Not All Genetic Discrimination is Illegal in the US
June 24th, 2011 § Leave a Comment
It struck me that the Genetic Information Nondiscrimination Act (GINA), the flagship US law against genetic discrimination, signed by President George W. Bush in 2008, does not cover life insurance, disability insurance, or long-term care insurance.
In other words, in the US it’s still possible to be discriminated against for genetic reasons. How is this situation likely to change in the near future?
When I look more closely at it in the NIH factsheet for GINA it is stated that
As genomic medicine is poised to revolutionize medicine, patients will be able to utilize advances in genetic testing to create highly personalized care and treatment plans without fear of discrimination.
I have two main objections with that:
- The current level of protection is not near enough to dissipate the fears for people to ‘utilize advances in genetic testing’. The law itself states that GINA sets a ‘minimum standard’ but still does not cover some crucial scenarios where being discriminated against may be devastating (e.g. life insurance).
- If Direct-to-consumer (DTC) genetic testing is only going to be available via medical prescription, as many in the FDA are seeking to implement, does this approach help in any way the advancement of personalized health care? According to Misha Angrist [1], the top requestor for genetic tests in 2006 in the US was a Spa company; i.e. rich people with disposable income who wanted to understand their health risks. The impression I gather is that private demand is the main driver for DTC genetic testing and not the institutionalized health care. If the worst fears of DTC providers become a reality it may well be the end of an open, accessible, democratized personal genomics field as we now it. This does not take away the grave obligation DTC providers and relevant governmental agencies have to educate the public to ensure that they understand their results and that rigorous standards are met in reported findings.
Genetic Information Nondiscrimination Act Factsheet
It also strikes me that, in GINA, concepts like ‘privacy’ and the ‘right not to know’ are not even mentioned. My family and I chose to share our genotypes with the world, which should be as respectable as not sharing this data in the strictest sense. Both being open or protective of one’s genetic data should be an informed personal choice and not something imposed. I thus think this should be an important aspect that remains to be addressed at the juridical level.
In the NIH GINA factsheet it also reads that
Insurers cannot use genetic information obtained intentionally or unintentionally in decisions about enrollment or coverage.
To my mind this means that there is no restriction in collecting genetic information even though it cannot be used. I’ve heard say that some insurers may be collecting genetic data in anticipation of changes in laws that would allow them future use of the data.
Ignorance is the worst possible kind of discrimination
The need to educate the public and help them make an informed decision on genetic testing should remain the single most important priority by governments and regulatory agencies alike. Trying to impose on the public tight regulations on access to DTC genetic testing may create the wrong public opinion and backfire in the same way as GM food and crops did in Europe. Now is the time, when opinions are still to be formed, to make people aware of what DTC can or can’t tell them about themselves. The worst form of discrimination I know is ignorance and only ignorance can lead to the wrong interpretation of test results. Perhaps the solution does not lie so much in restricting access to DTC genetics testing as it is in helping consumers choose the right option for themselves.
[1] Here Is a Human Being: At the Dawn of Personal Genomics (2010) HaperCollins Books.
Benefits for Publishing Family Genomes on the Internet
June 6th, 2011 § 6 Comments
It has been for a long while since I’ve been wanting to write about the stuff that Mike Cariaso, founder of SNPedia, has been doing with my family genotypes. Initially, he performed their data analysis with Promethease for assignment of traits and annotation to observed SNPs. More recently, he has also developed a tool for visualization and comparison of genotypes between different people. He has used my family’s and Manu Sporny’s genotypes as test cases.
This is an unanticipated benefit we have experienced as a family for publishing our genomes on the Internet. Using Promethease’s report we were able to learn that dad is lactose intolerant. The fact that he did not like milk and had not taken milk in years kind of made sense when we discovered that his two SNPs rs4988235(C;C) and rs182549(C;C) make him unlikely to digest lactose with 70% probability. This result regarding lactose intolerance was in fact in the 23andMe report but we missed it.
It is clear that Direct-to-consumer genetic companies do try to cater to the non-expert, i.e. the majority of its customer base. The novel SNPedia visualization tool will be an useful addition to those of us who strive to DIY our own discoveries about our personal genomes data.
Using his visualization tool, when I compare all my SNPs with those of my sister’s, I find that 68% of mine are identical to hers, a total of 389,250 (see below).
SNP comparison between my sister and myself
Note that the graph is using a logarithmic scale. Of all our analyzed SNPs, 25% are halfmatch (i.e. one of the alleles is common to both of us) and about 2% are conflicts. Example of conflicts may include different SNPs with the same position. This, according to Mike, may not be an accident. Because I know that we were analyzed in two different array platforms, version 2 and version 3 respectively, I can now tell the number of SNPs that are different between both of us, i.e. not present in either genotype. Of the total 0.5 Million plus SNPs in my genome about 29,082 do not match hers.
The other nice feature this tool provides is an actual graphical representation of chromosomal SNPs in a map of pixels, colored consistently with the above notations: light blue means match, dark blue halfmatch, red conflict and grey different SNPs:
Pixelated map of chromosome 1/chromosome 1 comparison between me and sister
The above figure shows two representations for chromsome/chromosome comparison between my chromosome 1 and my sister’s. Clearly most of the area is light blue, indicating complete match. Also the number of differences, halfmatches and conflicts are reported. Clicking on any of these links, one can find the actual SNPs in conflict, getting an output that looks like this:
1 rs9729550 1 1125105 CC AA 2 rs12142199 1 1239050 GG AA 3 rs7531583 1 1696020 GG AA 4 rs6681938 1 1771080 CC TT 5 rs41307846 1 1949559 GG -- 6 rs3128296 1 2058766 TT GG 7 rs262654 1 2079386 AA GG 8 rs262688 1 2103425 GG TT 9 rs6659405 1 2362949 TT GG 10 rs4648482 1 2739781 CC TT 11 rs2483266 1 3225901 CC TT 12 rs868688 1 3290667 TT CC 13 rs10492939 1 3292731 AA GG 14 rs2493268 1 3298358 TT CC 15 rs871822 1 3302774 GG TT 16 rs12024847 1 3310659 TT CC 17 rs2821017 1 3510731 GG AA 18 rs3765761 1 3620336 CC TT 19 rs3765766 1 3624520 TT CC 20 rs4233262 1 4136842 CC TT 21 rs966321 1 4215064 GG TT 22 rs964715 1 4216644 TT CC 23 rs1390136 1 4241703 CC TT 24 rs4654545 1 4425464 TT CC 25 rs446529 1 4695274 CC TT
This table shows that for the first SNP, rs9729550, I have CC while my sister has AA.
In conclusion, Promethease and the SNPedia visualization tool is helping me learn more about my SNP genotype results, complementing the information that I initially got from my Direct-to-consumer provider. Hopefully I will be able to do some additional research based on the results hereby obtained.
If you want to see my family’s genomes with Mike Cariaso’s tool you can find it here:
Don’t forget to send me any exciting findings that you might encounter!
How did I inherit my prostate cancer highest risk SNP?
March 3rd, 2011 § Leave a Comment
In a previous entry for this blog, I wrote that my highest risk factor in my 23andMe genotype is the rs10993994 SNP, which produces a 1.3 increase in odds for me to develop prostate cancer. Navigating my genome using myKaryoView I was able to learn that this gene is located 56 base pairs before the start of the MSMB gene. As it happens, this gene is a tumor suppressor that has been involved in prostate cancer. The rs10993994 SNP is located in the promoter region and the TT genotype, the one I contain, seems to down-regulate this tumor’s suppressor gene, increasing the chances of developing prostate cancer.
The most contributing SNP (highest column in red) corresponds to rs10993994 SNP.
Up to that point I was not able to learn anything else from my genome. Making use of last Christmas’ offer in 23andMe, that offered genome analyses from $99 and the keen collaboration of my family, I was able to buy 23andMe analysis kits for my Mum, Dad, Sister and Aunt. It was only last week when I finally got the results back. I have now uploaded all of these genomes in the DAS protocol and hence can query them using myKaryoView.
Region around rs10993994 SNP using myKaryoView. Pop up windows show the genotype of this SNP for the three individuals
The first thing I did was to establish how I inherited my TT genotype for rs10993994 SNP. I learnt that my mum and dad both have CT for the genotype of this SNP. So in fact I was unlucky enough to inherit one T from each parent. My sister was luckier, well she does not have a prostate, but at least the genotype she inherited, CT, is not the one that has been related to the greater health risk.
Millions of Genomes
February 15th, 2011 § Leave a Comment
This was that title of a talk recently given by Richard Durbin at the Wellcome Trust Sanger Institute. Excitement and expectation, reassured by a continuous trend of exponential growth, made inspired listeners feel the same way Google or Facebook employees must have felt at their company’s peak time.
Some numbers presented by Richard gave context to the startling prediction that by 2015 millions of individual genomes will be sequenced. This is in fact the expected number if the current pattern of growth continues. Ten years have now been celebrated after the draft for the first Human Genome was released in 2001. By 2006, with next generation sequencing in full swing and sequencing centers churning out many gigabases per week, tens of genomes had been sequenced. Today the number of individual genomes is in the order of thousands, meaning that every year a 4 fold growth is predicted. Extrapolating this estimation to five years from now makes thus the number of genomes sequenced 1024 times (45) our current number, hence millions of genomes.
Having such an incredible amount of data will clearly create challenges which we are just beginning to find. How are we going to hold all this data when processing capacity in computers “only” grows 2 fold every year? The answer is that as more genomes become available, an individual’s data will not be stored in its totality but only the differences that define his/her particular variations.
Although many genomes may have been sequenced by now, accessing them is not a trivial matter. Stored in many different places, with different restrictions and inconsistent levels of detail, the bulk of this data is likely to remain at least mildly challenging to handle. Results of investigations will certainly be accessible, but think of the effort it could cost to access every single database containing public individual genome data. I do not believe that a great number of genomes will be optimally researched unless more straightforward and standardized access protocols are put in place, something that currently is lacking. Times for excitement are reasonably justified, yet base pair to bedside medicine may be delayed if current data sharing procedures are not streamlined.
Personal Genomic Software: A Review of What Is Available
February 14th, 2011 § 5 Comments
Readers may have seen that a few previous entries in Manuel Corpas’ Blog have been dedicated to myKaryoView, a personal genome visualization free software. In this post I review some of the software that is currently available for analysis of personal genomes. These are all free third party packages independent from providers such as 23andMe, Navigenics or deCODEme.
Andrew Scheidecker’s Personal Genome Explorer apparently is the first piece of software that was created for analysis of 23andMe personal data. This is a console application that allows 23andme data import, deCODEme data import, SNP database import from SNPedia, analysis of genome based on SNPedia metadata and random genome generation based on population frequency data.
I found that Personal Genome Explorer is a light-weight application that can be easily downloaded and installed. A lot of potential information can be extracted and browsed from a database based on SNPedia data. I tried to upload my own 23andMe chromosome 16 with file extention ‘.txt’ and unfortunately it did not recognize it or gave a clue as to what kind of extension it accepts.
Personal Genome Explorer showing randomly selected SNPs
SNPTips is a firefox plugin extension that allows customers of 23andMe to access their SNP genotype information. SNPTips allows one to hover the mouse cursor over the SNP id in any article text or webpage. Clicking on the SNP icon it creates, a pop up window appears with one’s genotype (i.e. the DNA letters found in your analysis) with links to SNPedia, Google Scholar and dbSNP. I tried to upload my 23andMe chromosome 16 and it worked quickly and neatly. Unfortunately it does not allow simultaneous visualization of more than one personal genome.
Enlis Genome is another tool that can be downloaded as a console. The interface is quite intuitive and it managed to upload my chromosome 16 SNPs in about a couple of minutes. The report it gave back was very neat. However it seemed to provide a very similar kind of information to what is already available to 23andMe customers. The main added value I could find in this tool was that it colated most information provided by a 23andMe’s customer report into a sort of document that can be easily handled. It was unfortunate though that the report concluded I was female. How it infers my gender when I only provided autosome data puzzles me slightly.
My results for Enlis Genome uploading my 23andMe chromosome 16.
myKaryoView is to my knowledge the only personal genomics tool that allows navigation and visualization of this genetic data directly as a genome browser. myKaryoView uses the DAS technology, which makes it capable of representing any available DAS source together with one’s genome, such as known genes, OMIM genes, normally variant regions, etc. Currently, adding one’s genome into a DAS source is a process that requires expert knowledge of another tool called easyDAS. Once the DAS source for one’s genome is created, the url where the DAS source genome is located can be added to myKaryoView for exploration via its interface. myKaryoView does not require any download for installation, as it is a web tool, and many personal genomes can be navigated at the same time.
myKaryoView showing my personal genome SNPs in green for a subregion of 10q11.23
The Perfect Tool
If I was able to pick the strengths of each of the reviewed softwares and put them together into one piece I would choose the richness of SNP information from the Personal Genome Explorer, the ease for uploading one’s genome from SNPTips, the reporting capabilities of Enlis Genome and the navigation and visualization capabilities of myKaryoView. Since all of these implementations are already available, the winner of this software “market” will be the one that combines all of these strengths in manner that is easily accessible to lay people. I think 23andMe has a lesson to teach in terms of making accessible to all of us the ability to analyze one’s genome and reporting the relevant information succintly.
Conclusion
Several tools are now available specifically tailored to the analysis and discovery of information related to one’s personal genome. Not a single tool is perfect and to some extent all require some computer and biology knowledge in order to properly operate and understand them. This is clearly not the ideal situation for lay people who are curious to know a bit more about their own personal genome. Certainly if all the strong points of each of the above were combined a much better tool and service to the community could be rendered. Personal genome coders: it’s time to join forces!
The Meaning of Red
February 5th, 2011 § Leave a Comment
A previous post in Manuel Corpas’ Blog in March 2010 noted that there was disagreement among the leading Copy Number Variation (CNV) repositories in one small but significant detail. Some of them displayed gains in green, others in blue. The same with loses: no consensus existed in the way deleted regions were colored. Not agreeing to such an obvious standard was troublesome for users, especially when comparing data from different resources.
I am pleased to know that decision makers in DECIPHER, the Database of Genomic Variants, ISCA, the NCBI and the UCSC Genome browser have finally agreed on a common color scheme that defines gains in blue and loses in red. To be more precise, here are the hexadecimal colors:
- #0000FF (blue): gain
- #FF0000 (red): loss
Part of the drive in agreeing to this standard has been prompted by some users affected of color blindness who complained that they were not able to distinguish between red and green. This trigger accelerated the change.
Having a common standard coloring scheme will not only help color blind people but all users. A consistent way of illustrating gains and loses means all users will be able to grasp the science more quickly. This is great news for the whole community and a cause for celebration.
A Genetic Poem
December 24th, 2010 § Leave a Comment
A poetry contest is being organized by 23andMe. The five lucky winners each get a free entry to the 2011 edition of the Personalized World Medicine Conference. The rules are simple, include at least 5 words from a list provided and send it by December 31st. Any formats and number of tries are allowed. Unfortunately I am not eligible because it is only open for US residents and travel costs are not covered.
Since Christmas is a time that tends to be free of distractions for me :-) I decided to give it a try and write a genetic poem, even though I am not eligible for the prize. I will share it for now but I might take it down later if I feel more embarrassed about it.
My Genes and Me
Genes between probes
Vary my transcription
Condition their relation
And length of ear lobes
My credit card spelled out
The questionnaires went
I too gave consent
To have my test done, no doubt
A saliva drop fell
As the parcel came home
I shook it all well
The lab got it, I logged on
Risks and Ancestry I found
Some were fine, some were high
I Scrolled up, I scrolled down
Browsed data through the night
Then something else happened
A 5th cousin had chatted
That shared my haplogroup
Was she part of my troupe?
Yet it was all the genotypes
That 23andMe found alike
Without it I wouldn’t know
Such tales of my chromosomes
For how can a little SNP count
If these traits are all mine
My phenotypes all defined
When I had just DNA out
And if this was not enough
Hope you’ll let me certify
GWAS is good for some stuff
Even if it’s hard to identify
myKaryoView v2: Navigate Your Own Genome!
December 16th, 2010 § Leave a Comment
Following on the release of a Nature article on the rise of genome bloggers, in which Manuel Corpas’ Blog is linked, I would like to take this opportunity to announce the release of myKaryoView v2, an open source visualization software for personal genomics. Combining Rafael Jimenez’s and my own efforts, we have significantly augmented myKaryoView’s capabilities to allow users to visualize their personal genomes.
Visualization of one’s own personal genome is done via Bernat Gel’s easyDAS tool. This tool converts files with biological annotations into a DAS source. DAS sources can be thought of as tracks in a genome browser. The beauty of DAS is that it does not require any data to be stored locally and, as long as the reference coordinates are the same, any kind of biological features can be easily integrated.
Exploring My Own Genome With myKaryoView
23andMe analyses results report that I have a 28.1% risk of developing prostate cancer as opposed to a 17.8% average risk in males. This risk is calculated analysing the genotypes of 12 SNPs. The SNP marker rs10993994 shows the greatest risk among the 12 reported markers, a 1.3 increased odds. This SNP is located in 10q11, near the MSMB gene and the found allele (T) has been shown to affect its expression levels, decreasing its cancer suppressor function [1].
Having no history of prostate cancer in close relatives, I wanted to find more information about this SNP in order to confirm results. My whole genome profile, containing > 570,000 SNPs, was downloaded from 23andMe and a DAS source was created using easyDAS. The resulting data source was held privately in my newly created easyDAS account. Once easyDAS creates a new DAS source, the data is available through a URL. I pasted the URL for my genome data into the myKaryoView interface, selecting all accompanying tracks to be shown in its zoom view.
I typed the ‘MSMB’ gene myKaryoView’s query search box and once results were returned, I zoomed out to have a better overview of my 10q11 chromosome region, shown below.
Visualization of MSMB region with myKaryoView
My genomic profile is the bottom track with SNPs in green. The top track in purple corresponds to genes involved in mendelian inheritance diseases (taken from OMIM), in red all existing genes, blue and green normal CNV regions and in yellow somatic mutations found in cancer (from the COSMIC database). I clicked on the gene and SNPs feature bars to find further information. Clicking on the MSMB gene feature, I found that this gene’s start position is 51219559, only 57 bp after the rs10993994 SNP position. The track with yellow features (COSMIC) also contained four reported mutations for MSMB (MSMB:ENST00000358559), indicative of the involvement of this gene in cancer, but all of them within the genes exons, i.e. not outside the gene. Following MSMB’s link to OMIM revealed also its implication in prostate cancer.
By seeing all these data sources in myKaryoView I feel more confident with the validity of 23andMe’s reported risk. It is true that all the sources visualized in myKaryoView can be found if searched for in the Internet. The merit of this tool is, I think, that it provides a one stop shop for a first step in analyzing original data sources for one’s personal genomic results.
About myKaryoView
myKaryoView is a web tool for visualization of genomic data specifically designed for direct-to-consumer genomic tests that uses publicly available data distributed throughout the Internet. It does not require data to be locally held and it is capable of rendering any feature as long as it conforms to a standard protocol named DAS. Configuration and addition of sources in myKaryoView can be done through the interface. myKaryoView should be considered a prototype and not a finalized tool. Here offer a proof of principle of myKaryoView’s ability to display personal genomics data with 23andMe genome data sources. Prior to publication, please acknowledge Rafael Jimenez and Manuel Corpas if using myKaryoView.
[1] Proc Natl Acad Sci U S A. 2009 May 12;106(19):7933-8. Epub 2009 Apr 21.
myKaryoView: First Open Source Visualization Software for 23andMe Data
September 1st, 2010 § Leave a Comment
myKaryoView Logo
Following my previous post on the First Publicly Available Genome Via DAS I would like to present an open source software that Rafael Jimenez and myself have developed for visualization of genomic data. Here we have it configured to display 23andMe data as a test case. We call it myKaryoView and it is available for free use and download. Its website is located at the following address:
myKaryoView works in most contemporary browsers without lengthy installations and uses publicly available data distributed throughout the Internet via DAS. This means that there is no need to hold the data locally and that it is capable of visualizing any data as long as it is available via DAS. In order to visualize 23andMe data, myKaryoView requires the set up of a DAS source, which currently limits myKaryoView’s usage to those familiar with this technology. However, configuration and addition of sources are extremely simple and the amount of data able to display is limited only to the time of request completion and data rendering.
Here we show myKaryoView to display personal genomics data with a dummy 23andMe genome data source. This source is based on real 23andMe results data from my own genome, randomly modified in a manner that is irrecognizably different.
The myKaryoView website shows an implementation that allows search of genome data via gene name or genome coordinates. For example, type in the search box 1:2000000,6000000 and hit “Submit Query”.
myKaryoView Zoom and Chromosome views.
The figure above shows results of that query, with two tracks containing the source from 23andMe with dummy data plus genes for a subchromosomal region in chromosome 1, Start: 2000000, End: 60000000. Gene names and SNP data and are shown in red and blue respectively. Different color shades indicate the density of annotation for any given point. If the “Gene Names” data track name is clicked, a popup window appears with a link “Display Original Data Source” that allows the download of the raw data from its DAS source. Any feature can be clicked for retrieval of specific information contained in the DAS source. Here a blue SNP mark is clicked and a popup window appears describing the selected SNP and a link to its corresponding dbSNP entry.
A simple manual explaining how to install and configure myKaryoView to show different data sources is provided from the website. myKaryoView is still in beta testing and any feedback is welcome. We have some plans for the near future for myKaryoView, which we will reveal in due time. Meanwhile I hope you find it interesting and useful.
By the way, the claim that this is the First Open Source Visualization for 23andMe data is, of course, arguable.
