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Powerful one-dimensional scan to detect heterotic quantitative trait loci

Author

Listed:
  • Guoliang Li

    (Corrensstraße 3)

  • Renate H. Schmidt

    (Corrensstraße 3)

  • Yusheng Zhao

    (Corrensstraße 3)

  • Jochen C. Reif

    (Corrensstraße 3)

  • Yong Jiang

    (Corrensstraße 3)

Abstract

To meet the growing global demand for food, increasing yields through heterosis in agriculture is crucial. A deep understanding of the genetic basis of heterosis has led to the development of a quantitative genetic framework that incorporates both dominance and epistatic effects. However, incorporating all pairwise epistatic interactions is computationally challenging due to the large sequencing depth and population sizes needed to uncover the genes behind complex traits. In this study, we develop hQTL-ODS, a one-dimensional scanning method that directly assesses the net contribution of each quantitative trait locus to heterosis. Simulations show that hQTL-ODS reduces computational time while offering higher power and lower false-positive rate. We apply this method to a population of 5243 wheat hybrids with whole-genome sequenced profile, revealing key epistatic hubs that play a critical role in determining heterosis.

Suggested Citation

  • Guoliang Li & Renate H. Schmidt & Yusheng Zhao & Jochen C. Reif & Yong Jiang, 2025. "Powerful one-dimensional scan to detect heterotic quantitative trait loci," Nature Communications, Nature, vol. 16(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-65563-9
    DOI: 10.1038/s41467-025-65563-9
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    References listed on IDEAS

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    1. repec:plo:pgen00:1006869 is not listed on IDEAS
    2. Boyang Fu & Ali Pazokitoroudi & Mukund Sudarshan & Zhengtong Liu & Lakshminarayanan Subramanian & Sriram Sankararaman, 2023. "Fast kernel-based association testing of non-linear genetic effects for biobank-scale data," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    3. Legarra, Andres, 2016. "Comparing estimates of genetic variance across different relationship models," Theoretical Population Biology, Elsevier, vol. 107(C), pages 26-30.
    4. Xuehui Huang & Shihua Yang & Junyi Gong & Yan Zhao & Qi Feng & Hao Gong & Wenjun Li & Qilin Zhan & Benyi Cheng & Junhui Xia & Neng Chen & Zhongna Hao & Kunyan Liu & Chuanrang Zhu & Tao Huang & Qiang Z, 2015. "Genomic analysis of hybrid rice varieties reveals numerous superior alleles that contribute to heterosis," Nature Communications, Nature, vol. 6(1), pages 1-9, May.
    5. Xuehui Huang & Shihua Yang & Junyi Gong & Qiang Zhao & Qi Feng & Qilin Zhan & Yan Zhao & Wenjun Li & Benyi Cheng & Junhui Xia & Neng Chen & Tao Huang & Lei Zhang & Danlin Fan & Jiaying Chen & Congcong, 2016. "Genomic architecture of heterosis for yield traits in rice," Nature, Nature, vol. 537(7622), pages 629-633, September.
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