- Record: found
- Abstract: found
- Article: found
Prevalence of single nucleotide polymorphism among 27 diverse alfalfa genotypes as assessed by transcriptome sequencing
research-article
Author(s):
Xuehui Li
1 ,
Ananta Acharya
2 ,
Andrew D Farmer
3 ,
John A Crow
3 ,
Arvind K Bharti
3 ,
Robin S Kramer
3 ,
Yanling Wei
1 ,
Yuanhong Han
1 ,
Jiqing Gou
1 ,
Gregory D May
3 ,
Maria J Monteros
1 ,
E Charles Brummer
1
,
Publication date (Electronic):
29 October 2012
Journal:
BMC Genomics
Publisher:
BioMed Central
Read this article at
There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.
Abstract
Background
Alfalfa, a perennial, outcrossing species, is a widely planted forage legume producing highly nutritious biomass. Currently, improvement of cultivated alfalfa mainly relies on recurrent phenotypic selection. Marker assisted breeding strategies can enhance alfalfa improvement efforts, particularly if many genome-wide markers are available. Transcriptome sequencing enables efficient high-throughput discovery of single nucleotide polymorphism (SNP) markers for a complex polyploid species.
Result
The transcriptomes of 27 alfalfa genotypes, including elite breeding genotypes, parents of mapping populations, and unimproved wild genotypes, were sequenced using an Illumina Genome Analyzer IIx. De novo assembly of quality-filtered 72-bp reads generated 25,183 contigs with a total length of 26.8 Mbp and an average length of 1,065 bp, with an average read depth of 55.9-fold for each genotype. Overall, 21,954 (87.2%) of the 25,183 contigs represented 14,878 unique protein accessions. Gene ontology (GO) analysis suggested that a broad diversity of genes was represented in the resulting sequences. The realignment of individual reads to the contigs enabled the detection of 872,384 SNPs and 31,760 InDels. High resolution melting (HRM) analysis was used to validate 91% of 192 putative SNPs identified by sequencing. Both allelic variants at about 95% of SNP sites identified among five wild, unimproved genotypes are still present in cultivated alfalfa, and all four US breeding programs also contain a high proportion of these SNPs. Thus, little evidence exists among this dataset for loss of significant DNA sequence diversity from either domestication or breeding of alfalfa. Structure analysis indicated that individuals from the subspecies falcata, the diploid subspecies caerulea, and the tetraploid subspecies sativa (cultivated tetraploid alfalfa) were clearly separated.
Conclusion
We used transcriptome sequencing to discover large numbers of SNPs segregating in elite breeding populations of alfalfa. Little loss of SNP diversity was evident between unimproved and elite alfalfa germplasm. The EST and SNP markers generated from this study are publicly available at the Legume Information System ( http://medsa.comparative-legumes.org/) and can contribute to future alfalfa research and breeding applications.
Related collections
Most cited references31
- Record: found
- Abstract: found
- Article: not found
The Medicago Genome Provides Insight into the Evolution of Rhizobial Symbioses
Nevin D Young, Frédéric Debellé, Giles E D Oldroyd … (2011)
Legumes (Fabaceae or Leguminosae) are unique among cultivated plants for their ability to carry out endosymbiotic nitrogen fixation with rhizobial bacteria, a process that takes place in a specialized structure known as the nodule. Legumes belong to one of the two main groups of eurosids, the Fabidae, which includes most species capable of endosymbiotic nitrogen fixation 1 . Legumes comprise several evolutionary lineages derived from a common ancestor 60 million years ago (Mya). Papilionoids are the largest clade, dating nearly to the origin of legumes and containing most cultivated species 2 . Medicago truncatula (Mt) is a long-established model for the study of legume biology. Here we describe the draft sequence of the Mt euchromatin based on a recently completed BAC-assembly supplemented with Illumina-shotgun sequence, together capturing ~94% of all Mt genes. A whole-genome duplication (WGD) approximately 58 Mya played a major role in shaping the Mt genome and thereby contributed to the evolution of endosymbiotic nitrogen fixation. Subsequent to the WGD, the Mt genome experienced higher levels of rearrangement than two other sequenced legumes, Glycine max (Gm) and Lotus japonicus (Lj). Mt is a close relative of alfalfa (M. sativa), a widely cultivated crop with limited genomics tools and complex autotetraploid genetics. As such, the Mt genome sequence provides significant opportunities to expand alfalfa’s genomic toolbox.
- Record: found
- Abstract: found
- Article: found
High-throughput gene and SNP discovery in Eucalyptus grandis, an uncharacterized genome
Evandro Novaes, Derek Drost, William Farmerie … (2008)
Background Benefits from high-throughput sequencing using 454 pyrosequencing technology may be most apparent for species with high societal or economic value but few genomic resources. Rapid means of gene sequence and SNP discovery using this novel sequencing technology provide a set of baseline tools for genome-level research. However, it is questionable how effective the sequencing of large numbers of short reads for species with essentially no prior gene sequence information will support contig assemblies and sequence annotation. Results With the purpose of generating the first broad survey of gene sequences in Eucalyptus grandis, the most widely planted hardwood tree species, we used 454 technology to sequence and assemble 148 Mbp of expressed sequences (EST). EST sequences were generated from a normalized cDNA pool comprised of multiple tissues and genotypes, promoting discovery of homologues to almost half of Arabidopsis genes, and a comprehensive survey of allelic variation in the transcriptome. By aligning the sequencing reads from multiple genotypes we detected 23,742 SNPs, 83% of which were validated in a sample. Genome-wide nucleotide diversity was estimated for 2,392 contigs using a modified theta (θ) parameter, adapted for measuring genetic diversity from polymorphisms detected by randomly sequencing a multi-genotype cDNA pool. Diversity estimates in non-synonymous nucleotides were on average 4x smaller than in synonymous, suggesting purifying selection. Non-synonymous to synonymous substitutions (Ka/Ks) among 2,001 contigs averaged 0.30 and was skewed to the right, further supporting that most genes are under purifying selection. Comparison of these estimates among contigs identified major functional classes of genes under purifying and diversifying selection in agreement with previous researches. Conclusion In providing an abundance of foundational transcript sequences where limited prior genomic information existed, this work created part of the foundation for the annotation of the E. grandis genome that is being sequenced by the US Department of Energy. In addition we demonstrated that SNPs sampled in large-scale with 454 pyrosequencing can be used to detect evolutionary signatures among genes, providing one of the first genome-wide assessments of nucleotide diversity and Ka/Ks for a non-model plant species.
- Record: found
- Abstract: found
- Article: found
Genetic Architecture of Aluminum Tolerance in Rice (Oryza sativa) Determined through Genome-Wide Association Analysis and QTL Mapping
Adam Famoso, Keyan Zhao, Randy T Clark … (2011)
Introduction Aluminum (Al) toxicity is the major constraint to crop productivity on acid soils, which comprise over 50% of the world's arable land [1]. Under highly acidic soil conditions (pH 80% ancestry). Admixed accessions share 80% ancestry) the Japonica varietal group (consisting of the temperate japonica, tropical japonica and aromatic subpopulations) was significantly more Al tolerant than the Indica varietal group (indica and aus subpopulations) (p 80% ancestry to either varietal group. A one-way ANOVA demonstrated that subpopulation explained 57% of the phenotypic variation observed for Al tolerance (TRG-RRG) among the 274 accessions that carried a subpopulation classification. Despite the differences in mean TRG-RRG between subpopulations, considerable variation was also detected within each subpopulation (Figure S1). QTL Analysis Two immortalized QTL mapping populations were analyzed for Al tolerance. One consisted of 134 recombinant inbred lines (RIL) derived from the cross IR64/Azucena [69], and the other was comprised of 78 backcross inbred lines (BIL) derived from the cross Nipponbare/Kasalath//Nipponbare [70]. These populations were used to evaluate Al tolerance using three different indices of relative root growth (RRG), (1) longest root growth (LRG-RRG), (2) primary root growth (PGR-RRG) and (3) total root growth (TRG-RRG) (see Materials and Methods for details). The phenotypic distribution was approximately normal for each population, no matter which root screening index was used (illustrated for TRG-RRG in Figure S2A and S2B). The QTL mapping populations allowed us to determine which of the three root evaluation methods would be most useful for evaluating the diversity panel as a whole. The method of phenotyping, specifically, the RRG index used to estimate Al tolerance, directly impacted the significance of QTLs detected by composite interval mapping (Figure 2A–2C and Figure S3A–S3C). In the RIL population, three Al tolerance (Alt) QTL were detected using total root growth (the TRG-RRG index), Alt TRG 1.1 on chromosome 1, Alt TRG 2.1 on chromosome 2, and Alt TRG 12.1 on chromosome 12 (Figure 2A–2C Table 1). The Azucena allele conferred increased tolerance at the loci on chromosomes 1 and 12 and reduced tolerance at the locus on chromosome 2. QTLs were detected in the same positions on chromosomes 1 and 12 using RRG based on primary root growth (the PRG-RRG index), although with lower LOD scores (Figure 2A–2C; Table 1). Using longest root growth (the LRG-RRG index), a single QTL was detected on chromosome 9, Alt LRG 9.1, and this QTL was not detected when the other root indices were used. The major QTL on chromosome 12 (Alt TRG 12.1), which explained >19% of the variation in Al tolerance based on TRG-RRG, is located between 2.69–5.10 Mb and encompasses the Al sensitive rice mutant art1, which is located at 3.59 Mb [19]. 10.1371/journal.pgen.1002221.g002 Figure 2 QTLs Identified in IR64 × Azucena RIL Mapping Population. A–C) Composite interval mapping output for QTL detected in the RIL mapping population using three Al tolerance RRG indices. The Y-axis is the LOD score and the horizontal line is the significant LOD threshold based on 1000 permutations. QTL name and approximate physical position are along bottom of figure and co-localization of QTLs identified with different Al tolerance indices are indicated with dashed vertical lines. A) Total root growth (TRG-RRG); B) Primary root growth (PRG-RRG); C) Longest root growth (LRG-RRG). 10.1371/journal.pgen.1002221.t001 Table 1 Summary of significant QTLs (1000 permutations) identified by composite interval mapping in the RIL and BIL populations. Trait Index Population Chr. QTL Peak Marker Peak Mb Position Flanking Markers LOD1 L (Mb) LOD1 R (Mb) LOD Additive effect R2 TRG-RRG RIL 1 AltTRG 1.1 RM265 35.2 RM319/RM315 34.32 36.10 4.56 2.58 (Azu) 0.095 PRG-RRG RIL 1 AltPRG 1.1 RM265 35.2 RM319/RM315 34.36 35.93 3.29 3.84 (Azu) 0.081 TRG-RRG BIL 1 AltTRG 1.2 RM6333 38 RM5448/RM8231 37.70 38.68 3.44 −10.58 (Nip) 0.117 TRG-RRG RIL 2 AltTRG 2.1 RM221 27.61 RM526/RM318 26.79 29.17 2.9 −2.08 (IR64) 0.059 PRG-RRG BIL 6 AltPRG 6.1 L688 5.81 R1954/G200 2.82 6.67 3.95 12.78 (Kas) 0.143 LRG-RRG RIL 9 AltLRG 9.1 RM242 18.81 RM257/RM160 18.15 19.40 6.57 4.42 (Azu) 0.165 TRG-RRG RIL 12 AltTRG 12.1 RM247 3.19 RM453/RM512 2.88 3.89 7.85 3.76 (Azu) 0.193 PRG-RRG RIL 12 AltPRG 12.1 RM247 3.19 RM453/RM512 2.75 4.54 4.94 4.75 (Azu) 0.126 TRG-RRG BIL 12 AltTRG 12.2 R2708 23.36 R1709/G2140 22.33 25.00 3.49 12.3 (Kas) 0.128 Al tolerance (RRG) QTLs were identified using three root growth parameters, total root growth (TRG), primary root growth (PRG), and longest root growth (LRG). The parent contributing the tolerance allele is indicated in parentheses under additive effect. In the BIL population, two QTL were detected using the TRG index, Alt TRG 1.2 on chromosome 1, which co-localized with the Alt TRG 1.1 QTL identified in the RIL population, and Alt TRG 12.2 on chromosome 12, which did not overlap with the Alt TRG 12.1 identified in the RIL population (Figure 2A–2C, Figure S3A–S3C, Table 1). The Nipponbare allele conferred tolerance at the chromosome 1 locus and the Kasalath allele conferred tolerance at the Alt TRG 12.2 locus. No QTLs were detected on chromosome 2 in the BIL population. Using the PRG-RRG index, one QTL was detected on chromosome 6, where the Kasalath allele conferred resistance. No QTLs were detected using the LRG-RRG index in the BIL population. The Al tolerance index used for evaluating the phenotype directly affected both the identity and the significance of the QTLs detected. Al tolerance index-specific QTLs were detected in both populations and no QTL locus was detected across all three indices. Based on number of QTL detected, significance of QTL, and variance explained by the QTL, total root growth (TRG) proved to be the single most powerful Al tolerance index. However, rice QTLs detected using different evaluation methods are likely to confer Al tolerance by different mechanisms, such as tolerance of primary, secondary, lateral, or all roots, and thus they are complementary and together provide a robust evaluation of the genetic architecture of Al tolerance than any single index alone. Identification of Al Tolerance Loci through GWA Mapping To identify Al tolerance loci based on genome-wide association (GWA) mapping, we used an existing genotypic dataset consisting of 36,901 SNPs [65], and the total root growth (TRG-RRG) Al tolerance phenotype generated on 373 O. sativa accessions over the course of this study. GWA mapping was conducted, using SNPs with a MAF>0.05, across all 373 genotypes as well as independently within the indica, aus, temperate japonica, and tropical japonica subpopulations (Figure 3). The Efficient Mixed-Model Association (EMMA) [71] model was used in each analysis (both within and across subpopulations) to correct for confounding effects due to subpopulation structure and relatedness between individuals. As the subpopulation structure was highly correlated with Al tolerance, it was observed that analyzing all samples (373) together with the EMMA model resulted in an overcorrection (causing type 2 error) and a corresponding reduction in SNP significance (Figure S4). To address this problem, a PCA approach was also employed when analyzing all (373) samples together. However, the PCA approach resulted in a slight under-correction for population structure (Figure S4), demonstrating that results from each GWA method has limitations when used across all germplasm in this highly structured diversity panel. 10.1371/journal.pgen.1002221.g003 Figure 3 GWA Analysis of Al Tolerance within and across Rice Subpopulations. GWA analysis across and within subpopulations (IND = indica; AUS = aus; TRJ = tropical japonica; TEJ = temperate japonica). A priori candidate genes are listed across the top, with those identified within 200 kb of significant SNPs colored red. Color bands indicate the 23 bi-parental QTL positions from previous reports (grey) or from this study (yellow). SNP color indicates co-localization with QTLs (blue) or candidate genes (red). A total of ∼48 distinct Al tolerance genomic regions were identified by GWA mapping (Figure 3). Twenty-one regions were detected (p 0.05) were detected in the temperate japonica or tropical japonica subpopulations. The GWA mapping results indicate that the majority of significant loci are subpopulation-specific and that phenotypic variation for Al tolerance within given subpopulations is largely controlled by alleles that are unique to that subpopulation. SNPs identified by GWA were also compared to a set of 46 a priori candidate genes as well as to positions of QTL regions identified through bi-parental mapping (this study and previous reports) (Table 1 and Figure 3). Two regions of highly significant SNP clusters, one within the aus (8 SNPs; p = 2.8E-07) subpopulation on chr. 2 and one within the indica (32 SNPs; p = 2.9E-07) subpopulation on chr. 3, co-localized to previously reported QTLs in populations in which an aus and indica parent served as the susceptible parents, respectively [17], [23]. The list of 46 a-priori Al tolerance candidate genes (Table 2) was compiled based on published information on Al sensitive mutants from rice and Arabidopsis [20]–[22], [24], cloned Al tolerance genes from wheat and sorghum [14], [15], expression profiles from Al treated maize and rice roots [19], [73], and an association study on specific candidate Al tolerance genes of maize [74]. Significant SNPs (p 0.0001) outside of the a priori and QTL regions. The 200 kb window was selected to fall within the estimated window of LD decay in rice (∼50–500 kb [45]–[49] and the upper-limit false discovery rate for the a priori genes was 42%. In addition, four of the 46 gene candidates (∼9%) were located within a 200 kb window enriched for GWA SNPs in this study (Figure 3 and Table 2). One of the candidate genes (Nrat1) on chr. 2, co-localized with both GWA SNPs and a previously reported QTL (Figure 3). The relationship between the four candidates that co-localized with GWA SNPs are discussed in order of their positions on the rice genome below. 10.1371/journal.pgen.1002221.t002 Table 2 List of 46 a priori Al tolerance candidate genes. LOC ID Reference Chr. Mb Pos. (Homolog) Description GWA detection p-value 1 LOC_Os01g178300 [19] 1 4.07 OSCDT3 2 LOC_Os01g46350 [19] 1 26.37 proteins of unknown function 3 LOC_Os01g53090 [19] 1 30.51 pathogen-related protein, putative 4 LOC_Os01g56080 [19] 1 32.28 expressed protein 5 LOC_Os01g64120 [19] 1 37.24 2Fe-2S iron-sulfur cluster binding 6 LOC_Os01g64890 [19] 1 37.66 CorA-like magnesium transporter 7 LOC_Os01g69010 [15] 1 40.09 (SbMATE) MATE efflux protein 8 LOC_Os01g69020 [19] 1 40.10 retrotransposon protein, putative 9 NP_001044070 [19] 1 33.05 SAM-dependen methyltransferase 10 LOC_Os02g03900 [19], [20] 2 1.66 (Nrat1) metal transporter Nramp6 AUS 4.99E-07 11 LOC_Os02g09390 [19] 2 4.82 cytochrome P450, putative 12 LOC_Os02g38200 [74] 2 23.10 dehydrogenase, putative, expressed 13 LOC_Os02g51930 [19] 2 31.80 cytokinin-O-glucosyltransferase 2 14 LOC_Os02g53130 [19] 2 32.51 nitrate reductase, putative, expressed 15 LOC_Os03g11734 [74] 3 6.13 MATE efflux protein 16 LOC_Os03g19170 [19] 3 10.75 GCRP7 - Glycine and cysteine rich 17 LOC_Os03g21950 [74] 3 12.54 fumarate hydratase 18 LOC_Os03g54790 [19], [21], [24] 3 31.14 (ALS1) ABC transporter, ATP-binding protein 19 LOC_Os03g55290 [19] 3 31.46 GASR3 - Gibberellin-regulated 20 Os03g0760800 [19] 3 35.66 GA-regulated protein family 21 Os03g0126900 [19] 3 1.75 hypothetical protein 22 LOC_Os04g34010 [74] 4 20.42 (ALMT1) aluminum-activated malate transporter 23 LOC_Os04g41750 [19] 4 24.56 expressed protein 24 LOC_Os04g49410 [19] 4 29.30 expansin precursor 25 LOC_Os05g02750 [22], [24] 5 0.99 (ALS3 and STAR2) ABC transporter All-PCA 3.5E-05 26 LOC_Os05g02780 [74] 5 1.00 glycine-rich protein A3, putative All-PCA 3.5E-05 27 LOC_Os05g08810 [74] 5 4.85 phosphatidylinositol 3-kinase 28 LOC_Os05g09440 [74] 5 5.29 malic enzyme 29 LOC_Os06g36450 [74] 6 21.40 ferroportin1 protein 30 LOC_Os06g48060 [19], [24] 6 29.07 (STAR1) ABC transporter, ATP-binding 31 LOC_Os07g23710 [74] 7 13.38 cytochrome P450, putative 32 LOC_Os07g34520 [74] 7 20.69 isocitrate lyase IND 4.49E-05 33 LOC_Os07g39860 [19] 7 23.90 expressed protein 34 LOC_Os09g25850 [19] 9 15.49 WAX2, oxidoreductase; 35 LOC_Os09g30250 [19] 9 18.41 OsSub58 - Putative Subtilisin 36 LOC_Os10g12080 [74] 10 6.73 cytochrome P450, putative 37 LOC_Os10g13940 [19] 10 7.59 MATE efflux protein 38 LOC_Os10g26680 [74] 10 13.86 pectinesterase, putative, expressed 39 LOC_Os10g38080 [19] 10 20.32 OsSub61 - Putative Subtilisin homologue 40 LOC_Os10g42780 [19] 10 23.00 lrgB-like family protein, expressed 41 LOC_Os11g26850 [74] 11 14.96 erythronate-4-phosphate dehydrogenase 42 LOC_Os11g29680 [19] 11 16.74 expressed protein 43 LOC_Os11g29780 [19] 11 16.82 plant-specific domain TIGR01627 44 LOC_Os12g03899 [74] 12 1.61 major facilitator superfamily 45 LOC_Os12g05860 [74] 12 2.69 Cupin domain containing protein 46 LOC_Os12g12590 [19] 12 6.93 NADP-dependent oxidoreductase Genes identified within 200 kb of SNPs detected by GWA analysis (p 500 kb, and encompassed two significant regions detected across all samples (PCA), one of which was also detected within the indica subpopulation. STAR2 is the rice ortholog of the Arabidopsis Al sensitive mutant als3 [21]. It encodes the transmembrane domain of a bacterial-type ATP binding cassette (ABC) transporter and the star2 mutant is Al sensitive [24]. STAR2 was also found to be part of a gene network showing altered expression in response to Al in the art1 mutant compared to the ART1 wild type [19]. This study provides the first evidence that there may be natural variation for Al tolerance in rice at the STAR2 locus; however it is important to recognize that the PCA approach may under-correct for the effect of subpopulation in this study, thus it will be necessary to confirm the effect of the STAR2 alleles identified in this diversity panel. A significant GWAS region identified in the indica subpopulation on chromosome 7 co-localized with LOC_Os07g34520, a rice ortholog of a maize isocitrate lyase a priori candidate gene associated with Al tolerance in maize [73], [74]. The LD decay across this region within the indica subpopulation was 250 kb. Three highly significant regions detected within indica were further investigated to identify whether any clear Al tolerance candidate genes were located within these SNP clusters. The first region was a cluster of 32 significant SNPs (p = 3.0E-7) between 28.782–27.863 Mb on chr. 3 that co-localized with a previously reported QTL (Nguyen et al., 2002). Two clear candidates were identified among the 13 genes in this cluster; a nucleobase-ascorbate transporter (LOC_Os03g48810) and a chloride channel protein (LOC_Os03g48940). The second region was a 10 SNP cluster (p = 9.3E-12) between 26.986–27.479 Mb on chr. 7. Of the 80 genes in this region, 34 of which were retrotransposons, there were three strong candidate genes; a glycosyl transferase protein (LOC_Os07g45260), a cytochrome P450 protein (LOC_Os07g45290) and a zing finger RING type protein (LOC_Os07g45350). This region on chr. 7 was also identified in the introgression analysis as a localized introgressed region from Japonica into the highly tolerant Indica outliers (discussed below). The third region was an 8 SNP cluster between 4.892–5.164 Mb on chr. 11. Among the 48 genes in this region, there were two major classes of candidate genes observed, including 12 F-box proteins and a zinc finger CCHC protein. Haplotype Analysis of Nrat1 Gene Region on Chromosome 2 We chose to further investigate the variation in and around the Nrat1 gene on chromosome 2 because multiple independent lines of evidence supported the existence of a gene(s) in this region responsible for a significant portion of the variation for Al tolerance in rice. Evidence included a strong GWA peak in the aus subpopulation, a previously reported QTL [26], and the localization of the Nrat1 Al transporter gene. Using the 44 K SNP data, LD in this region was calculated to be ∼150 kb in the aus subpopulation and 11 distinct haplotypes were observed in the entire diversity panel across a 139 kb region around the Nrat1 gene (1.536 Mb–1.675 Mb on chr. 2) (Figure 4A). Haplotype 1 (Hap. 1), which was unique to the aus subpopulation, was found in 8 Al sensitive aus accessions and one Al sensitive aus/indica admixed line. These 9 genotypes were among the least Al tolerant (7th percentile, mean RRG = 0.16) of the 373 accessions screened (Table S1). Haplotype 1 explained 40% of the phenotypic variation for Al tolerance within the aus subpopulation (Figure S5). In addition, four aus accessions that were highly or moderately Al tolerant were found to contain a tropical japonica introgression across this region (described in the section on Introgression analysis below). 10.1371/journal.pgen.1002221.g004 Figure 4 Haplotype analysis of the Nrat1 gene region. A) Haplotypes observed in 373 accessions using the 44,000 SNP data. Haplotype 1 was unique to aus ancestry and associated with Al susceptibility within the aus subpopulation, explaining 40% of the Al tolerance variation within aus. Haplotypes 1, 2, and 3 share the same 4-SNP haplotype (id2001231-id2001243) flanking the Nrat1 gene (1.66 Mb). SNP positions are based on MSU6 annotation and subpopulations are abbreviated as follows: IND = indica, TEJ = temperate japonica, TRJ = tropical japonica, G.V. = groupV/aromatic, Admix = admixed lines without 80% ancestry to any one subpopulation. B) Haplotypes at the Nrat1 gene (1.66 Mb) in the (9) aus and (6) indica accessions sharing the 4-SNP haplotype flanking the Nrat1 gene. Polymorphisms are identified with numbers along bottom of figure. A STOP codon occurs in exon 13 between polymorphism 17 and 18. Gray shaded cells represent the reference allele and plant ID# 173 is the reference genotype ‘Nipponbare’. Yellow shaded cells represent polymorphisms in introns or synonymous polymorphisms in exons. Red shaded cells represent polymorphisms that result in amino acid substitutions (Indel or non-synonymous), unshaded cells marked with “−” indicate missing data, and +* indicates an intron insertion >500 bp. Haplotype 2 (Hap. 2) was found in one aus and one indica accession, and was most similar to Hap. 1, differing at only 2/14 SNPs (Figure 4A). The two lines containing haplotype 2 had very different levels of Al tolerance; the aus variety, Kasalath (ID 85), was highly susceptible, with a RRG = 0.2, while the indica variety, Taducan (ID 163), was tolerant, with a RRG = 0.8, suggesting that this extensive 14-SNP haplotype across the 139 kb region was not predictive of Al tolerance. However, when the haplotype was built using only the four SNPs immediately flanking the Nrat1 gene, a group of 16 accessions sharing the same haplotype at these four SNPs was clearly identified. These 16 accessions, included the 10 susceptible aus accessions (including one aus/indica admixed line) carrying haplotype 1 and haplotype 2 and six indica accessions (of varying Al tolerance) carrying haplotype 2 and haplotype 3 (Figure 4A). To determine if the four-SNP haplotype flanking the Nrat1 gene could be further resolved, we focused more deeply on the Nrat1 gene itself. We sequenced all 13 exons (including introns) of Nrat1 (1874 bp) in 26 susceptible and tolerant varieties representing the aus, indica, tropical japonica and temperate japonica subpopulations (Figure 4B). The accessions carried haplotypes 1, 2, 3, 6 and 11, as described in Figure 4A; where haplotype 1 was aus-specific and corresponded to the most sensitive group of accessions in the diversity panel; haplotype 2 was found in phenotypically divergent aus and indica accessions as described above; haplotype 3 was found in moderately tolerant indica varieties; haplotype 6, which appeared to be the ancestral haplotype, was the most common haplotype in all subpopulations and was associated with moderately high levels of tolerance; and haplotype 11, which was found in a majority of tropical japonica varieties, all of which were Al tolerant. Based on the 22 SNPs and/or indels identified across the 1,874 bp of Nrat1 sequence, highly resolved, gene haplotypes were constructed (Figure 4B). The gene haplotypes corresponded fairly well to the extended haplotype groups that had been constructed using the data from the 44 K SNP chip, except in the case of haplotype 2, where varieties differed at 10/22 (45%) of the SNPs across the Nrat1 gene. This fully resolved haplotype at the Nrat1 gene resulted in the susceptible Kasalath clustering with the other highly susceptible aus varieties and the tolerant Taducan clustering with other highly tolerant varieties (Figure 4). Three non-synonymous SNPs (polymorphisms 4, 16, 17) were shared among the 9 highly susceptible aus accessions. When the Eukaryotic Linear Motif resource (http://elm.eu.org) was used to identify functional sites in the Nrat1 gene, polymorphism 16 was identified as a functional site where a C→T SNP caused an amino acid change from valine→alanine (amino acid 500). This protein site was predicted to be involved in PKA-type AGC kinase phosphorylation, with the functional site spanning amino acids 497–503. Thus, polymorphism 16 was identified as a strong functional polymorphism candidate underlying natural variation in Nrat1. The fact that polymorphism 16 was also observed in two Al tolerant temperate japonica and one moderately tolerant tropical japonica accession (haplotype 11) suggested that SNP 16 alone was not predictive of Al tolerance. However, a combination of polymorphisms 4, 16, and 17 was entirely predictive of Al susceptibility. This study demonstrates the power of whole genome association analysis to integrate divergent pieces of evidence from independent bi-parental and mutant studies, enabling us to associate gene-based diversity with germplasm resources and natural variation that is of immediate use to plant breeders. Introgression Analysis There is a clear difference in the degree of Al tolerance found in the Japonica varietal group and the Indica varietal group, with the 10th percentile of Al tolerance of Japonica (0.53) being nearly equal to the 90th percentile of Indica (0.55) (Figure 1B). However, there are clear outliers within each varietal group. Five Indica accessions are highly Al tolerant (ID 30, 66, 142, 163, 337), ranging from 2.1–3.2 times the mean Indica Al tolerance, and three Japonica accessions (ID 12, 52, 112) are highly susceptible, each approximately 0.19 of the mean Japonica Al tolerance (Figure 1B and Table S1). To determine if these outliers were the result of introgressions across varietal groups, we calculated the allele ancestry of 5,467 SNPs distributed throughout the genome and identified specific genomic regions where historical Indica×Japonica admixture was detected only in the respective Indica or Japonica outlier lines. To do this, Japonica introgressions identified in highly Al tolerant Indica lines were used to query all other Indica accessions and only those Japonica introgressions that were uniquely present in the highly Al tolerant outlier Indica lines were considered as candidate regions underlying the outlier phenotype. When the five Indica outliers were used for this analysis, a few, well-defined regions comprising 2.4–4.9% of the genome corresponded to regions of Japonica introgression (Table 3). In the case of the three highly Al susceptible Japonica varieties, the genetic background was highly heterogeneous and the small number of lines precluded doing any admixture analysis. Therefore, the admixture analysis was conducted only on the five highly tolerant Indica outliers. 10.1371/journal.pgen.1002221.t003 Table 3 Summary of Japonica introgressions in the Indica outliers. Chr. Introgres- sion I.D. Line # Introgression (MSU6 Mb pos.) Size (Mb) GWA Signal Previous QTL 1 1.1 30, 163 41.69–42.06 0.37 IND none 2 2.1 66, 142a 21.93–23.10 1.17 none Nguyen V, 2001 7 7.1 30b, 66c, 142d, 163e 27.05–27.62 0.57 IND none 8 8.1f 30, 142, 163 0.032–0.42 0.39 none none 8 8.2 30, 163g 7.61–7.82 0.21 none Nguyen V, 2002 11 11.1 30, 66, 163h 19.06–20.05 0.99 IND none Indica outliers ranged from 94.6–97.6% Indica ancestry throughout the genome. Six regions were identified where the outliers shared unique introgressions from Japonica that were observed only in Al tolerant Indica outliers and were not present in any other Indica. Five of the six introgressed segments encompass regions identified in GWA analysis or bi-parental QTL analysis. Three introgressed regions encompass SNPs identified within the indica (IND) subpopulation, across all subpopulations (All), or both. Two of the introgressions encompass previously reported QTLs. a Line 142: introgression 2.1 is 21.93–23.80 Mb and TRG-RRG = 1.15. b Line 30: introgression 7.1 is 27.05–29.65 Mb and TRG-RRG = 0.76. c Line 66: introgression 7.1 is 27.05–27.62 Mb and TRG-RRG = 1.00. d Line 142: introgression 7.1 is 27.05–29.65 Mb and TRG-RRG = 1.15. e Line 163: introgression 7.1 is 25.98–29.65 and TRG-RRG = 0.80. f Introgression 8.1 is a novel locus that does not co-localize with GWA or QTL loci. g Line 163: introgression 8.2 is 7.61–10.14 Mb and TRG-RRG = 0.80. h Line 163: introgression 11.1 is 18.43–20.05 Mb and TRG-RRG = 0.80. In the five outlier Indica accessions, 6 Japonica introgressions (median size = 780 kb) were identified that were specific only to these 5 lines. Three of these introgressions were present in two genotypes, two of the introgressions were present in three genotypes, and one introgression was present in four of the outliers (Table 3). Three introgressions encompass SNPs identified by GWA analysis and two co-localized with bi-parental QTL. The introgression that was present in four of the indica outlier genotypes was located on chromosome 7 between 27.05–28.62 Mb and contained 94 annotated genes. This introgression included a cluster of GWA SNPs that were highly significant within the indica subpopulation (p = 2.6×10−5, MAF = 0.10) and was one of the top 100 most significant SNPs identified when the diversity panel as a whole was analyzed. Discussion Utilization of GWA and Bi-Parental QTL Mapping In this study, we utilized bi-parental QTL mapping and GWA analysis to examine the genetic architecture of Al tolerance in rice and to identify Al tolerance loci. Phenotyping of the diversity panel provided valuable information about the range and distribution of Al tolerance in O. sativa and offered new insights into the evolution of the trait. The mean Al tolerance in Japonica was twice that of Indica (p tropical japonica>aromatic>indica = aus) was consistent with the level of genetic relatedness among them [42], [44] and suggests that temperate and tropical japonica germplasm contain alleles that would be useful sources of genetic variation for enhancing levels of Al tolerance within indica and aus. This is supported by the identification of highly tolerant indica varieties from the rice diversity panel that contain introgressions from Japonica in regions characterized by GWA peaks. The highly tolerant Indica outliers demonstrate the feasibility of using a targeted approach to increase Al tolerance in Indica varieties by introgressing genes from Japonica. While less obvious, our QTL analysis demonstrated the ability to increase Al tolerance in Japonica using targeted introgressions from Indica. This was demonstrated within both QTL populations by the identification of two loci in which alleles from the highly susceptible Kasalath parent conferred enhanced levels of Al tolerance in the Nipponbare genome (temperate japonica) and one locus where the moderately susceptible IR64 parent conferred enhanced tolerance in crosses with Azucena (tropical japonica) (Table 1). To date, only a few indica and aus accessions have been used in QTL mapping populations and the identification of a large number of GWA loci in indica, coupled with the fact that indica is significantly more diverse than all other O. sativa subpopulations [40], [42] suggests that there are likely to be many novel alleles that could be mined from the indica subpopulation. Further evidence of the value of this approach in the context of plant breeding comes from the transgressive variation observed in both QTL populations, where some RILs and BILs exceeded the Al tolerance observed in the tolerant tropical and temperate japonica parents, Azucena and Nipponbare, respectively, due to alleles derived from the susceptible indica (IR64) or aus (Kasalath) parents, respectively. The significant differences in Al tolerance among varietal groups and subpopulations, and evidence that different genes and/or alleles contribute to Al tolerance within the major varietal groups, is consistent with Indica and Japonica domestication from pre-differentiated, wild O. rufipogon gene pools that differed in Al tolerance. Future experiments will test this hypothesis by comparing levels of Al tolerance found in wild populations of O. rufipogon. The inherently higher levels of Al tolerance found in the Japonica varietal group may help explain why tropical japonica varieties are so often found in the acid soils of upland environments. Compared to QTL mapping, GWA significantly increases the range of natural variation that can be surveyed in a single experiment and the number of significant regions that are likely to be identified. Furthermore, GWA provides higher resolution than QTL mapping, facilitating fine-mapping and gene discovery. This was illustrated by the two highly significant regions detected by GWA that overlapped with previously reported QTLs. GWA detected a highly significant cluster of 32 SNPs (p = 2.9E-07) on chr. 3 within the indica subpopulation, defining the candidate region to 81 kb window containing 13 genes, while the previously reported QTL interval was 1,720 kb [17], containing 260 genes. Similarly, the Nrat1 locus identified within the aus subpopulation on chromosome 2 initially narrowed the target region to 139 kb containing 27 genes by GWA, while the previously reported [26] QTL interval was 1,360 kb and contained 234 genes. Surprising, the Nrat1 region was not significant in the BIL population, in which the resistant parent (Nipponbare) contained a resistant haplotype at Nrat1 and the susceptible parent (Kasalath) contained the susceptible haplotype at Nrat1. The fact that a significant signal was not detected in the BIL population can likely be explained by one or more of the following: 1) the bias inherent in the small population size (78 BILs), 2) the backcross population structure in which only 11 individuals (14% of BILs) contained the Kasalath allele at the Nrat1 locus and/or 3) the effects of genetic background on the Nrat1 QTL region. The Nrat1 QTL region was detected in one previous QTL study by Ma et al. [23] where a BIL population consisting of 183 lines was used, with Kasalath as the susceptible aus parent and Koshihikari as the tolerant temperate japonica parent [23]. In that study, the Nrat1 QTL region was of minor significance (LOD = 2.81; R2 = 7%), and it is noteworthy that the two other (more significant) QTLs detected in that study were the two QTLs detected in our BIL population using only 78 lines. The fact that the Nrat1 QTL region was not detected in our BIL mapping population and was of low significance in the Ma et al. QTL study suggests that the effect of the Kasalath allele is likely to be influenced by genetic background effects (GXG). In an aus genetic background, the Nrat1 susceptible haplotype explains 40% of the phenotypic variation, and the diversity panel contains enough aus varieties for this to be statistically significant using GWA; however, in the BIL population where Nipponbare served as the recurrent parent, the aus alleles exist in a largely temperate japonica background. Given the extent of GXG observed in inter-sub-population crosses, and the small size of our BIL population, this appears to be the most likely explanation as to why the Nrat locus was not detected in our QTL experiment. Although GWA significantly increased the power and resolution of QTL detection, nearly all the significant loci detected were subpopulation-specific. This is entirely consistent with the strong subpopulation structure in rice and the high correlation of Al tolerance with subpopulation, justifying our GWA analysis on each subpopulation independently. So the question might be asked as to why it is also necessary to conduct GWA in the diversity panel as a whole? The answer to this question lies in the complex biology and demographic or breeding history of O. sativa. In this study GWA was conducted both within and across subpopulations, and it demonstrated that GWA on the diversity panel as a whole leveraged power to detect alleles that were segregating across multiple subpopulations, even if they were rare within any one subpopulation group, while when used on independent subpopulations, it was useful in detecting alleles that segregated only within one or two subpopulations but tended to be fixed in others. This is what would be expected from what we know about the evolutionary history of rice with its examples of shared domestication alleles [35], [75] coupled with myriad subpopulation-specific alleles [41], [48], [76]–[78] that provide each subpopulation with its specific identity and spectrum of ecological adaptations. There are cases in which QTLs discovered by bi-parental mapping are not detected by GWA analysis. One reason for this is that QTL mapping can readily detect alleles that are rare in a diversity panel, are subpopulation-specific, or where the phase of the allelic association differs across subpopulations, while GWA analysis has limited power to do so. This is important in the case of rice, because of the degree of differentiation between the subpopulations and the significant evolutionary differences between the Indica and Japonica varietal groups, as discussed above. Thus, while variation that is strongly correlated with subpopulation structure is undetectable by GWA analysis, these loci can be easily detected by QTL analysis if crosses between sub-populations are used. This is illustrated by the identification of the Al tolerance QTL, (Alt TRG 12.1) encompassing the ART1 locus on chromosome 12. This large-effect QTL (LOD = 7.85, R2 = 0.193) was clearly detected in the RIL population but was not detected by GWA analysis. The QTL mapping populations utilized in this study were of limited population size and thus largely underpowered [79]. As a result it is likely that some QTL effects were overestimated and that other small effect QTL were not detected. Although we cannot be certain of the exact amount of variance explained by a particular QTL, it is reasonable to conclude that the major QTL detected (Alt TRG 12.1) is, in fact, the most significant QTL in the population. GWA mapping also provides a valuable link between functional genomics and natural variation, and in the case of rice, highlights the subpopulation-specific distribution of specific alleles and phenotypes. We implicate the involvement of the STAR2 (chr. 6)/ALS3 (Arabidopsis Al sensitive mutant) gene, previously identified as induced mutations in rice and Arabidopsis, respectively [22], [23], and document the detection of highly resolved, novel Al tolerance loci in the indica and aus subpopulations. This is a critical bridge for germplasm managers and plant breeders who look for alleles of interest in germplasm collections rather than as sequences in GenBank. Analysis of Nrat1 Gene Our strongest example of the value of linking functional genomics and natural variation is illustrated by the GWA region on chromosome 2, where we demonstrate that the aus-specific susceptible haplotype in this region is functionally related to an Nramp gene. This gene was previously identified to have altered expression in the art1 (transcription factor) Al sensitive mutant [19] and was recently reported as Nrat1 (for Nramp aluminum transporter), an Al transporter localized to the plasma membrane of root cells, which when knocked out, enhances Al susceptibility. This is consistent with this transporter serving to mediate Al uptake by moving it directly into root cells, presumably into the vacuole, and away from the root cell wall [20]. Our haplotype analysis of the GWA region on chromosome 2 and sequence analysis of the Nrat1 gene identified putative sensitive and tolerant haplotypes that implicate the Nrat1 gene, and further identified two putative functional polymorphisms specific to the Al sensitive aus accessions. These data provides valuable information for identifying Nrat1 alleles that can be used to test the hypothesis put forth by Xia et al. [20], namely that Al tolerance is conferred by reducing Al concentrations in the cell wall. It will be interesting to see if the sensitive alleles of this gene encode an Nramp transporter that is less effective at mediating Al uptake. Furthermore, the observation that three of the four most Al tolerant aus accessions contain tropical japonica introgressions across this gene region strongly suggests that Al tolerance of aus genotypes can be increased by the targeted introgression of tropical japonica DNA at the Nrat 1 region. Phenotyping Methods Affect QTL Detection One of the objectives of this study was to determine if the Al tolerance index employed (longest root growth [LRG], primary root growth [PRG], or total root growth [TRG]) affected the detection and/or significance of Al tolerance QTL. In a recent publication from our research team, it was demonstrated that significantly different Al tolerance scores were obtained with the different indices [8]. In all previous QTL studies, Al tolerance was determined based on relative root growth (RRG) of the longest root. This study demonstrated that the Al tolerance index has a direct effect on the detection and significance of QTLs. Total root growth (TRG) was the single most powerful Al tolerance index, based on number of QTL detected, significance of QTL and variance explained by the QTL. However, it is relevant to point out that LRG-RRG identified a large-effect QTL (Alt LRG9.1) in the RIL population that was not detected using any other index, and PRG-RRG identified a unique QTL on chromosome 6 where the susceptible Kasalath variety carried the resistance allele. These observations suggest that different root evaluation methods are likely to identify Al tolerance QTLs that confer tolerance mediated by different types of roots, or possibly by different patterns of gene expression detectable only when specific phenotypic evaluation protocols are used. The strongest example of the importance of utilizing the TRG-RRG index is demonstrated by the identification of the Alt TRG 12.1 QTL in the RIL mapping population. The ART1 gene, a C2H2-type zinc finger-type transcription factor that causes Al hypersensitivity when mutated, is located close to the center of the Alt12.1 QTL peak. When this gene was first identified, it was suggested that it was not involved in natural variation of Al tolerance in rice, as no QTL had ever been identified in the region [19]. Based on our results, it is likely that this QTL was not previously identified because relative root growth was measured only based on LRG, rather than on TRG-RRG. Further fine-mapping of this locus, along with sequence and expression analysis, is underway to determine whether the ART1 locus underlies this QTL and to understand the mechanism by which it contributes to natural variation for Al tolerance. Previous studies in other cereals have reported that the correlation of Al tolerance between hydroponics and field conditions is >70% [80] and studies on rice Al tolerance mutants have demonstrated that tolerance/susceptibility observed in hydroponics screens is also observed under soil conditions [24]. To accurately assess the value of the loci detected in this study as targets of selection in rice breeding programs, we are currently developing experiments to determine the effect of the key loci detected in this work under Al-toxic field conditions. Furthermore, four sets of reciprocal NILs (8 NILs total) for the four QTLs detected in the RIL population are being developed to determine the effect of each QTL under both hydroponic and field conditions. Finally, field experiments will be conducted to determine which hydroponic root measurement phenotype (TRG, PRG, or LRG) is the best for predicting a genotypes Al tolerance under field conditions. Implications for Rice Breeding This study provides the most comprehensive analysis of the genetic architecture of Al tolerance in rice to date. It demonstrates the power of whole genome association analysis to identify phenotype-genotype relationships and to integrate disparate pieces of evidence from QTL studies, mutant analysis, and candidate gene evaluation into a coherent set of hypotheses about the genes and genomic regions underlying quantitative variation. By tracing the origin of Al tolerance alleles within and between rice subpopulations, we provide new insights into the evolution and combinatorial potential of different alleles that will be invaluable in breeding new varieties for acid soil environments. This work demonstrates how genetic and phenotypic diversity is partitioned by subpopulation in O. sativa and provides support for the hypothesis that the most efficient approach to enhancing many quantitative traits in rice is to selectively introgress genes/alleles from one subpopulation into another. Our study also lays the foundation for understanding the genetic basis of Al tolerance mechanisms that enable rice to withstand significantly higher levels of Al than do other cereals. It not only facilitates more efficient selection of tolerant genotypes of rice, but it points the way toward using this knowledge to enhance levels of Al tolerance in other plant species. Materials and Methods Plant Growth Conditions and Germplasm Plants were grown hydroponically in a growth chamber as described by Famoso et al. [8]. Al tolerance was determined based on relative root growth (RRG) after three days in Al (160 µM Al3+) or control solution. The hydroponic solution used in this study was chemically designed and optimized for rice Al tolerance screening; for a detailed comparison of the phenotypic procedures employed in this work compared to previously published rice Al tolerance work see Famoso et al. (2010). To obtain uniform seedlings, 80 seeds were germinated and the 30 most uniform seedlings were visually selected and transferred to a control hydroponic solution for a 24 hour adjustment period. After the 24 hour adjustment period, root length was measured with a ruler and the 20 most uniform seedlings were selected and distributed to fresh control solution (0 uM Al3+) or Al treatment solution (160 uM Al3+). Plants were grown in their respective treatments for ∼72 hours and the total root system growth was quantified using an imaging and root quantification system as described by Famoso et al. (2010). The mean total root growth was calculated for Al treated and control plants and RRG was calculated as mean growth (Al)/mean growth (control). The 373 genotypes screened for Al tolerance and used in the association analysis are part of a set of 400 O. sativa genotypes that have been genotyped with 44,000 SNPs as described by Zhao et al. [65]. QTL Analysis and Heritability The QTL populations consisted of a population of 134 recombinant inbred lines (RILs) derived from a cross between Azucena (tolerant tropical japonica) and IR64 (susceptible indica) [67], [70] and a population of 78 backcross introgression lines (BILs) derived from a cross between Nipponbare (tolerant temperate japonica) and Kasalath (susceptible aus) and backcrossed to Nipponbare. The Al3+ activity at which Al tolerance was screened was determined by identifying the Al3+ activity that provided the greatest difference in tolerance between the parents. The tolerant parent of the RIL population, Azucena, and the tolerant parent of the BIL population, Nipponbare, are similar in Al tolerance, whereas the susceptible parent of the RIL population, IR64, is significantly more tolerant than the susceptible parent of the BIL population, Kasalath (Figure 1A). To ensure that a normal distribution was obtained in each population, a different Al3+ concentration was used for each mapping population. The RIL population was screened at 250 µM Al3+ because the Azucena parent is very Al tolerant and the IR64 parent is only moderately susceptible. The BIL population was screened at 120 µM Al3+ because the Kasalath parent is extremely Al sensitive, though the Nipponbare parent is very Al tolerant. Figure 1 displays the Al tolerance of each mapping parent in reference to the 373 genetically diverse rice accessions screened at 160 µM Al3+. The genetic component of the phenotypic variance was calculated as VarG = VarG+Var(GxE)+error. QTL analysis was conducted using composite interval mapping (CIM) function in QTL Cartographer [81]. The significance threshold was determined by 1000 permutations. Genome-Wide Association Analysis Genome-Wide Association Analysis was performed using three approaches in all samples (373) with phenotypes. The first approach was the naïve approach, which is simply the linear regression of phenotype on the genotype for each SNP marker. The second approach was principle component analysis (PCA), where we obtained the four main PCs (principle components) that reflect the global main subpopulations in the sample to correct population structure estimated from software EIGENSOFT. [82]. The first four PCs are included as cofactors in the regression model to correct population structure: . Here β and γ are coefficient vectors for SNP effects and subpopulation PCs respectively. and are the corresponding SNP vector and first 4 PC vectors, and is the random error term. The third approach was the linear mixed model proposed by [62], [63], implemented in the R package EMMA [71], which models the different levels of population structure and relatedness. The model can be written in a matrix form as: y = Xβ+Cγ+Zμ+e where β and γ are the same as above, both of which are fixed effects, and is the random effect accounting for structures and relatedness, is corresponding design matrices, and is the random error term. Assume μ∼N(0,σ2 gK) and e∼N(0,σ2 eI), and K is the IBS matrix, as in [62]. We also conducted GWA using both the naïve approach and the mixed model approach in each of the four main subpopulations (IND, AUS, TEJ, TRJ). For the mixed model, the model was changed to y = Xβ+Zu+e, since there was no main subpopulation division within each subpopulation sample. Linkage disequilibrium decay and haploblocks were calculated at specific chromosome/gene regions using Haploview software [83]. Admixture Analysis Population structure was analyzed employing Expectation-Maximization techniques on an HMM model of per-marker ancestry along a chromosome with a weak linkage model between adjacent markers on the same chromosome induced by the HMM's state dependence on the previous marker's subpopulation assignment (M. Wright, Cornell University, personal communication). The 5,467 SNPs used for admixture analysis were a subset of the 36,901 high quality SNPs on the 44 K chip, and were selected based on their information content and ability to distinguish genetic groups, rather than individuals. The two main criteria used to select the subset of SNPs were a) good genomic distribution and minimal LD among those used in the analysis, and b) MAF>0.05 in at least one subpopulation. The state of the HMM at each marker corresponds to the subpopulation of origin for the marker (and by extension, the region containing the marker and its adjacent markers). The number of a priori distinct subpopulations was K = 5, consistent with that reported previously by Garris et al. 2005 and Ali et al., 2011 [40], [66]. A set of 50 standard non-admixed “control” lines, 10 representing each of the Garris et al. subpopulations, that were genotyped on the 44 K rice SNP array were used to develop and evaluate the method. All 50 lines were correctly assigned to each of the subpopulations and concordant with previous results using STRUCTURE [84], with little or no admixture or introgressions detected. The EM/HMM method was favored over the corresponding “linkage model” of recent versions of STRUCTURE because the EM/HMM model explicitly modeled inbreeding and estimated the inbreeding coefficient for each line independently, permitting lines in various stages of purification or inbreeding to homozygosity to be analyzed. The lines phenotyped in this study that were also genotyped on the 44 K SNP array were then analyzed, combined with these 50 control lines and the local ancestry along chromosomes were assigned by maximizing the state path of the HMM while simultaneously estimating subpopulation specific allele frequencies using the forward-backward algorithm. Using this method, introgressions from a foreign subpopulation into a line with a vast majority of the genetic background originating from a single subpopulation were detected. Supporting Information Figure S1 Distribution of Al tolerance (TRG-RRG) by subpopulation (>80% ancestry). Subpopulation explains 57% of phenotypic variation, however significant variation exist within each subpopulation. IND = indica, TEJ = temperate japonica, TRJ = tropical japonica, G.V. = groupV/aromatic, Admix = admixed lines without 80% ancestry to any one subpopulation. Phenotypic outliers were detected within the indica (five tolerant, one susceptible), temperate japonica (one tolerant, one susceptible), and tropical japonica (two tolerant) subpopulations. (EPS) Click here for additional data file. Figure S2 Distribution of Al tolerance in RIL and BIL mapping populations. A) Al tolerance (TRG-RRG at 250 µM Al3+) observed in 134 RILs derived from Azucena (tolerant tropical japonica) and IR64 (susceptible indica). The RIL population had a mean TRG-RRG of 39%, with a range of 21–67%. Under control conditions, the genetic component of phenotypic variation was 0.46, while in the Al3+ treatment, the genetic component of phenotypic variation was 0.35. Transgressive segregation was observed in 20% of the RILs, with 10% of the population demonstrating greater Al tolerance than Azucena (the tolerant parent) and 10% demonstrating greater susceptibility than IR64 (the susceptible parent). Three Al tolerant outliers were observed in the RIL population. B) Distribution of TRG-RRG Al tolerance at 120 µM Al3+ observed in 78 BILs derived from Nipponbare (tolerant temperate japonica) and Kasalath (susceptible aus). The BIL population had a mean TRG-RRG value of 73%, with a range of 45–120%. In control conditions, the genetic component of phenotypic variation was 0.45 while in the Al3+ treatment, the genetic component of phenotypic variation was 0.55. Transgressive segregation was only observed for increased Al tolerance, as no BIL was more susceptible than the Kasalath parent. One Al tolerant outlier was observed in the BIL population and the Kasalath parent was an Al susceptible outlier. (EPS) Click here for additional data file. Figure S3 Composite interval mapping in the BIL mapping population using three Al tolerance RRG indices. The Y-axis is the LOD score and the horizontal line is the significant LOD threshold based on 1000 permutations. A) Total root growth; B) Primary root growth; C) Longest root growth. (EPS) Click here for additional data file. Figure S4 Quantile–Quantile plot comparing p-values for the mixed model, PCA, and naïve models. Grey dashed line represents the null distribution. Colored solid lines of the observed ordered −log10(p-value) on the Y-axis vs expected log10(p-value) on the X-axis from bottom to top correspond to different methods: Mixed model, PCA and Naïve. The Naïve model does not correct for subpopulation structure or relatedness, resulting in highly inflated −log10 p-values. The PCA model accounts for major subpopulation structure, but not the more subtle correlation among accessions within subpopulation (measured as Identical By State matrix), resulting in a slight inflation of observed −log10 p-values, while the Mixed Model resulted in a slight overcorrection of subpopulation structure and a reduction in the observed −log10 p-values. (EPS) Click here for additional data file. Figure S5 Oneway ANOVA for Al tolerance within the aus subpopulation (55 accessions). The presence/absence of the susceptible haplotype flanking the Nrat1 gene region in the aus subpopulation explained 40% of the phenotypic variation for Al tolerance in the aus subpopulation. (EPS) Click here for additional data file. Table S1 Aluminum tolerance and subpopulation identity of 383 genotypes from the rice diversity panel. Ten genotypes denoted with asterisk (*), did not have existing SNP genotype data at the time of GWA analysis and were not included in the GWA analysis. Subpopulation ancestry was based off 80% identity: AUS = aus; IND = indica; TRJ = tropical japonica; TEJ = temperate japonica; Group V is also known as aromatic. Any line with less than 80% subpopulation identity was considered an admixture (ADMIX). The two major varietal groups are Indica and Japonica; the Indica varietal group is comprised of the aus and indica subpopulations and the Japonica varietal group is comprised of the temperate japonica, tropical japonica, and group V subpopulations. (DOC) Click here for additional data file. Table S2 Evaluation criteria for selecting candidate SNPs based on P-values from EMMA within and across subpopulations and a priori knowledge of candidate genes. SNPs within a 200 kb window around 46 a priori candidate genes were considered a priori SNPs. Other SNPs were those that fell outside of the 200 kb window surrounding candidate genes, including those identified in the 23 QTL regions. (DOC) Click here for additional data file.
Author and article information
Journal
Journal ID (nlm-ta): BMC Genomics
Journal ID (iso-abbrev): BMC Genomics
Title:
BMC Genomics
Publisher:
BioMed Central
ISSN
(Electronic):
1471-2164
Publication date Collection: 2012
Publication date
(Electronic):
29
October
2012
Volume: 13
Page: 568
Affiliations
Article
Publisher ID: 1471-2164-13-568DOI: 10.1186/1471-2164-13-568
PMC ID: 3533575
PubMed ID: 23107476
SO-VID: e38fe1dd-afbd-4465-93a7-4f0ce98dcb8e
Copyright © Copyright ©2012 Li et al.; licensee BioMed Central Ltd.
License:
This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
History
Date
received
: 17
April
2012
Date
accepted
: 18
October
2012
Categories
Subject:
Research Article
ScienceOpen disciplines: Genetics
Data availability:
ScienceOpen disciplines: Genetics