Microarray genotyping platforms report high accuracy. Of course, this is given that you use their protocols, ideal conditions, etc. Depending on the genotyping facility, this accuracy may be even more tenuous. I recently set out to get some good estimates for the rate of genotyping errors from the Illumina assay employed by 23andMe.

First let me refer back to a previous post about determining haplotype when you have two parents and a child. By determining haplotypes for all of my siblings and I, I am able to compare regions where we share both parental haplotypes, or shared diplotypes. In my family, there are four siblings (including myself), and I have had us all tested at 23andMe. To analyze genotyping errors, I decided to compare informative SNPs from shared haplotypes between my three brothers and I.

Experimental Outline:

1. Determine Transmitted Parental Haplotypes from all four siblings.
2. Determine Where Both alleles are Shared for all four Siblings.
3. Find out how many genotyping errors occurred in this region.

Determining the parental haplotypes is simple, and my method for doing this has already been described. Determining where the four siblings share alleles was accomplished using a program I wrote called NucleOlap. This program compares informative SNPs from paternal and maternal haplotypes between children and produces a nice output. It is designed to recognize candidate regions responsible for dominant or recessive genetic mutations given each child’s affected or unaffected status. However, if all children are affected, it is the same thing as analyzing haplotype sharing. Check out the documentation.

For every pair of siblings, it is expected that, on average, they will share both parental haplotypes for 25% of their genome. Add a 3rd sibling, and that 25% falls to 6.25%. For four siblings, it is expected that both haplotypes are shared for only 1.56% of their genome. According to release 36.1 of the Human Genome, the haploid length of the autosomes is 2,864,255,922 base pairs! I can expect that my three brothers and I share both parental haplotypes for 44,753,999 haploid base pairs.

The NucleOlap analysis found that my siblings and I all shared both haplotypes in six regions for a total of 47,656,130 haploid base pairs. Pretty good! The ideogram to the left shows the regions where my three brothers and I share the exact same genes from both parents. Shared regions occur on chromosomes 1, 2, 6, 13, 16, and 18. NucleOlap also provided me with the starting and ending positions (and SNPs) for each region. To determine where genotyping errors occured, I compared the raw data for these regions with each child (the program output is not affected by genotyping errors because it is able to recognize and ignore them).

The analysis occurred by gathering the SNPs in the identified shared regions and lining them up parallel to one another in Microsoft Excel. I then checked to see that all four siblings had the same genotype for each SNP (as they are expected to). A sample of how this worked is shown in the picture to the right.

My analysis revealed that 10,079 SNPs were contained within the regions where my brothers and I share diplotypes. Of these 10,079 SNPs, only 86 of them had any genotyping errors! This means that the genotyping calling was 99.15% accurate for these regions. Moreover, of the errors recorded, 79 of them occurred when there was a genotype call for some of the siblings and a null call (–) for others. Only 7 errors occurred where there was inconsistency in the genotype assigned to the siblings. The results are summarized in the table to the left.

My conclusion: the genotyping error rate is very low, less than 1% for the Illumina platform used by 23andMe. Even taking null calls into account, this number is still below 1%. My siblings and I shared 99.15% genotype identity in a region where we all share both parental haplotypes. I am very pleased with the accuracy.

This has to be the longest title of any post, but it covers the diverse functions of the identity-by-state method of analyzing high-density SNP data.

Elisha Roberson and Jonathan Pevsner published a paper this August titled, “Visualization of Shared Genomic Regions and Meiotic Recombination in High-Density SNP Data.” This paper describes methods and results for comparing the genome between any two individuals in search of shared genomic regions.

While many methods for determining genomic similarity rely on knowledge of family tree structure (identity by descent methods), the method described by Roberson and Pevsner compares SNPs based on identity by state, which allows them to compare any two individuals without knowledge of the family structure.

Genomic Comparison via Identity by State: A Brief Overview

Identity by State examines pairs of SNPs between two individuals and puts them into one of three categories:

1. Identical: Both individuals have the same genotype call (Ex. AA and AA; BB and BB; AB and AB).

2. One-Allele Shared: Only one call is shared between both individuals (Ex. AA and AB; AB and BB).

3. No alleles shared: No alleles are the same (Ex. AA and BB).

For individual SNPs, this type of analysis really does not provide any extra information. The real advantage is gained when high-density SNP information is taken for the whole genome (23andMe customers have this, and many other Illumina and Affymetrix platforms cover the whole genome).

In the paper, identical Identity by State (IBS) is referred to as situation 2; one-allele shared is situation 1; and no alleles shared is situation 0. For each chromosome, these three different “situations” can be plotted along an ideogram separated based on IBS. Here are a few images from the paper.

As we can see, when we plot the various IBS calls across chromosome 10 between two parents (unrelated individuals – as far as we know), there are tons of 2′s and 1′s, and even large numbers of 0′s. The data does not seem to have any rhyme to it. In fact, a distribution of 1′s 2′s and 0′s like this is evidence that the two are very unrelated. I can only see one small region that seems to be devoid of 0′s (arrow), which signifies something special!

Whenever there is a consistent lack of 0′s (a lack of unshared SNPs), it is evidence that the two individuals share actual haplotypes. In the example above, the region without the 0′s does contain a good number of 1′s and 2′s. This indicates that these two individuals share one haplotype in that region. If there were no 1′s in that region, that would be evidence that the individuals shared both haplotypes. The chart to the left summarizes how to interpret what is shared given the IBS calls in a region.

Roberson and Pevsner continue to show what type of IBS call distributions are made by various combinations of relations. For example:

Mother V. Son

Siblings

The results from comparing a mother to her child show that only 2′s and 1′s are expected to be visible (the occasional 0 might slip in due to genotyping error, or more rarely, mutation). This is consistent with the fact that the child receives one of his/her chromosomes from the mother.

The situation when the siblings are compared is a bit more complicated, but also more informative! We can see regions where 1′s, 2′s, and 0′s are all present, which indicates that the children inherited completely different alleles from the parents. Regions where only 1′s and 2′s are present indicate that the same allele was inherited from one parent, but a different allele from another. Finally, regions where only 2′s are present indicate that the children inherited the same allele from each parent in that region. Transitions from regions with 2,1,0 to 2,1 to 1 and vice versa indicate a meiotic recombination event had occurred. Very cool!

Exploring What We Can Learn with IBS Analysis

It has been shown pretty well that IBS analysis can be used to look at recombination events and allele sharing between siblings. This is extremely useful. However, IBS Analysis can apply towards a few other items:

• Examination of Hemizygous Deletions: Normally, parent-child comparisons only present with regions of IBS-2 and 1. We wouldn’t expect any 0′s since the child HAS TO inherit something from the parent. Except, of course, if the parent does not pass on any genes in that region. That is why, if a region of heavy IBS-0 is seen in a parent-child analysis, it indicates that a hemizygous deletion has been passed on from that parent to the child.
• Identification of Relatives/Potential Inbreeding: 23andMe recently released their relative finder. As a bad scientist and a worse journalist, I have done little investigation into how it works, and even less playing around with it. However, IBS is certainly one way relatives may be identified. Potential inbreeding will not show up as pronounced as relatives because there is likely more distance between people considering marriage than closer relatives, but IBS analysis on a couple who come from the same population produced the following picture:

The program which I wrote to create the above output colors the chromosome gray in regions with no SNP information, black in regions with no allele sharing, red in regions where one allele is shared, and green in regions where both alleles are shared. Two completely unrelated individuals would have completely black chromosomes. The picture above is of unrelated individuals with a more recent common ancestor, likely due to the fact that they come from the same population.

Implementing IBS Analysis: GenomicRelator!

Again, I got bored around 3 am one night and decided to write a program. Now I’m going to be giving it away for free, because I’m cool like that (I have been told to write the following: If you wish this program (or a version of it) for commercial use, you must contact me). Sweet.

Genome Relator is the program I have for you. I decided to not write a GUI, but instead make it double-clickable within a working directory (don’t worry, I’ll cover all this). It does two things:

1. General IBS Calls: It compares any two genome files and it draws pictures of the actual IBS calls on each chromosome (Green/Red/Black).
2. IBS Data Smoothing: IBS Calls are not as perfect in real life as they are in the world of published papers (for some reason), but we can look at the frequency of different calls across a fixed number of SNPs and decide whether or not the region contains 0′s, 1′s, or 2′s. It paints a prettier, easier to understand picture this way.

The Program

Download here. It’s a ZIP file with four JAR files inside: GenomeRelator, GenomeRelatorRAW, LaunchGenomeRelator, LaunchGenomeRelatorRAW. To use this program, simply extract the files to a folder, and place both genomes you would like to compare (named file1.txt and file2.txt respectively) into that same folder. Then just double click the proper JAR file.

You will need the most recent Java Runtime Environment on your computer as well. You should always click on the file with Launch in front of the name when using since it helps the computer allocate more memory (otherwise you run out of RAM and nothing happens). Read a little more to find out what the program does, the difference between RAW and normal, and the rules for the input files.

GenomeRelatorRAW compares the two files and processes them according to the IBS calls producing the following picture:

This just presents the data to you in a cool picture without really interpreting it, although you can visually interpret quite a bit from it. This takes much longer to render than the other version of the program since each SNP is actually evaluated and then illustrated (23andMe files might take 20 minutes to process!). I find these useful to compare to the output from the regular version of the program.

The regular version, GenomeRelator, produces output that “smooths” the data. The following picture comes from the regular (not RAW) version of the program:

Essentially, it moves throughout the genome with some preset rules: 250 SNPs at a time, the region must have greater than 1% IBS-0 and greater than 5% IBS-1, then it is assumed to have 2,1,0 (no relation). If it has less than 1% IBS-0 and greater than 5% IBS-1, then it is assumed to have 2, 1 (one allele shared). If it has less than 1% IBS-0 and less than 5% IBS-1, then it is assumed to have 2 (both alleles shared). These numbers 250, 1%, and 5%, are the variables that are at play in the program, and hopefully I will come out with a GUI version that allows you to decide what you want to set it to.

The rules for the input files:

1. The input files must be called file1.txt and file2.txt, and they must be placed within the same folder as the program (the jar files).
2. The input files must have one line of headers (no more no less).
3. The input files must have the same SNPs. For 23andMe customers, I am not entirely sure, but in my experience, male files have contained four extra SNPs (rs3091244, rs1229984, rs4420638, and rs34276300). Remove any SNPs necessary until both files have only the same ones.
4. The input files must contain four columns in the following order: rsid, chromosome, position, genotype. The genotype column must be one or two letters (yes, I fixed it so that male X chromosomes can survive when only one letter is there).

Program Output

The program will not only draw a picture, but if you run the regular version, it will also generate a number of files. A text file will be generated for each chromosome. This is just an intermediary file that the program needs in the middle of the process. A file called “IBS Summary” will also be generated for each chromosome. This file contains the list of regions (250 SNPs at a time) by their starting and ending genomic positions (the position in the genotype file). These are useful if you want to more closely examine regions of overlap (or deletion).

No Hosting On Your Own

I’m sorry, but I only want people to be able to download this from me, so just send them a link here if you want to distribute the program to others. It’s free for “academic” use and for “fun” use. No making money. Thanks to any who’ve read and used the program. I’d appreciate any feedback and please report all errors to me so I can do my best to fix them!

Photo by: liber

So I’ve already written a post about the challenges of phasing genotype data, but now I’m here to help you accomplish that task. Let’s go through a checklist of what will be needed:

• Your personal SNP information (through either 23andMe, Navigenics, deCODEme, etc.)
• The SNP information for your parents (preferably through the same company/microarray platform)

If you want to use my specific method you also need:

• The most recent Java Runtime Environment.

Of course, you can implement your own version of this phasing protocol with basic familiarity in a programming language or (more tediously) with some macros and if statements in Microsoft Excel.

How to Phase Your Genome: A Conceptual Overview

With information from both parents, it is possible to phase your genome (for the vast majority of SNP calls). We rely on the fact that for most situations, you can identify exactly what was inherited from your father and exactly what was inherited from your mother.

For example: If at a particular position, your genotype call is AT, your father’s genotype call is AA, and your mother’s genotype call is TT, then you know that the A must have come from your father, and the T must have come from your mother. Simple! We will refer to situations where phase can be determined as informative.

The chart to the right outlines exactly which situations are informative. The good news is that every situation is informative with the exception of one: when both parents and the child are heterozygous. Here, we are unable to say for certain what allele was inherited from each parent.

A sample implementation of how to phase a child’s DNA is illustrated below:

Implementing this Phasing Strategy: I’m Here to Help

If you have all the files mentioned above and would like to phase your genome, then I am more than happy to provide you with a Java archive that will allow you to accomplish this task. Even more, I will provide detailed instructions as to how to use this archive (it’s really simple, I swear).

You can download the Java program here as a zip file. Once finished, unzip the contents into the same fold. You should see Launcher.jar and PhaseME.jar. To launch the program, click Launcher.jar (I thought that was pretty obvious), and the GUI pictured below should appear:

The input files need to contain four columns in this order: rsid, chromosome, position, genotype. The genotype data needs to simply be AA,  AT, TT, etc. without any slashes or quotation marks.

Before running the program, you do need to make sure that your data is the same length and contains the same SNPs between both parents and the child. I have not incorporated any checks into the program for this. My recommendation is to use an IF statement in Microsoft Excel (version 2007) to make sure that all three files line up. Also, make sure that only one row of headers exists in the files.

Finally, select the files to be compared (father, mother, child), and select an output location and choose a name for the outputs. There will be 23 outputs with the following filenames: <yourchosenname>.chr<chromosome>.phased.txt. Each chromosome has its own output that shows you the haplotype inherited from the father and the haplotype inherited from the mother. This program just ignores Y and MT data. However, the program does have the ability to recognize whether the child is male or female, and it assigns the X chromosome haplotypes accordingly.

Let me know about any problems with the program (ex. If it does not produce any output), and I will check to see (1. If your input files are the problem, 2. If the program is the problem).

WANTED: 23andMe, Navigenics, and deCODEme customers.

The Direct-to-Consumer (DTC) Genetics Business is strange because it is often difficult to pinpoint exactly what it is that drives consumers to purchase genetic information.

Sudeepa Abeysinghe, a PhD student at The Austrailian National Univerity School of Sciences is conducting a short survey to determine “users’ experiences and perspectives of direct-to-consumer genomic testing.” The ultimate goal is to help understand what type of utility these tests currently provide.

The survey is only 20 minutes long, and can be anonymously taken. Please take some time to add your voice in shaping research into DTC genetics while also helping out a PhD student. The survey can be found here.

So it’s official, I’m waiting for the Spit Kit to arrive. After some consideration, a group of us decided that 23andMe would be the best service to use to get our genes read (I’m careful not to use the words sequenced so as not to mislead people). I did it in a group of six and got the holiday discount, so it was actually less than $400.

I’ve spent some time at their site recently, and one of the more entertaining objects I have found is their human prehistory animation. Although the video is cute and likely does a good job of giving the non-biology person some bearings in the area, they should be careful with such a video. Some groups (Fundamentalist Christians for example) might view the video as the devil’s propaganda. I recommend taking a look, it’s funny AND educational.

23andMe Prehistory Animation

Nonetheless, I’m really excited to learn more about myself (and my family), despite the possibility of hearing some negative news (nothing I can do about my genetic make-up now…maybe in the future though, who knows). I’m going to be updating the status of my experience along with some advice I have on how to take the data further than the analysis that is given on the 23andMe website.

Share/Save