UCLA

With the development of next-generation sequencing technologies, we can detect numerous genetic variants associated with many diseases or complex traits over the past decades. Genome-wide association studies (GWAS) have been one of the most effective methods to identify those variants. It discovers disease-associated variants by comparing the genetic information between controls and cases. This approach is simple and effective and has been used by many studies.

Before performing GWAS, we need to detect the genetic variants of the sample population. A subset of these variants, however, may have poor sequencing quality due to limitations in NGS or variant callers. In genetic studies that analyze a large number of sequenced individuals, it is critical to detect and remove those variants with poor quality as they may cause spurious findings. Here, I will present ForestQC, an efficient statistical tool for performing quality control on variants identified from NGS data by combining a traditional filtering approach and a machine learning approach, which outperforms widely used methods by considerably improving the quality of variants to be included in the analysis.

Once this association is identified, the next step is to understand the genetic mechanism of rare variants on how the variants influence diseases, especially whether or how they regulate gene expression as they may affect diseases through gene regulation. However, it is challenging to identify the regulatory effects of rare variants because it often requires large sample sizes and the existing statistical approaches are not optimized for it. To improve statistical power, I will introduce a new approach, LRT-q, based on a likelihood ratio test that combines effects of multiple rare variants in a nonlinear manner and has higher power than previous approaches. I apply LRT-q to the GTEx dataset and find many novel biological insights.

Recent studies have shown that omics data can be used for automatic disease diagnosis with machine learning algorithms. I will introduce an accurate and automated machine learning pipeline for the diagnosis of atopic dermatitis (AD) based on transcriptome and microbiota data. I will demonstrate that this classifier can accurately differentiate subjects with AD and healthy individuals. It also identifies a set of genes and microorganisms that are predictive for AD. I will show that they are directly or indirectly associated with AD.