DELISHUS: an efficient and exact algorithm for genome-wide detection of deletion polymorphism in autism

Department of Computer Science, Center for Computational Molecular Biology, Brown University, Providence, RI, USA.
Bioinformatics (Impact Factor: 4.98). 06/2012; 28(12):i154-62. DOI: 10.1093/bioinformatics/bts234
Source: PubMed


The understanding of the genetic determinants of complex disease is undergoing a paradigm shift. Genetic heterogeneity of rare mutations with deleterious effects is more commonly being viewed as a major component of disease. Autism is an excellent example where research is active in identifying matches between the phenotypic and genomic heterogeneities. A considerable portion of autism appears to be correlated with copy number variation, which is not directly probed by single nucleotide polymorphism (SNP) array or sequencing technologies. Identifying the genetic heterogeneity of small deletions remains a major unresolved computational problem partly due to the inability of algorithms to detect them.
In this article, we present an algorithmic framework, which we term DELISHUS, that implements three exact algorithms for inferring regions of hemizygosity containing genomic deletions of all sizes and frequencies in SNP genotype data. We implement an efficient backtracking algorithm-that processes a 1 billion entry genome-wide association study SNP matrix in a few minutes-to compute all inherited deletions in a dataset. We further extend our model to give an efficient algorithm for detecting de novo deletions. Finally, given a set of called deletions, we also give a polynomial time algorithm for computing the critical regions of recurrent deletions. DELISHUS achieves significantly lower false-positive rates and higher power than previously published algorithms partly because it considers all individuals in the sample simultaneously. DELISHUS may be applied to SNP array or sequencing data to identify the deletion spectrum for family-based association studies.
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Available from: Bjarni V Halldórsson, Oct 05, 2015
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    ABSTRACT: A major contribution to the genome variability among individuals comes from deletions and duplications - collectively termed copy number variations (CNVs) - which alter the diploid status of DNA. These alterations may have no phenotypic effect, account for adaptive traits or can underlie disease. We have compiled published high-quality data on healthy individuals of various ethnicities to construct an updated CNV map of the human genome. Depending on the level of stringency of the map, we estimated that 4.8-9.5% of the genome contributes to CNV and found approximately 100 genes that can be completely deleted without producing apparent phenotypic consequences. This map will aid the interpretation of new CNV findings for both clinical and research applications.
    Nature Reviews Genetics 02/2015; 16(3). DOI:10.1038/nrg3871 · 36.98 Impact Factor