The effect of artificial selection on phenotypic plasticity in maize

Remarkable productivity has been achieved in crop species through artificial selection and adaptation to modern agronomic practices. Whether intensive selection has changed the ability of improved cultivars to maintain high productivity across variable environments is unknown. Understanding the genetic control of phenotypic plasticity and genotype by environment (G × E) interaction will enhance crop performance predictions across diverse environments. Here we use data generated from the Genomes to Fields (G2F) Maize G × E project to assess the effect of selection on G × E variation and characterize polymorphisms associated with plasticity. Genomic regions putatively selected during modern temperate maize breeding explain less variability for yield G × E than unselected regions, indicating that improvement by breeding may have reduced G × E of modern temperate cultivars. Trends in genomic position of variants associated with stability reveal fewer genic associations and enrichment of variants 0–5000 base pairs upstream of genes, hypothetically due to control of plasticity by short-range regulatory elements

Maize Genomes to Fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets

Objectives: Crop improvement relies on analysis of phenotypic, genotypic, and environmental data. Given large, well-integrated, multi-year datasets, diverse queries can be made: Which lines perform best in hot, dry environments? Which alleles of specific genes are required for optimal performance in each environment? Such datasets also can be leveraged to predict cultivar performance, even in uncharacterized environments. The maize Genomes to Fields (G2F) Initiative is a multi-institutional organization of scientists working to generate and analyze such datasets from existing, publicly available inbred lines and hybrids. G2F’s genotype by environment project has released 2014 and 2015 datasets to the public, with 2016 and 2017 collected and soon to be made available. Data description: Datasets include DNA sequences; traditional phenotype descriptions, as well as detailed ear, cob, and kernel phenotypes quantified by image analysis; weather station measurements; and soil characterizations by site. Data are released as comma separated value spreadsheets accompanied by extensive README text descriptions. For genotypic and phenotypic data, both raw data and a version with outliers removed are reported. For weather data, two versions are reported: a full dataset calibrated against nearby National Weather Service sites and a second calibrated set with outliers and apparent artifacts removed

ThermoAlign: a genome-aware primer design tool for tiled amplicon resequencing

Publisher's PDFIsolating and sequencing specific regions in a genome is a cornerstone of molecular biology. This has been facilitated by computationally encoding the thermodynamics of DNA hybridization for automated design of hybridization and priming oligonucleotides. However, the repetitive composition of genomes challenges the identification of target-specific oligonucleotides, which limits genetics and genomics research on many species. Here, a tool called ThermoAlign was developed that ensures the design of target-specific primer pairs for DNA amplification. This is achieved by evaluating the thermodynamics of hybridization for full-length oligonucleotide-template alignments — thermoalignments — across the genome to identify primers predicted to bind specifically to the target site. For amplificationbased resequencing of regions that cannot be amplified by a single primer pair, a directed graph analysis method is used to identify minimum amplicon tiling paths. Laboratory validation by standard and long-range polymerase chain reaction and amplicon resequencing with maize, one of the most repetitive genomes sequenced to date (≈85% repeat content), demonstrated the specificity-by-design functionality of ThermoAlign. ThermoAlign is released under an open source license and bundled in a dependency-free container for wide distribution. It is anticipated that this tool will facilitate multiple applications in genetics and genomics and be useful in the workflow of high-throughput targeted resequencing studies.University of Delaware. Center for Bioinformatics & Computational Biology.University of Delaware. Department of Plant and Soil Sciences

High-Throughput Resequencing of Maize Landraces at Genomic Regions Associated with Flowering Time

Publisher's PDFDespite the reduction in the price of sequencing, it remains expensive to sequence and assemble whole, complex genomes of multiple samples for population studies, particularly for large genomes like those of many crop species. Enrichment of target genome regions coupled with next generation sequencing is a cost-effective strategy to obtain sequence information for loci of interest across many individuals, providing a less expensive approach to evaluating sequence variation at the population scale. Here we evaluate amplicon-based enrichment coupled with semiconductor sequencing on a validation set consisting of three maize inbred lines, two hybrids and 19 landrace accessions. We report the use of a multiplexed panel of 319 PCR assays that target 20 candidate loci associated with photoperiod sensitivity in maize while requiring 25 ng or less of starting DNA per sample. Enriched regions had an average on-target sequence read depth of 105 with 98% of the sequence data mapping to the maize ‘B73’ reference and 80% of the reads mapping to the target interval. Sequence reads were aligned to B73 and 1,486 and 1,244 variants were called using SAMtools and GATK, respectively. Of the variants called by both SAMtools and GATK, 30% were not previously reported in maize. Due to the high sequence read depth, heterozygote genotypes could be called with at least 92.5% accuracy in hybrid materials using GATK. The genetic data are congruent with previous reports of high total genetic diversity and substantial population differentiation among maize landraces. In conclusion, semiconductor sequencing of highly multiplexed PCR reactions is a cost-effective strategy for resequencing targeted genomic loci in diverse maize materials.Department of Plant and Soil Science

Quantification of sources of variation and accuracy of sequence discrimination in a replicated microarray experiment

cDNA microarray spot variability arises from many sources, and different systems have varying requirements for achieving the desired level of precision. We determined relative contributions to variance and investigated sequence discrimination using a multiple-array experimental design, with arrays subdivided to determine position and pin effect. Related fragments of 67 resistance gene homologs (RGHs) isolated from Theobroma cacao L. and grouped by sequence similarity were spotted onto arrays, using two of the same RGHs in the fluorescent dye channels (Cy™3, Cy5) of the hybridization solution in a “dye-flip” design. A comprehensive statistical model accounted for variability well, giving a coefficient of variation (CV) based on experimental error of 2.12%. Although we were able to separate 85% of RGH group means clearly, some groups more similar to the target were indistinguishable due to nonspecific hybridization. Genetic factors together contributed 72.2% of the total variation, while position and pin effects and their interactions contributed 9.8%. Replication effect was statistically significant. Otherwise, no tests for position effects were significant. The results of the analysis indicate that our Genetic Microsystems 417™ arrayer and Affymetrix 428™ scanner are performing with sufficient precision, and we produced useful information for planning efficient future experiments

Identification and Characterization of Regions of the Rice Genome Associated With Broad-Spectrum, Quantitative Disease Resistance

Much research has been devoted to understanding the biology of plant-pathogen interactions. The extensive genetic analysis of disease resistance in rice, coupled with the sequenced genome and genomic resources, provides the opportunity to seek convergent evidence implicating specific chromosomal segments and genes in the control of resistance. Published data on quantitative and qualitative disease resistance in rice were synthesized to evaluate the distributions of and associations among resistance loci. Quantitative trait loci (QTL) for resistance to multiple diseases and qualitative resistance loci (R genes) were clustered in the rice genome. R genes and their analogs of the nucleotide binding site–leucine-rich repeat class and genes identified on the basis of differential representation in disease-related EST libraries were significantly associated with QTL. Chromosomal segments associated with broad-spectrum quantitative disease resistance (BS-QDR) were identified. These segments contained numerous positional candidate genes identified on the basis of a range of criteria, and groups of genes belonging to two defense-associated biochemical pathways were found to underlie one BS-QDR region. Genetic dissection of disease QTL confidence intervals is needed to reduce the number of positional candidate genes for further functional analysis. This study provides a framework for future investigations of disease resistance in rice and related crop species

Selection Mapping of Loci for Quantitative Disease Resistance in a Diverse Maize Population

The selection response of a complex maize population improved primarily for quantitative disease resistance to northern leaf blight (NLB) and secondarily for common rust resistance and agronomic phenotypes was investigated at the molecular genetic level. A tiered marker analysis with 151 simple sequence repeat (SSR) markers in 90 individuals of the population indicated that on average six alleles per locus were available for selection. An improved test statistic for selection mapping was developed, in which quantitative trait loci (QTL) are identified through the analysis of allele-frequency shifts at mapped multiallelic loci over generations of selection. After correcting for the multiple tests performed, 25 SSR loci showed evidence of selection. Many of the putatively selected loci were unlinked and dispersed across the genome, which was consistent with the diffuse distribution of previously published QTL for NLB resistance. Compelling evidence for selection was found on maize chromosome 8, where several putatively selected loci colocalized with published NLB QTL and a race-specific resistance gene. Analysis of F2 populations derived from the selection mapping population suggested that multiple linked loci in this chromosomal segment were, in part, responsible for the selection response for quantitative resistance to NLB

Validation and Characterization of Maize Multiple Disease Resistance QTL

Southern Leaf Blight, Northern Leaf Blight, and Gray Leaf Spot, caused by ascomycete fungi, are among the most important foliar diseases of maize worldwide. Previously, disease resistance quantitative trait loci (QTL) for all three diseases were identified in a connected set of chromosome segment substitution line (CSSL) populations designed for the identification of disease resistance QTL. Some QTL for different diseases co-localized, indicating the presence of multiple disease resistance (MDR) QTL. The goal of this study was to perform an independent test of several of the MDR QTL identified to confirm their existence and derive a more precise estimate of allele additive and dominance effects. Twelve F2:3 family populations were produced, in which selected QTL were segregating in an otherwise uniform genetic background. The populations were assessed for each of the three diseases in replicated trials and genotyped with markers previously associated with disease resistance. Pairwise phenotypic correlations across all the populations for resistance to the three diseases ranged from 0.2 to 0.3 and were all significant at the alpha level of 0.01. Of the 44 QTL tested, 16 were validated (identified at the same genomic location for the same disease or diseases) and several novel QTL/disease associations were found. Two MDR QTL were associated with resistance to all three diseases. This study identifies several potentially important MDR QTL and demonstrates the importance of independently evaluating QTL effects following their initial identification