Genome Wide Association Mapping of Grain and Straw Biomass Traits in the Rice Bengal and Assam Aus Panel (BAAP) Grown Under Alternate Wetting and Drying and Permanently Flooded Irrigation

Abstract

Growing demand for staple crops like rice will need to be achieved predominately through agricultural intensification and more efficient use of inputs. To meet this demand it is essential that the genetic diversity within rice is fully utilized. The aus subpopulation is considered an underappreciated resource within that diversity. A new rice panel, the Bengal and Assam Aus Panel (BAAP) of 266 aus accessions was generated with ∼2 million informative SNPs obtained using skim sequencing at ∼4× depth. The BAAP was grown in the field in Bangladesh in the ‘boro’ season under both continuously flooded and Alternate Wetting and Drying (AWD) irrigation during 2013 and 2014 in Mymensingh and during 2014 in Madhupur. Heading date, grain mass, straw biomass and harvest index were measured. The majority (94%) of BAAP accessions flowered within a relatively small window of 10 days. The AWD irrigation treatment generally caused an increase in grain mass, but no significant genotype by treatment interactions were detected for this trait. Shoot biomass was the only trait that showed evidence of genotype by treatment interaction. The average LD (Linkage Disequilibrium) decay across the genome was 243 Kbp. Genome wide association mapping revealed 115 quantitative trait loci (QTLs). There was little evidence of QTLs specific to the irrigation treatment, and only a few QTLs co-localized with known genes. However, some QTLs were detected across multiple sites and years. These QTLs should be targets for breeding, and include a region around 2.2 Mbp on chromosome 1, a large region in the middle of chromosome 7 and two regions on chromosome 11 (∼10 Mbp and ∼29 Mbp). The BAAP appears to be a valuable addition to the growing collection of GWA mapping populations of rice.

Genomic prediction of arsenic tolerance and grain yield in rice. Contribution of trait-specific markers and multi environment models

“…The plant material was composed of 220 accessions belonging to the Aus genetic group of O. sativa. They were extracted from the Aus diversity panel of 266 accessions developed by Norton et al (2018). The selection criterion for the 220 accessions was the availability of phenotypic data for GAs (Supplementary Table 1).…”

“…The genotypic data used in this study were extracted from the "Bengal and Assam Aus Panel (BAAP)" SNP database, containing 2,053,863 SNPs (Norton et al 2018). Using Gigwa software (Sempéré et al 2019), the BAAP database was filtered for minor allele frequency (MAF > 5%), heterozygosity (H < 5%) and absence of missing data.…”

“…Using the 1600k SNP dataset, two types of smaller datasets were extracted with the aim of (i) identifying the threshold of marker density below which the accuracy of GEBV declines, and (ii) analysing the effect of accounting for trait-specific markers on the performances of genomic predictions. The selection of trait specific markers was based on GWAS results reported by Norton et al (2018) for DTF and GY, and by Norton et al (2019) for GAs and SAs. For DTF, 2-3 SNPs were selected along each of the 24 chromosomic segments with "notable association" detected in one of the two years of field experiment; the total number of DTF-specific SNP was 64.…”

“…The present study aims at evaluating the effectiveness of the above-mentioned options of genomic prediction for the improvement of As-tolerance in rice. Using the publically available large genotypic data and phenotypic data for the Bengal and Assam Aus panel (BAAP) (Norton et al 2018), we explore the predictive ability of genomic prediction for GAs, with single-environment models, accounting or not for trait-specific markers reported in Norton et al (2019), and the performances of multi-environment models, and multi-trait and multienvironment genomic prediction models. As the phenotypic dataset includes grain yield (GY) and days to flowering (DTF), the scope of the study encompasses all complex traits.…”

Identification of Loci Through Genome-Wide Association Studies to Improve Tolerance to Sulfur Deficiency in Rice

“…The rice aus group is a minor sub-population which originated from Bangladesh and India (Klush, 1997;Norton et al, 2018). It harbors novel loci that, confer tolerance to abiotic stress (Famoso et al, 2011;Lee et al, 2018), and enhance root development (Mori et al, 2016).…”