Enriching targeted sequencing experiments for rare disease alleles.

Edwards TL, Song Z, Li C
Bioinformatics. 2011 27 (15): 2112-8

PMID: 21700677 · PMCID: PMC3137214 · DOI:10.1093/bioinformatics/btr324

MOTIVATION - Next-generation targeted resequencing of genome-wide association study (GWAS)-associated genomic regions is a common approach for follow-up of indirect association of common alleles. However, it is prohibitively expensive to sequence all the samples from a well-powered GWAS study with sufficient depth of coverage to accurately call rare genotypes. As a result, many studies may use next-generation sequencing for single nucleotide polymorphism (SNP) discovery in a smaller number of samples, with the intent to genotype candidate SNPs with rare alleles captured by resequencing. This approach is reasonable, but may be inefficient for rare alleles if samples are not carefully selected for the resequencing experiment.

RESULTS - We have developed a probability-based approach, SampleSeq, to select samples for a targeted resequencing experiment that increases the yield of rare disease alleles substantially over random sampling of cases or controls or sampling based on genotypes at associated SNPs from GWAS data. This technique allows for smaller sample sizes for resequencing experiments, or allows the capture of rarer risk alleles. When following up multiple regions, SampleSeq selects subjects with an even representation of all the regions. SampleSeq also can be used to calculate the sample size needed for the resequencing to increase the chance of successful capture of rare alleles of desired frequencies.

SOFTWARE - http://biostat.mc.vanderbilt.edu/SampleSeq

MeSH Terms (14)

Algorithms Alleles Base Sequence Computational Biology Computer Simulation Genome Genome-Wide Association Study Genotype Humans Polymorphism, Single Nucleotide Probability Rare Diseases Sequence Analysis, DNA Software

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