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• W3137823786 abstract "For decades, practical limitations and cost have created a bias in plant genetic studies toward species that tolerate self pollination. An important work by Chen et al., 2021Chen M. Fan W. Ji F. Hua H. Liu J. Yan M. Ma Q. Fan J. Wang Q. Zhang S. Liu G. Sun Z. et al.Genome-wide identification of agronomically important genes in outcrossing crops using OutcrossSeq.Mol. Plant. 2021; https://doi.org/10.1016/j.molp.2021.01.003Abstract Full Text Full Text PDF Scopus (8) Google Scholar in this issue provides key tools to overcome this bias and paves the way toward more efficient genetics and breeding in outcrossing crops. Plants that tolerate full or partial inbreeding can be maintained in perpetuity and increased infinitely. Conversely, outcrossing species that do not tolerate inbreeding can be difficult to study due to their genetic makeup. For instance, heterozygous individuals segregating in a population cannot be grown as replicated “genotypes,” but instead must be grown and phenotyped as individual plants. Due to the heavy influence of macro- and micro-environmental factors on the performance of most plants, studies that use phenotypes from single individuals are rarely implemented except when absolutely necessary due to time or mating system (e.g., Müller et al., 2019Müller B.S.F. de Almeida Filho J.E. Lima B.M. Garcia C.C. Missiaggia A. Aguiar A.M. Takahashi E. Kirst M. Gezan S.A. Silva-Junior O.B. et al.Independent and Joint-GWAS for growth traits in eucalyptus by assembling genome-wide data for 3373 individuals across four breeding populations.New Phytol. 2019; 221: 818-833Crossref PubMed Scopus (26) Google Scholar), with a few notable exceptions in crops (e.g., Gyawali et al., 2019Gyawali A. Shrestha V. Guill K.E. Flint-Garcia S. Beissinger T.M. Single-plant GWAS coupled with bulk segregant analysis allows rapid identification and corroboration of plant-height candidate SNPs.BMC Plant Biol. 2019; 19 (1-15): 412https://doi.org/10.1186/s12870-019-2000-yCrossref PubMed Scopus (8) Google Scholar). In addition to challenges surrounding phenotyping, genotyping challenges must also be overcome when studying heterozygous populations of outcrossers. For instance, quantitative trait locus (QTL) mapping requires genomic segments within each individual in the mapping population to be assigned to a corresponding parent. In double haploid, recombinant-inbred, or F2 mapping populations, the mathematics behind assigning genotypes to “parent 1” or “parent 2” is trivial. But, when these parents are themselves segregating for various alleles across loci, the math becomes difficult or impossible, especially for the case of polyploids. Despite the statistical precision and mathematical simplicity of working with replicable inbreds or families, these benefits come at a cost in terms of time and efficiency. Generating inbred lines or related families takes multiple generations and years of labor. Field experiments on plots containing many identical genotypes consume space and money. In their recent paper, Chen et al., 2021Chen M. Fan W. Ji F. Hua H. Liu J. Yan M. Ma Q. Fan J. Wang Q. Zhang S. Liu G. Sun Z. et al.Genome-wide identification of agronomically important genes in outcrossing crops using OutcrossSeq.Mol. Plant. 2021; https://doi.org/10.1016/j.molp.2021.01.003Abstract Full Text Full Text PDF Scopus (8) Google Scholar break ground by releasing a pipeline and accompanying software, named OutcrossSeq, to implement mapping on heterozygous populations. Their pipeline is effective for use with low-coverage sequence data, which is becoming increasingly affordable to generate, and allows the pipeline to be implemented with large panels of individual plants. The application of OutcrossSeq to long-lived, clonal, or self-incompatible species opens doors allowing more research to take place on these species, and brings efficiency improvements that will also benefit major crops, such as maize, that do tolerate inbreeding. OutcrossSeq proposes three computation algorithms, as outlined in Figure 1. These algorithms, named Diploid-Outcrossing, Double-Cross, and Autopolyploid-Plant, are designed to perform the genotyping, imputation, and haplotyping steps by considering the specificities associated with each of these outcrossing types. The Autopolyploid-Plant module performs genotype imputation by using an SNP partitioning method based on linkage disequilibrium patterns. When measured in terms of imputation accuracy on simulated data and on data from sweet potato, this module outperformed previously published software specialized in the imputation of missing data for polyploid species, including Poppoly (Motazedi et al., 2019Motazedi E. Maliepaard C. Finkers R. Visser R. de Ridder D. Family-based haplotype estimation and allele dosage correction for polyploids using short sequence reads.Front. Genet. 2019; 10: 335Crossref PubMed Scopus (4) Google Scholar) and PolyRAD (Clark et al., 2019Clark L.V. Lipka A.E. Sacks E.J. polyRAD: genotype calling with uncertainty from sequencing data in polyploids and diploids.G3 Genes Genomes Genet. 2019; 9: 663-673Google Scholar), which are two methods based on a Bayesian probabilistic framework. By applying OutcrossSeq to an F1 sweet potato population, a fine-scale genotype map containing more than 1.6 M SNPs was obtained and used for genetic mapping on 16 phenotypic traits. OutcrossSeq also provides a Diploid-Outcrossing module, for which the imputation step uses fixed windows defined by physical distance. Based on a clustering analysis that exploits genotype similarity between individuals computed within each window, the algorithm can trace the parental identity of haplotype blocks in each F1 individual. In addition, this module allows haplotype phasing. When evaluated across various levels of low-density coverage, the algorithm's specificity decreases as the coverage declines, but remains acceptable when using at least 1× coverage. This sensitivity to coverage level was much less significant when applying the Double-Cross module, intended to be used on double-cross diploid populations. In their paper, Chen et al., 2021Chen M. Fan W. Ji F. Hua H. Liu J. Yan M. Ma Q. Fan J. Wang Q. Zhang S. Liu G. Sun Z. et al.Genome-wide identification of agronomically important genes in outcrossing crops using OutcrossSeq.Mol. Plant. 2021; https://doi.org/10.1016/j.molp.2021.01.003Abstract Full Text Full Text PDF Scopus (8) Google Scholar systematically investigate the reliability of their genetic mapping results obtained from real datasets with published genetic studies. The genetic analyses conducted on sweet potato revealed candidate genes that were consistent with previous studies, such as a locus associated with anthocyanin biosynthesis (Mano et al., 2007Mano H. Ogasawara F. Sato K. HIgo H. Minobe Y. Isolation of a regulatory gene of anthocyanin biosynthesis in tuberous roots of purple-fleshed sweet potato.Plant Physiol. 2007; 143: 1252-1268Crossref PubMed Scopus (199) Google Scholar), or another locus involved in storage root development (Noh et al., 2012Noh S.H. Lee H.-S. Kim Y.-S. Paek K.-H. Shin J.S. Bae J.M. Down-regulation of the IbEXP1 gene enhanced storage root development in sweetpotato.J. Exp. Bot. 2012; 64: 129-142Crossref PubMed Scopus (52) Google Scholar). The candidate genes for heading date and plant height they identified in the double-cross rice population were in agreement with previously identified QTLs (Zhang et al., 2015Zhang L. Li Q. Dong H. He Q. Liang L. Tan C. Han Z. Yao W. Li G. Zhao H. et al.Three CCT domain-containing genes were identified to regulate heading date by candidate gene-based association mapping and transformation in rice.Sci. Rep. 2015; 5 (1-11): 7663Crossref PubMed Scopus (51) Google Scholar). Therefore, as demonstrated by results achieved on both simulated and real datasets, the imputing algorithms developed by the authors can generate high-density genotype maps of high quality, which can be utilized for reliable QTL studies. OutcrossSeq addresses a burning problem in global food security: developing high-density, high-quality genotype maps for outcrossing species that can be used in genetic studies and breeding (Chen et al., 2021Chen M. Fan W. Ji F. Hua H. Liu J. Yan M. Ma Q. Fan J. Wang Q. Zhang S. Liu G. Sun Z. et al.Genome-wide identification of agronomically important genes in outcrossing crops using OutcrossSeq.Mol. Plant. 2021; https://doi.org/10.1016/j.molp.2021.01.003Abstract Full Text Full Text PDF Scopus (8) Google Scholar). For example, cassava is one of the most important starch sources for human consumption (Ceballos et al., 2015Ceballos H. Kawuki R.S. Gracen V.E. Yencho G.C. Hershey H.H. Conventional breeding, marker-assisted selection, genomic selection and inbreeding in clonally propagated crops: a case study for cassava.Theor. Appl. Genet. 2015; 128: 1647-1667Crossref PubMed Scopus (75) Google Scholar, Ceballos et al., 2020Ceballos H. Rojanaridpiched C. Phumichai C. Becerra L.A. Kittipadakul P. Iglesias C. Gracen V.E. Excellence in cassava breeding: perspectives for the future.Crop Breed Genet. Genom. 2020; 2: e200008https://doi.org/10.20900/cbgg20200008Crossref Google Scholar), is clonally propagated, and is diploid (Ceballos et al., 2015Ceballos H. Kawuki R.S. Gracen V.E. Yencho G.C. Hershey H.H. Conventional breeding, marker-assisted selection, genomic selection and inbreeding in clonally propagated crops: a case study for cassava.Theor. Appl. Genet. 2015; 128: 1647-1667Crossref PubMed Scopus (75) Google Scholar). However, breeding and genetics in cassava lag behind other crops. This is partially due to cassava's nature as a crop that uses heterozygous parents to produce F1s, which are then released as clonal hybrids (Ceballos et al., 2015Ceballos H. Kawuki R.S. Gracen V.E. Yencho G.C. Hershey H.H. Conventional breeding, marker-assisted selection, genomic selection and inbreeding in clonally propagated crops: a case study for cassava.Theor. Appl. Genet. 2015; 128: 1647-1667Crossref PubMed Scopus (75) Google Scholar, Ceballos et al., 2020Ceballos H. Rojanaridpiched C. Phumichai C. Becerra L.A. Kittipadakul P. Iglesias C. Gracen V.E. Excellence in cassava breeding: perspectives for the future.Crop Breed Genet. Genom. 2020; 2: e200008https://doi.org/10.20900/cbgg20200008Crossref Google Scholar). OutcrossSeq's Diploid-Outcrossing module has the potential to be applied in cassava to efficiently enable breeding and genetics research, and may ultimately lead to the identification of genes that can be used for breeding a more healthy and robust crop (Chen et al., 2021Chen M. Fan W. Ji F. Hua H. Liu J. Yan M. Ma Q. Fan J. Wang Q. Zhang S. Liu G. Sun Z. et al.Genome-wide identification of agronomically important genes in outcrossing crops using OutcrossSeq.Mol. Plant. 2021; https://doi.org/10.1016/j.molp.2021.01.003Abstract Full Text Full Text PDF Scopus (8) Google Scholar). For instance, one challenge cassava breeders face is difficulty synchronizing flowering to conduct crosses (Ceballos et al., 2015Ceballos H. Kawuki R.S. Gracen V.E. Yencho G.C. Hershey H.H. Conventional breeding, marker-assisted selection, genomic selection and inbreeding in clonally propagated crops: a case study for cassava.Theor. Appl. Genet. 2015; 128: 1647-1667Crossref PubMed Scopus (75) Google Scholar). We envision a future where tools, such as OutcrossSeq, may assist in identifying flowering time genes with effects that can be leveraged to match parents and overcome this challenge. In addition to gene discovery, Chen et al., 2021Chen M. Fan W. Ji F. Hua H. Liu J. Yan M. Ma Q. Fan J. Wang Q. Zhang S. Liu G. Sun Z. et al.Genome-wide identification of agronomically important genes in outcrossing crops using OutcrossSeq.Mol. Plant. 2021; https://doi.org/10.1016/j.molp.2021.01.003Abstract Full Text Full Text PDF Scopus (8) Google Scholar emphasize that OutcrossSeq can also be applied directly during the F1 stage of a breeding program. When combined with genomic selection, this may allow better predictions of offspring performance and ultimately more rapid genetic gain. Ceballos et al., 2015Ceballos H. Kawuki R.S. Gracen V.E. Yencho G.C. Hershey H.H. Conventional breeding, marker-assisted selection, genomic selection and inbreeding in clonally propagated crops: a case study for cassava.Theor. Appl. Genet. 2015; 128: 1647-1667Crossref PubMed Scopus (75) Google Scholar, their Figure 2) demonstrate a recurrent selection scheme for which this application may be particularly effective. Although OutcrossSeq cannot create inbred lines in species where they do not exist, it does address and overcome difficulties encountered when working with heterozygous plants. Namely, it generates parent–offspring genotype matrices and enables imputation for panels of heterozygous parents, progeny, or both. It is effective even for low-density sequence data, which enables the method to be implemented on large panels of individuals as necessary when working with single-plant phenotypes. Using cost-efficient tools, such as OutcrossSeq, to improve genetic gain is useful everywhere, but it will be of even greater importance in countries and for crops where the public sector is more involved in breeding and operates on a low budget. Overall, tools such as OutcrossSeq provide tremendous value for the plant research community—and for agriculture more generally—by enabling studies in outcrossing species that are major components of human diets. No conflict of interest declared." @default.
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