supplementary information for - nature research · 2010-11-01 · wang, lu wang, liuwei deng,...

Supplementary Information for

Genome-wide association studies of 14 agronomic traits

in rice landraces

Xuehui Huang, Xinghua Wei, Tao Sang, Qiang Zhao, Qi Feng, Yan Zhao, Canyang Li,

Chuanrang Zhu, Tingting Lu, Zhiwu Zhang, Meng Li, Danlin Fan, Yunli Guo, Ahong

Wang, Lu Wang, Liuwei Deng, Wenjun Li, Yiqi Lu, Qijun Weng, Kunyan Liu, Tao Huang,

Taoying Zhou, Yufeng Jing, Wei Li, Zhang Lin, Edward S. Buckler, Qian Qian, Qi-Fa

Zhang, Jiayang Li & Bin Han

This PDF file includes:

Supplementary Note

Supplementary Tables 2-4 and 7-8

Supplementary Figures 1-25

(Supplementary Tables 1, 5, 6, and 9 are provided in the separate Excel files)

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Nature Genetics: doi:10.1038/ng.695

Supplementary Note SNP identification and annotation & Estimation of specificity and

sensitivity & Data imputation algorithm & Association signals on known loci

Supplementary Table 1 The list of 517 landrace accessions sampled in this study.

Supplementary Table 2 The accuracy of the low-coverage consensus sequences

estimated against four sets of sequencing data.

Supplementary Table 3 Specificity and missing data rate of the genotype dataset before

and after missing genotypes are inferred.

Supplementary Table 4 The detailed list of predicted effects of annotated SNPs.

Supplementary Table 5 The list of genes over-represented for large-effect changes.

Supplementary Table 6 The list of genes that contained large-effect

complete-differentiation SNPs.

Supplementary Table 7 Genome-wide significant association signals of fourteen

agronomic traits using the simple model.

Supplementary Table 8 Genome-wide significant association signals of fourteen

agronomic traits from both the simple model and the compressed MLM.

Supplementary Table 9 The genotype dataset of indica landraces on the causal

polymorphic sites of three known genes.

Supplementary Figure 1 Geographic origins of 517 Chinese rice landraces sampled in

this study.

Supplementary Figure 2 Number and accuracy of SNPs called for 517 landraces plus 3

varieties as internal accuracy controls under various stringency rules.

Supplementary Figure 3 SNP density and distribution across the genome.

Supplementary Figure 4 Functional and evolutionary analyses of SNPs.

Supplementary Figure 5 Divergence and geographic origins of 517 rice landraces of

China.

Supplementary Figure 6 Sequence diversity and population genetic differentiation along

chromosomes in indica.

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Supplementary Figure 7 Sequence diversity and population genetic differentiation along

chromosomes in japonica.

Supplementary Figure 8 LD decay rate across the genome.

Supplementary Figure 9 An example of missing genotype imputation.

Supplementary Figure 10 Missing data rate and sequencing accuracy as the function of

sequencing coverage.

Supplementary Figure 11 Frequency distribution of variation of fourteen traits in 373

indica landraces.

Supplementary Figure 12 Genome-wide association analysis of tiller number.

Supplementary Figure 13 Genome-wide association analysis of flag leaf angle.

Supplementary Figure 14 Genome-wide association analysis of grain length.

Supplementary Figure 15 Genome-wide association analysis of grain weight.

Supplementary Figure 16 Genome-wide association analysis of spikelet number per

panicle.

Supplementary Figure 17 Genome-wide association analysis of gelatinization

temperature.

Supplementary Figure 18 Genome-wide association analysis of amylose content.

Supplementary Figure 19 Genome-wide association analysis of apiculus color.

Supplementary Figure 20 Genome-wide association analysis of pericarp color.

Supplementary Figure 21 Genome-wide association analysis of hull color.

Supplementary Figure 22 Genome-wide association analysis of drought tolerance.

Supplementary Figure 23 Genome-wide association analysis of degree of seed

shattering.

Supplementary Figure 24 Regions of the genome showing association signals around

known genes controlling heading date.

Supplementary Figure 25 Steps of missing genotype imputation.

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Supplementary Note SNP identification and annotation

We integrated single–base pair genotypes of 520 individuals to screen for SNPs across

the genome. Discrepancies with rice reference genome were called as candidate SNPs.

Unreliable sites were then filtered according to the following criteria: 1) candidate SNP

loci must be bi-allelic; 2) candidate SNP loci must be more than 10 bp away from each

other; and 3) all the singleton SNPs were excluded. Sites passing these criteria were

retained and called as common SNPs.

SNPs in coding regions were called coding SNPs on the basis of the gene models in

the Rice Annotation Projects Database (release 2) and only gene models with full-length

cDNAs or ESTs support were used (http://rapdb.dna.affrc.go.jp/). The coding SNPs were

then annotated to be synonymous or non-synonymous SNPs, which was used to calculate

the nonsynonymous-to-synonymous ratio for each gene. SNPs with large-effect changes

were annotated and partitioned to be SNPs that introduced stop codons, SNPs that disrupt

stop codons, SNPs that disrupt initiation codons and SNPs that disrupt splice sites.

We used three steps to find gene families under relaxed selection: 1) Genes with the

same Pfam domain were grouped to be a gene family. We then calculated the numbers of

coding SNPs within genes of each family. Only those families with 300 or more SNPs

that permitted sufficient power of statistical tests are left. 2) To avoid the impact of

potential pseudogenes, genes with large-effect SNPs were removed from the family

before further statistic test. 3) A chi-square test was then used for each family. The

observed class is the nonsynonymous-to-synonymous ratio of each family, while the

expected one is the nonsynonymous-to-synonymous ratio of total families.

Estimation of specificity and sensitivity

We used four sets of sequencing data to assess genotyping accuracy. For indica cv.

Guangluai-4, the 21 Mb BAC-based sequences36 and whole-genome sequences generated

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from 20-fold coverage Illumina sequencing were used. The BAC-based sequences of

Guangluai-4 were composed of 273 BACs covering 65.7% of chromosome 4, of which

the accession numbers were listed at our website

(http://www.ncgr.ac.cn/english/edatabasei.htm). The 20-fold Illumina sequences of

Guangluai-4 are available in the EBI European Nucleotide Archive (ftp://ftp.era.ebi.ac.uk)

with accession number ERP000235. For japonica, we used the BAC-based sequences of

japonica cv. Nipponbare8 and genome sequences of japonica cv. Nongken-58 generated

from 14-fold coverage Illumina GA sequencing. The BAC-based sequences of japonica

cv. Nipponbare were the rice reference genome (IRGSP 4.0,

http://rgp.dna.affrc.go.jp/IRGSP/Build4/build4.html). The 14-fold Illumina sequences of

Nongken-58 are available in the EBI European Nucleotide Archive (ftp://ftp.era.ebi.ac.uk)

with accession number ERP000236.

Genotype calls of consensus sequences at positions with reads covered were then

compared with the above four sets of sequencing data respectively. The number of total

sites for comparison and the number of concordant sites were calculated, which gave the

estimates of specificity (also called “accuracy” in the RESULTS; Supplementary Table

2). Referenced to the four independent standards, the estimated specificities were all

above 99.9%, indicating that the quality control procedures were effective.

The estimate of sensitivity of the genotype calling (also referred to as "recall rate" in

the RESULTS) was based on BAC-based sequences of indica cv. Guangluai-4. Direct

comparison of the BAC-based sequences of Guangluai-4 with their corresponding

regions of the reference genome resulted in a total of 103,965 SNPs, of which 20,888

SNPs were detected by one-fold coverage Illumina sequencing of Guangluai-4, giving a

recall rate of 20.1%. Data imputation algorithm Beginning with the first window at the top of the chromosome, the local haplotype

similarity is exploited. A similarity score is calculated between all pairs of individuals in

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ftp://ftp.era.ebi.ac.uk/

http://rgp.dna.affrc.go.jp/IRGSP/Build4/build4.html

ftp://ftp.era.ebi.ac.uk/

the studied population with the size of N individuals:

, 1

( )w

ij ijz

S s=

=∑ z

where sij (z) is the similarity score between individuals i and j at the z-th SNP. The

overall similarity score for the window is the sum of similarity scores of all SNPs in the

window of size w (Supplementary Fig. 25a).

At each SNP, the major and minor alleles are denoted by “0” and “1” respectively,

while the missing genotype is denoted by “?”. The single SNP similarity scores are: dij =

1 if genotypes of two individuals are identical (0 vs. 0 or 1 vs. 1); dij = 0.5 if one or both

genotypes are missing (0 vs. ?, 1 vs. ?, or ? vs. ?); dij = p if genotypes are different (0 vs.

1); p is allowed to vary and normally takes negative values (Supplementary Fig. 25a).

This is a penalty set to avoid recognizing different haplotypes as the nearest neighbors

while the difference might be caused by sequencing errors.

For the population with N individuals, a matrix of Sij is obtained (Supplementary

Fig. 25b). To infer the missing genotype at a SNP site of individual i, the nearest

neighbors of this individual are identified. The nearest neighbors of individual i are

defined as those individuals with the highest to k-th highest Sij scores (Supplementary

Fig. 25c). For the nearest neighbors, the major allele frequency at a SNP is calculated. At

a SNP site where the genotype is missing from individual i, the genotype of this

individual is determined to be the same as the major allele of the nearest neighbors if the

major allele frequency is higher than a threshold, f. This filling procedure is conducted for

the first SNP site in a window for all individuals with missing genotype and continues as

the window slides along the chromosome. All SNP sites of the last window of a

chromosome are filled at once.

One step is to determine the values of the four variables, w, p, k, and f, for the optimal

results of missing data imputation. Because testing a large number of data points of these

variables for individual windows is computationally impractical, we tested three values of

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each variable and obtained the optimized value of each variable for the entire genome.

Based on the genomic and populational properties of indica landraces and through

extensive cross validations, the following testing values were set for these variables: w

(50, 65, 80), p (-3, -5, -7), k (3, 5, 7), and f (0.7, 0.75, 0.8), which gave 81 combination of

values to test for the 373 indica landraces plus the internal control, Guangluai-4.

To optimize the variables for the indica landraces, 1200 chromosomal regions each

containing 300 consecutive SNPs were randomly selected from the genome for cross

validation. In these regions, we randomly masked 1% genotypes to make them missing

data. Two criteria were adopted for evaluating the performance of missing data

imputation. One is the imputation accuracy, A, defined as the percentage of correctly

inferred genotype in the 1% masked genotypes. The other is the filling rate, F, defined as

the percentage of missing genotypes that are inferred and filled. For each of the 81

combinations of the four variables, A and F were calculated for each of the 1200

chromosome regions. The mean of these 1200 A and F values are calculated. A total of 81

means for A and for F were obtained and plotted (Supplementary Fig. 25d). According

to the distribution of A and F, the combination of w = 80, p = -7, k = 5, and f = 0.7 was

judged to be the best because it yielded the highest F = 98.1% and the nearly highest A =

98.0%. After the missing genotypes were inferred, the imputation accuracy was

calculated by comparing inferred genotypes of Guangluai-4 with its accurate genome

sequences.

Association signals on known loci Among the association signals we identified, several were in the close proximity of

known loci, which were identified previously via mutants or crosses. The strongest signal

of apiculus color located within one known loci. The peak SNP was ~20 kb away from

OsC1, the gene previously identified to control the coloration (Fig. 5a)23. For pericarp

color, the strongest signal was ~26 kb away from Rc, the gene known to underlie red

pericarp of rice (Fig. 5b) 24. For hull color, the strongest signal was near the ibf locus, but

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the causal gene has not been identified and confirmed up to date25.

For grain quality, we studied two traits related to cooking and eating properties,

including gelatinization temperature and amylose content. The strongest association

signal was detected for gelatinization temperature, which was ~21 kb away from ALK,

the previously identified starch synthase modifying cooking property (Fig. 5c)26. For

amylose content, the strongest one has the peak SNP ~1 kb away from waxy, the starch

synthase known to control amylose content in rice (Fig. 5d) 27,28.

Grain width and length are two major characteristics for grain size. For grain width,

the peak SNP with the strongest signal was ~6 kb away from qSW5, the gene previously

identified for grain width variation (Fig. 5e)29. For grain length, one strong signals was

<1 kb away from GS3, the gene known to control grain length of rice Fig. 5f)30.

Primers for amplification and sequencing across the regions of the causal

polymorphisms of there known genes were designed using Primer 3 (v 0.2). The

sequences of the PCR primers were provided in Supplementary Table 9.

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Supplementary Table 2 The accuracy of the low-coverage consensus sequences estimated against four sets of sequencing data.

Sequences used for validation Number of total bases

Number of concordance Accuracy

Guangluai-4 20x Illumina data 111,725,302 111,678,146 99.96%Guangluai-4 BAC sequences 7,324,676 7,318,437 99.91%Nongken-58 14x Illumina data 137,144,714 137,108,004 99.97%Nipponbare reference genome 104,871,563 104,837,452 99.97%

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Supplementary Table 3 Specificity and missing data rate of the genotype dataset before and after missing genotypes are inferred. Type Before imputing After imputing Specificity Guangluai-4 20x Illumina data 98.7% 98.7%

Guangluai-4 BAC sequences 98.4% 98.5% Nongken-58 14x Illumina data 99.7% 98.6% Nipponbare reference genome 99.6% 98.7% Missing data rate The entire set of 517 landraces 61.7% 2.9% The subset of 373 indica landraces 61.4% 2.6% The subset of 131 japonica landraces 62.5% 3.8%

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Supplementary Table 4 The detailed list of predicted effects of annotated SNPs. Type of predicted effects Number of SNPs Number of Genes Large-effect 3,625 3,039 SNPs that introduced stop codons 2,005 1,709 SNPs that disrupt stop codons 200 200 SNPs that disrupt initiation codons 374 374 SNPs that disrupt splice sites 1,046 994 Synonymous 74,849 21,019 Non-Synonymous 92,665 22,342

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Supplementary Table 7 Genome-wide significant association signals of fourteen agronomic traits using the simple model.

Trait Chr. Position (IRGSP4)

Major allele

Minor allele

Minor allele freq

-log10P (Simple model)

Amylose content 3 18,705,982 C T 0.06 16.81 Amylose content 6 1,757,040 a C G 0.14 67.75 Amylose content 6 6,189,558 A T 0.11 34.76 Amylose content 6 6,709,537 C T 0.19 35.45 Amylose content 12 10,993,688 G T 0.06 15.93 Apiculus Color 6 5,335,519 a A G 0.33 48.44Apiculus Color 6 7,681,502 G A 0.32 17.31Apiculus Color 12 460,120 T C 0.22 9.92 Drought tolerance 1 5,536,395 G T 0.11 9.97 Drought tolerance 2 1,489,158 T C 0.12 9.32 Drought tolerance 5 2,275,357 A C 0.06 14.24 Drought tolerance 6 28,243,628 C T 0.09 11.98 Drought tolerance 11 21,161,361 G C 0.08 15.28 Gelatinization Temperature 2 31,298,733 T C 0.24 8.08 Gelatinization Temperature 5 28,906,320 A T 0.47 8.48 Gelatinization Temperature 6 6,726,587 a C T 0.19 14.68 Gelatinization Temperature 8 19,334,399 C A 0.27 8.33 Gelatinization Temperature 11 22,564,279 A C 0.24 9.01 Grain length 1 5,966,086 C T 0.08 12.31 Grain length 3 17,379,260 a A C 0.07 17.64 Grain length 3 17,637,475 C A 0.08 18.10 Grain length 3 23,349,781 A C 0.13 14.47 Grain length 4 1,135,241 T G 0.11 11.60 Spikelet number 1 26,933,074 A G 0.36 8.53 Spikelet number 3 436,658 G A 0.10 9.08 Grain weight 1 23,687,541 A G 0.23 13.15 Grain weight 4 3,580,191 A C 0.21 12.66 Grain weight 7 15,905,023 G A 0.11 13.06 Grain weight 8 6,288,077 T G 0.09 11.85 Grain weight 8 20,985,668 T C 0.18 11.99

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Grain width 5 4,942,020 A T 0.15 16.14 Grain width 5 5,341,575 a G A 0.17 23.38 Grain width 7 14,364,359 C T 0.42 10.98 Grain width 8 6,191,511 A G 0.05 9.07 Grain width 12 13,725,521 T G 0.09 9.69 Heading date 1 23,908,112 T C 0.23 44.14 Heading date 4 4,142,083 T A 0.21 54.45 Heading date 4 17,728,075 A G 0.06 46.56 Heading date 6 28,818,321 A G 0.29 38.66 Heading date 7 24,952,910 C T 0.33 37.49 Hull color 6 10,378,142 T C 0.06 9.81 Hull color 8 20,947,967 T C 0.18 8.39 Hull color 9 7,366,211 T C 0.20 19.77 Leaf angle 1 5,605,379 A T 0.08 11.39 Leaf angle 1 25,367,568 C T 0.22 11.51 Leaf angle 1 35,465,422 G A 0.09 12.52 Leaf angle 4 24,729,628 A G 0.08 8.43 Leaf angle 5 21,842,625 A G 0.40 11.27 Pericarp Color 2 34,824,975 A C 0.07 9.59 Pericarp Color 3 31,599,995 T A 0.18 9.54 Pericarp Color 7 6,127,089 a G T 0.33 60.96Pericarp Color 7 17,543,383 G T 0.23 8.13 Pericarp Color 8 12,483,076 T G 0.21 16.79 Shattering degree 3 31,392,925 A G 0.09 8.54 Shattering degree 5 898,443 C T 0.05 10.24 Shattering degree 8 25,498,378 A T 0.23 8.98 Shattering degree 11 2,231,172 C T 0.05 9.18 Shattering degree 12 16,521,488 C G 0.05 8.93 Tiller number 1 4,759,534 G A 0.21 8.92 Tiller number 1 6,840,011 T C 0.25 8.51 Tiller number 2 5,541,400 C T 0.12 8.80 Tiller number 2 25,083,473 A T 0.13 8.41 Tiller number 8 25,453,518 T C 0.11 8.31

Chr. Chromosome. a Known loci with identified genes, which are reported in Supplementary Note.

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Supplementary Table 8 Genome-wide significant association signals of fourteen agronomic traits from both the simple model and the compressed MLM.

Trait Chr. Position (IRGSP4)

Major allele

Minor allele

Minor allele freq

-log10P (Simple model)

-log10P (Compressed MLM)

Amylose content 3 18,705,982 C T 0.06 16.81 1.83 Amylose content 6 1,757,040 b C G 0.14 67.75 25.30 (1,770,929 a)Amylose content 6 6,189,558 A T 0.11 34.76 7.52 Amylose content 6 6,709,537 C T 0.19 35.45 11.13 Amylose content 12 10,993,688 G T 0.06 15.93 1.78 Apiculus Color 6 5,335,519 b A G 0.33 48.44 26.25 (5,335,519 a)Apiculus Color 6 7,681,502 G A 0.32 17.31 8.48Apiculus Color 12 460,120 T C 0.22 9.92 1.60 Drought tolerance 1 5,536,395 G T 0.11 9.97 6.39 Drought tolerance 2 1,489,158 T C 0.12 9.32 4.57 Drought tolerance 5 2,275,357 A C 0.06 14.24 7.61 Drought tolerance 6 28,243,628 C T 0.09 11.98 8.47 Drought tolerance 11 21,161,361 G C 0.08 15.28 11.07 Gelatinization Temperature 2 31,298,733 T C 0.24 8.08 1.66 Gelatinization Temperature 5 28,906,320 A T 0.47 8.48 0.73 Gelatinization Temperature 6 6,726,587 b C T 0.19 14.68 8.15(6,726,252 a)Gelatinization Temperature 8 19,334,399 C A 0.27 8.33 2.99

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Gelatinization Temperature 11 22,564,279 A C 0.24 9.01 0.61 Grain length 1 5,966,086 C T 0.08 12.31 0.65 Grain length 3 17,379,260 b A C 0.07 17.64 9.88 (17,371,398 a)Grain length 3 17,637,475 C A 0.08 18.10 10.57 Grain length 3 23,349,781 A C 0.13 14.47 6.48 Grain length 4 1,135,241 T G 0.11 11.60 0.40 Grain length 5 5,343,949 A G 0.20 4.73 6.76 Grain length 11 3,072,370 C T 0.11 1.59 6.43 Spikelet number 1 26,933,074 A G 0.36 8.53 3.77 Spikelet number 3 436,658 G A 0.10 9.08 5.54 Spikelet number 7 18,005,615 C T 0.44 0.86 7.15 Spikelet number 10 5,976,140 C T 0.06 1.85 6.90 Grain weight 1 23,687,541 A G 0.23 13.15 2.17 Grain weight 4 3,580,191 A C 0.21 12.66 1.91 Grain weight 7 15,905,023 G A 0.11 13.06 3.31 Grain weight 8 6,288,077 T G 0.09 11.85 4.26 Grain weight 8 20,985,668 T C 0.18 11.99 4.30 Grain width 5 4,942,020 A T 0.15 16.14 8.58 (4,907,158 a)Grain width 5 5,341,575 b G A 0.17 23.38 17.14Grain width 7 14,364,359 C T 0.42 10.98 3.92 Grain width 8 6,191,511 A G 0.05 9.07 0.84 Grain width 12 13,725,521 T G 0.09 9.69 2.51 Heading date 1 23,908,112 T C 0.23 44.14 1.94 Heading date 2 1,439,288 G A 0.42 2.99 6.41 Heading date 2 30,818,552 G C 0.07 2.87 6.42 Heading date 4 4,142,083 T A 0.21 54.45 0.79

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Heading date 4 17,728,075 A G 0.06 46.56 NAHeading date 4 18,773,995 A T 0.25 41.19 6.52 Heading date 6 11,083,237 G A 0.05 12.91 7.18 Heading date 6 28,818,321 A G 0.29 38.66 NAHeading date 7 24,952,910 C T 0.33 37.49 0.23 Heading date 9 10,738,885 C A 0.06 4.66 9.56 Heading date 11 28,247,391 C T 0.12 7.00 8.38 Heading date 12 18,324,888 G A 0.06 3.88 6.85 Hull color 6 10,378,142 T C 0.06 9.81 6.43 Hull color 8 20,947,967 T C 0.18 8.39 5.36 Hull color 9 7,366,211 T C 0.20 19.77 12.48 Leaf angle 1 5,605,379 A T 0.08 11.39 1.89 Leaf angle 1 25,367,568 C T 0.22 11.51 4.29 Leaf angle 1 35,465,422 G A 0.09 12.52 5.41 Leaf angle 4 24,729,628 A G 0.08 8.43 2.66 Leaf angle 5 21,842,625 A G 0.40 11.27 4.77 Pericarp Color 2 27,066,598 A G 0.24 3.49 8.65 Pericarp Color 2 34,824,975 A C 0.07 9.59 0.66 Pericarp Color 3 31,599,995 T A 0.18 9.54 3.57 Pericarp Color 7 6,127,089 b G T 0.33 60.96 51.68 (6,123,504 a)Pericarp Color 7 17,543,383 G T 0.23 8.13 NAPericarp Color 8 12,483,076 T G 0.21 16.79 10.89Shattering degree 2 25,025,325 C T 0.16 1.80 7.33 Shattering degree 3 31,392,925 A G 0.09 8.54 4.34 Shattering degree 5 898,443 C T 0.05 10.24 6.59 (948,266 a)Shattering degree 8 25,498,378 A T 0.23 8.98 6.05 Shattering degree 10 2,319,249 T G 0.06 3.08 6.66

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Shattering degree 11 2,231,172 C T 0.05 9.18 4.79 Shattering degree 12 16,521,488 C G 0.05 8.93 3.33 Tiller number 1 4,759,534 G A 0.21 8.92 4.11 Tiller number 1 6,840,011 T C 0.25 8.51 2.60 Tiller number 2 5,541,400 C T 0.12 8.80 2.35 Tiller number 2 25,083,473 A T 0.13 8.41 3.87 Tiller number 4 3,760,194 A T 0.20 2.97 6.50 Tiller number 8 25,453,518 T C 0.11 8.31 3.24 Tiller number 9 23,332,559 A G 0.34 2.39 6.82 Tiller number 10 15,239,407 T A 0.10 2.16 6.39

Chr. Chromosome; NA, Not available. a The positions of some peak SNPs from the compressed MLM were slightly different those from the simple model. b Known loci with identified genes, which are reported in Supplementary Note.

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supplementary information for - nature research · 2010-11-01 · wang, lu wang, liuwei deng,...

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