Matched Ascertainment of Informative Families for Complex Genetic Modelling

Family data are used extensively in quantitative genetic studies to disentangle the genetic and environmental contributions to various diseases. Many family studies based their analysis on population-based registers containing a large number of individuals composed of small family units. For binary trait analyses, exact marginal likelihood is a common approach, but, due to the computational demand of the enormous data sets, it allows only a limited number of effects in the model. This makes it particularly difficult to perform joint estimation of variance components for a binary trait and the potential confounders. We have developed a data-reduction method of ascertaining informative families from population-based family registers. We propose a scheme where the ascertained families match the full cohort with respect to some relevant statistics, such as the risk to relatives of an affected individual. The ascertainment-adjusted analysis, which we implement using a pseudo-likelihood approach, is shown to be efficient relative to the analysis of the whole cohort and robust to mis-specification of the random effect distribution

Genetic Variations in IL28B and Allergic Disease in Children

Environmental changes affecting the relationship between the developing immune system and microbial exposure have been implicated in the epidemic rise of allergic disease in developed countries. While early developmental differences in T cell function are well-recognised, there is now emerging evidence that this is related to developmental differences in innate immune function. In this study we sought to examine if differences associated with innate immunity contribute to the altered immune programming recognised in allergic children. Here, we describe for the first time, the association of carriage of the T allele of the tagging single nucleotide polymorphism rs12979860 3 kb upstream of IL28B, encoding the potent innate immune modulator type III interferon lambda (IFN-λ3), and allergy in children (p = 0.004; OR 4.56). Strikingly, the association between rs12979860 genotype and allergic disease is enhanced in girls. Furthermore, carriage of the T allele at rs12979860 correlates with differences in the pro-inflammatory profile during the first five years of life suggesting this contributes to the key differences in subsequent innate immune development in children who develop allergic disease. In the context of rising rates of disease, these immunologic differences already present at birth imply very early interaction between genetic predisposition and prenatal environmental influences

Genetic Association Analysis Using Sibship Data: A Multilevel Model Approach

Family based association study (FBAS) has the advantages of controlling for population stratification and testing for linkage and association simultaneously. We propose a retrospective multilevel model (rMLM) approach to analyze sibship data by using genotypic information as the dependent variable. Simulated data sets were generated using the simulation of linkage and association (SIMLA) program. We compared rMLM to sib transmission/disequilibrium test (S-TDT), sibling disequilibrium test (SDT), conditional logistic regression (CLR) and generalized estimation equations (GEE) on the measures of power, type I error, estimation bias and standard error. The results indicated that rMLM was a valid test of association in the presence of linkage using sibship data. The advantages of rMLM became more evident when the data contained concordant sibships. Compared to GEE, rMLM had less underestimated odds ratio (OR). Our results support the application of rMLM to detect gene-disease associations using sibship data. However, the risk of increasing type I error rate should be cautioned when there is association without linkage between the disease locus and the genotyped marker

A General Framework for Formal Tests of Interaction after Exhaustive Search Methods with Applications to MDR and MDR-PDT

The initial presentation of multifactor dimensionality reduction (MDR) featured cross-validation to mitigate over-fitting, computationally efficient searches of the epistatic model space, and variable construction with constructive induction to alleviate the curse of dimensionality. However, the method was unable to differentiate association signals arising from true interactions from those due to independent main effects at individual loci. This issue leads to problems in inference and interpretability for the results from MDR and the family-based compliment the MDR-pedigree disequilibrium test (PDT). A suggestion from previous work was to fit regression models post hoc to specifically evaluate the null hypothesis of no interaction for MDR or MDR-PDT models. We demonstrate with simulation that fitting a regression model on the same data as that analyzed by MDR or MDR-PDT is not a valid test of interaction. This is likely to be true for any other procedure that searches for models, and then performs an uncorrected test for interaction. We also show with simulation that when strong main effects are present and the null hypothesis of no interaction is true, that MDR and MDR-PDT reject at far greater than the nominal rate. We also provide a valid regression-based permutation test procedure that specifically tests the null hypothesis of no interaction, and does not reject the null when only main effects are present. The regression-based permutation test implemented here conducts a valid test of interaction after a search for multilocus models, and can be applied to any method that conducts a search to find a multilocus model representing an interaction

Randomized trial of achieving healthy lifestyles in psychiatric rehabilitation: the ACHIEVE trial

<p>Abstract</p> <p>Background</p> <p>Overweight and obesity are highly prevalent among persons with serious mental illness. These conditions likely contribute to premature cardiovascular disease and a 20 to 30 percent shortened life expectancy in this vulnerable population. Persons with serious mental illness need effective, appropriately tailored behavioral interventions to achieve and maintain weight loss. Psychiatric rehabilitation day programs provide logical intervention settings because mental health consumers often attend regularly and exercise can take place on-site. This paper describes the Randomized Trial of Achieving Healthy Lifestyles in Psychiatric Rehabilitation (ACHIEVE). The goal of the study is to determine the effectiveness of a behavioral weight loss intervention among persons with serious mental illness that attend psychiatric rehabilitation programs. Participants randomized to the intervention arm of the study are hypothesized to have greater weight loss than the control group.</p> <p>Methods/Design</p> <p>A targeted 320 men and women with serious mental illness and overweight or obesity (body mass index ≥ 25.0 kg/m²) will be recruited from 10 psychiatric rehabilitation programs across Maryland. The core design is a randomized, two-arm, parallel, multi-site clinical trial to compare the effectiveness of an 18-month behavioral weight loss intervention to usual care. Active intervention participants receive weight management sessions and physical activity classes on-site led by study interventionists. The intervention incorporates cognitive adaptations for persons with serious mental illness attending psychiatric rehabilitation programs. The initial intensive intervention period is six months, followed by a twelve-month maintenance period in which trained rehabilitation program staff assume responsibility for delivering parts of the intervention. Primary outcomes are weight loss at six and 18 months.</p> <p>Discussion</p> <p>Evidence-based approaches to the high burden of obesity and cardiovascular disease risk in person with serious mental illness are urgently needed. The ACHIEVE Trial is tailored to persons with serious mental illness in community settings. This multi-site randomized clinical trial will provide a rigorous evaluation of a practical behavioral intervention designed to accomplish and sustain weight loss in persons with serious mental illness.</p> <p>Trial Registration</p> <p>Clinical Trials.gov NCT00902694</p

HIV-1 Envelope Subregion Length Variation during Disease Progression

The V3 loop of the HIV-1 Env protein is the primary determinant of viral coreceptor usage, whereas the V1V2 loop region is thought to influence coreceptor binding and participate in shielding of neutralization-sensitive regions of the Env glycoprotein gp120 from antibody responses. The functional properties and antigenicity of V1V2 are influenced by changes in amino acid sequence, sequence length and patterns of N-linked glycosylation. However, how these polymorphisms relate to HIV pathogenesis is not fully understood. We examined 5185 HIV-1 gp120 nucleotide sequence fragments and clinical data from 154 individuals (152 were infected with HIV-1 Subtype B). Sequences were aligned, translated, manually edited and separated into V1V2, C2, V3, C3, V4, C4 and V5 subregions. V1-V5 and subregion lengths were calculated, and potential N-linked glycosylation sites (PNLGS) counted. Loop lengths and PNLGS were examined as a function of time since infection, CD4 count, viral load, and calendar year in cross-sectional and longitudinal analyses. V1V2 length and PNLGS increased significantly through chronic infection before declining in late-stage infection. In cross-sectional analyses, V1V2 length also increased by calendar year between 1984 and 2004 in subjects with early and mid-stage illness. Our observations suggest that there is little selection for loop length at the time of transmission; following infection, HIV-1 adapts to host immune responses through increased V1V2 length and/or addition of carbohydrate moieties at N-linked glycosylation sites. V1V2 shortening during early and late-stage infection may reflect ineffective host immunity. Transmission from donors with chronic illness may have caused the modest increase in V1V2 length observed during the course of the pandemic

Ancillary human health benefits of improved air quality resulting from climate change mitigation

<p>Abstract</p> <p>Background</p> <p>Greenhouse gas (GHG) mitigation policies can provide ancillary benefits in terms of short-term improvements in air quality and associated health benefits. Several studies have analyzed the ancillary impacts of GHG policies for a variety of locations, pollutants, and policies. In this paper we review the existing evidence on ancillary health benefits relating to air pollution from various GHG strategies and provide a framework for such analysis.</p> <p>Methods</p> <p>We evaluate techniques used in different stages of such research for estimation of: (1) changes in air pollutant concentrations; (2) avoided adverse health endpoints; and (3) economic valuation of health consequences. The limitations and merits of various methods are examined. Finally, we conclude with recommendations for ancillary benefits analysis and related research gaps in the relevant disciplines.</p> <p>Results</p> <p>We found that to date most assessments have focused their analysis more heavily on one aspect of the framework (e.g., economic analysis). While a wide range of methods was applied to various policies and regions, results from multiple studies provide strong evidence that the short-term public health and economic benefits of ancillary benefits related to GHG mitigation strategies are substantial. Further, results of these analyses are likely to be underestimates because there are a number of important unquantified health and economic endpoints.</p> <p>Conclusion</p> <p>Remaining challenges include integrating the understanding of the relative toxicity of particulate matter by components or sources, developing better estimates of public health and environmental impacts on selected sub-populations, and devising new methods for evaluating heretofore unquantified and non-monetized benefits.</p