The Influence of Nursing Home, Ward, and Eldercare Workers on the Number of Resident Handlings Performed Per Shift in Eldercare

The purpose of our study was to investigate which organizational levels and factors determine the number of resident handlings in eldercare. We conducted a multi-level study, stratified on day and evening shifts, including information on four levels: nursing homes (n = 20), wards within nursing homes (day, n = 120; evening, n = 107), eldercare workers within wards (day, n = 619; evening, n = 382), and within eldercare workers (i.e., days within eldercare workers; day, n = 5572; evening, n = 2373). We evaluated the influence of each level on the number of resident handlings using variance components analysis and multivariate generalized linear mixed models. All four levels contributed to the total variance in resident handlings during day and evening shifts, with 13%/20% at “nursing homes”, 21%/33% at “wards within nursing homes”, 25%/31% at “elder-care workers within wards”, and 41%/16% “within eldercare workers”, respectively. The percentage of residents with a higher need for physical assistance, number of residents per shift, occupational position (only within day shifts), and working hours per week (only within day shifts) were significantly associated with the number of resident handlings performed per shift. Interventions aiming to modify number of resident handlings in eldercare ought to target all levels of the eldercare organization.

Blood-based epigenome-wide analyses of cognitive abilities

We present a blood-based epigenome-wide association study and variance-components analysis of cognitive functions (n=9,162). Individual differences in DNA methylation (DNAm) accounted for up to 41.5% of the variance in cognitive functions; together, genetic and epigenetic markers accounted for up to 70.4% of the variance. A DNAm predictor accounted for 3.4% and 4.5% (P≤9.9x10-6) of the variance in general cognitive ability, independently of a polygenic score, in two external cohorts.

Pseudoreplication in genomics-scale datasets

In genomics-scale datasets, loci are closely packed within chromosomes and hence provide correlated information. Averaging across loci as if they were independent creates pseudoreplication, which reduces the effective degrees of freedom (n’) compared to the nominal degrees of freedom, n. This issue has been known for some time, but consequences have not been systematically quantified across the entire genome. Here we measured pseudoreplication (quantified by the ratio n’/n) for a common metric of genetic differentiation (FST) and a common measure of linkage disequilibrium between pairs of loci (r2). Based on data simulated using models (SLiM and msprime) that allow efficient forward-in-time and coalescent simulations while precisely controlling population pedigrees, we estimated n’ and n’/n by measuring the rate of decline in variance of mean FST and mean r2 as more loci were used. For both indices, n’ increases with Ne and genome size, as expected. However, even for large Ne and large genomes, n’ for r2 plateaus after a few thousand loci, and a variance components analysis indicates that the limiting factor is uncertainty associated with sampling individuals rather than genes. Pseudoreplication is less extreme for FST, but n’/n ≤0.01 can occur in datasets using tens of thousands of loci. Commonly-used block-jackknife methods consistently overestimated var(FST), producing very conservative confidence intervals. Predicting n’ based on our modeling results as a function of Ne, L, S, and genome size provides a robust way to quantify precision associated with genomics-scale datasets.

The reliability and heritability of cortical folds and their genetic correlations across hemispheres

Cortical folds help drive the parcellation of the human cortex into functionally specific regions. Variations in the length, depth, width, and surface area of these sulcal landmarks have been associated with disease, and may be genetically mediated. Before estimating the heritability of sulcal variation, the extent to which these metrics can be reliably extracted from in-vivo MRI must be established. Using four independent test-retest datasets, we found high reliability across the brain (intraclass correlation interquartile range: 0.65–0.85). Heritability estimates were derived for three family-based cohorts using variance components analysis and pooled (total N > 3000); the overall sulcal heritability pattern was correlated to that derived for a large population cohort (N > 9000) calculated using genomic complex trait analysis. Overall, sulcal width was the most heritable metric, and earlier forming sulci showed higher heritability. The inter-hemispheric genetic correlations were high, yet select sulci showed incomplete pleiotropy, suggesting hemisphere-specific genetic influences.

Efficient variance components analysis across millions of genomes

Variance components analysis has emerged as a powerful tool in complex trait genetics, with applications ranging from heritability estimation to association mapping. While the application of these methods to large-scale genetic datasets can potentially reveal important insights into genetic architecture, existing methods for fitting variance components do not scale well to these datasets. Here, we present a new algorithm for variance components analysis that is accurate and highly efficient, capable of estimating one hundred variance components on a million individuals genotyped at a million SNPs in a few hours. We illustrate the utility of our method in estimating variation in a trait explained by genotyped SNPs (SNP heritability) as well in partitioning heritability across population and functional genomic annotations. Analyzing 22 diverse traits with genotypes from 300, 000 individuals across about 8 million common and low frequency SNPs (minor allele frequency > 0.1%), we observe that the allelic effect size increases with decreasing MAF (minor allele frequency) and LD (linkage disequilibrium) across the analyzed traits consistent with the action of negative selection. Partitioning heritability across 28 functional annotations, we observe enrichment of heritability in FANTOM5 enhancers in asthma, eczema, thyroid and autoimmune disorders.

The Reliability of 2-Station Clerkship Objective Structured Clinical Examinations in Isolation and in Aggregate

Background: Most medical schools in the United States report having a 5- to 10-station objective structured clinical examination (OSCE) at the end of the core clerkship phase of the curriculum to assess clinical skills. We set out to investigate an alternative OSCE structure in which each clerkship has a 2-station OSCE. This study looked to determine the reliability of clerkship OSCEs in isolation to inform composite clerkship grading, as well as the reliability in aggregate, as a potential alternative to an end-of-third-year examination. Design: Clerkship OSCE data from the 2017-2018 academic year were analyzed: the generalizability coefficient (ρ2) and index of dependability (φ) were calculated for clerkships in isolation and in aggregate using variance components analysis. Results: In all, 93 students completed all examinations. The average generalizability coefficient for the individual clerkships was .47. Most often, the largest variance component was the interaction between the student and the station, indicating inconsistency in the performance of students between the 2 stations. Aggregate clerkship OSCE analysis demonstrated good reliability for consistency (ρ2 = .80). About one-third (33.8%) of the variance can be attributed to students, 8.2% can be attributed to the student by clerkship interaction, and 42.6% can be attributed to the student by block interaction, indicating that students’ relative performances varied by block. Conclusions: Two-station clerkship OSCEs have poor to fair reliability, and this should inform the weighting of the composite clerkship grade. Aggregating data results in good reliability. The largest source of variance in the aggregate was student by block, suggesting testing over several blocks may have advantages compared with a single day examination.

Identifying the phenotypic effect of rare variants by including between-pedigree coancestry in variance components linkage analysis

The contribution of rare variants to disease burden has become an important focus in genetic epidemiology. These effects are difficult to detect in population-based datasets, and as a result, interest in family-based study designs has resurfaced. Linkage analysis tools will need to be updated to accommodate the scale of data generated by modern genotyping and sequencing technologies.In conventional linkage analysis individuals in different pedigrees are assumed to be independent of each other. However, cryptic relatedness is often present in populations and haplotypes that harbor rare variants may be shared between pedigrees as well as within them.With millions of polymorphisms, Identity-by-descent (IBD) states across the genome can now be inferred without use of pedigree information. This is done by identifying long runs of identical-by-state genotypes which are unlikely to arise without IBD. Previously, IBD had to be estimated in pedigrees from recombination events in a sparse set of markers.We present a method for variance-components linkage that can incorporate large number of markers and allows for between-pedigree relatedness. We replace the IBD matrix generated from pedigree-based analysis with one generated from a genotype-based method. All pedigrees in a dataset are considered jointly, allowing between-pedigree IBD to be included in the model.In simulated data, we show that power is increased in the scenario when there is a haplotype shared IBD between members of different pedigrees. If there is no between-pedigree IBD, the analysis reduces to conventional variance-components analysis. By determining IBD states by long runs of dense IBS genotypes, linkage signals can be determined from their physical position, allowing more precise localization.