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Full-Text Articles in Genetics and Genomics
Why Odds Ratio Estimates Of Gwas Are Almost Always Close To 1.0, Yutaka Yasui
Why Odds Ratio Estimates Of Gwas Are Almost Always Close To 1.0, Yutaka Yasui
COBRA Preprint Series
“Missing heritability” in genome-wide association studies (GWAS) refers to the seeming inability for GWAS data to capture the great majority of genetic causes of a disease in comparison to the known degree of heritability for the disease, in spite of GWAS’ genome-wide measures of genetic variations. This paper presents a simple mathematical explanation for this phenomenon, assuming that the heritability information exists in GWAS data. Specifically, it focuses on the fact that the great majority of association measures (in the form of odds ratios) from GWAS are consistently close to the value that indicates no association, explains why this occurs, …
Sparse Integrative Clustering Of Multiple Omics Data Sets, Ronglai Shen, Sijian Wang, Qianxing Mo
Sparse Integrative Clustering Of Multiple Omics Data Sets, Ronglai Shen, Sijian Wang, Qianxing Mo
Memorial Sloan-Kettering Cancer Center, Dept. of Epidemiology & Biostatistics Working Paper Series
High resolution microarrays and second-generation sequencing platforms are powerful tools to investigate genome-wide alterations in DNA copy number, methylation, and gene expression associated with a disease. An integrated genomic profiling approach measuring multiple omics data types simultaneously in the same set of biological samples would render an integrated data resolution that would not be available with any single data type. In a previous publication (Shen et al., 2009), we proposed a latent variable regression with a lasso constraint (Tibshirani, 1996) for joint modeling of multiple omics data types to identify common latent variables that can be used to cluster patient …