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ABSTRACT Miscanthusholds a promise as a biocrop due to its high yield, perenniality and ability to grow on infertile soils. However, the current commercial biomass production ofMiscanthusis mostly limited to a single sterile triploid clone ofM.×giganteus. Nevertheless, parental species ofM.×giganteus, MiscanthussacchariflorusandMiscanthussinensiscontain vast genetic diversity for crop improvement. WithM. sacchariflorushaving a natural geographic distribution in cold‐temperate northeast China and eastern Russia, we hypothesised that it has substantial variation in physiological response to chilling. Using a semi‐high‐throughput method, we phenotyped 209M. sacchariflorusgenotypes belonging to six genetic groups for non‐photochemical quenching (NPQ) and photosystem II efficiency (ΦPSII) kinetics under warm and chilling treatments in three growing seasons. In response to the chilling treatment, all genetic groups exhibited an increase in NPQ induction rate indicating faster activation of NPQ in light. Notably, under chilling, the Korea/NE China/Russia 2x and N China 2x groups stood out for the highest NPQ rate in light and the highest steady‐state NPQ in light. This NPQ phenotype may contribute adaptation to chilling during bright, cold mornings of spring and early autumn in temperate climates, when faster NPQ would better protect from oxidative stress. Such enhanced adaptation could expand the growing season and thus productivity at a given location or expand the range of economically viable growing locations to higher latitudes and altitudes. A genome‐wide association study identified 126 unique SNPs associated with NPQ and ΦPSII traits. Among the identified candidate genes were enzymes involved in the ascorbate recycle and shikimate pathway, gamma‐aminobutyric acid and cation efflux transporters. Identifying natural variation and genes involved in NPQ and ΦPSII kinetics considerably enlarges the toolbox for breeding and/or engineeringMiscanthuswith optimised photosynthesis under warm and chilling conditions for sustainable feedstock production for bioenergy. Chilling affects the productivity and geographical distribution of most crops. Using a semi‐high‐throughput approach to investigate photosynthesis‐related traits, we characterised variation existing in the bioenergy cropMiscanthusunder chilling and warm conditions and identified potential genes associated with it. Under chilling, two genetic groups from the northern edge ofMiscanthusdistribution stood out for faster activation of photoprotection. This trait may contribute adaptation to chilling in temperate climates, when faster photoprotection would better defend from oxidative stress. Enhanced chilling adaptation could expand the growing season and thus productivity or enlarge the range of growing locations. 

Abstract BackgroundGiven the economic and environmental importance of allopolyploids and other species with highly duplicated genomes, there is a need for methods to distinguish paralogs, i.e. duplicate sequences within a genome, from Mendelian loci, i.e. single copy sequences that pair at meiosis. The ratio of observed to expected heterozygosity is an effective tool for filtering loci but requires genotyping to be performed first at a high computational cost, whereas counting the number of sequence tags detected per genotype is computationally quick but very ineffective in inbred or polyploid populations. Therefore, new methods are needed for filtering paralogs. ResultsWe introduce a novel statistic,H_ind/H_E, that uses the probability that two reads sampled from a genotype will belong to different alleles, instead of observed heterozygosity. The expected value ofH_ind/H_Eis the same across all loci in a dataset, regardless of read depth or allele frequency. In contrast to methods based on observed heterozygosity, it can be estimated and used for filtering loci prior to genotype calling. In addition to filtering paralogs, it can be used to filter loci with null alleles or high overdispersion, and identify individuals with unexpected ploidy and hybrid status. We demonstrate that the statistic is useful at read depths as low as five to 10, well below the depth needed for accurate genotype calling in polyploid and outcrossing species. ConclusionsOur methodology for estimatingH_ind/H_Eacross loci and individuals, as well as determining reasonable thresholds for filtering loci, is implemented in polyRAD v1.6, available athttps://github.com/lvclark/polyRAD. In large sequencing datasets, we anticipate that the ability to filter markers and identify problematic individuals prior to genotype calling will save researchers considerable computational time. 

Low or uneven read depth is a common limitation of genotyping-by-sequencing (GBS) and restriction site-associated DNA sequencing (RAD-seq), resulting in high missing data rates, heterozygotes miscalled as homozygotes, and uncertainty of allele copy number in heterozygous polyploids. Bayesian genotype calling can mitigate these issues, but previously has only been implemented in software that requires a reference genome or uses priors that may be inappropriate for the population. Here we present several novel Bayesian algorithms that estimate genotype posterior probabilities, all of which are implemented in a new R package, polyRAD. Appropriate priors can be specified for mapping populations, populations in Hardy-Weinberg equilibrium, or structured populations, and in each case can be informed by genotypes at linked markers. The polyRAD software imports read depth from several existing pipelines, and outputs continuous or discrete numerical genotypes suitable for analyses such as genome-wide association and genomic prediction.