At the risk of turning Friday Links into a self-trumpet-blowing occasion, we are happy to report that a number of GNZ contributors (Jeff, Carl and Luke) are authors on a new Crohn’s disease GWAS meta-analysis of 6000 patients that came out in Nature Genetics this week. The study brings the number of Crohn’s associations up to 71, with 30 novel, bringing the proportion of heritability explained up to about 24%; also worth noting that all of the associations from the previous meta-analysis were replicated it this one, showing how the cross-platform independent replication experiments that are now standard have largely obliterated false positives in GWAS. There were also 5 loci that showed evidence of a second, independent signal, which I think is a promising sign of things to come.
Tag Archive for 'GWAS'
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In the recent report from the US Government Accountability Office on direct-to-consumer genetic tests, much was made of the fact that risk predictions from DTC genetic tests may not be applicable to individuals from all ethnic groups. This observation was not new to the report – it has been commented on by numerous critics ever since the inception of the personal genomics industry.
So, why does risk prediction accuracy vary between individuals and what can be done to combat this? Are the DTC companies really to blame?
To explore these questions it is first necessary to understand what is meant by the odds ratio (OR). In genetic case-control association studies the OR typically represents the ratio of the odds of disease if allele A is carried compared to if allele B is carried. If all else is equal, genetic loci with a higher OR are more informative for disease prediction – so getting an accurate estimate is extremely important if prediction underpins your business model. However, getting an accurate estimate of OR is far from easy because many, often unmeasured, factors can cause OR estimates to vary. In this post I will try to break down the concept of a single, fixed odds ratio for a disease association, and highlight a number of factors that can cause odds ratios to vary using examples from the scientific literature.
The current issue of Cell has some important correspondence in response to an essay published by Jon McClellan and Mary Claire King in April. Daniel covered the original piece and hosted a guest post from Kai Wang which detailed some of the more obvious flaws in their argument. Now, Wang and his colleagues from Philadelphia have published an official response in Cell, in parallel with a similar letter from Robert Klein and colleagues from New York. Accompanying these is a further reply from McClellan and King. Read on for an overview of three contentious statements made in the original piece, and the rebuttals to each.
(This is an extended version of a short piece written as part of a series organized by the excellent Mary Carmichael at Newsweek. Readers eager for more detail on the statistics behind risk prediction should read Kate’s excellent discussion posted yesterday.)
In 2003 Francis Collins, having just led the human genome project to completion, made a prediction: within ten years, “predictive genetic tests will exist for many common conditions” and “each of us can learn of our individual risks for future illness”. The deadline of his prophecy is fast approaching, but how close are we to realizing his vision of being able to get a read-out of disease risk from a person’s DNA?
Continue reading ‘Why prediction is a risky business’
As any avid follower of genomics or medical genetics knows, genome-wide association studies (GWAS) have been the dominant tool used by complex disease genetics researchers in the last five years. There’s a very active debate in the field about whether GWAS have revolutionized our understanding of disease genetics or whether they were a waste of money for little tangible gain. No matter where you fall in that spectrum, however, you need only to browse the table of contents of any recent issue of Nature Genetics to see how ubiquitous they are. Since GWAS provide so much of the fodder for unzipping your genome, and in order to help you cut through the hype in the mainstream press coverage of GWAS, I’ve put together a quick primer on how to go straight to the original paper and decide for yourself whether it’s a landmark finding or a dud.