SSR marker-based study of the effects of genomic regions on Fe, Mn, Zn, and protein content in a rice diversity panel

Abstract: The objective of this work was to identify single-nucleotide polymorphism (SNP) markers linked with quantitative trait loci (QTLs) associated with increased contents of protein and oil in soybean. A total of 169 Brazilian soybean varieties, genotyped with 6,000 SNP markers, were evaluated. Protein and oil contents were obtained with the near-infrared reflectance method. Correlation and multiple linear regression analyses were used to identify linkage disequilibrium between SNP markers and the QTLs associated with the two characteristics. Seven QTLs were found to be associated with protein content, on six chromosomes (2, 6, 11, 12, 13, and 16), explaining 60.9% of the variation in this trait. For oil content, eight QTLs were identified on six chromosomes (1, 4, 5, 6, 17, and 19), explaining 78.3% of the variation in the trait. The correlation between the number of loci containing favorable alleles and the evaluated characteristics was 0.49 for protein content and 0.60 for oil content. The molecular markers identified are mapped in genomic regions containing QTLs previously mapped for both characteristics, which reinforces the association between these regions and the genetic control of oil and protein contents in soybean.

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In silico QTL mapping of maternal nurturing ability with the mouse diversity panel

Physiological Genomics ◽

10.1152/physiolgenomics.00159.2011 ◽

2012 ◽

Vol 44 (16) ◽

pp. 787-798 ◽

Cited By ~ 8

Author(s):

D. L. Hadsell ◽

J. Wei ◽

W. Olea ◽

L. A. Hadsell ◽

A. Renwick ◽

...

Keyword(s):

Qtl Mapping ◽

In Silico ◽

Inbred Mice ◽

Inbred Strains ◽

Daily Weight Gain ◽

Lactation Performance ◽

Haplotype Blocks ◽

Diversity Panel ◽

Maternal Performance ◽

Genomic Regions

Significant variation exists for maternal nurturing ability in inbred mice. Although classical mapping approaches have identified quantitative trait loci (QTL) that may account for this variation, the underlying genes are unknown. In this study, lactation performance data among the mouse diversity panel were used to map genomic regions associated with this variation. Females from each of 32 inbred strains ( n = 8–19 dams/strain) were studied during the first 8 days of lactation by allowing them to raise weight- and size-normalized cross-foster litters (10 pups/litter). Average daily weight gain (ADG) of litters served as the primary indicator of milk production. The number of pups successfully reared to 8 days (PNUM8) also served as a related indicator of maternal performance. Initial haplotype association analysis using a Bonferroni-corrected, genome-wide threshold revealed 10 and 15 associations encompassing 11 and 13 genes for ADG and PNUM8, respectively. The most significant of these associated haplotype blocks were found on MMU 8, 11, and 19 and contained the genes Nr3c2, Egfr, Sec61g, and Gnaq. Lastly, two haplotype blocks on MMU9 were detected in association with PNUM8. These overlapped with the previously described maternal performance QTL, Neogq1. These results suggest that the application of in silico QTL mapping is a useful tool in discovering the presence of novel candidate genes involved in determining lactation capacity in mice.

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QTL mapping of protein content and seed characteristics under water-stress conditions in sunflower

Genome ◽

10.1139/g09-020 ◽

2009 ◽

Vol 52 (5) ◽

pp. 419-430 ◽

Cited By ~ 6

Author(s):

A. Ebrahimi ◽

P. Maury ◽

M. Berger ◽

A. Calmon ◽

P. Grieu ◽

...

Keyword(s):

Water Stress ◽

Water Treatment ◽

Protein Content ◽

Recombinant Inbred Lines ◽

Kernel Weight ◽

Stress Conditions ◽

Phenotypic Variance ◽

Seed Density ◽

Genomic Regions ◽

Water Treatments

The purpose of this study was to identify genomic regions controlling seed protein content, kernel and hull weights, and seed density in water-stress conditions in sunflower ( Helianthus annuus L.). The experiments consisted of a split-plot design (water treatment and recombinant inbred lines) with three blocks in two environments (greenhouse and field). High significant variation was observed between genotypes for all traits as well as for water treatment × genotype interaction. Several specific and nonspecific QTLs were detected for all traits under well-watered and water-stress conditions. Two SSR markers, ORS671_2 and HA2714, linked to protein content were identified that have no interaction with water treatments in greenhouse conditions. We also detected the E35M60_4 marker associated with kernel weight that had no interaction with water treatments. A specific QTL for protein content was detected with important phenotypic variance (17%) under water-stress conditions. Overlapping QTLs for protein content and seed density were identified in linkage group 15. This region probably has a peliotropic effect on protein content and seed density. QTLs for protein content colocated with grain weight traits were also identified.

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