Search

An immense amount of observable diversity exists for all traits and across global populations. In the post-genomic era, equipped with efficient sequencing capabilities and better genotyping methods, we are now able to more fully appreciate how regulation of gene expression is consequential to one's genotypes in coding and non-coding DNA. The identification of genetic loci that contribute to quantifiable variation in genetic expression is critical in further improving our understanding of the biological regulation of complex traits. Expression quantitative traits loci (eQTLs) mapping studies have provided a powerful suite of techniques for genome wide analysis to detect these regulatory effects. However, a typical eQTL analysis relies on a large number of samples with many genetic variants to achieve robust power and significance for detection. With this in mind, eQTL analysis brings about 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 relationships between genetic variation and genetic expression. In this chapter, we provide a comprehensive review of statistical and machine learning methods. We will present various machine learning methods based upon regularization terms and several other statistical analysis methods. Finally, we will discuss prior knowledge integration and hyperparameter optimization.

Expression quantitative trait locus (eQTL) analysis has proven to be a powerful method to describe how variation in phenotypes may be attributed to a given genotype. While the field of bioinformatics and genomics has experienced exponential growth with modern technological advances, an unintended consequence arises as a lack of a gold standard for many applications and methods, which may be compounded with ever-improving computational capabilities. Researchers working on eQTL analysis have at their disposal a multitude of bioinformatics software, each with different assumptions and algorithms, which may produce confusion as to their respective applicability. In this chapter, we will introduce eQTLs, survey commonly used software to conduct a mapping study, as well as provide data correction methods to avoid the pitfalls of such analyses.

Expression quantitative trait locus (eQTL) analysis is a powerful method to understand the association between genetic variant and gene expression; it also has potential impact for the study of transcription medicine for human complex disease. In the past two decades, the researchers focus on studying the eQTL, while more and more evidence shows that the regulatory genetic variants locating noncoding region have strong effect for the gene expression. More and more researchers working on eQTL analysis realize the importance of other types of QTLs beyond eQTL. In this chapter, we will explore some QTLs beyond eQTLs that show the regulatory association with eQTLs and explain the underlying link among these types of QTLs.