NgsAdmixTutorial

We will go through a simple and more complex example on how to use NGSadmix with visualization of the data.

Example of NGSadmix - Simple

In our first example, we will infer admixture proportions for low depth NGS data using a small dataset from 30 human samples.

1. Every time you open a new terminal window, set directories to all required programs and the data you will use depending on where you installed them

Set the path to NGSadmix, for example:

NGSADMIX=~/Software/NGSadmix

Test the link

ls $NGSADMIX

2. Create the directories that will be used for working:

mkdir Demo

cd Demo

mkdir Data

mkdir Results

3. Set the paths to your local directories, for example:

IN_DIR=Demo/Data

OUT_DIR=Demo/Results

Test the links

ls $IN_DIR

ls $OUT_DIR

4. The Input file

NGSadmix uses Genotype Likelihoods (GLs) in .beagle format as input. The input file has been created for you.

Download the files and move them to your input folder (for example, $IN_DIR):

wget popgen.dk/software/download/NGSadmix/data/Demo1input.gz

wget popgen.dk/software/download/NGSadmix/data/Demo1pop.info

mv Demo1input.gz $IN_DIR

mv Demo1pop.info $IN_DIR

We will use a very reduced data set:

-10 individuals from each population: 10 from Nigeria (YRI), 10 from Japan (JPT) and 10 with European Ancestry (CEU).

-a very reduced genome 30 x 100k random regions across the autosomes

-each individual is sequenced at 2-6X

CEU	Europeans (mostly of British ancestry)
JPT	East Asian - Japanese individuals
YRI	West African - Nigerian Yoruba individuals

To view a summary of the population information file, cut the first column, sort and count:

cut -f 1 -d " " $IN_DIR/Demo1pop.info | sort | uniq -c

Lets make a population label file and place it in the output directory

cut -f1 -d" " $IN_DIR/Demo1pop.info > $OUT_DIR/poplabel

5. Let’s analyze the input file:

• In general, the first three columns of a beagle file contain marker name and the two alleles, allele1 and allele2, present in the locus (in beagle A=0, C=1, G=2, T=3). All following columns contain genotype likelihoods (three columns for each individual: first GL for homozygote for allele1, then GL for heterozygote and then GL for homozygote for allele2). Note that the GL values sum to one per site for each individual. This is just a normalization of the genotype likelihoods in order to avoid underflow problems in the beagle software, but it does not mean that they are genotype probabilities.

• In order to see the first 10 columns and 10 lines of the input file, type:

gunzip -c $IN_DIR/Demo1input.gz | head -n 10 | cut -f 1-10 | column -t

• Use this command to count the number of lines of the input file. The number of lines, indicates the number of loci for which there are GLs plus one (as the command includes the count of the header line):

gunzip -c $IN_DIR/Demo1input.gz | wc -l

6. To run an analysis of the GLs with NGSadmix, assuming the number of ancestral populations is K=3, type the following command:

$NGSADMIX -likes $IN_DIR/Demo1input.gz -K 3 -minMaf 0.05 -seed 1 -o $OUT_DIR/Demo1NGSadmix

For a reference on the parameters that can be used to run NGSadmix, please go to [1]

7. The output:

The analysis performed by NGSadmix produces 4 files:

• Log likelihood of the estimates

A .log file that summarizes the run.

Let’s take a look at the log file to determine the log likelihood of the estimates achieved by NGSadmix which is called the “best like” in the file:

cat $OUT_DIR/Demo1NGSadmix.log

• Estimated allele frequency

A zipped .fopt file, that contains an estimate of the allele frequency in each of the 3 assumed ancestral populations (there is a line for each locus).

We can use this file to obtain the estimated allele frequency of the first 5 SNPs (one per line) of the three assumed ancestral populations, by typing the following command:

zcat $OUT_DIR/Demo1NGSadmix.fopt.gz | head -n 5

• Estimated admixture proportions

A .qopt file, that contains an estimate of the individual's ancestry proportion from each of the three assumed ancestral populations (there is a line for each individual).

To obtain the estimated admixture proportions for the first 5 individuals, type the following command:

head -n 5 Demo1NGSadmix.qopt

• DemoNGSadmix.filter if the filter was used, it will show the sites that were left out.

To see the header file, type:

head -n 5 $OUT_DIR/Demo1NGSadmix.filter

no lines means that all where used

8. Follow these instructions to make a simple plot of the estimated admixture proportions for all individuals in R:

Make sure you stand on the output directory

cd $OUT_DIR

Type “R” in the terminal and press enter and paste the following code into R:

# Get ID and pop info for each individual
pop<-scan("poplabel",what="theFuck")


# Read inferred admixture proportions file
q<-read.table("Demo1NGSadmix.qopt")

# Plot them (ordered by population)
ord = order(pop)
par(mar=c(7,4,1,1))
barplot(t(q)[,ord],col=c(2,1,3),names=pop[ord],las=2,ylab="Demo1 Admixture proportions",cex.names=0.75)

The y-axis of the plot show the admixture proportions and the individuals in the samples are plotted in the x-axis.

Each color represents a different ancestral population.

The proportion of each color shows the different admixture of the individuals for each ancestral population.

The plot is sorted by the population of origin of each individual in the sample, and therefore, it shows blocks with prevalence of a certain color, which represents the population to which each individual belongs.

NB As you could tell from the number of loci included in the analysis, the above analysis is based on data from very few loci (actually we on purpose only analyzed data from a small part of the genome to make sure the analysis ran fast). Results from an analysis of data from the entire genome can be seen here [LINK ANDERS]

Example of NGSadmix - Complex

Now that you know how to make input data to NGSadmix, how to run NGSadmix and what the output looks like, let's try to look at a more realistic size dataset. More specifically let's try to run NGSadmix on data from the 1000 genomes project from the following populations:

ASW	HapMap African ancestry individuals from SW US
CEU	European individuals
CHB	Han Chinese in Beijing
JPT	Japanese individuals
YRI	Yoruba individuals from Nigeria
MXL	Mexican individuals from LA California

The input file Demo2input.gz with genotype likelihoods from 100 individuals in .beagle format, and a file with population info are given. Note: please make sure you have created and set up the working directories as indicated in the previous tutorial.

1. Download and copy the files to your input folder (for example, $IN_DIR):

wget popgen.dk/software/download/NGSadmix/data/Demo2input.gz $IN_DIR

wget popgen.dk/software/download/NGSadmix/data/Demo2pop.info $OUT_DIR

2. Take a quick look at the population data by making a summary by cutting the first column, sorting and counting:

cut -f 1 -d " " $IN_DIR/Demo2pop.info | sort | uniq -c

3. Run an analysis of the data with NGSadmix with K=3 (-K 3), using 1 cpu (-P 1), using only SNPs with minor allele frequency above 0.05 (-minMaf 0.05), set the seed set to 21 (-seed 21), and set the prefix of the output files to Demo2NGSadmixK3. For a reference on the parameters that can be used to run NGSadmix, please go to [2]

NGSADMIX -likes $IN_DIR/Demo2input.gz -K 3 -P 1 -minMaf 0.05 -seed 21 -o $OUT_DIR/Demo2NGSadmixK3

4. Plot the estimated admixture proportions by running the following code in R:

Make sure you stand on the output directory

cd $OUT_DIR

Type “R” in the terminal and press enter and paste the following code into R:

# read population labels and estimated admixture proportions
pop<-read.table("Demo2pop.info",as.is=T)
q<-read.table("Demo2NGSadmixK3.qopt")

# order according to population
ord<-order(pop[,1])
barplot(t(q)[,ord],col=2:10,space=0,border=NA,xlab="Individuals",ylab="Demo2 Admixture proportions for K=3")
text(tapply(1:nrow(pop),pop[ord,1],mean),-0.05,unique(pop[ord,1]),xpd=T)
abline(v=cumsum(sapply(unique(pop[ord,1]),function(x){sum(pop[ord,1]==x)})),col=1,lwd=1.2)

Note that like in the previous example, the order of the individuals in the plot is not the same as in the .qopt file. Instead, to provide a better overview, the individuals have been ordered according to their population labels.

5. Try to run NGSadmix with K=4 and compare the plots.

$NGSADMIX -likes $IN_DIR/Demo2input.gz -K 4 -P 1 -minMaf 0.05 -seed 21 -o $OUT_DIR/Demo2NGSadmixK4

Type “R” in the terminal and press enter and paste the following code into R:

# read population labels and estimated admixture proportions
pop<-read.table("Demo2pop.info",as.is=T)
q<-read.table("Demo2NGSadmixK4.qopt")

# order according to population
ord<-order(pop[,1])
barplot(t(q)[,ord],col=2:10,space=0,border=NA,xlab="Individuals",ylab="Demo2 Admixture proportions for K=4")
text(tapply(1:nrow(pop),pop[ord,1],mean),-0.05,unique(pop[ord,1]),xpd=T)
abline(v=cumsum(sapply(unique(pop[ord,1]),function(x){sum(pop[ord,1]==x)})),col=1,lwd=1.2)