Evaluation and development of rare variant analysis methods for novel disease or trait-related gene or region discovery using whole exome sequencing data

It is very important and it is also our aim to evaluate/utilize/develop the most powerful rare variant analysis methods in the discovery of novel cancer predisposition genes. The advance of sequencing technology and the refinement of reference sequence bring us cost-effective sequencing platforms and better accuracies, however, they have also generated very heterogeneous sequencing data sets, such as using different reference builds, different mapping software (versions).
Objective: 1) evaluate/develop methods for building a pipeline in harmonizing different available cohorts to reduce false positives due to sequencing artifacts or batch effects; 2) evaluate/develop rare variant methods in analyzing large scale sequencing data, for example, identify which tests are robust and powerful, how to define regions or SNP sets, how to incorporate variant information.
Study design: because large sample size is critical to achieve enough power, we will combine the data sets including TCGA, ADSP, as well as our inhouse sequencing data such as pediatric cancer sequence data or Amyotrophic lateral sclerosis (ALS) sequencing data. We need to deal with potential batch effects of different datasets. Then we will do case-control based association test, e.g., using one trait versus other traits to evaluate the enrichment of rare variants in each particular trait. Other public control data sets, such as 1000 Genomes or gnomAD might also help filtering potential pathogenic variants.

For sequenced rare variants, it is very important to develop powerful methods in identifying trait specific predisposition genes or variants, which can
incorporate as much prior information as possible and control potential batch effects due to different processing platforms. We will use the TCGA,
ADSP and our inhouse pediatric and ALS sequencing dataset for evaluation and development of methods for identifying predisposition variations of
each trait, for example, cancer risk genes, or neurodegenerative risk genes.