On the usefulness of parental lines GWAS for predicting low heritability traits in tropical maize hybrids

Galli, Giovanni; Alves, Filipe Couto; Morosini, Júlia Silva; Fritsche‐Neto, Roberto

doi:10.1371/journal.pone.0228724

Cited by 27 publications

(24 citation statements)

References 40 publications

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“…Additionally, Galli et al (2020) observed negative covariance between single-crosses genetic values predicted from marker effects estimated from parental lines and single-crosses. Hence, this reinforces the inability to predict single-crosses performance based on parental information with additive effects.…”

Section: Introductionmentioning

confidence: 92%

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Reduction of genotyping marker density for genomic selection is not an affordable approach to long-term breeding in cross-pollinated crops

DoVale¹,

Sabadin

et al. 2021

Preprint

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The selection of informative markers has been studied massively as an alternative to reduce genotyping costs for the genomic selection (GS) application. Low-density marker panels are attractive for GS because they decrease computational time-consuming and multicollinearity beyond more individuals can be genotyped with the same cost. Nevertheless, these inferences are usually made empirically using "static" training sets and populations, which are adequate only to predict a breeding program's initial cycles but might not for long-term cycles. Moreover, to the best of our knowledge, none of these inferences considered the inclusion of dominance into the GS models, which is particularly important to predict cross-pollinated crops. Therefore, that reveals an important and unexplored topic for allogamous long-term breeding. To achieve this goal, we employed two approaches: the former used empirical maize datasets, and the latter simulations of long-term breeding cycles of phenotypic and genomic recurrent selection (intrapopulation and reciprocal). Then, we observed the reducing marker density effect on populations (mean, the best genotypes performance, accuracy, additive variance) over cycles and models (additive, additive-dominance, specific combining ability (SCA)). Our results indicate that the markers reduction based on different linkage disequilibrium (LD) levels is viable only within a cycle and brings a significant decrease in predictive ability over generations. Furthermore, in the long-term, regardless of the selection scheme adopted, the more makers, the better because they buffer LD losses caused by recombination over breeding cycles. Finally, regarding the accuracy, the additive-dominant models tend to outperform the additive ones and perform similar to the SCA.

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Section: Introductionmentioning

confidence: 92%

“…Single-crosses were evaluated at each site and year under two nitrogen fertilization levels (ideal and low N), composing eight environments. More details about the experimental design, cultivation practices for HEL and USP datasets can be accessed in Sousa et al (2017) and Galli et al (2020), respectively.…”

Section: Phenotypic Datamentioning

confidence: 99%

Reduction of genotyping marker density for genomic selection is not an affordable approach to long-term breeding in cross-pollinated crops

DoVale¹,

Sabadin

et al. 2021

Preprint

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“…Drought tolerance (DT) is a complex trait composed of multiple morphological and physiological sub-traits. Progress of selection based on yield itself has been slow due to the low heritability of yield (Galli et al ., 2020). Consequently, improvement in drought-sensitive areas can only be made through identifying secondary morpho-physiological traits associated with DT, stable across environments and correlated to yield (Hamwieh et al ., 2013; Tucker et al ., 2020).…”

Section: Introductionmentioning

confidence: 99%

Molecular characterization of QTL-allele system for drought tolerance at seedling stage and optimal genotype design using multi-locus multi-allele genome-wide association analysis in a half-sib population of soybean (Glycine max (L.) Merr.)

Khan

Tong

Wang

et al. 2020

Plant Genet. Resour.

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Characterizing the whole genetic architecture of drought tolerance (DT) is a persistent challenge for the breeders. Here we developed a half-sib population comprising of 404 lines of two recombinant inbred line (RIL) populations with M8206 as the joint parent (M8206 × TongShan and ZhengYang × M8206) and tested for its DT under sand culture. The population was sequenced using restriction-site-associated DNA sequencing filtered with minor allele frequency ≥0.01; 55,936 single nucleotide polymorphisms (SNPs) were obtained and assembled into 6137 SNPLDBs (SNP linkage disequilibrium blocks). The restricted two-stage multi-locus genome-wide association analysis characterized with error and false-positive control identified 40 QTLs with 93 alleles on an average of 34.75% of the phenotypic variance (PV) collectively for relative root length (RRL) and relative shoot length (RSL) that served as potential DT indicators. Among these, eight loci corresponded to previously reported QTLs, whereas 32 loci were therefore novel. The identified QTLs with their corresponding alleles for RRL and RSL were organized into QTL-allele matrices, depicting the comprehensive DT genetic architecture of the three parents/half-sib population. From the matrices, we predicted the possible best/optimal genotype with weighted average value (WAV) 1.553 over two indicators, while for the top 10 single crosses among RILs with 95th percentile WAV was 1.218–1.257, transgressive over the parents (0.693–0.794) yet much less than 1.553. From the detected QTL-allele system, 65 potential candidate genes collectively for both indicators explaining on an average of 24.41% PV were annotated and χ2-tested as a DT candidate gene system involving nine biological processes.

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“…Regarding molecular makers, single nucleotide polymorphism (SNP) are abundant and uniformly distributed throughout the genome of crop species (Gupta et al, 2008). The lower cost, high read accuracy, and competitive sequencing systems are increasing the availability of SNP markers for crop improvement purposes (Thomson, 2014;Kang et al, 2016), especially in genetic diversity analysis (Frascaroli et al, 2013;Zhang et al, 2016), genome-wide association studies (GWAS) (Morosini et al, 2017;Vidotti et al, 2019;Galli et al, 2020), and genomic prediction (Technow et al, 2012;.…”

Section: Models Abstractmentioning

confidence: 99%

“…2). The high amount of markers may explain it with low MAF (MAF < 0.10), since the range of allele frequencies of a given locus, maximizes allelic substitution effect and, consequently, the additive variance Galli et al, 2020). In contrast, SNP-array captured a higher proportion of the variance caused by the dominance deviations than GBS-based approaches, especially for GY (Fig.…”

Section: The Impact From Mock Reference Genome On Genomic Predictionmentioning

confidence: 99%

Haploid maize seeds prediction using deep learning and using mock reference genomes for genomic prediction of hybrids

Sabadin¹

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Haploid maize seeds prediction using deep learning and using mock reference genomes for genomic prediction of hybrids Prediction is a key concept for animal and plant breeding. Accurate estimates of phenotypic and genetic values are crucial for the selection of the best genotypes. For this reason, several tools have been used to improve the accuracy of these estimates, from molecular markers, used to access genetic information, to high-throughput phenotyping, used to increase sample size and phenotypic precision. Here, we present two studies involving the use of different approaches and tools in the prediction process. First, we describe a study using deep learning and images for seed phenotyping. We built a convolutional neural network (CNN) model to classify images from putative and true haploid maize seeds based on the R1-nj phenotype. Our results reveal that the CNN model could classify putative haploid maize seeds with high accuracy (97%). However, the CNN model was unable to recognize true haploid seeds. Finally, we provide a highly accurate and trained CNN model to the scientific community to classify haploid maize seeds via R1-nj. In the latter, we studied using mock genomes to discover markers and their effect on estimates of genetic diversity and genomic prediction of hybrids. Moreover, we compared them with SNP markers from SNP-array and genotyping-bysequencing (GBS) scored in the reference genome B73. Our results show that using mock genomes delivers estimates comparable to standard platforms when considering simple traits and additive effects. However, for complex traits and dominance effects, the estimates were slightly worse. We believe that these studies provide relevant knowledge for the phenotypic and genomic prediction applied to plant breeding.

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On the usefulness of parental lines GWAS for predicting low heritability traits in tropical maize hybrids

Cited by 27 publications

References 40 publications

Reduction of genotyping marker density for genomic selection is not an affordable approach to long-term breeding in cross-pollinated crops

Reduction of genotyping marker density for genomic selection is not an affordable approach to long-term breeding in cross-pollinated crops

Molecular characterization of QTL-allele system for drought tolerance at seedling stage and optimal genotype design using multi-locus multi-allele genome-wide association analysis in a half-sib population of soybean (Glycine max (L.) Merr.)

Haploid maize seeds prediction using deep learning and using mock reference genomes for genomic prediction of hybrids

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