1. Genetics and Genomics

Genome-wide association study in quinoa reveals selection pattern typical for crops with a short breeding history

  1. Dilan Sarange Rajapaksha Patiranage
  2. Elodie Rey
  3. Nazgol Emrani  Is a corresponding author
  4. Gordon Wellman
  5. Karl Schmid
  6. Sandra M Schmöckel
  7. Mark Tester
  8. Christian Jung  Is a corresponding author
  1. Christian-Albrechts University of Kiel, Germany
  2. King Abdullah University of Science and Technology, Saudi Arabia
  3. University of Hohenheim, Germany
https://doi.org/10.7554/eLife.66873
Share this article
Cite this article
  1. Dilan Sarange Rajapaksha Patiranage
  2. Elodie Rey
  3. Nazgol Emrani
  4. Gordon Wellman
  5. Karl Schmid
  6. Sandra M Schmöckel
  7. Mark Tester
  8. Christian Jung
(2022)
Genome-wide association study in quinoa reveals selection pattern typical for crops with a short breeding history
eLife 11:e66873.
https://doi.org/10.7554/eLife.66873
  2. Download BibTeX
  3. Download .RIS

Abstract

Quinoa germplasm preserves useful and substantial genetic variation, yet it remains untapped due to a lack of implementation of modern breeding tools. We have integrated field and sequence data to characterize a large diversity panel of quinoa. Whole-genome sequencing of 310 accessions revealed 2.9 million polymorphic high confidence SNP loci. Highland and Lowland quinoa were clustered into two main groups, with FST divergence of 0.36 and LD decay of 6.5 and 49.8 Kb, respectively. A genome-wide association study using multi-year phenotyping trials uncovered 600 SNPs stably associated with 17 traits. Two candidate genes are associated with thousand seed weight, and a resistance gene analog is associated with downy mildew resistance. We also identified pleiotropically acting loci for four agronomic traits important for adaptation. This work demonstrates the use of re-sequencing data of an orphan crop, which is partially domesticated to rapidly identify marker-trait association and provides the underpinning elements for genomics-enabled quinoa breeding.

Data availability

The raw sequencing data have been submitted to the NCBI Sequence Read Archive (SRA) under the BioProject PRJNA673789. Quinoa reference genome version 2 is available at CoGe database under genome id 53523. Phenotype data and ready-use genotype data (vcf file) are available at https://doi.org/10.5061/dryad.zgmsbcc9m. A detailed description of the germplasm, phenotyping methods, and phenotypes are available at https://quinoa.kaust.edu.sa/#/ (Stanschewski et al., 2021). Seeds are available from the public gene banks such as IPK Gatersleben (https://www.ipk-gatersleben.de/en/genebank/) and the U.S. National Plant Germplasm System (https://npgsweb.ars-grin.gov/gringlobal/search).Custom scripts used for SNP calling are available on GitHub: https://github.com/IBEXCluster/ IBEX-SNPcaller/blob/master/workflow.sh. Additional information of other custom scripts used for making plots are available at https://github.com/DilanSarange/quinoaDPgwas

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Dilan Sarange Rajapaksha Patiranage

    Plant Breeding Institute, Christian-Albrechts University of Kiel, Kiel, Germany
    Competing interests
    The authors declare that no competing interests exist.

  2. Elodie Rey

    Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
    Competing interests
    The authors declare that no competing interests exist.

  3. Nazgol Emrani

    Plant Breeding Institute, Christian-Albrechts University of Kiel, Kiel, Germany
    For correspondence
    n.emrani@plantbreeding.uni-kiel.de
    Competing interests
    The authors declare that no competing interests exist.

  4. Gordon Wellman

    Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
    Competing interests
    The authors declare that no competing interests exist.

  5. Karl Schmid

    Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Stuttgart, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5129-895X

  6. Sandra M Schmöckel

    Department of Physiology of Yield Stability, University of Hohenheim, Stuttgart, Germany
    Competing interests
    The authors declare that no competing interests exist.

  7. Mark Tester

    Biological and Environmental Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5085-8801

  8. Christian Jung

    Plant Breeding Institute, Christian-Albrechts University of Kiel, Kiel, Germany
    For correspondence
    c.jung@plantbreeding.uni-kiel.de
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8149-7976

Funding

King Abdullah University of Science and Technology (OSR-2016-CRG5- 466 2966-02)

  • Dilan Sarange Rajapaksha Patiranage

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2022, Patiranage et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 3,650
    views
  • 672
    downloads
  • 22
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Dilan Sarange Rajapaksha Patiranage
  2. Elodie Rey
  3. Nazgol Emrani
  4. Gordon Wellman
  5. Karl Schmid
  6. Sandra M Schmöckel
  7. Mark Tester
  8. Christian Jung
(2022)
Genome-wide association study in quinoa reveals selection pattern typical for crops with a short breeding history
eLife 11:e66873.
https://doi.org/10.7554/eLife.66873

Share this article

https://doi.org/10.7554/eLife.66873

Categories and tags

Further reading

    1. Computational and Systems Biology
    2. Genetics and Genomics

    Risk factors affecting polygenic score performance across diverse cohorts

    Daniel Hui, Scott Dudek ... Marylyn D Ritchie
    Research Article

    Apart from ancestry, personal or environmental covariates may contribute to differences in polygenic score (PGS) performance. We analyzed the effects of covariate stratification and interaction on body mass index (BMI) PGS (PGSBMI) across four cohorts of European (N = 491,111) and African (N = 21,612) ancestry. Stratifying on binary covariates and quintiles for continuous covariates, 18/62 covariates had significant and replicable R2 differences among strata. Covariates with the largest differences included age, sex, blood lipids, physical activity, and alcohol consumption, with R2 being nearly double between best- and worst-performing quintiles for certain covariates. Twenty-eight covariates had significant PGSBMI–covariate interaction effects, modifying PGSBMI effects by nearly 20% per standard deviation change. We observed overlap between covariates that had significant R2 differences among strata and interaction effects – across all covariates, their main effects on BMI were correlated with their maximum R2 differences and interaction effects (0.56 and 0.58, respectively), suggesting high-PGSBMI individuals have highest R2 and increase in PGS effect. Using quantile regression, we show the effect of PGSBMI increases as BMI itself increases, and that these differences in effects are directly related to differences in R2 when stratifying by different covariates. Given significant and replicable evidence for context-specific PGSBMI performance and effects, we investigated ways to increase model performance taking into account nonlinear effects. Machine learning models (neural networks) increased relative model R2 (mean 23%) across datasets. Finally, creating PGSBMI directly from GxAge genome-wide association studies effects increased relative R2 by 7.8%. These results demonstrate that certain covariates, especially those most associated with BMI, significantly affect both PGSBMI performance and effects across diverse cohorts and ancestries, and we provide avenues to improve model performance that consider these effects.

    1. Genetics and Genomics

    Dynamics and regulatory roles of RNA m6A methylation in unbalanced genomes

    Shuai Zhang, Ruixue Wang ... Lin Sun
    Research Article

    N6-methyladenosine (m6A) in eukaryotic RNA is an epigenetic modification that is critical for RNA metabolism, gene expression regulation, and the development of organisms. Aberrant expression of m6A components appears in a variety of human diseases. RNA m6A modification in Drosophila has proven to be involved in sex determination regulated by Sxl and may affect X chromosome expression through the MSL complex. The dosage-related effects under the condition of genomic imbalance (i.e. aneuploidy) are related to various epigenetic regulatory mechanisms. Here, we investigated the roles of RNA m6A modification in unbalanced genomes using aneuploid Drosophila. The results showed that the expression of m6A components changed significantly under genomic imbalance, and affected the abundance and genome-wide distribution of m6A, which may be related to the developmental abnormalities of aneuploids. The relationships between methylation status and classical dosage effect, dosage compensation, and inverse dosage effect were also studied. In addition, we demonstrated that RNA m6A methylation may affect dosage-dependent gene regulation through dosage-sensitive modifiers, alternative splicing, the MSL complex, and other processes. More interestingly, there seems to be a close relationship between MSL complex and RNA m6A modification. It is found that ectopically overexpressed MSL complex, especially the levels of H4K16Ac through MOF, could influence the expression levels of m6A modification and genomic imbalance may be involved in this interaction. We found that m6A could affect the levels of H4K16Ac through MOF, a component of the MSL complex, and that genomic imbalance may be involved in this interaction. Altogether, our work reveals the dynamic and regulatory role of RNA m6A modification in unbalanced genomes, and may shed new light on the mechanisms of aneuploidy-related developmental abnormalities and diseases.

    1. Genetics and Genomics

    Down Syndrome: How a gene fuels ear infections

    Sedigheh Delmaghani, Aziz El-Amraoui
    Insight

    The DYRK1A enzyme is a pivotal contributor to frequent and severe episodes of otitis media in Down syndrome, positioning it as a promising target for therapeutic interventions.