The Application of Population Genomics in Crop Research

Population genomics is a rapidly developing discipline at the crossroads of population genetics and genomics. This term was first proposed in 1988 by Gulcher and Stefansson [1]. Luikart et al. [2] defined population genomics as using high-density genome-wide markers (DNA, RNA, and epigenetic marks) to identify genomic regions linked to traits or evolutionary processes such as fitness, phenotypes, and selection. With the development of high-throughput sequencing technologies, the application of population genomics in plants has witnessed substantial growth. Simultaneously, the advancement of software, statistical methods, and models has also facilitated the extraction of valuable insights from extensive population genetic data.

Gene chips and high-throughput sequencing, such as genotyping-by-sequencing (GBS) and whole-genome sequencing (WGS), have drastically expanded the scale of population genomic variation detection in plants. Large-scale genomic data (thousands to millions of single nucleotide polymorphisms (SNPs) or other molecular markers) provide researchers with the opportunity to observe the genetic characteristics of the entire genome. One prevalent application of population genomic methodologies is to estimate the genetic parameters and evolutionary relationships of a population. This entails the assessment of crucial factors such as population size, population structure, phylogeography, demographic history, and phylogenetic relationships [3]. Additionally, population genomics makes it possible to analyze adaptive genetic variations that underlie selective sweeps and investigate the genetic architecture underlying adaptive changes, allowing us to understand the molecular basis of species adaptation and evolution. Moreover, landscape genomics, as an extension of population genomics, focuses on the interaction between adaptive genetic variation and environmental heterogeneity, thereby revealing the adaptability and evolutionary mechanisms of species, which serve as a fundamental basis in the domains of conservation biology and selective breeding [4]. Furthermore, genome-wide association studies (GWAS), which are increasingly applied in population genomics studies, have enabled the identification of genomic regions associated with phenotypic variation, offering deeper insights into the genetic mechanisms underlying important agronomic traits.

In conclusion, the application of population genomics can help researchers gain a deeper understanding of genetic variations in plant populations and reveal the origin and evolution of populations. In crop science, population genomics is often used to identify gene loci that are associated with agronomic traits through methods such as genome-wide association studies, facilitating breakthrough discoveries in heredity and crop breeding.

The following section provides an exposition of research in the field of agronomy utilizing population genomics. A total of 10 articles published in the journal Agronomy over the past two years are reviewed, encompassing studies on seven diverse crop species, including major crops like rice, upland cotton, and rape.

The study by Kimwemwe et al. [5] employed DArTseq-derived SNP markers to evaluate the population structure and genetic diversity of 94 rice (Oryza sativa L.) genotypes from the Democratic Republic of Congo. Population structure analysis, using the ADMIXTURE program, identified five distinct sub-populations, including admixtures. Regarding genetic diversity, significant variations between sub-populations and between genotypes were observed using the AMOVA statistical method, which could be due to the limited gene flow. Furthermore, the average Euclidean genetic distance was high, which indicated substantial genetic diversity among the rice germplasms.

In the study by Shen et al. [6], they explored the relationship between introgression and geographic distribution in upland cotton using publicly re-sequenced data from 890 accessions. They found that an introgression pattern was associated with the similarity of the adapted environment. Additionally, the study revealed the impact of artificial selection on introgression. Through a genome-wide association study (GWAS) meta-analysis, they located 261 fiber-related QTLs. Among these, 67 QTLs showed signals of introgression, whereas 123 QTLs exhibited selection signals.

In the study by Yuan et al. [7], the genetic diversity and selection signatures of Brassica juncea from the Yunnan–Guizhou Plateau were investigated through whole-genome resequencing. The researchers re-sequenced the 193 accessions and identified 1.04 million high-quality SNPs and 3.23 million InDels. Based on population structure, principal component analyses, phylogenetic analysis, and phenotype analysis, the study revealed significant genetic diversity, and the accessions were divided into four distinct genetic groups. The study further conducted a selection sweep analysis and a genome-wide association study to identify candidate genes associated with seed color and fatty acid biosynthesis.

In the study by Miao et al. [8], they analyzed genetic diversity in non-heading Chinese cabbage resistance to clubroot disease. In total, 121 varieties were re-sequenced to explore the population structure, genetic diversity, population differentiation index, and selection sweep based on SNP markers. By analyzing population structure, the accessions were separated into four subgroups. Subgroups C and D exhibited potential selfing, while heterozygosity might have occurred in subgroups A and B. Subgroups B and C displayed a moderate level of genetic variation. In addition, the study employed selection sweep analysis. GO enrichment and KEGG enrichment analysis found two candidate genes related to disease resistance (BraA01g042910.3.5C and BraA06g019360.3.5C).

In the study by Nie et al. [9], the investigation focused on the genetic diversity and population differentiation of Dongxiang wild rice (DXWR). Resequencing 220 DXWR lines from nine natural populations revealed SNPs and Indels, with almost half of these variations absent in cultivated rice or other wild rice. Two subpopulations, G1 and G2, were identified through structure and PCA analysis, with G1 showing greater genetic diversity. Examining the correlation between relative genetic diversity and genetic differentiation between G1 and G2 elucidated directional selection’s potential impact on reduced diversity in certain regions. Highly differentiated regions between G1 and G2 harbored functional genes and QTLs.

In the research by Qin et al. [10], they investigated the genetic basis of the Harvest Index (HI) and related traits in Brassica napus L. This research involved an analysis of HI and 13 related traits across 104 core breeding lines. HI showed a complex relationship with other traits. Employing the Bnapus50K array, genome-wide association studies identified 212 significant SNPs associated with these traits and pinpointed 22 stable SNPs. Subsequently, a network correlating traits and SNPs was established, and 39 potential candidate genes were predicted.

In the research conducted by Xu et al. [11], the genetic mechanisms underlying hemicellulose content in rapeseed stalks were explored. The study involved 139 rapeseed accessions from the provinces of Guizhou, Hubei, and Anhui. By using a 60 K single nucleotide polymorphism chip, genotypic data were obtained. The GWAS revealed 28 significant SNPs associated with hemicellulose content. Notably, major loci such as qHCs.C02, qHCs.C05, and qHCs.C08 were linked with stalk hemicellulose, while qHCt.A09, qHCt.C05, and qHCt.C08 were associated with taproot hemicellulose. This investigation unveiled seven candidate genes involved in hemicellulose synthesis, with RNA-seq analysis confirming two significant and differentially expressed genes, namely BnaC05G0092200ZS and BnaC05G0112400ZS, as underlying QTL.

Khadgi et al. [12] employed a genome-wide association analysis to explore the genetic regulation of the prickle development in red raspberries. The study utilized a segregating population derived from a cross between the prickle-free cultivar Joan J (ss) with the prickled cultivar Caroline (Ss). By utilizing GBS to develop 8474 SNP markers and conducting GWAS, associations between these SNPs and the prickle phenotype were unveiled. This analysis revealed four SNPs located on chromosome 4 that were closely linked to the prickle trait.

Reddy et al. [13] investigated the genetic factors influencing the efficiency of phosphorous uptake and the utilization of efficiency traits in mungbean. The study employed an association mapping panel of 120 genotypes, which were phenotyped for various parameters under low P (LP) and normal P (NP) conditions in a hydroponic setup. Employing a genotyping-by-sequencing (GBS)-based genome-wide association study (GWAS), the research unveiled 116 SNPs associated with 61 protein-coding genes, with 16 of these genes enhancing phosphorous uptake and phosphorous utilization efficiency. Moreover, six genes with high expression across root, shoot apical meristem, and leaf were identified, and the SNPs present in three genes were validated via Sanger sequencing.

In the study conducted by Ryu et al. [14], genotyping-by-sequencing (GBS) was employed to identify single nucleotide polymorphisms (SNPs). Building upon these SNPs, an association study was carried out to investigate the flowering time, crude fat, and fatty acid contents in 46 rapeseed mutant lines derived from gamma radiation. Hierarchical clustering analysis revealed eight groups based on flowering time and fatty acid compositions. Forty significant SNPs associated with flowering time (1 SNP), crude fat content (2 SNPs), and fatty acid content (37 SNPs) were identified. Among the fatty acid content SNPs, 21 genes were annotated, with nine genes notably enriched in reproductive processes.

The application of population genomics enables researchers to gain a profound comprehension of genetic variation within crop populations and unveil the origins and domestication of crops. In all the aforementioned articles, each study diligently employed diverse methodologies to explore various aspects of genetic diversity, population structure, selection pressures, and genetic loci associated with agronomic traits in distinct crop populations. Consequently, these investigations have bestowed invaluable insights into crop breeding and genetic research endeavors.