Haploid, diploid, and pooled exome capture recapitulate features of biology and paralogy in two non‐model tree species

Abstract

Despite their suitability for studying evolution, many conifer species have large and repetitive giga‐genomes (16–31 Gbp) that create hurdles to producing high coverage SNP data sets that capture diversity from across the entirety of the genome. Due in part to multiple ancient whole genome duplication events, gene family expansion and subsequent evolution within Pinaceae, false diversity from the misalignment of paralog copies creates further challenges in accurately and reproducibly inferring evolutionary history from sequence data. Here, we leverage the cost‐saving benefits of pool‐seq and exome‐capture to discover SNPs in two conifer species, Douglas‐fir (Pseudotsuga menziesii var. menziesii (Mirb.) Franco, Pinaceae) and jack pine (Pinus banksiana Lamb., Pinaceae). We show, using minimal baseline filtering, that allele frequencies estimated from pooled individuals show a strong, positive correlation with those estimated by sequencing the same population as individuals (r > .948), on par with such comparisons made in model organisms. Further, we highlight the utility of haploid megagametophyte tissue for identifying sites that are probably due to misaligned paralogs. Together with additional minor filtering, we show that it is possible to remove many of the loci with large frequency estimate discrepancies between individual and pooled sequencing approaches, improving the correlation further (r > .973). Our work addresses bioinformatic challenges in non‐model organisms with large and complex genomes, highlights the use of megagametophyte tissue for the identification of paralogous artefacts, and suggests the combination of pool‐seq and exome capture to be robust for further evolutionary hypothesis testing in these systems.

Evaluating the accuracy of variant calling methods using the frequency of parent‐offspring genotype mismatch

“…Due to the substantial size and complexity of conifer genomes, exome sequence capture is the preferred genotyping method for the taxon (Lind et al, 2022). We designed our capture probes based on previous Pinus contorta exome capture probes (Suren et al, 2016) and removed all probes which failed to effectively capture the target sequences.…”

“…For instance, conifers often have exceptionally large genomes (20–40 Gbp; Neale et al, 2017) with histories of whole‐genome duplication (Zheng et al, 2015), gene family expansion (Scott et al, 2020; De La Torre et al, 2014), transposable element dynamics (Scott et al, 2020; Wang et al, 2020; Yi et al, 2018), and extensive repeat regions (Wegrzyn et al, 2014). These complexities present a major challenge for NGS data analysis and downstream hypothesis testing in conifers (Lind et al, 2022; Shu & Moran, 2020). Such challenges can be alleviated by quantifying the accuracy of SNP calling when using the above‐mentioned variant calling tools.…”

Genomic architecture of resistance and tolerance to Swiss needle cast and Rhabdocline needle cast diseases in Douglas-fir

“…We used a pool-sequencing approach that targeted exon regions. For probe design details see (Lind et al, 2022). Briefly, the sequence capture probes were designed using genes identified in Douglas-fir RNA-seq data from (1) daily and cyclic induced experiments (Cronn et al, 2017) and (2…”

“…As in (Lind et al, 2022), we used the VarScan pipeline (Lind, 2021a) to process our genomic data. Raw sequence reads were trimmed with fastp (v0.19.5, Chen et al 2018) by trimming reads that did not pass quality filters of less than twenty N's, a minimum mean Phred quality score of 30 for sliding windows of five base pairs, and a final length of 75 base pairs with no more than 20 base pairs called as N (-n 20 -M 30 -W 5 -l 75 -g -3).…”

“…SNP contingency tables were generated by calculating REF/ALT allele counts by multiplying the total ploidy of the pool (2 * number of individuals) by the ALT allele frequency or 1-ALT allele frequency. SNPs flagged as being paralogs by (Lind et al, 2022) with a threshold of <0.05 for significance. Finally, we tested for enrichment in outlier status between groups of SNPs identified among the three SNC case-control comparisons using the hypergeometric test in R v4.0.5.…”