The k‐in‐a‐row up‐and‐down design,revisited

The percentile‐finding experimental design known variously as ‘forced‐choice fixed‐staircase’, ‘geometric up‐and‐down’ or ‘k‐in‐a‐row’ (KR) was introduced by Wetherill four decades ago. To date, KR has been by far the most widely used up‐and‐down (U&D) design for estimating non‐median percentiles; it is implemented most commonly in sensory studies. However, its statistical properties have not been fully documented, and the existence of a unique mode in its asymptotic treatment distribution has been recently disputed. Here we revisit the KR design and its basic properties. We find that KR does generate a unique stationary mode near its target percentile, and also displays better operational characteristics than two other U&D designs that have been studied more extensively. Supporting proofs and numerical calculations are presented. A recent experimental example from anesthesiology serves to highlight some of the ‘up‐and‐down’ design family's properties and advantages. Copyright © 2009 John Wiley & Sons, Ltd.     

Sex‐ and Age‐of‐Onset‐Based Locus Heterogeneity in Asthma

Asthma is a complex disease with a genetic component. The results of genome‐wide linkage studies imply that locus heterogeneity is likely to be an important feature of the genetics of asthma. To attempt to reduce locus heterogeneity, we hypothesized that the following may form the bases for locus heterogeneity at some asthma susceptibility loci: sex of affected individuals, parental origin of alleles shared by affected sib pairs, and age of onset of wheeze. Analysis of such strata may assist in the identification of novel susceptibility loci, or reveal the basis for locus heterogeneity at previously identified loci. Genotype and phenotype data from genome‐wide linkage searches for asthma susceptibility loci from three populations were analyzed. Some regions demonstrated evidence for linkage to affected individuals of a particular sex. There was evidence for excess maternal allele sharing at regions on chromosomes 9 and 11. Regions on chromosomes 2 and 6 were linked to late and early age at onset of wheeze in asthma, respectively. These analyses suggest that the bases that we selected for stratification may be appropriate at certain susceptibility loci for asthma, and may therefore assist in the fine mapping of such loci. Differences in such variables between studies may explain apparent nonreplication of linkage results. © 2001 Wiley‐Liss, Inc.     

Nutritional practices in full‐day‐care pre‐schools

Background: Full‐day‐care pre‐schools contribute significantly to the nutritional intake and acquisition of dietary habits of the pre‐school child. The present study investigated nutritional practices in full‐day‐care pre‐schools in Dublin, Ireland, aiming to determine the nutritional support that pre‐school managers deem necessary, thereby facilitating the amelioration of existing pre‐school nutritional training and practices. Methods: A telephone questionnaire completed by pre‐school managers (n = 54) examined pre‐school dietary practices, food provision and the association between these and pre‐school size, nutritional training attendance, possession of the Food and Nutrition Guidelines for Pre‐school Services and having a healthy eating policy. Nutritional training needs were also investigated. Results: Twenty‐five pre‐schools provided all food for attending children; parents were sole providers in six. Thirty‐four pre‐schools had a written healthy eating policy. Attendance at nutritional training was reported by 40. Possession of the Guidelines (n = 40) did not consistently result in their use. Poor parental and staff involvement in policy and menu development was cited. Although the delayed introduction of iron‐containing foods and a feeding beaker in infants was clearly evident, inappropriate beverages and snacks were served to children aged 1–5 years in 43 and 37 pre‐schools, respectively. Training priorities cited by managers included parental education and the provision of information regarding menu planning and healthy food choices. Conclusions: Nutritional training should advocate whole staff familiarity with and use of current guidelines, in addition to encouraging nutritional policy development and enforcement. Parental education is warranted. Dietary education should focus specifically on appropriate weaning practices, healthy beverage and snack provision and menu planning.     

A Multi‐Locus Likelihood Method for Assessing Parent‐of‐Origin Effects Using Case‐Control Mother‐Child Pairs

Parent‐of‐origin effects have been pointed out to be one plausible source of the heritability that was unexplained by genome‐wide association studies. Here, we consider a case‐control mother‐child pair design for studying parent‐of‐origin effects of offspring genes on neonatal/early‐life disorders or pregnancy‐related conditions. In contrast to the standard case‐control design, the case‐control mother‐child pair design contains valuable parental information and therefore permits powerful assessment of parent‐of‐origin effects. Suppose the region under study is in Hardy‐Weinberg equilibrium, inheritance is Mendelian at the diallelic locus under study, there is random mating in the source population, and the SNP under study is not related to risk for the phenotype under study because of linkage disequilibrium (LD) with other SNPs. Using a maximum likelihood method that simultaneously assesses likely parental sources and estimates effect sizes of the two offspring genotypes, we investigate the extent of power increase for testing parent‐of‐origin effects through the incorporation of genotype data for adjacent markers that are in LD with the test locus. Our method does not need to assume the outcome is rare because it exploits supplementary information on phenotype prevalence. Analysis with simulated SNP data indicates that incorporating genotype data for adjacent markers greatly help recover the parent‐of‐origin information. This recovery can sometimes substantially improve statistical power for detecting parent‐of‐origin effects. We demonstrate our method by examining parent‐of‐origin effects of the gene PPARGC1A on low birth weight using data from 636 mother‐child pairs in the Jerusalem Perinatal Study.     

Two‐step estimation in ratio‐of‐mediator‐probability weighted causal mediation analysis

This study investigates appropriate estimation of estimator variability in the context of causal mediation analysis that employs propensity score‐based weighting. Such an analysis decomposes the total effect of a treatment on the outcome into an indirect effect transmitted through a focal mediator and a direct effect bypassing the mediator. Ratio‐of‐mediator‐probability weighting estimates these causal effects by adjusting for the confounding impact of a large number of pretreatment covariates through propensity score‐based weighting. In step 1, a propensity score model is estimated. In step 2, the causal effects of interest are estimated using weights derived from the prior step's regression coefficient estimates. Statistical inferences obtained from this 2‐step estimation procedure are potentially problematic if the estimated standard errors of the causal effect estimates do not reflect the sampling uncertainty in the estimation of the weights. This study extends to ratio‐of‐mediator‐probability weighting analysis a solution to the 2‐step estimation problem by stacking the score functions from both steps. We derive the asymptotic variance‐covariance matrix for the indirect effect and direct effect 2‐step estimators, provide simulation results, and illustrate with an application study. Our simulation results indicate that the sampling uncertainty in the estimated weights should not be ignored. The standard error estimation using the stacking procedure offers a viable alternative to bootstrap standard error estimation. We discuss broad implications of this approach for causal analysis involving propensity score‐based weighting.     

Two‐phase designs for joint quantitative‐trait‐dependent and genotype‐dependent sampling in post‐GWAS regional sequencing

We evaluate two‐phase designs to follow‐up findings from genome‐wide association study (GWAS) when the cost of regional sequencing in the entire cohort is prohibitive. We develop novel expectation‐maximization‐based inference under a semiparametric maximum likelihood formulation tailored for post‐GWAS inference. A GWAS‐SNP (where SNP is single nucleotide polymorphism) serves as a surrogate covariate in inferring association between a sequence variant and a normally distributed quantitative trait (QT). We assess test validity and quantify efficiency and power of joint QT‐SNP‐dependent sampling and analysis under alternative sample allocations by simulations. Joint allocation balanced on SNP genotype and extreme‐QT strata yields significant power improvements compared to marginal QT‐ or SNP‐based allocations. We illustrate the proposed method and evaluate the sensitivity of sample allocation to sampling variation using data from a sequencing study of systolic blood pressure.     

3‐carboxy‐4‐methyl‐5‐propyl‐2‐furanpropanoic acid (CMPF): A metabolite identified after consumption of fish oil and fish

3‐carboxy‐4‐methyl‐5‐propyl‐2‐furanpropanoic acid (CMPF) is a known metabolite of furan fatty acids and was first referred to as a urofuran fatty acid, as it was found in urine of humans and other species after consumption of furan fatty acids or foods containing furan fatty acids. More recently, CMPF has been identified as a highly prominent metabolite following the consumption of fish oil, fish oil fractions and diets rich in fish, and can be regarded as biomarker of oil‐rich fish or fish oil intakes. As furan fatty acids are known to occur in fish and fish oil (at a low level), it is possible that the CMPF in plasma arises from these furan fatty acids. On a structural basis, this is a likely explanation rather than the CMPF being an actual metabolite of long‐chain marine omega‐3 fatty acids. Recent studies in high fat‐fed mice given purified CMPF suggest that CMPF might contribute to the improved metabolic effects observed following consumption of long‐chain marine omega‐3 fatty acids but much is still to be known about the relationships between CMPF and health.     

A comparison of the results of intent‐to‐treat,per‐protocol,and g‐estimation in the presence of non‐random treatment changes in a time‐to‐event non‐inferiority trial

While intent‐to‐treat (ITT) analysis is widely accepted for superiority trials, there remains debate about its role in non‐inferiority trials. It has often been said that ITT analysis tends to be anti‐conservative in demonstrating non‐inferiority, suggesting that per‐protocol (PP) analysis may be preferable for non‐inferiority trials, despite the inherent bias of such analyses. We propose using randomization‐based g‐estimation analyses that more effectively preserve the integrity of randomization than do the more widely used PP analyses. Simulation studies were conducted to investigate the impacts of different types of treatment changes on the conservatism or anti‐conservatism of analyses using the ITT, PP, and g‐estimation methods in a time‐to‐event outcome. The ITT results were anti‐conservative for all simulations. Anti‐conservativeness increased with the percentage of treatment change and was more pronounced for outcome‐dependent treatment changes. PP analysis, in which treatment‐switching cases were censored at the time of treatment change, maintained type I error near the nominal level for independent treatment changes, whereas for outcome‐dependent cases, PP analysis was either conservative or anti‐conservative depending on the mechanism underlying the percentage of treatment changes. G‐estimation analysis maintained type I error near the nominal level even for outcome‐dependent treatment changes, although information on unmeasured covariates is not used in the analysis. Thus, randomization‐based g‐estimation analyses should be used to supplement the more conventional ITT and PP analyses, especially for non‐inferiority trials. Copyright © 2010 John Wiley & Sons, Ltd.     

PhredEM: a phred‐score‐informed genotype‐calling approach for next‐generation sequencing studies

A fundamental challenge in analyzing next‐generation sequencing (NGS) data is to determine an individual's genotype accurately, as the accuracy of the inferred genotype is essential to downstream analyses. Correctly estimating the base‐calling error rate is critical to accurate genotype calls. Phred scores that accompany each call can be used to decide which calls are reliable. Some genotype callers, such as GATK and SAMtools, directly calculate the base‐calling error rates from phred scores or recalibrated base quality scores. Others, such as SeqEM, estimate error rates from the read data without using any quality scores. It is also a common quality control procedure to filter out reads with low phred scores. However, choosing an appropriate phred score threshold is problematic as a too high threshold may lose data, while a too low threshold may introduce errors. We propose a new likelihood‐based genotype‐calling approach that exploits all reads and estimates the per‐base error rates by incorporating phred scores through a logistic regression model. The approach, which we call PhredEM, uses the expectation–maximization (EM) algorithm to obtain consistent estimates of genotype frequencies and logistic regression parameters. It also includes a simple, computationally efficient screening algorithm to identify loci that are estimated to be monomorphic, so that only loci estimated to be nonmonomorphic require application of the EM algorithm. Like GATK, PhredEM can be used together with a linkage‐disequilibrium‐based method such as Beagle, which can further improve genotype calling as a refinement step. We evaluate the performance of PhredEM using both simulated data and real sequencing data from the UK10K project and the 1000 Genomes project. The results demonstrate that PhredEM performs better than either GATK or SeqEM, and that PhredEM is an improved, robust, and widely applicable genotype‐calling approach for NGS studies. The relevant software is freely available.     

Impacts of care‐giving and sources of support: a comparison of end‐of‐life and non‐end‐of‐life caregivers in Canada

This is the second in a series of papers that deal with care‐giving in Canada, as based on data available from the Canadian General Social Survey (2007). Building on the first paper, which reviewed the differences between short‐term, long‐term and end‐of‐life (EOL) caregivers, this paper uniquely examines the caregiver supports employed by EOL caregivers when compared to non‐EOL caregivers (short‐term and long‐term caregivers combined). Both papers employ data from Statistics Canada's General Social Survey (GSS Cycle 21: 2007). The GSS includes three modules, where respondents were asked questions about the unpaid home care assistance that they had provided in the last 12 months to someone at EOL or with either a long‐term health condition or a physical limitation. The objective of this research paper was to investigate the link between the impact of the care‐giving experience and the caregiver supports received, while also examining the differences in these across EOL and non‐EOL caregivers. By way of factor analysis and regression modelling, we examine differences between two types of caregivers: (i) EOL and (ii) non‐EOL caregivers. The study revealed that with respect to socio‐demographic characteristics, health outcomes and caregiver supports, EOL caregivers were consistently worse off. This suggests that although all non‐EOL caregivers are experiencing negative impacts from their care‐giving role, comparatively greater supports are needed for EOL caregivers.     

Meta‐analysis of gene‐environment interaction exploiting gene‐environment independence across multiple case‐control studies

Multiple papers have studied the use of gene‐environment (G‐E) independence to enhance power for testing gene‐environment interaction in case‐control studies. However, studies that evaluate the role of G‐E independence in a meta‐analysis framework are limited. In this paper, we extend the single‐study empirical Bayes type shrinkage estimators proposed by Mukherjee and Chatterjee (2008) to a meta‐analysis setting that adjusts for uncertainty regarding the assumption of G‐E independence across studies. We use the retrospective likelihood framework to derive an adaptive combination of estimators obtained under the constrained model (assuming G‐E independence) and unconstrained model (without assumptions of G‐E independence) with weights determined by measures of G‐E association derived from multiple studies. Our simulation studies indicate that this newly proposed estimator has improved average performance across different simulation scenarios than the standard alternative of using inverse variance (covariance) weighted estimators that combines study‐specific constrained, unconstrained, or empirical Bayes estimators. The results are illustrated by meta‐analyzing 6 different studies of type 2 diabetes investigating interactions between genetic markers on the obesity related FTO gene and environmental factors body mass index and age.     

Pathway‐Based Approaches for Sequencing‐Based Genome‐Wide Association Studies

For analyzing complex trait association with sequencing data, most current studies test aggregated effects of variants in a gene or genomic region. Although gene‐based tests have insufficient power even for moderately sized samples, pathway‐based analyses combine information across multiple genes in biological pathways and may offer additional insight. However, most existing pathway association methods are originally designed for genome‐wide association studies, and are not comprehensively evaluated for sequencing data. Moreover, region‐based rare variant association methods, although potentially applicable to pathway‐based analysis by extending their region definition to gene sets, have never been rigorously tested. In the context of exome‐based studies, we use simulated and real datasets to evaluate pathway‐based association tests. Our simulation strategy adopts a genome‐wide genetic model that distributes total genetic effects hierarchically into pathways, genes, and individual variants, allowing the evaluation of pathway‐based methods with realistic quantifiable assumptions on the underlying genetic architectures. The results show that, although no single pathway‐based association method offers superior performance in all simulated scenarios, a modification of Gene Set Enrichment Analysis approach using statistics from single‐marker tests without gene‐level collapsing (weighted Kolmogrov‐Smirnov [WKS]‐Variant method) is consistently powerful. Interestingly, directly applying rare variant association tests (e.g., sequence kernel association test) to pathway analysis offers a similar power, but its results are sensitive to assumptions of genetic architecture. We applied pathway association analysis to an exome‐sequencing data of the chronic obstructive pulmonary disease, and found that the WKS‐Variant method confirms associated genes previously published.     

A general multistage procedure for k‐out‐of‐n gatekeeping

We generalize a multistage procedure for parallel gatekeeping to what we refer to as k‐out‐of‐n gatekeeping in which at least k out of n hypotheses ( 1 ? k ? n) in a gatekeeper family must be rejected in order to test the hypotheses in the following family. This gatekeeping restriction arises in certain types of clinical trials; for example, in rheumatoid arthritis trials, it is required that efficacy be shown on at least three of the four primary endpoints. We provide a unified theory of multistage procedures for arbitrary k, with k = 1 corresponding to parallel gatekeeping and k = n to serial gatekeeping. The theory provides an insight into the construction of truncated separable multistage procedures using the closure method. Explicit formulae for calculating the adjusted p‐values are given. The proposed procedure is simpler to apply for this particular problem using a stepwise algorithm than the mixture procedure and the graphical procedure with memory using entangled graphs. Copyright © 2013 John Wiley & Sons, Ltd.     

Genome‐Wide Analysis of Gene‐Gene and Gene‐Environment Interactions Using Closed‐Form Wald Tests

Despite the successful discovery of hundreds of variants for complex human traits using genome‐wide association studies, the degree to which genes and environmental risk factors jointly affect disease risk is largely unknown. One obstacle toward this goal is that the computational effort required for testing gene‐gene and gene‐environment interactions is enormous. As a result, numerous computationally efficient tests were recently proposed. However, the validity of these methods often relies on unrealistic assumptions such as additive main effects, main effects at only one variable, no linkage disequilibrium between the two single‐nucleotide polymorphisms (SNPs) in a pair or gene‐environment independence. Here, we derive closed‐form and consistent estimates for interaction parameters and propose to use Wald tests for testing interactions. The Wald tests are asymptotically equivalent to the likelihood ratio tests (LRTs), largely considered to be the gold standard tests but generally too computationally demanding for genome‐wide interaction analysis. Simulation studies show that the proposed Wald tests have very similar performances with the LRTs but are much more computationally efficient. Applying the proposed tests to a genome‐wide study of multiple sclerosis, we identify interactions within the major histocompatibility complex region. In this application, we find that (1) focusing on pairs where both SNPs are marginally significant leads to more significant interactions when compared to focusing on pairs where at least one SNP is marginally significant; and (2) parsimonious parameterization of interaction effects might decrease, rather than increase, statistical power.