biotech

Of some interest with respect to DNA databases and privacy concerns: "DNA links 1991 killing to Colonial-era family".

The DNA sample was taken in the death of 16-year-old Sarah Yarborough, who was killed on her high school campus in Federal Way, Washington, in December 1991. The King County Sheriff's Office has circulated two composite sketches of a possible suspect -- a man in his 20s at the time with shoulder-length blonde or light brown hair -- but had been unable to put a name to the sketch.

In December, though, the department sent the DNA profile to California-based forensic consultant Colleen Fitzpatrick. Fitzpatrick compared the profile to others in genealogy databases and found the closest match was to the family of Robert Fuller, who settled in Salem, Massachusetts, in 1630 and had relatives who came over before him on the Mayflower.

This is a Y chromosome match based on the genealogical research of people who may be completely unknown to the "suspect". Fitzpatrick offers that a Y-chromosome match may be expected to share a surname, which is probative in the forensic situation. Obviously there are many possible scenarios in which such information will not lead to discovery of a suspect: the chance of non-acknowledged paternity events across 200 years is very high. I don't view the result as strongly actionable, but I do think it raises important questions about the future of genealogy databases.

We are near the time when whole-genome sequencing will make this kind of identification much more likely because unique genetic matches to 3rd and 4th degree relatives will be plausible. Finding a handful of rare mutations shared between a crime scene sample and an individual in a whole-gneome database would be a strong indication of a relationship. It's possible that the databases for whole genomes will grow faster than the technology will allow reliable whole-genome sequencing from a crime scene sample. So in this case, the issues with database use may be primary.

It would be an interesting exercise to estimate the fraction of unknown samples from crime scene Y chromosome and mtDNA that could be matched to a 10th-degree relative in the Genographic (or any other large) dataset.

The New York Times notices DNA sequencing's Malthusian trap: "DNA sequencing caught in deluge of data."

That is a decline [in sequencing costs] by a factor of more than 800 over four years. By contrast, computing costs would have dropped by perhaps a factor of four in that time span.

The lower cost, along with increasing speed, has led to a huge increase in how much sequencing data is being produced. World capacity is now 13 quadrillion DNA bases a year, an amount that would fill a stack of DVDs two miles high, according to Michael Schatz, assistant professor of quantitative biology at the Cold Spring Harbor Laboratory on Long Island.

I have spoken with several scientists in other fields, like astronomy and particle physics, who deal with truly big datasets. Until now, biology data has actually been pretty small potatoes compared with the sheer amount pumped out by large projects in other fields. But that's changing. The Times article points out a unique aspect of the data problem in genetics: There are now thousands of labs that can generate large datasets, many of whom have no special plan for data archiving or availability.

“Google has enough capacity to do all of genomics in a day,” said Dr. Schatz of Cold Spring Harbor, who is trying to apply Google’s techniques to genomics data. Prodded by Senator Charles E. Schumer, Democrat of New York, Google is exploring cooperation with Cold Spring Harbor.

Google’s venture capital arm recently invested in DNAnexus, a bioinformatics company. DNAnexus and Google plan to host their own copy of the federal sequence archive that had once looked as if it might be closed.

I don't see Google as a deus ex machina for this one -- although I do observe that several other big data projects are sponsored by large Microsoft investors or founders.

The new Genome Biology has a perspective piece by Jacob Tennessen and colleagues, titled "The promise and limitations of population exomics for human evolution studies" [1]. Exomics is the study of the coding part of the genome, which is only 30 megabases as opposed to the 3 gigabases of a whole genome. Today it is possible to apply methods that sequence only the protein-coding parts of the genome, by combining methods that capture such regions with next-generation sequencing. The result is vastly cheaper than a whole genome, and some of this cost savings can be applied to increase the coverage, which increases the sequence accuracy.

Tossing away 99% of the genome is not an ideal sampling strategy for many purposes. However, when it comes to phenotype prediction, we can make some predictions about how changes in amino acid sequences will affect protein function. Many important phenotypic changes are caused by non-coding variations in gene regulation, but genetics has not yet reached a state of knowledge where these can be readily predicted. So, if we're sequencing people's genomes for the purposes of finding disease or phenotype variants, exome sequences give much of the information that we can presently evaluate.

James Hadfield noted the spree of exome sequencing publications at his blog, Core Genomics ("Exome capture comparison publication splurge"). He tags the rationale for

A lot of people I have talked to are now looking at screening pipelines which use Exome-Seq ahead of WGS to reduce the number of whole Human genomes to be sequenced. The idea being that the exome run will find mutations that can be followed up in many cases and only those with no hits can be selected for WGS.

I have heard a number of geneticists looking at exome sequencing as an intermediate step in population genetics, a way to increase the size of samples more affordably than whole genome sequencing makes possible at present. I don't think this will last long, as whole genomes offer much more for population genetic analysis and are rapidly dropping in price, but that depends on how technology develops. If we are consistently in the situation where researchers can multiplex 50 exomes at high coverage for the same price as one whole genome, it may make sense to use that strategy for a long time.

23andMe is starting an exome sequencing project. Daniel MacArthur's comments on G+ and the subsequent reader comments are interesting.

References

The Wall Street Journal has an op-ed by Matt Ridley, on the topic of possible regulation of consumer genetic testing. He writes that after years of relative non-interest in such tests, he ordered his own because of the likelihood that the FDA will limit their ability in the near future.

The champions of regulation respond that some firms in the direct-to-consumer genetic-testing industry are sometimes much exaggerating the health benefits of genotyping. As I said above, most results are anodyne and close to useless in terms of telling you how to live your life, but that is not how it sounds on the websites. However, this is not an argument for FDA medical-device regulation or requiring doctors' prescriptions before testing. It is an argument for plain, old-fashioned truth-in-advertising regulation of the kind effected by the Federal Trade Commission.

The AMA always seems to think it's fighting Doc Brinkley. Personally, I'd say the supplement industry is a far greater threat to the public health than DTC genetic testing, and is surely a better use of the FDA's time.

UPDATE (2011-04-24): More on Gene Expression. Much tweeting of the final line of the op-ed, as well:

Genetic knowledge, whether the high priests like it or not, is going to be a crowd-sourced phenomenon.