Author Archive for Luke Jostins

Review of the Lumigenix “Comprehensive” personal genome service

03/02/2012
Categories: Products and Test Review
Written by Daniel MacArthur, Luke Jostins, Katherine Morley, Joe Pickrell, and Dan Vorhaus

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.

Overview

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

09/01/2012
Categories: Journal Club
Written by Luke Jostins

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.


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Size matters, and other lessons from medical genetics

23/11/2011
Categories: Journal Club
Written by Joe Pickrell, Jeff Barrett, Daniel MacArthur, and Luke Jostins

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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Debating the future of genome sequencing in medicine

21/10/2011
Categories: ELSI
Written by Luke Jostins

This is a cross-post from my more technical blog, Genetic Inference. However, I thought that it might be of interest to non-specialists who like to keep up with the ongoing debates about the role of genomics in health and medicine.

Last week many of us at Genomes Unzipped (along with over 7000 other geneticists) were at the International Congress of Human Genetics in Montreal. A highlight of the meeting was a large debate entitled “Current and Emerging Sequencing Technologies: Changing the Practice of Medical Genetics”. The panel and the audience were both packed with research scientists, clinicians and industry researchers (you can see the full list of panel participants here), and as you’d expect the discussion was at times pretty lively.

Different perspectives

Joris Veltman described his exome sequencing of 500 individuals with intractable disease, and noted that there has been much success, and very little evidence of harm. Ségolène Aymé mentioned NIH targts that hope to see almost all genetic diseases diagnosed by 2020, and new treatments for rare diseases to be developed simultaneously. There seemed to be a solid consensus across the panel that sequencing should be rolled out as a standard tool in the diagnosis of genetic diseases, provided that the approach is a targeted one, restricted to finding the pathogenic mutation(s) causing the disease.

More controversial was the role of sequencing of healthy individuals, and the general return of data to patients or doctors for any reason other than directly diagnosing a genetic disease. Rade Drmanac, chief scientific officer of Complete Genomics, was obviously strongly in favour of everyone having their genome sequenced, and made it clear that Complete Genomics intends to start offering sequencing to doctors in the future. In his vision, genomes are sequenced at birth, and an initial analysis of immediately actionable results (e.g. potential genetic diseases) is passed to the doctor and patient, with further analyses being carried out if and when they are required.

Michael Hayden immediately dismissed this as hype. He pointed out how unable the US is to handle medical sequencing, with no good systems of reimbursement, a massive shortage of genetic councilors, and a general lack of training in the medical profession.While more positive in general, Louanne Hudgins also expressed worries about the lack of knowledge of genetics among doctors, with some truly scary examples of MDs failing to understanding even the most basic concepts in genetics.

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Friday Links: Studying association studies, and success at last in psychiatric genetics

07/10/2011
Categories: Friday Links
Written by Luke Jostins

In PLoS Genetics this week there is a viewpoint article on data sharing in disease genetics. The authors systematically looked at 643 genome-wide association studies published between 2002 and 2010, to see how easily available the results of the studies are now. They found that the availability of full study results has gone down over time, and many groups that do share data have put more restrictions in place on its use. They put this down to fears over the privacy of research subjects, and in particular to the Homer et al study. The Homer et al result is somewhat complicated, but in essence it says that if you have stolen someone’s genotype data, you can use it to figure out if they have participated in any given research study by looking at the full results of the study.

It certainly seems possible that worries about privacy are reducing the free flow of information within the research community. However, whether on balance the decrease in information flow is worth the increase in security is an open question. For my own view, I feel that having the genome-wide results of genome-wide association studies freely available is very important to the field, and is more important than the the rather esoteric risk of someone stealing someone’s DNA and using it to figure out that they once took part in a research study of inflammatory bowel disease. [LJ]

Genome-wide association studies have been hugely successful in identifying dozens of common genetic risk factors for a large number of common diseases. However, one area that GWAS has not had much success in is the field of psychiatric illness, where finding common risk factors that replicate across studies has been consistently difficult. However, it looks like this is starting to change. The current issue of Nature Genetics has two papers from the Psychiatric GWAS Consortium, detailing some of the largest meta-analyses of schizophrenia and bipolar disease ever published.

The schizophrenia study robustly replicated two previously implicated variants, and discovered five new ones, and the bipolar disease study replicated one and discovered a new one. The new variants give us some pretty startling insights into the genetics of the diseases, in particular revealing the importance of a non-coding gene micro-RNA 137 in regulating a wide range of genes expressed in neurons. As always, these variants explain only a small proportion of the total genetic effect, but they show that psychiatric genetics has now truly entered the GWAS arena, with all the scientific benefits that this can bring to medical research. [LJ]

The images above, in order, are taken from the paper Temporal Trends in Results Availability from Genome-Wide Association Studies, and from Wikimedia Commons.

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Genetic risk prediction in complex disease

26/08/2011
Categories: Disease Risk and Journal Club
Written by Jeff Barrett and Luke Jostins

I thought I’d point out a review article in Human Molecular Genetics that just came out in (open access) preprint form by Luke and myself on genetic risk prediction in complex disease. In it we discuss some of the strengths and weaknesses of genetic and risk prediction compared to classical epidemiological predictors, different statistical modelling considerations, and the effect of GWAS on prediction. Readers of this space might find the conclusion of some interest, where we consider some of the societal aspects of trying to bring the interpretation of genomes into mainstream medical practice.


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Friday Links: New genes for multiple sclerosis, and a new list of DTC genomics companies

12/08/2011
Categories: Uncategorized
Written by Luke Jostins and Daniel MacArthur

This week sees the publication of a large study of the genetics of multiple sclerosis. A consortium of 23 research groups gathered together data on nearly 10,000 MS suffers, and discovered 29 new genetic variants that contribute to disease risk. Overall, genetic variants for MS can now explain around 20% of the overall heritability of the disease, and these genetic variants highlight pathways that are likely to be important in the disease (such as T-helper-cell differentiation). Notably, this study is published in Nature, which is pretty rare for genome-wide association studies such as this. Perhaps related to this is the wonderful degree of detail included in the figures, such as in the ancestry plots of individuals in the study (see left). It is also surprisingly readable, containing just 4 pages of main article, with the nitty-gritty relegated to 100 pages of supplemental text. [LJ]

The Genetics and Public Policy Center have released an updated version of their list of direct-to-consumer genetic testing companies. You can view the list as a rather user-unfriendly massive PDF matrix of companies versus diseases tested here. The list is certainly not as useful as it could be – for instance, there are no indications of test price or quality, and whole-genome sequencing companies are shown as not testing for any disease, rather than (effectively) testing for all diseases – but it would be a good starting point for a crowd-sourcing project to produce a more comprehensive database. Hmmm… [DM]


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A case study in personal genomics

29/06/2011
Categories: Disease Risk
Written by Luke Jostins

I have no strong family history of any disease, despite having 7 blood aunts and uncles and countless cousins. So when I sent my spit off to 23andMe at the start of the Genomes Unzipped project, I was expecting something very similar to Caroline’s experience: a 5% increase in risk here, a 2% decrease in risk there, nothing that would really tell my anything about my health.

However, this was not my experience. Along with a pretty interesting Y haplogroup, I also had three unexpected and potentially worrying health results. I am a cystic fibrosis carrier, a hemochromatosis compound heterozygote, and have a strongly elevated risk of age-related macular degeneration. This cocktail of genetic disease certainly was not what I came to the test expecting!

After some thinking, I decided to take my test results to my GP, and see if there was any advice or testing he would recommend. In the end, my GP referred me to a clinical geneticist, which started a cascade of appointments which in turn led to a number of important changes in how I treat my own health.

What was most interesting is how the whole experience got me thinking about my health as something I am in charge of. I have since made a number of important life-style changes, some of them directly related to my genotyping results, some more generally to improve my overall health.

The point of this post is just to go through some of the experiences, what I have learned about specific conditions, and what changes I have made to my life since. In some sense, I feel like my experience is a case-study in what good outcomes can come from personal genomics, both for specific conditions, and more generally for how genetic data can change your own approach to your health.

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DTC Genetic Testing and the FDA: is there an end in sight to the regulatory uncertainty?

16/06/2011
Categories: Law and Regulation and Opinion
Written by Dan Vorhaus, Daniel MacArthur, and Luke Jostins

Disclaimer: Genomes Unzipped received 12 free kits from Lumigenix for review purposes, and Dan Vorhaus has provided legal advice to the company. We plan to release a full review of the Lumigenix service in early July.

Last month three direct-to-consumer (DTC) genetic testing companies opened their mailboxes to find a slightly ominous but entirely expected letter from the FDA. The three recipients (Lumigenix, American International Biotechnology Services and Precision Quality DNA) received substantively equivalent letters, with the FDA warning each company that its genetic testing service “appears to meet the definition of a device as that term is defined in section 201(h) of the Federal Food Drug and Cosmetic Act,” and that the agency would like to meet with company representatives “to discuss whether the service [they] are promoting requires review by FDA and what information [they] would need to submit in order for [their] product to be legally marketed.”

Translated from bureaucratese, that means that the FDA views these services as ones that may need to be formally reviewed by the agency and either approved or cleared before they can be legally sold. The FDA letter asks each company to describe its service and to explain either (1) why it does not require FDA approval or (2) how the company plans to pursue such approval.

This is a strategy that the FDA has pursued with a growing cadre of DTC service providers. These letters (currently 23 and counting1) represent the only public and company-specific actions the agency has taken to date with respect to DTC genetic testing. While many DTC letter recipients are engaged in dialogue with the FDA, those conversations have occurred beyond the public’s view. Until now.

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Genetics of CF severity, a survey of DTC customers and the value of a genetic diagnosis

27/05/2011
Categories: Friday Links
Written by Luke Jostins

In this week’s Nature Genetics there is a genome-wide association study of lung disease severity in cystic fibrosis suffers (or at least the subset who carry the ΔF508 mutation). The authors report a number of variants with “suggestive evidence”, and one with genome-wide significant evidence . The one genome-wide significant variant is rs12793173, with the C allele increasing the severity of lung disease. The variant is downstream of the gene EHF, which is also believed to play a role in asthma; the hope is that the function of this gene may shed some light on what causes variation in CF severity. As a CF mutation carrier with a CC genotype, any children that I have would be at a at slightly increased of having worse lung function. However, the variant only explains 1-2% of variation in lung function, so I won’t be worrying too much. [LJ]

A reader got in touch with us to inform of research he is doing into the response of personal genomics customers to genetic information. He is looking for users and potential users of DTC genetic tests to fill out a survey; you can find the survey here. If you have the time, this would be worth doing. Arguments about DTC genetics are too often based on hypotheticals or guesses, but there is a rapidly growing field that looks at how individuals really response to genetic data. This sort of data is exactly what is needed to make sensible decisions about the impact of DTC genetics on society. [LJ]

Continuing her series of interviews with people who have taken genetic tests, Elaine Westwick interviewed Jane Gregory, the mother of a child with a complex developmental disorder that was finally diagnosed by a genetic test. The interview raises a lot of the classical issues that you often see in clinical genetics cases, including the power of finding a genetic cause, even when knowing the cause doesn’t add any new treatment options. Well worth reading. [LJ]

The image at the top of the post, “65 Roses”, was made by Tanya Dawn


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