Author Archive for Luke Jostins

Crowd-funding personalized bioscience

Human_Microbiome_Project_logoHere at Genomes Unzipped we love genomes. But there is more to the world of biology than genomics, there is more to understanding your own body than personal genetic tests. To understand the human body, you have to look not just at the DNA present, but also at what genes are turned on in what tissues, what cells are being produced in what numbers, what compounds are circulating in your blood, and even what other organisms are also living on your body. However, for the interested consumer the non-genetic aspects of personalized medicine have generally been less readily accessible than the genetic aspect. This post discusses a few companies that are trying to fill this gap, and who are looking to the general public to crowd-source funding for their products.

A quick note: I have not investigated these companies in detail, and, as with all crowd-sourcing, you should be aware that the company may not manage to produce the product as they describe it (or even get to make it at all).

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Looking closer at natural selection in inflammatory bowel disease

As I mentioned a few weeks ago, we recently published a large study into the genetics of inflammatory bowel disease (IBD), which included a number of analyses digging into the biology and evolutionary history of IBD genetic risk. Gratifyingly, our paper has stimulated a lot of discussion among other scientists, which has generated several ideas about future directions for this work. One question that was raised by several population-genetics experts at ASHG was about our natural selection analysis, and in particular our claim to discover an enrichment of balancing selection in IBD loci. In the paper, we found clear signals of natural selection on IBD loci, a subset of which we interpreted as balancing selection. In this post I will set out how I came to this conclusion, but then outline another explanation that could explain the results: recent local positive selection in Europeans.

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Dozens of new IBD genes, but can they predict disease?

Out in Nature this week is a paper by three Genomes Unzipped authors reporting 71 new genetic associations with inflammatory bowel disease (IBD). This breaks the record for the largest number of associations for any common disease, and includes many new and interesting biological insights that you should all go and read about in the paper itself (pay-to-access I’m afraid) or on the Sanger Institute’s website.

One thing that we did not discuss in the paper was genetic prediction of IBD (i.e. using the risk variants we have discovered to predict who will or will not develop the disease). In this post I want to outline some of the situations in which we have considered using genetic risk prediction of IBD, and discuss whether any of them would actually work in practice.

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Another “IQ gene”: new methods, old flaws

A very large genome-wide association study (GWAS) of brain and intracranial size has just been published in Nature Genetics. The study looked at brain scans and genetic information from over 20,000 individuals, and discovered two new genetic variants that affect brain and head morphology, one which affects the volume of the skull, and one of which affects the size of the hippocampus.

The main study is very well carried out, and the two associations look to me to be well established. However, there are a few little things about the paper that, when combined with some biased reporting in the press, that have been bothering me. Firstly, the main result that has been reported in the news is that the study found an “IQ gene”, but this was only a very small follow-on in the study, and the evidence underlying it is relatively weak (certainly not the “Best evidence yet that a single gene can affect IQ”, as reported by New Scientist). Secondly, the authors use a misleading reporting of statistics to hide the fact that one of their association could easily be cause by an (already well known) association to general body size.

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Misapplied statistics in the OXTR/Prosociality story

Out in the PNAS Early Edition is a letter to the editor from four Genomes Unzipped authors (Luke, Joe, Daniel and Jeff). We report that we found a statistical error that drove the seemly highly significant association between polymorphisms in the OXTR gene and prosocial behaviour. The original study involved a sample of 23 people, each of whom had their prosociality rated 116 times (giving a total of 2668 observations), but the authors inadvertantly used a method that implicitly assumed there were actually 2668 different individuals in the study.

The authors kindly provided us with the raw data, and we ran what are called “null simulations” on their dataset to check to see whether their method could generate false positives. This involved randomly swapping around the genotypes of the 23 individuals, and then analysing these randomised datasets using the same statistical method as the paper. These “null datasets” are random, and have no real association between prosociality and OXTR genotype, so if the author’s method was working properly it would almost never find an association in these datasets. The plot below shows the distribution of the “p-value” from the author’s method in the null datasets – if everything was working properly all of the bars would be the same size:

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Identical twins usually do not die from the same thing

Over at Nature News, Erika Check Hayden has a post about a recent Science Translational Medicine paper by Bert Vogelstein and colleages looking at the potential predictive power of genetics. The take-home message from the study (or at least the message that has been taken home by, e.g., this NYT article) is that DNA does not perfectly determine which disease or diseases you may get in the future. This take home message is true, and to me relatively obvious (in the same way that smoking doesn’t perfectly determine lung cancer, or body weight and dietary health doesn’t perfectly determine diabetes status).

A lot of researchers have had a pretty negative reaction to this paper (see Erika’s storify of the twitter coverage). There are lots of legitimate criticism (see Erika’s post for details), but to be honest I suspect that a lot of this is a mixture of indignation and sour grapes that this paper, a not particularly original or particularly well done attempt to answer a question that many other people have answered before, got so much press (including a feature in the NYT). A very large number of people have tried to quantify the potential predictive power of genetics for a number of years – why was there no news feature for me and Jeff, or David Clayton, or Naomi Wray and Peter Visccher, or any of the other large number of stat-gen folks who have been doing exactly these studies for years. ANGER RISING and so forth.

But of course, the reason is relatively obvious. Continue reading ‘Identical twins usually do not die from the same thing’

Making sequencing simpler with nanopores

The Advances in Genome Biology and Technology (AGBT) conference, one of the main go-to destinations for those who get excited by DNA sequencing technology, is currently going down in Florida. Sadly, no-one from GNZ could make it this year, but we are keeping up with the various announcements about new genomics tech as best we can. One that caught our attention was the announcement of a brand new sequencing machine from a company that has previously kept very quiet about its technology.

Oxford Nanopore, who we have written about before, today announced two new sequencing machines to come out this year. The announcement has caused quite a buzz amoungst, well, everyone. Nature, New Scientist, GenomeWeb, BioIT World and Forbes all have reported on it, and bloggers Nick Loman and Keith Robison have also had a chance to talk to some of the Oxford Nanopore peeps about their new toys.

A lot of the interest has come from the (very cool) MinION, a tiny, disposable USB-key sequencer (shown in the picture above) that can sequence about a billion base pairs of DNA, and cost around $500-$900 each. The applications of this are endless – the ability to pick up a bit of biological matter, mix it with a few chemicals, and read whatever DNA is in it, could help with diagnostics, epidemiology, ecology, forensics. It is also (though not quite) the price where hobbyists could consider having a play; perhaps in a few years plug-and-play DIY genetics could be a possibility.

Less immediately striking, but still just as interesting, is the GridION sequencing machine. This is the work-horse of the nanopore sequencing world, made for reading lots of DNA, and scaling up to massive sequencing centers. Obviously, many scientists are going to be very interested in many of the features (notably, the ability to read very long pieces of DNA, a trick that has previously been more-or-less impossible to do reliably). However, what will this announcement mean for those of us who are interested in personal genomics?

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Review of the Lumigenix “Comprehensive” personal genome service

This is the first of a new format on Genomes Unzipped: as we acquire tests from more companies, or get data from others who have been tested, we’ll post reviews of those tests here. The aim of this series is to help potential genetic testing customers to make an informed decision about the products on the market. We’re still tweaking the format, so if you have any suggestions regarding additional analyses or areas that should be covered in more detail, let us know in the comments.


Lumigenix is a relative newcomer to the personal genomics scene: the Australian-based company launched back in March this year, offering a SNP chip-based genotyping service similar in concept to those provided by 23andMe, deCODEme and Navigenics.

The company kindly provided Genomes Unzipped with 12 free “Comprehensive” kits, which provide genotypes at over 700,000 positions in the genome, to enable us to review their product. We note that the company offers several other services, including a lower-priced “Introductory” test that covers fewer SNPs, and whole-genome sequencing for the more ambitious personal genomics enthusiast. This review should be regarded as entirely specific to the Comprehensive test.
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Phantom Heritability: What it does and doesn’t mean

Just out in prepublication at PNAS is a paper from Eric Lander’s lab, entitled, somewhat provocatively The mystery of missing heritability: Genetic interactions create phantom heritability. The authors suggest that certain types of gene-gene interactions could be causing us to underestimate how much of the heritability of complex traits has been uncovered by our genetic studies to date.

There has been an awful lot of talk about this research since Eric Lander talked about it at ASHG a few years ago, and the paper itself has generated quite a bit of discussion on- and off-line. Razib Khan reported on the paper last week, giving a good summary. He mentioned a press release about the paper issued by the advocacy organisation GeneWatch, which confuses the additive heritability discussed in this paper with the total heritability of diseases (a distinction explained below), and uses this to draw conclusions about how this result alters the promise of personal genomics. This just goes to show how much confusion there already is out there about this subject.

I have a more detailed post up on Genetic Inference about this paper, the strength of the argument, and what it means for the field. Here I am just going to pull out what I think are some important take-home points about this paper:

1) Broad sense heritabilities (the kind that are clinically important for e.g. risk prediction) have NOT been significantly overestimated The type of heritability we ultimately care about, the broad or total heritability, is how much total phenotypic variation is captured by genetics, or equivalently the correlation between identical twins in uncorrelated environments. The figure at the top of this post shows a plot that I made using Zuk et al’s equations, comparing true broad sense heritabilities, against what would be estimated based on twin studies (I have matched the colouring etc to Figure 1 of the paper). The twin study estimator of heritability is a robust estimator of total heritability for heritabilities less than 0.5. Above that, LP epistasis causes growing overestimation – it can make a 50% heritable trait look like a 65%, and 70% look like a 95%. It does not make weakly heritable traits look strongly heritable, just strongly heritable traits look very strongly heritable.

2) This paper is discussing additive heritability. This is a specific form of heritability that acts “simply” – half of it is passed on to offspring, siblings share an amount proportion to how related they are, and the genes that underlie it do not interact with each other. We do not know how much heritability acts like this, but various lines of evidence have made us think that it is a relatively good model, and most competing models have been incompatible with this evidence, or look contrived. What Zuk et al have done is produce a set of plausible, simple and non-contrived models (Limiting Pathway or LP models) that look pretty much indistinguishable from additivity using many of the tests we have run, but can act very differently in twin studies. Under these models, twin studies will overestimate the additive heritability (i.e. make us think that a larger proportion of heritability acts “simply”). The equivalent plot to the top of the page for estimating additive heritability, which you can see here, shows massive overestimation of additive heritability across the spectrum.

3) There is no real evidence that these LP models apply (and in fact there are still a few reasons to believe additivity could still broadly apply, see my other post for details). The issue is that we cannot conclusively rule these models (or models like these) out, and therefore the heritability explained by the genetic variants we have found so far is very uncertain.

4) This is important because our measures of “heritability explained” by the genetic variants we have found look at how much additive heritability is explained. These measures have in general told us that we have only explained a small proportion (generally < 25%) of additive heritability – but if in fact the heritability is largely not additive, but we are treating it like it is, we could in fact have explained a higher proportion of heritability than we believe. This would mean that the “missing heritability” is missing not because we have not found the right genetic risk factors, but because we have not found the right model to use. This could be good news: the genetic variants we have discovered could in fact be used to predict disease a lot better than they we can at the moment, if only we can find the right model to use them with.

Size matters, and other lessons from medical genetics

Size really matters: prior to the era of large genome-wide association studies, the large effect sizes reported in small initial genetic studies often dwindled towards zero (that is, an odds ratio of one) as more samples were studied. Adapted from Ioannidis et al., Nat Genet 29:306-309.

[Last week, Ed Yong at Not Exactly Rocket Science covered a paper positing an association between a genetic variant and an aspect of social behavior called prosociality. On Twitter, Daniel and Joe dismissed this study out of hand due to its small sample size (n = 23), leading Ed to update his post. Daniel and Joe were then contacted by Alex Kogan, the first author of the study in question. He kindly shared his data with us, and agreed to an exchange here on Genomes Unzipped. In this post, we expand on our point about the importance of sample size; Alex’s reply is here.

Edit 01/12/11 (DM): The original version of this post included language that could have been interpreted as an overly broad attack on more serious, well-powered studies in psychiatric disease genetics. I’ve edited the post to reduce the possibility of collateral damage. To be clear: we’re against over-interpretation of results from small studies, not behavioral genetics as a whole, and I apologise for any unintended conflation of the two.]

In October of 1992, genetics researchers published a potentially groundbreaking finding in Nature: a genetic variant in the angiotensin-converting enzyme ACE appeared to modify an individual’s risk of having a heart attack. This finding was notable at the time for the size of the study, which involved a total of over 500 individuals from four cohorts, and the effect size of the identified variant–in a population initially identified as low-risk for heart attack, the variant had an odds ratio of over 3 (with a corresponding p-value less than 0.0001).

Readers familiar with the history of medical association studies will be unsurprised by what happened over the next few years: initial excitement (this same polymorphism was associated with diabetes! And longevity!) was followed by inconclusive replication studies and, ultimately, disappointment. In 2000, 8 years after the initial report, a large study involving over 5,000 cases and controls found absolutely no detectable effect of the ACE polymorphism on heart attack risk. In the meantime, the same polymorphism had turned up in dozens of other association studies for a wide range of traits ranging from obstet­ric cholestasis to menin­go­­coccal disease in children, virtually none of which have ever been convincingly replicated.
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