DeltaGen: Quick start manual

Transcription

1 1 DeltaGen: Quick start manual Dr. Zulfi Jahufer & Dr. Dongwen Luo CONTENTS Page Main operations tab commands 2 Uploading a data file 3 Matching variable identifiers 4 Data check 5 Univariate analysis 7 Pattern analysis (within univariate model option) 11 Univariate analysis Two trait combination 14 Guide to calculation of generating a GEBV Sample cost 16 Estimation of genetic gain (G) and its simulation 17 Multivariate analysis 19 Using Plot 20 o Biplots (on raw data) 20 o Matrix plots (phenotypic correlation- on raw data) 21 MANOVA (additive variance/co-variance & correlation) 22 Smith-Hazel selection index 24 Pattern analysis for multiple traits 25 Trial design instructions (to be completed) 29 Save session and Quit 30

2 2 Main operations tab commands Main operations tab commands Clicking on any of the commands above will open dropdown menus. Introduction This window is opened when DeltaGen is started. Trial Design This command will open a screen that will provide a range of experimental designs that DeltaGen can generate. A full description of this command will be provided under experimental design, the last section in this manual. Data Input: uploading a data file Clicking on this command will open a dropdown menu with: Input: Clicking on this command will open the Data Information screen shown on the next page.

3 Uploading a data file 3 Shows data files opened Upload enables data files to be uploaded using Browse; Examples* enables practice data sets within DeltaGen to be uploaded; Clipboard enables copied data to be uploaded. * The practice data sets will also provide information on format of data matrices. STEP 2 Click to upload external data files Data file types accepted by DeltaGen! CSV files are preferred save EXCEL data files in csv format for uploading into DeltaGen STEP 1! Missing values in the data matrix should be identified before uploading any data set. The dropdown menu provides 3 options: Empty or * or! Data file names cannot have PPl gaps between words. Follow STEPS 1 & 2 to upload a data set from an external file. Files can be saved in RData or CSV formats

4 Matching variable identifiers 4 After Uploading a data set following steps 1 & 2, the column identifiers of variables in the data matrix have to be matched with those already defined as (Year, Season, Location, Replicates, Row, Column, Sample and Line) in DeltaGen. as shown in STEP 3 Traits STEP 3 - To match a DeltaGen variable with an associated column identifier in the data matrix, click on the relevant dropdown menu and choose the matching column name; e.g. by clicking on the dropdown menu for Location, column Site was selected. Similarly, Rep for Replicates. If a variable is not in the uploaded data, this is left as Null. STEP 4 - Clicking on Run will submit the data for analysis. You are now ready to check or analyse your data.

5 Data check: Graphical or tabular summary of raw data is an optional data quality check before univariate or multivariate analysis. 5 The plot-type dropdown menu provides a range of plots; Histogram, Density, Scatter, Line, Box-plot, to illustrate the data. First click on the X-variable, in this case DM (dry matter). You can arrange the plots by defining the row and column layout; in the example presented using Histograms, the Locations are presented down rows and the dry matter in each of the 3 replicates within each location as columns.

6 6 Data check continued. The heat-map option from the dropdown menu under Pivot Table, illustrates the actual values and spatial distribution of summer dry matter raw data across a field experiment based on a row-column experimental design consisting of 3 replicates. This can also be used to identify data entry errors. Clicking on these headings will show the associated data below. High value Missing data Low value All factors, e.g. Replicates, Column, Row, can be moved across; point on a factor, left click on the mouse, hold down and move. This will result in changing the configuration of the table.

7 7 Univariate analysis: Click on the Models command and select Univariate. The screen shown will open. The Data Information panel provides a summary of the contents of the uploaded data set ready for analysis. The demonstration/practice data set used, consists of 144 lines (half-sib families) of switchgrass (Panicum virgatum L.) evaluated across 2 locations over 2 years using randomized complete block designs with 3 replicates. Data on 4 traits; dry matter yield, Klason lignin (KL), ethanol (ETOH) and high heating value (HHV) are included. Data file name: Case study 3 under Examples. The default settings for the linear mixed effects model are; modelling and half sib family. If simulation of genetic gain is to be conducted, the choice of half sib or full sib family is important. Alternately if the analysis is not for estimation of genetic components of variance or is based on a fixed effects model, continue using the the default option. Simulation must be selected only after conducting the variance component analysis. On opening the univariate analysis screen, the Primary Trait box will be at Null. Clicking on this box will open the dropdown menu with all the traits in the uploaded data matrix; in the example; Yield, KL, ETOH, HHV. Click Run to begin analysis Fixed terms: clicking in the fixed terms box will open a dropdown menu that will enable you to select the appropriate factors in the data set (years, locations or lines in the example). Select Null if no fixed terms are to be included in the model. Random terms: Select the appropriate factors from dropdown menu which opens in this box. Contains theory & references Traits for BLUP or BLUE estimates can be selected from the associated dropdown menus.

8 8 Univariate analysis (continued): The linear model - One trait The demonstration/practice data set used, consists of 125 lines (half-sib families) of perennial ryegrass (Lolium perenne L.) evaluated across 2 locations over 4 years and seasons; spring, summer, autumn and winter, of each year. Experimental design: row column with 4 replicates. Data file name: MultiYearSea-Growth under Examples. Select Primary trait to be analysed, (From our example data set Spring growth has been selected), Select Fixed terms and their interactions, (if an additional term that does not appear in the dropdown menu needs to be added, double click in the fixed terms box, enter the term and click on Add that appears) Select Random terms and interactions, (if an additional term that does not appear in the dropdown menu needs to be added, double click in the random terms box, enter the term and click on Add that appears) Select Heritability if appropriate, Select BLUP if lines are random or BLUE if fixed, Click Run to begin analysis.

9 9 Mixed model analysis Output of spring growth of the 125 half sib families across years and locations. As the lines were considered as random effects in the linear model and the BLUP estimate option was selected, Clicking on the BLUP button has provided this analysis output, BLUP values for spring growth. Results for Fixed effects Half sib families (HS) Variance component Associated ± standard error Heritability was estimated as this option was selected.

10 10 Mixed model analysis of growth of the 125 half sib families across 4 years, 4 seasons and 2 locations. Results for Fixed effects The demonstration/practice data set, Data file name: AcrossYearSeaSite-Growth (1) under Examples. Half sib families (HS)

11 11 Pattern analysis (within Univariate model option) Multi-location (more than 2 locations) The demonstration/practice data set, Data file name: AcrossYearSeaSite-Growth (2) under Examples. The first step in Pattern analysis is to select Line-by-location interaction. BLUP estimates for each individual line within each location (in the example Rua, PN and KERI) will be generated. Click on pattern analysis Click Run.

12 12 Location groups: 1, 2 & 3. Clicking on Pattern Analysis Cluster will result in generating Line groups based on performance across sites and also associated dendrograms of Locations and Lines. Dendrograms of Location and Line grouping Locations: KERI, Rua, PN

13 13 Clicking on Pattern Analysis PCA (Principle Component Analysis) will result in generating a biplot based on PC1 and PC2, the line groups and individual line labels. The directional vectors are the locations. Directional vectors Line clusters

14 14 Univariate analysis (continued): The linear model - Two trait combination ETOH Analysis with the secondary trait included provides an opportunity to simultaneously estimate narrow sense heritability for each trait, and their genetic correlation. These outputs are then automatically integrated into the simulation models for estimation of Correlated Response to Selection of the primary trait based on secondary trait selection. The demonstration/practice data set used, consists of 144 lines (half-sib families) of switchgrass (Panicum virgatum L.) evaluated across 2 locations over 2 years using randomized complete block designs with 3 replicates. Data on 4 traits; dry matter yield, Klason lignin (KL), ethanol (ETOH) and high heating value (HHV) are included. Data file name: CaseStudy3 under Examples. All the initial steps with regards the fixed and random term models for the primary trait are similar to the single trait analysis. The Secondary trait: KL Tick the box for secondary trait and select the trait from the dropdown menu, (From our example data set, trait KL was selected ) Click the MANOVA box and select the terms in the dropdown menu to conduct a variance/covariance analysis, Click Run

15 15 Univariate analysis Output two trait (ETOH/KL) combination Results for Fixed effects from primary trait analysis. Variance components for Random effects from primary trait analysis. Heritability of primary trait. Results from this analysis are similar to those from the single trait analysis, but also provide information on heritability of the secondary trait as well as the genetic correlation between the two traits.

16 16 This is only a guide for calculating the cost of generating a single GEBV referred to as Sample cost in GS Step Cost/sample Genotyping $53 DNA isolation $7 Library generation $9 DNA sequencing $37 SNP genotypes $10 (bioinformatics) Prediction of GEBV's $5 (statistical model) Total $68 Other notes: Assumes GBS as the genotyping method. Sequencing uses an Illumina HiSeq 2500 with version 4 chemistry Cost is for the 96-plex level which will change with the level of multiplexing. The suggested multipliers: (48-ples 2), (192-plex 0.5), (384-plex 0.25) Dodds, K.G.; McEwan, J.C.; Brauning, R.; Anderson, R.M.; van Stijn, T.C.; Kristjánsson, T.; Clarke, S.M. (2015). Construction of relatedness matrices using genotyping-by-sequencing data. BMC Genomics 16: 1047.

17 17 Estimation of Genetic Gain (G) and its simulation After variance component analysis of half sib (HS) or full sib (FS) families, for a given trait (ETOH in our example), click on Simulation. This will open a blank Breeding Strategy and Simulation window shown below: Clicking on the Strategy dropdown window will enable selection of any of the breeding strategies below:

18 18 Estimation of Genetic Gain (G) and its simulation continued These constants cannot be changed HS family based breeding models including Genomic selection (GS). Inputs automatically transferred from linear model analysis All the simulation variables have dropdown menus which provide a range of values to select from. All estimated costs ($) should be entered manually. Gc, gain estimate per cycle Ga, gain estimate per annum % (relative to parental mean) Click Update every time a breeding strategy, simulation variable or cost ($) is changed. This will update G estimates and associated costs. If Full Sib families, HS will change to FS. If two traits like ETOH and KL, primary and secondary, respectively, are analysed, then models for Correlated response to selection will be available.

19 19 Multivariate analysis Plot gives you options to generate a Biplot or a Matrix Plot of phenotypic correlation, based on raw data. To begin: Click on the Model command and select Multivariate. MANOVA (Multivariate analysis of variance) generates a variance and covariance matrix and genotypic or genetic correlation coefficients for the traits chosen from the dropdown menu in the Multiple traits box. Clicking on Selection index activates a window that enables use of the Smith-Hazel index. Used for highlighting groups in the Plot option. Clicking in this box will show you the list of traits, in the uploaded data matrix, to be selected for multivariate analysis based on the three options; Plot, MANOVA and Selection Index. The demonstration/practice data (File name: CaseStudy3 in Examples. You need to first upload this file using: Data Input), consists of 144 lines (half-sib families) of switchgrass (Panicum virgatum L.) evaluated across 2 locations over 2 years using randomized complete block designs at each location containing 3 replicates. Data on 4 traits; biomass dry matter yield (Yld), Klason lignin (KL), ethanol (ETOH) and high heating value (HHV) are included in matrix.

20 20 Using Plot! This Biplot is based on raw data.

21 Phenotypic correlation 21

22 22 MANOVA 2 1 1) Click on MANOVA, 2) Click on Multiple traits box and choose traits, 3) Click on MANOVA terms box and chose the effects for the completely random linear model (keep this model simple by choosing main effects and only their two way interactions), 4) Click Run. Multivariate analysis Output the estimates will be genetic if the raw data are generated from HS or FS families 3 4

23 23 Smith-Hazel selection index ) Click on Selection Index, 2) Click on Multiple traits box and choose traits, 3) Manually enter the Index weightings, 4) Click on LME fixed terms box and select the fixed effects or leave as Null, 5) Click on LME random terms box and select the random effects, 6) Click on MANOVA terms box and chose the effects for the completely random linear model (keep this model simple by choosing main effects and only their two way interactions), 7) Click on the Selection pressure box and choose the intensity of selection, 8) To estimated the genetic gain for each trait under selection (YLD, ETOH, KL) tick G, 9) Click Run

24 Smith-Hazel index - Output window 24 [P] 1 The Smith-Hazel index equation [b] = [P] 1 [A][w] [A] [b] Individual trait BLUP s Smith-Hazel index: the genetic worth (breeding values) of lines. Gc, gain estimate per selection cycle in unites of measurement of each trait.

25 25 Pattern analysis for Multiple Traits Step 1, upload the Line-by-Trait mean matrix into DeltaGen using Data Input. This example is based on the data file MultiTraitMatrix.csv found in examples. Step 2, Click on the Pattern Analysis Step 3, select the variables by clicking on them, and keep the standardized data option on, Step 4, Click on Run. Cluster analysis will produce Line groups and a heat map with Line and Trait dendrograms. The PCA BiPlot option will provide a graphical summary of the Line clusters and trait association (shown by the directional vectors).

26 Pattern analysis output. 26 1, 2 & 3.

27 27 Dendrograms of Trait and Line grouping Lines Traits

28 28 Line cluster groups: Magnification and quality of the contents of the biplot can be adjusted by moving the scale controllers The entries (lines, genotypes..) shown in the biplot can be shown as dots or labels by selecting the option in the dropdown menu. Directional vectors

29 29 Trial design instructions Will be uploaded soon. However, please do try using the design generator.

30 30 Save session and Quit Analysis reports can be saved as PDF, HTML and Word documents. Click on any of the three document format options followed by selecting Download. To Quit DeltaGen, click on Quit App.