Genetically predicted waist circumference and risk of atrial fibrillation

Introduction:Observational studies have revealed an association between waist circumference(WC)and atrial fibrillation(AF).However,it is difficult to infer a causal relationship from observational studies because the observed associations could be confounded by unknown risk factors.Therefore,the causal role of WC in AF is unclear.This study was designed to investigate the causal association between WC and AF using a two-sample Mendelian randomization(MR)analysis.Methods:In our two-sample MR analysis,the genetic variation used as an instrumental variable for MR was acquired from a genome-wide association study(GWAS)of WC(42 single nucleotide polymorphisms with a genetic significance of P<5×10^(-8)).The data of WC(from the Genetic Investigation of ANthropometric Traits consortium,containing 232,101 participants)and the data of AF(from the European Bioinformatics Institute database,containing 55,114 AF cases and 482,295 controls)were used to assess the causal role of WC on AF.Three different approaches(inverse variance weighted[IVW],MR-Egger,and weighted median regression)were used to ensure that our results more reliable.Results:All three MR analyses provided evidence of a positive causal association between high WC and AF.High WC was suggested to increase the risk of AF based on the IVW method(odds ratio[OR]=1.43,95%confidence interval[CI],1.30-1.58,P=2.51×10^(-13)).The results of MR-Egger and weighted median regression exhibited similar trends(MR-Egger OR=1.40[95%CI,1.08-1.81],P=1.61×10^(-2);weighted median OR=1.39[95%CI,1.21-1.61],P=1.62×10^(-6)).MR-Egger intercepts and funnel plots showed no directional pleiotropic effects between high WC and AF.Conclusions:Our findings suggest that greater WC is associated with an increased risk of AF.Taking measures to reduce WC may help prevent the occurrence of AF.

Ensemble Prediction of Monsoon Index with a Genetic Neural Network Model

After the consideration of the nonlinear nature changes of monsoon index,and the subjective determination of network structure in traditional artificial neural network prediction modeling,monthly and seasonal monsoon intensity index prediction is studied in this paper by using nonlinear genetic neural network ensemble prediction（GNNEP）modeling.It differs from traditional prediction modeling in the following aspects： （1）Input factors of the GNNEP model of monsoon index were selected from a large quantity of preceding period high correlation factors,such as monthly sea temperature fields,monthly 500-hPa air temperature fields,monthly 200-hPa geopotential height fields,etc.,and they were also highly information-condensed and system dimensionality-reduced by using the empirical orthogonal function（EOF）method,which effectively condensed the useful information of predictors and therefore controlled the size of network structure of the GNNEP model.（2）In the input design of the GNNEP model,a mean generating function（MGF）series of predictand（monsoon index）was added as an input factor;the contrast analysis of results of predic- tion experiments by a physical variable predictor-predictand MGF GNNEP model and a physical variable predictor GNNEP model shows that the incorporation of the periodical variation of predictand（monsoon index）is very effective in improving the prediction of monsoon index.（3）Different from the traditional neural network modeling,the GNNEP modeling is able to objectively determine the network structure of the GNNNEP model,and the model constructed has a better generalization capability.In the case of identical predictors,prediction modeling samples,and independent prediction samples,the prediction accuracy of our GNNEP model combined with the system dimensionality reduction technique of predictors is clearly higher than that of the traditional stepwise regression model using the traditional treatment technique of predictors,suggesting that the GNNEP model opens up a vast range of possibilities for operational weather prediction.