Outlier analyses and genome-wide association study identify glgC and ERD6-like 4 as candidate genes for foliar water-soluble carbohydrate accumulation in Trifolium repens

Front Plant Sci. 2023 Jan 9:13:1095359. doi: 10.3389/fpls.2022.1095359. eCollection 2022.

Abstract

Increasing water-soluble carbohydrate (WSC) content in white clover is important for improving nutritional quality and reducing environmental impacts from pastoral agriculture. Elucidation of genes responsible for foliar WSC variation would enhance genetic improvement by enabling molecular breeding approaches. The aim of the present study was to identify single nucleotide polymorphisms (SNPs) associated with variation in foliar WSC in white clover. A set of 935 white clover individuals, randomly sampled from five breeding pools selectively bred for divergent (low or high) WSC content, were assessed with 14,743 genotyping-by-sequencing SNPs, using three outlier detection methods: PCAdapt, BayeScan and KGD-FST. These analyses identified 33 SNPs as discriminating between high and low WSC populations and putatively under selection. One SNP was located in the intron of ERD6-like 4, a gene coding for a sugar transporter located on the vacuole membrane. A genome-wide association study using a subset of 605 white clover individuals and 5,757 SNPs, identified a further 12 SNPs, one of which was associated with a starch biosynthesis gene, glucose-1-phosphate adenylyltransferase, glgC. Our results provide insight into genomic regions underlying WSC accumulation in white clover, identify candidate genomic regions for further functional validation studies, and reveal valuable information for marker-assisted or genomic selection in white clover.

Keywords: genome-wide association study; genotyping-by-sequencing; outlier detection; water-soluble carbohydrate; white clover.

Grants and funding

The project and SP’s studentship were supported from the “Genomics for Production and Security in a Biological Economy” programme, funded by the New Zealand Ministry for Business, Innovation and Employment (C10X1306). Additional funding was provided by the Marsden Fund, Royal Society Te Apārangi “Improved modelling in evolutionary transcriptomics and proteomics will advance understanding of plant adaptation” (MAU1707).