SNPEVG---SNP Effect Viewing and Graphing

• MAIRA KHAN• HIRA BATOOL • MARYAM ZAHRAH• NADIA ASHRAF

GROUP MEMBERS:

INTRODUCTION

• SNPEVG is a set of graphical tools for instant global and local viewing and graphing of GWAS results for all chromosomes and for each trait. (Genome-wide association study investigates the association between a huge amount of genetic loci and different traits/diseases)

• Implemented in the C++ programming language

IMPORTANCE

• Exploring traits/diseases-associated SNPs (TASs) which have relatively high signals from GWAS or whole genome sequencing association study needs further downstream statistical inference and bioinformatics prediction.

• As indispensable steps, variants visualization, functional annotation and selection of risk associated locus greatly facilitate the discovery of true association between genetic marker and disease/trait.

• Detailed graphical examination of each chromosome is helpful for further understanding the GWAS results

SNPEVG Package-Version 3.2

• Includes three graphical programs: SNPEVG1 SNPEVG2SNPEVG3

The SNPEVG1 program

• Creates Manhattan plots and Q-Q plots providing global view of test results for each trait.

• SNPEVG1 supports a maximum 100 traits. • Assuming 30 traits and 30 chromosomes per trait, one

command of ‘Run’ produces 960 graphs including 30 Manhattan plots, 30 Q-Q plots and 900 chromosome graphs. This will greatly facilitate the digestion of GWAS results.

• The total number of graphs that can be generated by one ‘Run’ is n(c + 2), where n is number of ‘traits’ with 0<n≤100, and c is the number of chromosomes.

INPUT File

• SNPEVG1 requires a simple text input file with the following columns: CHR, POSITION, SNP, and P-VALUE columns

• CHR = chromosome number,• POSITION = chromosomal position of the SNP

marker, • SNP = name of the SNP marker, • P-VALUE is the P-value for a trait.• extension ‘snpe’.

EXAMPLESNPID Chr Position T1-geno T1-add T1-dom T2-geno T2-add T2-

domSNP01 1 571340 2.48E-03 1.11E-03 1.58E-03 2.74E-02 2.65E-01 1.11E-

03 SNP02 1 845494 7.02E-04 9.07E-04 8.91E-05 2.56E-02 8.02E-02 9.07E-

04SNP03 1 883895 1.87E-02 1.71E-01 3.07E-03 1.07E-02 2.81E-03 1.71E-

01SNP04 1 929617 1.79E-03 1.57E-02 1.39E-05 9.03E-02 2.99E-04 1.57E-

02

GUI

Trait Selection

Manhattan plot

• The Manhattan plot allows all SNP effects of all chromosomes per trait on one graph to quickly identify genome locations with the most significant SNP effects.

• Each Manhattan plot uses true chromosome size defined by the starting and ending SNP marker positions of the chromosome.

• P-values for the unknown chromosome are displayed in sequential order of SNP markers rather than chromosome positions

Manhattan plot setting for pixel sizes and chromosome colors (upper) andManhattan plot with threshold P-value line (lower).

Manhattan plot with threshold P-value line and without showing data pointsbelow cut-off P-value.

Q-Q Plot for all SNP Effects

• For each chromosome, P-values can be presented as connected lines (Fig. A) or separate symbols(Fig.B).

OUTPUT FILE FORMAT

• Any selected graph, or graphs for selected traits, or all graphs can be saved as graphical images(.png files) with publication quality by clicking a button on the GUI.

The SNPEVG2 program

• Multiple traits/values per graph• Any method of significance testing• Any values of variables• 2 Y-Axes.

FEATURES• Instant graphics of large quantities of genome-wide

significance testing results or original values from any selected significance effects and original values.

• Support of ‘Y1’ and ‘Y2’ axes and selection of values for either axis.

• Immediate viewing of all graphics within the SNPEVG2 GUI.• Scalable SNPEVG2 GUI allowing efficient and flexible use of

computer screen.• Graphics of Manhattan plot, Q-Q plot, and figures for all

chromosomes and all effects/values (up to 100 effects/values) by one click of ‘run’.

• Save of one graph or all graphs for all Manhattan plots, Q-Q plots, and chromosomes by one of ‘Current Graph’ or ‘All Graphs’.

GUI

Example of genetic effects and original values by chromosome with lines

Example of genetic effects and original values by chromosome with symbols

Features

• The Y2 axis can be used to display a variable unrelated to P-values such as minor allele frequency or allele frequency difference between the best and worst individuals, allowing the production of more flexible and informative graphs than using Y1 axis presenting P-values only.

• Option to select traits to display, to customize the color of each trait or switch Y1 and Y2 axes so that chromosome graphs may not be crowded .

• Each Y axis, Y1 or Y2, can have its own threshold P-value or cut-off P-value

A chromosome graph with two threshold value lines for Y1 and Y2 axes, and elimination of P-values below the line of cut-off P-value line.

Wang et al. BMC Bioinformatics 2012 13:319

INPUT/OUTPUT

• SNPEVG2 requires a simple text input file with the same format as for SNPEVG1, i.e., CHR, POSITION, SNP, and P-VALUE columns.

• OUTPUT: .png files

The SNPEVG Convert Program

• The SNPEVGconvert program is designed to convert an output file from any GWAS analysis software to the format of SNPEVG1 and SNPEVG2.

• To use this program, the user only needs to specify the number of columns in the original files and identify the column numbers to be printed in the input file for SNPEVG1 and SNPEVG2.

Example of the parameter file for SNPEVGconvert

• plink.assoc.linear # input file name• 5 # number of columns in the output file• 2 1 3 9 8 # positions of columns to print as input

file for SNPEVG1 and SNPEVG2• assoc.snpe # output file name

The SNPEVG3 program

• SNPEVG3 is developed for graphical analysis of GWAS using the output file of single-locus test results of EPISNP or EPISNPmpi as the input file for drawing figures.

• SNPEVG3 has similar GUI features as SNPEVG1, but it does not have the limit of 100 traits.

• This program draws graphs for P-values of additive, dominance and genotypic effects on the Y1 axis and draws sample size on the Y2 axis.

• The P-values can be displayed with lines connecting adjacent data points or use symbols without connecting lines.

• The user has an option to draw a figure by a sorted effect such as additive or dominance effect.

INPUT FILE FORMAT

Trait Chr Locus M A D #_IndTrait1 14 M112 0.704E+00 0.107E+01 0.285E+00 295Trait2 14 M112 0.492E+00 0.127E+00 0.864E+00 295trait1 14 M212 0.401E+00 0.689E-01 0.748E+00 295trait1 19 M7 0.184E+01 0.180E+01 0.803E+00 290

M= Name of marker# ind = No of individuals with this trait.

Chromosome figure of SNPEVG3 with connecting line between adjacent data points.

Chromosome figure of SNPEVG3 without connecting line between adjacent data points.

REFERENCES

• Wang et al. “SNPEVG: a graphical tool for GWAS graphing with mouse clicks”, BMC Bioinformatics, 2012 13:319 doi:10.1186/1471-2105-13-319

• http://www.biomedcentral.com/content/supplementary/1471-2105-13-319-s3.pdf