Computational modeling of genetic variations by multi-omics integration todecipher personal genome

Computational modeling of genetic variations by multi-omics integration to decipher personal genome
A person’s genome typically contains millions of genetic variants. Understanding these variants by assessing
their functional impact on a person’s phenotype, is currently of great interest in human genetics and precision
medicine. Though Genome-Wide Association Studies (GWAS) or Quantitative Trait Locus (QTL) studies have
successfully identified variants associated with traits or molecular phenotypes, most of them are in noncoding
regions and hampered by linkage disequilibrium, making the identification and interpretation of casual variants
difficult. Moreover, most of these discoveries are common variants, however, rare and individual-specific variants
in personal genome are underexplored. Understanding these variants will not only explain the missing heritability
from GWAS but also improve the precision medicine. Recently, the advent and popularity of whole genome
sequencing (WGS) and paired multi-omics functional assays provide an unprecedented opportunity to identify
rare and individual-specific casual variants. However, the sample sizes of most WGS studies are modest
compared to GWAS, making the WGS analysis particularly challenging. Nevertheless, statistical and
computational methods for analyzing WGS are underdeveloped. Given these challenges and my unique multi-
disciplinary training, the overall goals of my research program are to develop a novel class of machine learning,
statistical and system biology approaches for the identification, prioritization and interpretation of noncoding
variants by integrating GWAS, WGS and multi-omics functional assays, which will empower precision medicine
by identifying individualized biomarkers for disease prevention, diagnosis and treatment. Specifically, in the next
five years, my lab will (i) develop a novel transfer learning approach to improve the prediction of noncoding
casual variants using multi-dimensional omics features (ii) develop a multi-omics integrated omnibus scan test
to improve the identification of rare casual variants from whole-genome sequencing data (iii) develop an
integrative computational framework for scoring impact of noncoding variants in personal genome (iv) develop a
novel class of multi-trait methods to improve phenotype prediction using whole-genome genetic variations.
In
the meantime, supported by Indiana University Precision Health Initiative, we will apply the methodologies to
different studies from Indiana Alzheimer’s Disease Center and Indiana Multiple Myeloma Biobank for novel
scientific findings. We will work close with collaborated geneticists and clinician-scientists to interpret the
discoveries. Importantly, we will work with experimental labs to validate the findings. In line with our previous
work, we will continue to make all developed methods into open-source software tools that are accessible and
useful to the biomedical research community.

Grant Number: 5R35GM142701-06
NIH Institute/Center: NIH
Principal Investigator: Li Chen