The Next Generation of Molecular Markers From Massively Parallel Sequencing of Pooled DNA Samples

  • Futschik A
  • Schlotterer C
  • 2

    Readers

    Mendeley users who have this article in their library.
  • N/A

    Citations

    Citations of this article.

Abstract

Next generation sequencing (NGS) is about to revolutionize genetic analysis. Currently NGS techniques are mainly used to sequence individual genomes. Due to the high sequence coverage required, the costs for population-scale analyses are still too high to allow an extension to nonmodel organisms. Here, we show that NGS of pools of individuals is often more effective in SNP discovery and provides more accurate allele frequency estimates, even when taking sequencing errors into account. We modify the population genetic estimators Tajima's p and Watterson's u to obtain unbiased estimates from NGS pooling data. Given the same sequencing effort, the resulting estimators often show a better performance than those obtained from individual sequencing. Although our analysis also shows that NGS of pools of individuals will not be preferable under all circumstances, it provides a cost-effective approach to estimate allele frequencies on a genome-wide scale. N EXT generation sequencing (NGS) is about to revolutionize biology. Through a massive paral-lelization, NGS provides an enormous number of reads, which permits sequencing of entire genomes at a fraction of the costs for Sanger sequencing. Hence, for the first time it has become feasible to obtain the complete genomic sequence for a large number of individuals. For several organisms, including humans, Drosophila melanogaster, and Arabidopsis thaliana, large resequencing projects are well on their way. Neverthe-less, despite the enormous cost reduction, genome sequencing on a population scale is still out of reach for the budget of most laboratories. The extraction of as much statistical information as possible at cost as low as possible has therefore already attracted considerable interest. See, for instance, Jiang et al. (2009) for the modeling of sequencing errors and Erlich et al. (2009) for the efficient tagging of sequences. Current genome-wide resequencing projects collect the sequences individual by individual. To obtain full coverage of the entire genome and to have high confidence that all heterozygous sites were discovered, it is required that genomes are sequenced at a suffi-ciently high coverage. As many of the reads provide only redundant information, cost could be reduced by a more effective sampling strategy. In this report, we explore the potential of DNA pooling to provide a more cost-effective approach for SNP discovery and genome-wide population genetics. Sequencing a large pool of individuals simultaneously keeps the number of redundant DNA reads low and provides thus an economic alternative to the sequenc-ing of individual genomes. On the other hand, more care has to be taken to establish an appropriate control of sequencing errors. Obviously haplotype information is not available from pooling experiments, but this will often be outweighed by the increased accuracy in population genetic inference. Focusing on biallelic loci, our analysis shows that with sufficiently large pool sizes, pooling usually outper-forms the separate sequencing of individuals, both for estimating allele frequencies and for inference of population genetic parameters. When sequencing er-rors are not too common, pooling seems also to be a good choice for SNP detection experiments. To avoid the additional challenges encountered with individual sequencing of diploid individuals, we compare pooling with individual sequencing of haploid individuals. See Lynch (2008, 2009) for a discussion of next generation sequencing of diploid individuals. Our results for the pooling experiments should be also applicable to a diploid setting, as we are just merging pools of size 2 to a larger pool in this case, leading to a pool size of n ¼ 2n d for n d diploid individuals. In the methods section, we derive several mathematical expressions that permit us to compare pooling with separate sequencing of individuals. These formulas are then applied in the results section to illustrate the differences in accu-racy between the approaches. A reader who is in-terested only in the actual differences under several

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Authors

  • Andreas Futschik

  • C. Schlotterer

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free