Estimation of An Optimum Spatial Autocorrelation in Linear Mixed Models Containing Spatial Effects

Fay-Herriot model assumes that the random effects between regions (areas) are independent of each other. This allows the regions to be mutually independent so that the estimators obtained are unbiased estimators. In cases where the regions are not mutually independent, it can develop a model in which the assumptions are violated (not fulfilled) or allow the regions to be dependent. The development of this model is known as small area estimation (SAE) with spatial effects. In small area estimation with spatial effects, one of the important parameters is the spatial autocorrelation parameter. In various small area estimation with spatial effects, it is still very rare to generate estimators of the spatial autocorrelation parameter. Most of the parameter values used are known, that is by trying to enter several spatial autocorrelation parameter values to show that the addition of regional aspects can increase the accuracy of the small area estimation. In addition, they have also tried a restricted maximum likelihood approach in estimating the spatial autocorrelation and component variance but they still assume that the sampling variance is known (assigned). Therefore, this research proposes a concentrated log-likelihood function by means of numerical procedure to find an optimum estimate value for spatial autocorrelation coefficient where both a sampling variance and a component variance are unknown. Parameters estimators obtained in the models, fixed and random effects parameters, are proved to be consistent.

A scalable algorithm for clonal reconstruction from sparse time course genomic sequencing data

Long-term evolution experiments (LTEEs) reveal the dynamics of clonal compositions in an evolving bacterial population over time. Accurately inferring the haplotypes - the set of mutations that identify each clone, as well as the clonal frequencies and evolutionary history in a bacterial population is useful for the characterization of the evolutionary pressure on multiple correlated mutations instead of that on individual mutations. Here, we study the computational problem of reconstructing the haplotypes of bacterial clones from the variant allele frequencies (VAFs) observed during a time course in a LTEE. Previously, we formulated the problem using a maximum likelihood approach under the assumption that mutations occur spontaneously, and thus the likelihood of a mutation occurring in a specific clone is proportional to the frequency of the clone in the population when the mutation occurs. We also developed several heuristic greedy algorithms to solve the problem, which were shown to report accurate results of clonal reconstruction on simulated and real time course genomic sequencing data in LTEE. However, these algorithms are too slow to handle sparse time course data when the number of novel mutations occurring during the time course are much greater than the number of time points sampled. In this paper, we present a novel scalable algorithm for clonal reconstruction from sparse time course data. We employed a statistical method to estimate the sampling variance of VAFs derived from low coverage sequencing data and incorporated it into the maximum likelihood framework for clonal reconstruction on noisy sequencing data. We implemented the algorithm (named ClonalTREE2) and tested it using simulated and real sparse time course genomic sequencing data. The results showed that the algorithm was fast and achieved near-optimal accuracy under the maximum likelihood framework for the time course data involving hundreds of novel mutations at each time point. The source code of ClonalTREE2 is available at https://github.com/COL-IU/ClonalTREE2.

Comparison of Variance Estimators for Systematic Environmental Sample Surveys: Considerations for Post-Stratified Estimation

The estimation of the sampling variance of point estimators under two-dimensional systematic sampling designs remains a challenge, and several alternative variance estimators have been proposed in the past few decades. In this work, we compared six alternative variance estimators under Horvitz-Thompson (HT) and post-stratification (PS) point estimation regimes. We subsampled a multitude of species-specific forest attributes from a large, spatially balanced national forest inventory to compare the variance estimators. A variance estimator that assumes a simple random sampling design exhibited positive relative bias under both HT and PS point estimation regimes ranging between 1.23 to 1.88 and 1.11 to 1.78 for HT and PS, respectively. Alternative estimators reduced this positive bias with relative biases ranging between 1.01 to 1.66 and 0.90 to 1.64 for HT and PS, respectively. The alternative estimators generally obtained improved efficiencies under both HT and PS, with relative efficiency values ranging between 0.68 to 1.28 and 0.68 to 1.39, respectively. We identified two estimators as promising alternatives that provide clear improvements over the simple random sampling estimator for a wide variety of attributes and under HT and PS estimation regimes.

Subsampling Technique to Estimate Variance Component for UK-Biobank Traits

The estimation of heritability has been an important question in statistical genetics. Due to the clear mathematical properties, the modified Haseman–Elston regression has been found a bridge that connects and develops various parallel heritability estimation methods. With the increasing sample size, estimating heritability for biobank-scale data poses a challenge for statistical computation, in particular that the calculation of the genetic relationship matrix is a huge challenge in statistical computation. Using the Haseman–Elston framework, in this study we explicitly analyzed the mathematical structure of the key term tr(KTK), the trace of high-order term of the genetic relationship matrix, a component involved in the estimation procedure. In this study, we proposed two estimators, which can estimate tr(KTK) with greatly reduced sampling variance compared to the existing method under the same computational complexity. We applied this method to 81 traits in UK Biobank data and compared the chromosome-wise partition heritability with the whole-genome heritability, also as an approach for testing polygenicity.

Visual representations of meta-analyses of multiple outcomes: Extensions to forest plots, funnel plots, and caterpillar plots

Meta-analytic datasets can be large, especially when in primary studies multiple effect sizes are reported. The visualization of meta-analytic data is therefore useful to summarize data and understand information reported in primary studies. The gold standard figures in meta-analysis are forest and funnel plots. However, none of these plots can yet account for the existence of multiple effect sizes within primary studies. This manuscript describes extensions to the funnel plot, forest plot and caterpillar plot to adapt them to three-level meta-analyses. For forest plots, we propose to plot the study-specific effects and their precision, and to add additional confidence intervals that reflect the sampling variance of individual effect sizes. For caterpillar plots and funnel plots, we recommend to plot individual effect sizes and averaged study-effect sizes in two separate graphs. For the funnel plot, plotting separate graphs might improve the detection of both publication bias and/or selective outcome reporting bias.

ANALYSIS OF DESIGN EFFECT FOR INDONESIAN NATIONAL LABOUR FORCE SURVEY

The implementation of multistage sampling design is a good strategy to achieve the gain in efficiency of survey cost. However, in terms of sampling efficiency, it leads to the loss of precision indicated by the higher sampling variance compared to SRS design. Design effect measures the ratio of actual variance to the variance of SRS and can be decomposed to the effect of sample weight and the effect of clustering. This study aims to analyse the effect of sample weight and the effect of clustering on the estimation of labour variables resulted from the National labour Force Survey of Indonesia. The analysis is provided at the national level, stratum level, and province level. In general, the study finds that the design effect varies between labour variables. The effect of clustering is higher than the effect of the sample weight. There is also a high variability of the clustering effect between provinces and between strata (urban-rural). In contrast, the design effect due to the sample weight is similar between provinces, but it differs between strata. Allocating sample size proportionally to each stratum could be a good strategy for dealing with the high effect of weighting. On the other hand, for the future specific survey that measures the variable with a high clustering effect and high rate of homogeneity, the alternative strategy is increasing the sample size of the cluster and declining the sample size of households per cluster

Association Between Smoking and Premenstrual Syndrome: A Meta-Analysis

Results of basic science studies demonstrate shared actions of endogenous neuroactive steroid hormones and drugs of abuse on neurotransmission. As such, premenstrual syndrome (PMS) may be associated with smoking, however, results from studies examining this relationship have been mixed. Following PRISMA guidelines, we extracted unique studies examining the relationship between smoking and PMS. We used the escalc () function in R to compute the log odds ratios and corresponding sampling variance for each study. We based quality assessment on the nature of PMS diagnosis and smoking estimation, confounding adjustment, participation rate, and a priori specification of target population. Our final sample included 13 studies, involving 25,828 study participants. Smoking was associated with an increased risk for PMS [OR = 1.56 (95% CI: 1.25–1.93), p < 0.0001]. Stratified by diagnosis, the effect size estimate was higher for Premenstrual Dysphoric Disorder (PMDD) [OR = 3.15 (95% CI: 2.20–4.52), p < 0.0001] than for PMS [OR = 1.27 (95% CI: 1.16–1.39), p < 0.0001]. We review some of the basic mechanisms for the observed association between smoking and PMS. Given nicotine's rewarding effects, increased smoking behavior may be a mechanism to alleviate affective symptoms of PMS. However, smoking may lead to worsening of PMS symptoms because nicotine has effects on neurocircuitry that increases susceptibility to environmental stressors. Indeed, prior evidence shows that the hypothalamic-pituitary-adrenal (HPA) axis is already sub-optimal in PMS, hence, smoking likely further deteriorates it. Combined, this complicates the clinical course for the treatment of both PMS and Tobacco Use Disorder in this population.

Estimation of non-additive genetic variance in human complex traits from a large sample of unrelated individuals

Non-additive genetic variance for complex traits is traditionally estimated from data on relatives. It is notoriously difficult to estimate without bias in non-laboratory species, including humans, because of possible confounding with environmental covariance among relatives. In principle, non-additive variance attributable to common DNA variants can be estimated from a random sample of unrelated individuals with genome-wide SNP data. Here, we jointly estimate the proportion of variance explained by additive , dominance and additive-by-additive genetic variance in a single analysis model. We first show by simulations that our model leads to unbiased estimates and provide new theory to predict standard errors estimated using either least squares or maximum likelihood. We then apply the model to 70 complex traits using 254,679 unrelated individuals from the UK Biobank and 1.1M genotyped and imputed SNPs. We found strong evidence for additive variance (average across traits . In contrast, the average estimate of across traits was 0.001, implying negligible dominance variance at causal variants tagged by common SNPs. The average epistatic variance across the traits was 0.058, not significantly different from zero because of the large sampling variance. Our results provide new evidence that genetic variance for complex traits is predominantly additive, and that sample sizes of many millions of unrelated individuals are needed to estimate epistatic variance with sufficient precision.