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.
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.
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| 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 |
