A Comparison of Bone Mineral Density and Its Predictors in HIV-Infected and HIV-Uninfected Older Men

ObjectiveBone health in older individuals with HIV infection has not been well studied. This study aimed to compare bone mineral density (BMD), trabecular bone score (TBS), and bone markers between HIV-infected men and age- and body mass index (BMI)-matched HIV-uninfected men aged ≥60 years. We investigated the associations of risk factors related to fracture with BMD, TBS, and bone markers in HIV-infected men.MethodsThis cross-sectional study included 45 HIV-infected men receiving antiretroviral therapy and 42 HIV-uninfected men. Medical history, BMD and TBS measurements, and laboratory tests related to bone health were assessed in all the participants. HIV-related factors known to be associated with bone loss were assessed in the HIV-infected men.ResultsThe mean BMD, TBS, and osteopenia or osteoporosis prevalence were similar among the cases and controls. The HIV-infected men had significantly higher mean N-terminal propeptide of type 1 procollagen and C-terminal cross-linking telopeptide of type I collagen levels. Stepwise multiple linear regression analysis demonstrated that low BMI (lumbar spine, P = .015; femoral neck, P = .018; and total hip, P = .005), high C-terminal cross-linking telopeptide of type I collagen concentration (total hip, P = .042; and TBS, P = .010), and low vitamin D supplementation (TBS, P = .035) were independently associated with low BMD and TBS.ConclusionIn older HIV-infected men with a low fracture risk, the mean BMD and TBS were similar to those of the age- and BMI-matched controls. The mean bone marker levels were higher in the HIV group. Traditional risk factors for fracture, including low BMI, high C-terminal cross-linking telopeptide of type I collagen level, and low vitamin D supplementation, were significant predictors of low BMD and TBS.     

An Estimation Procedure for the Joint Distribution of Spatial Direction and Thickness of Flat Bodies Using Vertical Sections. Part II: An application in soil micromorphology

In soil micromorphology fissures are considered in vertical sections. To get information about the properties of the soil the joint distribution of spatial direction and width of these fissures is of interest. The fissures are mathematically generalized to flat bodies which are defined as stationary weighted surface processes with the weight “thickness”. In a typical point of the surface process suitable, joint parametric distributions of direction and thickness are assumed. The parameters have to be estimated from measurements on vertical sections which are taken from the soil. On these sections only a visible thickness and a visible angle can be observed. The joint distribution of these variables can be expressed by the joint distribution of spatial direction and thickness with the same parameters and in this indirect way the parameters can be estimated. The paper describes how to randomize the vertical section and how to measure the visible variables on the sections. The Chi-Square method is proposed for the parameter estimation. Further it is discussed how to derive good starting values for the numerical procedure. All this is demonstrated in a simulation study using the Bingham-Mardia distribution for the direction and the lognormal distribution for the thickness including a way to correlate the mean thickness and the direction. Finally an application in soil micromorphology is demonstrated for one soil horizon.     

Estimation of Individual Growth Curves by Empirically Weighted Least Squares

The simultaneous estimation of individual growth curves and a mean growth curve is accomplished by weighted least squares. A polynomial curve is fitted for each individual and the polynomial parameters are linear functions of parameters corresponding to covariates. A simple, computationally efficient variance-covariance estimator is derived. The resultant estimate is used in the weighted least squares estimation. The results are compared to empirical Bayes estimation.     

Mapping economic trait loci for somatic cell score in Holstein cattle using microsatellite markers and selective genotyping

Marker-assisted selection (MAS) uses genetic marker genotypes to predict an animal's production potential and will provide additional selection information for progeny testing. With the discovery of highly polymorphic microsatellite markers, the tools now exist to begin the search for economic trait loci (ETL), which is the first step toward MAS. The objective of this study was to identify ETL for somatic cell score in an existing Holstein population. Using the granddaughter design, sons from seven grandsire families were genotyped with 20 autosomal microsatellites from five chromosomes (4, 8, 13, 17, 23), with an emphasis on chromosome 23, which is the location of the bovine major histocompatibility complex (BoLA). Selective genotyping was used to reduce the number of genotypes required, in which the 10 highest and 10 lowest sons from the phenotypic distribution curve were tested (140 sons in seven families). One marker (513), located near BoLA, showed evidence of an ETL in three of five polymorphic families. Additional sons were genotyped from the five families to estimate the effect and to compare selective and ‘complete’ genotyping. Both methods detected an ETL at marker 513, but in different families. This study provides evidence of the usefulness of microsatellite markers and the granddaughter design in the detection of ETL; however, additional markers need to be evaluated to determine the usefulness of selective genotyping. Based on the results from the 20 studied markers, the most likely position of a somatic cell score ETL lies near marker 513, located on chromosome 23.     

Ecological optima and tolerances of Thelypteris limbosperma,Athyrium distentifolium,and Matteuccia struthiopteris along environmental gradients in Western Norway

The distribution and abundance of Thelypteris limbosperma, Athyrium distentifolium, and Matteuccia struthiopteris are modelled statistically in relation to 14 environmental variables along the major climatic, topographic, and edaphic gradients in western Norway. The data are from 624 stands from which measurements or estimates of mean January and mean July temperatures, humidity, altitude, aspect, and slope are available. From 182 of these stands eight soil variables have also been measured. The species responses are quantified by two numerical methods: Gaussian logit regression and weighted averaging (WA) regression. The estimated WA optima suggest that A. distentifolium has an ecological preference for low July and January temperatures, high altitudes, and soils of low-medium pH and base content. The species shows statistically significant Gaussian responses with summer temperature, humidity (= Martonnes humidity index), altitude, slope, aspect, pH, cation exchange capacity, and base saturation with optima of 8.7 °C, 188.9, 1220 m, 28°, 29°, 4.8, 13.77 mEq 100 g dry soil^-1, and 13.4%, respectively. These suggest that the occurrence and relative abundance of A. distentifolium are well predicted by summer temperature, topography, and soil pH and base status. T. limbosperma has WA optima that suggest that it favours moderately high winter and summer temperatures, high humidity, medium altitude, and soils of low pH and base content. It has significant Gaussian responses to summer temperature (optimum =12.6 °C), winter temperature (-1.8 °C), humidity (179.2), altitude (459.5 m), slope (22.5°), and Na (0.7 mg 100 g dry soil^-1). These suggest that climatic factors, altitude, and slope are significant predictors for its occurrence and abundance. M. struthiopteris has high WA optima for summer temperature, pH, Ca, Mg, K, Na, cation exchange capacity (CEC), and base saturation, and a low optima for humidity and winter temperature. Of these, summer temperature (16.0 °C), Ca (63.1 mg 100 g dry soil^-1), Mg (41.0 mg 100 g dry soil^-1), K (23.6 mg 100 g dry soil^-1), Na (5.0 mg 100 g dry soil^-1), CEC (60.7 mEq 100 g dry soil^-1), and base saturation (56.3%) have significant Gaussian logit responses, as do aspect (150.2°) and loss-on-ignition (9.4%). These results suggest that the occurrence and relative abundance of M. struthiopteris are well predicted by high soil base cations, a generally southern aspect, low organic content in the soil, and high July temperatures.     

Cross-trait prediction accuracy of summary statistics in genome-wide association studies

In the era of big data, univariate models have widely been used as a workhorse tool for quickly producing marginal estimators; and this is true even when in a high-dimensional dense setting, in which many features are “true,” but weak signals. Genome-wide association studies (GWAS) epitomize this type of setting. Although the GWAS marginal estimator is popular, it has long been criticized for ignoring the correlation structure of genetic variants (i.e., the linkage disequilibrium [LD] pattern). In this paper, we study the effects of LD pattern on the GWAS marginal estimator and investigate whether or not additionally accounting for the LD can improve the prediction accuracy of complex traits. We consider a general high-dimensional dense setting for GWAS and study a class of ridge-type estimators, including the popular marginal estimator and the best linear unbiased prediction (BLUP) estimator as two special cases. We show that the performance of GWAS marginal estimator depends on the LD pattern through the first three moments of its eigenvalue distribution. Furthermore, we uncover that the relative performance of GWAS marginal and BLUP estimators highly depends on the ratio of GWAS sample size over the number of genetic variants. Particularly, our finding reveals that the marginal estimator can easily become near-optimal within this class when the sample size is relatively small, even though it ignores the LD pattern. On the other hand, BLUP estimator has substantially better performance than the marginal estimator as the sample size increases toward the number of genetic variants, which is typically in millions. Therefore, adjusting for the LD (such as in the BLUP) is most needed when GWAS sample size is large. We illustrate the importance of our results by using the simulated data and real GWAS.