Titlebook: eQTL Analysis; Methods and Protocol Xinghua Mindy Shi Book 2020 Springer Science+Business Media, LLC, part of Springer Nature 2020 mining.g

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Genome-Wide Composite Interval Mapping (GCIM) of Expressional Quantitative Trait Loci in Backcross Pgenetic model to develop genome-wide composite interval mapping (GCIM). This chapter covers the GCIM procedure in a backcross or doubled haploid populations. We describe the genetic model, parameter estimation, multi-locus genetic model, hypothesis tests, and software. Finally, some issues related to the GCIM method are discussed.

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Expression Quantitative Trait Loci (eQTL) Analysis in Cancermorigenesis and development. Here we describe a detailed workflow for identifying eQTLs in cancer using existing packages and software. The key package is Matrix eQTL, which requires input data of genotypes, genes expression, and covariates. This pipeline can be easily applied in a related research field.

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Statistical and Machine Learning Methods for eQTL Analysist distinct computational and statistical challenges that require advanced methodological development to overcome. In recent years, many statistical and machine learning methods for eQTL analysis have been developed with the ability to provide a more complex perspective towards the identification of

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Sparse Regression Models for Unraveling Group and Individual Associations in eQTL Mapping. We perform extensive experiments on both simulated datasets and yeast datasets to demonstrate the effectiveness and efficiency of the proposed method. The results show that . can effectively detect both individual and group-wise signals and outperform the state-of-the-arts by a large margin. This