Genome-Wide Association Studies of Image Traits Reveal Genetic Architecture of Drought Resistance in Rice

Abstract

Understanding how plants respond to drought can benefit drought resistance (DR) breeding. Using a non-destructive phenotyping facility, 51 image-based traits (i-traits) for 507 rice accessions were extracted. These i-traits can be used to monitor drought responses and evaluate DR. High heritability and large variation of these traits was observed under drought stress in the natural population. A genome-wide association study (GWAS) of i-traits and traditional DR traits identified 470 association loci, some containing known DR-related genes. Of these 470 loci, 443 loci (94%) were identified using i-traits, 437 loci (93%) co-localized with previously reported DR-related quantitative trait loci, and 313 loci (66.6%) were reproducibly identified by GWAS in different years. Association networks, established based on GWAS results, revealed hub i-traits and hub loci. This demonstrates the feasibility and necessity of dissecting the complex DR trait into heritable and simple i-traits. As proof of principle, we illustrated the power of this integrated approach to identify previously unreported DR-related genes. OsPP15 was associated with a hub i-trait, and its role in DR was confirmed by genetic transformation experiments. Furthermore, i-traits can be used for DR linkage analyses, and 69 i-trait locus associations were identified by both GWAS and linkage analysis of a recombinant inbred line population. Finally, we confirmed the relevance of i-traits to DR in the field. Our study provides a promising novel approach for the genetic dissection and discovery of causal genes for DR.

Resequencing 200 Flax Cultivated Accessions Identifies Candidate Genes Related to Seed Size and Weight and Reveals Signatures of Artificial Selection

“…where y is a vector of phenotype; X represents genotype; S is a population structure matrix built up based on the top three PCs, and K is a kinship matrix built up based on simple matching coefficients calculated from the nucleotide polymorphisms; a and b represent fixed effects, and m represents random effects; Where e is a vector of random residual effects. The significance thresholds (1/n) was set to control the genome-wide type I error rate of GWAS by modified Bonferroni correction (Guo et al, 2018); n represents the effective number of independent SNPs (Li et al, 2012). The P-value thresholds for significance in the flax population were approximately 1.0× 10 −5 .…”

Crop genome‐wide association study: a harvest of biological relevance

“…Buckler et al ., ; for maize flowering; Huang et al ., ; for rice agronomic traits), to individual responses to environmental factors (biotic or abiotic stress tolerance) (e.g. Li et al ., for rice blast resistance; Wang et al ., and Guo et al ., for drought tolerance in maize and rice, respectively; Kuroha et al ., for periodic flooding adaptation), to large‐scale molecular‐level quantification (e.g. Fu et al ., and Chen et al ., for determination of the structural transcriptome of the maize kernel, respectively; Chen et al ., and Wen et al ., 2014a with respect to metabolism in rice and maize), to more complex phenotypic variations as long as they are heritable and measurable, such as rice heterosis (Huang et al ., ) and maize haploid male fertility (Ma et al ., 2018a).…”

Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives

“…Moreover, some image-based traits (i-traits) that are difficult to assess manually, such as leaf rolling and stay-green properties of drought-tolerant rice, can also be quantified (Duan et al, 2018). Together with GWASs and 51 i-traits, the HRPF can dissect the complex DR traits into heritable and simple i-traits and discover new genes for DR (Guo et al, 2018a). In addition, with minor adjustments of the image analysis pipeline, the HRPF can be extended to phenotype other species, including 3D wheat plant architecture (Fang et al, 2016), the leaf traits of rape seedlings (Xiong et al, 2017), and the genetic architecture of variation in maize growth (Zhang et al, 2017).…”