Datasets

CIMMYT Ethiopia

Replication Data for: Genome-Wide Association Mapping and Genomic Prediction Analyses Reveal the Genetic Architecture of Grain Yield and Agronomic Traits under Drought and Optimum Conditions in Maize

To dissect the genetic architecture of grain yield and flowering traits under drought stress, a genome-wide association study (GWAS) was conducted using 236 test-crossed maize lines. The materials were evaluated under managed drought and optimal growing conditions in multiple environments using seven multi-locus GWAS models (mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, pKWmEB, ISIS EM-BLASSO, and FARMCPU) from mrMLM and GAPIT R packages. Genomic prediction with RR-BLUP model was applied on BLUEs across locations under optimum and drought conditions. The GbS data used for the analyses are included in this dataset.

• agricultural-research
• agricultural-sciences
• dataset
• genomic-best-linear-unbiased-predictor
• +6 more

CIMMYT Ethiopia

Haplotype-based genome-wide association study unveils stable genomic regions for grain yield in CIMMYT spring bread wheat

Genetic architecture of grain yield (GY) has been extensively investigated in wheat using genome wide association study (GWAS) approach. However, most studies have used small panel sizes in combination with large genotypic data, typical examples of the so-called ‘large p small n’ or ‘short-fat data’ problem. Further, use of bi-allelic SNPs accentuated ‘missing heritability’ issues and therefore reported markers had limited impact in wheat breeding. We performed haplotype-based GWAS using 519 haplotype blocks on seven large cohorts of advanced CIMMYT spring bread wheat lines comprising overall 6,333 genotypes. In addition, epistatic interactions among the genome-wide haplotypes were investigated, an important aspect which has not yet been fully explored in wheat GWAS in order to address the missing heritability. Our results unveiled the intricate genetic architecture of GY controlled by both main and epistatic effects. The importance of these results from practical applications in the CIMMYT breeding program is discussed.

CIMMYT Ethiopia

Replication Data for: Genetic Dissection of Resistance to Gray Leaf Spot by Combining Genome-Wide Association, Linkage Mapping and Genomic Prediction in Tropical Maize Germplasm

Gray leaf spot (GLS) is a major maize foliar disease in sub-Saharan Africa that can substantially reduce yields for farmers. This dataset contains supporting phenotypic and genotypic data used in the analysis of the genetic architecture of GLS resistance in maize. The data in this paper relate to several different sets of germplasm: (1) The IMAS (Improved Maize for Africa Soils) panel with 430 lines assembled from diverse breeding programs from Africa, Asia and Latin America. (2) A DH population developed from crossing CML550 with CML494; (3) A DH population developed from crossing CML550 with CML504; (4) A DH population developed from crossing CML550 with CML511; and (5) several F3 populations The IMAS panel is a good source for genetic studies on resistance for several maize diseases and also for abiotic stress like low N conditions. The DH populations are a good source for low N discovery studies as well as for MLN and other disease resistance studies.

CIMMYT Ethiopia

Genomic selection models based on integration of GWAS loci and epistatic interactions

The potential to integrate consistent associations identified from GWAS as fixed variables in GP models to improve prediction accuracy for complex traits (for example, grain yield) has not been investigated comprehensively in wheat. Here, we untangled the genetic architecture of grain yield and yield stability by haplotypes-based GWAS and epistatic scan of the genome. We then integrated robust and stable associations (and interacting loci) as fixed effects in GP models to investigate the importance of these associations in improving prediction accuracies of the said traits. We concluded that the utility of GP incorporating GWAS results is noteworthy for GY when GWAS results identify significant and robust genomic regions.

• agricultural-research