An optimized, high-throughput and cost-effective genotyping method applicable to various crop breeding populations is very important in this genomic era. We have developed an optimized Nextera skim-sequencing (skim-seq) approach to genotype different populations that can be used for genetics studies and genomics-assisted breeding. We performed skim-seq on a variety of populations developed through doubled haploid (DH) technology, inter-specific recombinants developed through introgression, amphidiploid developed through wide crosses, and on known monosomic samples.

1. A doubled haploid (DH) population consisting of 48 lines from the cross of spring wheat (Triticum aestivum) cultivars CDC Stanley and CDC Landmark developed by the Crop Development Centre at the University of Saskatchewan. We genotyped these DH with skim-seq and identified the genomic segments contributed by each of the two parental lines.

2. A population of 335 back cross generation 1 (BC1) skim-seq samples for wheat-barley recombinants with group 7 translocations and 839 F1 wheat 5D monosomic lines (TA3059) along with 16 standard Chinese Spring lines as internal control.

3. A panel of 144 Thinopyrum intermedium x Triticum durum (IWG--durum) lines and 141 Thinopyrum intermedium (IWG) lines were evaluated to assess skim-seq genome coverage as well as amphiploidy levels.

The demultiplexed FASTQ files for all samples tested in the experiment are available at NCBI SRA public repository with respective BioProject accessions; DH lines [PRJNA729723], 5D monosomic line [PRJNA742385], wheat-barley recombinants [PRJNA738484], IWG-durum and IWG [PRJNA736976]. An example key file for the 5D monosomic line is also attached. 

This study indicated that skim-seq is an efficient approach for genomic evaluation of a range of different populations and applications. The scripts have been provided to implement skim-seq data for variant calling, identification of genomic segment dosage and alien introgression. Each step of the pipeline is described and implemented with similar sequencing data from skim-seq libraries.

Skim-sequencing data of different populations described in the manuscript were generated using Nextera library preparation method. The data processing steps were illustrated in the attached pipeline script.

We have Readme.pdf file which describes about the dataset.