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Analysis of trait heritability in functionally partitioned rice genomes.

Julong WeiWeibo XieRuidong LiShibo WangHan QuRenyuan MaXiang ZhouZhenyu Jia
Published in: Heredity (2019)
Knowledge of the genetic architecture of importantly agronomical traits can speed up genetic improvement in cultivated rice (Oryza sativa L.). Many recent investigations have leveraged genome-wide association studies (GWAS) to identify single nucleotide polymorphisms (SNPs), associated with agronomic traits in various rice populations. The reported trait-relevant SNPs appear to be arbitrarily distributed along the genome, including genic and nongenic regions. Whether the SNPs in different genomic regions play different roles in trait heritability and which region is more responsible for phenotypic variation remains opaque. We analyzed a natural rice population of 524 accessions with 3,616,597 SNPs to compare the genetic contributions of functionally distinct genomic regions for five agronomic traits, i.e., yield, heading date, plant height, grain length, and grain width. An analysis of heritability in the functionally partitioned rice genome showed that regulatory or intergenic regions account for the most trait heritability. A close look at the trait-associated SNPs (TASs) indicated that the majority of the TASs are located in nongenic regions, and the genetic effects of the TASs in nongenic regions are generally greater than those in genic regions. We further compared the predictabilities using the genetic variants from genic regions with those using nongenic regions. The results revealed that nongenic regions play a more important role than genic regions in trait heritability in rice, which is consistent with findings in humans and maize. This conclusion not only offers clues for basic research to disclose genetics behind these agronomic traits, but also provides a new perspective to facilitate genomic selection in rice.
Keyphrases
  • genome wide
  • dna methylation
  • copy number
  • genome wide association
  • healthcare
  • body mass index
  • gene expression
  • cell wall