Detecting selected haplotype blocks in evolve and resequence experiments

Shifting from the analysis of single nucleotide polymorphisms to the
reconstruction of selected haplotypes greatly facilitates the
interpretation of Evolve and Resequence (E&R) experiments. Merging
highly correlated hitchhiker SNPs into haplotype blocks reduces thousands
of candidates to few selected regions. Current methods of haplotype
reconstruction from Pool-Seq data need a variety of data-specific
parameters that are typically defined ad hoc and require haplotype
sequences for validation. Here, we introduce haplovalidate, a tool which
detects selected haplotypes in Pool-seq time series data without the need
for sequenced haplotypes. Haplovalidate makes data-driven choices of two
key parameters for the clustering procedure, the minimum correlation
between SNPs constituting a cluster and the window size. Applying
haplovalidate to simulated and experimental E&R data reliably
detects selected haplotype blocks with low false discovery rates.
Importantly, our analyses identified a restriction of the haplotype
block-based approach to describe the genomic architecture of adaptation.
We detected a substantial fraction of haplotypes containing multiple
selection targets. These blocks were considered as one region of selection
and therefore led to under-estimation of the number of selection targets.
We demonstrate that the separate analysis of earlier time points can
significantly increase the separation of selection targets into individual
haplotype blocks. We conclude that the analysis of selected haplotype
blocks has great potential for the characterisation of the adaptive
architecture with E&R experiments.