This project aims to develop ways in which the patterns of shared ancestral gene-flow for specific chromosomal segments can be inferred between seemingly-unrelated individuals and used to empower analyses of rare mutations discovered by sequencing, with respect to association with diseases such as schizophrenia. Identity-by-descent (IBD) implies that two or more individuals each carry an extended stretch of haploid sequence that is a direct copy, or descendant, of a single, ancestral haplotype that resides (or once resided) in a recent common ancestor of those individuals. In large samples it is not unusual to find many thousands of instances in which seemingly unrelated individuals are, for some fraction of their genome, related exactly as closely as are parent and offspring. In the context of large, population-based studies of rare and common genetic variation, we propose that layering a map of intra-individual IBD sharing on top of datasets of rare mutation and polymorphism from sequencing can help in the daunting challenge of relating genetic variation to risk for common disease. Specifically, we propose to use IBD sharing information in sequencing studies to 1) identify likely de novo and very recent (private) mutations, 2) prioritize rare variants for likely functional impact and 3) allow additional un-sequenced samples to prioritize rare alleles according to the likelihood they are causal given their IBD sharing with sequenced individuals. We will apply the methods developed here to two large schizophrenia sequencing studies, with whole-exome data on over 6,000 individuals and genome-wide SNP data on over 14,000. The statistical approaches developed here will be implemented and distributed as part of the PLINK/Seq software package.

Public Health Relevance

Studying families is a powerful approach in genetics, but often only population-based samples of unrelated individuals are available. Using genetic sequence information, this project aims to develop methods to make use of the shared genetic origins that exist even between unrelated individuals (who are, in fact, often related, albeit very distantly) and use this information to find disease genes for schizophrenia. Genetic studies have the potential to uncover genetic determinants for a large number of diseases and traits, which can be relevant for prediction of risk, and give insight into novel targets for treatments.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
1R01MH099126-01
Application #
8419791
Study Section
Behavioral Genetics and Epidemiology Study Section (BGES)
Program Officer
Lehner, Thomas
Project Start
2012-09-20
Project End
2015-07-31
Budget Start
2012-09-20
Budget End
2013-07-31
Support Year
1
Fiscal Year
2012
Total Cost
$374,425
Indirect Cost
$149,425
Name
Icahn School of Medicine at Mount Sinai
Department
Psychiatry
Type
Schools of Medicine
DUNS #
078861598
City
New York
State
NY
Country
United States
Zip Code
10029
Ruderfer, Douglas M; Charney, Alexander W; Readhead, Ben et al. (2016) Polygenic overlap between schizophrenia risk and antipsychotic response: a genomic medicine approach. Lancet Psychiatry 3:350-7
Ruderfer, Douglas M; Hamamsy, Tymor; Lek, Monkol et al. (2016) Patterns of genic intolerance of rare copy number variation in 59,898 human exomes. Nat Genet 48:1107-11
Rees, E; Kirov, G; Walters, J T et al. (2015) Analysis of exome sequence in 604 trios for recessive genotypes in schizophrenia. Transl Psychiatry 5:e607
Sham, Pak C; Purcell, Shaun M (2014) Statistical power and significance testing in large-scale genetic studies. Nat Rev Genet 15:335-46
Purcell, Shaun M; Moran, Jennifer L; Fromer, Menachem et al. (2014) A polygenic burden of rare disruptive mutations in schizophrenia. Nature 506:185-90
Fromer, Menachem; Pocklington, Andrew J; Kavanagh, David H et al. (2014) De novo mutations in schizophrenia implicate synaptic networks. Nature 506:179-84