A genetic tradeoff for tolerance to moderate and severe heat stress in US hybrid maize.

Global climate change is increasing both average temperatures and the frequencies of extreme high temperatures. Past studies have documented a strong negative effect of exposures to temperatures >30°C on hybrid maize yields. However, these studies could not disentangle genetic adaptation via artificial selection from changes in agronomic practices. Because most of the earliest maize hybrids are no longer available, side-by-side comparisons with modern hybrids under current field conditions are generally impossible. Here, we report on the collection and curation of 81 years of public yield trial records covering 4,730 maize hybrids, which enabled us to model genetic variation for temperature responses among maize hybrids. We show that selection may have indirectly and inconsistently contributed to the genetic adaptation of maize to moderate heat stress over this time period while preserving genetic variance for continued adaptation. However, our results reveal the existence of a genetic tradeoff for tolerance to moderate and severe heat stress, leading to a decrease in tolerance to severe heat stress over the same time period. Both trends are particularly conspicuous since the mid-1970s. Such a tradeoff poses challenges to the continued adaptation of maize to warming climates due to a projected increase in the frequency of extreme heat events. Nevertheless, given recent advances in phenomics, enviromics, and physiological modeling, our results offer a degree of optimism for the capacity of plant breeders to adapt maize to warming climates, assuming appropriate levels of R&D investment.

Empirical Comparisons of Different Statistical Models To Identify and Validate Kernel Row Number-Associated Variants from Structured Multi-parent Mapping Populations of Maize.

Advances in next generation sequencing technologies and statistical approaches enable genome-wide dissection of phenotypic traits via genome-wide association studies (GWAS). Although multiple statistical approaches for conducting GWAS are available, the power and cross-validation rates of many approaches have been mostly tested using simulated data. Empirical comparisons of single variant (SV) and multi-variant (MV) GWAS approaches have not been conducted to test if a single approach or a combination of SV and MV is effective, through identification and cross-validation of trait-associated loci. In this study, kernel row number (KRN) data were collected from a set of 6,230 entries derived from the Nested Association Mapping (NAM) population and related populations. Three different types of GWAS analyses were performed: 1) single-variant (SV), 2) stepwise regression (STR) and 3) a Bayesian-based multi-variant (BMV) model. Using SV, STR, and BMV models, 257, 300, and 442 KRN-associated variants (KAVs) were identified in the initial GWAS analyses. Of these, 231 KAVs were subjected to genetic validation using three unrelated populations that were not included in the initial GWAS. Genetic validation results suggest that the three GWAS approaches are complementary. Interestingly, KAVs in low recombination regions were more likely to exhibit associations in independent populations than KAVs in recombinationally active regions, probably as a consequence of linkage disequilibrium. The KAVs identified in this study have the potential to enhance our understanding of the genetic basis of ear development.

Intragenic Meiotic Crossovers Generate Novel Alleles with Transgressive Expression Levels.

Meiotic recombination is an evolutionary force that generates new genetic diversity upon which selection can act. Whereas multiple studies have assessed genome-wide patterns of recombination and specific cases of intragenic recombination, few studies have assessed intragenic recombination genome-wide in higher eukaryotes. We identified recombination events within or near genes in a population of maize recombinant inbred lines (RILs) using RNA-sequencing data. Our results are consistent with case studies that have shown that intragenic crossovers cluster at the 5' ends of some genes. Further, we identified cases of intragenic crossovers that generate transgressive transcript accumulation patterns, that is, recombinant alleles displayed higher or lower levels of expression than did nonrecombinant alleles in any of ∼100 RILs, implicating intragenic recombination in the generation of new variants upon which selection can act. Thousands of apparent gene conversion events were identified, allowing us to estimate the genome-wide rate of gene conversion at SNP sites (4.9 × 10-5). The density of syntenic genes (i.e., those conserved at the same genomic locations since the divergence of maize and sorghum) exhibits a substantial correlation with crossover frequency, whereas the density of nonsyntenic genes (i.e., those which have transposed or been lost subsequent to the divergence of maize and sorghum) shows little correlation, suggesting that crossovers occur at higher rates in syntenic genes than in nonsyntenic genes. Increased rates of crossovers in syntenic genes could be either a consequence of the evolutionary conservation of synteny or a biological process that helps to maintain synteny.

tGBS® genotyping-by-sequencing enables reliable genotyping of heterozygous loci.

Conventional genotyping-by-sequencing (cGBS) strategies suffer from high rates of missing data and genotyping errors, particularly at heterozygous sites. tGBS® genotyping-by-sequencing is a novel method of genome reduction that employs two restriction enzymes to generate overhangs in opposite orientations to which (single-strand) oligos rather than (double-stranded) adaptors are ligated. This strategy ensures that only double-digested fragments are amplified and sequenced. The use of oligos avoids the necessity of preparing adaptors and the problems associated with inter-adaptor annealing/ligation. Hence, the tGBS protocol simplifies the preparation of high-quality GBS sequencing libraries. During polymerase chain reaction (PCR) amplification, selective nucleotides included at the 3'-end of the PCR primers result in additional genome reduction as compared to cGBS. By adjusting the number of selective bases, different numbers of genomic sites are targeted for sequencing. Therefore, for equivalent amounts of sequencing, more reads per site are available for SNP calling. Hence, as compared to cGBS, tGBS delivers higher SNP calling accuracy (>97-99%), even at heterozygous sites, less missing data per marker across a population of samples, and an enhanced ability to genotype rare alleles. tGBS is particularly well suited for genomic selection, which often requires the ability to genotype populations of individuals that are heterozygous at many loci.