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Full-Text Articles in Genetics and Genomics
A Hidden Markov Random Field Model For Genome-Wide Association Studies, Hongzhe Li, Zhi Wei, J M. Maris
A Hidden Markov Random Field Model For Genome-Wide Association Studies, Hongzhe Li, Zhi Wei, J M. Maris
UPenn Biostatistics Working Papers
Genome-wide association studies (GWAS) are increasingly utilized for identifying novel susceptible genetic variants for complex traits, but there is little consensus on analysis methods for such data. Most commonly used methods include single SNP analysis or haplotype analysis with Bonferroni correction for multiple comparisons. Since the SNPs in typical GWAS are often in linkage disequilibrium (LD), at least locally, Bonferonni correction of multiple comparisons often leads to conservative error control and therefore lower statistical power. In this paper, we propose a hidden Markov random field model (HMRF) for GWAS analysis based on a weighted LD graph built from the prior …
U-Statistics-Based Tests For Multiple Genes In Genetic Association Studies, Zhi Wei, Mingyao Li Phd, Timothy Rebbeck, Hongzhe Li
U-Statistics-Based Tests For Multiple Genes In Genetic Association Studies, Zhi Wei, Mingyao Li Phd, Timothy Rebbeck, Hongzhe Li
UPenn Biostatistics Working Papers
Abstract: As our understanding of biological pathways and the genes that regulate these pathways increases, consideration of these biological pathways has become an increasingly important part of genetic and molecular epidemiology. Pathway-based genetic association studies often involve genotyping of variants in genes acting in certain biological pathways. Such pathway-based genetic association studies can potentially capture the highly heterogeneous nature of many complex traits, with multiple causative loci and multiple alleles at some of the causative loci. In this paper, we develop two nonparametric test statistics that consider simultaneously the effects of multiple markers. Our approach, which is based on data-adaptive …
A Hidden Spatial-Temporal Markov Random Field Model For Network-Based Analysis Of Time Course Gene Expression Data, Zhi Wei, Hongzhe Li
A Hidden Spatial-Temporal Markov Random Field Model For Network-Based Analysis Of Time Course Gene Expression Data, Zhi Wei, Hongzhe Li
UPenn Biostatistics Working Papers
Microarray time course (MTC) gene expression data are commonly collected to study the dynamic nature of biological processes. One important problem is to identify genes that show different expression profiles over time and pathways that are perturbed during a given biological process. While methods are available to identify the genes with differential expression levels over time, there is a lack of methods that can incorporate the pathway information in identifying the pathways being modified/activated during a biological process. In this paper, we develop a hidden spatial-temporal Markov random field (hstMRF)-based method for identifying genes and subnetworks that are related to …