Genome-Wide Association Study of Grain Size Traits in Indica Rice Multiparent Advanced Generation Intercross (MAGIC) Population

Abstract

Rice grain size plays a crucial role in determining grain quality and yield. In this study, two multiparent advanced generation intercross (MAGIC) populations, DC1 and BIM, were evaluated for grain size across three environments and genotyped with 55K array-based SNP detection and genotype-by-sequencing (GBS), respectively, to identify QTLs and SNPs associated with grain length, grain width, grain length-width ratio, grain thickness, and thousand grain weight. A total of 18 QTLs were identified for the five grain sizerelated traits and explained 6.43-63.35% of the total phenotypic variance. Twelve of these QTLs colocalized with the cloned genes, GS3, GW5/qSW5, GW7/GL7/SLG7, and GW8/OsSPL16, of which the first two genes showed the strongest effect for grain length and grain width, respectively. Four potential new genes were also identified from the QTLs, which exhibited both genetic background independency and environment stability and could be validated in future studies. Moreover, the significant SNP markers identified are valuable for direct utilization in marker-assisted breeding to improve rice grain size.

Genome-Wide Association Study Using a Multiparent Advanced Generation Intercross (MAGIC) Population Identified QTLs and Candidate Genes to Predict Shoot and Grain Zinc Contents in Rice

“…A set of rice MAGIC populations has been developed by IRRI to better integrate QTL discovery and breeding. Their use in QTL mapping for a range of agronomic traits has been previously reported [36][37][38][39]44]. We previously identified QTLs associated with the toxicity tolerance of rice to three essential metals (Fe, Zn, and Al) by using three of the MAGIC populations, including DC1, DC2, and eight-way populations, genotyped using a 55 K SNP array [37].…”

“…To date, no QTLs have been fine-mapped or cloned for Zn 2+ accumulation in rice. In recent years, multiparent advanced generation intercross (MAGIC) populations have become popular population types for mapping and developing breeding lines with multiple desirable traits [36][37][38][39]. MAGIC populations have a relatively wide genetic background without significant population structure, which is a major constraint in association mapping using diversity panels.…”

Multi-parent populations in crops: a toolbox integrating genomics and genetic mapping with breeding

“…In a MAGIC rice population of over 1316 RILs, the mapping intervals were~700 Kbp on average (Raghavan et al 2017). However, as discussed above, empirical (Descalsota et al 2018) Bacterial leaf streak and bacterial leaf blight (Bossa-Castro et al 2018) Grain shape Grain quality, cooking and taste attributes (Ponce et al 2018) Yield, plant height, heading date, grain quality and biofortification (Zaw et al 2019) Grain length, grain width, grain thickness, and thousand grain weight (Ponce et al 2020) Heading date (Han et al 2020) Sorghum Plant height (Ongom and Ejeta 2018) Strawberry Fruit quality-related traits (Wada et al 2017) Tomato Fruit weight (Pascual et al 2015) Fruit weight and flowering time plasticity in response to water, salinity and heat stress (Diouf et al 2020) mapping resolution may be determined by the QTL effect size and population size. The wheat MAGIC populations developed at CSIRO (four Australian spring wheat founders, Huang et al 2012) and NIAB (eight UK winter wheat founders, Mackay et al 2014) are examples of MPPs that have been used to map genes controlling important yield related traits.…”

Scanning the rice Global MAGIC population for dynamic genetic control of seed traits under vegetative drought

“…Among the known candidate loci controlling grain size and weight in rice, our GWAS peaks colocalize with GW5, GL7, and GW8. These major QTL have key roles in either cell division or elongation during grain development [24,[54][55][56] and have been identified in previous genetic analysis of seed size and weight in indica MAGIC populations [46,57]. According to the recent genetic diversity analysis based on whole-genome sequencing data of 3,010 rice accessions [38], ten of the 16 Global MAGIC parents are indica rice accessions, which could explain the common genetic control of seed-related traits between our genetic material and other indica multi-parent populations.…”

“…SmartGrain [17] has been adopted to analyze seed images and measure grain size and shape in MAGIC populations [46,47]. However, SmartGrain works only on Windows platforms, and it does not allow to parallelize a pipeline across multiple images.…”