Limited haplotype diversity underlies polygenic trait architecture across 70 years of wheat breeding

Abstract

Background Selection has dramatically shaped genetic and phenotypic variation in bread wheat. We can assess the genomic basis of historical phenotypic changes, and the potential for future improvement, using experimental populations that attempt to undo selection through the randomizing effects of recombination. Results We bred the NIAB Diverse MAGIC multi-parent population comprising over 500 recombinant inbred lines, descended from sixteen historical UK bread wheat varieties released between 1935 and 2004. We sequence the founders’ genes and promoters by capture, and the MAGIC population by low-coverage whole-genome sequencing. We impute 1.1 M high-quality SNPs that are over 99% concordant with array genotypes. Imputation accuracy only marginally improves when including the founders’ genomes as a haplotype reference panel. Despite capturing 73% of global wheat genetic polymorphism, 83% of genes cluster into no more than three haplotypes. We phenotype 47 agronomic traits over 2 years and map 136 genome-wide significant associations, concentrated at 42 genetic loci with large and often pleiotropic effects. Around half of these overlap known quantitative trait loci. Most traits exhibit extensive polygenicity, as revealed by multi-locus shrinkage modelling. Conclusions Our results are consistent with a gene pool of low haplotypic diversity, containing few novel loci of large effect. Most past, and projected future, phenotypic changes arising from existing variation involve fine-scale shuffling of a few haplotypes to recombine dozens of polygenic alleles of small effect. Moreover, extensive pleiotropy means selection on one trait will have unintended consequences, exemplified by the negative trade-off between yield and protein content, unless selection and recombination can break unfavorable trait-trait associations.

The Haplotype-Based Analysis of Aegilops tauschii Introgression Into Hard Red Winter Wheat and Its Impact on Productivity Traits

“…The latter makes sequenced founders an ideal reference panel for imputing genotypes in a breeding population genotyped using low-coverage or complexity-reduced sequencing. This was recently demonstrated by imputing genotypes in the wheat MAGIC population genotyped by low-coverage sequencing (Scott et al, 2021). The composition of our introgression population, including multiple biparental cross families (Nyine et al, 2020), also shifts the population allele frequency toward more common variants, which could be imputed with a higher accuracy than rare variants (Huang et al, 2015).…”

Metabolomics for Crop Breeding: General Considerations

“…This improvement appeared to be based on (1) the search for the optimal combination of alleles with little effect by "shuffling" these alleles for polygenic traits and (2) the introduction of a small number of loci with a large effect from wide crossings, such as Rht semi-dwarfing alleles and introgression of large disease-resistant genomic segments from other species. This study indicated that most QTL are likely to have pleiotropic effects that make it harder to improve crops, as improving one trait can negatively affect others; for example, an increase in yield usually results in a decrease in protein content [13].…”

“…However, it was concluded that the selection for dozens of loci makes crop improvement possible. It is expected that further improvement of crops will be assisted by genomic selection and driven by the introgression of selected exotic alleles and genome-wide introduction of diverse haplotypes [13].…”

“…NMR measurements are quantitative, highly reproducible, can be performed with high throughput, although longer measurements and measurements for both 1 H and 13 C strongly improve the results. The downsides of NMR are low sensitivity, especially for 13 C and nitrogen 15 N, and low resolving power. Thus, just up to the 100 most abundant metabolites can be recognized and quantified.…”

“…However, in addition, NMR spectra have a vast number of minor peaks (more than ten thousand), which cannot be assigned for specific metabolites but can be utilized to characterize biological samples as molecular features (MF). Two-dimensional NMR (usually 1 H and 13 C) helps in the identification of some MFs as specific molecules; however, the 13 C abundancy in natural samples is low and 13 C NMR provides information on the metabolites with a high concentration in the sample. Thus, NMR is well-suited for untargeted metabolomic studies.…”