By Meredith W. Salisbury
There’s a lot of talk about how genotyping is the poor man’s genome sequence — a temporary solution while whole-genome sequencing remains financially beyond reach. But until the much-hyped $1,000 genome becomes a reality, genotyping technologies will dominate the growing field of large-scale population genetics and association studies.
Indeed, the recent news that microarray behemoth Affymetrix acquired genotyping startup ParAllele BioScience for $120 million shows that this field —stopgap technology solution though it may be — continues to heat up. “We’re just starting to see an explosion [of interest] in this type of study,” says Sarah Shaw Murray, staff genotyping science manager for Illumina, about the rise in whole-genome association research.
Except for members of the largest genome centers, most scientists using genotyping for research have until lately relied on sequencing-based technologies. “Up until a few years ago people used microsatellite-based [genotyping],” says Murray. These days, more and more scientists are gaining access to higher-throughput methods based on mass spectrometry and microarrays. Just in the past year, Murray adds, Illumina has installed its mega-throughput, array-based system in a number of core labs and has signed several smaller labs to service contracts allowing access to the company’s genotyping production lab.
As these tools become more accessible to genome researchers, it’s a good time to take a look at the various kinds of technologies available. Keep reading to find out which might be best suited for your applications.
What’s the Difference?
Unlike sequence-based methods, which track down a known locus with a primer and then sequence the base or bases in question to determine the identity of a particular SNP, mass spec- and microarray-based methods can find and identify polymorphisms in a much higher-throughput manner.
Microarrays work best for looking at the highest number of SNPs. Scientists using Affymetrix arrays can test thousands of SNPs with a single assay. Known polymorphic sequences are hybridized to a chip, and then the sample in question is run over that array to locate and identify the genotypes present in that sample. Technology from the joint efforts between Affymetrix and ParAllele, for instance, allow for up to 10,000 SNPs on a panel; researchers can select from pre-chosen arrays or customized ones.
Like Affymetrix, Illumina’s genotype products use arrays — but the difference lies in the company’s bead technology, which Illumina’s Murray says allows for sample multiplexing that cannot be done on the Affy platform. (Representatives from Affymetrix were unavailable for comment.) Illumina’s tool uses oligonucleotides bound to beads, which are arrayed on a chip and can test up to 1,536 SNPs per reaction. That’s certainly fewer than can be screened with competitor Affy’s technology, but Murray says Illumina’s tool allows for running up to 96 samples per plate, whereas scientists using Affymetrix chips have to run one chip per sample.
Last month, Illumina released a chip that could process 100,000 SNPs on a single array; subsequent chips that would hold 250,000 and then 500,000 SNPs are currently in development, according to Murray. “These are the types of products that people would be using for whole-genome association studies,” she says.
Because of the sheer volume of SNPs they can process, microarray technologies are seen as the best discovery tools for polymorphisms. Mass spec, on the other hand, is known to have lower throughput when it comes to number of SNPs, but that technology has its own advantages, says Edvin Munk, director of marketing at Sequenom. “You always need follow-up work after the microarray,” he says. “This is where Sequenom is very powerful.”
Sequenom, the major player for mass spec-based genotyping, bases its SNP calls on data gathered from time-of-flight spectrometry to determine the mass of fragments and, therefore, the identity of the base in question. Even when multiplexed, the volume of SNPs a researcher can look at with Sequenom’s MassArray platform is significantly lower than its array competitors — its latest product, iPlex, can examine 24 polymorphisms at a time, Munk says —but where Sequenom finds its throughput is in sample number. Mass spec genotyping allows for studies with tens of thousands of samples, according to Munk, making it a particularly good technology for candidate gene or candidate region association studies.
That’s the kind of work for which beta tester Stacey Gabriel, the director of the human haplotype map group at the Broad Institute, says she expects to use Sequenom’s iPlex product. Gabriel, who runs the genotyping facility at the Broad and also uses technology from Illumina and Affymetrix, says the mass spec-based approach is good for looking at “a targeted set of SNPs.” She’s been testing iPlex since this spring and says that while her team has done minimal testing on it so far — validating about 100 SNPs on it — she was pleased with the results, adding that the extra multiplexing lowered “fully loaded” costs to somewhere under 10 cents per genotype. She says the technology should prove useful in some large upcoming cohort studies — involving diabetes and other complex diseases — where there are a few candidate genes but huge numbers of DNA samples to run.
Choosing Your Tech
Though Gabriel is well versed in the various methods for genotyping, she says that ultimately all she’s worried about is getting “an accurate genotype at the appropriate scale and cost.” Whether it’s mass spec, microarrays, or even the old microsatellites that get her there is essentially immaterial.
But most researchers don’t have the luxury of having all of those platforms at their disposal. How to choose the best one for your needs? It really comes down to throughput and type of study.
For basic SNP discovery, most agree that technologies like the Affymetrix and ParAllele arrays give you more for your money, finding the highest number of polymorphisms with a familiar tool. Of course, once scientists have more than a handful of samples to run, things aren’t quite so simple. Sarah Shaw Murray at Illumina maintains that for more than a few samples, the Illumina tool serves scientists better because the whole process is automated and can run up to 96 samples at once. (Using the Affy system for as many samples would require the separate handling of a chip for each sample, notes Murray — opening the door for human error and potential differences in protocols between experiments.)
Those who want to use such technology but can’t justify the throughput for their own research can sign up for service contracts using Illumina’s production lab, which runs more than 1 million genotypes daily, Murray says.
That’s not to say that anyone with lower throughput should sign on for this kind of service. Murray says, “For anyone who’s only looking at 10 SNPs for a study, our platform would not be for them.” Illumina’s product works most efficiently for studies of at least 384 polymorphisms, she adds.
But if you are the kind of researcher with 10 SNPs to look at, Edvin Munk at Sequenom will be happy to talk to you. He views his company’s mass spec technology as “very complementary” to microarray tools because of its ability to look at many samples across relatively few SNPs. “If somebody wants to make a discovery and have a hypothesis-free study where he uses 100,000 SNPs, this is not going to be the technology that he wants to pick,” Munk says. He’d recommend the researcher try a discovery technology like Affy’s, and then come back in a year, when he has winnowed his study to a few SNPs and wants to investigate them across, say, 20,000 samples. Munk says the average Sequenom customer looks at about 2,000 SNPs in tens of thousands of samples.
As these technologies make their way into more labs, no doubt vendors will come up with a host of solutions aimed at varying throughput levels. In the meantime, expect to see a continued race for the highest possible volume — for both SNPs and samples —while large-scale genotyping remains the purview of major research centers. The trickle-down to smaller labs will really depend on funding priorities, says Illumina’s Murray. “Even though the cost per genotype has gotten quite cheap, it’s not like everyone can [afford to] do these studies,” she says. “As people start these [whole-genome association] studies and find things, the funding will continue [to grow].”