[Written by Dr Joseph Glessner]

In the analysis of genetic variation, individual-level data is of great value. Research sites generating such data on human subjects are typically mandated by their institutional review boards (IRBs) not to share this information between institutions and other entities. However, the ability to combine large genetic datasets across research sites is an important tool in understanding the genomic architecture of common complex diseases. Indeed, methods to combine different genome-wide analysis studies (GWAS) of single nucleotide polymorphism (SNP) markers are well established, and they have proved extremely powerful for delving deeper into common diseases such as type 2 diabetes and childhood obesity. However, similar trans-institutional approaches for analyses of copy number variants (CNVs) are relatively in their infancy. Some of the main reasons for this discrepancy include:

1) Non-standard and variable methods to infer CNVs from genotyping data
2) A lack of robust methods for imputation of CNVs across genotyping chipsets
3) The accounting complexities of CNVs across subjects having variable boundaries

This research plan seeks to establish a genome-wide approach to meta-analyze CNVs across sites that comprise the Early Growth Genetic (EGG) Consortium.