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mrMLM v4.0.2: An R Platform for Multi-locus Genome-wide Association Studies

Genomics, Proteomics & Bioinformatics 2020年第4期18卷 481-487页

作者： Ya-Wen Zhang cox lwaka tamba Yang-Jun Wen Pei Li Wen-Long Ren Yuan-Li Ni Jun Gao Yuan-Ming Zhang Crop Information Center College of Plant Science and TechnologyHuazhong Agricultural UniversityWuhan 430070China Department of Mathematics Egerton UniversityEgerton 536-20115Kenya State Key Laboratory of Crop Genetics and Germplasm Enhancement Nanjing Agricultural UniversityNanjing 210095China Department of Epidemiology and Medical Statistics School of Public HealthNantong UniversityNantong 226019China College of Informatics Huazhong Agricultural UniversityWuhan 430070China

Previous studies have reported that some important loci are missed in single-locus genome-wide association studies(GWAS),especially because of the large phenotypic error in field *** solve this issue,multi-locus GWAS methods have been ***,only a few software packages for multi-locus GWAS are ***,we developed an R software named mr MLM *** software integrates mr MLM,FASTmr MLM,FASTmr EMMA,p LARm EB,p KWm EB,and ISIS EM-BLASSO methods developed by our *** are four components in mr MLM v4.0.2,including dataset input,parameter setting,software running,and result *** fread function in *** is used to quickly read datasets,especially big datasets,and the do Parallel package is used to conduct parallel computation using multiple *** addition,the graphical user interface software mr *** v4.0.2,built upon Shiny,is also *** confirm the correctness of the aforementioned programs,all the methods in mr MLM v4.0.2 and three widely-used methods were used to analyze real and simulated *** results confirm the superior performance of mr MLM v4.0.2 to other methods currently *** positive rates are effectively controlled,albeit with a less stringent significance *** MLM v4.0.2 is publicly available at Bio Code(https://***/biocode/tools/BT007077)or R(https://***/web/packages/mr ***/***)as an open-source software.

关键词： Genome-wide association study Linear mixed model mrMLM Multi-locus genetic model R

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Interval Estimation in a Two Parameter Weibull Distribution Based on Type-2 Censored Data

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Open Journal of Statistics 2020年第6期10卷 1039-1056页

作者： Raphael Masila Mweleli Luke Akong’o Orawo cox lwaka tamba Justin Obwoge Okenye Department of Mathematics Egerton University Egerton Kenya

In this paper, we consider the construction of the approximate profile- likelihood confidence intervals for parameters of the 2-parameter Weibull distribution based on small type-2 censored samples. In previous research works, the traditional Wald method has been used to construct approximate confidence intervals for the 2-parameter Weibull distribution under type-2 censoring scheme. However, the Wald technique is based on normality assumption and thus may not produce accurate interval estimates for small samples. The profile-likelihood and Wald confidence intervals are constructed for the shape and scale parameters of the 2-parameter Weibull distribution based on simulated and real type-2 censored data, and are hence compared using confidence length and coverage probability.

关键词： Two-Parameter Weibull Distribution Interval Estimation Relative Likelihood Function Maximum Relative Likelihood Function Profile-Likelihood Interval Coverage Probability

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Dynamic Spatio-Temporal Modeling in Disease Mapping

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Open Journal of Statistics 2023年第6期13卷 893-916页

作者： Flavian Awere Otieno cox lwaka tamba Justin Obwoge Okenye Luke Akong’o Orawo Department of Mathematics Egerton University Egerton Kenya

Spatio-temporal models are valuable tools for disease mapping and understanding the geographical distribution of diseases and temporal dynamics. Spatio-temporal models have been proven empirically to be very complex and this complexity has led many to oversimply and model the spatial and temporal dependencies independently. Unlike common practice, this study formulated a new spatio-temporal model in a Bayesian hierarchical framework that accounts for spatial and temporal dependencies jointly. The spatial and temporal dependencies were dynamically modelled via the matern exponential covariance function. The temporal aspect was captured by the parameters of the exponential with a first-order autoregressive structure. Inferences about the parameters were obtained via Markov Chain Monte Carlo (MCMC) techniques and the spatio-temporal maps were obtained by mapping stable posterior means from the specific location and time from the best model that includes the significant risk factors. The model formulated was fitted to both simulation data and Kenya meningitis incidence data from 2013 to 2019 along with two covariates;Gross County Product (GCP) and average rainfall. The study found that both average rainfall and GCP had a significant positive association with meningitis occurrence. Also, regarding geographical distribution, the spatio-temporal maps showed that meningitis is not evenly distributed across the country as some counties reported a high number of cases compared with other counties.

关键词： Spatio-Temporal Model Matern Exponential Covariance Function Spatial and Temporal Dependencies Markov Chain Monte Carlo (MCMC)

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