Residual Variance Research Articles

16 Genetic Parameter Estimates for Age at First Calving in Simmental and Red Angus Heifers

Abstract The objective of this study was to determine genetic parameter estimates for age at first calving (AFC) in Red Angus and Simmental heifers. The ability for a heifer to calve earlier has implications on calf performance and the heifer's successive re-breeding. Heifer pregnancy data were obtained from International Genetic Solutions, where females with exposure records were included in the population. Females with a subsequent record of calving between 630 and 810 days of age were included in the analysis. Females with embryo transfer or multiple birth progeny were excluded. Records from single-animal and contemporary groups with no variation were eliminated and after data filtering, there were 149,379 Simmental with a mean Simmental breed proportion of 0.58 ± 0.25, and 83,191 Red Angus records with a mean Red Angus breed proportion of 0.80 ± 0.13. A 3-generation pedigree file containing 291,530 Simmental animals with 25,585 unique sires and 161,921 unique dams, and a 3-generation pedigree file containing 174,914 Red Angus animals with 19,733 unique sires and 99,464 unique dams were used for the separate analysis. The mean AFC in Simmental and Red Angus was 724.8 ± 28.64 d, and 724.2 ± 29.93 d, respectively. Data for each breed were modeled separately using a single trait animal model with random additive genetic and residual effects, and the fixed effect of contemporary group using ASReml 3.0. Contemporary group was defined as breeder, herd, year born, birth group and yearling group. The additive, residual, and phenotypic variances for Simmental were 120.08, 520.37, and 640.45 d2, respectively. For Red Angus, 85.67, 543.56, and 629.24 d2 were the additive, residual, and phenotypic variances, respectively. The heritability estimates of AFC for Simmental and Red Angus were 0.19 ± 0.0059 and 0.14 ± 0.0076, respectively. These results demonstrate AFC in days is lowly to moderately heritable. Lower residual and higher additive variance in Simmental compared to Red Angus may suggest the Simmental population has larger variation for underlying fertility influences such as age at puberty. The mean breeding values for AFC in Simmental was 6.1 ± 8.0, with a minimum of -24.4 and maximum of 54.7. Whereas Red Angus breeding values for AFC resulted in a mean of 4.0 ± 5.6 and a minimum and maximum of -16.1, and 37.3, respectively. The comparable heritability estimates, low standard error, and range in breeding values suggest selection for improved AFC can be attained. It is worth further exploring the impact of breed effects and heterosis influences on age at first calving. While this study does not account for heifers that did not calve in their first year, tandem selection for heifer fertility and age at calving should be heavily emphasized due to the economic importance of both traits.

Predicting Fragmented Care: Beneficiary, Physician, Practice, and Market Characteristics.

Understanding what drives fragmented ambulatory care (care spread across multiple providers without a dominant provider) can inform the design of future interventions to reduce unnecessary fragmentation. To identify the characteristics of beneficiaries, primary care physicians, primary care practice sites, and geographic markets that predict highly fragmented ambulatory care in the United States. Cross-sectional analysis of Medicare claims data for beneficiaries attributed to primary care physicians and practices in 2018. We used hierarchical linear models with random intercepts and an extensive list of explanatory variables to predict the likelihood of high fragmentation. A total of 3,540,310 Medicare fee-for-service beneficiaries met the inclusion criteria, attributed to 26,344 primary care physicians in 9300 practice sites, and 788 geographic markets. We defined high care fragmentation as a reversed Bice-Boxerman Index score above 0.85. Explanatory variables explained only 6% of the variation in highly fragmented care. Unobserved differences between primary care physicians, between practice sites, and between markets together accounted for 4%. Instead, 90% of the variation in high fragmentation was unobserved residual variance. We identified the characteristics of beneficiaries (age, reason for original Medicare entitlement, and dually eligible for Medicaid insurance), physicians (comprehensiveness of care), and practices (size, being part of a system/hospital) that had small associations with high fragmentation. Variation in fragmentation was not explained by observed beneficiary, primary care provider, practice site, or market characteristics. Instead, the aggregate behavior of diverse health care providers beyond primary care, along with unmeasured patient preferences and behaviors, seem to be important predictors.

Segregation analysis of 17,425 population-based breast cancer families: Evidence for genetic susceptibility and risk prediction

SummaryRare pathogenic variants in known breast cancer-susceptibility genes and known common susceptibility variants do not fully explain the familial aggregation of breast cancer. To investigate plausible genetic models for the residual familial aggregation, we studied 17,425 families ascertained through population-based probands, 86% of whom were screened for pathogenic variants in BRCA1, BRCA2, PALB2, CHEK2, ATM, and TP53 via gene-panel sequencing. We conducted complex segregation analyses and fitted genetic models in which breast cancer incidence depended on the effects of known susceptibility genes and other unidentified major genes and a normally distributed polygenic component. The proportion of familial variance explained by the six genes was 46% at age 20–29 years and decreased steadily with age thereafter. After allowing for these genes, the best fitting model for the residual familial variance included a recessive risk component with a combined genotype frequency of 1.7% (95% CI: 0.3%–5.4%) and a penetrance to age 80 years of 69% (95% CI: 38%–95%) for homozygotes, which may reflect the combined effects of multiple variants acting in a recessive manner, and a polygenic variance of 1.27 (95% CI: 0.94%–1.65), which did not vary with age. The proportion of the residual familial variance explained by the recessive risk component was 40% at age 20–29 years and decreased with age thereafter. The model predicted age-specific familial relative risks consistent with those observed by large epidemiological studies. The findings have implications for strategies to identify new breast cancer-susceptibility genes and improve disease-risk prediction, especially at a young age.

Measurement invariance of the Interpersonal Needs Questionnaire (INQ-15) across sexual orientation, gender identity, and race/ethnicity in a sample of sexual minority young adults.

Sexual minority (e.g., gay, lesbian, bisexual) people are at increased risk for suicidal thoughts and behaviors compared to their heterosexual peers. The interpersonal theory of suicide proposes that perceived burdensomeness and thwarted belongingness are central to the desire to die, and both are associated with suicidal ideation in sexual minority samples. The Interpersonal Needs Questionnaire (INQ) was developed to measure these risk factors and has become the most commonly used measure. However, it is unknown whether the INQ demonstrates similar measurement properties across subgroups of sexual minority people. Therefore, the goal of this study was to examine whether the 15-item version of the INQ exhibited measurement invariance (MI) across sexual orientation (gay/lesbian vs. bi +), gender identity (cisgender men vs. cisgender women vs. transgender/gender diverse individuals), and race/ethnicity (non-Latinx White individuals vs. people of color) in a sample of 792 sexual minority young adults (ages 18-29). A series of multigroup measurement invariance models indicated that the INQ-15 met strict invariance (i.e., equal factor loadings, item intercepts, and residual variances) across all three dimensions of identity. This indicates that it can be used and compared across diverse samples of sexual minority young adults. Results also indicated that perceived burdensomeness was greater for transgender/gender diverse individuals than for cisgender men and women, and that perceived burdensomeness and thwarted belongingness were greater for people of color than for non-Latinx White individuals. In contrast, gay/lesbian and bi + individuals did not differ. Additional research is needed to understand the factors that account for these group differences. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Heteroscedastic Reaction Norm Models Improve the Assessment of Genotype by Environment Interaction for Growth, Reproductive, and Visual Score Traits in Nellore Cattle.

Simple SummaryIn animal breeding, the different climatic conditions and production systems in beef cattle can result in the genotype by environment interaction. These interactions also need to be considered in genetic evaluations. In this study, five reaction norm models were tested on eleven traits related to growth, reproduction, and visual score measured in Nellore cattle breeding programs to check changes in the estimated genetic values of sires according to the selection environment. Using the best fitted statistical model, the presence of genotype by environment interaction was observed for some traits such as age at first calving, scrotal circumference, weaning to yearling weight gain, and yearling weight. For these traits, it is possible to select the best sires to increase productivity and reduce environmental sensitivity. Overall, the reaction norms trajectories for these traits seem to be affected by a non-linear component, and selecting robust animals for these traits is an alternative to increase production and reduce environmental sensitivity.The assessment of the presence of genotype by environment interaction (GxE) in beef cattle is very important in tropical countries with diverse climatic conditions and production systems. The present study aimed to assess the presence of GxE by using different reaction norm models for eleven traits related to growth, reproduction, and visual score in Nellore cattle. We studied five reaction norm models (RNM), fitting a linear model considering homoscedastic residual variance (RNM_homo), and four models considering heteroskedasticity, being linear (RNM_hete), quadratic (RNM_quad), linear spline (RNM_l-l), and quadratic spline (RNM_q-q). There was the presence of GxE for age at first calving (AFC), scrotal circumference (SC), weaning to yearling weight gain (WYG), and yearling weight (YW). The best models were RNM_l-l for YW and RNM_q-q for AFC, SC, and WYG. The heritability estimates for RNM_l-l ranged from 0.07 to 0.20, 0.42 to 0.61, 0.24 to 0.42, and 0.47 to 0.63 for AFC, SC, WYG, and YW, respectively. The heteroskedasticity in reaction norm models improves the assessment of the presence of GxE for YW, WYG, AFC, and SC. Additionally, the trajectories of reaction norms for these traits seem to be affected by a non-linear component, and selecting robust animals for these traits is an alternative to increase production and reduce environmental sensitivity.

PSXII-8 Gaussian Kernel Based on Geographic Information to Model Farm Effects in Genetic Evaluation of Pasture-Raised Beef Cattle

Abstract The notion of contemporary groups (CG) is routinely used in beef cattle breeding programs for adjusting phenotypic observations for environmental effects. Although CG include the effect of farms, they do not allow information borrowing across farms as the CG are generally modeled either as fixed effects or as statistically independent random effects. Nonetheless, farms that are located closer to each other may share similarities in terms of climate as well as soil and forage quality. We proposed a Gaussian kernel approach to model farm effects based on their geographical position. The data consisted of 487,905 yearling weight observations of Nellore cattle from 394 farms located throughout Brazil and a pedigree file with 868,003 animals from the Embrapa-Geneplus Breeding Program. The benchmark model (M0) included the fixed effects of sex, age (linear covariate), and season at measurement, as well as direct genetic and residual effects as random, independently distributed with variance Aσ a2 and Iσ e2, respectively, where A is the numerator relationship matrix. From the M0, three models were analyzed using REML/BLUP method. M1, M2, and M3 included, respectively, the farm effect as fixed, as independent-random, and correlated-random using a Gaussian kernel with different bandwidths (h; ranging from 0.05 to 10). The AIC was used to compare the models’ goodness-of-fit. The incorporation of farm as a random effect had a negative impact on the overall fit and, consequently, the M2 and M3 presented the largest AIC (5,159,839, and 5,159,838, respectively). The M3 presented the best fit using h equal to 0.05. For this model, when the h value was increased (values close to one off-diagonal), the model fit was proportionally penalized. No gain was achieved with the correlated farm effect because a more informative kernel may have resulted in multi-collinearity issues. In the best model (M1, AIC= 2,555,877) the estimated genetic and residual variance were equal to 391.5, and 1,586 kg2, respectively.

Basal Reactivity Evaluated by Infrared Thermography in the "Caballo de Deporte Español" Horse Breed According to Its Coat Color.

Simple SummaryThe importance of the coat color in horses has been present since their domestication. The interactive effect of coat color-determinant genes on other traits, such as behavior, makes it a factor of importance in horse selection, as it determines the way the horse perceives and reacts to its environment. Eye temperature at rest (ETR) assessed with infrared thermography was used as a suitable non-invasive tool to assess basal reactivity. The aim of this study was to evaluate the influence of coat color on basal reactivity assessed with infrared thermography, determine their relationship with the results obtained in show jumping competitions, and to estimate the genetic parameters for ETR to test its suitability for genetic selection. Our results indicated differences in ETR due to six different factors. We also discovered a positive correlation with ranking in show jumping, indicating that less reactive horses were more likely to achieve better rankings. The genetic results indicated that part of the variability present in the basal reactivity due to coat color has an environmental origin. The heritability values indicate that ETR has a suitable genetic basis to be used in the breeding program to select for basal reactivity due to coat color.Horses have been valued for their diversity of coat color since prehistoric times. In particular, the pleiotropic effect that coat color genes have on behavior determines the way the horse perceives and reacts to its environment. The primary aim of this study was to evaluate the influence of coat color on basal reactivity assessed with infrared thermography as eye temperature at rest (ETR), determine their relation with the results obtained by these horses in Show Jumping competitions and to estimate the genetic parameters for this variable to test its suitability for genetic selection. A General Linear Model (GLM) and Duncan post-hoc analysis indicated differences in ETR due to coat color, sex, age, location, and breed-group factors. A Spearman’s rank correlation of 0.11 (p < 0.05) was found with ranking, indicating that less reactive horses were more likely to achieve better rankings. Heritability values ranged from 0.17 to 0.22 and were computed with a model with genetic groups and a model with residual variance heterogeneity. Breeding values were higher with the last genetic model, thus demonstrating the pleiotropic effect of coat color. These results indicate that ETR has a suitable genetic basis to be used in the breeding program to select for basal reactivity due to coat color.

33 Genetic Evaluation of Show Jumping Performance in the Irish Sport Horse

Abstract The current genetic evaluations of the Irish Sport Horse are undertaken on a single trait called the Lifetime Performance Rating (LPR). The LPR represents the highest level a horse has achieved two double clear rounds in show jumping in both national and international performances. The objective of this study was to compare the method currently used in these evaluations (Model 1, Table 1) to a modified methodology to determine the most accurate method for estimating breeding values (EBV) for the LPR. Genetic parameters and EBVs were generated based on the LPR of 30,355 horses using eight different animal models. Each of the eight models considered the additive genetic variance as the random effect and a combination of following fixed effects: sex, year the highest level was first achieved, age the highest level was achieved, maximum age of the animal in the competition data, Thoroughbred percentage (TB%), and TB% divided into eights. (Table 1). The goodness-of-fit of each model was evaluated using the Akaike’s information criteria and the genetic parameters produced by each model. Heritability estimates for LPR ranged from 0.26 to 0.32 depending on the model used (Table 1). The inclusion of age or maximum age in the model decreased the residual variances, and thus, increased the heritability estimates of LPR; these models also performed better than those that did not consider age in any form. The models that considered the effect of TB% were not a significantly better fit than those that did not consider this effect. In conclusion, future genetic evaluations of LPR in the Irish Sport Horse would benefit from using a model that considers either the age or maximum age of performance of each horse, alongside the year the highest level was achieved, and the sex of the horse.

Methods for estimation of genetic parameters in soybeans: an alternative to adjust residual variability

Selection practices are maximized when plant breeders have the availability of consolidated parameters, which will guide direct and indirect selection methods. This study aimed to apply a biometric alternative to minimize residual variance and maximize selection parameters by parent-progeny regression, interim controls, and mixed linear models intrinsic to breeding. The obtained data were subjected to the assumptions of the statistical model, which identified the normality and homogeneity of the residual variances and model additivity. Subsequently, two analysis scenarios were created. The first preserved all information obtained in the experiment, both from segregating families and pure-line cultivars, and was called original scenario. The other scenario preserved progeny data, but the residual variability of controls was restricted using as criterion observations contained between the interval of the first sample standard deviation. Thereby, an acceptable residue limit could be obtained. Both scenarios were submitted to three consolidated frequentist methods (genitor-progeny regression; sum of squares of augmented block design with interim controls; and mixed linear models, wherein random genetic effects are taken as weighted genetic parameters by the genealogical matrix). Restricting residual variation in parents or controls can maximize genetic parameters and genetic gains in soybean breeding. Significant heritability estimate gains were obtained in the augmented blocks with interim control approach. Mixed linear models with random genetic effects can be considered a great tool to obtain genetic parameters in experiments with a high magnitude of common and regular treatments.

Comprehensive analysis of superconducting gravimeter data, GPS, and hydrology modelling in support of lunar laser ranging at Apache Point Observatory, New Mexico

SUMMARYThe Apache Point Observatory Lunar Laser-ranging Operation (APOLLO) project began in 2006 using a 3.5 m telescope on a high peak in the Sacramento Mountains of New Mexico. It has been one of the best performing (in terms of returned photons per viewing period and range uncertainty) of a handful of similar sites worldwide. The purpose of installing a superconducting gravimeter (SG) in 2009 was to improve the determination of local deformation, directly affecting the telescope motions, as part of an effort to reduce the error in lunar distance to the mm level. We have now accumulated 10 yr of gravity measurements, together with data from a nearby permanent GPS Plate Boundary Observatory (P027) site. This paper describes the traditional analysis of SG data to produce a local tidal model and to determine a number of necessary dynamic corrections that are specific to the site, of which local atmospheric and hydrology attraction and loading are the most important. Loading corrections are available through the Ecole et Observatoire des Sciences de la Terre (EOST) website as part of the International Earth Rotation Service, and we consider 12 different EOST loading series for global atmosphere and hydrology loading and attraction. Nonetheless, local hydrology can only be addressed at the required level of detail using local models determined directly from the gravity data. We devote a major portion of the paper to the development of several water storage and conceptual tank models that succeed in reducing the large seasonal variance of the SG residuals by more than 90 per cent. Yet there remain episodes in the corrected residuals that depart by up to 4 $\mu {\rm{Gal}}\,$ from the hydrological models, and the mean error of our fit remains between 1.5–2.0 $\mu {\rm{Gal}}\,$ for our two data sets, 2009–2012 and 2013–2018 respectively. Unfortunately the vertical GPS data relies on the UNAVCO processing, and has typically poor vertical accuracy. We find the correlation between the GPS and gravity residuals to be correspondingly weak, and then discuss briefly the incorporation of the displacement and gravity data into the Planetary Ephemeris Program for solving for the lunar distance.

GDR: A Game Algorithm Based on Deep Reinforcement Learning for Ad Hoc Network Routing Optimization

Ad Hoc networks have been widely used in emergency communication tasks. For dynamic characteristics of Ad Hoc networks, problems of node energy limited and unbalanced energy consumption during deployment, we propose a strategy based on game theory and deep reinforcement learning (GDR) to improve the balance of network capabilities and enhance the autonomy of the network topology. The model uses game theory to generate an adaptive topology, adjusts its power according to the average life of the node, helps the node with the shortest life to decrease the power, and prolongs the survival time of the entire network. When the state of the node changes, reinforcement learning is used to automatically generate routing policies to improve the average end-to-end latency of the network. Experiments show that, under the condition of ensuring connectivity, GDR has smaller residual energy variance, longer network lifetime, and lower network delay. The delay of the GDR model is 10.5% higher than that of existing methods on average.

The impulsive behavior short scale-8 (I-8): A comprehensive validation of the English-language adaptation.

The Impulsive Behavior Short Scale–8 (I-8) measures the psychological construct of impulsivity with four subscales comprising two items each (completion time < 1 min). The aim of the present study was threefold: (1) to assess the psychometric properties (objectivity, reliability, and validity) of the English-language I-8; (2) to compare these psychometric properties with those of the original German-language source version of the scale; and (3) to test the cross-national comparability of the scale via measurement invariance tests. For this purpose, we used heterogeneous quota samples from the UK and Germany. Our results indicate that I-8 is a reliable and valid short scale with highly comparable psychometric properties across both language versions. In addition, I-8 showed a highly similar correlational pattern with various extraneous variables across the two nations. Furthermore, partial scalar invariance and full invariance of residual variances held, allowing the comparison of latent means and observed (co)variances across nations. I-8 lends itself as a measure of impulsive behavior especially in surveys in which assessment time is limited, such as in large-scale cross-national surveys.

The Impact of Functional Form Complexity on Model Overfitting for Nonlinear Mixed-Effects Models

Nonlinear mixed-effects models (NLMEMs) allow researchers to model curvilinear patterns of growth, but there is ambiguity as to what functional form the data follow. Often, researchers fit multiple nonlinear functions to data and use model selection criteria to decide which functional form fits the data “best.” Frequently used model selection criteria only account for the number of parameters in a model but overlook the complexity of intrinsically nonlinear functional forms. This can lead to overfitting and hinder the generalizability and reproducibility of results. The primary goal of this study was to evaluate the performance of eight model selection criteria via a Monte Carlo simulation study and assess under what conditions these criteria are sensitive to model overfitting as it relates to functional form complexity. Results highlighted criteria with the potential to capture overfitting for intrinsically nonlinear functional forms for NLMEMs. Information criteria and the stochastic information complexity criterion recovered the true model more often than the average or conditional concordance correlation. Results also suggest that the amount of residual variance and sample size have an impact on model selection for NLMEMs. Implications for future research and recommendations for application are also provided.

The tropical biomass & carbon project–An application for forest biomass and carbon estimates

This article introduces the Tropical Biomass & Carbon Application – the ‘TB&C App’, a web application available on the permanent link www.tropicalbiomass.com. The TB&C App requires as input attributes ‘the smallest and largest diameters’, ‘number of trees ha−1’, basal area ha−1, and ‘parameters of the diameter (beta) distribution’ describing stand structure. The App delivers outputs at two levels: (1) Stand level, including mean aboveground biomass (AGB) and carbon (AGC), in Mg ha−1, along with confidence intervals (CIs) as measures of uncertainty, and; (2) Tree level estimates, with AGB and diameter for every simulated tree. Phase 1 of the project TB&C comprises four Brazilian forest (and non-forest) formations: Campinarana, Floresta estacional, Floresta ombrofila, and Savana. This article aims to (i) describe the algorithm written for the TB&C App, and (ii) present results of Phase 1. This first phase counts on a standardized database of 1,428 trees with field-measured dry AGB, from plots across the different formations, which is the largest tree-biomass database compiled so far in Brazil. Model uncertainties were incorporated into the modeling process, allowing computation of CIs through an uncertainty approach. The total variance of residuals of AGB was also modeled, aiming at predicting CIs as a function of the quantity of AGB. An analysis of reliability of the equations implemented in the TB&C App indicates that more than 95% (n = 64,000) of the true AGB's fit into the CI outputted by the TB&C App. A comparison with other approaches in the literature shows significant agreement with previous estimates and more conservative estimates where previously-published estimates disagreed with the TB&C App. We cite as advantages of the TB&C App; (i) reliability of the outputs, (ii) a user-friendly layout, (iii) AGB and AGC estimates provided along with robust CIs, and (iv) estimates at the stand and tree levels with consistent totals. A biomass dataset containing information on 64,000 plots is also delivered as supplement of this paper.

Relative importance of genotype, gene expression, and DNA methylation on complex traits in perennial ryegrass.

The growing demand for food and feed crops in the world because of growing population and more extreme weather events requires high-yielding and resilient crops. Many agriculturally important traits are polygenic, controlled by multiple regulatory layers, and with a strong interaction with the environment. In this study, 120 F2 families of perennial ryegrass (Lolium perenne L.) were grown across a water gradient in a semifield facility with subsoil irrigation. Genomic (single-nucleotide polymorphism [SNP]), transcriptomic (gene expression [GE]), and DNA methylomic (MET) data were integrated with feed quality trait data collected from control and drought sections in the semifield facility, providing a treatment effect. Deep root length (DRL) below 110 cm was assessed with convolutional neural network image analysis. Bayesian prediction models were used to partition phenotypic variance into its components and evaluated the proportion of phenotypic variance in all traits captured by different regulatory layers (SNP, GE, and MET). The spatial effects and effects of SNP, GE, MET, the interaction between GE and MET (GE × MET) and GE× treatment (GEControl and GEDrought ) interaction were investigated. Gene expression explained a substantial part of the genetic and spatial variance for all the investigated phenotypes, whereas MET explained residual variance not accounted for by SNPs or GE. For DRL, MET also contributed to explaining spatial variance. The study provides a statistically elegant analytical paradigm that integrates genomic, transcriptomic, and MET information to understand the regulatory mechanisms of polygenic effects for complex traits.

Endocrine flexibility.

Endocrine flexibility.

Mendelian randomization mixed-scale treatment effect robust identification and estimation for causal inference.

Standard Mendelian randomization (MR) analysis can produce biased results if the genetic variant defining an instrumental variable (IV) is confounded and/or has a horizontal pleiotropic effect on the outcome of interest not mediated by the treatment variable. We provide novel identification conditions for the causal effect of a treatment in the presence of unmeasured confounding by leveraging a possibly invalid IV for which both the IV independence and exclusion restriction assumptions may be violated. The proposed Mendelian randomization mixed-scale treatment effect robust identification (MR MiSTERI) approach relies on (i) an assumption that the treatment effect does not vary with the possibly invalid IV on the additive scale; (ii) that the confounding bias does not vary with the possibly invalid IV on the odds ratio scale; and (iii) that the residual variance for the outcome is heteroskedastic with respect to the possibly invalid IV. Although assumptions (i) and (ii) have, respectively, appeared in the IV literature, assumption (iii) has not; we formally establish that their conjunction can identify a causal effect even with an invalid IV. MR MiSTERI is shown to be particularly advantageous in the presence of pervasive heterogeneity of pleiotropic effects on the additive scale. We propose a simple and consistent three-stage estimator that can be used as a preliminary estimator to a carefully constructed efficient one-step-update estimator. In order to incorporate multiple, possibly correlated, and weak invalid IVs, a common challenge in MR studies, we develop a MAny Weak Invalid Instruments (MR MaWII MiSTERI) approach for strengthened identification and improved estimation accuracy. Both simulation studies and UK Biobank data analysis results demonstrate the robustness of the proposed methods.

A Distributed Method for Self-Calibration of Magnetoresistive Angular Position Sensor within a Servo System

Magnetoresistive angle position sensors are, beside Hall effect sensors, especially suitable for usage within servo systems due to their reliability, longevity, and resilience to unfavorable environmental conditions. The proposed distributed method for self-calibration of magnetoresistive angular position sensor uses the data collected during the highest allowed speed shaft movement for the identification of the measurement process model parameters. Data acquisition and initial data processing have been realized as a part of the control process of the servo system, whereas the identification of the model parameters is a service of an application server. The method of minimizing of the sum of algebraic distances of the sensor readings and the parametrized model is employed for the identification of parameters of linear compensation, whereas the average shaft rotation speed has been used as a high precision reference for the identification of parameters of harmonic compensation. The proposed method, in addition to a fast convergence, provides for the increase in measurement accuracy for an order of magnitude. Experimentally obtained measurement uncertainty was better than 0.5°, with the residual variance less than 0.02°, comparable to the sensor resolution.

Additive and non-additive genetic effects of humoral immune traits in Japanese quail

In breeding programs, using appropriate models to estimate the variance components with high accuracy is essential. Considering the non-additive genetic effects along with additive effects in evaluation analysis models can be effective in increasing the accuracy of estimates. This study was conducted to fit the best model for estimation of variance components of humoral immune responses (antibody titers against sheep red blood cells [SRBC] and Newcastle disease virus [NDV]), especially non-additive genetic effects (dominance and epistasis) in Japanese quail. The collected records of humoral immune responses were used to estimate genetic parameters using the Bayesian method via Gibbs sampling by fitting 24 different models. The best model for each trait was selected based on Deviance Information Criteria (DIC). Moreover, the residual and additive genetic variance changes were evaluated comparing the models. The results showed that dominance, epistasis, and maternal genetic effects were influenced on humeral immune responses traits except for immunoglobulin Y (IgY). The direct heritability for total antibody titer (AbT), the titer of immunoglobulin Y (IgY), the titer of immunoglobulin M (IgM) against SRBC, antibody titer against NDV (AbNDV), and immune system performance index (IgF) were 0.065, 0.208, 0.117, 0.100, and 0.132, respectively. The contribution of dominance and epistasis as a proportion of phenotypic variance for all traits ranged from 0.070 to 0.221, and 0.087 to 0.251, respectively. A reduction in residual variance were observed in all traits, moreover accuracy of estimated breeding values increased in some traits with adding non-additive genetic effects in the models. Furthermore, the additive genetic variance in the best models decreased for AbT, IgF, and AbNDV traits compared with the simple model (without non-additive genetic effects). The results suggested including the non-additive genetic effects in the genetic models for evaluation of humoral immune responses of quail to obtain precise genetic parameters.

Psychometric properties of the Coparenting Relationship Scale in Chinese parents

AbstractObjectiveThis study aimed to investigate the psychometric properties of the Coparenting Relationship Scale (CRS) in Chinese samples.BackgroundThe coparenting relationship serves an important role in raising children. The CRS was originally developed to measure the multidimensionality of the coparenting relationship. However, its latent structure and psychometric properties have not been extensively addressed, particularly in non‐Western cultures.MethodData were collected from a sample of 991 Chinese families (991 mothers, 967 fathers, 31–57 years of age). Confirmatory factor analyses were utilized to provide evidence of the theoretical factorial structure. Internal consistency was examined by Cronbach's α and omega coefficients. Correlations with the Dyadic Adjustment Scale were to verify criterion validity. Configural, metric, scalar, and residual variance invariance were examined across the gender of parents in dyadic perspective.ResultsIn contrast to a poor fit for the seven‐factor structure of the original 35‐item CRS, the Chinese version of the CRS (CRS‐C) showed a good fit in a six‐factor structure with 27 items. The CRS‐C showed strict measurement invariance across gender of the parent, satisfactory internal consistency, and criterion‐related validity as indicated by marital quality.ConclusionsThese findings provided preliminary evidence that the CRS‐C is reliable and valid for measuring the coparenting relationship among Chinese parents.ImplicationsCoparenting relationship and its measurement will benefit from attention in non‐Western contexts and cross‐cultural studies.