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KMID : 0381120220440020145
Genes and Genomics
2022 Volume.44 No. 2 p.145 ~ p.154
Identification of yield-related genes through genome-wide association: case study of weeping forsythia, an emerging medicinal crop
Li Yong

Wu Qiong
Liu Hong-Li
Pei Nan-Cai
He Yan-Xia
Quan Jine
Abstract
Background: The genetic basis of crop yield is an agricultural research hotspot. Identifying the genes related to yield traits is the key to increase the yield. Weeping forsythia is an emerging medicinal crop that currently lacks excellent varieties. The genes related to fruit yield in weeping forsythia have not been identified.

Objective: Thus, we aimed to screen the candidate genes related to fruit yield of weeping forsythia by using genome-wide association analysis.

Methods: Here, 60 samples from the same field and source of weeping forsythia were collected to identify its yield-related candidate genes. Association analysis was performed on the variant loci and the traits related to yield, i.e., fruit length, width, thickness, and weight.

Results: Results from admixture, neighbor-joining, and kinship matrix analyses supported the non-significant genetic differentiation of these samples. Significant association was found between 2 variant loci and fruit length, 8 loci and fruit width, 24 loci and fruit thickness, and 13 loci and fruit weight. Further search on the 20 kb up/downstream of these variant loci revealed 1 gene related to fruit length, 16 genes related to fruit width, 12 genes related to fruit thickness, and 13 genes related to fruit weight. Among which, 4 genes, namely, WRKY transcription factor 35, salicylic acid-binding protein, auxin response factor 6, and alpha-mannosidase were highly related to the fruit development of weeping forsythia.

Conclusion: This study identify four candidate genes related to fruit development, which will provide useful information for the subsequent molecular-assisted and genetic breeding of weeping forsythia.
KEYWORD
Forsythia suspensa, Fruit shape, Fruit weight, Genotyping by sequencing, Genome-wide association analysis
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