Quantitative genetics: pan-genomes, SVs, and k-mers for GWAS

Disease-Associated Streptococcus pneumoniae Genetic Variation

Streptococcus pneumoniae is an opportunistic pathogen that causes substantial illness and death among children worldwide. The genetic backgrounds of pneumococci that cause infection versus asymptomatic carriage vary substantially. To determine the evolutionary mechanisms of opportunistic pathogenicity, we conducted a genomic surveillance study in China. We collected 783 S. pneumoniae isolates from infected and asymptomatic children. By using a 2-stage genomewide association study process, we compared genomic differences between infection and carriage isolates to address genomic variation associated with pathogenicity. We identified 8 consensus k-mers associated with adherence, antimicrobial resistance, and immune modulation, which were unevenly distributed in the infection isolates. Classification accuracy of the best k-mer predictor for S. pneumoniae infection was good, giving a simple target for predicting pathogenic isolates. Our findings suggest that S. pneumoniae pathogenicity is complex and multifactorial, and we provide genetic evidence for precise targeted interventions.

Combined reference-free and multi-reference based GWAS uncover cryptic variation underlying rapid adaptation in a fungal plant pathogen

Microbial pathogens often harbor substantial functional diversity driven by structural genetic variation. Rapid adaptation from such standing variation threatens global food security and human health. Genome-wide association studies (GWAS) provide a powerful approach to identify genetic variants underlying recent pathogen adaptation. However, the reliance on single reference genomes and single nucleotide polymorphisms (SNPs) obscures the true extent of adaptive genetic variation. Here, we show quantitatively how a combination of multiple reference genomes and reference-free approaches captures substantially more relevant genetic variation compared to single reference mapping. We performed reference-genome based association mapping across 19 reference-quality genomes covering the diversity of the species. We contrasted the results with a reference-free (i.e., k-mer) approach using raw whole-genome sequencing data in a panel of 145 strains collected across the global distribution range of the fungal wheat pathogen Zymoseptoria tritici. We mapped the genetic architecture of 49 life history traits including virulence, reproduction and growth in multiple stressful environments. The inclusion of additional reference genome SNP datasets provides a nearly linear increase in additional loci mapped through GWAS. Variants detected through the k-mer approach explained a higher proportion of phenotypic variation than a reference genome-based approach and revealed functionally confirmed loci that classic GWAS approaches failed to map. The power of GWAS in microbial pathogens can be significantly enhanced by comprehensively capturing structural genetic variation. Our approach is generalizable to a large number of species and will uncover novel mechanisms driving rapid adaptation of pathogens.

Genetic analysis of iron, zinc and grain yield in wheat-Aegilops derivatives using multi-locus GWAS

BACKGROUND

Wheat is a major staple crop and helps to reduce worldwide micronutrient deficiency. Investigating the genetics that control the concentrations of iron (Fe) and zinc (Zn) in wheat is crucial. Hence, we undertook a comprehensive study aimed at elucidating the genomic regions linked to the contents of Fe and Zn in the grain.

METHODS AND RESULTS

We performed the multi-locus genome-wide association (ML-GWAS) using a panel of 161 wheat-Aegilops substitution and addition lines to dissect the genomic regions controlling grain iron (GFeC), and grain zinc (GZnC) contents. The wheat panel was genotyped using 10,825 high-quality SNPs and phenotyped in three different environments (E1-E3) during 2017-2019. A total of 111 marker-trait associations (MTAs) (at p-value < 0.001) were detected that belong to all three sub-genomes of wheat. The highest number of MTAs were identified for GFeC (58), followed by GZnC (44) and yield (9). Further, six stable MTAs were identified for these three traits and also two pleiotropic MTAs were identified for GFeC and GZnC. A total of 1291 putative candidate genes (CGs) were also identified for all three traits. These CGs encode a diverse set of proteins, including heavy metal-associated (HMA), bZIP family protein, AP2/ERF, and protein previously associated with GFeC, GZnC, and grain yield.

CONCLUSIONS

The significant MTAs and CGs pinpointed in this current study are poised to play a pivotal role in enhancing both the nutritional quality and yield of wheat, utilizing marker-assisted selection (MAS) techniques.

Genome-wide artificial introgressions of Gossypium barbadense into G. hirsutum reveal superior loci for simultaneous improvement of cotton fiber quality and yield traits

INTRODUCTION

The simultaneous improvement of fiber quality and yield for cotton is strongly limited by the narrow genetic backgrounds of Gossypium hirsutum (Gh) and the negative genetic correlations among traits. An effective way to overcome the bottlenecks is to introgress the favorable alleles of Gossypium barbadense (Gb) for fiber quality into Gh with high yield.

OBJECTIVES

This study was to identify superior loci for the improvement of fiber quality and yield.

METHODS

Two sets of chromosome segment substitution lines (CSSLs) were generated by crossing Hai1 (Gb, donor-parent) with cultivar CCRI36 (Gh) and CCRI45 (Gh) as genetic backgrounds, and cultivated in 6 and 8 environments, respectively. The kmer genotyping strategy was improved and applied to the population genetic analysis of 743 genomic sequencing data. A progeny segregating population was constructed to validate genetic effects of the candidate loci.

RESULTS

A total of 68,912 and 83,352 genome-wide introgressed kmers were identified in the CCRI36 and CCRI45 populations, respectively. Over 90 % introgressions were homologous exchanges and about 21 % were reverse insertions. In total, 291 major introgressed segments were identified with stable genetic effects, of which 66(22.98 %), 64(21.99 %), 35(12.03 %), 31(10.65 %) and 18(6.19 %) were beneficial for the improvement of fiber length (FL), strength (FS), micronaire, lint-percentage (LP) and boll-weight, respectively. Thirty-nine introgression segments were detected with stable favorable additive effects for simultaneous improvement of 2 or more traits in Gh genetic background, including 6 could increase FL/FS and LP. The pyramiding effects of 3 pleiotropic segments (A07:C45Clu-081, D06:C45Clu-218, D02:C45Clu-193) were further validated in the segregating population.

CONCLUSION

The combining of genome-wide introgressions and kmer genotyping strategy showed significant advantages in exploring genetic resources. Through the genome-wide comprehensive mining, a total of 11 clusters (segments) were discovered for the stable simultaneous improvement of FL/FS and LP, which should be paid more attention in the future.

k-mer-Based Genome-Wide Association Studies in Plants: Advances, Challenges, and Perspectives

Genome-wide association studies (GWAS) have allowed the discovery of marker-trait associations in crops over recent decades. However, their power is hampered by a number of limitations, with the key one among them being an overreliance on single-nucleotide polymorphisms (SNPs) as molecular markers. Indeed, SNPs represent only one type of genetic variation and are usually derived from alignment to a single genome assembly that may be poorly representative of the population under study. To overcome this, k-mer-based GWAS approaches have recently been developed. k-mer-based GWAS provide a universal way to assess variation due to SNPs, insertions/deletions, and structural variations without having to specifically detect and genotype these variants. In addition, k-mer-based analyses can be used in species that lack a reference genome. However, the use of k-mers for GWAS presents challenges such as data size and complexity, lack of standard tools, and potential detection of false associations. Nevertheless, efforts are being made to overcome these challenges and a general analysis workflow has started to emerge. We identify the priorities for k-mer-based GWAS in years to come, notably in the development of user-friendly programs for their analysis and approaches for linking significant k-mers to sequence variation.

Limitations and advantages of using metabolite-based genome-wide association studies: focus on fruit quality traits

In the last decades, linkage mapping has help in the location of metabolite quantitative trait loci (QTL) in many species; however, this approach shows some limitations. Recently, thanks to the most recent advanced in high-throughput genotyping technologies like next-generation sequencing, metabolite genome-wide association study (mGWAS) has been proposed a powerful tool to identify the genetic variants in polygenic agrinomic traits. Fruit flavor is a complex interaction of aroma volatiles and taste being sugar and acid ratio key parameter for flavor acceptance. Here, we review recent progress of mGWAS in pinpoint gene polymorphisms related to flavor-related metabolites in fruits. Despite clear successes in discovering novel genes or regions associated with metabolite accumulation affecting sensory attributes in fruits, GWAS incurs in several limitations summarized in this review. In addition, in our own work, we performed mGWAS on 194 Citrus grandis accessions to investigate the genetic control of individual primary and lipid metabolites in ripe fruit. We have identified a total of 667 associations for 14 primary metabolites including amino acids, sugars, and organic acids, and 768 associations corresponding to 47 lipids. Furthermore, candidate genes related to important metabolites related to fruit quality such as sugars, organic acids and lipids were discovered.

Structural variant-based pangenome construction has low sensitivity to variability of haplotype-resolved bovine assemblies

Advantages of pangenomes over linear reference assemblies for genome research have recently been established. However, potential effects of sequence platform and assembly approach, or of combining assemblies created by different approaches, on pangenome construction have not been investigated. Here we generate haplotype-resolved assemblies from the offspring of three bovine trios representing increasing levels of heterozygosity that each demonstrate a substantial improvement in contiguity, completeness, and accuracy over the current Bos taurus reference genome. Diploid coverage as low as 20x for HiFi or 60x for ONT is sufficient to produce two haplotype-resolved assemblies meeting standards set by the Vertebrate Genomes Project. Structural variant-based pangenomes created from the haplotype-resolved assemblies demonstrate significant consensus regardless of sequence platform, assembler algorithm, or coverage. Inspecting pangenome topologies identifies 90 thousand structural variants including 931 overlapping with coding sequences; this approach reveals variants affecting QRICH2, PRDM9, HSPA1A, TAS2R46, and GC that have potential to affect phenotype.

Omics-based interdisciplinarity is accelerating plant breeding

Plant breeding is one of the oldest and most important activities accompanying human civilization. During the past thousand years, plant breeding has achieved three significant innovations, each of which derives from introgression of new theories or technologies. These innovations have significantly increased the food supply and allowed for population development. However, with population increases and resource shortages, the world is continuously facing the challenge of food security, which calls for next innovation in plant breeding. Recent technological advances in multiple disciplines have boosted the development of omics, which is accelerating plant breeding. Here, we review the recent advances in omics and discuss our understanding of how interdisciplinary researches will prompt new innovations in plant breeding.

GWAS for genetics of complex quantitative traits: Genome to pangenome and SNPs to SVs and k-mers

The development of improved methods for genome-wide association studies (GWAS) for genetics of quantitative traits has been an active area of research during the last 25 years. This activity initially started with the use of mixed linear model (MLM), which was variously modified. During the last decade, however, with the availability of high throughput next generation sequencing (NGS) technology, development and use of pangenomes and novel markers including structural variations (SVs) and k-mers for GWAS has taken over as a new thrust area of research. Pangenomes and SVs are now available in humans, livestock, and a number of plant species, so that these resources along with k-mers are being used in GWAS for exploring additional genetic variation that was hitherto not available for analysis. These developments have resulted in significant improvement in GWAS methodology for detection of marker-trait associations (MTAs) that are relevant to human healthcare and crop improvement.