Quantitative Trait Locus Analysis of Leaf Morphology Indicates Conserved Shape Loci in Grapevine

Intra-leaf modeling of Cannabis leaflet shape produces leaf models that predict genetic and developmental identities

The iconic, palmately compound leaves of Cannabis have attracted significant attention in the past. However, investigations into the genetic basis of leaf shape or its connections to phytochemical composition have yielded inconclusive results. This is partly due to prominent changes in leaflet number within a single plant during development, which has so far prevented the proper use of common morphometric techniques. Here, we present a new method that overcomes the challenge of nonhomologous landmarks in palmate, pinnate, and lobed leaves, using Cannabis as an example. We model corresponding pseudo-landmarks for each leaflet as angle-radius coordinates and model them as a function of leaflet to create continuous polynomial models, bypassing the problems associated with variable number of leaflets between leaves. We analyze 341 leaves from 24 individuals from nine Cannabis accessions. Using 3591 pseudo-landmarks in modeled leaves, we accurately predict accession identity, leaflet number, and relative node number. Intra-leaf modeling offers a rapid, cost-effective means of identifying Cannabis accessions, making it a valuable tool for future taxonomic studies, cultivar recognition, and possibly chemical content analysis and sex identification, in addition to permitting the morphometric analysis of leaves in any species with variable numbers of leaflets or lobes.

Complex traits and candidate genes: estimation of genetic variance components across multiple genetic architectures

Large-effect loci-those statistically significant loci discovered by genome-wide association studies or linkage mapping-associated with key traits segregate amidst a background of minor, often undetectable, genetic effects in wild and domesticated plants and animals. Accurately attributing mean differences and variance explained to the correct components in the linear mixed model analysis is vital for selecting superior progeny and parents in plant and animal breeding, gene therapy, and medical genetics in humans. Marker-assisted prediction and its successor, genomic prediction, have many advantages for selecting superior individuals and understanding disease risk. However, these two approaches are less often integrated to study complex traits with different genetic architectures. This simulation study demonstrates that the average semivariance can be applied to models incorporating Mendelian, oligogenic, and polygenic terms simultaneously and yields accurate estimates of the variance explained for all relevant variables. Our previous research focused on large-effect loci and polygenic variance separately. This work aims to synthesize and expand the average semivariance framework to various genetic architectures and the corresponding mixed models. This framework independently accounts for the effects of large-effect loci and the polygenic genetic background and is universally applicable to genetics studies in humans, plants, animals, and microbes.

Leveraging the genetic correlation between traits improves the detection of epistasis in genome-wide association studies

Epistasis, commonly defined as the interaction between genetic loci, is known to play an important role in the phenotypic variation of complex traits. As a result, many statistical methods have been developed to identify genetic variants that are involved in epistasis, and nearly all of these approaches carry out this task by focusing on analyzing one trait at a time. Previous studies have shown that jointly modeling multiple phenotypes can often dramatically increase statistical power for association mapping. In this study, we present the "multivariate MArginal ePIstasis Test" (mvMAPIT)-a multioutcome generalization of a recently proposed epistatic detection method which seeks to detect marginal epistasis or the combined pairwise interaction effects between a given variant and all other variants. By searching for marginal epistatic effects, one can identify genetic variants that are involved in epistasis without the need to identify the exact partners with which the variants interact-thus, potentially alleviating much of the statistical and computational burden associated with conventional explicit search-based methods. Our proposed mvMAPIT builds upon this strategy by taking advantage of correlation structure between traits to improve the identification of variants involved in epistasis. We formulate mvMAPIT as a multivariate linear mixed model and develop a multitrait variance component estimation algorithm for efficient parameter inference and P-value computation. Together with reasonable model approximations, our proposed approach is scalable to moderately sized genome-wide association studies. With simulations, we illustrate the benefits of mvMAPIT over univariate (or single-trait) epistatic mapping strategies. We also apply mvMAPIT framework to protein sequence data from two broadly neutralizing anti-influenza antibodies and approximately 2,000 heterogeneous stock of mice from the Wellcome Trust Centre for Human Genetics. The mvMAPIT R package can be downloaded at https://github.com/lcrawlab/mvMAPIT.

Species relationships within the genus Vitis based on molecular and morphological data

The grape genus Vitis L. includes the domesticated V. vinifera, which is one of the most important fruit crop, and also close relatives recognized as valuable germplasm resources for improving cultivars. To resolve some standing problems in the species relationships within the Vitis genus we analyzed diversity in a set of 90 accessions comprising most of Vitis species and some putative hybrids. We discovered single nucleotide polymorphisms (SNPs) in SANGER sequences of twelve loci and genotyped accessions at a larger number of SNPs using a previously developed SNP array. Our phylogenic analyses consistently identified: three clades in North America, one in East Asia, and one in Europe corresponding to V. vinifera. Using heterozygosity measurement, haplotype reconstruction and chloroplast markers, we identified the hybrids existing within and between clades. The species relationships were better assessed after discarding these hybrids from analyses. We also studied the relationships between phylogeny and morphological traits and found that several traits significantly correlated with the phylogeny. The American clade that includes important species such as V. riparia and V. rupestris showed a major divergence with all other clades based on both DNA polymorphisms and morphological traits.

Increases in vein length compensate for leaf area lost to lobing in grapevine

PREMISE

Leaf lobing and leaf size vary considerably across and within species, including among grapevines (Vitis spp.), some of the best-studied leaves. We examined the relationship between leaf lobing and leaf area across grapevine populations that varied in extent of leaf lobing.

METHODS

We used homologous landmarking techniques to measure 2632 leaves across 2 years in 476 unique, genetically distinct grapevines from five biparental crosses that vary primarily in the extent of lobing. We determined to what extent leaf area explained variation in lobing, vein length, and vein to blade ratio.

RESULTS

Although lobing was the primary source of variation in shape across the leaves we measured, leaf area varied only slightly as a function of lobing. Rather, leaf area increases as a function of total major vein length, total branching vein length, and vein to blade ratio. These relationships are stronger for more highly lobed leaves, with the residuals for each model differing as a function of distal lobing.

CONCLUSIONS

For leaves with different extents of lobing but the same area, the more highly lobed leaves have longer veins and higher vein to blade ratios, allowing them to maintain similar leaf areas despite increased lobing. These findings show how more highly lobed leaves may compensate for what would otherwise result in a reduced leaf area, allowing for increased photosynthetic capacity through similar leaf size.

A predicted developmental and evolutionary morphospace for grapevine leaves

Using conventional statistical approaches there exist powerful methods to classify shapes. Embedded in morphospaces is information that allows us to visualise theoretical leaves. These unmeasured leaves are never considered nor how the negative morphospace can inform us about the forces responsible for shaping leaf morphology. Here, we model leaf shape using an allometric indicator of leaf size, the ratio of vein to blade areas. The borders of the observable morphospace are restricted by constraints and define an orthogonal grid of developmental and evolutionary effects which can predict the shapes of possible grapevine leaves. Leaves in the genus Vitis are found to fully occupy morphospace available to them. From this morphospace, we predict the developmental and evolutionary shapes of grapevine leaves that are not only possible, but exist, and argue that rather than explaining leaf shape in terms of discrete nodes or species, that a continuous model is more appropriate.

MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits

Large-scale phenotype data can enhance the power of genomic prediction in plant and animal breeding, as well as human genetics. However, the statistical foundation of multi-trait genomic prediction is based on the multivariate linear mixed effect model, a tool notorious for its fragility when applied to more than a handful of traits. We present MegaLMM, a statistical framework and associated software package for mixed model analyses of a virtually unlimited number of traits. Using three examples with real plant data, we show that MegaLMM can leverage thousands of traits at once to significantly improve genetic value prediction accuracy.

Vein-to-blade ratio is an allometric indicator of leaf size and plasticity

PREMISE

As a leaf expands, its shape dynamically changes. Previously, we documented an allometric relationship between vein and blade area in grapevine leaves. Larger leaves have a smaller ratio of primary and secondary vein area relative to blade area compared to smaller leaves. We sought to use allometry as an indicator of leaf size and plasticity.

METHODS

We measured the ratio of vein-to-blade area from the same 208 vines across four growing seasons (2013, 2015, 2016, and 2017). Matching leaves by vine and node, we analyzed the correlation between the size and shape of grapevine leaves as repeated measures with climate variables across years.

RESULTS

The proportion of leaf area occupied by vein and blade exponentially decreased and increased, respectively, during leaf expansion making their ratio a stronger indicator of leaf size than area itself. Total precipitation and leaf wetness hours of the previous year but not the current showed strong negative correlations with vein-to-blade ratio, whereas maximum air temperature from the previous year was positively correlated.

CONCLUSIONS

Our results demonstrate that vein-to-blade ratio is a strong allometric indicator of leaf size and plasticity in grapevines measured across years. Grapevine leaf primordia are initiated in buds the year before they emerge, and we found that total precipitation and maximum air temperature of the previous growing season exerted the largest statistically significant effects on leaf morphology. Vein-to-blade ratio is a promising allometric indicator of relationships between leaf morphology and climate, the robustness of which should be explored further.

Recent advances in Cannabis sativa genomics research

Cannabis (Cannabis sativa L.) is one of the oldest cultivated plants purported to have unique medicinal properties. However, scientific research of cannabis has been restricted by the Single Convention on Narcotic Drugs of 1961, an international treaty that prohibits the production and supply of narcotic drugs except under license. Legislation governing cannabis cultivation for research, medicinal and even recreational purposes has been relaxed recently in certain jurisdictions. As a result, there is now potential to accelerate cultivar development of this multi-use and potentially medically useful plant species by application of modern genomics technologies. Whilst genomics has been pivotal to our understanding of the basic biology and molecular mechanisms controlling key traits in several crop species, much work is needed for cannabis. In this review we provide a comprehensive summary of key cannabis genomics resources and their applications. We also discuss prospective applications of existing and emerging genomics technologies for accelerating the genetic improvement of cannabis.

Fine Mapping of Leaf Trichome Density Revealed a 747-kb Region on Chromosome 1 in Cold-Hardy Hybrid Wine Grape Populations

Segregation for leaf trichome density was observed in a cold-hardy hybrid grape population GE1025 (N = ∼125, MN1264 × MN1246) that was previously used to detect a quantitative trait locus (QTL) underlying foliar phylloxera resistance on chromosome 14. Our hypothesis was that high trichome density was associated with resistance to phylloxera. Existing literature found trichome density QTL on chromosomes 1 and 15 using a hybrid grape population of "Horizon" × Illinois 547-1 and suggested a few candidate genes. To validate the reported QTL and our hypothesis, interval mapping was conducted in GE1025 with previous genotyping-by-sequencing (GBS) single nucleotide polymorphism (SNP) genotype data and phenotypic scores collected using a 0-6 trichome density scale at several leaf positions. Evaluations were done on replicated forced dormant cuttings in 2 years and on field-grown leaves in 1 year. There was no strong relationship between trichome density and phylloxera resistance except for a Pearson's correlation (r) of about -0.2 between a few trichome density traits and phylloxera severity traits at 2 and 3 weeks after infestation. Two genetic regions were repeatedly detected for multiple trichome density traits: from 10 to 20.7 Mbp (∼10 Mbp) on chromosome 1 for ribbon and simple density traits and from 2.4 to 8.9 Mbp on chromosome 10 for ribbon density traits, explaining 12.1-48.2 and 12.6-27.5% of phenotypic variation, respectively. To fine map, we genotyped a larger population, GE1783 (N = ∼1,023, MN1264 × MN1246), with conserved rhAmpSeq haplotype markers across multiple Vitis species and phenotyped 233 selected potential recombinants. Evaluations were conducted on field-grown leaves in a single year. The QTL for ribbon trichome density on adaxial vein and adaxial leaf and simple density on abaxial vein was fine mapped to 12.63-13.38 Mbp (747 kb) on chromosome 1. We found variations of MN1264 and MN1246 at candidate genes NAC transcription factor 29, EF-hand protein, and MYB140 in this region and three other surrounding candidate genes proposed previously. Even though no strong relationship between foliar phylloxera resistance and trichome density was found, this study validated and fine mapped a major QTL for trichome density using a cold-hardy hybrid grape population and shed light on a few candidate genes that have implications for different breeding programs.

Composite modeling of leaf shape along shoots discriminates Vitis species better than individual leaves

PREMISE

Leaf morphology is dynamic, continuously deforming during leaf expansion and among leaves within a shoot. Here, we measured the leaf morphology of more than 200 grapevines (Vitis spp.) over four years and modeled changes in leaf shape along the shoot to determine whether a composite leaf shape comprising all the leaves from a single shoot can better capture the variation and predict species identity compared with individual leaves.

METHODS

Using homologous universal landmarks found in grapevine leaves, we modeled various morphological features as polynomial functions of leaf nodes. The resulting functions were used to reconstruct modeled leaf shapes across the shoots, generating composite leaves that comprehensively capture the spectrum of leaf morphologies present.

RESULTS

We found that composite leaves are better predictors of species identity than individual leaves from the same plant. We were able to use composite leaves to predict the species identity of previously unassigned grapevines, which were verified with genotyping.

DISCUSSION

Observations of individual leaf shape fail to capture the true diversity between species. Composite leaf shape-an assemblage of modeled leaf snapshots across the shoot-is a better representation of the dynamic and essential shapes of leaves, in addition to serving as a better predictor of species identity than individual leaves.