Limits...
Multiple interval QTL mapping and searching for PSTOL1 homologs associated with root morphology, biomass accumulation and phosphorus content in maize seedlings under low-P.

Azevedo GC, Cheavegatti-Gianotto A, Negri BF, Hufnagel B, E Silva Lda C, Magalhaes JV, Garcia AA, Lana UG, de Sousa SM, Guimaraes CT - BMC Plant Biol. (2015)

Bottom Line: Multiple interval mapping models for single (MIM) and multiple traits (MT-MIM) were combined and revealed 13 genomic regions significantly associated with the target traits in a complementary way.QTL mapping strategies adopted in this study revealed complementary results for single and multiple traits with high accuracy.Some QTLs, mainly the ones that were also associated with yield performance in other studies, can be good targets for marker-assisted selection to improve P-use efficiency in maize.

View Article: PubMed Central - PubMed

Affiliation: Departamento de Biologia Geral, Universidade Federal de Minas Gerais, Avenida Presidente Antônio Carlos, 6627, Belo Horizonte, MG, 31270-901, Brazil. gabrielcorradiazevedo@yahoo.com.br.

ABSTRACT

Background: Modifications in root morphology are important strategies to maximize soil exploitation under phosphorus starvation in plants. Here, we used two multiple interval models to map QTLs related to root traits, biomass accumulation and P content in a maize RIL population cultivated in nutrient solution. In addition, we searched for putative maize homologs to PSTOL1, a gene responsible to enhance early root growth, P uptake and grain yield in rice and sorghum.

Results: Based on path analysis, root surface area was the root morphology component that most strongly contributed to total dry weight and to P content in maize seedling under low-P availability. Multiple interval mapping models for single (MIM) and multiple traits (MT-MIM) were combined and revealed 13 genomic regions significantly associated with the target traits in a complementary way. The phenotypic variances explained by all QTLs and their epistatic interactions using MT-MIM (23.4 to 35.5 %) were higher than in previous studies, and presented superior statistical power. Some of these QTLs were coincident with QTLs for root morphology traits and grain yield previously mapped, whereas others harbored ZmPSTOL candidate genes, which shared more than 55 % of amino acid sequence identity and a conserved serine/threonine kinase domain with OsPSTOL1. Additionally, four ZmPSTOL candidate genes co-localized with QTLs for root morphology, biomass accumulation and/or P content were preferentially expressed in roots of the parental lines that contributed the alleles enhancing the respective phenotypes.

Conclusions: QTL mapping strategies adopted in this study revealed complementary results for single and multiple traits with high accuracy. Some QTLs, mainly the ones that were also associated with yield performance in other studies, can be good targets for marker-assisted selection to improve P-use efficiency in maize. Based on the co-localization with QTLs, the protein domain conservation and the coincidence of gene expression, we selected novel maize genes as putative homologs to PSTOL1 that will require further validation studies.

No MeSH data available.


QTLs identified for root traits, seedling dry weight and P content using single and multiple traits MIM analyses. The markers are represented as vertical traces along the horizontal lines, which represent the chromosomes and are numbered in centiMorgans (cM). The candidate genes are depicted below the red vertical traces. QTL profiles for single trait MIM are shown as colored lines according to the legend for root length (RL), root average diameter (RD), root surface area (SA), surface area of fine roots (SA2), root:shoot ratio (R:S), total seedling dry weight (TDW) and total P content (Pcont). Multi trait QTL profiles are shown as black line. The QTL peaks are depicted with an inverted triangle colored according to the legend followed by the bin. The confidence interval (95 %) for each QTL is represented by a horizontal line above the chromosomes colored according to the legend
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Fig2: QTLs identified for root traits, seedling dry weight and P content using single and multiple traits MIM analyses. The markers are represented as vertical traces along the horizontal lines, which represent the chromosomes and are numbered in centiMorgans (cM). The candidate genes are depicted below the red vertical traces. QTL profiles for single trait MIM are shown as colored lines according to the legend for root length (RL), root average diameter (RD), root surface area (SA), surface area of fine roots (SA2), root:shoot ratio (R:S), total seedling dry weight (TDW) and total P content (Pcont). Multi trait QTL profiles are shown as black line. The QTL peaks are depicted with an inverted triangle colored according to the legend followed by the bin. The confidence interval (95 %) for each QTL is represented by a horizontal line above the chromosomes colored according to the legend

Mentions: A linkage map was constructed using 292 markers that covered 1787.5 cM of the maize genome, with an average interval of 6.1 cM between adjacent markers (Additional file 2: Figure S1). In addition to SSR and SNP markers, six ZmPSTOL candidate genes and three genes previously associated with root morphology (Rth1, Bk2l3, and Rtcs) were mapped to their predicted physical positions. Multiple interval mapping models for single (MIM) and multiple traits (MT-MIM) provided statistical evidence for 13 genomic regions harboring QTLs on all maize chromosomes, with the exception of chromosome 5 (Tables 3 and 4). The QTL regions were named using the trait initials if they were detected through single trait analysis or as “multi” if they were detected by multiple trait analysis, followed by their genetic position in bin (Fig. 2). A bin is the interval of approximately 20 cM between two core markers previously defined and mapped in maize [47], which are designated with the chromosome number followed by a two-digit decimal.Table 3


Multiple interval QTL mapping and searching for PSTOL1 homologs associated with root morphology, biomass accumulation and phosphorus content in maize seedlings under low-P.

Azevedo GC, Cheavegatti-Gianotto A, Negri BF, Hufnagel B, E Silva Lda C, Magalhaes JV, Garcia AA, Lana UG, de Sousa SM, Guimaraes CT - BMC Plant Biol. (2015)

QTLs identified for root traits, seedling dry weight and P content using single and multiple traits MIM analyses. The markers are represented as vertical traces along the horizontal lines, which represent the chromosomes and are numbered in centiMorgans (cM). The candidate genes are depicted below the red vertical traces. QTL profiles for single trait MIM are shown as colored lines according to the legend for root length (RL), root average diameter (RD), root surface area (SA), surface area of fine roots (SA2), root:shoot ratio (R:S), total seedling dry weight (TDW) and total P content (Pcont). Multi trait QTL profiles are shown as black line. The QTL peaks are depicted with an inverted triangle colored according to the legend followed by the bin. The confidence interval (95 %) for each QTL is represented by a horizontal line above the chromosomes colored according to the legend
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4492167&req=5

Fig2: QTLs identified for root traits, seedling dry weight and P content using single and multiple traits MIM analyses. The markers are represented as vertical traces along the horizontal lines, which represent the chromosomes and are numbered in centiMorgans (cM). The candidate genes are depicted below the red vertical traces. QTL profiles for single trait MIM are shown as colored lines according to the legend for root length (RL), root average diameter (RD), root surface area (SA), surface area of fine roots (SA2), root:shoot ratio (R:S), total seedling dry weight (TDW) and total P content (Pcont). Multi trait QTL profiles are shown as black line. The QTL peaks are depicted with an inverted triangle colored according to the legend followed by the bin. The confidence interval (95 %) for each QTL is represented by a horizontal line above the chromosomes colored according to the legend
Mentions: A linkage map was constructed using 292 markers that covered 1787.5 cM of the maize genome, with an average interval of 6.1 cM between adjacent markers (Additional file 2: Figure S1). In addition to SSR and SNP markers, six ZmPSTOL candidate genes and three genes previously associated with root morphology (Rth1, Bk2l3, and Rtcs) were mapped to their predicted physical positions. Multiple interval mapping models for single (MIM) and multiple traits (MT-MIM) provided statistical evidence for 13 genomic regions harboring QTLs on all maize chromosomes, with the exception of chromosome 5 (Tables 3 and 4). The QTL regions were named using the trait initials if they were detected through single trait analysis or as “multi” if they were detected by multiple trait analysis, followed by their genetic position in bin (Fig. 2). A bin is the interval of approximately 20 cM between two core markers previously defined and mapped in maize [47], which are designated with the chromosome number followed by a two-digit decimal.Table 3

Bottom Line: Multiple interval mapping models for single (MIM) and multiple traits (MT-MIM) were combined and revealed 13 genomic regions significantly associated with the target traits in a complementary way.QTL mapping strategies adopted in this study revealed complementary results for single and multiple traits with high accuracy.Some QTLs, mainly the ones that were also associated with yield performance in other studies, can be good targets for marker-assisted selection to improve P-use efficiency in maize.

View Article: PubMed Central - PubMed

Affiliation: Departamento de Biologia Geral, Universidade Federal de Minas Gerais, Avenida Presidente Antônio Carlos, 6627, Belo Horizonte, MG, 31270-901, Brazil. gabrielcorradiazevedo@yahoo.com.br.

ABSTRACT

Background: Modifications in root morphology are important strategies to maximize soil exploitation under phosphorus starvation in plants. Here, we used two multiple interval models to map QTLs related to root traits, biomass accumulation and P content in a maize RIL population cultivated in nutrient solution. In addition, we searched for putative maize homologs to PSTOL1, a gene responsible to enhance early root growth, P uptake and grain yield in rice and sorghum.

Results: Based on path analysis, root surface area was the root morphology component that most strongly contributed to total dry weight and to P content in maize seedling under low-P availability. Multiple interval mapping models for single (MIM) and multiple traits (MT-MIM) were combined and revealed 13 genomic regions significantly associated with the target traits in a complementary way. The phenotypic variances explained by all QTLs and their epistatic interactions using MT-MIM (23.4 to 35.5 %) were higher than in previous studies, and presented superior statistical power. Some of these QTLs were coincident with QTLs for root morphology traits and grain yield previously mapped, whereas others harbored ZmPSTOL candidate genes, which shared more than 55 % of amino acid sequence identity and a conserved serine/threonine kinase domain with OsPSTOL1. Additionally, four ZmPSTOL candidate genes co-localized with QTLs for root morphology, biomass accumulation and/or P content were preferentially expressed in roots of the parental lines that contributed the alleles enhancing the respective phenotypes.

Conclusions: QTL mapping strategies adopted in this study revealed complementary results for single and multiple traits with high accuracy. Some QTLs, mainly the ones that were also associated with yield performance in other studies, can be good targets for marker-assisted selection to improve P-use efficiency in maize. Based on the co-localization with QTLs, the protein domain conservation and the coincidence of gene expression, we selected novel maize genes as putative homologs to PSTOL1 that will require further validation studies.

No MeSH data available.