Family-based association analysis with ordered categorical phenotypes, covariates and interactions

Genetic association analyses of family-based studies with ordered categorical phenotypes are often conducted using methods either for quantitative or for binary traits, which can lead to suboptimal analyses. Here we present an alternative likelihood-based method of analysis for single nucleotide polymorphism (SNP) genotypes and ordered categorical phenotypes in nuclear families of any size. Our approach, which extends our previous work for binary phenotypes, permits straightforward inclusion of covariate, gene-gene and gene-covariate interaction terms in the likelihood, incorporates a simple model for ascertainment and allows for family-specific effects in the hypothesis test. Additionally, our method produces interpretable parameter estimates and valid confidence intervals. We assess the proposed method using simulated data, and apply it to a polymorphism in the c-reactive protein (CRP) gene typed in families collected to investigate human systemic lupus erythematosus. By including sex interactions in the analysis, we show that the polymorphism is associated with anti-nuclear autoantibody (ANA) production in females, while there appears to be no effect in males.

Meta-analysis: a unifying meta-likelihood approach framing unobserved heterogeneity, study covariates, publication bias, and study quality

OBJECTIVES: This contribution provides a unifying concept for meta-analysis integrating the handling of unobserved heterogeneity, study covariates, publication bias and study quality. It is important to consider these issues simultaneously to avoid the occurrence of artifacts, and a method for doing so is suggested here. METHODS: The approach is based upon the meta-likelihood in combination with a general linear nonparametric mixed model, which lays the ground for all inferential conclusions suggested here. RESULTS: The concept is illustrated at hand of a meta-analysis investigating the relationship of hormone replacement therapy and breast cancer. The phenomenon of interest has been investigated in many studies for a considerable time and different results were reported. In 1992 a meta-analysis by Sillero-Arenas et al. concluded a small, but significant overall effect of 1.06 on the relative risk scale. Using the meta-likelihood approach it is demonstrated here that this meta-analysis is due to considerable unobserved heterogeneity. Furthermore, it is shown that new methods are available to model this heterogeneity successfully. It is argued further to include available study covariates to explain this heterogeneity in the meta-analysis at hand. CONCLUSIONS: The topic of HRT and breast cancer has again very recently become an issue of public debate, when results of a large trial investigating the health effects of hormone replacement therapy were published indicating an increased risk for breast cancer (risk ratio of 1.26). Using an adequate regression model in the previously published meta-analysis an adjusted estimate of effect of 1.14 can be given which is considerably higher than the one published in the meta-analysis of Sillero-Arenas et al. In summary, it is hoped that the method suggested here contributes further to a good meta-analytic practice in public health and clinical disciplines.

A likelihood ratio appropach to family-based association studies with covariates

We introduce a procedure for association based analysis of nuclear families that allows for dichotomous and more general measurements of phenotype and inclusion of covariate information. Standard generalized linear models are used to relate phenotype and its predictors. Our test procedure, based on the likelihood ratio, unifies the estimation of all parameters through the likelihood itself and yields maximum likelihood estimates of the genetic relative risk and interaction parameters. Our method has advantages in modelling the covariate and gene-covariate interaction terms over recently proposed conditional score tests that include covariate information via a two-stage modelling approach. We apply our method in a study of human systemic lupus erythematosus and the C-reactive protein that includes sex as a covariate.

Phylogenomics of eukaryotes: Impact of missing data on large alignments

Resolving the relationships between Metazoa and other eukaryotic groups as well as between metazoan phyla is central to the understanding of the origin and evolution of animals. The current view is based on limited data sets, either a single gene with many species (e.g., ribosomal RNA) or many genes but with only a few species. Because a reliable phylogenetic inference simultaneously requires numerous genes and numerous species, we assembled a very large data set containing 129 orthologous proteins (similar to30,000 aligned amino acid positions) for 36 eukaryotic species. Included in the alignments are data from the choanoflagellate Monosiga ovata, obtained through the sequencing of about 1,000 cDNAs. We provide conclusive support for choanoflagellates as the closest relative of animals and for fungi as the second closest. The monophyly of Plantae and chromalveolates was recovered but without strong statistical support. Within animals, in contrast to the monophyly of Coelomata observed in several recent large-scale analyses, we recovered a paraphyletic Coelamata, with nematodes and platyhelminths nested within. To include a diverse sample of organisms, data from EST projects were used for several species, resulting in a large amount of missing data in our alignment (about 25%). By using different approaches, we verify that the inferred phylogeny is not sensitive to these missing data. Therefore, this large data set provides a reliable phylogenetic framework for studying eukaryotic and animal evolution and will be easily extendable when large amounts of sequence information become available from a broader taxonomic range.

Missing the logic of the text: Lord Butler’s report on intelligence on Iraqi weapons of mass destruction

This article focuses on the final report of Lord Butler’s review of British intelligence on weapons of mass destruction (WMD), specifically on its treatment of the accuracy of the use of intelligence on Iraqi WMD in a government dossier published in September 2002 ahead of the 2003 Iraq war. In the report, the demonstration of the accuracy of the “September Dossier” hinges on the insertion of tables that compare side-by-side quotations from this document and from intelligence assessments. The analysis of the textual and visual methods by which the report is written reveals how the logic of the comparative tables is missed in the Butler report: the logic of these tables requires that the comparison between quotations from the two documents should be performed at the level of their details but the Butler report performs its comparison only at a broad and general level.

Are the debates on water privatization missing the point? Experiences from Africa, Asia and Latin America

This paper has two principal aims: first, to unravel some of the arguments mobilized in the controversial privatization debate, and second, to review the scale and nature of private sector provision of water and sanitation in Africa, Asia and Latin America. Despite being vigorously promoted in the policy arena and having been implemented in several countries in the South in the 1990s, privatization has achieved neither the scale nor benefits anticipated. In particular, the paper is pessimistic about the role that privatization can play in achieving the Millennium Development Goals of halving the number of people without access to water and sanitation by 2015. This is not because of some inherent contradiction between private profits and the public good, but because neither publicly nor privately operated utilities are well suited to serving the majority of low-income households with inadequate water and sanitation, and because many of the barriers to service provision in poor settlements can persist whether water and sanitation utilities are publicly or privately operated. This is not to say that well-governed localities should not choose to involve private companies in water and sanitation provision, but it does imply that there is no justification for international agencies and agreements to actively promote greater private sector participation on the grounds that it can significantly reduce deficiencies in water and sanitation services in the South.

Tobit estiamtion with unknown point of censoring with an application to milk market participation in the Ethiopian highlands

Data augmentation is a powerful technique for estimating models with latent or missing data, but applications in agricultural economics have thus far been few. This paper showcases the technique in an application to data on milk market participation in the Ethiopian highlands. There, a key impediment to economic development is an apparently low rate of market participation. Consequently, economic interest centers on the “locations” of nonparticipants in relation to the market and their “reservation values” across covariates. These quantities are of policy interest because they provide measures of the additional inputs necessary in order for nonparticipants to enter the market. One quantity of primary interest is the minimum amount of surplus milk (the “minimum efficient scale of operations”) that the household must acquire before market participation becomes feasible. We estimate this quantity through routine application of data augmentation and Gibbs sampling applied to a random-censored Tobit regression. Incorporating random censoring affects markedly the marketable-surplus requirements of the household, but only slightly the covariates requirements estimates and, generally, leads to more plausible policy estimates than the estimates obtained from the zero-censored formulation

Promotion carryover as a missing data problem

An important feature of agribusiness promotion programs is their lagged impact on consumption. Efficient investment in advertising requires reliable estimates of these lagged responses and it is desirable from both applied and theoretical standpoints to have a flexible method for estimating them. This note derives an alternative Bayesian methodology for estimating lagged responses when investments occur intermittently within a time series. The method exploits a latent-variable extension of the natural-conjugate, normal-linear model, Gibbs sampling and data augmentation. It is applied to a monthly time series on Turkish pasta consumption (1993:5-1998:3) and three, nonconsecutive promotion campaigns (1996:3, 1997:3, 1997:10). The results suggest that responses were greatest to the second campaign, which allocated its entire budget to television media; that its impact peaked in the sixth month following expenditure; and that the rate of return (measured in metric tons additional consumption per thousand dollars expended) was around a factor of 20.