Gains in QTL detection using an ultra-high density SNP map based on population sequencing relative to traditional RFLP/SSR markers.

Abstract

Huge efforts have been invested in the last two decades to dissect the genetic bases of complex traits including yields of many crop plants, through quantitative trait locus (QTL) analyses. However, almost all the studies were based on linkage maps constructed using low-throughput molecular markers, e.g. restriction fragment length polymorphisms (RFLPs) and simple sequence repeats (SSRs), thus are mostly of low density and not able to provide precise and complete information about the numbers and locations of the genes or QTLs controlling the traits. In this study, we constructed an ultra-high density genetic map based on high quality single nucleotide polymorphisms (SNPs) from low-coverage sequences of a recombinant inbred line (RIL) population of rice, generated using new sequencing technology. The quality of the map was assessed by validating the positions of several cloned genes including GS3 and GW5/qSW5, two major QTLs for grain length and grain width respectively, and OsC1, a qualitative trait locus for pigmentation. In all the cases the loci could be precisely resolved to the bins where the genes are located, indicating high quality and accuracy of the map. The SNP map was used to perform QTL analysis for yield and three yield-component traits, number of tillers per plant, number of grains per panicle and grain weight, using data from field trials conducted over years, in comparison to QTL mapping based on RFLPs/SSRs. The SNP map detected more QTLs especially for grain weight, with precise map locations, demonstrating advantages in detecting power and resolution relative to the RFLP/SSR map. Thus this study provided an example for ultra-high density map construction using sequencing technology. Moreover, the results obtained are helpful for understanding the genetic bases of the yield traits and for fine mapping and cloning of QTLs.

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Gains in QTL Detection Using an Ultra-High Density SNP
Map Based on Population Sequencing Relative to
Traditional RFLP/SSR Markers
Huihui Yu, Weibo Xie, Jia Wang, Yongzhong Xing, Caiguo Xu, Xianghua Li, Jinghua Xiao, Qifa Zhang*
National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan, China
Abstract
Huge efforts have been invested in the last two decades to dissect the genetic bases of complex traits including yields of
many crop plants, through quantitative trait locus (QTL) analyses. However, almost all the studies were based on linkage
maps constructed using low-throughput molecular markers, e.g. restriction fragment length polymorphisms (RFLPs) and
simple sequence repeats (SSRs), thus are mostly of low density and not able to provide precise and complete information
about the numbers and locations of the genes or QTLs controlling the traits. In this study, we constructed an ultra-high
density genetic map based on high quality single nucleotide polymorphisms (SNPs) from low-coverage sequences of a
recombinant inbred line (RIL) population of rice, generated using new sequencing technology. The quality of the map was
assessed by validating the positions of several cloned genes including GS3 and GW5/qSW5, two major QTLs for grain length
and grain width respectively, and OsC1, a qualitative trait locus for pigmentation. In all the cases the loci could be precisely
resolved to the bins where the genes are located, indicating high quality and accuracy of the map. The SNP map was used
to perform QTL analysis for yield and three yield-component traits, number of tillers per plant, number of grains per panicle
and grain weight, using data from field trials conducted over years, in comparison to QTL mapping based on RFLPs/SSRs.
The SNP map detected more QTLs especially for grain weight, with precise map locations, demonstrating advantages in
detecting power and resolution relative to the RFLP/SSR map. Thus this study provided an example for ultra-high density
map construction using sequencing technology. Moreover, the results obtained are helpful for understanding the genetic
bases of the yield traits and for fine mapping and cloning of QTLs.
Citation: Yu H, Xie W, Wang J, Xing Y, Xu C, et al. (2011) Gains in QTL Detection Using an Ultra-High Density SNP Map Based on Population Sequencing Relative
to Traditional RFLP/SSR Markers. PLoS ONE 6(3): e17595. doi:10.1371/journal.pone.0017595
Editor: Hiroaki Matsunami, Duke University, United States Of America
Received November 21, 2010; Accepted January 28, 2011; Published March 3, 2011
Copyright: ? 2011 Yu et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This research was supported by grants from the National Program on Key Basic Research Project, National Special Key Project on Functional Genomics
of Major Plants and Animals, and National Natural Science Foundation of China. The funders had no role in study design, data collection and analysis, decision to
publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: qifazh@mail.hzau.edu.cn
Introduction
Natural variations of complex traits are usually controlled by
multiple genetic factors, each of which is regarded as a quantitative
trait locus (QTL). Crop yield is one of the most complex traits.
Grain yield of rice per plant is composed of three components:
number of tillers (panicles) per plant, number of grains per panicle
and grain weight. All these traits are quantitatively inherited and
regulated by multiple genes each having an apparently small effect
that is sensitive to environmental modifications. We have conduc-
ted a series of studies to characterize the genetic and molecular
bases of rice yield using populations derived from a cross between
two elite rice lines, Zhenshan 97 and Minghui 63, the parents of
Shanyou 63, the most widely cultivated hybrid in China [1]. A
large number of QTLs controlling yield traits have been
genetically mapped, and some of them have been cloned. A
limitation associated with the previous studies is that all the
analyses were based on linkage maps using restriction fragment
length polymorphism (RFLP) and simple sequence repeat (SSR)
markers in which many regions were sparsely represented, thus it
is not possible to obtain precise and complete information about
the numbers and locations of the QTLs.
Recent advances in genome research have provided a range of
molecular-marker techniques for constructing high-density genetic
maps. For example, microarray-based genotyping can provide a
large number of markers in parallel [2]. In particular, oligo-
nucleotide microarrays, composed of millions of probes, can detect
thousands of polymorphisms in a single experiment. Samples of
genomic DNA or cRNA are hybridized to microarrays and
differential hybridization intensities indicate polymorphisms in the
corresponding probe sequences between the genotypes, which are
referred to as single feature polymorphisms (SFPs) [3–7]. Recently,
SFPs have been used to detect polymorphisms between varieties,
and also provided high-density genetic markers for studying gene
expression QTLs (eQTLs) [8–10]. However, the recovery of
polymorphisms depends on the probes fixed on the microarrays
which restricts the markers used in the study. Moreover, the
technique for SFP analysis is costly and time consuming if a large
segregating population is genotyped.
The development of new sequencing technologies has made it
practical to use DNA sequencing technology for directly obtaining
single nucleotide polymorphism (SNP) markers for population
genotyping [11–13]. Using the bar-coded multiplexed sequencing
technology and Illumina Genome Analyzer, Huang et al [14]
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performed genomic sequencing of a rice recombinant inbred line
(RIL) population. They adopted a sliding window approach to call
genotypes of RILs, constructed a bin map based on the SNPs
between the two parents and located a QTL of large effect for
plant height in a 100-kb region containing the rice ‘‘green
revolution’’ gene [14]. Xie et al [15] developed a parent-
independent genotyping method to identify SNP markers using
only low-coverage RIL sequences without deep sequencing the
parents.
In this study, we constructed an ultra-high density SNP map of
a well studied rice RIL population using the method of Xie et al
[15]. The quality of the SNP map was assessed using several
cloned genes. We performed QTL analysis of yield and yield-
component traits using the new map in comparison with the
results from the traditional RFLP/SSR map. It was shown that the
ultra-high density SNP map is advantageous in QTL detection
and resolution.
Results
Genotyping RILs with high-density SNPs and
constructing bin map
The 241 RILs derived from the cross between Zhenshan 97
and Minghui 63 were genomic sequenced to obtain a ,0.06-fold
coverage of the rice genome for each RIL. A total of 270,820
high quality SNPs were identified based on the data of the 241
RILs, yielding a genome-wide SNP density about 1 SNP/1.37 kb
(Figure S1). The SNP genotype for each RIL was obtained using
the hidden Markov model (HMM) analysis followed by
imputation [15]. To assess the mapping quality, genotyping data
of the raw SNPs of the 241 RILs were compared with RFLPs and
SSRs previously generated for the same population [16–19]. In
doing so, the sequences of the RFLP/SSR markers were obtained
from Gramene (http://www.gramene.org/) and NCBI (http://
www.ncbi.nlm.nih.gov/), or the probes were sequenced for the
RFLP markers without sequence information. The RFLP/SSR
markers were anchored to the reference genome Nipponbare
(TIGR Rice Genome Pseudomolecules Release version 6.1,
annotated by MSU) [20] by BLAST analysis of the sequences
[21]. Totally 211 polymorphic loci with the physical locations in
agreement with their genetic positions were used as the
framework map, and physical locations of another 9 polymorphic
loci were calculated based on the physical/genetic locations of the
flanking markers, resulting in 220 polymorphic loci in the RLFP/
SSR map (Table S1). RILs with SNP marker data in agreement
with RFLP/SSR data were kept for the subsequent analyses. The
redundant RILs and the ones with unexpected high ratio of
heterozygous genotypes were also excluded. In this way, 210
RILs were obtained as of high quality and used for subsequent
analyses.
Bin maps were constructed for the 210 RILs based on individual
SNPs and adjacent bins with the same genotype were lumped (see
Materials and Methods for details), resulting in a map consisting of
1,619 recombinant bins without missing data (Table S2, Figure 1).
The physical lengths of the bins ranged from 6.2 kb to 7.9 Mb,
with an average of 230 kb and a median of 126 kb. Totally 97.5%
of bins were less than 1 Mb in length, with 13 bins more than
2 Mb, 11 of which were located in centromeric or pericentromeric
regions of the respective chromosomes where recombination was
suppressed (Table 1, Figure S2). The other 2 big bins were in the
regions with very low SNP density but high recombination
frequencies, on chromosomes 2 (Bin 311: 20.6–28.6 Mb) and 9
(Bin1218: 12.2–14.4 Mb) respectively, where RFLP/SSR markers
were disconnected in the map [16].
Using each bin as a marker, a genetic linkage map based on
recombination frequency was constructed, which was 1,625.5 cM in
length,approximately1.0 cMperbin,correspondingto 230 kb (Table
S2), representing a great increase in marker density compared to
8.7 cM between adjacent markers in the RFLP/SSR map [16] and
2.4 cM/bin in the SFP map [9]. The SNP bin map was highly
consistent to maps produced with different genotyping methods of the
same population, in the sense of collinearity and recombination break
points (Figure 2).The sequence-based approachcaptured some double
recombination events that previous RFLP/SSR markers could not
detect. Moreover, the genotype of a bin was usually supported by
several high quality SNPs and thus was highly accurate, compared to
low density RFLP/SSR marker genotyping, for which a single
genotyping error may influence the genotype of a RIL in a large
chromosomal region.
The quality and accuracy of the map
The quality and accuracy of this map for genetic analysis were
evaluated by locating the cloned genes, including OsC1 for apicule
color [22], a qualitative trait, and GS3 for grain length [23–24],
and GW5/qSW5 for grain width [25–26], both were QTLs.
Apicule color controlled by the C gene was already used as a
morphological marker in the previous map [16]. The parent
Zhenshan 97 and 91 of the 210 RILs had purple leaf sheaths,
auricles, stigmas and apiculus, while the other parent Minghui 63
and the remaining RILs had no purple pigmentation in these
tissues. This trait co-segregated completely with the genotypes of
Bin868 on rice chromosome 6 (Figure 3). The bin was 117 kb and
contained the rice homolog of maize C1, OsC1, which belongs to
the group of R2R3-Myb factors and was identified as the
candidate for rice C gene [22].
ThemajorQTLforgrainlengthGS3wasidentified usingthesame
RIL population [27] and has been cloned using map-based cloning
method [23–24]. QTL mapping of grain length using the SNP bin
map with the data obtained in 1998 by Tan et al [27] revealed that
the most significant peak pointed the bin on chromosome 3
containing GS3 (,197 kb) (Figure 4A, B). The same result was
obtained using the phenotype data collected in 2008.
When analyzing QTLs for grain width, we found that the QTL
with largest effect was mapped to the bin of about 123 kb in length
containing the GW5/qSW5 locus on chromosome 5. This was the
case for the data of both 1998 and 2008 (Figure 4C, D).
When the mapping results above were compared with those
obtained using the RFLP/SSR map, it was shown that the
distance between the markers flanking GS3 was more than 10 cM
in that map corresponding to about 6 Mb [27]. The GW5/qSW5
locus was located in a big gap (.30 cM) on chromosome 5 in the
RFLP/SSR map, and the interval of that QTL was more than
10 Mb [27], which also underestimated the QTL effect (Table 2).
Clearly, the SNP bin map constructed is highly accurate and of
high quality for gene mapping and QTL identification.
QTL analysis of rice yield and yield-component traits
In order to investigate the efficiency of this new map for
analyzing complex traits, we performed QTL analysis of rice yield
traits using the SNP bin map in comparison to the RFLP/SSR
map. Phenotype data for yield per plant, number of tillers per
plant, number of grains per panicle and 1000-grain weight were
obtained from Xing et al [16] collected in 1997 (Xing1997) and
1998 (Xing1998), and Hua et al [17–18] collected in 1998
(Hua1998) and 1999 (Hua1999). Totally we had 16 trait values for
each of the 210 RILs (4 traits 64 trials), with which QTLs were
identified using composite interval mapping (CIM) [28] employing
permutation tests to decide the LOD thresholds.
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For the RFLP/SSR genetic map, the LOD thresholds at
P=0.05 ranged from 3.93 to 4.41, with the average LOD value
4.07, for 16 sets of data. Totally, 3 QTLs for yield/plant, 4 QTLs
for tillers/plant, 4 QTLs for grains/panicle and 7 QTLs for 1000-
grain weight were identified by the respective LOD thresholds in
four trials (Table S3). Some of them were identified only in one
trial and others could be recovered in two or more trials (Table 3).
Most of the QTLs were also identified by Xing et al [16] and the
intervals of the flanking markers were also consistent.
With the ultra-high density SNP bin map, the LOD thresholds
at P=0.05 ranged from 4.76 to 5.10, with the average LOD value
4.97, for the 16 data sets. Three QTLs for yield/plant, 4 QTLs for
Figure 1. Recombination bin map constructed using high quality SNPs from sequencing genotyping of the RIL population. (A) Whole
map of 1,619 recombination bins for the 210 RILs. Chromosomes are separated by vertical gray lines. (B) The map of the first 50 bins on chromosome
1 for the first 20 RILs. The vertical gray lines indicate the recombination breakpoints. A region between two vertical lines across all RILs is recognized
as a recombination bin. Physical positions are based on rice TIGR6.1. Red, Zhenshan 97 genotype; Blue, Minghui 63 genotype.
doi:10.1371/journal.pone.0017595.g001
Table 1. Distribution of size ranges of recombination bins in
the ultra-high density SNP map constructed using RILs of the
Zhenshan 97/Minghui 63 cross based on population
sequencing.
Size range
, ,0.1
Mb
0.1–0.5
Mb
0.5–1.0
Mb
1.0–2.0
Mb
. .2 MbTotal
Number
of bins
677 79011227 131,619
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tillers/plant, 4 QTLs for grains/panicle and 11 QTLs for 1000-
grain weight were identified above the LOD thresholds in four
trials (Figure 5, Table S4). When the results obtained with the two
maps were compared, it was shown that the numbers of QTLs
above the thresholds were similar for the first three traits, but the
SNP bin map identified a greater number of QTLs for 1000-grain
weigh than did the RFLP/SSR map (Table 3).
We further presented details of the QTLs for number of grains
per panicle and grain weight as QTLs detected for these two traits
were more repeatable (Table 4). Three QTLs for grains/panicle,
located on chromosomes 1, 3 and 7, respectively, were detected in
at least two trails using the SNP bin map. One of them, gn7 (Ghd7),
has been cloned [29]. The QTL with apparently the largest effect,
gn3, in which the allele from Zhenshan 97 increased the number of
grains per panicle, was recovered in all the four trails. The gn3
region spanned a genetic distance of about 7 cM, corresponding to
a physical distance of about 8 Mb, locating in the centromeric
region of chromosome 3 (16–24 Mb) (Figure 6A). Analysis using
the SNP bin map detected several peaks of similar heights on the
QTL LOD curves in the gn3 region, indicating the likelihood that
several linked loci with similar small effects contributed to the
phenotype variation in the population (Figure 6A). However, the
RFLP/SSR map could only reveal a single peak (Figure 6B).
For 1000-grain weight, 6 QTLs were identified in at least two
trials by using SNP bin map, distributed on chromosomes 1, 3, 5
and 9 respectively. The most significant two QTLs, kgw3a (GS3)
and kgw5 (GW5/qSW5), have been cloned. At kgw3a, the allele
from Minghui 63 increased the grain weight, and conversely at
kgw5 the allele from Zhenshan 97 had positive effect (Table 4).
Using the new SNP bin map, kgw5 could be accurately limited into
a 123-kb region containing GW5/qSW5, and kgw3a was mapped to
a region of 1.0–1.5 Mb containing GS3. However, using the
RFLP/SSR map, kgw5 was located to a 4.6–Mb interval. The
flanking markers of kgw3a were C1087-RZ403 or RZ403-R19 and
Figure 2. Comparative genotyping of R001 on chromosome 1 with different markers. (A) RFLPs and SSRs. (B) Microarray-based SFPs. (C)
Bin map based on SNPs constructed in this study. All positions are transformed to physical positions according to rice TIGR6.1.
doi:10.1371/journal.pone.0017595.g002
Figure 3. Cosegregation analysis of the trait values of apicule color and genotypes of the recombination bins. The x-axis shows the
positions of the bins distributed on rice 12 chromosomes. Chromosomes are separated by the vertical gray lines. The y-axis indicates the number of
RILs that the apicule color cosegregating with the genotypes at each bin. The peak is at Bin868 on chromosome 6, showing complete cosegregation
for all the 210 RILs.
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the closest marker was RZ403 according to the results of the four
trials (Table S3), but in fact GS3 (at 16.7 Mb on chromosome 3) is
located in the interval of G144-C1087 (15.3–21.1 Mb). Using the
SNP bin map, 2 QTLs were detected on chromosome 1 in at least
three trails, while using RFLP/SSR map, only one QTL was
identified on chromosome 1.
Among the four traits, the number of QTLs resolved for grain
weight was the largest, using both the SNP bin map and RFLP/
SSR map, although the numbers differed with the maps. Grain
weight is determined by grain size and grain plumpness, and the
former is specified by its three dimensions, length, width and
thickness. We further analyzed QTLs for grain length and grain
width for the data of 1998 and 2008. Totally, 4 QTLs for grain
length (3 were repeatable) and 3 QTLs for grain width (2 repea-
table) were identified above the LOD thresholds at P=0.05 using
the SNP bin map (Figure 7, Table 5). The most significant QTL
for grain length, gl3a, was the same as GS3 and the QTL for grain
weight kgw3a. It contributed greater to grain length than to grain
weight. The most significant QTL for grain width was gw5a, which
was the same as GW5/qSW5 as well as kgw5 for grain weight.
While using the RFLP/SSR map, only 3 QTLs for grain length
and 2 QTLs for grain width were detected above the LOD
Figure 4. Precise locations of GS3 and GW5/qSW5 in the SNP bin map. (A) LOD curves of QTL mapping for grain length on chromosome 3.
Short lines on x-axis indicate the genetic positions of the bins. (B) Physical mapping of GS3. Short lines on x-axis indicate the boundaries of the bins.
The exact position of GS3 is indicated by the black dash line. (C) LOD curves of QTL mapping for grain width on chromosome 5. Short lines on x-axis
indicate the genetic positions of the bins. (D) Physical mapping of GW5/qSW5. Short lines on x-axis indicate the boundaries of the bins. The exact
position of GW5/qSW5 is indicated by the black dash line. Red curves indicate the data from 1998 and blue curves indicate the data from 2008.
doi:10.1371/journal.pone.0017595.g004
Table 2. Comparison of QTL mapping for GS3 for grain length and GW5/qSW5 for grain width in the RIL population of the
Zhenshan 97/Minghui 63 cross using different genetic maps.
Genetic mapGS3 for grain lengthGW5/qSW5 for grain width
Interval Var (%)c
Interval Var (%)
RFLP/SSR genetic map6.0 Mba
57.612.4 Mbb
44.0
Sequence-based SNP bin map197 kb57.1 123 kb52.7
aThe flanking markers were RG393 and C1087 [27].
bThe flanking markers were RG360 and C734 [27].
cPercentage of variation explained.
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thresholds (Table S5), of which only one QTL for grain length
(gl3a) was identified in both years.
For grain length, two QTLs were identified on each of
chromosomes 1 and 3 using the SNP bin map, compared to one
QTL identified on each of these two chromosomes using the
RFLP/SSR map. Like in grain weight, the effects of the two QTLs
on chromosome 1 contributed in different directions to grain
length. At gl1a, allele from Zhenshan 97 increased the grain length
and thus increased grain weight (kgw1a), while at gl1b allele from
Minghui 63 increased the grain length and thus increased grain
weight (kgw1b). For grain width, two QTLs on chromosome 5
identified by using RFLP/SSR map were apparently due to the
same QTL (gw5) identified using the SNP bin map, because of the
low density markers and high recombination frequency in the
Table 3. Number of QTLs identified for yield and yield-component traits for the data of four trials from the RIL population of the
Zhenshan 97/Minghui 63 cross using the RFLP/SSR (map1) and ultra-high density SNP bin (map2) maps, with LOD thresholds
obtained by permutation tests at P=0.05.
Yield/plant Tillers/plant Grains/panicle Grain weight
Map1 Map2Map1Map2 Map1 Map2Map1Map2
Xing1997a
01101245
Xing1998b
21222147
Hua1998c
10124345
Hua1999d
11102357
Repeatable QTLse
10102346
aFor the data of 1997 from Xing et al [16].
bFor the data of 1998 from Xing et al [16].
cFor the data of 1998 from Hua et al [17–18].
dFor the data of 1999 from Hua et al [17–18].
eQTLs identified in at least two trials.
doi:10.1371/journal.pone.0017595.t003
Figure 5. QTL mapping for yield and yield-component traits using the SNP bin map. The phenotype data are from Xing et al [16] collected
in 1997 (Xing1997) and 1998 (Xing1998), and Hua et al [17–18] collected in 1998 (Hua1998) and 1999 (Hua1999). Four traits, grain yield/plant, tillers/
plant, grains/panicle and grain weight, are shown from top to bottom. A triangle indicates a QTL detected above the LOD threshold by the
permutation test (1000 permutations, P=0.05) in only one trail. An arrow indicates a QTL identified in at least two trials.
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GW5/qSW5 region of RFLP/SSR map. Using the SNP bin map,
another two QTLs with small effects were identified on
chromosomes 6 and 8, respectively, which were not detectable
using the RFLP/SSR map.
Discussion
Advantages of sequence-based genotyping
We have shown that the sequence-based genotyping method
can provide an ultra-high density genetic map of high quality
SNPs, based on low-coverage sequences of a rice RIL population.
As discussed by Xie et al [15], the method is of high throughput
and time- and cost-effective, and the map is of high quality and
accuracy for genetic analysis and QTL mapping. In addition, the
large number of high-quality SNP markers between Minghui 63
and Zhenshan 97, both of which are among the most frequently
used breeding lines of indica rice, provided useful markers for
genetic analyses and breeding applications in indica rice.
Compared to RFLP/SSR and array-based SFP genotyping
methods, the sequence-based method produces a map of the highest
density. The accuracy and thus the quality of the SNP markers
identified using sequencing genotyping was enhanced by using
information of adjacent SNPs to form bins, which is also an advantage
compared to other marker types. The known physical positions of the
sequencing-based SNP markers allow detection of false double
crossovers between adjacent markers, which would otherwise be
incorrectly incorporated in genetic maps based on markers such as
RFLPs or SSRs causing inaccuracy in the analysis [9].
Table 4. QTLs identified for yield and yield-component traits in at least two of the four trials by using the high density SNP bin
map (showing only the most significant QTLs in the four trials).
Trait QTLChr. Position (cM)LODInterval (Mb)a
Addb
VarRepeatesd
(%)c
Grains/panicle gn1133.89 5.32 5.4–6.56.20 4.782
gn33 98.0915.49 22.9–23.7
210.74 21.664
gn77 54.7312.62 8.4–15.4 10.43 19.672
1000-grain weightkgw1a1 36.308.28 6.2–8.4
20.777.514
kgw1b1 148.14 6.73 32.9–360.64 5.433
kgw3a393.75 20.28 16.2–17.21.26 21.844
kgw3b3 139.378.10 29.9–30.3 0.788.992
kgw55 29.7118.52 5.3–5.4
21.20 21.414
kgw99 86.57 5.6619.1–20.9
20.614.832
a1.5-LOD support interval of the QTL.
bAddictive effect: positive values of the additive effect indicate that alleles from Minghui 63 were in the direction of increasing the trait score.
cPercentage of variation explained by the QTL.
dNumber of trials in which the QTL was detected.
doi:10.1371/journal.pone.0017595.t004
Figure 6. Comparison of QTL mapping for gn3 using different maps. LOD curves for number of grains per panicle in gn3 region on
chromosome 3 are shown. The phenotype data are from Xing et al [16] collected in 1997 (black lines) and 1998 (red lines), and Hua et al [17–18]
collected in 1998 (green lines) and 1999 (blue lines). Physical positions are indicated in x-axis. (A) Using the SNP bin map. The short lines on x-axis
indicate the positions of the recombination breakpoints. (B) Using the RFLP/SSR map. The short lines on x-axis indicate the positions of the markers.
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The sequence-based genotyping differs from conventional
marker-based genotyping approach in the following aspects: (1)
Only a few RILs are genotyped at a given SNP site with the raw
sequence data while data for the majority of the RILs are
missing [14]. (2) Because of sequencing errors, the SNPs
obtained could not be directly used as markers, and a bin
supported by several adjacent SNPs in a chromosomal segment
with no recombination event is used as the unit for genotyping,
which is very different from the interval defined by two flanking
markers in traditional marker systems. (3) The precision of the
recombination breakpoint depends on the local density of the
SNPs, the breakpoint could be more precisely identified with
higher density of the SNPs in the region. (4) The genotypes
between the boundaries of the bins are imputed, which may
cause inaccuracy in the analysis in the SNP marker sparse
regions. However, it may have little effect on primary QTL
mapping to locate a QTL to an interval, like a 1.5-LOD drop
interval used in this study.
Figure 7. QTL mapping for grain length and grain width using the SNP bin map. Red lines show the LOD curves for the phenotypic data
from Tan et al [27] collected in 1998 and blue lines show the LOD curves for the phenotypic data collected in 2008. A triangle indicates a QTL
detected above the LOD threshold by the permutation test (1000 permutations, P=0.05) in only one year. An arrow indicates a QTL identified in two
years.
doi:10.1371/journal.pone.0017595.g007
Table 5. QTLs identified for grain length and grain width for the data of 1998 and 2008 from the RIL population of the Zhenshan
97/Minghui 63 cross, using the high density SNP bin map, with LOD thresholds obtained by permutation tests at P=0.05.
TraitQTLBinChr. Position (cM)LOD Interval (Mb)a
Addb
Var (%)c
Grain length gl1a Bin891 82.71 7.5013.53–18.78
20.135.08
(1998)gl1b Bin1581 139.695.45 30.19–32.720.11 3.85
gl3aBin4393 93.7544.5216.72–16.91 0.44 57.13
Grain length gl1a Bin891 82.71 9.33 14.62–19.52
20.14 4.93
(2008)gl1bBin1491 137.296.70 30.15–31.850.12 4.11
gl3a Bin4393 93.7550.7016.72–16.91 0.43 60.98
gl3bBin5093 136.946.16 29.59–30.40.10 3.13
Grain widthgw5Bin7295 29.7140.50 5.25–5.38
20.1652.65
(1998)gw6Bin9226 82.14 6.41 21.28–22.110.06 6.32
Grain widthgw5Bin7295 29.7156.35 5.25–5.38
20.17 62.10
(2008)gw6Bin9306 88.0711.13 22.11–23.920.066.75
gw8 Bin11418 62.63 6.9119.69–21.08
20.04 3.88
a–cSee footnotes of Table 4 for explanations.
doi:10.1371/journal.pone.0017595.t005
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Factors affecting QTL mapping
Several factors may affect the efficiency of QTL mapping. For a
given trait in a particular population, marker density may be a key
factor. In general, increasing marker density can increase the
resolution of the genetic map, thus enhancing the precision of
QTL mapping. Our results showed that the detection power and
resolution of QTL mapping were significantly improved by using
the ultra-high density SNP bin map. For example, when analyzing
rice yield and yield-component traits using LOD thresholds
obtained by permutation tests at P=0.05, a larger number of
QTLs for grain weight were detected by using SNP bin map than
using RFLP/SSR map, which is also the case for the component
traits, grain length and grain width, indicating increase in
detection power. The two main QTLs for grain size, GS3 for
grain length and GW5/qSW5 for grain width, were delimited to
genomic regions ,200 kb, compared to .5 Mb using RFLP/SSR
marker-based genetic map [27] indicating greatly improved
precision. Furthermore, analysis using the high-density SNP bin
map resolved several closely linked peaks with similar small effects
in the region previously identified as gn3 for number of grains per
panicle, as opposed to a single peak detected using the RFLP/SSR
map.
The resolution of QTL mapping also depends on the
recombination frequency in the local QTL region. This is clearly
exemplified by the analysis of the two main QTLs for grain weight,
kgw3a (GS3) and kgw5 (GW5/qSW5). Although these two QTLs
had similar large effect on grain weight, the 1.5-LOD drop support
interval of kgw5 was 123 kb on the bin containing the gene GW5/
qSW5, using data of every experimental trail, whereas the support
interval of kgw3a was more than 1 Mb. This could be explained by
the fact that GW5 is located in a recombination hotspot [26], while
GS3 is located in a pericentromeric region [23] where the
recombination frequency is relatively low. An even more dramatic
example is gn7 (Ghd7). Although it was characterized to be a major
QTL with pleiotropic effect on number of grains, plant height and
heading date [29], the 1.5-LOD drop support interval was as large
as 7 Mb and it could not even be detected in some of the trails in
this analysis using the SNP bin map. This is due to the fact that
this locus is located in a recombination suppressing region where
1 cM corresponds to 7368 kb, about 32-fold lower than the
genome average of approximately 230 kb/cM. Thus local
recombination frequency also has a large effect on QTL detection.
Many studies show that the population size affects the number
and the effects of QTLs detected, as well as the accuracy and
precision of QTL estimates [30–32]. In general, increasing
population size would reduce experimental errors thus improving
the power of detection. An additional gain from increased
population size for sequence-based SNP bin map is an increase
in the number of recombination events in the population, which
would increase the total number of bins accompanied by reduced
bin sizes. This by itself may result in very fine-scale mapping of
QTLs, narrowing the candidate to one or a few genes.
With the rapid accumulation of genomic information and
resources such as genomic sequences [33], expression profiles and
regulatory network [9,34–35], and mutant libraries [36–40], it
may be feasible to identify the candidate genes, by sequencing
genotyping of a sufficiently large population. This approach may
even be more promising for QTLs with large effects or less
environmental errors, and QTLs located in recombination
hotspots.
Gains in QTL recovery and the stringency of detection
The results of QTL detection we presented here were based on
LOD thresholds estimated by permutation tests at P=0.05. We
believe that this could apply the same statistical stringency to QTL
detection using maps of very different densities to make the results
directly comparable. However, the thresholds adopted here were
much higher than the empirical ones (e.g. LOD 3.0 or lower) used
in many QTL studies in rice. Such highly stringent tests might
miss QTLs of smaller effects, which may bring in bias in the
comparison of gains from the high-density SNP map. To evaluate
such possible effect, we also attempted to use a single LOD
threshold 3.0 for QTL claiming for all the traits using both maps,
with the results given in Table S6. As expected, some of the
undetected QTLs by the RFLP/SSR map emerged, especially for
grain weight. Thus some small effect QTLs could be detected only
with relative low stringency using the RFLP/SSR map, but could
be detected with higher stringency using the SNP bin map,
indicating that the high-density SNP bin map improved the QTL
detection power. It can also be seen from Table S6 that with LOD
threshold 3.0, the number of QTLs detected in each of the trails
was also larger using the SNP bin map than using the RFLP/SSR
map, although the number of QTLs that were repeatedly detected
were similar using the two maps. This comparison again indicated
that the high-density SNP map could recover more information in
QTL detection than the RFLP/SSR map. Therefore, we recom-
mend the use of permutation tests to decide LOD thresholds to
control type II error.
Materials and Methods
Plant materials
The population used in this study consisted of 241 recombinant
inbred lines (RILs) derived by single-seed descent from a cross
between two elite rice lines of indica subspecies, Zhenshan 97 and
Minghui 63, the parents of Shanyou 63, the most widely cultivated
hybrid in China. Most of the data used in this study were obtained
from the datasets of previous studies [16–18,27]. All of them were
collected from field trials on the experimental farm of Huazhong
Agricultural University in Wuhan, China. In addition, RILs and
the parents were field planted again in the rice growing season in
Wuhan in 2008 to obtain data for grain length and grain width,
and in 2009 to observe the colors of leaf sheaths, auricles, stigmas
and apiculus.
In 2008, leaves were bulk-harvested from 5–10 plants per line
grown in the field for genomic DNA extraction. DNA samples of
the 241 RILs and the two parents were sequenced using the
Illumina Genome Analyze (GA) as described by Xie et al [15].
Bin map construction
The bar-coded multiplex sequencing of RILs and the
construction of high density bin map were as described by Xie
et al [15]. Briefly, after obtaining the raw sequences of the RILs,
potential SNPs were identified on the basis of assuming a biallelic
state for each polymorphism site. Drafts of parental genotypes
were obtained with the assistance of low coverage parental
Zhenshan 97 sequences using a maximum parsimonious inference
of recombination (MPR), implemented in an R package MPR
[15]. High-quality SNPs were identified through filtering-out low-
quality ones by permutations involving resampling of windows of
SNPs (the function globalMPRRefine in MPR package) followed by
Bayesian inference (the function genotypeCallsBayes). The genotypes
of RILs were determined using a hidden Markov model approach
(the function correctGeno with parameter ‘‘correct.FUN = correct-
FUNHMM’’), with heterozygotes set to missing. Consecutive SNP
sites with the same genotype were lumped into blocks and a
breakpoint was assumed at the transition between two different
genotype blocks. Blocks with length less than 250 kb in which the
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Page 10

number of sequenced SNPs was fewer than five were masked as
missing data to avoid false double recombinations. The genotypic
maps of the RILs were aligned and split into recombination bins
[14,41] according to the recombination breakpoints. Bins less than
5-kb were merged to the next bin. Genotypes of bins for regions at
the transitions between two different genotype blocks were set to
missing data and imputed using R/qtl package function fill.geno
with the ‘‘argmax’’ method [42]. The genetic linkage map based
on the bins was constructed using the R/qtl package function
est.map with Haldane map method [42].
QTL analysis
The same datasets of the RILs for the traits were used for QTL
analyses of both SNP bin and RFLP/SSR maps, using R/qtl
package [42]. Composite interval mapping (CIM) [28] was
performed for each trait using the R/qtl function cim [42] with a
10-cM scan window and covariates of 5 markers. For the high-
density SNP bin map, the walking speed was set to zero because
the bins were clearly defined which was different from the nature
of traditional molecular markers. The likelihood ratio statistic was
computed for each bin. The LOD threshold was obtained based
on permutation test (1000 permutations, P=0.05) for each data
set. A 1.5 LOD-drop support interval was used for each QTL as
described by Wang et al [9]. The QTL addictive effect and
variation explained by each QTL were determined using the
linear QTL model involving all the detected QTLs using the R
function lm [43]. For the RFLP/SSR genetic map, the walking
speed was set to 2.0 cM. We used the distance between the
flanking markers to represent the QTL interval and used the most
closely linked marker to estimate the QTL effect.
Supporting Information
Figure S1
identified from low-coverage sequences of 241 RILs. The
physical positions on each chromosome are based on rice
TIGR6.1. The short blue lines indicate the SNP density (SNPs/
50-kb). The average density is about 36 SNPs/50-kb (,1 SNP/
1.37-kb). A height more than 150 SNPs/50-kb is set to 150 SNPs/
50-kb. The pink point on each chromosome indicates the
centromere.
(TIF)
Distribution of 270,820 high quality SNPs
Figure S2
on the SNP markers in the rice genome. Physical positions
are based on rice TIGR6.1. Adjacent bins are separated by short
lines on each chromosome. Yellow arrows indicate centromeres.
Red boxes indicate bins of more than 2 Mb in length.
(TIF)
Distribution of 1,619 recombinant bins based
Table S1
detected by RFLP/SSR markers for the 210 RILs from the
Zhenshan 97/Minghui 63 cross.
(XLS)
Map information for the 220 polymorphic loci
Table S2
from the Zhenshan 97/Minghui 63 cross based on high quality
SNPs obtained from population sequencing.
(XLS)
Map information for all 1,619 bins for the 210 RILs
Table S3
in four trials from the RIL population of the Zhenshan 97/
Minghui 63 cross using the RFLP/SSR genetic map. Trial 1, in
1997 from Xing et al [16] (Xing1997); Trial 2, in 1998 from Xing
et al [16] (Xing1998); Trial 3, in 1998 from Hua et al [17–18]
(Hua1998); Trial 4, in 1999 from Hua et al [17–18] (Hua1999).
(XLS)
QTLs identified for yield and yield-component traits
Table S4
in four trials from the RIL population of the Zhenshan 97/
Minghui 63 cross using the ultra-high density SNP bin map. Trial
1, in 1997 from Xing et al [16] (Xing1997); Trial 2, in 1998 from
Xing et al [16] (Xing1998); Trial 3, in 1998 from Hua et al [17–
18] (Hua1998); Trial 4, in 1999 from Hua et al [17–18]
(Hua1999).
(XLS)
QTLs identified for yield and yield-component traits
Table S5
the data of 1998 and 2008 from the RIL population of the
Zhenshan 97/Minghui 63 cross, using the RFLP/SSR map.
(XLS)
QTLs identified for grain length and grain width for
Table S6
component traits for the data of four trials from the RIL
population of the Zhenshan 97/Minghui 63 cross using the
RFLP/SSR (map1) and ultra-high density SNP bin (map2) maps,
with LOD threshold 3.0.
(XLS)
Number of QTLs identified for yield and yield-
Acknowledgments
We thank Dr. Bin Han and his group in the National Center for Gene
Research, Shanghai, China for whole-genome Solexa sequencing of the
RIL population.
Author Contributions
Conceived and designed the experiments: HY WX QZ. Performed the
experiments: HY JW. Analyzed the data: HY WX. Contributed reagents/
materials/analysis tools: YX CX XL JX. Wrote the paper: HY WX QZ.
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