Genome-Wide Association Mapping of Fusarium Head Blight Resistance in Wheat using Genotyping-by-Sequencing.

Fusarium head blight (FHB) is one of the most important wheat ( L.) diseases worldwide, and host resistance displays complex genetic control. A genome-wide association study (GWAS) was performed on 273 winter wheat breeding lines from the midwestern and eastern regions of the United States to identify chromosomal regions associated with FHB resistance. Genotyping-by-sequencing (GBS) was used to identify 19,992 single-nucleotide polymorphisms (SNPs) covering all 21 wheat chromosomes. Marker-trait associations were performed with different statistical models, the most appropriate being a compressed mixed linear model (cMLM) controlling for relatedness and population structure. Ten significant SNP-trait associations were detected on chromosomes 4A, 6A, 7A, 1D, 4D, and 7D, and multiple SNPs were associated with on chromosome 3B. Although combination of favorable alleles of these SNPs resulted in lower levels of severity (SEV), incidence (INC), and deoxynivalenol concentration (DON), lines carrying multiple beneficial alleles were in very low frequency for most traits. These SNPs can now be used for creating new breeding lines with different combinations of favorable alleles. This is one of the first GWAS using genomic resources from the International Wheat Genome Sequencing Consortium (IWGSC).

Genomic Selection for Predicting Fusarium Head Blight Resistance in a Wheat Breeding Program.

Genomic selection (GS) is a breeding method that uses marker-trait models to predict unobserved phenotypes. This study developed GS models for predicting traits associated with resistance to Fusarium head blight (FHB) in wheat (Triticum aestivum L.). We used genotyping-by-sequencing (GBS) to identify 5054 single-nucleotide polymorphisms (SNPs), which were then treated as predictor variables in GS analysis. We compared how the prediction accuracy of the genomic-estimated breeding values (GEBVs) was affected by (i) five genotypic imputation methods (random forest imputation [RFI], expectation maximization imputation [EMI], k-nearest neighbor imputation [kNNI], singular value decomposition imputation [SVDI], and the mean imputation [MNI]); (ii) three statistical models (ridge-regression best linear unbiased predictor [RR-BLUP], least absolute shrinkage and operator selector [LASSO], and elastic net); (iii) marker density (p = 500, 1500, 3000, and 4500 SNPs); (iv) training population (TP) size (nTP = 96, 144, 192, and 218); (v) marker-based and pedigree-based relationship matrices; and (vi) control for relatedness in TPs and validation populations (VPs). No discernable differences in prediction accuracy were observed among imputation methods. The RR-BLUP outperformed other models in nearly all scenarios. Accuracies decreased substantially when marker number decreased to 3000 or 1500 SNPs, depending on the trait; when sample size of the training set was less than 192; when using pedigree-based instead of marker-based matrix; or when no control for relatedness was implemented. Overall, moderate to high prediction accuracies were observed in this study, suggesting that GS is a very promising breeding strategy for FHB resistance in wheat.

Association and linkage analysis of aluminum tolerance genes in maize.

BACKGROUND: Aluminum (Al) toxicity is a major worldwide constraint to crop productivity on acidic soils. Al becomes soluble at low pH, inhibiting root growth and severely reducing yields. Maize is an important staple food and commodity crop in acidic soil regions, especially in South America and Africa where these soils are very common. Al exclusion and intracellular tolerance have been suggested as two important mechanisms for Al tolerance in maize, but little is known about the underlying genetics. METHODOLOGY: An association panel of 282 diverse maize inbred lines and three F2 linkage populations with approximately 200 individuals each were used to study genetic variation in this complex trait. Al tolerance was measured as net root growth in nutrient solution under Al stress, which exhibited a wide range of variation between lines. Comparative and physiological genomics-based approaches were used to select 21 candidate genes for evaluation by association analysis. CONCLUSIONS: Six candidate genes had significant results from association analysis, but only four were confirmed by linkage analysis as putatively contributing to Al tolerance: Zea mays AltSB like (ZmASL), Zea mays aluminum-activated malate transporter2 (ALMT2), S-adenosyl-L-homocysteinase (SAHH), and Malic Enzyme (ME). These four candidate genes are high priority subjects for follow-up biochemical and physiological studies on the mechanisms of Al tolerance in maize. Immediately, elite haplotype-specific molecular markers can be developed for these four genes and used for efficient marker-assisted selection of superior alleles in Al tolerance maize breeding programs.