Structural Variation Detection And Mining Candidate Genes Related To Seedless Trait Of ’Shijiwuhe’ Pear

Pear is an important fruit species of rosaceae,widely distributed and cultivated all over the world.Pear plants have genetic diversity of thousands of species.In addition to single nucleotide polymorphisms(SNPs)and small insertions and deletions(InDels),structural variation(SVs)can better understand the genetic diversity among species and the genetic basis of complex traits.Previous studies have shown that SVs can influence crop traits,domestication and evolution,but there are few studies about SVs in Pyrus.In recent years,many SVs detection algorithms and corresponding software packages have been developed,but most SV detection software is developed and tested using the human genome or the genome of the model plant-Arabidopsis thaliana,and SV detection software is not applied in the pear genome.Because spatially-integrated SVs detection in the pear genome does not have a gold-standard set,this thesis research is focused on how to use the SV detection software packages to obtain SVs with high confidence,test most appropriate sequencing depth for SVs detection using pear genome,and combine the SVs detection and transcriptome data to mining the differentially expressed genes within SVs which contributed to seedless trait of ’Shijiwuhe’ pear.Specific research results are as follows:1.A pipeline to detect SVs using next-generation and long-read sequencing data was constructed.The performances of seven types of SV detection software using next-generation sequencing(NGS)data and two types of software using long-read sequencing data(SVIM and Sniffles),which are based on different algorithms,were compared.Of the nine software packages evaluated,SVIM identified the most SVs,and Sniffles detected SVs with the highest accuracy(>90%).When the results from multiple SV detection tools were combined,the SVs identified by both MetaSV and IMR/DENOM,which use NGS data,were more accurate than those identified by both SVIM and Sniffles,with mean accuracies of 98.7%and 96.5%,respectively.The software packages using longread sequencing data required fewer CPU cores and less memory and ran faster than those using NGS data.In addition,according to the performances of assembly-based algorithms using NGS data,we found that a sequencing depth of 50×is appropriate for detecting SVs in the pear genome.This study provides strong evidence that more than one SV detection software package,each based on a different algorithm,should be used to detect SVs with higher confidence,and that long-read sequencing data are better than NGS data for SV detection.The SV detection pipeline that we have established will facilitate the study of diversity in other crops.2.Seedless fruits are highly marketable because they are easier to eat than fruits with seeds.‘Shijiwuhe’ is a seedless pear cultivar that is a mutant derived from an F1 hybridization population(’Bartlett’ ×’Yali’).Little is known about the key genes controlling seedless pear fruit.In this study,field experiments revealed that seedless’Shijiwuhe’ pear was not due to parthenocarpy,and that it was self-incompatible.Single nucleotide poly-morphisms(SNPs),small insertions and deletions(InDels)and structural variations(SVs)were characterized using DNA sequencing data between ’Shijiwuhe’ and parental cultivars.A total of 1498 genes were found to be affected by SV and over 50%of SVs were located in promoter regions.Transcriptome analysis was conducted at three time points(4,8,and 12 days after cross-pollination)during early fruit development of’Shijiwuhe’,’Bartlett’,and ’Yali’.In total,1438 differentially expressed genes(DEGs)were found between ’Shijiwuhe’ and parental cultivars ’Bartlett’ and ’Yali’.We found 1193 SVs that caused differential expression of genes at 4 DACP.Among them,over 100 genes were in pathways related to seed nutrition and energy storage and 41 candidate genes encoded several important transcription factors,such as MYB,WRKY,NAC,and bHLH,which might play important roles in seed development.The qRT-PCR results also confirmed that the candidate genes with SVs showed differential expression between’Shijiwuhe’ pear and ’Bartlett’ or ’Yali’.This study,which combined field experiments,SV detection,and transcriptome analysis might provide an effective way to predict the candidate genes regulating the seedless trait and important gene resources for genetic improvement of pear.