An algorithm for detecting high frequency copy number polymorphisms using SNP arrays

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

Motivation: 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.

Results: 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.

Availability: DELISHUS is available at http://www.brown.edu/Research/Istrail_Lab/.

Contact:Eric_Morrow@brown.edu and Sorin_Istrail@brown.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

PAIR: polymorphic Alu insertion recognition

Background

Alu polymorphisms are some of the most common polymorphisms in the genome, yet few methods have been developed for their detection.

Methods

We present algorithms to discover Alu polymorphisms using paired-end high throughput sequencing data from multiple individuals. We consider the problem of identifying sites containing polymorphic Alu insertions.

Results

We give efficient and practical algorithms that detect polymorphic Alus, both those that are inserted with respect to the reference genome and those that are deleted. The algorithms have a linear time complexity and can be run on a standard desktop machine in a very short amount of time on top of the output of tools standard for sequencing analysis.

Conclusions

In our simulated dataset we are able to locate 98.1% of Alus inserted with respect to the reference and 97.7% of Alus deleted, our simulations also show an excellent correlations between the deletions detected in parents and children. We further run our algorithms on publicly available data from the 1000 genomes project and find several thousand Alu polymorphisms in each individual.