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. 2019 Jan 7;14(1):e0208614. doi: 10.1371/journal.pone.0208614

Imputation accuracy of wheat genotyping-by-sequencing (GBS) data using barley and wheat genome references

Hadi Alipour 1,2, Guihua Bai 3, Guorong Zhang 1,*, Mohammad Reza Bihamta 4, Valiollah Mohammadi 4, Seyed Ali Peyghambari 4
Editor: Khalil Kashkush5
PMCID: PMC6322752  PMID: 30615624

Abstract

Genotyping-by-sequencing (GBS) provides high SNP coverage and has recently emerged as a popular technology for genetic and breeding applications in bread wheat (Triticum aestivum L.) and many other plant species. Although GBS can discover millions of SNPs, a high rate of missing data is a major concern for many applications. Accurate imputation of those missing data can significantly improve the utility of GBS data. This study compared imputation accuracies among four genome references including three wheat references (Chinese Spring survey sequence, W7984, and IWGSC RefSeq v1.0) and one barley reference genome by comparing imputed data derived from low-depth sequencing to actual data from high-depth sequencing. After imputation, the average number of imputed data points was the highest in the B genome (~48.99%). The D genome had the lowest imputed data points (~15.02%) but the highest imputation accuracy. Among the four reference genomes, IWGSC RefSeq v1.0 reference provided the most imputed data points, but the lowest imputation accuracy for the SNPs with < 10% minor allele frequency (MAF). The W7984 reference, however, provided the highest imputation accuracy for the SNPs with < 10% MAF.

Introduction

Wheat (Triticum aestivum L.) is a major staple food crop in the world. The fast-growing world population demands wheat production to be increased up to 70% by 2050 to feed estimated world population of approximately nine billion [1–3]. Application of advanced genomic technologies in breeding can speed up genetic improvement of new wheat varieties to meet the challenge [4]. Next-generation-sequencing (NGS) technologies have revolutionized throughput and greatly reduced DNA sequencing cost, which makes it feasible for routine screening of breeding materials [5]. Genotyping-by-sequencing (GBS) is one application that sequences a subset of a complex genome and also multiplexes various numbers of samples to lower the genotyping cost [6]. GBS can discover and genotype single nucleotide polymorphisms (SNPs) simultaneously and it is a valuable platform for crop breeding and genomic research [6].

SNPs are the most abundant type of sequence variations in plant genomes [7] and therefore suitable for studies that require a large number of markers to be assayed such as marker-trait association analysis, genetic map construction, quantitative trait locus (QTL) screening, genomic selection, and analysis of population structure and genetic variation [8]. High-throughput SNP genotyping platforms have been successfully used for diploid crops such as maize [9] and barley [10]. Wheat, however, is polyploid and has a huge genome (~17 Gb) with abundant repetitive DNA (> 80%), which present major challenges to direct sequencing the genome for developing high-density SNP maps [11]. Recently, a GBS protocol [5] has been optimized for cereal crops including wheat and can generate thousands of SNPs with reasonably low cost [12–14]. However, abundance of missing data due to low sequencing coverage significantly reduces number of usable SNPs and lowers marker density [5]. High marker density will improve accuracy of many downstream analyses such as QTL mapping, genome-wide association studies (GWAS) and genomic selection [15, 16]. Sequencing depth and library complexity and quality may all affect number of missing data. Increase in sequencing depth can lower missing data rate, but also increase sequencing cost. Marker imputation using available information from reference genomes can increase usable SNPs without increasing sequencing cost. Several imputation algorithms including IMPUTE [17], MaCH [18], fastPHASE [19], BEAGLE [20] have been developed to assign allelic status of missing values to genotypic data. Among those algorithms, IMPUTE and MaCH use hidden Markov model (HMM) and Markov chain Monte Carlo (MCMC) iterations to conduct subsampling, and the haplotypes in each iteration are considered as a sample from the haplotype pool. FastPHASE and BEAGLE, however, cluster haplotypes and collapsed total number of haplotypes into a smaller number of “ancestral” haplotypes [21]. Although both BEAGLE and fastPHASE use a hidden Markov model, BEAGLE is more parsimonious by allowing fewer possible transitions and emissions. In addition, fastPHASE fixes the number of clusters in the model, whereas BEAGLE allows dynamic change of number of clusters to fit localized linkage disequilibrium (LD) patterns [22]. Therefore, BEAGLE has been used to impute missing data in many studies [23–25]. Length of LD blocks greatly affects imputation accuracy because recombination breaks allelic associations. The markers that are common between samples and a reference panel serve as anchors to guide genotype imputation of any missing haplotypes within an LD block [26].

Imputation strategies may vary from species to species depending on availability of reference genomes and well-saturated reference linkage maps in a species. A more complete reference genome allows proper alignment and ordering of the sequenced tags and helps impute low coverage data [27]. To date, three wheat reference genomes and one barley reference genome have been reported [28]. Among the three wheat reference genomes, Chinese Spring survey sequence (CSSS) [29] has 10.2 Gb of sequences generated from Illumina NGS and W7984 reference has 9.1 Gb of sequences that were assembled using large-insert libraries and the three homoeologous genomes were assembled separately [30]. W7984 reference has lower genome coverage than CSSS, but higher assembly quality. Wheat IWGSC RefSeq v1.0 reference is the newest version of wheat reference genome with the best assembly quality, contains 14.5 Gb sequences with 94% genome coverage and was assembled using POPSEQ data and HiC map (chromosome conformation capture) [https://wheat-urgi.versailles.inra.fr/Seq-Repository/Assemblies]. Here we used the four reference genomes to compare imputation efficiencies and accuracies of wheat GBS data.

Materials and methods

Plant materials

A total of 384 accessions of Iranian wheat accessions [http://biogeo.ucdavis.edu/projects/iranwheat] were kindly provided by the United States Department of Agriculture (USDA) germplasm collection (https://npgsweb.ars-grin.gov/gringlobal/search.aspx), International Center for the Improvement of Maize and Wheat (CIMMYT), University of Tehran (UT), and Seed and Plant Improvement Institute (SPII), Karaj, Iran [31]. The wheat collection includes 276 Iranian landraces collected from different climates between 1937 and 1968 and 108 cultivars released in Iran between 1942 and 2014. Genomic DNA of the accessions were extracted from two-week-old seedling leaves using a modified cetyltrimethyl ammonium bromide (CTAB) method [32]. DNA concentration was quantified using the Quant-iT PicoGreen dsDNA Assay (Life Technologies Inc., NY) and normalized to 20 ng/μl.

GBS library preparation and sequencing

The GBS library was constructed following Poland et al. [13]. In brief, genomic DNA of each sample was double-digested with PstI (CTGCAG) and MspI (CCGG) restriction enzymes (New England BioLabs Inc., Ipswich, MA, USA), and ligated to barcoded adapters using T4 ligase (New England BioLabs Inc.). All the ligated products were pooled and cleaned up using the QIAquick PCR Purification Kit (Qiagen Inc., Valencia, CA, USA). Primers complementary to both adaptors were used for PCR. PCR amplification started at 95 °C for 5 min, followed by 16 cycles of 95 °C for 30 s, 62 °C for 20 s and 68 °C for 1 min and ended by a final extension step at 72 °C for 5 min. The PCR product was then cleaned up again using the QIAquick PCR Purification Kit, and quantified using Bioanalyzer 7500 Agilent DNA Chips (Agilent Technologies, Inc.). After size-selection for 250–300 bp fragments in an E-gel system (Life Technologies Inc.), concentration of the library was evaluated using a Qubit 2.0 fluorometer and Qubit dsDNA HS Assay Kit (Life Technologies Inc.). The size-selected library was sequenced on an Ion Proton system (Life Technologies Inc.).

Sequence reads were first trimmed to 64 bp, and identical reads were grouped into sequence tags. Unique sequence tags were aligned internally to identify SNPs within the tags allowing mismatches of up to 3 bp. SNPs were called using the Universal Network Enabled Analysis Kit (UNEAK) GBS pipeline [33] in TASSEL 3.0 bioinformatics analysis package [34]. Tags with low quality score (< 15) were removed. SNPs with heterozygotes or a minor allele frequency > 10% were discarded to reduce the false positive markers. Only SNPs with lower than 80% missing data were used for this study. BLASTn analysis was carried out to align sequence tags to the four genome references including one from the barley reference genome [28], and three from wheat reference genomes, the flow-sorted Chinese Spring survey sequence (CSSS) [29], the Popseq W7984 sequence reference [30] and IWGSC RefSeq v1.0 [https://wheat-urgi.versailles.inra.fr/Seq-Repository/Assemblies]. The purpose of using the barley reference genome is to show efficiency of using reference genomes of closely related species to impute missing data in cases where reference genome sequence is absent in some species. If a SNP could be mapped in multiple chromosome positions, the position with the lowest E-value was used to represent the SNP location.

In this study, imputation was performed using BEAGLE v3.3.2 [20] and the four genome reference genomes. BEAGLE used a phasing algorithm to determine haplotype phase for each individual and to impute the missing values based upon allele frequencies. This was done by constructing local haplotype clusters and then sampling a number of haplotypes for each individual from a special class of HMM. Probability of each possible haplotype was estimated using the genotypic information and a forward-backward algorithm [35]. Then, new haplotypes for the individuals were sampled according to the conditional probabilities to reconstruct the local haplotype cluster as input for next iteration. This process was repeated several times. To achieve a high level of phasing accuracy in the end, the most-likely haplotypes for each individuals were imputed using the Viterbi algorithm [35].

Imputation accuracy was calculated by comparing the imputed SNPs after five, six and seven sequencing runs to the actual SNPs called from eight sequencing runs. Two files including one file of the actual SNP data from five (359 million reads), six (421 million reads), seven (488 million reads) and eight (566 million reads) sequencing runs, and an imputed data file generated from the five, six and seven sequencing runs were compared to calculate the number of correctly imputed data points. The ratio between correctly imputed and total imputed data points was used to estimate imputation accuracy [36]. To evaluate the relationship between imputation accuracy and allele frequency, allele frequencies from the original data file were calculated for each SNP.

Results

Two GBS libraries were generated for the 384 wheat accessions with 276 landraces and 12 cultivars in library 1 and 96 cultivars in library 2. To minimize missing data, an average of two sequencing runs was performed for each plate of 96 samples, therefore, library 1 with three plates of samples was run a total of six times and library 2 with one plate of samples was run twice. Eight sequencing runs generated a total of 566,439,207 reads from the two libraries with 81% (458,363,607) of high-quality barcoded reads, from which 133,039 unique SNPs were identified including 16,506, 38,642 and 65,560 SNPs with <20%, <50% and <80% missing data, respectively. To determine the relationship between number of GBS-SNPs and number of sequencing runs, numbers of SNPs were calculated for each increased run from first to six sequencing runs of the library 1 (Fig 1). The number of SNPs with <20% missing data was concave up as the run number increased (Fig 1a) but concave down for the numbers of SNPs with <50% and <80% missing data (Fig 1b and 1c).

Fig 1. Relationship between numbers of sequencing runs and the numbers of SNPs with (a) <20%, (b) <50%, and (c) <80% missing data for the first library of 288 Iranian wheat genotypes.

Fig 1

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The average SNP density was 3.87 SNPs per Mbp when the SNPs with <80% missing data were counted (Table 1). The sequence tags containing the SNPs with <80% missing data were used to blast against each of the four references to map those SNPs to unique chromosome locations. The barley reference genome mapped the lowest percentage of sequencing tags (23.14%, Table 1), whereas IWGSC RefSeq v1.0 reference mapped the highest (94.94%, Table 2) among the four references. CSSS (55.34%, Table 3) and W7984 (85.61%, Table 4) were in between. Among the three wheat genomes, B genome had the most mapped SNPs and D genome the least across the four references, thus B chromosomes had much higher marker density than that in the D chromosomes. Among the four reference genomes, IWGSC RefSeq v1.0 reference provided the highest SNP density in almost all chromosomes. Among 21 chromosomes, chromosomes 2B and 3B had the highest SNP density, and chromosome 4D had the lowest (Tables 1–4).

Table 1. Summary of single nucleotide polymorphism types identified in three wheat genomes using the barley genome reference.

Allele 1A 2A 3A 4A 5A 6A 7A A genome 1B 2B 3B 4B 5B 6B 7B B genome 1D 2D 3D 4D 5D 6D 7D D genome NA Total
Chromosome_size(Mbp) 798 899 828 856 827 705 814 5,727 849 928 993 821 870 913 900 6,274 605 729 771 649 750 713 728 4,945 - 16,946
No. of SNP 803 1,211 574 316 410 874 977 5,165 910 1,750 1,537 471 1,277 689 613 7,247 414 709 160 57 330 470 620 2,760 50,388 65,560
Density (SNP/Mbp) 1.01 1.35 0.69 0.37 0.50 1.24 1.20 0.90 1.07 1.89 1.55 0.57 1.47 0.75 0.68 1.16 0.68 0.97 0.21 0.09 0.44 0.66 0.85 0.56 - 3.87
Transition 537 812 378 196 280 603 639 3,445 623 1,161 1,035 298 836 454 417 4,824 247 444 98 43 200 288 394 1,714 35,012 44,995
A/G 257 413 160 83 135 287 307 1,642 300 512 477 131 390 215 196 2,221 108 213 42 24 86 132 193 798 16,495 21,156
C/T 234 322 182 86 126 242 269 1,461 261 491 419 134 363 191 173 2,032 104 172 43 16 87 128 161 711 14,387 18,591
T/C 23 39 14 17 9 34 38 174 29 68 67 15 31 29 26 265 16 32 6 2 14 13 21 104 1,957 2,500
G/A 23 38 22 10 10 40 25 168 33 90 72 18 52 19 22 306 19 27 7 1 13 15 19 101 2,173 2,748
Transversion 266 399 196 120 130 271 338 1,720 287 589 502 173 441 235 196 2,423 167 265 62 14 130 182 226 1,046 15,376 20,565
A/T 37 69 26 20 17 43 41 253 35 77 81 32 62 47 22 356 29 38 12 3 16 21 37 156 2,137 2,902
A/C 52 113 42 30 26 58 75 396 60 139 119 40 118 53 45 574 46 56 16 1 32 41 50 242 3,597 4,809
T/A 3 8 6 4 4 5 15 45 6 11 16 2 3 8 5 51 3 3 1 0 1 1 2 11 314 421
T/G 3 7 2 1 2 5 8 28 9 25 18 6 7 4 8 77 5 6 2 1 2 7 3 26 453 584
C/A 8 10 5 2 5 4 9 43 9 10 11 2 9 1 8 50 3 11 3 0 2 6 3 28 437 558
C/G 88 115 71 41 37 97 102 551 101 194 131 44 152 65 69 756 48 87 12 5 46 61 67 326 4,665 6,298
G/T 64 69 36 18 31 44 70 332 54 108 108 41 75 46 33 465 25 54 13 3 24 32 52 203 3,140 4,140
G/C 11 8 8 4 8 15 18 72 13 25 18 6 15 11 6 94 8 10 3 1 7 13 12 54 633 853
Ts % 66.87 67.05 65.85 62.03 68.29 68.99 65.40 66.70 68.46 66.34 67.34 63.27 65.47 65.89 68.03 66.57 59.66 62.62 61.25 75.44 60.61 61.28 63.55 62.10 69.48 68.63
Tv % 33.13 32.95 34.15 37.97 31.71 31.01 34.60 33.30 31.54 33.66 32.66 36.73 34.53 34.11 31.97 33.43 40.34 37.38 38.75 24.56 39.39 38.72 36.45 37.90 30.52 31.37
Ts/Tv ratio 2.02 2.04 1.93 1.63 2.15 2.23 1.89 2.00 2.17 1.97 2.06 1.72 1.90 1.93 2.13 1.99 1.48 1.68 1.58 3.07 1.54 1.58 1.74 1.64 2.28 2.19
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Table 2. Summary of single nucleotide polymorphism types identified in three wheat genomes using the wheat the IWGSC RefSeq v1.0.

Allele 1A 2A 3A 4A 5A 6A 7A A genome 1B 2B 3B 4B 5B 6B 7B B genome 1D 2D 3D 4D 5D 6D 7D D genome NA
No. of SNP 3,399 4,001 2943 3,684 2,850 2,683 4,556 24,116 4,424 5,083 5,350 2,055 4,054 4,776 4,055 29,797 1,370 2,029 1,162 466 798 993 1,510 8,328 3,319
Density (SNP/Mbp) 4.26 4.45 3.55 4.3 3.45 3.81 5.6 4.21 5.21 5.48 5.39 2.5 4.66 5.23 4.51 4.75 2.26 2.78 1.51 0.72 1.06 1.39 2.07 1.68 -
Transition 2,390 2,873 2,023 2,523 1,946 1,833 3,183 16,771 3,072 3,502 3,729 1,389 2,774 3,337 2,872 20,675 863 1,310 754 321 537 623 958 5,366 2,183
A/G 1,121 1,397 975 1,185 886 847 1,452 7,863 1,482 1,643 1,721 650 1,273 1,587 1,344 9,700 412 604 340 160 251 305 466 2,538 1,055
C/T 1,004 1,172 812 1,051 855 766 1,347 7,007 1,263 1,384 1,529 596 1,190 1,380 1,196 8,538 353 526 315 129 217 253 392 2,185 861
T/C 131 157 112 130 85 105 186 906 145 221 215 67 144 181 162 1,135 45 94 48 12 31 32 55 317 142
G/A 134 147 124 157 120 115 198 995 182 254 264 76 167 189 170 1,302 53 86 51 20 38 33 45 326 125
Transversion 1,009 1,128 920 1,161 904 850 1,373 7,345 1,352 1,581 1,621 666 1,280 1,439 1,183 9,122 507 719 408 145 261 370 552 2,962 1,136
A/T 140 181 126 180 116 109 184 1,036 195 234 236 96 162 207 145 1,275 69 98 56 21 45 54 97 440 151
A/C 219 296 209 263 207 204 341 1,739 307 373 390 154 306 357 285 2,172 116 160 94 28 61 89 115 663 235
T/A 18 28 23 22 19 10 28 148 27 36 41 13 25 29 23 194 9 12 11 2 4 6 4 48 31
T/G 17 25 20 29 36 20 33 180 33 56 59 20 33 40 45 286 15 22 14 2 6 12 12 83 35
C/A 24 31 21 28 25 29 38 196 36 41 43 19 44 28 43 254 6 23 9 4 9 12 15 78 30
C/G 314 319 297 362 262 274 397 2,225 429 468 456 187 390 440 385 2,755 160 234 125 45 68 103 180 915 403
G/T 243 213 183 212 207 167 302 1,527 264 308 335 154 267 276 218 1,822 104 134 83 36 59 74 101 591 200
G/C 34 35 41 65 32 37 50 294 61 65 61 23 53 62 39 364 28 36 16 7 9 20 28 144 51
Ts % 70.31 71.81 68.74 68.49 68.28 68.32 69.86 69.54 69.44 68.90 69.70 67.59 68.43 69.87 70.83 69.39 62.99 64.56 64.89 68.88 67.29 62.74 63.44 64.43 65.77
Tv % 29.69 28.19 31.26 31.51 31.72 31.68 30.14 30.46 30.56 31.10 30.30 32.41 31.57 30.13 29.17 30.61 37.01 35.44 35.11 31.12 32.71 37.26 36.56 35.57 34.23
Ts/Tv ratio 2.37 2.55 2.20 2.17 2.15 2.16 2.32 2.28 2.27 2.22 2.30 2.09 2.17 2.32 2.43 2.27 1.70 1.82 1.85 2.21 2.06 1.68 1.74 1.81 1.92
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Table 3. Summary of single nucleotide polymorphism types identified in three wheat genomes using the wheat Chinese Spring survey sequence (CSSS) assembly.

Allele 1A 2A 3A 4A 5A 6A 7A A genome 1B 2B 3B 4B 5B 6B 7B B genome 1D 2D 3D 4D 5D 6D 7D D genome NA
No. of SNP 2,151 2,578 1,188 1,647 898 1,926 2,057 12,445 2,464 3,782 4,307 1,519 3,408 1,661 1,517 18,658 906 1,019 348 114 657 911 1,220 5,175 29,282
Density (SNP/Mbp) 2.7 2.87 1.43 1.92 1.09 2.73 2.53 2.17 2.9 4.08 4.34 1.85 3.92 1.82 1.69 2.97 1.5 1.4 0.45 0.18 0.88 1.28 1.68 1.05 -
Transition 1,490 1,830 813 1,113 606 1,326 1,417 8,595 1,707 2,671 2,994 1,026 2,372 1,116 1,049 12,935 568 647 216 92 440 574 796 3,333 20,132
A/G 704 897 371 489 273 591 645 3,970 793 1,214 1,394 472 1,114 527 474 5,988 263 293 88 53 210 262 379 1,548 9,650
C/T 632 732 346 478 277 577 619 3,661 722 1,117 1,219 449 995 472 462 5,436 235 271 93 33 183 251 326 1,392 8,102
T/C 74 104 46 68 19 69 75 455 89 153 169 56 116 59 65 707 36 45 13 4 25 29 46 198 1,140
G/A 80 97 50 78 37 89 78 509 103 187 212 49 147 58 48 804 34 38 22 2 22 32 45 195 1,240
Transversion 661 748 375 534 292 600 640 3,850 757 1,111 1,313 493 1,036 545 468 5,723 338 372 132 22 217 337 424 1,842 9,150
A/T 87 136 50 71 39 90 91 564 106 145 195 76 125 93 58 798 45 57 19 4 33 50 61 269 1,271
A/C 146 199 83 130 56 125 159 898 155 267 313 96 253 131 120 1,335 77 83 33 4 51 77 106 431 2,145
T/A 9 17 9 8 13 8 17 81 15 18 29 10 17 21 9 119 7 7 1 0 1 3 4 23 198
T/G 8 13 8 8 12 17 17 83 20 36 46 19 28 9 18 176 13 9 4 1 6 14 8 55 270
C/A 17 22 8 11 7 21 15 101 21 39 32 10 40 6 16 164 8 12 3 0 8 11 9 51 242
C/G 203 197 127 174 91 193 181 1,166 240 335 368 147 316 151 156 1,713 106 121 38 8 66 98 138 575 2,844
G/T 167 142 76 103 62 126 138 814 165 238 282 118 215 105 74 1,197 68 68 24 5 43 63 80 351 1,778
G/C 24 22 14 29 12 20 22 143 35 33 48 17 42 29 17 221 14 15 10 0 9 21 18 87 402
Ts % 69.27 70.99 68.43 67.58 67.48 68.85 68.89 69.06 69.28 70.62 69.51 67.54 69.60 67.19 69.15 69.33 62.69 63.49 62.07 80.70 66.97 63.01 65.25 64.41 68.75
Tv % 30.73 29.01 31.57 32.42 32.52 31.15 31.11 30.94 30.72 29.38 30.49 32.46 30.40 32.81 30.85 30.67 37.31 36.51 37.93 19.30 33.03 36.99 34.75 35.59 31.25
Ts/Tv ratio 2.25 2.45 2.17 2.08 2.08 2.21 2.21 2.23 2.25 2.40 2.28 2.08 2.29 2.05 2.24 2.26 1.68 1.74 1.64 4.18 2.03 1.70 1.88 1.81 2.20
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Table 4. Summary of single nucleotide polymorphism types identified in three wheat genomes using the wheat W7984 assembly.

Allele 1A 2A 3A 4A 5A 6A 7A A genome 1B 2B 3B 4B 5B 6B 7B B genome 1D 2D 3D 4D 5D 6D 7D D genome NA
No. of SNP 2,907 3,434 2,645 3,299 1,921 2,455 4,023 20,684 3,813 4,649 5,067 1,986 3,686 4,455 3,792 27,448 1,232 1,804 1,128 428 897 1,039 1,465 7,993 9,435
Density (SNP/Mbp) 3.64 3.82 3.19 3.85 2.32 3.48 4.94 3.61 4.49 5.01 5.10 2.42 4.24 4.88 4.21 4.37 2.04 2.47 1.46 0.66 1.20 1.46 2.01 1.62 -
Transition 2,061 2,454 1,819 2,291 1,296 1,689 2,803 14,413 2,615 3,172 3,541 1,348 2,521 3,103 2,671 18,971 776 1,149 717 296 588 662 934 5,122 6,489
A/G 957 1,190 858 1,053 597 787 1,294 6,736 1,250 1,465 1,635 609 1,160 1,468 1,240 8,827 368 518 325 160 276 330 434 2,411 3,182
C/T 881 1,000 741 975 570 703 1,184 6,054 1,081 1,274 1,455 591 1,083 1,287 1,123 7,894 322 469 302 108 243 270 395 2,109 2,534
T/C 108 139 103 112 46 92 158 758 136 205 203 75 126 165 149 1,059 32 86 46 16 29 34 53 296 387
G/A 115 125 117 151 83 107 167 865 148 228 248 73 152 183 159 1,191 54 76 44 12 40 28 52 306 386
Transversion 846 980 826 1,008 625 766 1,220 6,271 1,198 1,477 1,526 638 1,165 1,352 1,121 8,477 456 655 411 132 309 377 531 2,871 2,946
A/T 99 170 112 149 83 98 165 876 174 199 226 84 159 188 135 1,165 67 88 53 17 48 59 90 422 439
A/C 189 252 193 229 130 181 299 1,473 273 356 357 142 274 337 272 2,011 107 145 98 28 69 86 99 632 693
T/A 12 22 26 14 19 12 19 124 24 31 37 16 23 26 24 181 9 12 11 1 3 5 8 49 67
T/G 18 19 18 28 28 23 22 156 32 49 56 21 32 36 43 269 10 21 12 5 7 10 14 79 80
C/A 19 28 21 24 14 24 32 162 33 37 42 20 39 26 43 240 9 23 5 3 11 12 13 76 80
C/G 276 273 247 315 186 248 371 1,916 377 447 426 184 355 414 358 2,561 143 215 130 34 91 109 171 893 928
G/T 211 186 171 196 144 151 265 1,324 232 299 325 149 239 270 199 1,713 89 120 83 35 66 71 109 573 530
G/C 22 30 38 53 21 29 47 240 53 59 57 22 44 55 47 337 22 31 19 9 14 25 27 147 129
Ts % 70.90 71.46 68.77 69.45 67.46 68.80 69.67 69.68 68.58 68.23 69.88 67.88 68.39 69.65 70.44 69.12 62.99 63.69 63.56 69.16 65.55 63.72 63.75 64.08 68.78
Tv % 29.10 28.54 31.23 30.55 32.54 31.20 30.33 30.32 31.42 31.77 30.12 32.12 31.61 30.35 29.56 30.88 37.01 36.31 36.44 30.84 34.45 36.28 36.25 35.92 31.22
Ts/Tv ratio 2.44 2.50 2.20 2.27 2.07 2.20 2.30 2.30 2.18 2.15 2.32 2.11 2.16 2.30 2.38 2.24 1.70 1.75 1.74 2.24 1.90 1.76 1.76 1.78 2.20
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Transitions were the most observed nucleotide variations (68.63%) including A/G (32.27%), C/T (28.36%), C/G (9.61%), A/C (7.34%), G/T (6.31%) and A/T (4.43%) transition types (Table 1). Using the barley reference genome, more transition-type SNPs were identified in A (3,445) and B (4,824) genomes than that in the D genome (1,714). The transition/transversion (Ts/Tv) SNP ratios from the A and B genomes (2.0) were relatively higher than that (1.64) from the D genome (Table 1). A similar trend in SNP types was observed for the CSSS assembly, but its transition/transversion SNP ratios were higher than those from the barley reference genome with 2.23 for the A genome, 2.26 for the B genome and 1.81 for the D genome (Table 3). The W7984 assembly and IWGSC RefSeq v1.0 had similar transition/transversion ratios to the CSSS assembly, but with slightly higher numbers of total SNPs (Tables 2 and 4).

The numbers of SNPs per chromosome were significantly correlated to the chromosome sizes (Mbp) in all four references. Although they were all significant, the correlations were much lower for the barley reference genome (R2 ~ 0.41**, Fig 2a) and wheat CSSS assembly (R2 ~ 0.54**, Fig 2b) than those for W7984 assembly (R2 ~ 0.75**, Fig 2c) and IWGSC RefSeq v1.0 (R2 ~ 0.74**, Fig 2d).

Fig 2. Relationship between the number of SNPs per chromosome and chromosome sizes when (a) barley genome, (b) Chinese Spring survey sequence (CSSS), (c) W7984, and (d) IWGSC RefSeq v1.0 reference was used to call SNPs.

Fig 2

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Five sequencing runs (three runs of the library 1 and two runs of the library 2) generated 355,197,375 total reads and 5,571 SNPs with less than 20% missing data. The number of SNPs was almost doubled (10,213) after adding one additional sequencing run of library 1 (total six runs) and tripled (16,506) after adding three sequencing runs of library 1 (total eight runs). For the number of SNPs with <80% missing data, increasing number of sequencing runs from five (45,624 SNPs) to eight (65,560 SNPs) only increased 44% SNPs. However, imputation reduced much more missing data than increasing sequencing runs from five to eight although imputation efficiency is different among the four references (Table 5). The numbers of SNPs with <20% missing data increased only three times when sequencing runs were increased from five to eight (Table 5), but increased 2.8 times (the barley reference genome), 5.1 times (CSSS), 7.0 times (W7984) and 7.8 times (IWGSC RefSeq v1.0) after imputation from the data of five runs. Imputation using IWGSC RefSeq v1.0 reference generated the most SNPs with only 4.9% missing data in the final imputed dataset; the barley reference genome imputed the least SNPs with 65.5% missing data after imputation; and wheat CSSS and W7984 were in between with 38.0% and 12.5% missing data after imputation (Table 5). These results indicate that although both imputation and increasing sequencing depth can quickly fill up missing data, imputation can reduce more missing data and therefore detect more SNPs than increasing sequencing depth with the highest increase for SNPs with <20% missing data.

Table 5. Numbers of SNPs called after five (3 runs of the library 1 and 2 runs of the library 2), six (4 runs of the library 1 and 2 runs of the library 2), seven (5 runs of the library 1 and 2 runs of the library 2) and eight (6 runs of the library 1 and 2 runs of the library 2) sequencing runs of the two GBS libraries with and without imputation using barley genome and Chinese Spring survey sequence (CSSS) and W7984 assembly and Wheat IWGSC RefSeq v1.0 reference.

Imputation methods Missing data Number of SNPs
Five sequencing runs (355,197,375 reads) Six sequencing runs (419,373,662 reads) Seven sequencing runs (486,646,481 reads) Eight sequencing runs (566,439,207 reads)
<20% <50% <80% <20% <50% <80% <20% <50% <80% <20% <50% <80%
Without imputation 5,571 26,773 45,624 10,213 31,472 53,241 13,746 35,380 59,766 16,506 38,642 65,560
Barley 15,738 30,442 45,624 20,142 35,320 53,241 23,526 39,269 59,766 26,284 42,706 65,560
Wheat CSSS 28,285 36,811 45,624 34,135 42,782 53,241 38,783 47,831 59,766 42,877 52,357 65,560
Wheat W7984 39,906 42,532 45,624 46,819 49,634 53,241 52,529 55,605 59,766 57,703 60,900 65,560
Wheat IWGSC RefSeq v1.0 43,365 44,410 45,624 50,735 51,924 53,241 57,065 58,361 59,766 62,740 64,083 65,560
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Imputation accuracy was calculated for each reference by comparing the SNP data imputed from five, six or seven sequencing runs to the real SNP data from eight runs (Table 6). All four references provided high imputation accuracy. Among them, the IWGSC RefSeq v1.0 provided the lowest imputation accuracy (84.16%) although it imputed the most data points in all run combinations. The other three references had relatively higher accuracies from 87.31% for CSSS reference to 89.80% for W7984 reference (Table 6). The number of imputed data points was the highest using five sequencing runs, and the lowest using the data from eight sequencing runs. For all references and sequencing runs, even though the imputed data points per chromosome were much lower in the D genome than those in the A and B genomes in general, the D genome had the highest imputation accuracy and the B genome the lowest (Table 6).

Table 6. Numbers of data points per chromosome after imputation with barley genome, Chinese Spring survey sequence (CSSS), W7984 and IWGSC RefSeq v1.0 references and imputation accuracy calculated by comparing imputed data of five, six and seven runs with actual SNP data generated from eight sequencing runs.

Run Reference Missing data points A genome B genome D genome Total
Five runs Barley Total data points 157,689 221,204 85,281 464,174
Correctly imputed 141,111 195,450 77,230 413,791
Accuracy 89.49 88.36 90.56 89.15
CSSS Total data points 374,452 448,904 125,864 949,220
Correctly imputed 339,919 401,393 114,507 855,819
Accuracy 90.78 89.42 90.98 90.16
W7984 Total data points 556,620 637,680 186,281 1,380,581
Correctly imputed 502,417 568,400 169,075 1,239,892
Accuracy 90.26 89.14 90.76 89.81
IWGSC RefSeq v1.0 Total data points 691,863 841,893 243,017 1,776,773
Correctly imputed 584,917 694,667 209,385 1,488,969
Accuracy 84.54 82.51 86.16 83.80
Six runs Barley Total data points 23,138 22,979 11,552 57,669
Correctly imputed 20,356 20,016 10,294 50,666
Accuracy 87.98 87.11 89.11 87.86
CSSS Total data points 74,520 103,888 29,095 207,503
Correctly imputed 63,312 86,484 25,494 175,290
Accuracy 84.96 83.25 87.62 84.48
W7984 Total data points 393,307 517,916 154,857 1,066,080
Correctly imputed 363,152 473,970 143,550 980,672
Accuracy 92.33 91.51 92.70 91.99
IWGSC RefSeq v1.0 Total data points 467,224 574,541 162,471 1,204,236
Correctly imputed 397,638 475,980 140,790 1,014,408
Accuracy 85.11 82.85 86.66 84.24
Seven runs Barley Total data points 25,242 33,810 12,195 71,247
Correctly imputed 22,637 29,634 11,207 63,478
Accuracy 89.68 87.65 91.90 89.10
CSSS Total data points 67,293 104,296 25,254 196,843
Correctly imputed 59,247 89,933 22,647 171,827
Accuracy 88.04 86.23 89.68 87.29
W7984 Total data points 154,672 214,222 59,594 428,488
Correctly imputed 135,735 186,183 53,429 375,347
Accuracy 87.76 86.91 89.65 87.60
IWGSC RefSeq v1.0 Total data points 233,321 289,654 80,048 603,023
Correctly imputed 199,037 240,517 69,725 509,279
Accuracy 85.31 83.04 87.10 84.45
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The imputation accuracy increased with the increase in allele frequency from 25.7% accuracy for allele frequency < 5% and 99.7% accuracy for allele frequency > 95% (Fig 3). The positive correlations were observed for all four references. The imputation accuracy reached 94% when allele frequency was >65%. Relatively lower imputation accuracy of IWGSC RefSeq v1.0 than other references mainly occurred at those alleles with frequency <35% (Fig 3b and 3c) where W7984 assembly provided more accurate imputation than other references. About 75% of imputed SNPs were distributed in allele frequencies between 0.55 and 0.95 (Fig 4), and had high mean imputation accuracies from 88.0% to 99.7%. The difference in imputation accuracy in this allele frequency range was negligible among the four references (Fig 4).

Fig 3. Relationship between imputation accuracy and allele frequency for (a) five, (b) six, (c) seven runs imputed with eight runs using barley genome, Chinese Spring survey sequence (CSSS), W7984 and IWGSC RefSeq 1.0 in Iranian wheat GBS data.

Fig 3

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Fig 4. Number of total and correctly imputed alleles for different allele frequencies for (a) five, (b) six, (c) seven runs imputed with eight runs using barley genome, Chinese Spring survey sequence (CSSS), W7984 and IWGSC RefSeq 1.0 in Iranian wheat GBS data.

Fig 4

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Discussion

Advancements in next-generation sequencing technology and high-throughput SNP genotyping can greatly accelerate crop breeding process if properly deployed [37]. GBS technology not only significantly improves throughput, but also greatly reduces SNP genotyping costs by reducing genome complexity and multiplexing samples [13]. Although GBS can generate a large number of SNP markers, its application in association mapping and genomics-assisted breeding can be limited by massive amount of missing data when low coverage sequencing is conducted [4, 38]. Biologically, missing SNP calls in GBS datasets can be due to presence-absence variation and/or differential methylation in restriction sites. Technically, genome complexity, low library quality, and sequence coverage [27] are among the major contributors. Library complexity can be reduced by digesting sample DNA with restriction enzymes such as PstI and MspI. A combination of PstI and MspI enzymes has been successfully used for high quality wheat library construction [13]. The sequencing coverage is a function of genome complexity, multiplexing level, and output of a NGS platform [39]. In the current study, we constructed two libraries, library 1 with three plates of samples and library 2 with one plate of samples. The library 1 had six sequencing runs and the library 2 had two, thus each sample in both libraries had the same sequence depth. However, the samples in the library 1 produced more SNPs (14,781, 32,926 and 49,717 SNPs at <20%, <50%, and <80% missing data, respectively) than those from the library 2 (10,630, 23,931 and 26,198 SNPs at <20%, <50%, and <80% missing data, respectively), suggesting that raising multiplexing level and sequencing multiple times can significantly increase SNP number and reduce missing data in comparison with a library at lower multiplex level with the same sequencing depth. The results in this study showed that increasing number of SNPs with <20% missing data was concave up by increasing run number (Fig 1a) while increasing numbers of SNPs with <50% and <80% missing data were concave down (Fig 1b and 1c), suggesting increasing run number can quickly reduce missing data, but slowly increase in total numbers of SNPs.

Typically, two strategies can be used to reduce missing data: increasing sequencing depth or imputing missing data using a reference genome. Increasing sequence depth can be achieved through lowering multiplexing level in a fixed run number or increasing sequencing runs of a highly multiplexed library. Both methods will result in an increase in per-sample cost. As indicated previously, lowering multiplex level may not increase SNP number as expected. Thus, increasing number of sequencing runs can be an option. In this study, the first library was run six times and the number of SNPs was significantly increased especially for the number of SNPs with <20% missing data (Fig 1), indicating that increasing number of runs significantly decreased the number of missing data and therefore increased number of usable SNPs. However, this also increased the per-sample cost significantly. Imputing missing data is an effective approach to minimize missing data without increasing sequencing costs. Imputed data can be very accurate if a high-quality genome reference is available and SNP markers can be accurately aligned on the physical map [27]. However, in the case that genome reference is absent or incomplete, such imputation is challenging. In this study, we compared imputation efficiency and accuracy among four genome references including the barley reference genome and three wheat references (CSSS, W7984 and IWGSC RefSeq v1.0) and found that all the references are useful for ordering GBS-SNPs and can significantly reduce missing data points and provide accurate imputation to leverage the application GBS markers in wheat [4] although imputation efficiency varied with completeness of genome references.

Before imputation, we were able to bioinformatically map 15,172 (~23.14% of total SNPs called), 36,278 (~55.34%), 56,125 (~85.61%) and 62,241 (94.94%) SNPs out of 65,560 SNPs with <80% missing data to the barley reference genome (Table 1), CSSS (Table 2), W7984 (Table 3) and IWGSC RefSeq v1.0 (Table 4) references, respectively. That the most SNPs were mapped to IWGSC RefSeq v1.0 among the four reference genomes may be due to that the newest reference has the best genome coverage. For all four references, the highest number of SNPs were mapped to the B genome with 47.77%, 51.43%, 48.91% and 47.87% of totally mapped SNPs to the barley reference genome, CSSS, W7984 and IWGSC RefSeq v1.0 references, respectively, and the lowest number of SNPs were mapped to the D genome with 18.19%, 14.26%, 14.24% and 13.38% of totally mapped SNPs, respectively (Tables 1, 2, 3 and 4). The number of SNPs mapped to the A and B genomes were 1.8 and 3.6 times higher than those mapped on the D genome, whereas the differences in the numbers of mapped SNPs between the D genome and the A and B genomes from previous reports were even higher (about two-fold higher) than observed in this study [40–42], reflecting the most recent polyploidy bottleneck of hexaploid wheat [2, 43]. During the evolution of modern bread wheat, there has been extensive gene flow between hexaploid T. aestivum and tetraploid emmer wheat (AABB), while gene flow between the hexaploid and Ae. tauschii (DD) might have not occurred [44–47], which might explain higher polymorphism on the A and B genomes than on the D genome [31, 48]. The greatest number of SNPs was mapped to the chromosome 3B and the least number of SNPs was mapped to the chromosome 4D, which agrees with Edae et al. [4] using W7984 and CSSS assemblies.

The number of transition-type SNPs (68.63%) with majority of A/G (32.27%) and C/T (28.36%) transition was much higher than transversion-type SNPs with an average Ts/Tv SNP ratio of 2.19 (Table 1), which agrees with several previous studies on hexaploid wheat [31, 49–53] and barley [54–56] where Ts/Tv SNP ratios was from 1.59 to 2.12. A/G and C/T types of mutations are usually due to methylation of cytosine that can be easily achieved from spontaneous deamination and transition to a thymine [57]. In this study, the Ts/Tv SNP ratios from the A and B genomes were significantly higher than that from the D genome, which most likely due to high methylation occurred in the A and B genomes during the two rounds of polyploidization [49], whereas D genome has been through only one round of such polyploidization during the hexaploid wheat evolution [58].

Among the four reference genomes, IWGSC RefSeq v1.0 has the best wheat genome sequence coverage and assembly quality therefore it is expected that the IWGSC RefSeq v1.0 generated the most imputed data points (1,776,773) from the five sequencing runs and the barley reference genome (464,174) generated the least (Table 6). However, W7984 assembly had the highest imputation accuracy. An obvious relationship was not observed between imputation accuracy and chromosome size and between imputation accuracy and number of missing data per chromosome. The percentages of imputed SNPs were much higher in the A (~38.69%) and B (~48.03%) genomes than that in the D genome (~13.28). The numbers of imputed data points in the A and B chromosomes were much higher than that from the D chromosomes, but imputation accuracy of the SNPs in the D chromosomes was the highest (Table 6). This could be owing to the low polymorphism level that resulted in a relatively low number of imputed SNPs in the D genome [59]. A high LD level on the D chromosomes may also contribute to its higher imputation accuracy than that in the A and B genomes [43, 60–63] as observed in several other studies [36, 64–66].

A high positive correlation was observed between imputation accuracy and allele frequency. Based on different references and runs, the greatest number of imputed data points was observed in allele frequencies of 0.55 to 0.95 with imputation accuracy from 88 to 99% (Fig 4a, 4b and 4c). Although the numbers of imputed data points using IWGSC RefSeq v1.0 were higher than those using other three references in different allele frequencies, especially after six runs, the imputation accuracy using IWGSC RefSeq v1.0 was much lower than other references in the lower allele frequency. However, imputation accuracy for 75% of missing data were similar among four genome references, and only the missing data with lower allele frequencies showed difference in imputation accuracy among references. SNPs with a low minor allele frequency (MAF < 10%) was reported to have significantly lower power to detect true trait-marker association [67], thus, they were removed in many GWAS [68]. Since IWGSC RefSeq v1.0 imputed the most missing data points, it can be used to impute missing data if SNPs with MAF < 10% was removed in a study. However, in the cases where rare variants with MAF <10% might play a more important role than common SNPs with a MAF >10% [69], imputation using W7984 may improve imputation accuracy.

Conclusions

Imputation using genome references is an effective tool to fill up massive missing genotypic data generated from GBS. Among the four references (the barley reference genome and wheat reference genomes of CSSS, W7984 and IWGSC RefSeq v1.0) used for imputation, IWGSC RefSeq v1.0 imputed the greatest number of missing data points with adequate imputation accuracy, especially for those alleles with high frequencies. For those alleles with low allele frequency, W7984 assembly showed the best imputation accuracy although imputed number of missing data points was slightly lower than the IWGSC RefSeq v1.0 reference. Therefore, they both can be used as reference genomes to impute missing GBS data in wheat breeding and genetic research.

Acknowledgments

Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the USDA. USDA is an equal opportunity provider and employer.

Data Availability

All relevant data are within the paper and SNP data files are deposited in Figshare. SNP data files for 5, 6, 7, and 8 runs can be each accessed at https://figshare.com/s/92cd9f23d9dddb224304; https://figshare.com/s/b402031d86d969a7cb0f; https://figshare.com/s/83b4abe205a8a5409017; https://figshare.com/s/253e39735d226b78b6bd. All the original GBS reads will be available upon request. Please send request to Guihua.Bai@ARS.USDA.GOV, Hard Winter Wheat Genetics Research, ARS-USDA, Manhattan, KS 66506, USA.

Funding Statement

This project is partly funded by US Wheat and Barley Scab Initiative and the National Research Initiative Competitive Grants 2017-67007-25939 to GB and 2017-67007-25929 to GB from the National Institute of Food and Agriculture, U.S. Department of Agriculture. Funders had no role in the study design, data collection and analysis, decision to publication, or manuscript preparation.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

All relevant data are within the paper and SNP data files are deposited in Figshare. SNP data files for 5, 6, 7, and 8 runs can be each accessed at https://figshare.com/s/92cd9f23d9dddb224304; https://figshare.com/s/b402031d86d969a7cb0f; https://figshare.com/s/83b4abe205a8a5409017; https://figshare.com/s/253e39735d226b78b6bd. All the original GBS reads will be available upon request. Please send request to Guihua.Bai@ARS.USDA.GOV, Hard Winter Wheat Genetics Research, ARS-USDA, Manhattan, KS 66506, USA.

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