Assessing how genetic variation influences phenotype is a fundamental goal of genetics, and using multi-omics sequencing approaches can improve variant-to-function pipelines. Collecting different levels of molecular data in cell type- or tissue-specific contexts will help to accelerate our understanding of the mechanisms linking genotype to phenotype. In the past decades, large-scale genome-wide studies have identified thousands of loci associated with various diseases and complex traits. The primary challenge for the field now is to obtain mechanistic understanding about the biology underlying these statistical associations, in order to harness the genetic information and use it to inform prevention strategies, drug development or other public health goals.
Because most GWAS loci fall within non-coding regions, assigning function to these SNPs is non-trivial. Multi-omics sequencing is a powerful approach for evaluating variant effects through measurement of multiple layers of information. Integrating whole-genome or whole-exome sequencing data with transcriptome information (from RNAseq) and epigenetic information (from methylation arrays) in tissue types of interest can help identify genes and pathways that have a role in particular diseases.
Associate Professor of Genetics and Genomics,
Neuroscience and Psychiatry
Icahn School of Medicine at Mount Sinai
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