Hordeum vulgare

Analyses of the 86 bp sequencing reads were based upon the unfiltered qseq files, since the filtering process that produces fastq files sometimes discarded good reads that aligned perfectly to the reference genome for at least 64 bases. Starting with the qseq files from a flow cell, we first filtered for reads that (1) perfectly matched one of the barcodes and the expected four-base remnant of the ApeKI cut site (CWGC), (2) were not adapter/adapter dimers, and (3) contained no “Ns” in their first 72 bases. These reads were sorted into separate files according to their barcode, with the barcode removed and the remainder of the sequence trimmed to 64 bases (including the initial CWGC). If either the full ApeKI site (from partial digestion or chimera formation) or the first 8 bases of common adapter (from ApeKI fragments less than 64 bases) were detected within 64 bases, the read was truncated appropriately and then filled to 64 bases with polyA.
For maize, subsequent filtering of the reads was then done in two different ways, depending on our purpose. To generate a reference set of 64 base sequence tags to be included in a presence/absence genotype table, only reads with a minimum Q-score of 10 across the first 72 bases) and that occurred at least twice were kept. We opted to use this somewhat low-stringency minimum Q-score cutoff to maximize the number of useful sequence tags. Sequence tags containing random sequencing errors should not occur multiple times in multiple samples and should not map genetically, so they should be filtered out in subsequent steps. To this set of reference tags, the expected 64 base tags from an in silico ApeKI digest of the maize reference genome, B73 RefGen v1 [21] (link), were added (with fragments shorter than 64 bases filled with polyA, as above). To fill in the observed counts in the genotype table, a second pass across the reads for each DNA sample was performed. In this second pass, 64 base reads were counted for each sample (and the count added to the genotype table) if they perfectly matched one of the reference tags, regardless of their minimum Q score. The resulting genotype table was then filtered to remove tags that occurred in 10 or fewer DNA samples; this should remove most of the sequencing errors. For barley, the absence of a reference genome prevented anchoring reads to a physical map. Sequence reads were simply filtered for unique 64 base sequence reads that were present in five or more lines and these were mapped genetically as described below.
All maize and barley sequences were submitted to the National Center for Biotechnology Information (NCBI) Short Read Archive (study SRP004282.1).

The N. benthamiana 370 bp PDS (NbPDS; GenBank accession: AJ571700) and 400 bp plastid transketolase (NbTK; GenBank accession: HQ200305) fragments were obtained by RT-PCR using the NbPDS-1/NbPDS-2 and NbTK-1/NbTk-2 primer pairs respectively (Table S1). The resulting fragments were integrated into pCa-γbLIC in the sense orientations to generate pCa-γb:NbPDS₃₇₀ and pCa-γb:NbTK₄₀₀. In a similar fashion, 200 and 400 bp wheat PDS (TaPDS; GenBank accession: FJ517553), 250, 300 and 547 bp wheat ChlH (TaChlH; TIGR accession: TC169257), 300 bp barley ChlH (HvChlH; GenBank accession: U26545), 300 and 400 bp barley PDS (HvPDS; GenBank accession: AY062039), and 102, 303 and 402 bp B. distachyon PDS (BdPDS; GenBank accession: HQ317869) fragments were amplified by RT-PCR with appropriate primer pairs (Table S1) and inserted into pCa-γbLIC.

For assay design, SNPs were filtered to remove those that (i) had sequences showing similarity to the repeats (e-value ≤1e−10) identified by comparing 100 bp SNP-flanking sequences with the GIRI (http://www.girinst.org/repbase/) and ITMI Triticeae Repeat Sequence databases (wheat.pw.usda.gov/ITMI/Repeats) and (ii) were located in close proximity (<50 bp) to the exon–intron junctions identified in the wheat genome assembly (Brenchley et al., 2012 (link)). The selected SNPs were then submitted to the Illumina Assay Design Tool for design score calculation (www.illumina.com). A total of 91 829 SNPs were included into the assay design (Table S5).
Synonymous or nonsynonymous SNPs were annotated by comparing sequences with the nonredundant protein database at NCBI (https://www.ncbi.nlm.nih.gov/) using the blastx program with the e-value threshold of ≤1e⁻¹⁰. For functional annotation, RTs were translated into six reading frames and compared against the protein sequences (blastx e-value threshold ≤1e⁻⁰⁵) predicted in the rice, sorghum, maize and barley genomes. The output of the blastx program was used for automated functional annotation using blast2GO (http://www.blast2go.de/).

The nucleotide and amino acid sequences for tobacco (Nicotiana tabacum) KED [6 (link)] were used as a starting point for the initial search using BLASTN and BLASTP tools against the GenBank and OneKP database including non-redundant nucleotide and protein sequences, whole-genome shot gun, expressed sequence tags, high throughput genomic sequences, UniProtKB, transcriptome shotgun assembly proteins and protein data bank. Initial search using both the coding nucleotide sequences and the amino acid sequences identified 32 eudicots and at least one monocot (Elaeis guineensis). Subsequent searches were performed against the E. guineensis amino acid sequence through Liliopsida (monocotyledons) database to identify matching sequences of monocots. Likewise, using retrieved sequences to systematically search databases of the same orders and families of eudicotyledons yielded more species of possible matching sequences. Similar strategy was used to identified KED sequences from gymnosperms. Further searches were done sequentially by narrowing organism groups to find matches from more closely related species. However, it must be pointed out that the database search was aimed at surveying broadly the possible taxonomic presence of the KED gene and the retrieved sequences are not by no means an exhaustive outcome due to genomic sequence availability and the annotation quality of the public databases.
To search for possible KED-rich sequences in charophytes, bacteria and animals, KED protein and nucleotide sequences from plants were first repeatedly blasted through each of the intended organism groups in the databases. Then each match was further examined by retrieving its sequence from the database. Translation tool was used to generate open reading frames, followed by amino acid composition analysis, specifically for K+E+D content, to score the putative KED candidates. Once a KED sequence was identified from one taxon group (for example, charophyte), this sequence was used to search the entire available entries from this group. This way, sequences predicting KED-rich open reading frames in genomes of several charophyte, bacterial and animal species were identified.
During the course of searching animal KED candidates, a 6,229-amino acid microtubule-associated protein futsch from honeybee (Apis cerana) was found to contain an internal KED-rich region, whereas its N- and C-terminus portions have normal K, E and D contents. To illustrate examples of the presence of KED sequences in animal species, this 750-amino acid internal KED-rich region was arbitrarily taken out for demonstration in this study.
All retrieved sequences of possible matches were manually reviewed and verified for proper open reading frames and translated sequences. Wherever applicable, both genomic sequences and mRNA sequences were matched to verify the correct coding sequences. The full-length, translated sequences with considerable sequence identity and a high percentage of KED (K+E+D% greater than 30%) were designated as a candidate match.
Only partial KED sequences were available for two plants: cedar (Cryptomeria japonica, a gymnosperm; without C-terminus) and barley (Hordeum vulgare, a monocot, angiosperm; without N-terminus). However, they both still possessed the conserved domain (see “Results” below), therefore were included in sequence comparison analysis. But because their KED protein lengths were unknown and would distort the analysis parameters, they were excluded from the dataset for phylogenetic analysis described below.

Soro is one of the administrative districts of Hadiyya zone which is located in south central Ethiopia. It is situated approximately 272 km southwest of Addis Ababa and in a close proximity to the Gimicho town. Sibiya Arera is geographically located in 7° 9′ 0″–7° 11′ 0″ N latitude and 37° 52′ 30″–37° 54′ 0″ E longitude (Figure 1).
Rainfall distribution in the study area is bimodal, characterized by heavy rainy season from June to September, and light rainy season from March to May. The annual long term average rainfall is 1,107 mm and peak rainfall in September. The long term average annual temperature is 17.2°C [10 (link)]. The mean monthly temperature ranges from 15.98°C in December to 18.91°C in March (Figure 2). These favorable climatic conditions and high population have made the district to be one of the intensively cultivated areas in the south central highlands of Ethiopia. Rain-fed agriculture is the only source of livelihood for the majority of population. It is characterized by a smallholder mixed crop-livestock production.
Soil is a good indicator of the influence of soil parent material and the spatial variability in the degree of weathering, geological, and other factors are responsible for soil formation and development [11 ]. The dominant soil type of the study area is Nitisols that cover extensive areas of agricultural fields are highly suitable for crop production. The local geology is characterized by volcanic basalt flows and Cenozoic pyroclastic fall deposits [10 (link)].
The major land use/land cover types in the district include cultivated land, grazing land, forest land, and built-up areas. Cultivated land is the dominant land use type with 50,454 hectares (73.3% of the total area). At the present time, the local community has been implementing different practices to protect the adverse effect of erosion on their farmland and to improve soil fertility. Sibiya Arera is one of the areas with better implementation of soil and water conservation practices. Model farmers adopted biological and physical conservation practices, however, there is still land without conservation technologies which owned by reluctant farmers showing no willingness to implement soil and water conservation measures.
The farming system of the study area is mainly subsistence farming based on mixed crop-livestock production. Major crops grown in the area include wheat (Triticum aestivum L.), maize (Zea mays L.), barley (Hordeum vulgare L.), sorghum (Sorghum bicolor (L.) Moench), and teff (Eragrostis tef (Zucc.) Trotter). All farmers of the area have been practicing rain-fed agriculture based on continuous cultivation. Previously, diammonium phosphate (DAP) and urea were the main fertilizer types used by a large number of people. However, currently, farmers in the study area have started to use blended fertilizers such as nitrogen, phosphorus, sulfur (NPS), and nitrogen, phosphorus, sulfur, and boron (NPSB).
Arable lands are composed of the landscape without conservation practice, physical soil and water conservation structures (fanya juu), and physical soil and water conservation structures combined with biological practices (fanya juu stabilized with desho grass). Soil and water conservation practices are mechanisms used to reduce erosion and associated nutrient loss, reducing the risk of production; however, are not constructed in some agricultural lands in the study area. As a result soil erosion is major deterioration processes which lead to soil degradation and declining agricultural productivity in nonconserved agricultural land. Fanya juu structures integrated with biological practices are permanent features made of earth, designed to protect the soil from uncontrolled runoff and erosion and retain water where needed. It seeks to increase the amount of water seeping into the soil, reducing the speed and amount of water running off. Erosion is prevented by keeping enough vegetation cover on embankment to protect the soil surface and binds the soil together and maintains soil structure.