Integration of eQTL and machine learning to dissect causal genes with pleiotropic effects in genetic regulation networks of seed cotton yield

• DOI: 10.1016/j.celrep.2023.113111
• Publication Date: 2023-09-06T13:05:14Z
• Authors: Ting Zhao; Hongyu Wu; Xutong Wang; Yongyan Zhao; Luyao Wang; Jiaying Pan; Huan Mei; Jin Han; Siyuan Wang; Kening Lu; Menglin Li; Mengtao Gao; Zeyi Cao; Hailin Zhang; Ke Wan; Jie Li; Lei Fang; Tianzhen Zhang; Xueying Guan
• Keywords: Genome-wide Association Study; Germ plasm; Trait; Genetic Association; Gene regulatory network

The dissection of a gene regulatory network (GRN) that complements the genome-wide association study (GWAS) locus and crosstalk underlying multiple agronomical traits remains major challenge. In this study, we generate 558 transcriptional profiles lint-bearing ovules at one day post-anthesis from selective core cotton germplasm, which 12,207 expression quantitative trait loci (eQTLs) are identified. Sixty-six known phenotypic GWAS colocalized with 1,090 eQTLs, forming 38 functional GRNs associated predominantly seed yield. Of eGenes, 34 exhibit pleiotropic effects. Combining eQTLs within yield significantly increases portion narrow-sense heritability. extreme gradient boosting (XGBoost) machine learning approach is applied to predict phenotypes on basis expression. Top-ranking eGenes (NF-YB3, FLA2, GRDP1) derived effects validated, along their potential roles by correlation analysis, domestication selection transgenic plants.