Incorporating common genetic risk factors into the models typically used to calculate a women's breast cancer risk has little impact on clinical decision-making, such as whether an individual should consider earlier or more frequent mammography or prophylactic drugs, according to a paper published today in the New England Journal of Medicine. The results follow similar studies for diabetes and cardiovascular disease, echoing what has now become a common criticism of genome-wide association studies; that this approach is unlikely to identify genetic risk factors of diagnostic value.

Over the last few years, researchers have used DNA-studded microarrays to quickly search tens of thousands of human genomes for common genetic variations linked to various diseases, dubbed genome-wide association studies. Despite the enormous size of these studies, they have only identified a fraction of the source of the genetic risk of disease. And the vast majority account for a very small change in risk in a given individual. (This is in contrast to rare genetic variants, like BRCA1, which substantially increase a women's risk of developing breast cancer.)

To analyze the potential impact of common, breast cancer-linked variations, researchers from the National Cancer Institute combined data from five studies of breast cancer. Taken together, these studies compared 5,590 breast cancer patients to 5,998 women without cancer, mostly white and age 50 and 79. The team employed a commonly breast cancer risk model, which uses medical, reproductive and family history, to calculate an individual's risk of developing cancer over the next five years. They found the risk score was similar to that calculated using 10 breast cancer variations recently identified in genome wide association studies. But combining the two risk models had little impact. "When we included these newly discovered genetic factors, we found some improvement in the performance of risk models for breast cancer, but it was not enough improvement to matter for the great majority of women." said Sholom Wacholder, Ph.D., senior investigator in NCI's Division of Cancer Epidemiology and Genetics (DCEG), in a statement.

For most women in the study, the inclusive model did not substantially change their personal estimated risk of developing breast cancer beyond the Gail model calculations. Overall, using the inclusive model reclassified 26 percent of women to a higher risk category; 28 percent to a lower risk category; and left 46 percent in the same category of risk score. The shifts from one category to another were generally too small to influence clinical decision-making.

That's probably not welcome news to direct-to-consumer genetic testing companies, such as Navigenics and DecodeMe, which screen for these types of variants. Kari Stefansson, founder of Decode, argues that the results do show that common variants are useful, but that we still have a way to go.

My hope is that this entire argument is soon moot. Whole genome sequencing, which can identify both rare and common variants, seems finally poised to fulfill its role in illuminating the genomics of disease. A handful of papers published over the last few months have demonstrated that sequencing can identify genetic variants linked to some rare diseases, and scientists hope the same approach can be applied to more common ones. While a $48,000 genome sequence--the cost of Illumina's personal sequencing service--is still a lot compared to Decode's $500 cancer screen, the price is dropping rapidly. (No one knows yet how much more bang you'd get for your buck.) Complete Genomics, a startup in California, will soon offer bulk sequencing services for about $20,000 a genome, with a $5,000 price tag not far behind.

The Icelandic genomics company DeCode announced details of its rebirth yesterday, most notably dropping its drug development efforts. The company, which has churned out a huge body of genetic research in the last decade--much of it published in high profile scientific journals--filed for chapter 11 bankruptcy protection in the U.S late last year. The company has spent more than $600 million but never turned a profit.

Decode began as a sort of national genomics effort, capitalizing on Iceland's self-contained population, well-documented genealogies, and openness to genetic research to search for genes linked to different diseases. (In the early days, these characteristics allowed scientists to pinpoint these genes more easily than in a general population.) As genomics technology advanced, the company moved to whole genome association studies, identifying genes linked to heart disease, stroke, skin cancer and schizophrenia. In the process, DeCode generated a massive genomics database, as well as expertise in collecting and analyzing huge volumes of data, which it now hopes to capitalize on. Rather than trying to develop drugs on its own, the company plans to partner with pharmaceutical firms.

The company will continue to offer its consumer genetic-testing website, deCodeME, launched in 2007.

According to the New York Times,

The new DeCode is owned by Saga Investments, an alliance that includes two leading life science investment companies, Polaris Ventures and ARCH Venture Partners. Terrance McGuire, a general partner at Polaris, said DeCode had been recapitalized because its research and database, formed from the medical records of the Icelandic population, were a valuable asset. "From an investor's perspective, it was the power of the content being created," he said.

...Dr. Stefansson said that the company's genetic research in Iceland would carry on just as before and that DeCode would "continue to outperform" its mostly university-based rivals in the United States and England. The commercial operation will be lead by Mr. Collier in the United States, Dr. Stefansson said.

For more of TR's coverage of DeCode, check out this Q&A with Kari Stefansson from 2006, which highlights the company's high hopes for drug development. And this feature describing its early efforts.

A couple of years ago, spurred by a strong family history of diabetes and journalistic curiosity, I took one of the newly available genetic tests to asses my risk for the disease. The test assessed whether I carried a variant called TCF7L2, which was discovered by scientists at deCode in 2005--almost 20 percent of people with type 2 diabetes carry two copies of the high-risk version of the gene.

Since then, scientists have discovered a number of other genetic risk factors for type 2 diabetes, and tests to detect them are available directly to consumers via the internet, along with an analysis of that person's lifetime risk. But a new study confirms what some have said all along--that these analyses add little to our ability to predict who will get the disease. So-called phenotypic factors, such as age, body mass index, waist circumference, and cholesterol levels are much more accurate predictors.

Risk of developing type 2 diabetes is highly dependent on environmental factors, notably diet and obesity. But it also has a strong genetic component--someone with a sibling with the disease is much more likely to get it than someone without, regardless of diet. Companies including 23andMe and Navigenics have marketed genetic tests to assess risk of type 2 diabetes and other diseases with the idea that this information can help people try to prevent diseases for which they are genetically at risk. But the new study calls that into question.

According to GenomeWeb:

Researchers from the University College London followed thousands of individuals in London's Whitehall district over roughly 20 years. When they looked specifically at factors affecting type 2 diabetes risk, they found that two non-genetic risk models were better predictors of diabetes risk than a genetic model based on nearly two dozen risk alleles.

...When the researchers assessed the so-called Cambridge and Framingham type 2 diabetes risk models, which are based on non-genetic factors such as age, sex, family history, waist circumference, body mass index, smoking behavior, cholesterol levels and so on, they found that both predicted risk of the disease better than a genetic risk model based on 20 common, independently inherited risk SNPs.

The Cambridge model had 19.7 percent sensitivity for detecting type 2 diabetes cases in the Whitehall cohort based on a five percent false positive rate, while the Framingham model had 30.6 percent sensitivity. The gene count score, meanwhile, detected 6.5 percent of cases at a five percent false positive rate and 9.9 percent of cases at a 10 percent false positive rate. In addition, the team noted, adding genetic risk information did not significantly improve the ability to identify individuals at risk of type 2 diabetes over either non-genetic risk model alone.

The researchers who conducted the study didn't discount studies searching for genetic risk factors for diabetes. They said that the most useful outcome of those studies will be a better understanding of the biological basis of the disease, potentially pointing toward new drug targets.

Direct-to-consumer genomics companies say it's too soon to disregard the value to the individual. It's not yet clear whether people will be more motivated by a genetic risk than an environmental one. Or whether the ability to intervene very early on, as genetic risk factors would allow, is more effective than later interventions.

Here's 23andMe's response, via GenomeWeb:

Joanna Mountain, senior director of research for direct-to-consumer genetics firm 23andMe, agreed that non-genetic factors are important contributors to type 2 diabetes risk. But she says there is still a place for genetic testing--particularly for a subset of individuals with a relatively high genetic risk for the disease.

"One point that the authors don't make is that a small fraction of individuals learn from genetic data that their type 2 diabetes risk is very high," Mountain told GWDN by e-mail. "The results for these individuals don't influence the statistic that is used heavily in this study, but for those individuals, the genetic information can be very valuable."

Mountain also noted that genetic testing allows for earlier risk assessment than non-genetic factors. And, she said, in contrast to weight, body mass index, and other non-genetic factors, genetic profiles do not change over time.

23andMe currently evaluates eight of the 20 alleles tested in the study. Mountain said the company will continue adding genetic markers for type 2 diabetes risk as these alleles meet their scientific criteria.