Identification of Rare Variants from Exome Sequence in a Large Pedigree with Autism
Author(s) -
Elizabeth E. Marchani,
N.H. Chapman,
Christina Cheung,
Katy Ankenman,
Ian B. Stanaway,
Hilary Coon,
Deborah A. Nickerson,
R. Bernier,
Zoran Brkanac,
Ellen M. Wijsman
Publication year - 2012
Publication title -
human heredity
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.423
H-Index - 62
eISSN - 1423-0062
pISSN - 0001-5652
DOI - 10.1159/000346560
Subject(s) - exome sequencing , exome , genetics , biology , imputation (statistics) , haplotype , computational biology , genotype , gene , phenotype , missing data , computer science , machine learning
We carried out analyses with the goal of identifying rare variants in exome sequence data that contribute to disease risk for a complex trait. We analyzed a large, 47-member, multigenerational pedigree with 11 cases of autism spectrum disorder, using genotypes from 3 technologies representing increasing resolution: a multiallelic linkage marker panel, a dense diallelic marker panel, and variants from exome sequencing. Genome-scan marker genotypes were available on most subjects, and exome sequence data was available on 5 subjects. We used genome-scan linkage analysis to identify and prioritize the chromosome 22 region of interest, and to select subjects for exome sequencing. Inheritance vectors (IVs) generated by Markov chain Monte Carlo analysis of multilocus marker data were the foundation of most analyses. Genotype imputation used IVs to determine which sequence variants reside on the haplotype that co-segregates with the autism diagnosis. Together with a rare-allele frequency filter, we identified only one rare variant on the risk haplotype, illustrating the potential of this approach to prioritize variants. The associated gene, MYH9, is biologically unlikely, and we speculate that for this complex trait, the key variants may lie outside the exome.
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