measured early in life. While in females, maternal genetic effect was greater than males, in males, maternal permanent environmental effect was greater which together with higher estimates of residual variances revealed greater sensitivity of male lambs to environmental conditions. Cross-sex additive genetic correlation (ra) was 0.86 for BW and 1.00 for the other studied traits. Maternal correlations between sexes were close to 1.00 indicating lack of sexual dimorphism for maternal effects on body weights of Baluchi lambs. Based on an approximate 95% confidence interval, the difference between sexes regarding direct heritability were not significant, indicating no need or opportunity for different selection strategies in two sexes. Therefore, a model including lambs of both sexes and with sex effects included in the model can be used for genetic evaluation.

Genetic parameters and genetic trends of conformation and management traits in Dairy Gir cattle

ABSTRACT The objective of this study was to estimate genetic parameters and genetic trends of different conformation and management traits regularly measured within the context of the National Dairy Gir Breeding Program (PNMGL). The estimation of genetic and residual variances for each trait was performed using average information restricted maximum likelihood (AI-REML) procedure in AIREMLF90 program software. The population was divided into three subpopulations constituted by measured females (with phenotype records), all females, and males. Linear regressions were applied for each trait, considering two periods of birth (1st period: 1938-1996; 2nd period: 1997-2012). The estimated heritability of conformation and management traits varied from 0.01 to 0.53, denoting a perspective of genetic improvement through selection and corrective matings for purebred Dairy Gir populations. The average genetic changes in conformation and management traits were, in general, variable and inexpressive, showing that the selection of Dairy Gir may have had been directed essentially to increase milk yield. The analysis of the two periods of birth indicated that some linear traits present progress (although inexpressive) in the 2nd period (more recent period).

Genetic analysis of egg production traits in turkeys (Meleagris gallopavo) using a single-step genomic random regression model

BackgroundEgg production traits are economically important in poultry breeding programs. Previous studies have shown that incorporating genomic data can increase the accuracy of genetic prediction of egg production. Our objective was to estimate the genetic and phenotypic parameters of such traits and compare the prediction accuracy of pedigree-based random regression best linear unbiased prediction (RR-PBLUP) and genomic single-step random regression BLUP (RR-ssGBLUP). Egg production was recorded on 7422 birds during 24 consecutive weeks from first egg laid. Hatch-week of birth by week of lay and week of lay by age at first egg were fitted as fixed effects and body weight as a covariate, while additive genetic and permanent environment effects were fitted as random effects, along with heterogeneous residual variances over 24 weeks of egg production. Predictions accuracies were compared based on two statistics: (1) the correlation between estimated breeding values and phenotypes divided by the square root of the trait heritability, and (2) the ratio of the variance of BLUP predictions of individual Mendelian sampling effects divided by one half of the estimate of the additive genetic variance.ResultsHeritability estimates along the production trajectory obtained with RR-PBLUP ranged from 0.09 to 0.22, with higher estimates for intermediate weeks. Estimates of phenotypic correlations between weekly egg production were lower than the corresponding genetic correlation estimates. Our results indicate that genetic correlations decreased over the laying period, with the highest estimate being between traits in later weeks and the lowest between early weeks and later ages. Prediction accuracies based on the correlation-based statistic ranged from 0.11 to 0.44 for RR-PBLUP and from 0.22 to 0.57 for RR-ssGBLUP using the correlation-based statistic. The ratios of the variances of BLUP predictions of Mendelian sampling effects and one half of the additive genetic variance ranged from 0.17 to 0.26 for RR-PBLUP and from 0.17 to 0.34 for RR-ssGBLUP. Although the improvement in accuracies from RR-ssGBLUP over those from RR-PBLUP was not uniform over time for either statistic, accuracies obtained with RR-ssGBLUP were generally equal to or higher than those with RR-PBLUP.ConclusionsOur findings show the potential advantage of incorporating genomic data in genetic evaluation of egg production traits using random regression models, which can contribute to the genetic improvement of egg production in turkey populations.

Joint Modeling of Genetics and Field Variation in Plant Breeding Trials Using Relationship and Different Spatial Methods: A Simulation Study of Accuracy and Bias

Modelling field spatial patterns is standard practice for the analysis of plant breeding. Jointly fitting the genetic relationship among individuals and spatial information enables better separability between the variance due to genetics and field variation. This study aims to quantify the accuracy and bias of estimative parameters using different approaches. We contrasted three settings for the genetic term: no relationship (I), pedigree relationship (A), and genomic relationship (G); and a set of approaches for the spatial variation: no-spatial (NS), moving average covariate (MA), row-column adjustment (RC), autoregressive AR1 × AR1 (AR), spatial stochastic partial differential equations, or SPDE (SD), nearest neighbor graph (NG), and Gaussian kernel (GK). Simulations were set to represent soybean field trials at F2:4 generation. Heritability was sampled from a uniform distribution U(0,1). The simulated residual-to-spatial ratio between residual variance and spatial variance (Ve:Vs) ranged from 9:1 to 1:9. Experimental settings were conducted under an augmented block design with the systematic distribution of checks accounting for 10% of the plots. Relationship information had a substantial impact on the accuracy of the genetic values (G > A > I) and contributed to the accuracy of spatial effects (30.63–42.27% improvement). Spatial models were ranked based on an improvement to the accuracy of estimative of genetic effects as SD ≥ GK ≥ AR ≥ NG ≥ MA > RC ≥ NS, and to the accuracy of estimative of spatial effects as GK ≥ SD ≥ NG > AR ≥ MA > RC. Estimates of genetic and spatial variance were generally biased downwards, whereas residual variances were biased upwards. The advent of relationship information reduced the bias of all variance components. Spatial methods SD, AR, and GK provided the least biased estimates of spatial and residual variance.

Cranial Morphology of the Flat-Headed Bat Myotis planiceps (Chiroptera: Vespertilionidae) in the Context of American Myotis

Myotis planiceps is an insectivorous species with a distinctive flat-headed morphology among Myotis species. Its distribution is restricted to the northeastern Mexico highlands, and uses trees of Yucca carnerosana to roost. The particular ecological and morphological characteristics of this species make it a likely resource-use specialist. We evaluated the morphological specialization of M. planiceps as compared with thirteen American congeners belonging to different dietary categories, using a geometric morphometric protocol and multivariate statistical analyses. We estimated morphological differences across dietary categories, including M. planiceps as a separate group, and evaluated the effect of cranial size and diet on variations in cranial morphology. We found a significant differentiation of this species in all the cranial shape characters, with soft insectivores as the dietary group closest to M. planiceps. Additionally, diet explained similar percentages of shape variance in cranial and mandibular characters, and the highest interaction between diet and cranial size was recorded in the braincase. In general, the smallest sizes and thinner skulls were observed in soft insectivores. Our findings do not support a significant relationship between skull shape characters, described by the first principal component, and hardness of food items, but we found a significant association with the mandible shape. No phylogenetic structure was observed in the residual variance of these models. We found trends of change in cranial morphology associated with specialized habitats, the mandibular characters being more contrasting in the fishing bat M. vivesi, and the braincase morphology being more contrasting in M. planiceps, a species that lives in restricted habitats and probably feeds on soft insects.

Heat stress level as an alternative to fixed regression modeling for fat and protein yield traits in Holstein cattle

To date, no study has tested the inclusion of bioclimatic variables or heat stress indicators in the formation of fixed regression for the genetic evaluation of dairy cattle. The objective of the present study was to investigate the inclusion of heat stress level in modeling of fixed regression in random regression models to predict population response and individual genetic components for fat and protein yield in test-day, using data from Holstein cattle in tropical environment. The data comprised 52,012 test-day fat and protein records of 9,858 first-parity Holstein cows from Brazil, collected from 1997 to 2013, and bioclimatic data (temperature-humidity index – THI, and diurnal temperature variation – DTV) from 18 weather stations. Least square linear regression models were used to determine THI and DTV thresholds for fat and protein yield losses caused by heat stress. In addition to the standard model (SM, without bioclimatic variables), THI and DTV were analyzed as fixed effects (considering the average of two days before the test-day control), and heat stress level was considered in formation of fixed regression, totaling three models for each trait. THI and DTV thresholds for fat and protein losses was THI = 74 (−0.030 kg/day/ THI) and DTV = 16 (−0.020 kg/day/ DTV), for both traits. The model that included THI and DTV as fixed effects, and used heat stress level in formation of fixed regression (fixed curve), presented a better fit (Akaike's information criterion, Schwarz's Bayesian information criterion, and residual variance). Estimated breeding values (EBV) are improved when using this model, and there is an increase in reliability of estimates. There is an important reranking of sires when heat stress indicators are included in the model to evaluate fat and protein yield in test-day. The increase in reliability of EBV by including heat stress level in the fixed regression shows a better fit of model to the data. It is possible to conclude that the inclusion of heat stress level in the formation of fixed regression is an important factor to be considered in dairy cattle reared in tropical regions to minimize environmental effects and optimize the process of evaluation and genetic selection.

Modeling heat stress effects on dairy cattle milk production in a tropical environment using test-day records and random regression models

In tropical environments, dairy cattle production is constrained by several factors, including climate. The seasonal loss of milk due to heat stress is a recurring challenge for many dairy producers. The objective of this study was to detect heat stress thresholds, milk yield loss and individual animal variations using random regression models for dairy cattle from test-day milk records. Data were obtained from the Kenya Livestock Breeders Organization for the years 2000–2017 and merged with weather data. The weather parameters were grid-interpolated solar and meteorological data obtained from the National Aeronautics and Space Administration/Prediction Of Worldwide Energy Resources (NASA/POWER). After editing, the records comprised 49 993, 45 251 and 36 136 test-day records for first, second, and third lactations, respectively, for the four main dairy breeds: Friesian (68.0%), Ayrshire (21.1%), Jersey (7.6%) and Guernsey (3.3%). Variance components were estimated using Restricted Maximum Likelihood in ASReml software. Random regression models with third-order Legendre polynomials were fitted to the average and individual lactation curves and the reaction norms. An extended factor analytic variance structure for the random cow effects was used to estimate (co)variances between days in milk and thermal load. The daily average temperature (TA) and temperature humidity index (THI) were identified as the most suitable thermal load indicators for assessing milk yield losses. Considering a one day lag, the estimated heat stress thresholds were about 22 °C and 69 index units for TA and THI, respectively. Almost no differences were observed for estimated residual variances between the thermal load indicators, indicating there was no better model fit by TA or THI. The heat stress thresholds and milk loss patterns are important for management of dairy production systems in the tropics with climatic conditions similar to this study. Data recording should be improved as a tool to monitor the expected impacts of climate change and mitigation measures.

Statistical modelling of measurement error in wet chemistry soil data

AbstractThere is a growing demand for high‐quality soil data. However, soil measurements are subject to many error sources. We aimed to quantify uncertainties in synthetic and real‐world wet chemistry soil data through a linear mixed‐effects model, including batch and laboratory effects. The use of synthetic data allowed us to investigate how accurately the model parameters were estimated for various experimental measurement designs, whereas the real‐world case served to explore if estimates of the random effect variances were still accurate for unbalanced datasets with few replicates. The variance estimates for synthetic data were unbiased, but limited laboratory information led to imprecise estimates. The same was observed for unbalanced synthetic datasets, where 20, 50 and 80% of the data were removed randomly. Removal led to a sharp increase of the interquartile range (IQR) of the variance estimates for batch effect and the residual. The model was also fitted to real‐world and total organic carbon (TOC) data, provided by the Wageningen Evaluating Programmes for Analytical Laboratories (WEPAL). For , the model yielded unbiased estimates with relatively small IQRs. However, the limited number of batches with replicate measurements (5.8%) caused the batch effect to be larger than expected. A strong negative correlation between batch effect and residual variance suggested that the model could not distinguish well between these two random effects. For TOC, batch effect was removed from the model as no replicates were available within batches. Again, unbiased model estimates were obtained. However, the IQRs were relatively large, which could be attributed to the smaller dataset with only a single replicate measurement. Our findings demonstrated the importance of experimental measurement design and replicate measurements in the quantification of uncertainties in wet chemistry soil data.Highlights Accurate uncertainty quantification depends on the experimental measurement design. Linear mixed‐effects models can be used as a tool to quantify uncertainty in wet chemistry soil data. Lack of replicate measurements leads to poor estimates of error variance components. Measurement error in wet chemistry soil data should not be ignored.

ESTIMATE OF GENETIC COMPONENTS OF BIRTH WEIGHT USING MULTI-BREED MODELS WITH DIFFERENT PEDIGREE STRUCTURES IN MESTIZO SHEEP

The objectives of this paper were to verify the influence of the multi-breed model and pedigree structure in the estimates of the genetic components to birth weight in a mestizo herd. Using 1234 birth weight records in two ways, one dataset with complete pedigree information (n = 1028) and another with incomplete pedigree information (n = 1234). The pedigree was composed for 10 sires, 366 and 448 dams for complete and incomplete pedigree, respectively. Used for analysis the maximum restricted likelihood method about the animal model, considering the birth weight trait, for a dataset with or not complete pedigree, used the WOMBAT program. The direct heritability coefficients in both datasets did not present large difference, 0.06 and 0.09 to best models in the complete and incomplete pedigree, respectively. However, in the incomplete pedigree, the maternal heritability was 0.29. The residual variance, which on the best model of the dataset with complete pedigree presented 0.51 and 0.35 for the best model of the dataset with incomplete pedigree. Concluding, we find the influence of de multi-breed model only in the dataset with complete pedigree, that considered the direct and breed effects. In the dataset with incomplete pedigree, the better model was that considered the direct and maternal additive effects.

Reservoir Scheduling Using a Multi-Objective Cuckoo Search Algorithm under Climate Change in Jinsha River, China

Changes in rainfall and streamflow due to climate change have an adverse impact on hydropower generation reliability and scheduling of cascade hydropower stations. To estimate the impact of climate change on hydropower, a combination of climate, hydrological, and hydropower scheduling models is needed. Here, we take the Jinsha River as an example to estimate the impact of climate change on total power generation of the cascade hydropower stations and residual load variance of the power grid. These two goals are solved by applying an improved multi-objective cuckoo search algorithm, and a variety of strategies for the optimal dispatch of hydropower stations are adopted to improve the efficiency of the algorithm. Using streamflow prediction results of CMIP5 climate data, in conjunction with the Xinanjiang model, the estimated results for the next 30 years were obtained. The results indicated that the negative correlation between total power generation and residual load variance under the RCP 2.6 scenario was weaker than that under the RCP 8.5. Moreover, the average power generation and the average residual load variance in RCP 2.6 was significantly larger than that in RCP 8.5. Thus, reducing carbon emissions is not only beneficial to ecological sustainability, but also has a positive impact on hydropower generation. Our approaches are also applicable for cascade reservoirs in other river catchments worldwide to estimate impact of climate change on hydropower development.

Fragility Analysis for Subway Station Using Artificial Neural Network

ABSTRACT To avoid the limitations of the assumptions for the traditional probabilistic seismic demand model (PSDM), this study proposed a novel PSDM for conducting fragility analysis of subway stations based on artificial neural network (ANN). The proposed ANN-based PSDM consists of an ANN-based trend model and a probabilistic-neural-network (PNN)-based error model. A two-story and three-span subway station in Shanghai was taken as a case. The results show that compared with the traditional PSDM, the proposed ANN-based PSDM can better predict the seismic responses of structures and describes the nonhomogeneous variance of residuals of the predicted seismic responses.

Optically synchronized fibre links using spectrally pure chip-scale lasers

Precision frequency and phase synchronization between distinct fiber interconnected nodes is critical for a wide range of applications, including atomic timekeeping, quantum networking, database synchronization, ultra-high-capacity coherent optical communications and hyper-scale data centers. Today, many of these applications utilize precision, tabletop laser systems, and would benefit from integration in terms of reduced size, power, cost, and reliability. In this paper we report a record low 3x10^-4 rad^2 residual phase error variance for synchronization based on independent, spectrally pure, ultra-high mutual coherence, photonic integrated lasers. This performance is achieved with stimulated Brillouin scattering lasers that are stabilized to independent microcavity references, realizing sources with 30 Hz integral linewidth and a fractional frequency instability less than or equal to 2x10^-13 at 50 ms. This level of low phase noise and carrier stability enables a new type of optical-frequency-stabilized phase-locked loop (OFS-PLL) that operates with a less than 800 kHz loop bandwidth, eliminating traditional power consuming high bandwidth electronics and digital signal processors used to phase lock optical carriers. Additionally, we measure the residual phase error down to a received carrier power of -34 dBm, removing the need to transmit in-band or out-of-band synchronized carriers. These results highlight the promise for a path to spectrally pure, ultra-stable, integrated lasers for network synchronization, precision time distribution protocols, quantum-clock networks, and multiple-Terabit per second coherent DSP-free fiber-optic interconnects.

Genotype-by-environment interactions for reproduction, body composition, and growth traits in maternal-line pigs based on single-step genomic reaction norms

BackgroundThere is an increasing need to account for genotype-by-environment (G × E) interactions in livestock breeding programs to improve productivity and animal welfare across environmental and management conditions. This is even more relevant for pigs because selection occurs in high-health nucleus farms, while commercial pigs are raised in more challenging environments. In this study, we used single-step homoscedastic and heteroscedastic genomic reaction norm models (RNM) to evaluate G × E interactions in Large White pigs, including 8686 genotyped animals, for reproduction (total number of piglets born, TNB; total number of piglets born alive, NBA; total number of piglets weaned, NW), growth (weaning weight, WW; off-test weight, OW), and body composition (ultrasound muscle depth, MD; ultrasound backfat thickness, BF) traits. Genetic parameter estimation and single-step genome-wide association studies (ssGWAS) were performed for each trait.ResultsThe average performance of contemporary groups (CG) was estimated and used as environmental gradient in the reaction norm analyses. We found that the need to consider heterogeneous residual variance in RNM models was trait dependent. Based on estimates of variance components of the RNM slope and of genetic correlations across environmental gradients, G × E interactions clearly existed for TNB and NBA, existed for WW but were of smaller magnitude, and were not detected for NW, OW, MD, and BF. Based on estimates of the genetic variance explained by the markers in sliding genomic windows in ssGWAS, several genomic regions were associated with the RNM slope for TNB, NBA, and WW, indicating specific biological mechanisms underlying environmental sensitivity, and dozens of novel candidate genes were identified. Our results also provided strong evidence that the X chromosome contributed to the intercept and slope of RNM for litter size traits in pigs.ConclusionsWe provide a comprehensive description of G × E interactions in Large White pigs for economically-relevant traits and identified important genomic regions and candidate genes associated with GxE interactions on several autosomes and the X chromosome. Implementation of these findings will contribute to more accurate genomic estimates of breeding values by considering G × E interactions, in order to genetically improve the environmental robustness of maternal-line pigs.

How temperature variation affects white-rot fungi mycelial growth dynamics: a nonlinear mixed models approach

This study aimed to model mycelial growth in a factorial experiment with two species of white rot fungi growing at five temperatures. The nonlinear models evaluated were logistic, Gompertz, Weibull and von Bertalanffy. The adjustments were performed first by evaluating the fixed and mixed-effects models with random effects, added to the fixed parameters. Then, the best adjusted model was improved by an adequated covariance structure, and dummy variables were added to the parameters asymptote (α) and abscissa of the inflection point (β) in the model in order to verify the effect of the experiment factors, species and temperatures on the regression parameters. The criteria used to compare models were residual variance and Akaike information criterion. Gompertz and von Bertalanffy mixed-effects models were better adjusted. The parameters differed between species. Moreover, the linear and quadratic effects of temperature evaluated in each species were significant. The models were reparametrized in order to consider two parameters of interest: velocity (μ) at the inflection point and lag time (λ). The maximum growth velocity was obtained at 25.4 °C by S. ostrea, while T. villosa was achieved at 30.5 °C; both fungi suffered less lag time by increasing the temperature.

Evaluating Cluster-Level Factor Models with lavaan and Mplus

Background: Researchers frequently use the responses of individuals in clusters to measure cluster-level constructs. Examples are the use of student evaluations to measure teaching quality, or the use of employee ratings of organizational climate. In earlier research, Stapleton and Johnson (2019) provided advice for measuring cluster-level constructs based on a simulation study with inadvertently confounded design factors. We extended their simulation study using both Mplus and lavaan to reveal how their conclusions were dependent on their study conditions. Methods: We generated data sets from the so-called configural model and the simultaneous shared-and-configural model, both with and without nonzero residual variances at the cluster level. We fitted models to these data sets using different maximum likelihood estimation algorithms. Results: Stapleton and Johnson’s results were highly contingent on their confounded design factors. Convergence rates could be very different across algorithms, depending on whether between-level residual variances were zero in the population or in the fitted model. We discovered a worrying convergence issue with the default settings in Mplus, resulting in seemingly converged solutions that are actually not. Rejection rates of the normal-theory test statistic were as expected, while rejection rates of the scaled test statistic were seriously inflated in several conditions. Conclusions: The defaults in Mplus carry specific risks that are easily checked but not well advertised. Our results also shine a different light on earlier advice on the use of measurement models for shared factors.

Structural and functional characterization of multiple myeloma associated cytoplasmic poly(A) polymerase FAM46C.

BackgroundMultiple myeloma (MM) is a hematologic malignancy characterized by the accumulation of aberrant plasma cells within the bone marrow. The high frequent mutation of family with sequence similarity 46, member C (FAM46C) is closely related with the occurrence and progression of MM. Recently, FAM46C has been identified as a non‐canonical poly(A) polymerase (PAP) that functions as a tumor suppressor in MM. This study aimed to elucidate the structural features of this novel non‐canonical PAP and how MM‐related mutations affect the structural and biochemical properties of FAM46C, eventually advancing our understandings towards FAM46C mutation‐related MM occurrence.MethodsWe purified and crystallized a mammalian FAM46C construct, and solved its structure. Next, we characterized the property of FAM46C as a PAP through a combination of structural analysis, site‐directed mutagenesis and biochemical assays, and by comparison with its homolog FAM46B. Finally, we structurally analyzed MM‐related FAM46C mutations and tested the enzymatic activity of corresponding mutants.ResultsWe determined the crystal structure of a mammalian FAM46C protein at 2.35 Å, and confirmed that FAM46C preferentially consumed adenosine triphosphate (ATP) and extended A‐rich RNA substrates. FAM46C showed a weaker PAP activity than its homolog FAM46B, and this difference was largely dependent on the residue variance at particular sites. Of them, residues at positions 77, 290, and 298 of mouse FAM46C were most important for the divergence in enzymatic activity. Among the MM‐associated FAM46C mutants, those residing at the catalytic site (D90G and D90H) or putative RNA‐binding site (I155L, S156F, D182Y, F184L, Y247V, and M270V) showed abolished or compromised PAP activity of FAM46C, while N72A and S248A did not severely affect the PAP activity. FAM46C mutants D90G, D90H, I155L, S156F, F184L, Y247V, and M270V had significantly lower inhibitory effect on apoptosis of RPMI‐8226 cells as compared to wild‐type FAM46C.ConclusionsFAM46C is a prokaryotic‐like PAP with preference for A‐rich RNA substrates, and showed distinct enzymatic efficiency with its homolog FAM46B. The MM‐related missense mutations of FAM46C lead to various structural and biochemical outcomes to the protein.

CONTRIBUTION OF THE ADDITIVE GENETIC EFFECTS IN SOYBEAN BREEDING AIMING AT THE AGRONOMIC IDEOTYPE

The development of new genotypes and their availability to the agricultural market is decisive for the success of the Brazilian soybean production chain. This study aimed to estimate the components of variance and genetic parameters of aspects of agronomic importance and to predict the selection of transgressive soybean populations and families based on the agronomic ideotype. The segregating families were conducted in an enlarged block design, composed of 285 F3 segregating families corresponding to the unusual treatments, and together with seven commercial cultivars assigned as common treatments, arranged in four replications. The characters of agronomic interest were measured the first pod insertion height, plant height, number of pods on the main stem, number of pods on the branches, pods containing only one seed, pods containing two seeds, pods containing three seeds, pods containing four seeds and seed mass. Were estimated additive genetic variance, genetic variance of the F3 segregating families, genetic variance of F2 segregating population, residual variance, phenotypic variance, narrow sense heritability, coefficient of determination for the effects of the F3 segregating family, coefficient of determination for the effects of the progeny, genetic variation coefficient, residual variation coefficient and predicted genetic ranking for the best F2 segregating populations and F3 segregating families. The pronounced additive genetic gains in the progeny showed higher values for the characters measured: first pod insertion height with 1.84%, for the family and population, the additivity presented the highest values for the number of pods with four grains. The selection of the progenies through the agronomic ideotype based on the greatest genetic gains of the morphological characters weighted by the largest seed mass will thus lead these populations to joint selection for the other yield components.