“I have called this principle, by which each slight variation, if useful, is preserved, by the term natural selection, in order to mark its relation to man’s power of selection.”
-Charles Darwin, On the Origin of Species
We revolutionized our agriculture sector by taking advantage of the selection process. While natural selection helps an organism adapt to its surroundings for survival, artificial selection enhances desirable traits to meet present and future demands. Developing hybrids with superior performance compared to their parents significantly influenced the design of a breeding pipeline aimed to develop varieties with desirable traits. The phenotypic superiority of hybrids, called heterosis or hybrid vigor, was first observed by Charles Darwin et. al. (1877). This phenomenon is highly advantageous for plants in terms of reproduction and adaptation to environmental changes. It plays a significant role in agriculture, particularly in increasing economically important traits such as yield and stress tolerance in crops through hybrid breeding.
Heterosis has been recognized in plants for over a century, and its occurrence and degree vary among different species and within the same species. Cross-pollinated plants such as maize exhibit stronger heterosis compared to self-pollinated plants such as rice. Additionally, hybrids from genetically divergent clads tend to show stronger heterosis than those from genetically close accessions.
To figure out the molecular underpinning of heterosis magnitude in plants, it is crucial to characterize the gene loci responsible for heterosis through statistical genetics methods. Advances in omics data analysis, including genomics, transcriptomics, metabolomics, and population genetics approaches such as genome-wide association studies (GWAS) and functional genomics, offer opportunities to identify key allelic variations related to heterosis (Liu et al., 2020). Heterosis is the outcome of a series of interactions in the genomes, which demands a combination of -omics and population genetics approaches to reveal the wholistic picture of these interaction patterns (Das et al., 2021).
A few examples of case studies where omics data were used to understand and use heterosis in molecular breeding are as follows: Complex heterotic traits in maize were predicted by integrating omics data from genome-wide single nucleotide polymorphism (SNP) markers and metabolomic profiles (Riedelsheimer et al., 2012). The analysis helped in narrowing down the elite hybrids, saving time and resources in the maize breeding program. Transcriptomics data was used to identify differentially expressed genes and genomic variations in hybrid rice compared to their parents (Huang et al., 2016). The study identified that the Hd3a gene showed an advantage in grain number per panicle and overall grain yield with increased lateral branching and panicle development, giving insights in designing better hybrids by selecting parental lines. Significant epistatic interactions between multiple loci were detected contributing to the heterosis in various traits through a study conducted using QTL mapping and genetic interaction analysis in Arabidopsis hybrids (Melchinger et al., 2007). Multi-omics data analysis, including transcriptomics and genomics from maize hybrids and their parental lines, revealed microRNAs (miRNAs) and small interfering RNAs (siRNAs) exhibiting non-additive expression patterns in hybrids, influencing genes related to vegetative growth and stress response (Zhang et al., 2023). Comparative transcriptomic analysis was used to identify differentially expressed genes in hybrids and parental lines at two developmental stages in canola (Brassica napus) (Xiong et al., 2022). The study found the dominance effect as the major contributor to the observed heterosis, with a significant number of genes exhibiting expression levels akin to one of the parents.
Several studies have also explored population genomics in understanding heterosis. Complex interplay between genetic and environmental factors was studied on rice hybrids using high-density SNP data in a GWAS study (Xie et al., 2022). The study helped in determining the degree of heterosis in hybrids and sensitivity to environmental changes compared to their inbred parents. The study proposed that heterosis in most cases was not due to heterozygote advantage but homozygote disadvantage under insufficient genetic background. In a large-scale multi-environmental analysis in cotton using restriction-site-associated DNA sequences, quantitative trait nucleotides (QTNs) and potential candidate genes responsible for heterosis were uncovered (Sarfraz et al., 2021). The findings contributed to understanding how specific loci influenced hybrid performance, providing a foundation for breeding programs aimed at improving cotton production through the exploitation of heterosis.
These studies demonstrate the power of multi-omics and population genomics approaches in dissecting the complexity of heterosis. By combining data from various molecular layers, researchers can gain a comprehensive understanding of the genetic, epigenetic, and metabolic factors contributing to hybrid vigor. This knowledge is crucial for designing molecular breeding strategies, developing hybrids, and selecting elite varieties with enhanced performance. Using advanced computational methods such as machine learning and deep learning to integrate and analyze large-scale data can improve inferences and predictions of heterotic performance and facilitate the design of superior hybrids.
References
Darwin, C., 1877. The effects of cross and self fertilisation in the vegetable kingdom. D. Appleton.
Das, A.K., Choudhary, M., Kumar, P., Karjagi, C.G., Kr, Y., Kumar, R., Singh, A., Kumar, S., Rakshit, S., 2021. Heterosis in Genomic Era: Advances in the Molecular Understanding and Techniques for Rapid Exploitation. Crit. Rev. Plant Sci. 40, 218–242. https://doi.org/10.1080/07352689.2021.1923185
Huang, X., Yang, S., Gong, J., Zhao, Q., Feng, Q., Zhan, Q., Zhao, Y., Li, W., Cheng, B., Xia, J., Chen, N., Huang, T., Zhang, L., Fan, D., Chen, J., Zhou, C., Lu, Y., Weng, Q., Han, B., 2016. Genomic architecture of heterosis for yield traits in rice. Nature 537, 629–633. https://doi.org/10.1038/nature19760
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Melchinger, A.E., Piepho, H.-P., Utz, H.F., Muminović, J., Wegenast, T., Törjék, O., Altmann, T., Kusterer, B., 2007. Genetic Basis of Heterosis for Growth-Related Traits in Arabidopsis Investigated by Testcross Progenies of Near-Isogenic Lines Reveals a Significant Role of Epistasis. Genetics 177, 1827–1837. https://doi.org/10.1534/genetics.107.080564
Riedelsheimer, C., Czedik-Eysenberg, A., Grieder, C., Lisec, J., Technow, F., Sulpice, R., Altmann, T., Stitt, M., Willmitzer, L., Melchinger, A.E., 2012. Genomic and metabolic prediction of complex heterotic traits in hybrid maize. Nat. Genet. 44, 217–220. https://doi.org/10.1038/ng.1033
Sarfraz, Z., Iqbal, Muhammad Shahid, Geng, X., Iqbal, Muhammad Sajid, Nazir, M.F., Ahmed, H., He, S., Jia, Y., Pan, Z., Sun, G., Ahmad, S., Wang, Q., Qin, H., Liu, J., Liu, H., Yang, Jun, Ma, Z., Xu, D., Yang, Jinlong, Zhang, J., Li, Z., Cai, Z., Zhang, Xuelin, Zhang, Xin, Huang, A., Yi, X., Zhou, G., Li, L., Zhu, H., Pang, B., Wang, L., Sun, J., Du, X., 2021. GWAS Mediated Elucidation of Heterosis for Metric Traits in Cotton (Gossypium hirsutum L.) Across Multiple Environments. Front. Plant Sci. 12, 565552. https://doi.org/10.3389/fpls.2021.565552
Xie, J., Wang, W., Yang, T., Zhang, Q., Zhang, Zhifang, Zhu, X., Li, N., Zhi, L., Ma, X., Zhang, S., Liu, Y., Wang, X., Li, F., Zhao, Y., Jia, X., Zhou, J., Jiang, N., Li, G., Liu, M., Liu, S., Li, L., Zeng, A., Du, M., Zhang, Zhanying, Li, J., Zhang, Ziding, Li, Z., Zhang, H., 2022. Large-scale genomic and transcriptomic profiles of rice hybrids reveal a core mechanism underlying heterosis. Genome Biol. 23, 264. https://doi.org/10.1186/s13059-022-02822-8
Xiong, J., Hu, K., Shalby, N., Zhuo, C., Wen, J., Yi, B., Shen, J., Ma, C., Fu, T., Tu, J., 2022. Comparative transcriptomic analysis reveals the molecular mechanism underlying seedling biomass heterosis in Brassica napus. BMC Plant Biol. 22, 283. https://doi.org/10.1186/s12870-022-03671-0
Zhang, J., Xie, Y., Zhang, H., He, C., Wang, X., Cui, Y., Heng, Y., Lin, Y., Gu, R., Wang, J., Fu, J., 2023. Integrated Multi-Omics Reveals Significant Roles of Non-Additively Expressed Small RNAs in Heterosis for Maize Plant Height. Int. J. Mol. Sci. 24, 9150. https://doi.org/10.3390/ijms24119150
