Micro paddy lysimeter for monitoring solute transport in paddy environment

The micro paddy lysimeter (MPL) was developed and evaluated for its performance to simulate solute transport in paddy environment under laboratory conditions. MPLs were constructed using soil collected from Field Museum Honmachi of Tokyo University of Agriculture and Technology, Japan. For the physical characteristics of the hardpan layer, parameters such as thickness, and soil aggregate size, affecting the percolation rate were studied. For the plow layer, two types of plow soils, sieved and un-sieved soils were compared. The sieved soil plow layer was produced by mixing air-dried soils of different aggregate sizes of D > 9.50, 9.50 ≥ D > 4.75, 4.75 ≥ D > 2.0 mm and D ≤ 2.0 mm at 47.1, 19.5, 20.6, and 12.8%, respectively. The un-sieved plow layer soil was directly used after collecting from the field. Inert tracer was applied to ponding water with controlled boundary conditions to evaluate the reproducibility of the soil hydraulic characteristics. HYDRUS-1D was used to evaluate the movement of bromide tracer in the MPL. The proposed conditions of the MPL were that the hardpan layer can be made from soil aggregates smaller than 0.425 mm with 2 cm thickness and that the plow layer can be prepared with sieved or un-sieved soils. With these conditions, the obtained results proved that MPLs can be a useful tool to simulate solute transport in paddy environment.

In vivo evaluation of folate decorated cross-linked micelles for the delivery of platinum anticancer drugs

The biodistribution of micelles with and without folic acid targeting ligands were studied using a block copolymer consisting of acrylic acid (AA) and polyethylene glycol methyl ether acrylate (PEGMEA) blocks. The polymers were prepared using RAFT polymerization in the presence of a folic acid functionalized RAFT agent. Oxoplatin was conjugated onto the acrylic acid block to form amphiphilic polymers which, when diluted in water, formed stable micelles. In order to probe the in vivo stability, a selection of micelles were cross-linked using 1,8-diamino octane. The sizes of the micelles used in this study range between 75 and 200 nm, with both spherical and worm-like conformation. The effects of cross-linking, folate conjugation and different conformation on the biodistribution were studied in female nude mice (BALB/c) following intravenous injection into the tail vein. Using optical imaging to monitor the fluorophore-labeled polymer, the in vivo biodistribution of the micelles was monitored over a 48 h time-course after which the organs were removed and evaluated ex vivo. These experiments showed that both cross-linking and conjugation with folic acid led to increased fluorescence intensities in the organs, especially in the liver and kidneys, while micelles that are not conjugated with folate and not cross-linked are cleared rapidly from the body. Higher accumulation in the spleen, liver, and kidneys was also observed for micelles with worm-like shapes compared to the spherical micelles. While the various factors of cross-linking, micelle shape, and conjugation with folic acid all contribute separately to prolong the circulation time of the micelle, optimization of these parameters for drug delivery devices could potentially overcome adverse effects such as liver and kidney toxicity.

Genetic insights into common pathways and complex relationships among iImmune-mediated diseases

Shared aetiopathogenic factors among immune-mediated diseases have long been suggested by their co-familiality and co-occurrence, and molecular support has been provided by analysis of human leukocyte antigen (HLA) haplotypes and genome-wide association studies. The interrelationships can now be better appreciated following the genotyping of large immune disease sample sets on a shared SNP array: the 'Immunochip'. Here, we systematically analyse loci shared among major immune-mediated diseases. This reveals that several diseases share multiple susceptibility loci, but there are many nuances. The most associated variant at a given locus frequently differs and, even when shared, the same allele often has opposite associations. Interestingly, risk alleles conferring the largest effect sizes are usually disease-specific. These factors help to explain why early evidence of extensive 'sharing' is not always reflected in epidemiological overlap. © 2013 Macmillan Publishers Limited. All rights reserved.

Genome-wide association studies

Genome-wide association studies (GWAS) are a powerful hypothesis-free tool for the dissection of susceptibility to common heritable human diseases, including osteoporosis. To date, more than 2000 loci for common human diseases have been identified by GWAS. Success using the GWAS model depends on genetic risk being determined by shared stretches of DNA carried with different frequencies in cases and controls, inherited from ancient ancestors, termed the “common disease–common variant” hypothesis. Not all disease risk is caused by common variants, however, and thus GWAS will not detect all variants involved. Successful GWAS performance requires careful quality control, especially as the effect sizes under study are modest, and there are multiple potential sources of error. Conservative interpretation, use of stringent significance thresholds, and replication in independent cohorts are required to ensure results are robust. Despite these challenging parameters, much has been learnt from GWAS and, as the approach matures and is modified to identify a wider range of variants, significantly more will be learnt about the etiopathogenesis of common diseases such as osteoporosis.

'Sink or swim': An evaluation of the clinical characteristics of individuals with high bone mass

Summary High bone mineral density on routine dual energy X-ray absorptiometry (DXA) may indicate an underlying skeletal dysplasia. Two hundred fifty-eight individuals with unexplained high bone mass (HBM), 236 relatives (41% with HBM) and 58 spouses were studied. Cases could not float, had mandible enlargement, extra bone, broad frames, larger shoe sizes and increased body mass index (BMI). HBM cases may harbour an underlying genetic disorder. Introduction High bone mineral density is a sporadic incidental finding on routine DXA scanning of apparently asymptomatic individuals. Such individuals may have an underlying skeletal dysplasia, as seen in LRP5 mutations. We aimed to characterize unexplained HBM and determine the potential for an underlying skeletal dysplasia. Methods Two hundred fifty-eight individuals with unexplained HBM (defined as L1 Z-score ≥ +3.2 plus total hip Z-score ≥ +1.2, or total hip Z-score ≥ +3.2) were recruited from 15 UK centres, by screening 335,115 DXA scans. Unexplained HBM affected 0.181% of DXA scans. Next 236 relatives were recruited of whom 94 (41%) had HBM (defined as L1 Z-score + total hip Z-score ≥ +3.2). Fifty-eight spouses were also recruited together with the unaffected relatives as controls. Phenotypes of cases and controls, obtained from clinical assessment, were compared using random-effects linear and logistic regression models, clustered by family, adjusted for confounders, including age and sex. Results Individuals with unexplained HBM had an excess of sinking when swimming (7.11 [3.65, 13.84], p < 0.001; adjusted odds ratio with 95% confidence interval shown), mandible enlargement (4.16 [2.34, 7.39], p < 0.001), extra bone at tendon/ligament insertions (2.07 [1.13, 3.78], p = 0.018) and broad frame (3.55 [2.12, 5.95], p < 0.001). HBM cases also had a larger shoe size (mean difference 0.4 [0.1, 0.7] UK sizes, p = 0.009) and increased BMI (mean difference 2.2 [1.3, 3.1] kg/m 2, p < 0.001). Conclusion Individuals with unexplained HBM have an excess of clinical characteristics associated with skeletal dysplasia and their relatives are commonly affected, suggesting many may harbour an underlying genetic disorder affecting bone mass.

Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis

Multiple sclerosis is a common disease of the central nervous system in which the interplay between inflammatory and neurodegenerative processes typically results in intermittent neurological disturbance followed by progressive accumulation of disability. Epidemiological studies have shown that genetic factors are primarily responsible for the substantially increased frequency of the disease seen in the relatives of affected individuals, and systematic attempts to identify linkage in multiplex families have confirmed that variation within the major histocompatibility complex (MHC) exerts the greatest individual effect on risk. Modestly powered genome-wide association studies (GWAS) have enabled more than 20 additional risk loci to be identified and have shown that multiple variants exerting modest individual effects have a key role in disease susceptibility. Most of the genetic architecture underlying susceptibility to the disease remains to be defined and is anticipated to require the analysis of sample sizes that are beyond the numbers currently available to individual research groups. In a collaborative GWAS involving 9,772 cases of European descent collected by 23 research groups working in 15 different countries, we have replicated almost all of the previously suggested associations and identified at least a further 29 novel susceptibility loci. Within the MHC we have refined the identity of the HLA-DRB1 risk alleles and confirmed that variation in the HLA-A gene underlies the independent protective effect attributable to the class I region. Immunologically relevant genes are significantly overrepresented among those mapping close to the identified loci and particularly implicate T-helper-cell differentiation in the pathogenesis of multiple sclerosis. © 2011 Macmillan Publishers Limited. All rights reserved.

Genetic analyses in a sample of individuals with high or low BMD shows association with multiple Wnt pathway genes

INTRODUCTION Although the high heritability of BMD variation has long been established, few genes have been conclusively shown to affect the variation of BMD in the general population. Extreme truncate selection has been proposed as a more powerful alternative to unselected cohort designs in quantitative trait association studies. We sought to test these theoretical predictions in studies of the bone densitometry measures BMD, BMC, and femoral neck area, by investigating their association with members of the Wnt pathway, some of which have previously been shown to be associated with BMD in much larger cohorts, in a moderate-sized extreme truncate selected cohort (absolute value BMD Z-scores = 1.5-4.0; n = 344). MATERIALS AND METHODS Ninety-six tag-single nucleotide polymorphism (SNPs) lying in 13 Wnt signaling pathway genes were selected to tag common genetic variation (minor allele frequency [MAF] > 5% with an r(2) > 0.8) within 5 kb of all exons of 13 Wnt signaling pathway genes. The genes studied included LRP1, LRP5, LRP6, Wnt3a, Wnt7b, Wnt10b, SFRP1, SFRP2, DKK1, DKK2, FZD7, WISP3, and SOST. Three hundred forty-four cases with either high or low BMD were genotyped by Illumina Goldengate microarray SNP genotyping methods. Association was tested either by Cochrane-Armitage test for dichotomous variables or by linear regression for quantitative traits. RESULTS Strong association was shown with LRP5, polymorphisms of which have previously been shown to influence total hip BMD (minimum p = 0.0006). In addition, polymorphisms of the Wnt antagonist, SFRP1, were significantly associated with BMD and BMC (minimum p = 0.00042). Previously reported associations of LRP1, LRP6, and SOST with BMD were confirmed. Two other Wnt pathway genes, Wnt3a and DKK2, also showed nominal association with BMD. CONCLUSIONS This study shows that polymorphisms of multiple members of the Wnt pathway are associated with BMD variation. Furthermore, this study shows in a practical trial that study designs involving extreme truncate selection and moderate sample sizes can robustly identify genes of relevant effect sizes involved in BMD variation in the general population. This has implications for the design of future genome-wide studies of quantitative bone phenotypes relevant to osteoporosis.

Combined analysis of three whole genome linkage scans for Ankylosing Spondylitis

Objective. Ankylosing spondylitis (AS) is a debilitating chronic inflammatory condition with a high degree of familiality (λs=82) and heritability (>90%) that primarily affects spinal and sacroiliac joints. Whole genome scans for linkage to AS phenotypes have been conducted, although results have been inconsistent between studies and all have had modest sample sizes. One potential solution to these issues is to combine data from multiple studies in a retrospective meta-analysis. Methods: The International Genetics of Ankylosing Spondylitis Consortium combined data from three whole genome linkage scans for AS (n=3744 subjects) to determine chromosomal markers that show evidence of linkage with disease. Linkage markers typed in different centres were integrated into a consensus map to facilitate effective data pooling. We performed a weighted meta-analysis to combine the linkage results, and compared them with the three individual scans and a combined pooled scan. Results: In addition to the expected region surrounding the HLA-B27 gene on chromosome 6, we determined that several marker regions showed significant evidence of linkage with disease status. Regions on chromosome 10q and 16q achieved 'suggestive' evidence of linkage, and regions on chromosomes 1q, 3q, 5q, 6q, 9q, 17q and 19q showed at least nominal linkage in two or more scans and in the weighted meta-analysis. Regions previously associated with AS on chromosome 2q (the IL-1 gene cluster) and 22q (CYP2D6) exhibited nominal linkage in the meta-analysis, providing further statistical support for their involvement in susceptibility to AS. Conclusion: These findings provide a useful guide for future studies aiming to identify the genes involved in this highly heritable condition. . Published by on behalf of the British Society for Rheumatology.

The v-MFG test: Investigating maternal, offspring and maternal-fetal genetic incompatibility effects on disease and viability

The MFG test is a family-based association test that detects genetic effects contributing to disease in offspring, including offspring allelic effects, maternal allelic effects and MFG incompatibility effects. Like many other family-based association tests, it assumes that the offspring survival and the offspring-parent genotypes are conditionally independent provided the offspring is affected. However, when the putative disease-increasing locus can affect another competing phenotype, for example, offspring viability, the conditional independence assumption fails and these tests could lead to incorrect conclusions regarding the role of the gene in disease. We propose the v-MFG test to adjust for the genetic effects on one phenotype, e.g., viability, when testing the effects of that locus on another phenotype, e.g., disease. Using genotype data from nuclear families containing parents and at least one affected offspring, the v-MFG test models the distribution of family genotypes conditional on offspring phenotypes. It simultaneously estimates genetic effects on two phenotypes, viability and disease. Simulations show that the v-MFG test produces accurate genetic effect estimates on disease as well as on viability under several different scenarios. It generates accurate type-I error rates and provides adequate power with moderate sample sizes to detect genetic effects on disease risk when viability is reduced. We demonstrate the v-MFG test with HLA-DRB1 data from study participants with rheumatoid arthritis (RA) and their parents, we show that the v-MFG test successfully detects an MFG incompatibility effect on RA while simultaneously adjusting for a possible viability loss.

Localization of type 1 diabetes susceptibility to the MHC class I genes HLA-B and HLA-A

The major histocompatibility complex (MHC) on chromosome 6 is associated with susceptibility to more common diseases than any other region of the human genome, including almost all disorders classified as autoimmune. In type 1 diabetes the major genetic susceptibility determinants have been mapped to the MHC class II genes HLA-DQB1 and HLA-DRB1 (refs 1–3), but these genes cannot completely explain the association between type 1 diabetes and the MHC region4, 5, 6, 7, 8, 9, 10, 11. Owing to the region's extreme gene density, the multiplicity of disease-associated alleles, strong associations between alleles, limited genotyping capability, and inadequate statistical approaches and sample sizes, which, and how many, loci within the MHC determine susceptibility remains unclear. Here, in several large type 1 diabetes data sets, we analyse a combined total of 1,729 polymorphisms, and apply statistical methods—recursive partitioning and regression...

Model selection with misspecified spatial covariance structure

Spatial data analysis has become more and more important in the studies of ecology and economics during the last decade. One focus of spatial data analysis is how to select predictors, variance functions and correlation functions. However, in general, the true covariance function is unknown and the working covariance structure is often misspecified. In this paper, our target is to find a good strategy to identify the best model from the candidate set using model selection criteria. This paper is to evaluate the ability of some information criteria (corrected Akaike information criterion, Bayesian information criterion (BIC) and residual information criterion (RIC)) for choosing the optimal model when the working correlation function, the working variance function and the working mean function are correct or misspecified. Simulations are carried out for small to moderate sample sizes. Four candidate covariance functions (exponential, Gaussian, Matern and rational quadratic) are used in simulation studies. With the summary in simulation results, we find that the misspecified working correlation structure can still capture some spatial correlation information in model fitting. When the sample size is large enough, BIC and RIC perform well even if the the working covariance is misspecified. Moreover, the performance of these information criteria is related to the average level of model fitting which can be indicated by the average adjusted R square ( [GRAPHICS] ), and overall RIC performs well.

Weighted rank regression for clustered data analysis

We consider ranked-based regression models for clustered data analysis. A weighted Wilcoxon rank method is proposed to take account of within-cluster correlations and varying cluster sizes. The asymptotic normality of the resulting estimators is established. A method to estimate covariance of the estimators is also given, which can bypass estimation of the density function. Simulation studies are carried out to compare different estimators for a number of scenarios on the correlation structure, presence/absence of outliers and different correlation values. The proposed methods appear to perform well, in particular, the one incorporating the correlation in the weighting achieves the highest efficiency and robustness against misspecification of correlation structure and outliers. A real example is provided for illustration.

An optimal design for screening trials

Yao, Begg, and Livingston (1996, Biometrics 52, 992-1001) considered the optimal group size for testing a series of potentially therapeutic agents to identify a promising one as soon as possible for given error rates. The number of patients to be tested with each agent was fixed as the group size. We consider a sequential design that allows early acceptance and rejection, and we provide an optimal strategy to minimize the sample sizes (patients) required using Markov decision processes. The minimization is under the constraints of the two types (false positive and false negative) of error probabilities, with the Lagrangian multipliers corresponding to the cost parameters for the two types of errors. Numerical studies indicate that there can be a substantial reduction in the number of patients required.

Working correlation structure misspecification, estimation and covariate design: Implications for generalised estimating equations performance

The method of generalised estimating equations for regression modelling of clustered outcomes allows for specification of a working matrix that is intended to approximate the true correlation matrix of the observations. We investigate the asymptotic relative efficiency of the generalised estimating equation for the mean parameters when the correlation parameters are estimated by various methods. The asymptotic relative efficiency depends on three-features of the analysis, namely (i) the discrepancy between the working correlation structure and the unobservable true correlation structure, (ii) the method by which the correlation parameters are estimated and (iii) the 'design', by which we refer to both the structures of the predictor matrices within clusters and distribution of cluster sizes. Analytical and numerical studies of realistic data-analysis scenarios show that choice of working covariance model has a substantial impact on regression estimator efficiency. Protection against avoidable loss of efficiency associated with covariance misspecification is obtained when a 'Gaussian estimation' pseudolikelihood procedure is used with an AR(1) structure.

A generalized estimating equations approach for analysis of the impact of new technology on a trawl fishery

The article describes a generalized estimating equations approach that was used to investigate the impact of technology on vessel performance in a trawl fishery during 1988-96, while accounting for spatial and temporal correlations in the catch-effort data. Robust estimation of parameters in the presence of several levels of clustering depended more on the choice of cluster definition than on the choice of correlation structure within the cluster. Models with smaller cluster sizes produced stable results, while models with larger cluster sizes, that may have had complex within-cluster correlation structures and that had within-cluster covariates, produced estimates sensitive to the correlation structure. The preferred model arising from this dataset assumed that catches from a vessel were correlated in the same years and the same areas, but independent in different years and areas. The model that assumed catches from a vessel were correlated in all years and areas, equivalent to a random effects term for vessel, produced spurious results. This was an unexpected finding that highlighted the need to adopt a systematic strategy for modelling. The article proposes a modelling strategy of selecting the best cluster definition first, and the working correlation structure (within clusters) second. The article discusses the selection and interpretation of the model in the light of background knowledge of the data and utility of the model, and the potential for this modelling approach to apply in similar statistical situations.