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.

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).

While all the hype about getting your genome read is centered on discovering your geographical origins, learning which parent you got your personality from, and becoming aware of your various predispositions, there could be some negative consequences from getting your genome sequenced. A professor at Princeton University recently asked me if I was mentally prepared for the worst with my DNA test. At first I wasn’t sure what he meant, but after some brain storming and a bit of research to back it up, I have come up with the 5 worst things to learn from reading your chromosomes:

5. Your family is not from where they claim to be from:

If you walk into a classroom of average 5th graders in the United States and pose the question, “What nationality are you?” you can expect a range of answers. “Irish and Italian” might answer one young-un. “One-fourth German, One-fourth Dutch, and Half French” will claim another. Due to the cultural importance placed on ancestry in the United States, these kids will leave out the obious answer of U.S. Citizen.

Using your own genetic data along with the information from the HapMap Project, it is possible to determine the geographic origin of haplotypes – groups of associated SNPs – in your genome. For children and adults that have paraded around with the Italian flag their whole life, or those who don’t leave home with out their “luck of the Irish” jacket, it would be world-crushing to discover that instead, your genes come from France, or China.

4. You are at higher risk for cancer/alzheimer’s/heart disease/diabetes/etc.

While you may feel healthy now, it is possible that you might discover a bleaker future awaits you. Everyone is at riskfor developing certain diseases, but some genes greatly increase the chance of developing these maladies.

There is some fear that genetic risk factor information may cause people to react in an extreme manner. While the data from each individual study is statistically sound, their combined effects on each individual have not been tested. Should their values be added, or should they be multiplied?

My only word of caution with risk factor information is don’t feel like you need to go bungee jumping and skydiving to make sure you get your ‘last big thrill’ anytime soon. There is still much research to be done on exactly how each particular SNP is associated with the onset of each disease.

3. You were adopted…or perhaps there was a mix-up at the hospital

For families that get their genes read together, it is normally possible to see which part of a child’s chromosome comes from which parent (or grandparent even). In the event that a child’s DNA has very little overlap with either parent, it is evidence that they are not quite related (in the familial sense). Perhaps the parents were keeping an adoption a secret from the child?

Of course, there is an alternative hypothesis in this situation. Although not terribly common, it is a fear of new parents that their new baby might be accidentally swapped for another newborn. Last year, two Czech couples discovered that they had raised each others’ daughters for 10 months before deciding to swap back. If a DNA test reveals that a child’s genes don’t match the parents’, maybe there was a hospital mix-up!

2. Your father is not your real dad

While maternity is generally not disputed (with rare exceptions) since a mother physically gives birth to her child (again, not in all cases), paternity can sometimes be dubious. If a child gets tested and discovers that there is significant overlap with his mother’s genome, but very little with his father’s (or perhaps the child sees that the geographic origin of his DNA does not match his father’s), then it may suggest an extra-marital affair. Although some information found from these genetic tests may be trivial, this certainly qualifies as life-altering material. Cheaters beware, you can’t hide from science.

1. Girls: You may not be a girl…genetically at least

That’s right. Girls might discover that they are genetically males. Every person has two sex chromosomes (again, there are some exceptions). Girls have two X chromosomes, while boys have one X, and one Y chromosome. However, a rare genetic variation known as Andogen Insensitivity Syndrome (AIS) causes XY individuals (normally male) to develop a female external appearance.

Complete AIS generally results in external female development with internal testes. This information is definitely a life-changer. See this ABC special on AIS:

So there you have it. Not everything you discover during your commercial genetic test is going to be candy and unicorns. There could be some serious information, and perhaps some life-changing information. While I still maintain that there is significant value in being aware of your predispositions, knowing the possible worst case scenario will help you proceed with caution.

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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.

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You might be genetically inclined to get buzzed from drinking less. A study published on December 8th by researchers from the Earnest Gallo Clinic and Research Center indicates that the SNP rs1051730, is linked to a gene that affects how much alcohol you need to get buzzed.

The study is kind of funny. Participants were subjected to a “10 am 3 drink challenge,” and body sway was measured afterward. It was found that those homozygous for cytosine (C) at this particular locus responded quicker to the alcohol than did those homozygous for thymine (T). In fact, those who responded slower to alcohol (T individuals) were at a higher long-term risk to alcohol abuse.

What does this mean for me? For one thing, I could screen all of my dates in order to determine who would be the cheapest to take out drinking. More importantly, I could choose which combination to give my kid: do I prepare him to be the ultimate binge drinker in college by giving him two copies of the T gene? Or do I lower his risk for alcoholism by giving him two C’s, making him a lush? Such questions are going to plague me in my quest to build the perfect child.

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