Modernization and Its Discontents

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/67022/2/10.1177_000276427702100208.pd

Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure

Heart failure (HF) is a leading cause of morbidity and mortality worldwide. A small proportion of HF cases are attributable to monogenic cardiomyopathies and existing genome-wide association studies (GWAS) have yielded only limited insights, leaving the observed heritability of HF largely unexplained. We report results from a GWAS meta-analysis of HF comprising 47,309 cases and 930,014 controls. Twelve independent variants at 11 genomic loci are associated with HF, all of which demonstrate one or more associations with coronary artery disease (CAD), atrial fibrillation, or reduced left ventricular function, suggesting shared genetic aetiology. Functional analysis of non-CAD-associated loci implicate genes involved in cardiac development (MYOZ1, SYNPO2L), protein homoeostasis (BAG3), and cellular senescence (CDKN1A). Mendelian randomisation analysis supports causal roles for several HF risk factors, and demonstrates CAD-independent effects for atrial fibrillation, body mass index, and hypertension. These findings extend our knowledge of the pathways underlying HF and may inform new therapeutic strategies

Characterization theorems in random utility theory

To explain inconsistency in choice experiments, where a subject on repeated presentations of one particular subset of alternatives does not always select the same alternative, random utility theory models the subject's evaluation of a stimulus by a random variable sampled at each presentation of the stimulus. The problem addressed in this entry is the characterization of random utility theory in its most general form (i.e., with an arbitrary joint distribution of the random variables) in terms of the testable restrictions it imposes on the choice data. While for the experimental paradigm where choices are obtained for every subset of alternatives this characterization problem has been solved, it is still open for the case of binary choice probabilities, where just all 2-element subsets are offered. For this case, the problem turns out to be equivalent to finding a linear description of the linear ordering polytope, which constitutes the convex hull of all linear orders of the alternatives, identifying these orders with their indicator functions (0-1 vectors). It is illustrated that many necessary conditions for a random utility representation can be found by applying graph-theoretic techniques, but also that with increasing the number of alternatives there is a combinatorial explosion of such necessary conditions with no apparent structural regularities. The problem of a complete characterization for an arbitrary number of alternatives seems intractable at the moment. Finally it is shown how this characterization problem for binary choice probabilities generalizes to other instances of probabilistic measurement