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      Unraveling the Complex Trait of Harvest Index with Association Mapping in Rice ( Oryza sativa L.)

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          Abstract

          Harvest index is a measure of success in partitioning assimilated photosynthate. An improvement of harvest index means an increase in the economic portion of the plant. Our objective was to identify genetic markers associated with harvest index traits using 203 O. sativa accessions. The phenotyping for 14 traits was conducted in both temperate (Arkansas) and subtropical (Texas) climates and the genotyping used 154 SSRs and an indel marker. Heading, plant height and weight, and panicle length had negative correlations, while seed set and grain weight/panicle had positive correlations with harvest index across both locations. Subsequent genetic diversity and population structure analyses identified five groups in this collection, which corresponded to their geographic origins. Model comparisons revealed that different dimensions of principal components analysis (PCA) affected harvest index traits for mapping accuracy, and kinship did not help. In total, 36 markers in Arkansas and 28 markers in Texas were identified to be significantly associated with harvest index traits. Seven and two markers were consistently associated with two or more harvest index correlated traits in Arkansas and Texas, respectively. Additionally, four markers were constitutively identified at both locations, while 32 and 24 markers were identified specifically in Arkansas and Texas, respectively. Allelic analysis of four constitutive markers demonstrated that allele 253 bp of RM431 had significantly greater effect on decreasing plant height, and 390 bp of RM24011 had the greatest effect on decreasing panicle length across both locations. Many of these identified markers are located either nearby or flanking the regions where the QTLs for harvest index have been reported. Thus, the results from this association mapping study complement and enrich the information from linkage-based QTL studies and will be the basis for improving harvest index directly and indirectly in rice.

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          Rice yields decline with higher night temperature from global warming.

          S PENG, J. Huang, J. E. Sheehy … (2004)
          The impact of projected global warming on crop yields has been evaluated by indirect methods using simulation models. Direct studies on the effects of observed climate change on crop growth and yield could provide more accurate information for assessing the impact of climate change on crop production. We analyzed weather data at the International Rice Research Institute Farm from 1979 to 2003 to examine temperature trends and the relationship between rice yield and temperature by using data from irrigated field experiments conducted at the International Rice Research Institute Farm from 1992 to 2003. Here we report that annual mean maximum and minimum temperatures have increased by 0.35 degrees C and 1.13 degrees C, respectively, for the period 1979-2003 and a close linkage between rice grain yield and mean minimum temperature during the dry cropping season (January to April). Grain yield declined by 10% for each 1 degrees C increase in growing-season minimum temperature in the dry season, whereas the effect of maximum temperature on crop yield was insignificant. This report provides a direct evidence of decreased rice yields from increased nighttime temperature associated with global warming.
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            Association mapping in structured populations.

            J. Pritchard, M. Stephens, N. Rosenberg … (2000)
            The use, in association studies, of the forthcoming dense genomewide collection of single-nucleotide polymorphisms (SNPs) has been heralded as a potential breakthrough in the study of the genetic basis of common complex disorders. A serious problem with association mapping is that population structure can lead to spurious associations between a candidate marker and a phenotype. One common solution has been to abandon case-control studies in favor of family-based tests of association, such as the transmission/disequilibrium test (TDT), but this comes at a considerable cost in the need to collect DNA from close relatives of affected individuals. In this article we describe a novel, statistically valid, method for case-control association studies in structured populations. Our method uses a set of unlinked genetic markers to infer details of population structure, and to estimate the ancestry of sampled individuals, before using this information to test for associations within subpopulations. It provides power comparable with the TDT in many settings and may substantially outperform it if there are conflicting associations in different subpopulations.
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              Structure of linkage disequilibrium and phenotypic associations in the maize genome.

              D L Remington, J Thornsberry, Y Matsuoka … (2001)
              Association studies based on linkage disequilibrium (LD) can provide high resolution for identifying genes that may contribute to phenotypic variation. We report patterns of local and genome-wide LD in 102 maize inbred lines representing much of the worldwide genetic diversity used in maize breeding, and address its implications for association studies in maize. In a survey of six genes, we found that intragenic LD generally declined rapidly with distance (r(2) < 0.1 within 1500 bp), but rates of decline were highly variable among genes. This rapid decline probably reflects large effective population sizes in maize during its evolution and high levels of recombination within genes. A set of 47 simple sequence repeat (SSR) loci showed stronger evidence of genome-wide LD than did single-nucleotide polymorphisms (SNPs) in candidate genes. LD was greatly reduced but not eliminated by grouping lines into three empirically determined subpopulations. SSR data also supplied evidence that divergent artificial selection on flowering time may have played a role in generating population structure. Provided the effects of population structure are effectively controlled, this research suggests that association studies show great promise for identifying the genetic basis of important traits in maize with very high resolution.
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                Author and article information

                Contributors
                : Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosone
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, USA )
                ISSN (Electronic): 1932-6203
                Publication date Collection: 2012
                Publication date (Electronic): 23 January 2012
                Volume: 7
                Issue: 1
                Electronic Location Identifier: e29350
                Affiliations
                [1 ]State Key Lab of Rice Biology, International Atomic Energy Agency Collaborating Center, Zhejiang University, Hangzhou, People's Republic of China
                [2 ]Agricultural Research Service, United States Department of Agriculture, Dale Bumpers National Rice Research Center, Stuttgart, Arkansas, United States of America
                [3 ]Institue of Horticulture, Zhejiang Academy of Agricultural Sciences, Hangzhou, People's Republic of China
                [4 ]University of Arkansas, Rice Research and Extension Center, Stuttgart, Arkansas, United States of America
                [5 ]Agricultural Research Service, United States Department of Agriculture, Southern Plains Area, College Station, Texas, United States of America
                United States Department of Agriculture, Agricultural Research Service, United States of America
                Author notes

                Conceived and designed the experiments: WY AM DW KM. Performed the experiments: XL LJ AJ WY. Analyzed the data: XL LJ KY HA. Contributed reagents/materials/analysis tools: HA AJ. Wrote the paper: XL WY AM DW KM.

                Article
                Publisher ID: PONE-D-11-06191
                DOI: 10.1371/journal.pone.0029350
                PMC ID: 3264563
                PubMed ID: 22291889
                SO-VID: f4d32747-9b28-430e-ad5c-64b7e05af3fc
                Copyright © This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
                History
                Date received : 2 April 2011
                Date accepted : 27 November 2011
                Page count
                Pages: 10
                Categories
                Subject: Research Article
                Subject: Agriculture
                Subject: Agricultural Biotechnology
                Subject: Agricultural Production
                Subject: Crops
                Subject: Cereals
                Subject: Biology
                Subject: Computational Biology
                Subject: Developmental Biology
                Subject: Ecology
                Subject: Plant Ecology
                Subject: Genetics
                Subject: Genomics
                Subject: Model Organisms
                Subject: Plant and Algal Models
                Subject: Plant Science
                Subject: Plant Ecology
                Subject: Plants

                ScienceOpen disciplines: Uncategorized
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                ScienceOpen disciplines: Uncategorized

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