Variance Component Models Research Articles

Quantifying the Impact of Co-Housing on Murine Aging Studies.

Analysis of preclinical lifespan studies often assume that outcome data from co-housed animals are indepen-dent. In practice, treatments, such as controlled feeding or putative life-extending compounds, are applied to whole housing units, and as a result the outcomes are potentially correlated within housing units. We consider intra-class (here, intra-cage) correlation in three published and two unpublished lifespan studies of aged mice encompassing more than 20 thousand observations. We show that the independence assumption underlying common analytic techniques does not hold in these data, particularly for traits associated with frailty. We describe and demonstrate various analytical tools available to accommodate this study design and highlight a limitation of standard variance components models (i.e., linear mixed models) which are the usual statisti-cal tool for handling correlated errors. Through simulations, we examine the statistical biases resulting from intra-cage correlations with similar magnitudes as observed in these case studies and discuss implications for power and reproducibility.

Modeling spatial correlation between earthquake insured losses in New Zealand: A mixed-effects analysis.

Earthquake insurance is a critical risk management strategy that contributes to improving recovery and thus greater resilience of individuals. Insurance companies construct premiums without taking into account spatial correlations between insured assets. This leads to potentially underestimating the risk, and therefore the exceedance probability curve. We here propose a mixed-effects model to estimate losses per ward that is able to account for heteroskedasticity and spatial correlation between insured losses. Given the significant impact of earthquakes in New Zealand due to its particular geographical and demographic characteristics, the government has established a public insurance company that collects information about the insured buildings and any claims lodged. We thus develop a two-level variance component model that is based on earthquake losses observed in New Zealand between 2000 and 2021. The proposed model aims at capturing the variability at both the ward and territorial authority levels and includes independent variables, such as seismic hazard indicators, the number of usual residents, and the average dwelling value in the ward. Our model is able to detect spatial correlation in the losses at the ward level thus increasing its predictive power and making it possible to assess the effect of spatially correlated claims that may be considerable on the tail of lossdistribution.

Sources of specialist physician fee variation: Evidence from Australian health insurance claims data

This study explores the variation in specialist physician fees and examines whether the variation can be attributed to patient risk factors, variation between physicians, medical specialties, or other factors. We use health insurance claims data from a large private health insurer in Australia. Although Australia has a publicly funded health system that provides universal health coverage, about 44 % of the population holds private health insurance. Specialist physician fees in the private sector are unregulated; physicians can charge any price they want, subject to market forces.We examine the variation in fees using two price measures: total fees charged and out-of- pocket payments. We follow a two-stage method of removing the influence of patient risk factors by computing risk-adjusted prices at patient-level, and aggregating the adjusted prices over all claims made by each physician to arrive at physician-level average prices. In the second stage, we use variance-component models to analyse the variation in the physician-level average prices.We find that patient risk factors account for a small portion of the variance in fees and out-of-pocket payments. Physician-specific variation accounts for the bulk of the vari- ance. The results underscore the importance of understanding physician characteristics in formulating policy efforts to reduce fee variation.

Voxel-based texture similarity networks reveal individual variability and correlate with biological ontologies

The human brain is organized as a complex, hierarchical network. However, the structural covariance patterns among brain regions and the underlying biological substrates of such covariance networks remain to be clarified. The present study proposed a novel individualized structural covariance network termed voxel-based texture similarity networks (vTSNs) based on 76 refined voxel-based textural features derived from structural magnetic resonance images. Validated in three independent longitudinal healthy cohorts (40, 23, and 60 healthy participants, respectively) with two common brain atlases, we found that the vTSN could robustly resolve inter-subject variability with high test-retest reliability. In contrast to the regional-based texture similarity networks (rTSNs) that calculate radiomic features based on region-of-interest information, vTSNs had higher inter- and intra-subject variability ratios and test-retest reliability in connectivity strength and network topological properties. Moreover, the Spearman correlation indicated a stronger association of the gene expression similarity network (GESN) with vTSNs than with rTSNs (vTSN: r = 0.600, rTSN: r = 0.433, z = 39.784, P < 0.001). Hierarchical clustering identified 3 vTSN subnets with differential association patterns with 13 coexpression modules, 16 neurotransmitters, 7 electrophysiology, 4 metabolism, and 2 large-scale structural and 4 functional organization maps. Moreover, these subnets had unique biological hierarchical organization from the subcortex-limbic system to the ventral neocortex and then to the dorsal neocortex. Based on 424 unrelated, qualified healthy subjects from the Human Connectome Project, we found that vTSNs could sensitively represent sex differences, especially for connections in the subcortex-limbic system and between the subcortex-limbic system and the ventral neocortex. Moreover, a multivariate variance component model revealed that vTSNs could explain a significant proportion of inter-subject behavioral variance in cognition (80.0 %) and motor functions (63.4 %). Finally, using 494 healthy adults (aged 19–80 years old) from the Southwest University Adult Lifespan Dataset, the Spearman correlation identified a significant association between aging and vTSN strength, especially within the subcortex-limbic system and between the subcortex-limbic system and the dorsal neocortex. In summary, our proposed vTSN is robust in uncovering individual variability and neurobiological brain processes, which can serve as biologically plausible measures for linking biological processes and human behavior.

Oxygen targets following immediate percutaneous coronary intervention in comatose survivors of out-of-hospital cardiac arrest - a BOX sub-study

Abstract Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): The Novo Nordisk Foundation The Lundbeck Foundation. Background Treatment with supplemental oxygen in patients with acute myocardial infarction (AMI) has previously been associated with poorer outcomes. However, in patients with AMI resuscitated from out-of-hospital cardiac arrest (OHCA) and undergoing immediate revascularization, evidence regarding specific oxygen targets is sparse. Purpose To assess the impact of a restrictive versus a liberal oxygen target on infarct size in OHCA patients undergoing immediate percutaneous coronary intervention (PCI). Methods The current study is a sub-study of the BOX trial, a randomized trial with a 2-by-2 factorial design, in which comatose OHCA patients with a presumed cardiac cause of arrest were randomly assigned to either a restrictive oxygen target (partial pressure of arterial oxygen (Pao2) of 9 to 10 kPa) or a liberal oxygen target (Pao2 of 13 to 14 kPa). In this sub-study we included patients who underwent immediate revascularization with PCI. The primary outcome was myocardial infarct size as assessed by the cardiac enzyme troponin-T (TnT). TnT was measured repeatedly during the first 72 hours and analyzed with a repeated measures variance component model with baseline adjustment. Secondary outcome was one-year all-cause mortality assessed by Kaplan-Meier and Cox proportional hazards analyses. Results A total of 329 patients underwent immediate PCI, whereof 172 patients were randomized to the restrictive oxygen target and 157 to the liberal oxygen target. The oxygen intervention was successful with a significantly higher PaO2 in the liberal oxygen target group during the first 72 hours (p&amp;lt;0.0001). Baseline characteristics and findings on coronary angiography (CAG) were similar in the two oxygen target groups. Thus, median age was 63 (interquartile range (IQR) 55–71), 81% were male, and estimated left ventricular ejection fraction on arrival to hospital was 35% (IQR 25–45%). Further, 74% of the patients had an initial postresuscitation electrocardiogram with ST-segment elevations, and the left anterior descending artery was deemed as culprit lesion in 49% of the patients. There was no difference in peak TnT between the restrictive and liberal oxygen target group (median 2540 (IQR 925–6420) versus 3410 (792–8630), ng/l, p=0.23), nor did TnT differ between the groups during the first 72 hours after admission (Figure 1, p=1.00). Within one year from admission, 120 patients (36.5%) had died. There was no difference in mortality between the restrictive (34%) and liberal (39%) oxygen target group (hazard ratio, 0.80; 95% CI, 0.56-1.14; p=0.21) (Figure 2). Conclusion Targeting a restrictive versus a liberal oxygen level in comatose survivors of OHCA who underwent immediate revascularization did not affect infarct size nor did it result in a significant difference in one-year mortality rate.Figure 1:Troponin-T levelsFigure 2:Kaplan-Meier curves

Abstract 3565: The Cox proportional hazards mixed model enables accurate estimation of the total effect somatic SNVs have on treatment outcomes

Abstract Heritability is the variation in disease susceptibility attributed to genetic variation and is a fundamental parameter for interpreting disease architecture and making predictions. In the context of germline genetics, variance component models are commonly used to estimate heritability for continuous or case-control phenotypes (under the liability threshold model, LTM). However, models for time-to-event (TTE) outcomes have been largely unstudied. This has limited the characterization of age-of-onset and treatment response phenotypes, the latter particularly relevant for clinical studies. Here, we propose and evaluate a Cox proportional hazard Mixed Model (COXMM) to enable accurate estimation for TTE phenotypes with censoring. We apply these methods to Real World data from the Profile cohort from Dana-Farber Cancer Institute, a large prospectively collected tumor sequencing cohort with detailed clinical annotation. We implemented an efficient COXMM with a random effect modeled by the genetic relatedness across individuals, and benchmarked its performance in extensive simulations. We simulated phenotypes according to generative models reflecting either LTM or TTE models. For TTE phenotypes, data followed various Weibull distributions with independent censoring. Across TTE simulations, COXMM produced unbiased estimates while the classic case-control heritability estimator showed significant downward bias. Likewise, restricting only to cases and estimating the heritability of age-of-onset as a normalized continuous phenotype produced biased estimates, due to the artificial exclusion of controls. For example, for an age-of-onset with heritability of 0.8 and 40% of individuals in the study being cases, COXMM correctly inferred the heritability to be 0.80 +/- 0.007, compared to 0.32 +/- 0.004 for a conventional case-control model, and 0.09 +/- 0.02 for a conventional case-only age-of-onset model. In the Profile cohort, we analyzed the impact of somatic SNVs on progression free survival (PFS) and overall survival (OS) in patients with non-small cell lung cancer (N=954 and N=1796, respectively). We restricted to somatic SNVs present in at least 5% of patients. For OS, we observed that the COXMM heritability estimate was significantly larger than those computed by case-control status and using a normalized case-only age/duration (COXMM: 0.19 +/- 0.003; Case-Control: 0.07 +/- 0.2; Duration: 0.04 +/- 0.01). We observed a similar phenomenon for PFS as well (COXMM: 0.11 +/- 0.01; Case-Control: 0.03 +/- 0.4; Duration: 0.03 +/- 0.02). These results demonstrate that somatic features are most strongly associated with TTE and can be accurately estimated with COXMM. COXMM enables the efficient quantification of TTE heritability for diverse clinical phenotypes and outperforms existing methods. Citation Format: Kodi Taraszka, Alexander Gusev. The Cox proportional hazards mixed model enables accurate estimation of the total effect somatic SNVs have on treatment outcomes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3565.

Genetic and environmental influence on alcohol intent and alcohol sips among U.S. children-Effects across sex, race, and ethnicity.

Alcohol intent (the susceptibility to initiating alcohol use) and alcohol sips (the initiation of alcohol) in youth are a multifactorial puzzle with many components. This research aims to examine the connection between genetic and environmental factors across sex, race and ethnicity. Data was obtained from the twin hub of the Adolescent Brain Cognitive Development (ABCD) study at baseline (2016-2018). Variance component models were conducted to dissect the additive genetic (A), common (C) and unique environmental (E) effects on alcohol traits. The proportion of the total alcohol phenotypic variation attributable to additive genetic factors is reported as heritability (h2). The sample (n = 1,772) included an approximately equal male-female distribution. The 886 same-sex twin pairs were 60.4% dizygotic (DZ), 39.6% monozygotic (MZ), 65.4% non-Hispanic Whites, 13.9% non-Hispanic Blacks, 10.8% of Hispanics with a mean age of 121.2 months. Overall, genetic predisposition was moderate for alcohol intent (h2 = 28%, p = .006) and low for alcohol initiation (h2 = 4%, p = 0.83). Hispanics (h2 = 53%, p < .0001) and Blacks (h2 = 48%, p < .0001) demonstrated higher alcohol intent due to additive genetic factors than Whites (h2 = 34%, p < .0001). Common environmental factors explained more variation in alcohol sips in females (c2 = 63%, p = .001) than in males (c2 = 55%, p = .003). Unique environmental factors largely attributed to alcohol intent, while common environmental factors explained the substantial variation in alcohol initiation. Sex and racial/ethnic disparities in genetic and environmental risk factors for susceptibility to alcohol initiation can lead to significant health disparities. Certain populations may be at greater risk for alcohol use due to their genetic and ecological factors at an early age.

Short-Term Exposure to Nitrogen Dioxide Modifies Genetic Predisposition in Blood Lipid and Fasting Plasma Glucose: A Pedigree-Based Study.

(1) Background: Previous studies suggest that exposure to nitrogen dioxide (NO2) has a negative impact on health. But few studies have explored the association between NO2 and blood lipids or fasting plasma glucose (FPG), as well as gene-air pollution interactions. This study aims to fill this knowledge gap based on a pedigree cohort in southern China. (2) Methods: Employing a pedigree-based design, 1563 individuals from 452 families participated in this study. Serum levels of triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDLC), high-density lipoprotein cholesterol (HDLC), and FPG were measured. We investigated the associations between short-term NO2 exposure and lipid profiles or FPG using linear mixed regression models. The genotype-environment interaction (GenoXE) for each trait was estimated using variance component models. (3) Results: NO2 was inversely associated with HDLC but directly associated with TG and FPG. The results showed that each 1 μg/m3 increase in NO2 on day lag0 corresponded to a 1.926% (95%CI: 1.428-2.421%) decrease in HDLC and a 1.400% (95%CI: 0.341-2.470%) increase in FPG. Moreover, we observed a significant genotype-NO2 interaction with HDLC and FPG. (4) Conclusion: This study highlighted the association between NO2 exposure and blood lipid profiles or FPG. Additionally, our investigation suggested the presence of genotype-NO2 interactions in HDLC and FPG, indicating potential loci-specific interaction effects. These findings have the potential to inform and enhance the interpretation of studies that are focused on specific gene-environment interactions.

Rare loss-of-function variants in matrisome genes are enriched in Ebstein’s anomaly

Ebstein's anomaly, a rare congenital heart disease, is distinguished by the failure of embryological delamination of the tricuspid valve leaflets from the underlying primitive right ventricle myocardium. Gaining insight into the genetic basis of Ebstein's anomaly allows a more precise definition of its pathogenesis. In this study, two distinct cohorts from the Chinese Han population were included: a case-control cohort consisting of 82 unrelated cases and 125 controls without cardiac phenotypes and a trio cohort comprising 36 parent-offspring trios. Whole-exome sequencing data from all 315 participants were utilized to identify qualifying variants, encompassing rare (minor allele frequency<0.1% from East Asians in the gnomAD database) functional variants and high-confidence (HC) loss-of-function (LoF) variants. Various statistical models, including burden tests and variance-component models, were employed to identify rare variants, genes, and biological pathways associated with Ebstein's anomaly. Significant associations were noted between Ebstein's anomaly and rare HC LoF variants found in genes related to the matrisome, a collection of extracellular matrix (ECM) components. Specifically, 47 genes with HC LoF variants were exclusively or predominantly identified in cases, while nine genes showed such variants in the probands. Over half of unrelated cases (n= 42) and approximately one-third of probands (n= 12) were found to carry one or two LoF variants in these prioritized genes. These results highlight the role of the matrisome in the pathogenesis of Ebstein's anomaly, contributing to a better understanding of the genetic architecture underlying this condition. Our findings hold the potential to impact the genetic diagnosis and treatment approaches for Ebstein's anomaly.

Genetic and environmental influences on early-age susceptibility and initiation of nicotine-containing product use: A twin-pairs study.

Nicotine-containing products (NCPs) such as electronic nicotine delivery systems (ENDS) are increasingly common throughout the landscape of youth use of nicotine-containing products (NCP), and have overtaken traditional cigarette smoking modalities. This study seeks to examine the genetic and environmental influences on liability for susceptibility and initiation of ENDS and other NCPs among US children. Data were drawn from 886 monozygotic (MZ) and dizygotic (DZ) twin pairs aged 9-10 years in the Adolescent Brain & Cognitive Development (ABCD) study at the baseline during 2016-2018. Heritability (h2) measured the proportion of the total phenotypic variation attributable to genes. Variance component models were utilized to analyze influences from the common environment (c2) and unique environmental factors (e2), taking into account correlations within twin pairs. The national sample included 50% females, 69.5% of non-Hispanic Whites, 12.8% of non-Hispanic Blacks, and 11.6% of Hispanics, with a mean age of 121.5 months. The twin sets were 60% DZ and 40% MZ. Heritability was low for NCP susceptibility (h2=0) and moderate for NCP initiation (h2=39%, p=0.02). The variance associated with NCP susceptibility was primarily influenced by environmental factors, especially one's unique factors (c2=37%, p<0.0001 vs e2=63%, p<0.0001). In contrast, the variance associated with NCP initiation was split across common and unique environmental factors (c2=32%, p=0.02 vs e2=29%, p=0.02). In the era with ENDS use surging among youth, NCP initiation remains to be a heritable trait with joint influence from the environment. NCP susceptibility is largely influenced by environmental factors, especially unique environments. Continued assessment of gene × environment interaction can better inform future youth NCP interventions.

Parental Divorce and Trajectories of Alcohol Consumption in Men: A Genetically Informative Perspective.

Much of what is known about parental divorce and adult alcohol outcomes comes from cross-sectional comparisons of those who did and did not experience parental divorce. In contrast, far less is known about whether and how parental divorce is associated with alcohol consumption trajectories. We used a longitudinal perspective to investigate the associations between parental divorce and men's alcohol consumption trajectories as well as a genetically informative approach to evaluate whether the pattern of genetic and environmental influences on these trajectories differed for men who did and did not experience parental divorce. The sample included 1,614 adult men from a population-based twin registry in Virginia. Measures of parental divorce (before age 16) and alcohol consumption (between ages 10 and 40) came from interviews and life history calendars. Data were analyzed with growth curve and longitudinal biometric variance component models. In total, 11% of the sample experienced parental divorce. Parental divorce was associated with higher alcohol consumption intercepts that were sustained over time but was not associated with the linear slope or quadratic curvature of men's alcohol consumption trajectories. Longitudinal biometric variance components modeling indicated that genetic influences on alcohol consumption were higher in adolescence and young adulthood among those who experienced parental divorce compared with those who did not. Parental divorce is associated with the shape and relative influence of genetic and environmental factors on men's alcohol consumption trajectories from adolescence through adulthood.

Covariance Clustering: Modelling Covariance in Designed Experiments When the Number of Variables is Greater than Experimental Units

The size and complexity of datasets resulting from comparative research experiments in the agricultural domain is constantly increasing. Often the number of variables measured in an experiment exceeds the number of experimental units composing the experiment. When there is a necessity to model the covariance relationships that exist between variables in these experiments, estimation difficulties can arise due to the resulting covariance structure being of reduced rank. A statistical method, based in a linear mixed model framework, is presented for the analysis of designed experiments where datasets are characterised by a greater number of variables than experimental units, and for which the modelling of complex covariance structures between variables is desired. Aided by a clustering algorithm, the method enables the estimation of covariance through the introduction of covariance clusters as random effects into the modelling framework, providing an extension of the traditional variance components model for building covariance structures. The method was applied to a multi-phase mass spectrometry-based proteomics experiment, with the aim of exploring changes in the proteome of barley grain over time during the malting process. The modelling approach provides a new linear mixed model-based method for the estimation of covariance structures between variables measured from designed experiments, when there are a small number of experimental units, or observations, informing covariance parameter estimates.

Heritability and recursive influence of host genetics on the rumen microbiota drive body weight variance in male Hu sheep lambs

BackgroundHeritable rumen microbiota is an important modulator of ruminant growth performance. However, no information exists to date on host genetics-rumen microbiota interactions and their association with phenotype in sheep. To solve this, we curated and analyzed whole-genome resequencing genotypes, 16S rumen-microbiota data, and longitudinal body weight (BW) phenotypes from 1150 sheep.ResultsA variance component model indicated significant heritability of rumen microbial community diversity. Genome-wide association studies (GWAS) using microbial features as traits identified 411 loci-taxon significant associations (P < 10−8). We found a heritability of 39% for 180-day-old BW, while also the rumen microbiota likely played a significant role, explaining that 20% of the phenotypic variation. Microbiota-wide association studies (MWAS) and GWAS identified four marker genera (Bonferroni corrected P < 0.05) and five novel genetic variants (P < 10−8) that were significantly associated with BW. Integrative analysis identified the mediating role of marker genera in genotype influencing phenotype and unravelled that the same genetic markers have direct and indirect effects on sheep weight.ConclusionsThis study reveals a reciprocal interplay among host genetic variations, the rumen microbiota and the body weight traits of sheep. The information obtained provide insights into the diverse microbiota characteristics of rumen and may help in designing precision microbiota management strategies for controlling and manipulating sheep rumen microbiota to increase productivity.FHBZi7VG9hPqSjx265vQ6eVideo

Origins of attainment: do brother correlations in occupational status and income overlap?

Abstract We study the overlap in the overall impact of family background on two widely studied labour market outcomes by considering whether brother similarities in occupational status are rooted in the same underlying family characteristics that affect brother similarities in income. We extend previous research using sibling correlations as an omnibus measure of total family background impact on a given outcome by directly quantifying how brother correlations in occupational status and income overlap. We apply a novel variance components model to data from Denmark and the United States, two countries known to follow a contradictory pattern: While income mobility is much lower in the United States, occupational mobility is virtually similar. Apart from confirming this pattern, we find a substantial overlap, around 70 per cent, in brother similarities in income and occupational status in both countries. Conventional family background variables account for less than one-fifth of this overlap in each country, suggesting that shared family origins of attainment in these two domains are constituted by largely unknown family characteristics. We speculate what these characteristics might be.

Robust estimation of fixed effect parameters and variances of linear mixed models: the minimum density power divergence approach

Many real-life data sets can be analyzed using linear mixed models (LMMs). Since these are ordinarily based on normality assumptions, under small deviations from the model the inference can be highly unstable when the associated parameters are estimated by classical methods. On the other hand, the density power divergence (DPD) family, which measures the discrepancy between two probability density functions, has been successfully used to build robust estimators with high stability associated with minimal loss in efficiency. Here, we develop the minimum DPD estimator (MDPDE) for independent but non-identically distributed observations for LMMs according to the variance components model. We prove that the theoretical properties hold, including consistency and asymptotic normality of the estimators. The influence function and sensitivity measures are computed to explore the robustness properties. As a data-based choice of the MDPDE tuning parameter α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha$$\\end{document} is very important, we propose two candidates as “optimal” choices, where optimality is in the sense of choosing the strongest downweighting that is necessary for the particular data set. We conduct a simulation study comparing the proposed MDPDE, for different values of α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha$$\\end{document}, with S-estimators, M-estimators and the classical maximum likelihood estimator, considering different levels of contamination. Finally, we illustrate the performance of our proposal on a real-data example.

Cocktail-party listening and cognitive abilities show strong pleiotropy.

The cocktail-party problem refers to the difficulty listeners face when trying to attend to relevant sounds that are mixed with irrelevant ones. Previous studies have shown that solving these problems relies on perceptual as well as cognitive processes. Previously, we showed that speech-reception thresholds (SRTs) on a cocktail-party listening task were influenced by genetic factors. Here, we estimated the degree to which these genetic factors overlapped with those influencing cognitive abilities. We measured SRTs and hearing thresholds (HTs) in 493 listeners, who ranged in age from 18 to 91 years old. The same individuals completed a cognitive test battery comprising 18 measures of various cognitive domains. Individuals belonged to large extended pedigrees, which allowed us to use variance component models to estimate the narrow-sense heritability of each trait, followed by phenotypic and genetic correlations between pairs of traits. All traits were heritable. The phenotypic and genetic correlations between SRTs and HTs were modest, and only the phenotypic correlation was significant. By contrast, all genetic SRT-cognition correlations were strong and significantly different from 0. For some of these genetic correlations, the hypothesis of complete pleiotropy could not be rejected. Overall, the results suggest that there was substantial genetic overlap between SRTs and a wide range of cognitive abilities, including abilities without a major auditory or verbal component. The findings highlight the important, yet sometimes overlooked, contribution of higher-order processes to solving the cocktail-party problem, raising an important caveat for future studies aiming to identify specific genetic factors that influence cocktail-party listening.

Reference intervals for LC-MS /MS measurements of plasma renin activity, aldosterone, angiotensin II, and 24-hour urinary aldosterone in Northern Chinese Han population

BackgroundExamination of aldosterone to Renin Ratio (ARR) and plasma aldosterone concentration (PAC) or 24-h urinary aldosterone excretion (24-h UALD) was the necessary tests in confirmatory tests for primary aldosteronism (PA). We developed a combined liquid chromatography-tandem mass spectrometry method (LC-MS/MS) for plasma renin activity (PRA), PAC, and angiotensin II (Ang II) and investigated their reference intervals (RIs) in northern Chinese Han population. The RIs of 24-h UALD excretion were also studied using LC-MS/MS. MethodsA total of 309 healthy volunteers were recruited in 3 cities in China. PRA, PAC, Ang II, and 24-h UALD were measured using the laboratory-developed LC-MS/MS. Multiple linear regression and the variance component model were applied to determine if the RI needed to be split. The RIs of PRA, PAC, and Ang II were determined using the nonparametric percentile method. ResultsThe laboratory-developed LC-MS/MS method was verified and showed good performance. Standard deviation ratio (SDR) sex for PAC and SDR region for Ang II are 0.466 and 0.407, respectively, indicating that the RIs of PAC and Ang II must be divided by sex and region, respectively. In addition, the SDR 24hUK for 24-h UALD is 0.579, indicating that the RI of 24-h UALD must be partitioned by urine potassium. ConclusionRIs were established for tests related to the renin-angiotensin-aldosterone system in the apparently healthy northern Chinese Han population by the LC-MS/MS method.

Assessing Inter-rater Reliability With Heterogeneous Variance Components Models: Flexible Approach Accounting for Contextual Variables

Inter-rater reliability (IRR), which is a prerequisite of high-quality ratings and assessments, may be affected by contextual variables, such as the rater’s or ratee’s gender, major, or experience. Identification of such heterogeneity sources in IRR is important for the implementation of policies with the potential to decrease measurement error and to increase IRR by focusing on the most relevant subgroups. In this study, we propose a flexible approach for assessing IRR in cases of heterogeneity due to covariates by directly modeling differences in variance components. We use Bayes factors (BFs) to select the best performing model, and we suggest using Bayesian model averaging as an alternative approach for obtaining IRR and variance component estimates, allowing us to account for model uncertainty. We use inclusion BFs considering the whole model space to provide evidence for or against differences in variance components due to covariates. The proposed method is compared with other Bayesian and frequentist approaches in a simulation study, and we demonstrate its superiority in some situations. Finally, we provide real data examples from grant proposal peer review, demonstrating the usefulness of this method and its flexibility in the generalization of more complex designs.

A fast linkage method for population GWAS cohorts with related individuals.

Linkage analysis, a class of methods for detecting co-segregation of genomic segments and traits in families, was used to map disease-causing genes for decades before genotyping arrays and dense SNP genotyping enabled genome-wide association studies in population samples. Population samples often contain related individuals, but the segregation of alleles within families is rarely used because traditional linkage methods are computationally inefficient for larger datasets. Here, we describe Population Linkage, a novel application of Haseman-Elston regression as a method of moments estimator of variance components and their standard errors. We achieve additional computational efficiency by using modern methods for detection of IBD segments and variance component estimation, efficient preprocessing of input data, and minimizing redundant numerical calculations. We also refined variance component models to account for the biases in population-scale methods for IBD segment detection. We ran Population Linkage on four blood lipid traits in over 70,000 individuals from the HUNT and SardiNIA studies, successfully detecting 25 known genetic signals. One notable linkage signal that appeared in both was for low-density lipoprotein (LDL) cholesterol levels in the region near the gene APOE (LOD = 29.3, variance explained = 4.1%). This is the region where the missense variants rs7412 and rs429358, which together make up the ε2, ε3, and ε4 alleles each account for 2.4% and 0.8% of variation in circulating LDL cholesterol. Our results show the potential for linkage analysis and other large-scale applications of method of moments variance components estimation.

Gene-by-Environment Interaction in Non-Alcoholic Fatty Liver Disease and Depression: The Role of Hepatic Transaminases.

Non-alcoholic fatty liver disease (NAFLD) encompasses a range of liver conditions, from benign fatty accumulation to severe fibrosis. The global prevalence of NAFLD has risen to 25-30%, with variations across ethnic groups. NAFLD may advance to hepatocellular carcinoma, increases cardiovascular risk, is associated with chronic kidney disease, and is an independent metabolic disease risk factor. Assessment methods for liver health include liver biopsy, magnetic resonance imaging, ultrasound, and vibration-controlled transient elastography (VCTE by FibroScan). Hepatic transaminases are cost-effective and minimally invasive liver health assessment methods options. This study focuses on the interaction between genetic factors underlying the traits (hepatic transaminases and the FibroScan results) on the one hand and the environment (depression) on the other. We examined 525 individuals at risk for metabolic disorders. We utilized variance components models and likelihood-based statistical inference to examine potential GxE interactions in markers of NAFLD, including aspartate aminotransferase (AST), alanine aminotransferase (ALT), and the AST/ALT ratio, and Vibration-Controlled Transient Elastography (VCTE by FibroScan). We calculated the Fibroscan-AST (FAST) score (a score that identifies the risk of progressive non-alcoholic steatohepatitis (NASH) and screened for depression using the Beck Depression Inventory-II (BDI-II). We identified significant G × E interactions for AST/ALT ratio × BDI-II, but not AST, ALT, or the FAST score. Our findings support that genetic factors play a role in hepatic transaminases, especially the AST/ALT ratio, with depression influencing this relationship. These insights contribute to understanding the complex interplay of genetics, environment, and liver health, potentially guiding future personalized interventions.