DSRIG: Incorporating graphical structure in the regularized modeling of SNP data.

Genetic selection of farm animals plays an important role in genetic improvement programs. Regularized regression methods on single nucleotide polymorphism (SNP) data from a set of candidate genes can help to identify genes that are associated with the trait of interest. This complex task must also consider the relative effect sizes on the desired trait and account for the relationships among the candidate SNPs so that selection of a SNP does not promote other undesirable traits through breeding. We present the Doubly Sparse Regression Incorporating Graphical structure (DSRIG), a novel regularized method for genetic selection that exploits the relationships among candidate SNPs to improve prediction. DSRIG was applied in the prediction of skatole and androstenone levels, two compounds known to be associated with boar taint. DSRIG was shown to provide a predictive benefit when compared to ordinary least squares (OLS) and the least absolute shrinkage and selection operator (LASSO) in a cross-validation procedure. The relative sizes of the coefficient estimates over the cross-validation procedure were compared to determine which SNPs may have the greatest impact on expression of the boar taint compounds and a consensus graph was used to infer the relationships among SNPs.

Guelph Family Health Study: pilot study of a home-based obesity prevention intervention.

OBJECTIVE: To examine the feasibility and preliminary impact of a home-based obesity prevention intervention among Canadian families. METHODS: Families with children 1.5-5 years of age were randomized to one of three groups: (1) four home visits (HV) with a health educator, emails, and mailed incentives (4HV; n = 17); (2) two HV, emails, and mailed incentives (2HV; n = 14); or (3) general health advice through emails (control; n = 13). Parents randomized to the 2HV and 4HV groups completed post-intervention satisfaction surveys. At baseline and post-intervention, parents reported frequency of family meals and their children's fruit, vegetable, and sugar-sweetened beverage (SSB) intake. We assessed the children's physical activity, sedentary behaviour, and sleep using accelerometers and their % fat mass using bioelectrical impedance analysis. Differences in outcomes at post-intervention, controlling for baseline, were examined using generalized estimating equations. RESULTS: Of the 44 families enrolled, 42 (96%) had 6-month outcome data. Satisfaction with the intervention was high; 80% were "very satisfied" and 20% were "satisfied." At post-intervention, children randomized to the 4HV and 2HV groups had significantly higher fruit intake and children randomized to the 2HV group had significantly lower percentage of fat mass, as compared to the control. No significant intervention effect was found for frequency of family meals, the children's vegetable or SSB intake, physical activity, sedentary behaviour, or sleep. CONCLUSIONS: Our results suggest that the delivery of a home-based intervention is feasible among Canadian families and may lead to improved diet and weight outcomes among children. A full-scale trial is needed to test the effectiveness of this home-based intervention. CLINICAL TRIALS REGISTRATION NUMBER: NCT02223234.

Guelph Family Health Study's Home-Based Obesity Prevention Intervention Increases Fibre and Fruit Intake in Preschool-Aged Children.

PURPOSE: The Guelph Family Health Study (GFHS) pilot was designed to examine the feasibility and effectiveness of a home-based obesity prevention intervention on health behaviours and obesity risk. The objective of this analysis was to determine the effect of the 6-month intervention on preschool-aged children's dietary intakes. METHODS: Families with children aged 1.5-5 years old were randomized to receive one of the following: 4 home visits with a health educator as well as tailored emails and mailed incentives (4HV; n = 19 children); 2 home visits with a health educator as well as tailored emails and mailed incentives (2HV; n = 14 children); or general health advice through emails (control; n = 12 children). Three-day food records were completed by parents for their children before and after the 6-month intervention and analyzed for 3-day average intakes of energy, nutrients, and MyPlate food groups. RESULTS: After the 6-month intervention, the 4HV group had significantly higher fibre intake and the 4HV and 2HV groups had significantly higher fruit intake, both compared with the control group. CONCLUSIONS: This study provides support for a home-based intervention approach to improve the diet quality of preschool-aged children.

A comparison of confidence interval methods for the intraclass correlation coefficient in community-based cluster randomization trials with a binary outcome.

BACKGROUND: Many investigators rely on previously published point estimates of the intraclass correlation coefficient rather than on their associated confidence intervals to determine the required size of a newly planned cluster randomized trial. Although confidence interval methods for the intraclass correlation coefficient that can be applied to community-based trials have been developed for a continuous outcome variable, fewer methods exist for a binary outcome variable. The aim of this study is to evaluate confidence interval methods for the intraclass correlation coefficient applied to binary outcomes in community intervention trials enrolling a small number of large clusters. Existing methods for confidence interval construction are examined and compared to a new ad hoc approach based on dividing clusters into a large number of smaller sub-clusters and subsequently applying existing methods to the resulting data. METHODS: Monte Carlo simulation is used to assess the width and coverage of confidence intervals for the intraclass correlation coefficient based on Smith's large sample approximation of the standard error of the one-way analysis of variance estimator, an inverted modified Wald test for the Fleiss-Cuzick estimator, and intervals constructed using a bootstrap-t applied to a variance-stabilizing transformation of the intraclass correlation coefficient estimate. In addition, a new approach is applied in which clusters are randomly divided into a large number of smaller sub-clusters with the same methods applied to these data (with the exception of the bootstrap-t interval, which assumes large cluster sizes). These methods are also applied to a cluster randomized trial on adolescent tobacco use for illustration. RESULTS: When applied to a binary outcome variable in a small number of large clusters, existing confidence interval methods for the intraclass correlation coefficient provide poor coverage. However, confidence intervals constructed using the new approach combined with Smith's method provide nominal or close to nominal coverage when the intraclass correlation coefficient is small (<0.05), as is the case in most community intervention trials. CONCLUSION: This study concludes that when a binary outcome variable is measured in a small number of large clusters, confidence intervals for the intraclass correlation coefficient may be constructed by dividing existing clusters into sub-clusters (e.g. groups of 5) and using Smith's method. The resulting confidence intervals provide nominal or close to nominal coverage across a wide range of parameters when the intraclass correlation coefficient is small (<0.05). Application of this method should provide investigators with a better understanding of the uncertainty associated with a point estimator of the intraclass correlation coefficient used for determining the sample size needed for a newly designed community-based trial.

Multi-state models for investigating possible stages leading to bipolar disorder.

BACKGROUND: It has been proposed that bipolar disorder onsets in a predictable progressive sequence of clinical stages. However, there is some debate in regard to a statistical approach to test this hypothesis. The objective of this paper is to investigate two different analysis strategies to determine the best suited model to assess the longitudinal progression of clinical stages in the development of bipolar disorder. METHODS: Data previously collected on 229 subjects at high risk of developing bipolar disorder were used for the statistical analysis. We investigate two statistical approaches for analyzing the relationship between the proposed stages of bipolar disorder: 1) the early stages are considered as time-varying covariates affecting the hazard of bipolar disorder in a Cox proportional hazards model, 2) the early stages are explicitly modelled as states in a non-parametric multi-state model. RESULTS: We found from the Cox model thatthere was evidence that the hazard of bipolar disorder is increased by the onset of major depressive disorder. From the multi-state model, in high-risk offspring the probability of bipolar disorder by age 29 was estimated as 0.2321. Cumulative incidence functions representing the probability of bipolar disorder given major depressive disorder at or before age 18 were estimated using both approaches and found to be similar. CONCLUSIONS: Both the Cox model and multi-state model are useful approaches to the modelling of antecedent risk syndromes. They lead to similar cumulative incidence functions but otherwise each method offers a different advantage.

A competing risks model for correlated data based on the subdistribution hazard.

Family-based follow-up study designs are important in epidemiology as they enable investigations of disease aggregation within families. Such studies are subject to methodological complications since data may include multiple endpoints as well as intra-family correlation. The methods herein are developed for the analysis of age of onset with multiple disease types for family-based follow-up studies. The proposed model expresses the marginalized frailty model in terms of the subdistribution hazards (SDH). As with Pipper and Martinussen's (Scand J Stat 30:509-521, 2003) model, the proposed multivariate SDH model yields marginal interpretations of the regression coefficients while allowing the correlation structure to be specified by a frailty term. Further, the proposed model allows for a direct investigation of the covariate effects on the cumulative incidence function since the SDH is modeled rather than the cause specific hazard. A simulation study suggests that the proposed model generally offers improved performance in terms of bias and efficiency when a sufficient number of events is observed. The proposed model also offers type I error rates close to nominal. The method is applied to a family-based study of breast cancer when death in absence of breast cancer is considered a competing risk.

Soya protein does not affect glycaemic control in adults with type 2 diabetes.

Evidence from observational, animal and human studies supports a role for soya protein and its isoflavones in the improvement of glycaemic control in type 2 diabetes. The objective of the present study was to determine the effect of isoflavone-rich soya protein on markers of glycaemic control in adults with type 2 diabetes. Using a randomised, crossover, double-blind, placebo-controlled design, adults with diet-controlled type 2 diabetes (n 29) consumed soya protein isolate (SPI) and milk protein isolate (MPI) for 57 d each separated by a 4-week washout. Blood was collected on days 1 and 57 of each treatment period for analysis of fasting HbA1C, and fasting and postprandial glucose, insulin and calculated indices of insulin sensitivity and resistance. Urine samples of 24 h were collected at the end of each treatment period for analysis of isoflavones. Urinary isoflavone excretion was significantly greater following consumption of SPI compared with MPI, and 20.7 % of the subjects (n 6) were classified as equol excretors. SPI consumption did not significantly affect fasting or postprandial glucose or insulin, fasting HbA1C, or indices of insulin sensitivity and resistance. These data do not support a role for soya protein in the improvement of glycaemic control in adults with diet-controlled type 2 diabetes and contribute to a limited literature of human studies on the effects of soya protein on the management of type 2 diabetes.

Soy protein reduces serum LDL cholesterol and the LDL cholesterol:HDL cholesterol and apolipoprotein B:apolipoprotein A-I ratios in adults with type 2 diabetes.

Type 2 diabetes is highly prevalent in North America and is associated with increased risk of cardiovascular disease (CVD). Evidence supports a role for soy protein in the reduction of serum lipids related to CVD risk; however, few studies have focused on adults with type 2 diabetes who are not on lipid-lowering medications and/or do not have diabetic complications. The purpose of this study was to determine the effect of soy protein isolate (SPI) consumption on serum lipids in adults with diet-controlled type 2 diabetes. Using a double-blind, randomized, crossover, placebo-controlled intervention study design, adults with diet-controlled type 2 diabetes (n = 29) consumed SPI (80 mg/d aglycone isoflavones) or milk protein isolate (MPI) for 57 d each separated by a 28-d washout period. Twenty-four-hour urine samples were collected on d 54-56 of each treatment for analysis of isoflavones and blood was collected on d 1 and 57 of each treatment and analyzed for serum lipids and apolipoproteins. SPI consumption increased urinary isoflavones compared with MPI. SPI consumption reduced serum LDL cholesterol (P = 0.04), LDL cholesterol:HDL cholesterol (P = 0.02), and apolipoprotein B:apolipoprotein A-I (P = 0.05) compared with MPI. SPI did not affect serum total cholesterol, HDL cholesterol, triacylglycerol, apolipoprotein B, or apolipoprotein A-I. These data demonstrate that consumption of soy protein can modulate some serum lipids in a direction beneficial for CVD risk in adults with type 2 diabetes.

Meta-analysis of community-based cluster randomization trials with binary outcomes.

BACKGROUND: Cluster randomization trials are widely used to test the effect of an intervention when individuals are naturally found in groups such as communities. For several separate studies of a similar intervention, it may be of interest to combine their results using meta-analysis procedures. However, this task requires consideration of both the likely dependencies among cluster members (intracluster correlation) and stratification based on the studies considered. PURPOSE: In this article, several possible approaches for meta-analysis are considered for cluster randomization trials having a binary outcome. METHODS: It is first noted that the standard Mantel-Haenszel test is invalid in this context since it ignores dependencies among cluster members. Two modifications are therefore considered as well as a general inverse variance approach and a procedure based on the Woolf statistic which does not require the availability of trial-specific design effects. Empirical Type I errors and powers for the different procedures considered are evaluated using Monte Carlo simulation. To illustrate the techniques, data are used from trials performed in four countries to compare two antenatal care programs with respect to their effects on the risk of hypertension during pregnancy. RESULTS: For the simulation scenarios considered, an adjusted Mantel-Haenszel procedure provides a valid test with the greatest power slightly outperforming the general inverse variance approach. LIMITATIONS: The potential need to adjust for possible confounding was not considered. However, more detailed information on confounders would not likely be available for most meta-analyses. CONCLUSION: Two procedures performed well. However, the choice of analysis approach also inevitably depends on the nature and extent of the available data.

Confidence intervals for candidate gene effects and environmental factors in population-based association studies of families.

Complex diseases are influenced by both genetic and environmental factors. Studies of individuals or of families can be used to examine the association of genetic factors, such as candidate genes, and other risk factors with the presence or absence of complex disorders. If families are investigated, whether or not they are randomly ascertained, possible familial correlation among observations must be considered. We have compared two statistical approaches for analyzing correlated binary data from randomly ascertained nuclear families. The generalized estimating equations approach (GEE) can be used to adjust for familial correlation. The relationship between covariates and the response is modelled, and the correlations among family members are treated as nuisance parameters. For comparison, we have proposed two strategies from a hierarchical nonparametric bootstrap approach. One strategy (S1) samples family units, preserving the structure and correlation within each family. A second and novel strategy (S2) also samples family units but then randomly samples offspring with replacement in each family. We applied the methods to data from a study of cardiovascular disease, and followed up with a simulation study in which family data were generated from an underlying multifactorial genetic model. Although the bootstrap approach was more computationally demanding, it outperformed the GEE in terms of confidence interval coverage probabilities for all sample sizes considered.

Temporal clusters of Salmonella serovars in humans in Alberta, 1990-2001.

BACKGROUND: Temporal clusters of salmonellosis are believed to occur but have not been quantitatively explored, thus, our objectives were to describe trends, seasonal patterns, and clusters of salmonellosis in humans in Alberta by examining isolates reported through passive surveillance systems. METHODS: Cases of salmonellosis reported through Notifiable Disease Records between January 1990 and December 2001 were obtained from Alberta Health and Wellness. Least squares regression was used to characterize the distribution (long-term trends and seasonal patterns) of isolates. A cluster detection test was used to determine if and when isolates of specific serovars aggregated in time, over and above the background distribution. Comparisons were made to temporal patterns in Alberta livestock and to known outbreaks in humans. RESULTS: S. Typhimurium, Enteritidis, Hadar, Heidelberg, and Thompson were the five most common serovars of the 9,188 isolates reported. The annual number of isolates was relatively stable over time, with a distinct summertime seasonal pattern. Clusters were observed in 23 of 32 serovars examined. More clusters occurred in September and October than in other months, and in 1998 through 2001 than in the early to mid-1990s. Also, more clusters were of short duration than long. INTERPRETATION: Short-duration clusters likely indicate a point source of infection, while long-duration clusters may indicate exposure to a persistent common source or the occurrence of secondary infections. A sharp increasing trend and a large cluster of S. Heidelberg may have public health implications. Surveillance activities focussed on similarities between common serovars, trends, and temporal clusters in humans and animals, and studies on factors associated with autumn clusters may be useful in preventing outbreaks in humans.

A temporal study of Salmonella serovars in animals in Alberta between 1990 and 2001.

Passive laboratory-based surveillance data from Alberta Agriculture Food and Rural Development were analyzed for common Salmonella serovars, prevalences, trends, and for the presence of temporal clusters. There were 1767 isolates between October 1990 and December 2001 comprising 63 different serovars, including 961 isolates from chickens, 418 from cattle, 108 from pigs, 102 from turkeys, and 178 from all other species combined. Salmonella Typhimurium, Heidelberg, Hadar, Kentucky, and Thompson were the 5 most frequently isolated serovars. Approximately 60% of the S. Typhimurium were isolated from cattle, whereas over 90% of the S. Heidelberg, Hadar, Kentucky, and Thompson were isolated from chickens. Salmonella Enteritidis was rarely isolated. There was an increasing trend in isolates from chickens, cattle, and pigs, and a decreasing trend in isolates from turkeys. Temporal clusters were observed in 11 of 15 serovars examined in chickens (S. Anatum, Heidelberg, Infantis, Kentucky, Mbandaka, Montevideo, Nienstedten, Oranienburg, Thompson, Typhimurium, and Typhimurium var. Copenhagen), 5 of 5 serovars in cattle (S. Dublin, Montevideo, Muenster, Typhimurium, and Typhimurium var. Copenhagen), and 1 of 3 serovars in pigs (S. Typhimurium). Short-duration clusters may imply point source infections, whereas long-duration clusters may indicate an increase in the prevalence of the serovar, farm-to-farm transmission, or a wide-spread common source. A higher concentration of clusters in the winter months may reflect greater confinement, reduced ventilation, stressors, or increased exposure to wildlife vectors that are sharing housing during the winter. Detection of large clusters of Salmonella may have public health implications in addition to animal health concerns.