IC4R001-GWAS-2011-21829395 - Revision history

Zhennan at 09:48, 21 June 2016

2016-06-21T09:48:37Z

Zhennan: /* Research Findings */

2016-06-21T09:27:28Z

‎Research Findings

Zhennan: /* Research Findings */

2016-06-21T09:25:07Z

‎Research Findings

Zhennan: /* Research Findings */

2016-06-21T09:16:32Z

‎Research Findings

Xysj2012: /* The Background of This Project */

2016-06-21T09:04:26Z

‎The Background of This Project

Xysj2012 at 09:04, 21 June 2016

2016-06-21T09:04:12Z

Xysj2012 at 09:03, 21 June 2016

2016-06-21T09:03:47Z

Xysj2012: /* Research Findings */

2016-06-21T09:03:29Z

‎Research Findings

Zhennan: /* Research Findings */

2016-06-21T09:00:13Z

‎Research Findings

Zhennan: /* Research Findings */

2016-06-21T08:54:53Z

‎Research Findings

← Older revision		Revision as of 09:48, 21 June 2016
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	==Corresponding Author==		==Corresponding Author==
−	''' Susan R. McCouch'''(srm4@cornell.edu)	+	* ''' Susan R. McCouch'''(srm4@cornell.edu)

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 * To identify Al tolerance loci based on genome-wide association(GWA) mapping, we used an existing genotypic dataset consisting of 36,901 SNPs, 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 1)'''. The Efficient Mixed-Model Association (EMMA) 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. 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, demonstrating that results from each GWA method has limitations when used across all germplasm in this highly structured diversity panel.
 * 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, 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 2). 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 (7 th percentile, mean RRG=0.16) of the 373 accessions screened. Haplotype 1 explained 40% of the phenotypic variation for Al tolerance within the aus subpopulation. 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).
 == Labs working on this Project ==

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 * Two immortalized QTL mapping populations were analyzed for Al tolerance. One consisted of 134 recombinant inbred lines (RIL) derived from the cross IR64/Azucena , and the other was comprised of 78 backcross inbred lines (BIL) derived from the cross Nipponbare/Kasalath//Nipponbare. 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 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. 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.<br><br>
 * To identify Al tolerance loci based on genome-wide association(GWA) mapping, we used an existing genotypic dataset consisting of 36,901 SNPs, 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 1)'''. The Efficient Mixed-Model Association (EMMA) 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. 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, demonstrating that results from each GWA method has limitations when used across all germplasm in this highly structured diversity panel.
 == Labs working on this Project ==

@@ Line 12: / Line 12: @@
 * Two immortalized QTL mapping populations were analyzed for Al tolerance. One consisted of 134 recombinant inbred lines (RIL) derived from the cross IR64/Azucena , and the other was comprised of 78 backcross inbred lines (BIL) derived from the cross Nipponbare/Kasalath//Nipponbare. 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 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. 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.<br><br>
 * To identify Al tolerance loci based on genome-wide association(GWA) mapping, we used an existing genotypic dataset consisting of 36,901 SNPs, 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 1)'''. The Efficient Mixed-Model Association (EMMA) 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. 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, demonstrating that results from each GWA method has limitations when used across all germplasm in this highly structured diversity panel.
 == Labs working on this Project ==

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 ==The Background of This Project==
-[[File:gwas-1.PNG|1000px|thumb|right|'''Figure 1.''' '' GWA Analysis of Al Tolerance within and across Rice Subpopulations.'']]
+[[File:gwas-1.PNG|700px|thumb|right|'''Figure 1.''' '' GWA Analysis of Al Tolerance within and across Rice Subpopulations.'']]
 * While rice (Oryza sativa) is significantly more Al tolerant than other cereals, no genes underlying Al tolerance in rice have been reported. Using genome-wide association(GWA) and bi-parental QTL mapping, we investigated the genetic architecture of Al tolerance in rice. Japonica varieties were twice as Al tolerant as indica and aus varieties. Overall, 57% of the phenotypic variation was correlated with subpopulation, consistent with observations that different genes and genomic regions were associated with Al tolerance in different subpopulations. Four regions identified by GWA co-localized with a priori candidate genes, and two highly significant regions co-localized with previously identified quantitative trait loci(QTL). Haplotype and sequence analysis around the candidate gene, Nrat1, identified a susceptible haplotype explaining 40% of the Al tolerance variation within the aus subpopulation and three non-synonymous mutations within Nrat1 that were predictive of Al sensitivity. Using Indica 6 Japonica mapping populations, we identified QTLs associated with transgressive variation where alleles from a susceptible indica or aus parent enhanced Al tolerance in a tolerant japonica background. This work demonstrates the importance of subpopulation in interpreting and manipulating complex traits in rice and provides a roadmap for breeders aiming to capture genetic value from phenotypically inferior lines.

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 ==Research Findings==
 * Two immortalized QTL mapping populations were analyzed for Al tolerance. One consisted of 134 recombinant inbred lines (RIL) derived from the cross IR64/Azucena , and the other was comprised of 78 backcross inbred lines (BIL) derived from the cross Nipponbare/Kasalath//Nipponbare. 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 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. 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.<br><br>
-* To identify Al tolerance loci based on genome-wide association(GWA) mapping, we used an existing genotypic dataset consisting of 36,901 SNPs, 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) 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. 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, demonstrating that results from each GWA method has limitations when used across all germplasm in this highly structured diversity panel.
+* To identify Al tolerance loci based on genome-wide association(GWA) mapping, we used an existing genotypic dataset consisting of 36,901 SNPs, 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 1)'''. The Efficient Mixed-Model Association (EMMA) 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. 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, demonstrating that results from each GWA method has limitations when used across all germplasm in this highly structured diversity panel.
 [[File:gwas-1.PNG|500px|thumb|center|'''Figure 1.''' '' GWA Analysis of Al Tolerance within and across Rice Subpopulations.'']]