Details of data processing

Sequence alignment and variation identification

The assembly release version 7.0 of genomic pseudomolecules of japonica cv. Nipponbare was downloaded from Michigan State University and used as the reference genome.  Reads of all varieties were aligned to the pseudomolecules using the software BWA v0.7.12-r1039.  SNPs/INDELs were identified using GATK v3.3-0-g37228af. We first map the reads to the reference with BWA mem and then Generate GVCF per-sample with HaplotypeCaller (with parameters of -T HaplotypeCaller --emitRefConfidence GVCF --variant_index_type LINEAR --variant_index_parameter 128000, mapping quality ≥20 were used), after creating the GVCF file, we use  CombineGVCFs to generate VCF file. The variations identified by GATK were further filtered: the allele count in VCF file must >10, depth must >=50. 

Imputing missing genotype using an LD-KNN algorithm

After obtaining raw genotype calls from GATK, 33.4% of genotypes were missing due to low-coverage sequencing. We then performed imputation using an in-house modified k nearest neighbor algorithm.  In imputation, heterozygous calls were set to missing and we split the variations to 4058 bins (each 5000variations) for imputation.  For these missing genotype in high coverage region,  we set it to be 'DEL'. After imputation, we got an overall missing data rate reduced to 2.32% and overall DEL rate to 9.57%. The detailed precision rate and missing rate of each bin after imputation are shown below: 

 

      

Figure 1. Precision rate statistic after imputation

 

   

Figure 2. Missing rate  statistic after imputation

 

Data evaluation

To estimate the accuracy of imputed genotype, we genotyped 50 accessions using Illumina Infinium array RiceSNP50. There are 41709 high-quality SNP markers in the array and 41709 SNPs covered by the RiceVarMap v2 well-imputed SNPs. The accuracy of Infinium array is proved. Thus, the concordance of genotypes using array hybridization and sequencing can be used to estimate the accuracy of raw genotypes from direct sequencing and after imputation.    The results suggested an accuracy of 99.9% for raw genotypes and 99.8% for genotypes after imputation (Table 1).

Table 1. Concordance between genotyping results of array hybridization and sequencing on 41709 SNPs.
ID Raw Prop. Concordance Raw Num. Concordance Raw Num. Difference Imputed Prop. Concordance Imputed Num. Concordance  Imputed Num.  Difference
C146 0.9999443 35902 2 0.9999519 41606 2
C048 0.9998581 35222 5 0.999344 41132 27
C056 0.9997714 34987 8 0.9994436 41314 23
C149 0.99974 34606 9 0.9993461 41265 27
C052 0.9997377 34301 9 0.9994166 41117 24
C026 0.9997359 34075 9 0.9994922 41333 21
C016 0.9997203 35738 10 0.9990028 41073 41
C063 0.9997072 34145 10 0.9989585 41244

43

C116 0.9996554 34807 12 0.9993704 41273 26
C101 0.9996047 32875 13 0.996908 40624 126
C070 0.9995361 32870 14 0.999153 40613 127
C087 0.9995227 33504 16 0.9981672 40846 75
C079 0.9995153 32992 16 0.997913 40643 85
C130 0.9994939 33573 17 0.9975975 40692 98
C028 0.9994919 35407 18 0.9992482 41203 31
C082 0.9994542 32960 18 0.9975939 40632 98
C152 0.999408 33765 20 0.9983384 40857 68
C067 0.9994021 33433 20 0.9981647 40791 75
C144 0.9993756 33613 21 0.9974241 40658 105
C071 0.999375 33578 21 0.9982623 40787 71
C106 0.9993459 33612 22 0.9972781 40670 111
C035 0.9993267 34136 23 0.9973952 40588 106
C023 0.9993248 34041 23 0.9970509 40570 120
W249 0.999283 34841 25 0.9989298 41068

44

C034 0.9992642 33952 25 0.9973746 40649 107
C010 0.9992524 33414 25 0.9983731 40501 66
C003 0.999236 34006 26 0.9978706 40301 86
W006 0.9992323 35145 27 0.9985201 40484 60
C029 0.9992306 33766 26 0.9984585 40806 63
C074 0.9992114 32942 26 0.9971664 40469 115
C083 0.9992 32475 26 0.9964222 40104 144
C123 0.9991879 33220 27 0.9981806 40598 74
C111 0.9991183 33994 30 0.9970075 40647 122
C137 0.9991066 33548 30 0.9975201 40626 101
C014 0.9990678 33222 31 0.9981316 40600 76
C004 0.9989958 34818 35 0.9970967 40525 118
C119 0.9988537 33112 38 0.9978975 40818 86
W225 0.9988271 33211 39 0.9976852 40514 94
C153 0.9988045 32582 39 0.9982033 40557 73
C005 0.9987718 33342 41 0.9979786 40484 82
C051 0.9987332 32323 41 0.9974401 40523 104
C002 0.9986189 32538 45 0.9973611 39685 105
W252 0.9985072 32776 49 0.9970285 39592 118
W251 0.998462 33758 52 0.9975101 40463 101
C134 0.9975806 34636 84 0.9961547 40672 157
C145 0.9972605 29122 80 0.9946713 39759 213
C059 0.997162 28460 81 0.9944998 39417 218
C151 0.99716 28440 81 0.9946527 39434 212
Total 0.999129 1675414 1415 0.997853 2032184 4413

 

The genetic structure and diversity of the rice germplasms

The population structure of the 4,726 accessions was inferred using ADMIXTURE based on 210,521 SNPs which randomly selected from the genome (per 5Kb randomly pick out 3 SNPs, MAF >=0.01). The parameter of the number of ancient clusters K was set from 2 to 7 to obtain different inferences. Each accession was classified based on its maximum subpopulation component. Accessions with the maximum subpopulation component value differing from the second value less than 0.4 were classified as intermediate. 

When K=2, accessions were divided into indica and japonica varietal groups. 

At K=3, the aus cluster (Aus) appeared within the indica varietal group.

At K=4, the indica were further divided into two sub groups (indica I and indica III, also denote as IndI and IndIII), indica accessions with similar components of IndI and IndIII (<0.4) were classified as Indica Intermediate. 

At K=5, the indI were further divided into two sub groups (indica I and indica II, also denote as IndI and IndII), indica accessions with similar components of IndI and IndII (<0.4) were classified as Indica Intermediate. 

At K=6, japonica was divided into two sub groups, corresponding to tropical japonica (TrJ) and temperate japonica (TeJ), japonica accessions with similar components of TeJ and TrJ (<0.4) were classified as Japonica Intermediate. 

At K=7, an independent group (VI) emerged, which is an intermediate group between indica and japonica. Only fourteen accessions belonged to VI and we found that nine of them were with mutated fragrance gene fgr, which suggested that VI is corresponding to Group V/Aromatic group reported in other studies (Glaszmann et al. Theor Appl Genet, 1987, 74: 21-30; 1. Garris et al. Genetics, 2005, 169: 1631-1638). 

The set of 4729 rice accessions sequenced in this study was accordingly classified into 595 IndI, 465 IndII, 913 IndIII, 786 indica intermediate, 767 TeJ, 504 TrJ, 241 japonica intermediate, 269 Aus, 96 VI, and 90 intermediate, The details of classification and values of subpopulation component can be queried in Cultivar Information page. 

 

Figure 3. Neighbor-joining tree of 4729 accessions constructed from matching the distance of 210,521 even-distributed and randomly selected SNPs. Different subpopulations, indica I (IndI), indica II (IndII), Indica III (IndIII), Aus, temperate japonica (TeJ) and tropical japonica (TrJ) are shown in different color and the numbers of accessions in each subpopulation are marked. In this figure, the number of accessions of Intermediate contains VI group (denotes in pink).

Figure 4. The distribution of the estimated subpopulation components for each accession analyzing by ADMIXTURE under different assumptions of ancient clusters K = 2 to 7 for 4729 accessions.