Genetic research in a public-private research consortium: prospects for indirect use of Elige breeding germplasm in academic research

The creation of a public¿private research partnership between plant breeding industry and academia can be beneficial for all parties involved. Academic partners benefit from the material contributions by industry and a practically relevant research focus, while industry benefits from increased insights and methodology tailored to a relevant set of data. However, plant breeding industry is highly competitive and there are obvious limits to the data and material partners are willing and able to share. This will usually include current and historic released cultivated materials, but will very often not include the elite germplasm used in-house to create new cultivars. Especially for crops where hybrid cultivars dominate the market, parental lines of hybrid cultivars are considered core assets that are never provided to outside parties. However, this limitation often does not apply to DNA or genetic fingerprints of these parental lines. We developed a procedure to take advantage of elite breeding materials for the creation of new promising research populations, through indirect selection of parents. The procedure starts with the identification of a number of traits for further study based on the presence of marker-trait associations and a priori knowledge within the participating companies about promising traits for quality improvement. Next, regression-based multi-QTL models are fitted to hybrid cultivar data to identify QTLs. Fingerprint data of parental lines of a limited number of specific hybrids are then used to predict parental phenotypes using the multi-QTL model fitted on hybrid data. The specific hybrids spanned the whole of the sensory space adequately. Finally, a choice of parental lines is made based on the QTL model predictions and new promising line combinations are identified. Breeding industry is then asked to create and provide progeny of these line combinations for further research. This approach will be illustrated with a case study in tomato

Evaluation of sweetpotato (Ipomoea batatas (L.) Lam.) germplasm from north-eastern Uganda through a Farmer Participatory Approach

Ugandan farmers grow many landrace sweetpotato varieties, but some of these are relatively low yielding and susceptible to pests. The objective of the present research was to involve farmers in a large-scale assessment of Ugandan farmers’ varieties to rapidly identify those with superior yield performance, pest resistance and consumer acceptance. One hundred sixty distinct farmers’ varieties collected from Lira, Soroti, Katakwi, Kumi and Pallisa Districts of North-eastern Uganda were evaluated in on-station trials. Trials were conducted at two sites (Serere Agricultural and Animal Research Institute and Arapai Agricultural College) in Soroti District in the second rainy season of 1999. Twenty-five farmers from surrounding areas participated in trial harvest at each site. At harvest, fresh storage root yield, foliage yield, and dry matter content were determined by researchers. Farmers observed a number of characteristics and rated each entry with respect to the following variables: general impression, dry matter content, pests, and defects. A strong positive correlation was observed between farmers’ general impression and yield and harvest index in the trials. Farmers selected 10 superior varieties from each trial for further multi-environment, on-station and on-farm trials. Coincidentally, nine of the selected varieties were common to both sites

A method for sensitivity analysis to assess the effects of measurement error in multiple exposure variables using external validation data

Measurement error in self-reported dietary intakes is known to bias the association between dietary intake and a health outcome of interest such as risk of a disease. The association can be distorted further by mismeasured confounders, leading to invalid results and conclusions. It is, however, difficult to adjust for the bias in the association when there is no internal validation data

Phenomics data processing: A plot-level model for repeated measurements to extract the timing of key stages and quantities at defined time points

Decision-making in breeding increasingly depends on the ability to capture and predict crop responses to changing environmental factors. Advances in crop modeling as well as high-throughput eld phenotyping (HTFP) hold promise to provide such insights. Processing HTFP data is an interdisciplinary task that requires broad knowledge on experimental design, measurement techniques, feature extraction, dynamic trait modeling, and prediction of genotypic values using statistical models. To get an overview of sources of variation in HTFP, we develop a general plot-level model for repeated measurements. Based on this model, we propose a seamless step-wise procedure that allows for carry on of estimated means and variances from stage to stage. The process builds on the extraction of three intermediate trait categories; (1) timing of key stages, (2) quantities at de ned time points or periods, and (3) dose-response curves. In a rst stage, these intermediate traits are extracted from low-level traits’ time series (e.g., canopy height) using P-splines and the quarter of maximum elongation rate method (QMER), as well as nal height percentiles. In a second and third stage, extracted traits are further processed using a stage-wise linear mixed model analysis. Using a wheat canopy growth simulation to generate canopy height time series, we demonstrate the suitability of the stage-wise process for traits of the rst two above-mentioned categories. Results indicate that, for the rst stage, the P-spline/QMER method was more robust than the percentile method. In the subsequent two-stage linear mixed model processing, weighting the second and third stage with error variance estimates from the previous stages improved the root mean squared error. We conclude that processing phenomics data in stages represents a feasible approach if estimated means and variances are carried forward from one processing stage to the next. P-splines in combination with the QMER method are suitable tools to extract timing of key stages and quantities at de ned time points from HTFP data

Accurate genotype imputation in multiparental populations from low-coverage sequence

Many different types of multiparental populations have recently been produced to increase genetic diversity and resolution in QTL mapping. Low-coverage, genotyping-by-sequencing (GBS) technology has become a cost-effective tool in these populations, despite large amounts of missing data in offspring and founders. In this work, we present a general statistical framework for genotype imputation in such experimental crosses from low-coverage GBS data. Generalizing a previously developed hidden Markov model for calculating ancestral origins of offspring DNA, we present an imputation algorithm that does not require parental data and that is applicable to bi-and multiparental populations. Our imputation algorithm allows heterozygosity of parents and offspring as well as error correction in observed genotypes. Further, our approach can combine imputation and genotype calling from sequencing reads, and it also applies to called genotypes from SNP array data. We evaluate our imputation algorithm by simulated and real data sets in four different types of populations: the F2, the advanced intercross recombinant inbred lines, the multiparent advanced generation intercross, and the cross-pollinated population. Because our approach uses marker data and population design information efficiently, the comparisons with previous approaches show that our imputation is accurate at even very low (< 1 ×) sequencing depth, in addition to having accurate genotype phasing and error detection.</p

A two-stage approach for the spatio-temporal analysis of high-throughput phenotyping data

High throughput phenotyping (HTP) platforms and devices are increasingly used for the characterization of growth and developmental processes for large sets of plant genotypes. Such HTP data require challenging statistical analyses in which longitudinal genetic signals need to be estimated against a background of spatio-temporal noise processes. We propose a two-stage approach for the analysis of such longitudinal HTP data. In a first stage, we correct for design features and spatial trends per time point. In a second stage, we focus on the longitudinal modelling of the spatially corrected data, thereby taking advantage of shared longitudinal features between genotypes and plants within genotypes. We propose a flexible hierarchical three-level P-spline growth curve model, with plants/plots nested in genotypes, and genotypes nested in populations. For selection of genotypes in a plant breeding context, we show how to extract new phenotypes, like growth rates, from the estimated genotypic growth curves and their first-order derivatives. We illustrate our approach on HTP data from the PhenoArch greenhouse platform at INRAE Montpellier and the outdoor Field Phenotyping platform at ETH Zürich.Ministerio de Ciencia, Innovación y Universidades | Ref. BCAM Severo Ochoa accreditation SEV-2017-0718Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung | Ref. project PhenoCOOL (project no. 169542)Horizon 2020 Framework Programme | Ref. grant agreement ID 731013 (EPPN2020)Ministerio de Ciencia, Innovación y Universidades | Ref. MTM2017-82379-

Homoplasy corrected estimation of genetic similarity from AFLP bands, and the effect of the number of bands on the precision of estimation

AFLP is a DNA fingerprinting technique, resulting in binary band presence–absence patterns, called profiles, with known or unknown band positions. We model AFLP as a sampling procedure of fragments, with lengths sampled from a distribution. Bands represent fragments of specific lengths. We focus on estimation of pairwise genetic similarity, defined as average fraction of common fragments, by AFLP. Usual estimators are Dice (D) or Jaccard coefficients. D overestimates genetic similarity, since identical bands in profile pairs may correspond to different fragments (homoplasy). Another complicating factor is the occurrence of different fragments of equal length within a profile, appearing as a single band, which we call collision. The bias of D increases with larger numbers of bands, and lower genetic similarity. We propose two homoplasy- and collision-corrected estimators of genetic similarity. The first is a modification of D, replacing band counts by estimated fragment counts. The second is a maximum likelihood estimator, only applicable if band positions are available. Properties of the estimators are studied by simulation. Standard errors and confidence intervals for the first are obtained by bootstrapping, and for the second by likelihood theory. The estimators are nearly unbiased, and have for most practical cases smaller standard error than D. The likelihood-based estimator generally gives the highest precision. The relationship between fragment counts and precision is studied using simulation. The usual range of band counts (50–100) appears nearly optimal. The methodology is illustrated using data from a phylogenetic study on lettuce

Acción : diario de Teruel y su provincia: Año III Número 633 - (11/12/34)

New types of phenotyping tools generate large amounts of data on many aspects of plant physiology and morphology with high spatial and temporal resolution. These new phenotyping data are potentially useful to improve understanding and prediction of complex traits, like yield, that are characterized by strong environmental context dependencies, i.e., genotype by environment interactions. For an evaluation of the utility of new phenotyping information, we will look at how this information can be incorporated in different classes of genotype-to-phenotype (G2P) models. G2P models predict phenotypic traits as functions of genotypic and environmental inputs. In the last decade, access to high-density single nucleotide polymorphism markers (SNPs) and sequence information has boosted the development of a class of G2P models called genomic prediction models that predict phenotypes from genome wide marker profiles. The challenge now is to build G2P models that incorporate simultaneously extensive genomic information alongside with new phenotypic information. Beyond the modification of existing G2P models, new G2P paradigms are required. We present candidate G2P models for the integration of genomic and new phenotyping information and illustrate their use in examples. Special attention will be given to the modelling of genotype by environment interactions. The G2P models provide a framework for model based phenotyping and the evaluation of the utility of phenotyping information in the context of breeding programs.</p

Dynamics of senescence-related QTLs in potato

The study of quantitative trait’s expression over time helps to understand developmental processes which occur in the course of the growing season. Temperature and other environmental factors play an important role. The dynamics of haulm senescence was observed in a diploid potato mapping population in two consecutive years (2004 and 2005) under field conditions in Finland. The available time series data were used in a smoothed generalized linear model to characterize curves describing the senescence development in terms of its onset, mean and maximum progression rate and inflection point. These characteristics together with the individual time points were used in a Quantitative trait loci (QTL) analysis. Although QTLs occurring early in the senescence process coincided with QTLs for onset of senescence, the analysis of the time points made it difficult to study senescence as a continuous trait. Characteristics estimated from the senescence curve allowed us to study it as a developmental process and provide a meaningful biological interpretation to the results. Stable QTLs in the two experimental years were identified for progression rate and year-specific QTLs were detected for onset of senescence and inflection point. Specific interactions between loci controlling senescence development were also found. Epistatic interaction between QTLs on chromosomes 4, 5 and 7 were detected in 2004 and pleiotopic effects of QTLs on chromosomes 3 and 4 were observed in 2005