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

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

doi:10.1016/j.celrep.2023.113111

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

Cell Rep. 2023 Sep 26;42(9):113111. doi: 10.1016/j.celrep.2023.113111. Epub 2023 Sep 6.

Authors

Ting Zhao¹, Hongyu Wu², Xutong Wang³, Yongyan Zhao¹, Luyao Wang⁴, Jiaying Pan¹, Huan Mei², Jin Han², Siyuan Wang², Kening Lu⁵, Menglin Li⁵, Mengtao Gao⁵, Zeyi Cao², Hailin Zhang², Ke Wan⁵, Jie Li⁵, Lei Fang¹, Tianzhen Zhang¹, Xueying Guan⁶

Affiliations

¹ Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China; Hainan Institute of Zhejiang University, Building 11, Yonyou Industrial Park, Yazhou Bay Science and Technology City, Yazhou District, Sanya 572025, China.
² Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China.
³ Hubei Hongshan Laboratory, Wuhan 430070, China.
⁴ Hainan Institute of Zhejiang University, Building 11, Yonyou Industrial Park, Yazhou Bay Science and Technology City, Yazhou District, Sanya 572025, China.
⁵ State Key Laboratory of Crop Genetics and Germplasm Enhancement, Cotton Hybrid R & D Engineering Center (the Ministry of Education), College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China.
⁶ Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China; Hainan Institute of Zhejiang University, Building 11, Yonyou Industrial Park, Yazhou Bay Science and Technology City, Yazhou District, Sanya 572025, China. Electronic address: [email protected].

PMID: 37676770
DOI: 10.1016/j.celrep.2023.113111

Abstract

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

Keywords: CP: Genomics; CP: Plants; GWAS; XGBoost; cotton; eQTL; machine learning; seed size; yield.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Gene Regulatory Networks*
Genome-Wide Association Study*
Phenotype
Polymorphism, Single Nucleotide
Quantitative Trait Loci / genetics