Association mapping reveals novel genes and genomic regions controlling grain size architecture in mini core accessions of Indian National Genebank wheat germplasm collection

Development of a next generation SNP genotyping array for wheat

High-throughput genotyping arrays have provided a cost-effective, reliable and interoperable system for genotyping hexaploid wheat and its relatives. Existing, highly cited arrays including our 35K Wheat Breeder's array and the Illumina 90K array were designed based on a limited amount of varietal sequence diversity and with imperfect knowledge of SNP positions. Recent progress in wheat sequencing has given us access to a vast pool of SNP diversity, whilst technological improvements have allowed us to fit significantly more probes onto a 384-well format Axiom array than previously possible. Here we describe a novel Axiom genotyping array, the 'Triticum aestivum Next Generation' array (TaNG), largely derived from whole genome skim sequencing of 204 elite wheat lines and 111 wheat landraces taken from the Watkins 'Core Collection'. We used a novel haplotype optimization approach to select SNPs with the highest combined varietal discrimination and a design iteration step to test and replace SNPs which failed to convert to reliable markers. The final design with 43 372 SNPs contains a combination of haplotype-optimized novel SNPs and legacy cross-platform markers. We show that this design has an improved distribution of SNPs compared to previous arrays and can be used to generate genetic maps with a significantly higher number of distinct bins than our previous array. We also demonstrate the improved performance of TaNGv1.1 for Genome-wide association studies (GWAS) and its utility for Copy Number Variation (CNV) analysis. The array is commercially available with supporting marker annotations and initial genotyping results freely available.

New insights into QTNs and potential candidate genes governing rice yield via a multi-model genome-wide association study

BACKGROUND

Rice (Oryza sativa L.) is one of the globally important staple food crops, and yield-related traits are prerequisites for improved breeding efficiency in rice. Here, we used six different genome-wide association study (GWAS) models for 198 accessions, with 553,229 single nucleotide markers (SNPs) to identify the quantitative trait nucleotides (QTNs) and candidate genes (CGs) governing rice yield.

RESULTS

Amongst the 73 different QTNs in total, 24 were co-localized with already reported QTLs or loci in previous mapping studies. We obtained fifteen significant QTNs, pathway analysis revealed 10 potential candidates within 100kb of these QTNs that are predicted to govern plant height, days to flowering, and plot yield in rice. Based on their superior allelic information in 20 elite and 6 inferior genotypes, we found a higher percentage of superior alleles in the elite genotypes in comparison to inferior genotypes. Further, we implemented expression analysis and enrichment analysis enabling the identification of 73 candidate genes and 25 homologues of Arabidopsis, 19 of which might regulate rice yield traits. Of these candidate genes, 40 CGs were found to be enriched in 60 GO terms of the studied traits for instance, positive regulator metabolic process (GO:0010929), intracellular part (GO:0031090), and nucleic acid binding (GO:0090079). Haplotype and phenotypic variation analysis confirmed that LOC_OS09G15770, LOC_OS02G36710 and LOC_OS02G17520 are key candidates associated with rice yield.

CONCLUSIONS

Overall, we foresee that the QTNs, putative candidates elucidated in the study could summarize the polygenic regulatory networks controlling rice yield and be useful for breeding high-yielding varieties.