Dataset Information

An imputation platform to enhance integration of rice genetic resources.

ABSTRACT: As sequencing and genotyping technologies evolve, crop genetics researchers accumulate increasing numbers of genomic data sets from various genotyping platforms on different germplasm panels. Imputation is an effective approach to increase marker density of existing data sets toward the goal of integrating resources for downstream applications. While a number of imputation software packages are available, the limitations to utilization for the rice community include high computational demand and lack of a reference panel. To address these challenges, we develop the Rice Imputation Server, a publicly available web application leveraging genetic information from a globally diverse rice reference panel assembled here. This resource allows researchers to benefit from increased marker density without needing to perform imputation on their own machines. We demonstrate improvements that imputed data provide to rice genome-wide association (GWA) results of grain amylose content and show that the major functional nucleotide polymorphism is tagged only in the imputed data set.

SUBMITTER: Wang DR

PROVIDER: S-EPMC6115364 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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An imputation platform to enhance integration of rice genetic resources.

Wang Diane R DR Agosto-Pérez Francisco J FJ Chebotarov Dmytro D Shi Yuxin Y Marchini Jonathan J Fitzgerald Melissa M McNally Kenneth L KL Alexandrov Nickolai N McCouch Susan R SR

Nature communications 20180829 1

As sequencing and genotyping technologies evolve, crop genetics researchers accumulate increasing numbers of genomic data sets from various genotyping platforms on different germplasm panels. Imputation is an effective approach to increase marker density of existing data sets toward the goal of integrating resources for downstream applications. While a number of imputation software packages are available, the limitations to utilization for the rice community include high computational demand and ...[more]

PMID: 30158584

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Project description:BackgroundIn 2007, the Food and Agriculture Organization of the United Nations (FAO) initiated the Global plan of action for Farm Animal Genetic Resources (FAnGR). The main goal of this plan is to reduce further loss of genetic diversity in farm animals, so as to protect and promote the diversity of farm animal resources. An important step to reach this goal is to monitor and prioritize endangered breeds in the context of conservation programs.Methodology/web portal implementationThe GENMON WebGIS platform is able to monitor FAnGR and to evaluate the degree of endangerment of livestock breeds. The system takes into account pedigree and introgression information, the geographical concentration of animals, the cryo-conservation plan and the sustainability of breeding activities based on socio-economic data as well as present and future land use conditions. A multi-criteria decision tool supports the aggregation of the multi-thematic indices mentioned above using the MACBETH method, which is based on a weighted average using satisfaction thresholds. GENMON is a monitoring tool to reach subjective decisions made by a government agency. It relies on open source software and is available at http://lasigsrv2.epfl.ch/genmon-ch.Results/significanceGENMON allows users to upload pedigree-information (animal ID, parents, birthdate, sex, location and introgression) from a specific livestock breed and to define species and/or region-specific weighting parameters and thresholds. The program then completes a pedigree analysis and derives several indices that are used to calculate an integrated score of conservation prioritization for the breeds under investigation. The score can be visualized on a geographic map and allows a fast, intuitive and regional identification of breeds in danger. Appropriate conservation actions and breeding programs can thus be undertaken in order to promote the recovery of the genetic diversity in livestock breeds in need. The use of the platform is illustrated by means of an example based on three local livestock breeds from different species in Switzerland.

Project description:Exploration of and understanding diversity and variability in genotypes of germplasm determines the success of rice improvement programs. One of the most important determinants of the success of breeding programs is genetic diversity and inheritance of traits. Genetic variability analysis helps breeders to determine the appropriate selection method and standards to be used to improve the preferred trait. The aim of this study was to estimate genetic components, heritability and to obtain information about genetic diversity using cluster analysis and principal component analysis. Twenty rice genotypes with three replicates in a randomized complete block design were analyzed at the Experimental Farm at Sakha Agricultural Research Station, Sakha, Kafr El-Sheikh, Egypt, during the period from 2017 to 2020. The results of the analysis of variance showed that highly significant variations were recorded between the studied genotypes for all traits. The genotypic coefficient of variation (GCV%) and phenotypic (PCV%) coefficient of variation were moderate for plant height, panicles/plant, panicle weight, spikelets/panicle, filled grains/panicle, grain yield/plant and amylose content percentage for the first-year, second-year and combined data. This indicates the existence of beneficial genetic variability that can be exploited to improve these traits. The broad-sense estimates of heritability were high and recorded values higher than 60% for all the studied traits for the two-year and combined data, except for hulling percentage. This indicates that the selection of traits that have high heritability and are less affected by the environment leads to an acceleration of the improvement of these traits. The results from the cluster analysis and principal component analysis revealed a high level of genotypic variation among the studied genotypes and genetic diversity between them. One of the most important outcomes of this study is the successful utilization of genetic resources (germplasm) from ancient varieties and lines of rice in selecting and identifying 17 new restoration lines of rice, which have various improvement purposes in rice and hybrid rice breeding programs.

Dataset Information

An imputation platform to enhance integration of rice genetic resources.

Publications

An imputation platform to enhance integration of rice genetic resources.

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