Birth interval, mortality and growth of children in a rural area in Kenya.
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Birth Intervals↗
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Quantitative trait loci modifying cardiac atrial septal morphology and risk of patent foramen ovale in the mouse.
Atrial septal defect (ASD) is a common congenital heart disease (CHD) occurring in 5 to 7 per 10,000 live births. Mutations in 5 human genes (NKX2.5, TBX5, GATA4, MYHC, ACTC) are known to cause dominant ASD, but these account for a minority of cases. Human and mouse data suggest that ASD exists in an anatomical continuum with milder septal variants patent foramen ovale (PFO) and atrial septal aneurysm, strongly associated with ischemic stroke and migraine. We have previously shown in inbred mice that the incidence of PFO strongly correlates with length of the interatrial septum primum, defining a quantitative trait underlying PFO risk. To better understand genetic causation of atrial septal abnormalities, we mapped quantitative trait loci (QTL) influencing septal morphology using mouse strains (QSi5 and 129T2/SvEms) maximally informative for PFO incidence and 3 quantitative septal anatomical traits including septum primum length. [QSi5x129T2/SvEms]F2 intercross animals (n=1437) were phenotyped and a whole genome scan performed at an average 17-cM interval. Statistical methodology scoring PFO as a binary phenotype was developed as a confirmatory mapping technique. We mapped 7 significant and 6 suggestive QTL modifying quantitative phenotypes, with 4 supported by binary analysis. Quantitative traits, although strongly associated with PFO (P<0.001), correlated poorly with each other and in all but 1 case QTL for different traits were nonoverlapping. Thus, multiple anatomical processes under separate genetic control contribute to risk of PFO. Our findings demonstrate the feasibility of modeling the genetic basis of common CHD using animal genetic and genomic technologies.
Animals↗
Properties of R(2) statistics for logistic regression.
Various R(2) statistics have been proposed for logistic regression to quantify the extent to which the binary response can be predicted by a given logistic regression model and covariates. We study the asymptotic properties of three popular variance-based R(2) statistics. We find that two variance-based R(2) statistics, the sum of squares and the squared Pearson correlation, have identical asymptotic distribution whereas the third one, Gini's concentration measure, has a different asymptotic behaviour and may overstate the predictivity of the model and covariates when the model is mis-specified. Our result not only provides a theoretical basis for the findings in previous empirical and numerical work, but also leads to asymptotic confidence intervals. Statistical variability can then be taken into account when assessing the predictive value of a logistic regression model.
Biometry↗