A handful of mutation processes operate on the human germline to form small insertions and deletions (indels), large copy number variants (CNVs), inversions, translocations and more complex changes in chromosome structure. These diverse mutations are collectively referred to as structural variation (SV). Assessing the functional and pathogenic impact of singleton and rare structural variants in disease is one of the most pressing and understudied problems in human genetics today. Here we describe methodological innovations for integrating structural variation into eQTL studies, and then transforming knowledge learned from GTEx data into a probabilistic pathogenicity assessment tool that can be used by a wide range of researchers. We will pilot new approaches for integrating SVs and single nucleotide variants (SNVs) in a coherent framework. The centerpiece of this integrative effort will be a new model- based pathogenicity assessment method that will integrate (i) knowledge gleaned from GTEx analyses, (ii) recent breakthroughs in classification of Mendelian disease genes, and (iii) the rapidly expanding set of known disease mutations matriculating from array- and sequencing-based studies of severe Mendelian and other pediatric diseases. This method will be the first tool for generic functional assessment of both SVs and SNVs and will interpret variation affecting coding and/or non-coding regions.

Public Health Relevance

It is estimated that approximately 15% of cases of idiopathic intellectual disability and congenital defects are caused by chromosomal rearrangements. This number is certainly an underestimate, as no tools exist to evaluate the functional impact of most chromosomal rearrangements. In this project we will develop novel model-based methods for improved interpretation of this class of mutation, driven in part by the new availability of extensive gene expression data from multiple human tissues.

Agency
National Institute of Health (NIH)
Type
Research Project (R01)
Project #
5R01MH101810-02
Application #
8706981
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Addington, Anjene M
Project Start
Project End
Budget Start
Budget End
Support Year
2
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Washington University
Department
Genetics
Type
Schools of Medicine
DUNS #
City
Saint Louis
State
MO
Country
United States
Zip Code
63130