Skovbjerg Cathrine Kiel, Sarup Pernille, Wahlström Ellen, Jensen Jens Due, Orabi Jihad, Olesen Lotte, Jensen Just, Jahoor Ahmed, Ramstein Guillaume
Nordic Seed A/S, Odder, Denmark.
Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus C, Denmark.
Heredity (Edinb). 2025 Jan;134(1):33-48. doi: 10.1038/s41437-024-00733-x. Epub 2024 Nov 28.
Genome-wide association study (GWAS) is a powerful tool for identifying marker-trait associations that can accelerate breeding progress. Yet, its power is typically constrained in newly established breeding programs where large phenotypic and genotypic datasets have not yet accumulated. Expanding the dataset by inclusion of data from well-established breeding programs with many years of phenotyping and genotyping can potentially address this problem. In this study we performed single- and multi-population GWAS on heading date and lodging in four barley breeding populations with varying combinations of row-type and growth habit. Focusing on a recently established 6-rowed winter (6RW) barley population, single-population GWAS hardly resulted in any significant associations. Nevertheless, the combination of the 6RW target population with other populations in multi-population GWAS detected four and five robust candidate quantitative trait loci for heading date and lodging, respectively. Of these, three remained undetected when analysing the combined populations individually. Further, multi-population GWAS detected markers capturing a larger proportion of genetic variance in 6RW. For multi-population GWAS, we compared the findings of a univariate model (MP1) with a multivariate model (MP2). While both models surpassed single-population GWAS in power, MP2 offered a significant advantage by having more realistic assumptions while pointing towards robust marker-trait associations across populations. Additionally, comparisons of GWAS findings for MP2 and single-population GWAS allowed identification of population-specific loci. In conclusion, our study presents a promising approach to kick-start genomics-based breeding in newly established breeding populations.
全基因组关联研究（GWAS）是一种强大的工具，用于识别可加速育种进程的标记-性状关联。然而，在尚未积累大量表型和基因型数据集的新建立的育种计划中，其效力通常受到限制。通过纳入来自具有多年表型和基因分型数据的成熟育种计划的数据来扩大数据集，可能会解决这个问题。在本研究中，我们对四个具有不同行型和生长习性组合的大麦育种群体的抽穗期和倒伏情况进行了单群体和多群体GWAS分析。聚焦于一个最近建立的六行冬大麦群体（6RW），单群体GWAS几乎没有产生任何显著关联。然而，在多群体GWAS中，将6RW目标群体与其他群体相结合，分别检测到了四个和五个用于抽穗期和倒伏的稳健候选数量性状位点。其中，在单独分析合并群体时，有三个位点未被检测到。此外，多群体GWAS检测到的标记捕获了6RW中更大比例的遗传变异。对于多群体GWAS，我们比较了单变量模型（MP1）和多变量模型（MP2）的结果。虽然这两个模型在效力上都超过了单群体GWAS，但MP2具有更现实的假设，同时指向跨群体的稳健标记-性状关联，具有显著优势。此外，对MP2和单群体GWAS的GWAS结果进行比较，有助于识别群体特异性位点。总之，我们的研究提出了一种有前景的方法，可在新建立的育种群体中启动基于基因组学的育种。
