Whole-genome sequencing of pools of individuals (Pool-seq) is a cost-effective approach to determine genome-wide allele frequencies in an unbiased manner from a large number of individuals.
Once minimum quality criteria have been met, Pool-seq-based allele frequency estimates are accurate and reliable.
Typical issues of Pool-seq are alignment problems due to copy number variation or problems in the reference genome. The calling of low-frequency alleles is challenging owing to the difficulty in distinguishing them from sequencing errors.
Pool-seq has been successfully applied to a wide range of applications, including bulk segregant analyses, evolve and resequence studies, evolutionary genome analyses, analyses of time-series data and cancer genomics.
Owing to its cost-effectiveness, Pool-seq will continue to be a powerful tool for studies that require genome-wide allele frequency data in a large number of population samples. New technological and analytical advances will facilitate the extraction of haplotype information from Pool-seq data.
The analysis of polymorphism data is becoming increasingly important as a complementary tool to classical genetic analyses. Nevertheless, despite plunging sequencing costs, genomic sequencing of individuals at the population scale is still restricted to a few model species. Whole-genome sequencing of pools of individuals (Pool-seq) provides a cost-effective alternative to sequencing individuals separately. With the availability of custom-tailored software tools, Pool-seq is being increasingly used for population genomic research on both model and non-model organisms. In this Review, we not only demonstrate the breadth of questions that are being addressed by Pool-seq but also discuss its limitations and provide guidelines for users.
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The authors apologize to all colleagues who were not cited owing to space limitations. They are grateful to all colleagues who shared unpublished manuscripts, especially D. Kessner, Q. Long, M. Pérez Enciso, A. S. Fiston-Lavier and K. Schneeberger for comments and discussions. They thank members of the Institut für Populationsgenetik, in particular A. Betancourt, M. Dolezal, A. Futschik and A. Kalinka for discussion and comments on earlier versions of the manuscript. This work has been supported by the ERC (ArchAdapt) and the Austrian Science Funds (FWF, W1225).
The authors declare no competing financial interests.
- Next-generation sequencing
(NGS; also known as second-generation sequencing). An umbrella term for different sequencing platforms delivering millions of short DNA sequence reads.
DNA sequences that are generated by next-generation sequencing.
A sequencing technique in which sequencing libraries are not prepared from DNA of a single individual or cell but from a mixture of DNA fragments originating from different individuals or cells. In the context of this Review, Pool-seq is used to describe the unbiased sequencing of the entire genome.
The number of reads that span a given genomic position.
- Sequencing libraries
Sets of fragmented DNA extracted from one or more individuals that serve as the template for subsequent sequencing.
- Exome sequencing
A sequencing approach in which the complexity of the genome is reduced through hybridization to exonic sequences, which results in a higher sequence coverage of protein-coding regions.
- Restriction-site-associated DNA markers
Sequence polymorphisms in close proximity to a restriction enzyme recognition site.
- Linkage disequilibrium
(LD). Nonrandom association between alleles at two loci. In outcrossing diploid individuals, the genotypes need to be sorted into haplotypes in a statistical procedure called phasing.
- Genetic markers
Polymorphic loci that could be scored with a genotyping technique.
- F2 analysis
Analysis of mapping populations generated by the F2 design. The F1 progeny from crossing two phenotypically different parental strains are themselves crossed to produce an F2 population that is segregating for the phenotype of interest. The F2 mapping population may carry up to three genotypes at every marker and therefore allows the detection of additive and dominance effects, as well as interactions between loci.
- Phased genomic sequences
Genome sequences for which the haplotype phase (that is, the combination of alleles or genetic markers that coexist on a single chromosome) has been determined.
In statistics, it refers to the replacement of missing data with values. In genomics, it describes the use of haplotype sequences to fill in missing sequence information.
The combination of alleles or genetic markers that coexist on a single chromosome. Chromosomal regions carrying a haplotype are inherited as intact physical units until they are broken up by recombination.
- Pool genome-wide association studies
(Pool-GWASs). Genotype–phenotype mapping studies in which phenotypically extreme individuals are grouped and sequenced as pools. Causative variants are identified by contrasting the allele frequencies between the pools.
- Evolve and resequence studies
Studies that combine experimental evolution with next-generation sequencing. They make use of controlled environmental, demographic and selective variables to facilitate genotype–phenotype mapping.
- Forward genetics
An approach in which mutations induced by random mutagenesis that lead to the disruption of gene function are identified based on their phenotypes. The causative mutation is traditionally identified by positional cloning or by a candidate-gene approach.
- Bulk segregant analysis
(BSA). Analysis in which offspring from diverged parents are phenotyped and the DNA of individuals from opposing tails of the phenotypic distribution is combined (pooled). Causative variants are identified by contrasting allele frequency differences among the pools.
- Epistatic interactions
Non-additive interactions between genes in which the effect of an allele at one locus is modified by the genotypes at other loci in the genome. The resulting phenotype is different from that expected by summing the independent effects of the individual loci.
Introducing a genomic region from one strain or species into that of another by repeated backcrossing. By selecting for the phenotype of interest, the genomes become isogenic except for the chromosomal regions causing the selected phenotype.
- Paired-end reads
DNA fragments that were sequenced from both ends, yielding pairs of reads that are separated by a defined distance that is dependent on the library preparation protocol.
- Soft clipping
Substrings at either end of reads that were not aligned with a local alignment algorithm and are thereby excluded in the subsequent analysis.
- Proper pairs
Paired-end reads where both pairs can be mapped to the same chromosomes within a distance pre-specified by the insert size chosen during library preparation.
- Broken pairs
Paired-end reads that do not map as proper pairs.
- Mapping quality
Log (base 10) transformed measure of the probability that a read is incorrectly mapped multiplied by 10.
- Base quality
Log (base 10) transformed measure of the probability that a given base call is incorrect multiplied by 10.
- Insertions and deletions
(Indels). DNA sequences that have been inserted or deleted from a genomic region. As only phylogenetic analysis allows the distinction between insertions and deletions, indel has been used as an indifferent term.
- Strand bias
A variant that is significantly more likely to occur within reads that originate from one of the two strands of DNA.
Trait mapping studies that rely on a statistical test to determine associations between sequence variants and a given phenotype in natural populations.
The gradual change in phenotypes or allele frequencies along a geographical or environmental gradient.
The population genetic mechanism by which a neutral, or in some cases slightly deleterious, mutation increases in population frequency solely as a result of physical linkage with a positively selected mutation.
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Cite this article
Schlötterer, C., Tobler, R., Kofler, R. et al. Sequencing pools of individuals — mining genome-wide polymorphism data without big funding. Nat Rev Genet 15, 749–763 (2014). https://doi.org/10.1038/nrg3803
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