Variant calling using NGS and sequence capture data for population and evolutionary genomic inferences in Norway Spruce (Picea abies)

Abstract

Advances in next-generation sequencing methods and the development of new statistical and computational methods have opened up possibilities made for large-scale, high quality genotyping in most organisms. Conifer genomes are large and are known to contain a high fraction of repetitive elements and this complex genome structure has bearings for approaches that aim to use next-generation sequencing methods for genotyping. In this chapter we provide a detailed description of a workflow for variant calling using next-generation sequencing in Norway spruce ( Picea abies ). The workflow that starts with raw sequencing reads and proceeds through read mapping to variant calling and variant filtering. We illustrate the pipeline using data derived from both whole-genome resequencing data and reduced-representation sequencing. We highlight possible problems and pitfalls of using next-generation sequencing data for genotyping stemming from the complex genome structure of conifers and how those issues can be mitigated or eliminated.

Development of a highly efficient 50K single nucleotide polymorphism genotyping array for the large and complex genome of Norway spruce (Picea abies L. Karst) by whole genome resequencing and its transferability to other spruce species

“…First, we used whole genome resequenced data based on 35 Norway spruce samples, previously described in Bernhardsson et al. (2020) and Wang et al. (2020), for the initial SNP selection.…”

“…The 35 whole genome resequenced Norway spruce samples were originally collected from Russia (one), Romania (one), Poland (one), Belarus (one), Sweden (22), Norway (five) and Finland (four) (described in more detail in Bernhardsson et al., 2020 and Wang et al., 2020). The WGS samples were used to find and extract candidate genome sequences for probe design of the screening array.…”

“…The combined raw VCF‐file (containing more than 709 million SNPs and 43 million indels, Figure 1) across the 20 genomic subsets was filtered in several steps. First, only bi‐allelic SNPs > 5 bp away from an indel and that followed the filtering criteria based on gatk ’s “best practice” (https://gatkforums.broadinstitute.org/gatk/discussion/2806/howto-apply-hard-filters-to-a-call-set) were kept (Bernhardsson et al., 2020). Since six of the WGS samples had a quite low sequence coverage (average coverage ~ 6×) and thereby also a lower confidence in SNP calls, the VCF‐files were subset to only include 29 samples derived from Norway, Finland and Sweden (Fennoscandia), which all had high coverage (15–20 × for called sites on average).…”

Quantifying adaptive evolution and the effects of natural selection across the Norway spruce genome

“…The bioinformatics pipeline used to handle all sequencing data has previously been described in detail in Bernhardsson et al (2020) and . Briefly, all raw sequence reads were first mapped to the complete P. abies reference genome v.1.0 (Nystedt et al 2013) using BWA-MEM v0.7.15 (http://bio-bwa.sourceforge.net/bwa.shtml, Li 2013 with default settings.…”

“…SNPs that passed all the different hard filtering criteria were used in the downstream analyses. For more detailed information on the genotype hard filtering criteria used, please refer to Bernhardsson et al (2020) and .…”