Journal articles 2015
Documents
QTL IciMapping: Integrated software for genetic linkage map construction and quantitative trait locus mapping in biparental populations
Meng L, Li H, Zhang L and Wang J (2015). QTL IciMapping: Integrated software for genetic linkage map construction and quantitative trait locus mapping in biparental populations. The Crop Journal (DOI: 10.1016/j.cj.2015.01.001). In press; published online on 23 February 2015. (G8009.10)
Abstract: QTL IciMapping is freely available public software capable of building high-density linkage maps and mapping quantitative trait loci (QTL) in biparental populations. Eight functionalities are integrated in this software package: (1) BIN: binning of redundant markers; (2) MAP: construction of linkage maps in biparental populations; (3) CMP: consensus map construction from multiple linkage maps sharing common markers; (4) SDL: mapping of segregation distortion loci; (5) BIP: mapping of additive, dominant, and digenic epistasis genes; (6) MET: QTL-by-environment interaction analysis; (7) CSL: mapping of additive and digenic epistasis genes with chromosome segment substitution lines; and (8) NAM: QTL mapping in NAM populations. Input files can be arranged in plain text, MS Excel 2003, or MS Excel 2007 formats. Output files have the same prefix name as the input but with different extensions. As examples, there are two output files in BIN, one for summarizing the identified bin groups and deleted markers in each bin, and the other for using the MAP functionality. Eight output files are generated by MAP, including summary of the completed linkage maps, Mendelian ratio test of individual markers, estimates of recombination frequencies, LOD scores, and genetic distances, and the input files for using the BIP, SDL, and MET functionalities. More than 30 output files are generated by BIP, including results at all scanning positions, identified QTL, permutation tests, and detection powers for up to six mapping methods. Three supplementary tools have also been developed to display completed genetic linkage maps, to estimate recombination frequency between two loci, and to perform analysis of variance for multi-environmental trials.
Meng L, Li H, Zhang L and Wang J (2015). QTL IciMapping: Integrated software for genetic linkage map construction and quantitative trait locus mapping in biparental populations. The Crop Journal (DOI: 10.1016/j.cj.2015.01.001). In press; published online on 23 February 2015. (G8009.10)
Abstract: QTL IciMapping is freely available public software capable of building high-density linkage maps and mapping quantitative trait loci (QTL) in biparental populations. Eight functionalities are integrated in this software package: (1) BIN: binning of redundant markers; (2) MAP: construction of linkage maps in biparental populations; (3) CMP: consensus map construction from multiple linkage maps sharing common markers; (4) SDL: mapping of segregation distortion loci; (5) BIP: mapping of additive, dominant, and digenic epistasis genes; (6) MET: QTL-by-environment interaction analysis; (7) CSL: mapping of additive and digenic epistasis genes with chromosome segment substitution lines; and (8) NAM: QTL mapping in NAM populations. Input files can be arranged in plain text, MS Excel 2003, or MS Excel 2007 formats. Output files have the same prefix name as the input but with different extensions. As examples, there are two output files in BIN, one for summarizing the identified bin groups and deleted markers in each bin, and the other for using the MAP functionality. Eight output files are generated by MAP, including summary of the completed linkage maps, Mendelian ratio test of individual markers, estimates of recombination frequencies, LOD scores, and genetic distances, and the input files for using the BIP, SDL, and MET functionalities. More than 30 output files are generated by BIP, including results at all scanning positions, identified QTL, permutation tests, and detection powers for up to six mapping methods. Three supplementary tools have also been developed to display completed genetic linkage maps, to estimate recombination frequency between two loci, and to perform analysis of variance for multi-environmental trials.
Full article
QTL associated with lateral root plasticity in response to soil moisture fluctuation stress in rice
Niones JM, Inukai Y, Suralta RR and Yamauchi A (2015). QTL associated with lateral root plasticity in response to soil moisture fluctuation stress in rice. Plant and Soil Published online: 19 February 2015 (DOI: 10.1007/s11104-015-2404-x). (G3008.06)
Abstract: Background Lateral root (LR) plasticity is a key trait that plays a significant role in plant adaptation to fluctuating soil moisture stressed environments. We previously had demonstrated that promoted LR production (LR plasticity) contributed to the maintenance in shoot dry matter production and grain yield under soil moisture fluctuation (SMF) stress.
Aim To identify quantitative trait loci (QTLs) associated with LR plasticity under SMF condition and their contributions to shoot dry matter production.
Methods F2 lines derived from Nipponbare x chromosome segment substituted line number 47 (Nipponbare/Kasalath) backcrosses were used to analyze ten substituted chromosome regions with ‘Kasalath’ allele that are associated with root plasticity under SMF stress.
Results We mapped two closely linked QTLs on chromosome 12 region namely qTLRN-12 at seedling stage and qLLRN-12 at vegetative stage. Under SMF conditions, qTLRN-12 found at the flanking markers between TG154 and RM247 is responsible for the plasticity in total LR number while qLLRN-12 detected at the flanking markers between RM6296 and TG156 is associated with plasticity in L-type LR production. Kasalath genome contributed the corresponding alleles for increasing the mentioned root traits that resulted in a significant increase in shoot dry matter production under SMF stress.
Conclusion We identified two QTLs associated with LR plasticity on chromosome 12 which significantly contributed to the greater root system development and maintenance of total dry matter production under SMF stress.
Niones JM, Inukai Y, Suralta RR and Yamauchi A (2015). QTL associated with lateral root plasticity in response to soil moisture fluctuation stress in rice. Plant and Soil Published online: 19 February 2015 (DOI: 10.1007/s11104-015-2404-x). (G3008.06)
Abstract: Background Lateral root (LR) plasticity is a key trait that plays a significant role in plant adaptation to fluctuating soil moisture stressed environments. We previously had demonstrated that promoted LR production (LR plasticity) contributed to the maintenance in shoot dry matter production and grain yield under soil moisture fluctuation (SMF) stress.
Aim To identify quantitative trait loci (QTLs) associated with LR plasticity under SMF condition and their contributions to shoot dry matter production.
Methods F2 lines derived from Nipponbare x chromosome segment substituted line number 47 (Nipponbare/Kasalath) backcrosses were used to analyze ten substituted chromosome regions with ‘Kasalath’ allele that are associated with root plasticity under SMF stress.
Results We mapped two closely linked QTLs on chromosome 12 region namely qTLRN-12 at seedling stage and qLLRN-12 at vegetative stage. Under SMF conditions, qTLRN-12 found at the flanking markers between TG154 and RM247 is responsible for the plasticity in total LR number while qLLRN-12 detected at the flanking markers between RM6296 and TG156 is associated with plasticity in L-type LR production. Kasalath genome contributed the corresponding alleles for increasing the mentioned root traits that resulted in a significant increase in shoot dry matter production under SMF stress.
Conclusion We identified two QTLs associated with LR plasticity on chromosome 12 which significantly contributed to the greater root system development and maintenance of total dry matter production under SMF stress.
Abiotic stress responses in legumes: Strategies used to cope with environmental challenges
Araújo SS, Beebe S, Crespi M, Delbreil B, González EM, Gruber V, Lejeune-Henaut I, Link W, Monteros MJ, Prats E, Rao I, Vadez V and Vaz Patto MC (2015). Abiotic stress responses in legumes: Strategies used to cope with environmental challenges. Critical Reviews in Plant Sciences 34(1–3):237–280 (DOI: 10.1080/07352689.2014.898450). First published online in October 2014. Not open access; view abstract.
Araújo SS, Beebe S, Crespi M, Delbreil B, González EM, Gruber V, Lejeune-Henaut I, Link W, Monteros MJ, Prats E, Rao I, Vadez V and Vaz Patto MC (2015). Abiotic stress responses in legumes: Strategies used to cope with environmental challenges. Critical Reviews in Plant Sciences 34(1–3):237–280 (DOI: 10.1080/07352689.2014.898450). First published online in October 2014. Not open access; view abstract.
Novel QTLs in an interspecific backcross Oryza sativa x Oryza glaberrima for resistance to iron toxicity in rice
Dufey I, Draye X, Lutts S, Lorieux M, Martinez C and Bertin P (2015). Novel QTLs in an interspecific backcross Oryza sativa x Oryza glaberrima for resistance to iron toxicity in rice. Euphytica Published online: 1 February 2015 (DOI 10.1007/s10681-014-1342-7). Not open access; view abstract. (G3005.10)
Dufey I, Draye X, Lutts S, Lorieux M, Martinez C and Bertin P (2015). Novel QTLs in an interspecific backcross Oryza sativa x Oryza glaberrima for resistance to iron toxicity in rice. Euphytica Published online: 1 February 2015 (DOI 10.1007/s10681-014-1342-7). Not open access; view abstract. (G3005.10)
Interrelationship among yield and yield contributing traits in RILs and their parents in Chickpea (Cicer arietinum L.)
Joshi P and Yasin M (2015). Interrelationship among yield and yield contributing traits in RILs and their parents in Chickpea (Cicer arietinum L.). Indian Journal of Applied and Pure Biology 30(1):97–100. (G7009.02)
Abstract: Two hundred fifty two recombinant inbred lines generated from diverse Desi (ICC 283) and Kabuli (ICC 8261) parents were grown in RBD during rabi 2011-12 and interrelationship amongst yield traits was worked out. Seed yield showed positive association with biological yield, harvest index & plant height and negative with days to 50% flowering & days to maturity. Good plant vigour with high biological yield & plant height directly affect the yield. Long reproductive phase influence the high sink transfer and resulted high harvest index. Late flowering and late maturity observed in prostrate and spreading plant growth habit bearing RILs showed negative association with seed yield. The path analysis showed biological yield, plant height and harvest index is directly contributing to seed yield but delayed flowering and maturity increase vegetative period and reduce reproductive phase specially in prostrate and spreading plant growth habits bearing RILs showed negative indirect effect on seed yield.
Joshi P and Yasin M (2015). Interrelationship among yield and yield contributing traits in RILs and their parents in Chickpea (Cicer arietinum L.). Indian Journal of Applied and Pure Biology 30(1):97–100. (G7009.02)
Abstract: Two hundred fifty two recombinant inbred lines generated from diverse Desi (ICC 283) and Kabuli (ICC 8261) parents were grown in RBD during rabi 2011-12 and interrelationship amongst yield traits was worked out. Seed yield showed positive association with biological yield, harvest index & plant height and negative with days to 50% flowering & days to maturity. Good plant vigour with high biological yield & plant height directly affect the yield. Long reproductive phase influence the high sink transfer and resulted high harvest index. Late flowering and late maturity observed in prostrate and spreading plant growth habit bearing RILs showed negative association with seed yield. The path analysis showed biological yield, plant height and harvest index is directly contributing to seed yield but delayed flowering and maturity increase vegetative period and reduce reproductive phase specially in prostrate and spreading plant growth habits bearing RILs showed negative indirect effect on seed yield.
Translational genomics in agriculture: Some examples in grain legumes
Varshney RK, Kudapa H, Pazhamala L, Chitikineni A, Thudi M, Bohra A, Gaur PM, Janila P, Fikre A, Kimurto P and Ellis N (2015). Translational genomics in agriculture: Some examples in grain legumes. Critical Reviews in Plant Sciences 34(1–3):169–194 (DOI: 10.1080/07352689.2014.897909). First published online in October 2014.
Recent advances in genomics and associated disciplines like bioinformatics have made it possible to develop genomic resources, such as large-scale sequence data for any crop species. While these datasets have been proven very useful for the understanding of genome architecture and dynamics as well as facilitating the discovery of genes, an obligation for, and challenge to the scientific community is to translate genome information to develop products, i.e. superior lines for trait(s) of interest. We call this approach, “translational genomics in agriculture” (TGA). TGA is currently in practice for cereal crops, such as maize (Zea mays) and rice (Oryza sativa), mainly in developed countries and by the private sector; progress has been slow for legume crops. Grown globally on 62.8 million ha with a production of 53.2 million tons and a value of nearly 24.2 billion dollars, the majority of these legumes have low crop productivity (<1 ton/ hectare) and are in the developing countries of sub Saharan Africa, Asia and South America. Interestingly, the last five years have seen enormous progress in genomics for these legume crops. Therefore, it is time to implement TGA in legume crops in order to enhance crop productivity and to ensure food security in developing countries. Prospects, as well as some success stories of TGA, in addition to advances in genomics, trait mapping and gene expression analysis are discussed for five leading legume crops, chickpea (Cicer arietinum), common bean (Phaseolus vulgaris), groundnut (Arachis hypogaea), pigeonpea (Cajanus cajan) and soybean (Glycine max). Some efforts have also been outlined to initiate/ accelerate TGA in three additional legume crops namely faba bean (Vicia faba), lentil (Lens culinaris) and pea (Pisum sativum).
Varshney RK, Kudapa H, Pazhamala L, Chitikineni A, Thudi M, Bohra A, Gaur PM, Janila P, Fikre A, Kimurto P and Ellis N (2015). Translational genomics in agriculture: Some examples in grain legumes. Critical Reviews in Plant Sciences 34(1–3):169–194 (DOI: 10.1080/07352689.2014.897909). First published online in October 2014.
Recent advances in genomics and associated disciplines like bioinformatics have made it possible to develop genomic resources, such as large-scale sequence data for any crop species. While these datasets have been proven very useful for the understanding of genome architecture and dynamics as well as facilitating the discovery of genes, an obligation for, and challenge to the scientific community is to translate genome information to develop products, i.e. superior lines for trait(s) of interest. We call this approach, “translational genomics in agriculture” (TGA). TGA is currently in practice for cereal crops, such as maize (Zea mays) and rice (Oryza sativa), mainly in developed countries and by the private sector; progress has been slow for legume crops. Grown globally on 62.8 million ha with a production of 53.2 million tons and a value of nearly 24.2 billion dollars, the majority of these legumes have low crop productivity (<1 ton/ hectare) and are in the developing countries of sub Saharan Africa, Asia and South America. Interestingly, the last five years have seen enormous progress in genomics for these legume crops. Therefore, it is time to implement TGA in legume crops in order to enhance crop productivity and to ensure food security in developing countries. Prospects, as well as some success stories of TGA, in addition to advances in genomics, trait mapping and gene expression analysis are discussed for five leading legume crops, chickpea (Cicer arietinum), common bean (Phaseolus vulgaris), groundnut (Arachis hypogaea), pigeonpea (Cajanus cajan) and soybean (Glycine max). Some efforts have also been outlined to initiate/ accelerate TGA in three additional legume crops namely faba bean (Vicia faba), lentil (Lens culinaris) and pea (Pisum sativum).
Development of a SNP genotyping panel for detecting polymorphisms in Oryza glaberrima/O. sativa interspecific crosses
Pariasca-Tanaka J, Lorieux M, He C, McCouch S, Thomson MJ and Wissuwa M (2015). Development of a SNP genotyping panel for detecting polymorphisms in Oryza glaberrima/O. sativa interspecific crosses. Euphytica 201(1):67–78 (DOI: 10.1007/s10681-014-1183-4). First published in June 2014. Not open access; view abstract. (G3005.10)
Pariasca-Tanaka J, Lorieux M, He C, McCouch S, Thomson MJ and Wissuwa M (2015). Development of a SNP genotyping panel for detecting polymorphisms in Oryza glaberrima/O. sativa interspecific crosses. Euphytica 201(1):67–78 (DOI: 10.1007/s10681-014-1183-4). First published in June 2014. Not open access; view abstract. (G3005.10)
