Residual Variance Research Articles

KLKB1 and CLSTN2 are associated with HDL-mediated cholesterol efflux capacity in a genome-wide association study

Background and aimsHDL-mediated cholesterol efflux capacity (CEC) may protect from cardiovascular disease. Thus, we aimed to identify its genetic and non-genetic determinants. MethodsWe measured CEC to 2% apolipoprotein B-depleted serum using BODIPY-cholesterol and cAMP-stimulated J774A.1 macrophages using serum samples from 4,981 participants in the German Chronic Kidney Disease (GCKD) study. Variance of CEC explained by clinical and biochemical parameters in a multivariable linear regression model was calculated by proportional marginal variance decomposition. A genome-wide association study with 7,746,917 variants was performed based on an additive genetic model. The main model was adjusted for age, sex and principal components 1-10. Further models were selected for sensitivity analysis and to reduce residual variance by known CEC pathways. ResultsVariables that explained 1% and more of the variance of CEC were concentrations of triglycerides (12.9%), HDL-cholesterol (11.8%), LDL-cholesterol (3.0%), apolipoprotein A-IV (2.8%), PCSK9 (1.0%), and eGFR (1.0%). The KLKB1 (chr4) and APOE/C1 (chr19) loci were genome-wide significantly (p < 5x10−8) associated with CEC in our main model (p = 8.8x10−10 and p = 3.3x10−10, respectively). KLKB1 remained significantly associated after additional adjustment for either kidney parameters, HDL-cholesterol, triglycerides or apolipoprotein A-IV concentrations, while the APOE/C1 locus was not significantly associated anymore after adjustment for triglycerides. Adjustment for triglycerides also revealed an association with the CLSTN2 locus (chr3; p = 6.0x10−9). ConclusionsWe identified HDL-cholesterol and triglycerides as the main determinants of CEC. Furthermore, we newly found a significant association of CEC with the KLKB1 and the CLSTN2 locus and confirmed the association with the APOE/C1 locus, likely mediated by triglycerides.

Incorporating random effects in biopharmaceutical control strategies

ObjectiveRandom effects are often neglected when defining the control strategy for a biopharmaceutical process. In this article, we present a case study that highlights the importance of considering the variance introduced by random effects in the calculation of proven acceptable ranges (PAR), which form the basis of the control strategy.MethodsLinear mixed models were used to model relations between process parameters and critical quality attributes in a set of unit operations that comprises a typical biopharmaceutical manufacturing process. Fitting such models yields estimates of fixed and random effect sizes as well as random and residual variance components. To form PARs, tolerance intervals specific to mixed models were applied that incorporate the random effect contribution to variance.ResultsWe compared standardized fixed and random effect sizes for each unit operation and CQA. The results show that the investigated random effect is not only significant but in some unit operations even larger than the average fixed effect. A comparison between ordinary least squares and mixed models tolerance intervals shows that neglecting the contribution of the random effect can result in PARs that are too optimistic.ConclusionsUncontrollable effects such as week-to-week variability play a major role in process variability and can be modelled as a random effect. Following a workflow such as the one suggested in this article, random effects can be incorporated into a statistically sound control strategy, leading to lowered out of specification results and reduced patient risk.

Genomic prediction for grain yield and micro-environmental sensitivity in winter wheat.

Individuals within a common environment experience variations due to unique and non-identifiable micro-environmental factors. Genetic sensitivity to micro-environmental variation (i.e. micro-environmental sensitivity) can be identified in residuals, and genotypes with lower micro-environmental sensitivity can show greater resilience towards environmental perturbations. Micro-environmental sensitivity has been studied in animals; however, research on this topic is limited in plants and lacking in wheat. In this article, we aimed to (i) quantify the influence of genetic variation on residual dispersion and the genetic correlation between genetic effects on (expressed) phenotypes and residual dispersion for wheat grain yield using a double hierarchical generalized linear model (DHGLM); and (ii) evaluate the predictive performance of the proposed DHGLM for prediction of additive genetic effects on (expressed) phenotypes and its residual dispersion. Analyses were based on 2,456 advanced breeding lines tested in replicated trials within and across different environments in Denmark and genotyped with a 15K SNP-Illumina-BeadChip. We found that micro-environmental sensitivity for grain yield is heritable, and there is potential for its reduction. The genetic correlation between additive effects on (expressed) phenotypes and dispersion was investigated, and we observed an intermediate correlation. From these results, we concluded that breeding for reduced micro-environmental sensitivity is possible and can be included within breeding objectives without compromising selection for increased yield. The predictive ability and variance inflation for predictions of the DHGLM and a linear mixed model allowing heteroscedasticity of residual variance in different environments (LMM-HET) were evaluated using leave-one-line-out cross-validation. The LMM-HET and DHGLM showed good and similar performance for predicting additive effects on (expressed) phenotypes. In addition, the accuracy of predicting genetic effects on residual dispersion was sufficient to allow genetic selection for resilience. Such findings suggests that DHGLM may be a good choice to increase grain yield and reduce its micro-environmental sensitivity.

Heywood Cases in Unidimensional Factor Models and Item Response Models for Binary Data.

Heywood cases are known from linear factor analysis literature as variables with communalities larger than 1.00, and in present day factor models, the problem also shows in negative residual variances. For binary data, factor models for ordinal data can be applied with either delta parameterization or theta parametrization. The former is more common than the latter and can yield Heywood cases when limited information estimation is used. The same problem shows up as non convergence cases in theta parameterized factor models and as extremely large discriminations in item response theory (IRT) models. In this study, we explain why the same problem appears in different forms depending on the method of analysis. We first discuss this issue using equations and then illustrate our conclusions using a small simulation study, where all three methods, delta and theta parameterized ordinal factor models (with estimation based on polychoric correlations and thresholds) and an IRT model (with full information estimation), are used to analyze the same datasets. The results generalize across WLS, WLSMV, and ULS estimators for the factor models for ordinal data. Finally, we analyze real data with the same three approaches. The results of the simulation study and the analysis of real data confirm the theoretical conclusions.

Efficient penalized generalized linear mixed models for variable selection and genetic risk prediction in high-dimensional data.

Sparse regularized regression methods are now widely used in genome-wide association studies (GWAS) to address the multiple testing burden that limits discovery of potentially important predictors. Linear mixed models (LMMs) have become an attractive alternative to principal components (PCs) adjustment to account for population structure and relatedness in high-dimensional penalized models. However, their use in binary trait GWAS rely on the invalid assumption that the residual variance does not depend on the estimated regression coefficients. Moreover, LMMs use a single spectral decomposition of the covariance matrix of the responses, which is no longer possible in generalized linear mixed models (GLMMs). We introduce a new method called pglmm, a penalized GLMM that allows to simultaneously select genetic markers and estimate their effects, accounting for between-individual correlations and binary nature of the trait. We develop a computationally efficient algorithm based on penalized quasi-likelihood estimation that allows to scale regularized mixed models on high-dimensional binary trait GWAS. We show through simulations that when the dimensionality of the relatedness matrix is high, penalized LMM and logistic regression with PC adjustment fail to select important predictors, and have inferior prediction accuracy compared to pglmm. Further, we demonstrate through the analysis of two polygenic binary traits in a subset of 6731 related individuals from the UK Biobank data with 320K SNPs that our method can achieve higher predictive performance, while also selecting fewer predictors than a sparse regularized logistic lasso with PC adjustment. Our Julia package PenalizedGLMM.jl is publicly available on github: https://github.com/julstpierre/PenalizedGLMM. Supplementary data are available at Bioinformatics online.

Daily living skills scale: Development and preliminary validation of a new, open-source assessment of daily living skills.

Autistic individuals and individuals with a range of other neurodevelopmental conditions (NDD) often present with lower levels of daily living skills (DLS) when compared to their neurotypical peers. Importantly, lower levels of DLS have been linked to a range of negative outcomes, including lower rates of post-secondary education, lower employment rates, and higher daily support needs across autism and NDD. However, there are currently no open-source informant-reported instruments for capturing key aspects of DLS. This study describes the development, refinement, and initial psychometric evaluation of a new, relatively brief (53-item). Daily Living Skills Scale (DLSS) in a sample of 1,361 children aged 2-17 years, Confirmatory Factor Analysis demonstrated an excellent fit of unidimensional model to the data (CFI = 0.953, TLI = 0.951, RMSEA = 0.073 [95% CI: 0.071-0.074]). The single-factor CFA model showed evidence of measurement invariance of factor loadings, thresholds, and residual variance (strict invariance) across sex, age, race, and ethnicity. Model reliability and internal consistency were excellent (ω = 0.98; α = 0.97). Conditional reliability estimates indicated very good reliability (= 0.80) for the total DLS scale from very low (θ = -4.2) to high (θ = +2.4) scores. Conceptually derived self-care, homecare, and community participation subscales also showed strong reliability and internal consistency. With further replication, the EFS has excellent potential for wide adoption across research and clinical contexts.

Phase estimation at the point-ahead angle for AO pre-compensated ground to GEO satellite telecoms.

We present a new method to estimate the off-axis adaptive optics pre-compensation phase of a ground to GEO satellite telecom link suffering from point-ahead anisoplanatism. The proposed phase estimator relies on the downlink phase and log-amplitude measurements that are available at the optical ground station. We introduce the analytical tools, extended from the literature, to build the estimator as well as a general modal formalism to express the reciprocal residual phase covariance matrix resulting from any estimation linear with measurements. We use this residual phase covariance matrix to generate independent coupled flux samples thanks to a pseudo-analytical approach and study the gain offered by the proposed estimator on the coupled flux statistics, in various atmospheric conditions. The estimator is shown to reduce the anisoplanatic residual phase variance by at least 35%, and 46% at best, with a greater impact on the lower modes, especially on the tip and tilt residual phase variances. The phase variance reduction brings a gain up to 15 dB on the cumulative density function at probability 10-3. This gain should allow to relax the power constraints on the link budget at the OGS and renews the interest in large aperture diameter (60 cm class telescopes) for GEO Feeder links by reducing the atmospheric turbulence impact on the uplink coupled signal.

Head Shape Heritability in the Hungarian Meadow Viper Vipera ursinii rakosiensis.

Understanding heritability patterns in functionally relevant traits is a cornerstone for evaluating their evolutionary potential and their role in local adaptation. In this study, we investigated patterns of heritability in the head shape of the Hungarian meadow viper (Vipera ursinii rakosiensis). To this end, we used geometric morphometric data from 12 families composed of 8 mothers, 6 fathers and 221 offspring, bred in captivity at the Hungarian Meadow Viper Conservation Centre (Hungary). We separately evaluated maternal and paternal contributions to the offspring phenotype, in addition to additive genetic effects, all determined using a mixed animal model. Our results indicate a strong genetic and maternal contribution to head shape variations. In contrast, the paternal effects-which are rarely evaluated in wild-ranging species-as well as residual environmental variance, were minimal. Overall, our results indicate a high evolutionary potential for head shape in the Hungarian meadow viper, which suggests a strong contribution of this ecologically important trait in shaping the ability of this endangered species to adapt to changing conditions and/or habitats. Furthermore, our results suggest that maternal phenotypes should be carefully considered when designing captive breeding parental pairs for reinforcing the adaptive capacity of threatened populations, whereas the paternal phenotypes seem less relevant.

ICanClean Improves Independent Component Analysis of Mobile Brain Imaging with EEG.

Motion artifacts hinder source-level analysis of mobile electroencephalography (EEG) data using independent component analysis (ICA). iCanClean is a novel cleaning algorithm that uses reference noise recordings to remove noisy EEG subspaces, but it has not been formally tested in a parameter sweep. The goal of this study was to test iCanClean’s ability to improve the ICA decomposition of EEG data corrupted by walking motion artifacts. Our primary objective was to determine optimal settings and performance in a parameter sweep (varying the window length and r2 cleaning aggressiveness). High-density EEG was recorded with 120 + 120 (dual-layer) EEG electrodes in young adults, high-functioning older adults, and low-functioning older adults. EEG data were decomposed by ICA after basic preprocessing and iCanClean. Components well-localized as dipoles (residual variance < 15%) and with high brain probability (ICLabel > 50%) were marked as ‘good’. We determined iCanClean’s optimal window length and cleaning aggressiveness to be 4-s and r2 = 0.65 for our data. At these settings, iCanClean improved the average number of good components from 8.4 to 13.2 (+57%). Good performance could be maintained with reduced sets of noise channels (12.7, 12.2, and 12.0 good components for 64, 32, and 16 noise channels, respectively). Overall, iCanClean shows promise as an effective method to clean mobile EEG data.

Differential sensitivity of climate conditions on birth weight genetic values in mice divergently selected for birth weight residual variance.

After 32 generations of a divergent selection experiment for residual variance of birth weight in mice, two divergent lines were thus obtained: the heterogeneous line (H-line) and the homogeneous line (L-line). Throughout the generations, differences were observed between the two lines in traits such as litter size, survival at weaning and birth weight variability caused by unidentified environmental conditions. The L-line exhibited advantages in terms of higher survival rates, larger litter sizes, and less sensitivity to changes in food intake. The study is an examination of the effects of climate as an environmental factor on the performance of these animals. Climate factors including maximum, minimum, and mean temperature (T), humidity (H), and TH index; at three stages (the fecundation, a week before the parturition and the parturition), were linked to a birth weight dataset consisting of 22,614 records distributed as follows: 8,853 corresponding to the H-line, 12,649 to the L-line and 1,112 to the initial population. Out of the 27 analysed climatic variables, the maximum temperature one week before parturition (MXTW) was identified as the most influential when comparing heteroscedastic models with the deviance information criterion. The order of Legendre polynomial to apply in the following random regression model was tested by a cross-validation using homoscedastic models. Finally, MXTW was compared on how it affected the two divergent lines by analyzing predicted breeding values (PBV) obtained from a random regression heterosdedastic model. The mean PBV of the H-line in the first generation showed a range of 0.070g with a negative slope, which was 35 times higher than the range obtained for the L-line, which varied within 0.002g. In the last generation of selection, the H-line exhibited greater instability of PBV across temperatures, with a difference of 0.101g between the maximum and minimum mean PBV, compared to 0.017g for the L-line. The standard deviations of the slopes in the H-line were more dispersed than in the L-line. Unlike the H-line, the L-line had slopes that were not significantly different from 0 throughout the generations of selection, indicating greater stability in response to MXTW variations. The H-line exhibited a higher sensitivity to changes in MXTW, particularly in birth weight, with the L-line being more stable. The selection for uniformity of birth weight could lead to less sensitive animals under environmental changes.

Genetic parameters for fleece uniformity in alpacas.

Fiber diameter is the main selection objective and criterion in alpaca breeding programs, but it can vary across anatomic regions of the animal. As fiber diameter is usually registered from a unique sample from the mid side of the body, fiber diameter variability within fleece is never addressed and phenotypic and genetic differences may exist for fleece uniformity in alpaca populations. The objective of this work was to estimate the genetic parameters of fleece uniformity in an alpaca population. Fiber diameters measured in three different locations were used as repeated records of the same animal and studied for fitting a model that considers heterogeneous the residual variance of the model. Also, the logarithm of the standard deviation of the three measures was used as a measure of the fleece variability. Estimate of the additive genetic variance of the environmental variability was 0.43±0.14, enough high to suggest the existence of wide room to select for fleece uniformity. Genetic correlation of the trait with its environmental variability was 0.76±0.13 showing that fleece uniformity will be indirectly selected when aiming to reduce the fiber diameter. In the light of these parameters, and due to the cost of registering and the cost of opportunity, it looks no worthy to include uniformity as a selection criterion in alpaca breeding programs.

Pressure Sensor Fault Diagnosis by Using Adaptive Robust Observers

The model uncertaint of hydraulic systems is a key issue in observer-based fault diagnosis. In this research, for diagnosing pressure sensor faults in hydraulic systems, we present the application of adaptive robust observers design. For reducing model uncertainty and increasing the feasibility of the fault diagnosis scheme, this paper uses a robust filter and controlled parameter adaption to deal with model uncertainty. Then, according to the fault system model, the fault magnitude of the pressure sensor fault is estimated and the residual variance can be generated for diagnosing the sensor fault. By experiments, the fault diagnosis scheme is proven efficient, which is based on adaptive robust observers. Then, the fault magnitude can be estimated.

Raise Estimation: An Alternative Approach in The Presence of Problematic Multicollinearity

When adopting the Ordinary Least Squares (OLS) method to compute regression coefficients, the results become unreliable when two or more predictor variables are linearly related to one another. The confidence interval of the estimates becomes longer as a result of the increased variance of the OLS estimator, which also causes test procedures to have the potential to generate deceptive results. Additionally, it is difficult to determine the marginal contribution of the associated predictors since the estimates depend on the other predictor variables that are included in the model. This makes the determination of the marginal contribution difficult. Ridge Regression (RR) is a popular alternative to consider in this scenario; however, doing so impairs the standard approach for statistical testing. The Raise Method (RM) is a technique that was developed to combat multicollinearity while maintaining statistical inference. In this work, we offer a novel approach for determining the raise parameter, because the traditional one is a function of actual coefficients, which limits the use of Raise Method in real-world circumstances. Using simulations, the suggested method was compared to Ordinary Least Squares and Ridge Regression in terms of its capacity to forecast, stability of its coefficients, and probability of obtaining unacceptable coefficients at different levels of sample size, linear dependence, and residual variance. According to the findings, the technique that we designed turns out to be quite effective. Finally, a practical application is discussed.

PS-P04-10: IMPACT OF BLOOD PRESSURE VARIABILITY ON THE RISK OF STROKE AND PREMATURE DEATH

Objective: Visit-to-visit blood pressure variability (BPV) has been identified in several recent studies as a risk factor for stroke, independently of the level of BP. If true, this finding could help to identify patients at higher risk of stroke and to develop new preventive strategies. However, studies on BPV are exposed to important methodological challenges including conditioning on the future, which violates the assumption of the Cox model, using endogenous variables like the expected BP and its variance, and not handling properly the measurement error. Our objective was to address these issues by designing a joint model with heterogeneous variance for the repeated measures of BP, allowing us to analyze the association between BPV and the risk of stroke and the competing risk of death. Design and Method: We used data from PROGRESS (Perindopril Protection against Stroke Study), a placebo-controlled BP-lowering randomized trial for the prevention of stroke recurrence in 6105 patients with a previous history of stroke or TIA. During 5 years of follow-up, BP was measured 12 times, and 727 strokes and 438 non-stroke deaths were observed. We designed an innovative and dedicated joint model combining a linear mixed model, including a subject-specific random effect for the residual variance and two proportional hazard models for the two competing risks of stroke and death. In this model, the risk of each event may depend on the current value and current slope of the individual expected trajectory of BP as well as the intra-subject residual variance. Results: In separate analysis of each arm of the trial, we found that the risk of stroke increased with the current level of BP (HR = 1.13 per 5 mmHg, 95%CI = [1.08; 1.17], p-value &lt; 0.001) but was not associated with its intra-individual variability (HR = 0.89, 95%CI = [0.71; 1.12], p-value = 0.317). Conversely, the risk of non-stroke death increased with the intra-individual variability of BP (HR = 1.43, 95%CI = [1.08; 1.88], p-value = 0.011) but not with its current level (HR = 1.03, 95%CI = [0.97; 1.09], p-value = 0.317). Results were similar on the complete sample adjusting for the randomized treatment. Conclusion: Using appropriate modeling accounting for measurement error and limiting selection and other potential biases, we found a differential effect of expected BP and BPV on the risk of stroke and of death. These results, if confirmed and enlarged to other populations, would have important consequences to design efficient and individualized preventive strategies.

Growth curve mixed nonlinear models in quails.

Our aim was to evaluate the use and application of different nonlinear mixed models, as well as to compare them with approach in nonlinear fixed models, for describing the growth curve of meat-type quails according to gender. A total of 15,002 and 15,408 records of males and females were used, respectively. The body weights were regressed on age of the animals using nonlinear models (Brody; Gompertz; Logistic, Morgan-Mercer-Flodin, Richards and Von Bertalanffy). All model parameters were considered fixed, whereas parameters related to asymptotic weight and maturity rate were fitted as random effects. The Bayesian Information Criterion was used to find the model of best fit. For both genders, the model that used the Morgan-Mercer-Flodin function with the inclusion of asymptotic weight as a random effect was considered the best-fitting model because it reduced the residual variance and increased the accuracy. Based on the lower absolute growth rate and growth velocity of male quails compared to that of females, it can be inferred that males should be slaughtered later. Given the results of this study, it can contribute to the current knowledge about animal yield, specifically at the best moment to slaughter and, this sense, improv the quality genetic of the populations in time.

Estudio de la precisión de modelos de marea mediante la varianza de los residuos de alturas satelitales en la plataforma patagónica

This work proposes to compare the astronomical tidal predictions obtained by using the totality of the constituents provided by seven global tide models and by the Center for Topographic studies of the Ocean and Hydrosphere (CTOH). To quantify this comparison, the variance of residuals of satellite sea level heights are computed from 1992 to 2019, for each model and CTOH. As a case study, we focus on the Patagonian shelf. It is found that the most accurate tidal predictions are obtained when using FES2014 model and CTOH harmonic constants. It is also observed that the decrease in variance values is due to the addition of certain minor constituents. As an example, in the case of CTOH, a location yields a reduction of more than 57% in the variance values when 9 minor constituents are added to the nine common ones. Likewise, in the case of FES2014, variance reduction over this same location is more than 56% by incorporating 14 minor constituents. Finally, a comparison of the amplitudes and phases values of the common constituents among models and the CTOH shows that these values are practically the same. However, when comparing predictions, very dissimilar results are obtained among models

Mixed Models in Nonlinear Regression for Description of the Growth of Nelore Cattle.

Body weight records were used to characterize the growth curve of Nelore cattle. Body weight was regressed as a function of age, for both sexes, by using nonlinear models through the functions of Brody, Gompertz, Logistic, Richards, Meloun 1, Von Bertalanffy, and Von Bertalanffy. The quality of the model arrangements was evaluated by employing Akaike and Bayesian Schwarz information criteria. The Brody function provided the best adaptations by the evaluators and, considering the asymptotic weight and the maturation rate as random, a reduction in residual variance of 79% for males and 83% for females was obtained in relation to the models under fixed contexts. In males, the absolute and relative growth rates ranged from 0.921 to 0.261 kg/day and 2.39 to 0.08%, respectively. For the same rates, under another approach, females ranged from 0.922 to 0.198 kg/day and 2.55 to 0.06%, respectively. Males showed greater growth acceleration at the beginning of the growth trajectory, being equal to females at 397 days of age and from that age onward they presented lower estimates. The nonlinear regression model approach under the mixed-models context allows reduction of residual variance, increasing model accuracy.

Pathological functional impairment: Neuropsychological correlates of the shared variance between everyday functioning and brain volumetrics.

Given that several non-cognitive factors can contribute to difficulties with everyday functioning, examining the extent to which cognition is associated with brain-related changes in everyday functioning is critical to accurate characterization of cognitive disorders. In this study, we examined neuropsychological correlates of the shared variance between everyday functioning and pathological indicators of cognitive aging using MRI brain volumetrics. Participants were 600 adults aged 55 and older without dementia [432 cognitively normal; 168 mild cognitive impairment (MCI)] from the National Alzheimer's Coordinating Center cohort who underwent neuropsychological testing, informant-rated everyday functioning, and brain MRI scanning at baseline. The shared variance between everyday functioning and brain volumetrics (i.e., hippocampal volume, white matter hyperintensity volume) was extracted using the predicted value from multiple regression. The shared variance was used as an indicator of pathological everyday functional impairment. The residual variance from the regression analysis was used to examine functional reserve. Larger white matter hyperintensity volumes (p = 0.002) and smaller hippocampal volumes (p < 0.001) were significantly correlated with worse informant-rated everyday functioning. Among individuals with MCI, worse performances on delayed recall (p = 0.013) and category fluency (p = 0.012) were significantly correlated with pathological functional impairment in multiple regression analysis. In the cognitively normal group, only worse auditory working memory (i.e., digit span backward; p = 0.025) significantly correlated with pathological functioning. Functional reserve was inversely related to anxiety (p < 0.001) in the MCI group and was associated with depressive symptoms (p = 0.003) and apathy (p < 0.001) in the cognitively normal group. Subtle brain-related everyday functioning difficulties are evident in MCI and track with expected preclinical Alzheimer's disease cognitive phenotypes in this largely amnestic sample. Our findings indicate that functional changes occur early in the disease process and that interventions to target neuropsychiatric symptoms may help to bolster functional reserve in those at risk.

Fitting Genomic Prediction Models with Different Marker Effects among Prefectures to Carcass Traits in Japanese Black Cattle.

We fitted statistical models, which assumed single-nucleotide polymorphism (SNP) marker effects differing across the fattened steers marketed into different prefectures, to the records for cold carcass weight (CW) and marbling score (MS) of 1036, 733, and 279 Japanese Black fattened steers marketed into Tottori, Hiroshima, and Hyogo prefectures in Japan, respectively. Genotype data on 33,059 SNPs was used. Five models that assume only common SNP effects to all the steers (model 1), common effects plus SNP effects differing between the steers marketed into Hyogo prefecture and others (model 2), only the SNP effects differing between Hyogo steers and others (model 3), common effects plus SNP effects specific to each prefecture (model 4), and only the effects specific to each prefecture (model 5) were exploited. For both traits, slightly lower values of residual variance than that of model 1 were estimated when fitting all other models. Estimated genetic correlation among the prefectures in models 2 and 4 ranged to 0.53 to 0.71, all &lt;0.8. These results might support that the SNP effects differ among the prefectures to some degree, although we discussed the necessity of careful consideration to interpret the current results.

METACOGNITION AND EMOTIONS IN OLDER AFRICAN AMERICAN ADULTS

Abstract Older adults in the earliest stages of cognitive decline often present with subjective cognitive complaints which may not be fully reflected in objective measures of cognition. Previous research suggests that a relationship exists between negative emotions, stress and metacognition, but these relationships have not yet been examined in the context of COVID-19. The purpose of this study was to examine the role of stress and emotions in perceived cognition in the context of the COVID-19 pandemic. Telephone screenings were administered to 206 older African Americans (aged 64–94 years). Objective cognition (Telephone Interview for Cognitive Status [TICS]), subjective cognition (Cognitive Change Questionnaire [CCQ]), perceived stress scale 4 (PSS-4), and survey questions about affective responses to COVID-19 experiences were measured. Objective TICS scores predicted subjective CCQ executive function scores (F(1, 197)=4.37, p=.038, R2=.022). Discrepancy scores were calculated as the standardized residual variance between objective and subjective measures. Survey items describing emotional states were summarized with emodiversity scores following Quoidbach and colleagues’ (2014) formula. Discrepancy scores were correlated with perceived stress, as well as global and negative emodiversity (Spearman r=.294, .279, .318, p&amp;lt;.001). In conclusion, we have shown that objective and subjective measures of cognition are related, but discrepancies exist between objectively-measured and self-perceived cognition. Increased stress and greater negative emotions are associated with greater overestimation of cognitive difficulties relative to one’s objective level of cognition. As stress and negative emotions have increased for many during the pandemic, individuals may also have depreciated their self-appraisal of cognitive abilities in the present climate.