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PubMed2025

Assessment of genomic prediction capabilities of transcriptome data in a barley multi-parent RIL population.

Low-cost and high-throughput RNA sequencing data for barley RILs achieved GP performance comparable to or better than traditional SNP array datasets when combined with parental whole-genome sequencing SNP data. The field of genomic selection (GS) is advancing rapidly on many fronts including the utilization of multi-omics datasets with the goal of increasing prediction ability and becoming an integral part of an increasing number of breeding programs ensuring future food security. In this study, we used RNA sequencing (RNA-Seq) data to perform genomic prediction (GP) on three related barley RIL populations. We investigated the potential of increasing prediction ability by combining genomic and transcriptomic datasets, adding whole-genome sequencing (WGS) SNP data, functional annotation-based filtering, and empirical quality filtering. Our RNA-Seq data were generated cost-efficiently using small-footprint plant cultivation, high-throughput RNA extraction, and Library preparation miniaturization. We also examined sequencing depth reduction as an additional cost-saving measure. We used fivefold cross-validation to evaluate the prediction ability of the gene expression dataset, the RNA-Seq SNP dataset, and the consensus SNP dataset between the RNA-Seq and parental WGS data, resulting in prediction abilities between 0.73 and 0.78. The consensus SNP dataset performed best, with five out of eight traits performing significantly better compared to a 50K SNP array, which served as a benchmark. The advantage of the consensus SNP dataset was most prominent in the inter-population predictions, in which the training and validation sets originated from different RIL sub-populations. We were therefore able to not only show that RNA-Seq data alone are able to predict various complex traits in barley using RILs, but also that the performance can be further increased with WGS data for which the public availability will steadily increase.

Hordeum↗

PubMed2026

A GWAS-derived histone H4 variant linked to ear row number reveals functional insights into the maize ZmHistone gene family.

Ear row number (ERN) is a major yield determinant in maize and a key target for breeding of high-yielding varieties. This study utilized a multi-parent population (MPP) of 780 recombinant inbred lines (RILs) derived from seven inbred lines across three environments. Genotyping-by-sequencing (GBS) of the MPP yielded 638,646 high-quality SNPs. Using genome-wide association study (GWAS), we detected 80 significant SNPs including S2-15316355 and S4-224453431, which were consistently detected in all environments and best linear unbiased prediction (BLUP) analysis. A linkage disequilibrium-defined ±20 kb window around these two lead SNPs contained three positional candidate genes: Zm00001eb072840, Zm00001eb072850 and Zm00001eb202890. Zm00001eb072850 (ZmHistone12), a histone H4 variant, was prioritized for hypothesis-driven follow-up because the lead SNP lies within its coding sequence and the gene is expressed in ear-related tissues. Additionally, we identified 91 ZmHistone genes in the maize genome and described their phylogeny, promoter motif and expression patterns. Public transcriptome and qRT-PCR analysis in seven parental lines provide descriptive evidence of Histone variant genes in maize ear development. These results suggest a potential involvement of chromatin-associated regulation of ERN in maize and provide a foundation for future functional validation.

Ear development↗

PubMed2026

Long-read low-pass sequencing enhances variant detection in a peanut MAGIC population.

Accurate genotyping accelerates crop improvement, yet long-read sequencing remains underused in breeding due to cost. We present a scalable long-read low-pass (LRLP) sequencing framework for high-throughput variant discovery and trait mapping. Using PacBio HiFi reads in an allotetraploid peanut (Arachis hypogaea; AABB, 2n = 4x = 40) MAGIC population, we generated both LRLP and short-read low-pass (SRLP) data. At comparable depths, LRLP achieved substantially greater whole-genome and gene-space coverage than SRLP. Data were analyzed using both a single-reference genome and an 18-parent pangenome graph constructed with KhufuPan, a new tool for graph-based genotyping. Across analytical approaches, LRLP consistently identified more SNPs, indels (2-1,000 bp), and structural variants (>1 kb) than SRLP, improving genotype resolution and selection accuracy, particularly for large structural variants. By reducing cost barriers and increasing variant discovery in complex genomes, LRLP provides a practical path for deploying advanced genomics in under-resourced and orphan crops critical to global food security.

Arachis↗

PubMed2025

Uncovering the genetic basis of competitiveness and the potential for cooperation in plant groups.

Crop productivity was transformed by incorporating dwarfing genes that made plants smaller and less competitive (more cooperative). Beyond such major shifts in plant size, however, it is not clear how much variation in competitiveness remains and how to find its genetic basis. We performed plant density experiments, using 484 lines of the Arabidopsis thaliana multi-parent advanced generation inter-cross population, to compare methods for mapping the genetic basis of plant competitiveness. We first found that a major dwarfing gene, the erecta allele, caused reduced competitiveness and higher group productivity. Then, measuring competitiveness more generally, we found: (i) extensive variation in generic measures of competitiveness that extended beyond the effects of the erecta allele; (ii) a novel genomic region underlying variation in competitiveness; and (iii) that some measures of competitiveness were more useful than others. Our results show how modern genomic resources, including multi-parent populations, could uncover hidden genes for more cooperative crop plants.

Arabidopsis↗

PubMed2026

The MexMAGIC population reveals the genetic architecture of traits exhibiting clinal variation in Mexican native maize.

Defining the genetic basis of local adaptation is a key goal of evolutionary biology and crop improvement. Theory predicts that when selective pressures follow differences in the environment, a cline will be established. Clines can be exploited to uncover adaptive variation by association of alleles with the environment. However, monotonic phenotypic change over a cline is not necessarily mirrored in the behavior of genetic variants and population structure can further complicate analysis. To study genetic and phenotypic variation across the environment, we developed a multi-parent advanced generation inter-cross (MAGIC) population using eight Mexican native maize (Zea mays L. ssp. mays) varieties sourced from distinct agroecological zones. We evaluated the population in a common garden in Mexico and mapped tassel branching and flowering time, two traits that exhibit clinal variation. Variation in tassel branching was dominated by a single QTL with allele effects aligning to a negative elevational cline. By contrast, allele effects associated with 11 identified flowering time QTL were not consistently correlated with any one source environmental factor. Our observations support the prediction that genotype-environment association will be strongest under simple genetic architecture, although, even then, analysis in native populations may be confounded by population structure.

MAGIC↗