For those interested in clinical diagnostics of genetic diseases, the ability to use the molecular information presented within our various genomic databases is somewhat limited. If you attempt to locate the common p.Q12X mutation within the NCBI Reference Sequence of the AMPD1 gene (which causes Adenosine Monophosphate Deaminase Deficiency), you will find that the 12th codon does not correspond to Glutamic Acid (the mutation, according to the current RefSeq gene (NM_000036.2), is actually p.Q45X) . For researchers and clinicians looking for SNP primers or probes for specific mutations, the lack of fidelity and congruency between reported mutations and the “curated” databases is more than a headache. I have personally spent hours chasing down one mutation only to remain uncertain as to whether or not I had located the proper nucleotide in the end.

What is the issue? Did the original researchers not properly locate the mutation? Is the Reference Sequence incorrect? What can be done to increase the ease with which we can not only locate a specific phenotype-associated variant, but determine the nucleotides immediately surrounding that locus as well?

Understanding the Genomic Databases

The National Center for Biotechnology Information (NCBI) provides an excellent summary of the differences between its two major databases: RefSeq and GenBank. The major distinction for those interested in clinical genetics is the fact that RefSeq is curated and GenBank is not. GenBank is essentially a public repository of all DNA sequences made available by researchers who are responsible for updating and maintaining their own submitted sequences. RefSeq is the database that attempts to provide one sequences as the “Reference Sequence,” usually determined by looking at the most common variants among the GenBank entries. RefSeq sequences are occasionally updated which is why it is always important to include the accession number of the gene when using the Reference Sequence.

RefSeq has greater utility because it provides linked records between the genomic DNA, the mRNA transcript (and cDNA record), and the translated protein. It would seem that locating a non-synonymous mutation should take just a few clicks.

An Arcane Nomenclature System

Okay, I admit, arcane is a bit harsh, but it is time to update our nomenclature system. The Human Genome Variation Society (HGVS) provides the guidelines that are currently considered the best practice in naming sequence variants. They rely on coding DNA (cDNA) and essentially number the cDNA beginning with 1 for the first Adenine in the initiation (ATG) codon. There are obvious drawbacks to using cDNA, the most important being lack of numerical assignments for intronic nucleotides.

More and more intronic mutations are being found to account for deleterious phenotypes. Yet, the original (deprecated) system for naming intron variants looks something like this: IVS3+22G>A (interpretation: the G 22 basepairs from the beginning of intron 3 is changed to an A). There always seems to be some ambiguity when I come across mutations described this way: we can also write IVS3-98G>A (the G 98 basepairs from the end of intron 3 is changed to an A).

More recently, it has been recommend to describe intronic mutations according to the closest possible cDNA location. Our mutation above might now be described as c.195+22G>A (cDNA nucleotide 195 is the last nucleotide found on exon 3 (which immediately precedes intron 3). Or, we might write it as c.196-98G>A.

Let’s be practical for a second. This is terrible indexing. If I want to find an intronic mutation, it will be indexed according to the cDNA record of the relevant gene. Fine, but if I search the cDNA for nucleotide 195, the intron is spliced out of the transcript, so I cannot locate the sequence of interest there. Instead, I have to take the last few base pairs from exon 3 of the cDNA transcript, open up the gDNA (genomic DNA) transcript, and search for these base pairs. Then, once I have discovered where this exon ends in the genomic DNA, I must count 22 base pairs forward to locate the nucleotide of interest (this will all be in vain if the mutation was reported incorrectly in the literature).

The lesson here: although the RefSeq genomic DNA is linked to the cDNA and the protein records, the actual nucleotides/codons/amino acids are not indexed together.

A Standardized Index: Making Sure Mutation Nomenclature is Static

The cDNA naming convention grew out of the fact that the ability to completely sequence genomic DNA for the major part of the 20th century was very limited. It made sense for mutations to be reported based on the cDNA because it reflected the two more easily obtainable (and more static) records of that time: the mRNA transcript and the protein sequence. However, now that the Human Genome Project has paved the way for easier sequencing, and we are constantly improving a standard Reference Sequence, it makes much more sense to name mutations in terms of their genomic DNA (gDNA) location.

As reported by Flicek et al., the Locus Reference Genomic (LRG) sequence format is being developed specifically for the purpose of accurately indexing genomic variants. The LRG will provide a static record for every gene of interest, and this record WILL NOT CHANGE. It may be annotated, etc., but in the interest of preserving reported mutations, the backbone sequence (and subsequent base pair numbers will not change).

I have been using the Reference Sequence in a similar function whenever I locate a mutation for testing purposes. For example, I was searching for the p.G380R mutation in the FGFR3 gene which is a common cause of Achondroplasia. The cDNA variant I was interested in was c.G1138A. After searching for the mutation within the gene transcript, I located the surrounding base pairs, and determined that the genomic name for the mutation is g.G10458A. In my system, I start with the Adenine of the initiation codon as 1 for the gDNA as well as the cDNA. Thus, the gDNA and cDNA index numbers diverge once the first intron begins. For point mutations, I have written a script that automatically accomplishes this by interacting with the UCSC genome browser. An indexing revelation is what helps this script to work.

Relational Indexing: Relating the cDNA to the gDNA

I have been developing my own library of RefSeq genome transcripts, but I have been indexing them more efficiently. My gDNA files, as previously mentioned,  are labeled 1 at the beginning of the initiation codon, however, the non-coding transcribed region preceding the initiation codon is labeled from -1 downward beginning at the nucleotide immediately preceding ATG (there is no 0). From the start codon, the gDNA positions are numbered sequentially. Similiarly, the cDNA transcript begins with the start codon, and is numbered from 1 until the end of the stop codon.

To relate the cDNA to the gDNA, I use exon indexing. For cDNA, every nucleotide is assigned to an exon, and these nucleotides are numbered from 1 until the end of the exon. Thus, if exon 3 is 79 base pairs long, the nucleotides within exon 3 will also be numbered from 1 to 79. For gDNA, every nucleotide is assigned to an exon or intron, and these nucleotides are numbered from 1 until the end of the exon/intron. Thus, we can relate the cDNA position to the gDNA position through the exon indexing.

For example, if I want to determine the gDNA position of cDNA locus 285, the analysis is very simple:

1. Find nucleotide 285 within the cDNA file.
2. Determine the exon of nucleotide 285 (exon 2).
3. Determine the index within exon 2 of the nucleotide (exon 2, position 83).
4. Locate exon 2, position 83 within the gDNA file.
5. Determine the gDNA position associated with exon 2, position 83 (382).

In this way, we have effortlessly determined that c.285 corresponds to g.382.

Developing a Functional, Interactive Reference Assembly

Although the data files are linked between RefSeq gDNA, cDNA and amino acid sequences, the files are not indexed to the extent that it is entirely useful. Ultimately, I would like to be able to browse a cDNA/gDNA sequence, perform a base-pair change, determine if this change is synonymous or non-synonymous, and find out if the change has been associated with any disease phenotypes (which would entail linking individual nucleotides to OMIM records). The development of such a functional variation browser will require a lot of forethought, smart programming, and a great deal of curation. However, I believe that development of the Locus Reference Genomic (LRG) sequence is a step in the right direction.

References:

Dalgleish R, Flicek P, Cunningham F, Astashyn A, Tully RE, Proctor G, Chen Y, McLaren WM, Larsson P, Vaughan BW, Béroud C, Dobson G, Lehväslaiho H, Taschner PE, den Dunnen JT, Devereau A, Birney E, Brookes AJ, & Maglott DR (2010). Locus Reference Genomic sequences: an improved basis for describing human DNA variants. Genome medicine, 2 (4) PMID: 20398331

Inspired by one of my favorite sites, HackerNews, I’ve decided to launch my own version: The Chromosome Chronicles news section! Here, ANYONE can submit genetics news (I also have a few reputable news sites feeding to it). But I encourage anyone to submit, whether it is a new blog post, a funny genetics picture.The news allows all users and visitors to then vote up or vote down a submitted article. Also, I will be tweeting on my twitter account the most recent top stories @chromchron. Please submit!

The 22nd Blog Carnival of Evolution is now up at Beetles in the Bush! Check it out to see 26 great posts on evolution and perspectives on a diverse range of evolution-related fields. The Chromosome Chronicles article on In Silico Models of Evolution is featured in this edition. Stop over for some good old Darwinian reading.

Brigham Young and 21 of his wives.

Evolutionary fitness is one of the most important concepts in Darwinian evolution. Essentially, fitness can be regarded as a measure of how much of an individual’s genes are passed on to the next generation. More specifically, the higher proportion of the next generation that is comprised of your genes, the more fit, evolutionarily, you are.

Fitness: A short example

For example, say I have one copy of a particular version of a gene: Gene X. No one else in my population of 100 individuals has it. Since we all have two copies of every gene, the frequency of Gene X can be regarded as 1 out of 200. Let’s assume that I pass on Gene X to four of my children in the next generation of 100 individuals for this population. Gene X has now increased from 1 out of 200 to 4 out of 200. Since the frequency of Gene X increased from the first to the second generation, Gene X can be regarded as a gene of higher evolutionary fitness.

Maximizing Your Fitness

Okay, now that we’ve reviewed fitness as the measure for evolutionary success, it begs the question: how can I maximize my evolutionary success? Answer: have as many children as possible (who will survive to one day themselves reproduce). This way, your genes will constitute a higher proportion of the future generations of gene pools.

An obvious limitation to the number of children a person can have is childbirth itself. Nine months, a painful delivery, and then years of caring for the child requires a huge investment in resources. Moreover, women might be viewed as a “limiting step” in this process (I hope I don’t get shot for that one). In particular, mating with only one women would surely slow a man down if his main goal was to produce as many offspring as possible.

Polygamy: Evolutionarily Advantageous

Joseph Smith Jr. was on to something when he founded the Church of the Latter Day Saints. Specifically, his many, many, many wives made him highly successful (from a Darwinian standpoint), although he publicly condemned the practice. By some accounts he may have had 20 children with his multiple wives. While the privilege of polygamy had previously only been reserved for the highest of alpha males (like the one and only Genghis Khan, to whom a large proportion of China can claim some relation), Smith had labored to include multiple wives in the doctrine of his faith. Simply join his church and you become an evolutionary celebrity. To this day, there are many living in Utah who can claim some relation to Joseph Smith Jr.

Divorce/Second Marriages: The Compromise in the name of Monogamy

Let’s assume (or believe) that all human actions are still driven by primal evolutionary urges. Having multiple wives surely fits this bill since it allows for greater evolutionary fitness. However, polygamy is outlawed in western culture, so outside of those who practice it underground, it is not a reliable option for men who wish to increase their fitness.

For men, the divorce/second marriage life route allows for more than one family, more than one wife, just not at the same time. By Darwinian standards, those who get divorced and remarry are actually more successful than those who are monogamous their whole lives (assuming that they have more children). Men who get divorced and remarry will be passing on a higher proportion of genetic material into the next generation.

Let’s take this argument a step further. What if the behavior: “Get divorced, remarry” has a genetic factor that predisposes a man to engaging in this behavior. If this behavior results in higher fitness (higher % of genes passed on by those who exhibit the behavior), then one might argue that a higher proportion of men in the next generation will have genes that predispose them to get divorced. By this logic, if divorce is an activity that makes men more fit (evolutionarily), then we can expect to see divorce rates rise in future generations!

Is Maximizing Our Darwinian Fitness our True Goal in Life?

This question gets to the heart of our discussion. Is maximizing the number of children we have our major goal in life? For some species, yes. For humans, no. Our sentience seems to have made us immune to many of the evolutionary pressures that drive other animals’ behaviors. For example, if having your own genes passed on in your children was so important, then why is adoption so popular? Also, why do many couples elect to not have children ever? These actions seem to be at odds with the idea of evolutionary fitness, yet many members of our species engage in them.

Finally, if maximizing evolutionary fitness was really the ultimate goal in life, then why wouldn’t all men (and women) go around the world donating sperm (and eggs) to all of the banks they can find. I can think of no quicker way to maximize the number of genetic offspring you have. Hopefully, no one does that because it would be kind of creepy.

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.