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Genetic control of the leaf angle and leaf orientation value as revealed by ultra-high density maps in three connected maize populations.

Li C, Li Y, Shi Y, Song Y, Zhang D, Buckler ES, Zhang Z, Wang T, Li Y - PLoS ONE (2015)

Bottom Line: A total of 45 QTLs with phenotypic effects ranging from 1.2% to 29.2% were detected for four leaf architecture traits by using joint linkage mapping across the three populations.Four QTLs were located on small genomic regions where candidate genes were found.These results extend our understanding of the genetics of leaf traits and can be used in genomic prediction to accelerate plant architecture improvement.

View Article: PubMed Central - PubMed

Affiliation: Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.

ABSTRACT
Plant architecture is a key factor for high productivity maize because ideal plant architecture with an erect leaf angle and optimum leaf orientation value allow for more efficient light capture during photosynthesis and better wind circulation under dense planting conditions. To extend our understanding of the genetic mechanisms involved in leaf-related traits, three connected recombination inbred line (RIL) populations including 538 RILs were genotyped by genotyping-by-sequencing (GBS) method and phenotyped for the leaf angle and related traits in six environments. We conducted single population quantitative trait locus (QTL) mapping and joint linkage analysis based on high-density recombination bin maps constructed from GBS genotype data. A total of 45 QTLs with phenotypic effects ranging from 1.2% to 29.2% were detected for four leaf architecture traits by using joint linkage mapping across the three populations. All the QTLs identified for each trait could explain approximately 60% of the phenotypic variance. Four QTLs were located on small genomic regions where candidate genes were found. Genomic predictions from a genomic best linear unbiased prediction (GBLUP) model explained 45±9% to 68±8% of the variation in the remaining RILs for the four traits. These results extend our understanding of the genetics of leaf traits and can be used in genomic prediction to accelerate plant architecture improvement.

No MeSH data available.


Related in: MedlinePlus

Partitioning variations in LA, LL, LW, and LOV across three populations.
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pone.0121624.g001: Partitioning variations in LA, LL, LW, and LOV across three populations.

Mentions: Phenotypic variations were identified for LA, LL, LW, and LOV within three RIL populations (Table 1). The WEIFENG322 population had the greatest phenotypic variation for the four traits. The variation ranges for the four traits were similar between the HUOBAI and LV28 populations. The broad-sense heritability for LA, LL, LW, and LOV reached 0.68, 0.75, 0.63, and 0.64, respectively. Approximately 30.2% and 29.8% for LA and LOV variations across the three populations were attributed to environmental variations (Fig. 1). These variations were greater than that observed for LL or LW. Nonetheless, the manual phenotyping method for LA may confound measures of environmental variation.


Genetic control of the leaf angle and leaf orientation value as revealed by ultra-high density maps in three connected maize populations.

Li C, Li Y, Shi Y, Song Y, Zhang D, Buckler ES, Zhang Z, Wang T, Li Y - PLoS ONE (2015)

Partitioning variations in LA, LL, LW, and LOV across three populations.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4373667&req=5

pone.0121624.g001: Partitioning variations in LA, LL, LW, and LOV across three populations.
Mentions: Phenotypic variations were identified for LA, LL, LW, and LOV within three RIL populations (Table 1). The WEIFENG322 population had the greatest phenotypic variation for the four traits. The variation ranges for the four traits were similar between the HUOBAI and LV28 populations. The broad-sense heritability for LA, LL, LW, and LOV reached 0.68, 0.75, 0.63, and 0.64, respectively. Approximately 30.2% and 29.8% for LA and LOV variations across the three populations were attributed to environmental variations (Fig. 1). These variations were greater than that observed for LL or LW. Nonetheless, the manual phenotyping method for LA may confound measures of environmental variation.

Bottom Line: A total of 45 QTLs with phenotypic effects ranging from 1.2% to 29.2% were detected for four leaf architecture traits by using joint linkage mapping across the three populations.Four QTLs were located on small genomic regions where candidate genes were found.These results extend our understanding of the genetics of leaf traits and can be used in genomic prediction to accelerate plant architecture improvement.

View Article: PubMed Central - PubMed

Affiliation: Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.

ABSTRACT
Plant architecture is a key factor for high productivity maize because ideal plant architecture with an erect leaf angle and optimum leaf orientation value allow for more efficient light capture during photosynthesis and better wind circulation under dense planting conditions. To extend our understanding of the genetic mechanisms involved in leaf-related traits, three connected recombination inbred line (RIL) populations including 538 RILs were genotyped by genotyping-by-sequencing (GBS) method and phenotyped for the leaf angle and related traits in six environments. We conducted single population quantitative trait locus (QTL) mapping and joint linkage analysis based on high-density recombination bin maps constructed from GBS genotype data. A total of 45 QTLs with phenotypic effects ranging from 1.2% to 29.2% were detected for four leaf architecture traits by using joint linkage mapping across the three populations. All the QTLs identified for each trait could explain approximately 60% of the phenotypic variance. Four QTLs were located on small genomic regions where candidate genes were found. Genomic predictions from a genomic best linear unbiased prediction (GBLUP) model explained 45±9% to 68±8% of the variation in the remaining RILs for the four traits. These results extend our understanding of the genetics of leaf traits and can be used in genomic prediction to accelerate plant architecture improvement.

No MeSH data available.


Related in: MedlinePlus