Response transformations for random effect and variance component models

Random effect models have been popularly used as a mainstream statistical technique over several decades; and the same can be said for response transformation models such as the Box–Cox transformation. The latter aims at ensuring that the assumptions of normality and of homoscedasticity of the response distribution are fulfilled, which are essential conditions for inference based on a linear model or a linear mixed model. However, methodology for response transformation and simultaneous inclusion of random effects has been developed and implemented only scarcely, and is so far restricted to Gaussian random effects. We develop such methodology, thereby not requiring parametric assumptions on the distribution of the random effects. This is achieved by extending the ‘Nonparametric Maximum Likelihood’ towards a ‘Nonparametric profile maximum likelihood’ technique, allowing to deal with overdispersion as well as two-level data scenarios.

Causes of variation in epigenetic aging across the lifespan

Background DNA methylation-based biological age (DNAm age) is potentially an important biomarker for adult health. Studies in specific age ranges have found widely varying results about its causes of variation. We investigated these causes across the lifespan. Methods We pooled genome-wide DNA methylation data for 4,217 people aged 0-92 years from 1,871 families. DNAm age was calculated using the Horvath epigenetic clock. We estimated familial correlations in DNAm age for monozygotic (MZ) twin, dizygotic (DZ) twin, sibling, parent-offspring, and spouse pairs by cohabitation status. Genetic and environmental variance component models were fitted and compared. Results Twin pair correlations were -0.12 to 0.18 around birth, not different from zero (all P>0.29). For all pairs of relatives, their correlations increased with time spent living together (all P<0.02) at different rates (MZ>DZ and siblings>parent-offspring; P<0.001) and decreased with time spent living apart (P=0.02) at similar rates. These correlation patterns were best explained by cohabitation-dependent shared environmental factors, the effects of which were 1.41 (95% confidence interval [CI], 1.16 to 1.66) times greater for MZ pairs than for DZ and sibling pairs, and the latter were 2.03 (95% CI, 1.13 to 9.47) times greater than for parent-offspring pairs. Genetic factors explained 13% (95% CI, -10% to 35%) of variation (P=0.27). Conclusion Variation in DNAm age is mostly caused by environmental factors, including those shared to different extents by relatives while living together and whose effects persist into old age. The equal environment assumption of the classic twin study might not hold for epigenetic aging.

Fine-Tuning Multilevel Modeling of Risk Factors Associated with Nonsurgical Periodontal Treatment Outcome

This retrospective study aimed to investigate the effect of known risk factors on nonsurgical periodontal treatment (NSPT) response using a pocket depth fine-tuning multilevel linear model (MLM). Thirty-seven patients (24 males and 13 females) with moderate to severe chronic periodontitis were treated with nonsurgical periodontal therapy. Follow-up visits at 3, 6, and 12 months included measurement of several clinical periodontal parameters. Data were extracted from a database system. Probing depth (PD) and Clinical Attachment Loss (CAL) reductions after NSPT in an overall of 1416 initially affected sites (baseline PD &ge; 4 mm), distributed on 536 teeth, were analyzed against known risk factors at three hierarchical levels (patient, tooth and site). The variance component models fitted to assess the three-level variance of PD and CAL decrease for each post-treatment follow-up showed that all levels contributed significantly to the overall variance (P &lt; 0.001). Patients that underwent NSPT and were continually monitored had very curative results. All three hierarchical levels included risk factors who had impact on the to influence the magnitude of PD and CAL reduction. Specifically, the tooth&rsquo;s type, surfaces involved and teeth mobility site-level risk factors showed the highest influence on these reductions, being highly relevant factors for the NSPT success.

Relationship between LD Score and Haseman-Elston Regression

Estimating SNP-heritability from summary statistics using LD Score regression provides a convenient alternative to standard variance component models, because LD Score regression is computationally very fast and does not require individual genotype data. However, the mathematical relationship between variance component methods and LD Score regression is not clear; in particular, it is not known in general how much of an increase in standard error one incurs by working with summary data instead of individual genotypes. In this paper, I show that in samples of unrelated individuals, LD Score regression with constrained intercept is essentially the same as Haseman-Elston (HE) regression, which is currently the state-of-the-art method for estimating SNP-heritability from ascertained case/control samples. Similar results hold for SNP-genetic correlation.

How to deal with genotype uncertainty in variance component quantitative trait loci analyses

SummaryDealing with genotype uncertainty is an ongoing issue in genetic analyses of complex traits. Here we consider genotype uncertainty in quantitative trait loci (QTL) analyses for large crosses in variance component models, where the genetic information is included in identity-by-descent (IBD) matrices. An IBD matrix is one realization from a distribution of potential IBD matrices given available marker information. In QTL analyses, its expectation is normally used resulting in potentially reduced accuracy and loss of power. Previously, IBD distributions have been included in models for small human full-sib families. We develop an Expectation–Maximization (EM) algorithm for estimating a full model based on Monte Carlo imputation for applications in large animal pedigrees. Our simulations show that the bias of variance component estimates using traditional expected IBD matrix can be adjusted by accounting for the distribution and that the calculations are computationally feasible for large pedigrees.

A Characterization of Admissible Linear Estimator of Regression Coefficients in Variance Component Models

In the paper, for the variance component models we take the ordinary quadratic risk function, and consider the admissibility of the linear estimators of linear combinations of regression coefficients in the class of linear homogeneous and inhomogeneous estimators. We get the necessary and sufficient conditions for the linear estimators of linear combinations of regression coefficients to be admissible.