Quantitative trait loci-based genomics-assisted prediction for the degree of apple fruit cover color

Genomewide selection for fruit quality traits in apple: breeding insights gained from prediction and postdiction

Many fruit quality traits in apple (Malus domestica Borkh.) are controlled by multiple small-effect quantitative trait loci (QTLs). Genomewide selection (genomic selection) might be an effective breeding approach for highly quantitative traits in woody perennial crops with long generation times like apple. The goal of this study was to determine if genomewide prediction is an effective breeding approach for fruit quality traits in an apple scion breeding program. Representative apple scion breeding germplasm (n_individuals = 955), high-quality single nucleotide polymorphism (SNP) data (n_SNPs = 977), and breeding program fruit quality trait data at harvest were analyzed. Breeding parents `Honeycrisp' and `Minneiska' were highly represented. Moderate to high predictive abilities were observed for most fruit quality traits at harvest. For example, when 25% random subsets of the germplasm set were used as training sets, mean predictive abilities ranged from 0.35 to 0.54 across traits. Trait, training and test sets, family size for within family prediction, and number of SNPs per chromosome affected model predictive ability. Inclusion of large-effect QTLs as fixed effects resulted in higher predictive abilities for some traits (e.g. percent red overcolor). Postdiction (i.e. retrospective) analyses demonstrated the impact of culling threshold on selection decisions. The results of this study demonstrate that genomewide selection is a useful breeding approach for certain fruit quality traits in apple.

A single QTL harboring multiple genetic variations leads to complicated phenotypic segregation in apple flesh firmness and crispness

Within a QTL, the genetic recombination and interactions among five and two functional variations at MdbHLH25 and MdWDR5A caused much complicated phenotype segregation in apple FFR and FCR. The storability of climacteric fruit like apple is a quantitative trait. We previously identified 62 quantitative trait loci (QTLs) associating flesh firmness retainability (FFR) and flesh crispness retainability (FCR), but only a few functional genetic variations were identified and validated. The genetic variation network controlling fruit storability is far to be understood and diagnostic markers are needed for molecular breeding. We previously identified overlapped QTLs F16.1/H16.2 for FFR and FCR using an F1 population derived from 'Zisai Pearl' × 'Red Fuji'. In this study, five and two single-nucleotide polymorphisms (SNPs) were identified on the candidate genes MdbHLH25 and MdWDR5A within the QTL region. The SNP1 A allele at MdbHLH25 promoter reduced the expression and SNP2 T allele and/or SNP4/5 GT alleles at the exons attenuated the function of MdbHLH25 by downregulating the expression of the target genes MdACS1, which in turn led to a reduction in ethylene production and maintenance of higher flesh crispness. The SNPs did not alter the protein-protein interaction between MdbHLH25 and MdWDR5A. The joint effect of SNP genotype combinations by the SNPs on MdbHLH25 (SNP1, SNP2, and SNP4) and MdWDR5A (SNPi and SNPii) led to a much broad spectrum of phenotypic segregation in FFR and FCR. Together, the dissection of these genetic variations contributes to understanding the complicated effects of a QTL and provides good potential for marker development in molecular breeding.

Research Progress on Genetic Basis of Fruit Quality Traits in Apple (Malus × domestica)

Identifying the genetic variation characteristics of phenotypic traits is important for fruit tree breeding. During the long-term evolution of fruit trees, gene recombination and natural mutation have resulted in a high degree of heterozygosity. Apple (Malus × domestica Borkh.) shows strong ecological adaptability and is widely cultivated, and is among the most economically important fruit crops worldwide. However, the high level of heterozygosity and large genome of apple, in combination with its perennial life history and long juvenile phase, complicate investigation of the genetic basis of fruit quality traits. With continuing augmentation in the apple genomic resources available, in recent years important progress has been achieved in research on the genetic variation of fruit quality traits. This review focuses on summarizing recent genetic studies on apple fruit quality traits, including appearance, flavor, nutritional, ripening, and storage qualities. In addition, we discuss the mapping of quantitative trait loci, screening of molecular markers, and mining of major genes associated with fruit quality traits. The overall aim of this review is to provide valuable insights into the mechanisms of genetic variation and molecular breeding of important fruit quality traits in apple.

Root Breeding in the Post-Genomics Era: From Concept to Practice in Apple

The development of rootstocks with a high-quality dwarf-type root system is a popular research topic in the apple industry. However, the precise breeding of rootstocks is still challenging, mainly because the root system is buried deep underground, roots have a complex life cycle, and research on root architecture has progressed slowly. This paper describes ideas for the precise breeding and domestication of wild apple resources and the application of key genes. The primary goal of this research is to combine the existing rootstock resources with molecular breeding and summarize the methods of precision breeding. Here, we reviewed the existing rootstock germplasm, high-quality genome, and genetic resources available to explain how wild resources might be used in modern breeding. In particular, we proposed the 'from genotype to phenotype' theory and summarized the difficulties in future breeding processes. Lastly, the genetics governing root diversity and associated regulatory mechanisms were elaborated on to optimize the precise breeding of rootstocks.

Machine learning models outperform deep learning models, provide interpretation and facilitate feature selection for soybean trait prediction

Recent growth in crop genomic and trait data have opened opportunities for the application of novel approaches to accelerate crop improvement. Machine learning and deep learning are at the forefront of prediction-based data analysis. However, few approaches for genotype to phenotype prediction compare machine learning with deep learning and further interpret the models that support the predictions. This study uses genome wide molecular markers and traits across 1110 soybean individuals to develop accurate prediction models. For 13/14 sets of predictions, XGBoost or random forest outperformed deep learning models in prediction performance. Top ranked SNPs by F-score were identified from XGBoost, and with further investigation found overlap with significantly associated loci identified from GWAS and previous literature. Feature importance rankings were used to reduce marker input by up to 90%, and subsequent models maintained or improved their prediction performance. These findings support interpretable machine learning as an approach for genomic based prediction of traits in soybean and other crops.

A bulked segregant analysis tool for out-crossing species (BSATOS) and QTL-based genomics-assisted prediction of complex traits in apple

INTRODUCTION

Genomic heterozygosity, self-incompatibility, and rich-in somatic mutations hinder the molecular breeding efficiency of outcrossing plants.

OBJECTIVES

We attempted to develop an efficient integrated strategy to identify quantitative trait loci (QTLs) and trait-associated genes, to develop gene markers, and to construct genomics-assisted prediction (GAP) modes.

METHODS

A novel protocol, bulked segregant analysis tool for out-crossing species (BSATOS), is presented here, which is characterized by taking full advantage of all segregation patterns (including AB × AB markers) and haplotype information. To verify the effectiveness of the protocol in dealing with the complex traits of outbreeding species, three apple cross populations with 9,654 individuals were adopted.

RESULTS

By using BSATOS, 90, 60, and 77 significant QTLs were identified successfully and candidate genes were predicted for apple fruit weight (FW), fruit ripening date (FRD), and fruit soluble solid content (SSC), respectively. The gene-based markers were developed and genotyped for 1,396 individuals in a training population, including 145 Malus accessions and 1,251 F1 plants of the three full-sib families. GAP models were trained using marker genotype effect estimates of the training population. The prediction accuracy was 0.7658, 0.6455, and 0.3758 for FW, FRD, and SSC, respectively.

CONCLUSION

The BSATOS and GAP models provided a convenient and efficient methodology for candidate gene mining and molecular breeding in out-crossing plant species. The BSATOS pipeline can be freely downloaded from: https://github.com/maypoleflyn/BSATOS.

Continuous Mapping Identifies Loci Associated With Weevil Resistance [Cosmopolites sordidus (Germar)] in a Triploid Banana Population

The first step toward marker-assisted selection is linking the phenotypes to molecular markers through quantitative trait loci (QTL) analysis. While the process is straightforward in self-pollinating diploid (2x) species, QTL analysis in polyploids requires unconventional methods. In this study, we have identified markers associated with weevil Cosmopolites sordidus (Germar) resistance in bananas using 138 triploid (2n = 3x) hybrids derived from a cross between a tetraploid "Monyet" (2n = 4x) and a 2x "Kokopo" (2n = 2x) banana genotypes. The population was genotyped by Diversity Arrays Technology Sequencing (DArTSeq), resulting in 18,009 polymorphic single nucleotide polymorphisms (SNPs) between the two parents. Marker-trait association was carried out by continuous mapping where the adjusted trait means for the corm peripheral damage (PD) and total cross-section damage (TXD), both on the logit scale, were regressed on the marker allele frequencies. Forty-four SNPs that were associated with corm PD were identified on the chromosomes 5, 6, and 8, with 41 of them located on chromosome 6 and segregated in "Kokopo." Eleven SNPs associated with corm total TXD were identified on chromosome 6 and segregated in "Monyet." The additive effect of replacing one reference allele with the alternative allele was determined at each marker position. The PD QTL was confirmed using conventional QTL linkage analysis in the simplex markers segregating in "Kokopo" (AAAA × RA). We also identified 43 putative genes in the vicinity of the markers significantly associated with the two traits. The identified loci associated with resistance to weevil damage will be used in the efforts of developing molecular tools for marker-assisted breeding in bananas.

Intricate genetic variation networks control the adventitious root growth angle in apple

Background

The root growth angle (RGA) typically determines plant rooting depth, which is significant for plant anchorage and abiotic stress tolerance. Several quantitative trait loci (QTLs) for RGA have been identified in crops. However, the underlying mechanisms of the RGA remain poorly understood, especially in apple rootstocks. The objective of this study was to identify QTLs, validate genetic variation networks, and develop molecular markers for the RGA in apple rootstock.

Results

Bulked segregant analysis by sequencing (BSA-seq) identified 25 QTLs for RGA using 1955 hybrids of the apple rootstock cultivars ‘Baleng Crab’ (Malus robusta Rehd., large RGA) and ‘M9’ (M. pumila Mill., small RGA). With RNA sequencing (RNA-seq) and parental resequencing, six major functional genes were identified and constituted two genetic variation networks for the RGA. Two single nucleotide polymorphisms (SNPs) of the MdLAZY1 promoter damaged the binding sites of MdDREB2A and MdHSFB3, while one SNP of MdDREB2A and MdIAA1 affected the interactions of MdDREB2A/MdHSFB3 and MdIAA1/MdLAZY1, respectively. A SNP within the MdNPR5 promoter damaged the interaction between MdNPR5 and MdLBD41, while one SNP of MdLBD41 interrupted the MdLBD41/MdbHLH48 interaction that affected the binding ability of MdLBD41 on the MdNPR5 promoter. Twenty six SNP markers were designed on candidate genes in each QTL interval, and the marker effects varied from 0.22°-26.11°.

Conclusions

Six diagnostic markers, SNP592, G122, b13, Z312, S1272, and S1288, were used to identify two intricate genetic variation networks that control the RGA and may provide new insights into the accuracy of the molecular markers. The QTLs and SNP markers can potentially be used to select deep-rooted apple rootstocks.