Author Archive for Daniel MacArthur

Genome interpretation costs will not spiral out of control

Mo' genomes, mo' money?

An article in Genetic Engineering & Biotechnology News argues that as the cost of genome sequencing decreases, the cost of analysing the resulting data will balloon to extraordinary levels. Here is the crux of the argument:

We predict that in the future a large sum of money will be invested in recruiting highly trained and skilled personnel for data handling and downstream analysis. Various physicians, bioinformaticians, biologists, statisticians, geneticists, and scientific researchers will be required for genomic interpretation due to the ever increasing data.

Hence, for cost estimation, it is assumed that at least one bioinformatician (at $75,000), physician (at $110,000), biologist ($72,000), statistician ($70,000), geneticist ($90,000), and a technician ($30,000) will be required for interpretation of one genome. The number of technicians required in the future will decrease as processes are predicted to be automated. Also the bioinformatics software costs will plummet due to the decrease in computing costs as per Moore’s law.

Thus, the cost in 2011 for data handling and downstream processing is $285,000 per genome as compared to $517,000 per genome in 2017. These costs are calculated by tallying salaries of each person involved as well as the software costs.

These numbers would be seriously bad news for the future of genomic medicine, if they were even remotely connected with reality. Fortunately this is not the case. In fact this article (and other alarmist pieces on the “$1000 genome, $1M interpretation” theme) wildly overstate the economic challenges of genomic interpretation.

Since this meme appears to be growing in popularity, it’s worth pointing out why genome analysis costs will go down rather than up over time:
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All genomes are dysfunctional: broken genes in healthy individuals

Breakdown of the number of loss-of-function variants in a "typical" genome

I don’t normally blog here about my own research, but I’m making an exception for this paper. There are a few reasons to single this paper out: firstly, it’s in Science (!); and secondly, no fewer than five Genomes Unzipped members (me, Luke, Joe, Don and Jeff) are co-authors. For me it also represents the culmination of a fantastic postdoc position at the Wellcome Trust Sanger Institute (for those who haven’t heard on Twitter, I’ll be starting up a new research group at Massachusetts General Hospital in Boston next month).

Readers who don’t have a Science subscription can access a pre-formatted version of the manuscript here. In this post I wanted to give a brief overview of the study and then highlight what I see as some of the interesting messages that emerged from it.

First, some background

This is a project some three years in the making – the idea behind it was first conceived by my Sanger colleague Bryndis Yngvadottir and I back in 2009, and it subsequently expanded into a very productive collaboration with several groups, most notably Mark Gerstein’s group at Yale University, and the HAVANA gene annotation team at the Sanger Institute.

The idea is very simple. We’re interested in loss-of-function (LoF) variants – genetic changes that are predicted to be seriously disruptive to the function of protein-coding genes. These come in many forms, ranging from a single base change that creates a premature stop codon in the middle of a gene, all the way up to massive deletions that remove one or more genes completely. These types of DNA changes have long been of interest to geneticists, because they’re known to play a major role in really serious diseases like cystic fibrosis and muscular dystrophy.

But there’s also another reason that they’re interesting, which is more surprising: every complete human genome sequenced to date, including celebrities like James Watson and Craig Venter, has appeared to carry hundreds of these LoF variants. If those variants were all real, that would indicate a surprising degree of redundancy in the human genome. But the problem is we don’t actually know how many of these variants are real – no-one has ever taken a really careful look at them on a genome-wide scale.
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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.

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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On bad genetics reporting

This short article on the Independent’s website may not be the worst piece of genetics reporting ever, but given its brevity it may well take a new record for the density of errors and misconceptions. (To save you the trouble of hunting down the article it’s actually referring to, which of course is not linked, it’s this online article in Molecular Psychiatry).

Let’s start with the headline:

Sleeping is all in the genes

No. Data from twin studies suggest that the length of time people sleep for is around 44% heritable – that is, around 44% of the variation in this trait is due to inherited (and presumably mostly genetic) factors. The article being discussed in the piece provides no new information about the heritability of this trait.

Scientists have found the reason why some people need more sleep than others lies in their genes.

Scientists have found that one of the reasons people sleep longer than others is possibly a variant in a non-coding region of the gene ABCC9. Even if this association is real (and the evidence in the article is less than compelling), it explains just 5% of the variation in sleep length between people.

A survey of more than 10,000 people …

A survey of 4,251 people found the association between sleep length and the ABCC9 variant. This association was not replicated in a separate set of 5,949 individuals. The authors have a potential explanation for this lack of replication (based on the season in which the sleep length measurements were collected), and then did a post hoc re-analysis of their combined sample accounting for season that produced positive results.

showed those carrying the gene ABCC9, present in one in five of us,

The gene ABCC9 is present in all of us (hell, it’s even present in fruitflies). However, there is a genetic variation in one region of the ABCC9 gene, and one version of this variation is present in 17.3% of Europeans.
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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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Going green: lessons from plant genomics for human sequencing studies

This is a guest post by Jeffrey Rosenfeld. Jeff is a next-generation sequencing advisor in the High Performance and Research Computing group at the University of Medicine and Dentistry of New Jersey, working on a variety of human and microbial genetics projects. He is also a Visiting Scientist at the American Museum of Natural History where he focuses on whole-genome phylogenetics. He was trained at the University of Pennsylvania, New York University and Cold Spring Harbor Laboratory.

As human geneticists, it is all too easy to ignore papers published about non-human organisms – especially when those organisms are plants. After all, how much can the analysis of (say) Arabidopsis genome diversity possibly assist in my quest to better understand the human genome and determine which genes cause disease? Quite a bit, as it happens: a fascinating recent paper in Nature demonstrates a number of lessons that we can learn from our distant green relatives.

By exploiting the small genome size of Arabidopsis (~120 million bases, compared to the relatively gargantuan 3 billion bases of Homo sapiens), researchers were able to perform complete genome sequencing and transcriptome profiling in 18 different ecotypes of the plant (similar to what we would call strains of an animal).

In a normal genome re-sequencing experiment, the procedure is to obtain DNA from an individual, sequence the DNA, align it to a reference sequence and then to call variants (i.e. differences from the reference). This approach is used by the 1000 Genomes Project and basically all of the hundreds of disease-focused human sequencing projects currently underway around the world. This approach allows researchers to relatively easily identify single-base substitution (SNP) and small insertion/deletion (indel) differences between genomes. However, the amount of variability that can be identified is restricted by the use of a reference: regions where there is extreme divergence between the reference and sample genomes are often badly called, and more complex variants (e.g. large, recurrent rearrangements of DNA) can be missed. Additionally, and crucially, sequences that are not present in the reference genome will be completely missed by this approach.
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Results of Nature poll on personal genetic analysis by scientists

Last month we pointed to a poll over at Nature looking primarily at the use of personal genetic tests among scientists (Nature‘s Brendan Maher was kind enough to consult us when designing the poll, so we were able to pass on some of the lessons we learned when doing our own reader survey last year). The results are now in, and Brendan has a brief article taking a look at the results.

Firstly, there was a fantastic response to the survey – nearly 1,600 participants. Of those, 289 (18%) had taken some kind of genetic test; interestingly, a further 54% said they’d be interested in doing so if given the opportunity. The vast majority of genetic tests done were genome scans (50% 23andMe). The motivations of those who had tests done were very similar to those from our readers – intellectual curiosity ranked at the top, with interest in health, genealogy and ancestry ranking lower.

Brendan’s piece has some nice vignettes from survey respondents. He was also kind enough to pass on the raw (anonymised) data to us for further analysis, and we’ll be poking around in there over the next week or so. Some immediately interesting results emerge from the comparison of the results from participants who fell into the “biology” vs “medicine” discipline: of those who had taken genetic tests, biologists were far more likely to have been tested by 23andMe (73.3% vs 47.2%) and were more likely to have cited “intellectual curiosity” as a major factor in their decision (71.1% vs 42.6%), whereas “medicine” respondents were more likely to cite a specific health risk as a major factor (34.4% vs 18.7%), were more likely to have consulted a clinician beforehand (23.0% vs 7.8%) and were more likely to report negative outcomes to testing (8.2% vs 4.8%). Of those who hadn’t yet had a genetic test done, biologists were more likely to be interested in doing so if given the opportunity (74.4% vs 67.6%). Nothing terribly shocking, but some useful insight into the basis of the “culture war” between basic and medical researchers over the issues surrounding personal genomics.

Anyway, kudos to Brendan and the Nature team – and to their readers, of course – for generating such an interesting data-set. No doubt you’ll be hearing more about the results of this survey soon.

Complete Genomics to sequence 1500 whole genomes for pre-term birth study

Genome sequencing provider Complete Genomics has announced a deal with the non-profit Inova Translational Medicine Institute, under which the company would sequence 1,500 complete human genomes to help explore the genetic basis of premature birth.

The Inova collaboration is one of many large-scale genome sequencing studies currently being planned and performed around the world. In some respects the study is actually quite a small one – only 250 “cases” (i.e. premature babies) are being sequenced, along with 250 normal-term control babies, which means the researchers will have low statistical power by the standards of modern genomics. However, sequencing this number of complete genomes to high depth is (as far as I know) unprecedented, and the inclusion of the parents of all of the children in the study will provide the team with the ability to do some very interesting analyses – for instance, looking at “de novo” mutations that arise in the babies but weren’t present in either parent, as well as exploring potential effects of the maternal genome. Maternal genetics are known to be important in determining the risk of premature birth: girls born prematurely have a higher risk of delivering a pre-term baby themselves (with twin studies suggesting between 15 and 40% of the risk is heritable), while paternal genes seem to have almost no effect.
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Nature poll for scientists on personal genomic analysis

Brendan Maher from Nature has alerted us to a new poll that may well be of interest to many of our readers – an investigation of the attitudes of researchers towards personal genetic testing. Here’s the summary:

We aim to get a sense of how many researchers are actually peering into their own genomes. The responses will be analysed in aggregate to aid in a news story, and no identifying individual information will be used unless you agree to be contacted by a reporter. At the end of the survey you will have the chance to enter our prize draw to win a £100/$150 Amazon gift card.

You can fill in the survey here.

Direct-to-consumer genetic test results in a clinical setting: a case report

Dr Neeta Tailor is an anaesthetist working at the Royal Gwent Hospital in Wales. Dr Tailor recently treated a friend of Genomes Unzipped members (referred to here as Patient X) who required emergency surgery following some unusual and fairly horrible complications (believe me, I’ve seen the photos!) from wisdom tooth removal. The remarkable thing about this case: prior to surgery the patient volunteered information about her potential drug responses based on her 23andMe profile, including variation in one gene that could have had a profound effect on her response to a standard muscle relaxant. Dr Tailor kindly agreed to write up her experience in this guest post.

For those interested in the genetic details: Patient X’s 23andMe results suggest she is heterozygous for the rs1799807 SNP, which induces an aspartate to glycine change in the BCHE gene and is associated with a substantially prolonged apnea (loss of breathing) following administration of succinylcholine. This is one of three separate mutations in the BCHE gene tested by 23andMe. Although in this case the clinicians had already decided independently to avoid the use of succinylcholine, it’s intriguing to think about how rapidly this type of information could become useful to clinicians – and what steps will need to be taken to ensure DTC genetic testing results are trustworthy enough to justify their consideration in this kind of emergency setting. [DM]


Anaesthesia is classically described as the pharmacologically induced triad of amnesia (memory loss), analgesia (pain reduction) and the loss of muscle reflexes. Patients usually come across anaesthetists during their pre-operative anaesthetic assessment; we are the ones telling you that our job is to pop you off to sleep, although it is usually more complicated than that!

The patient described below works in the world of genetics and invited me to describe her case in order to illustrate how pharmacogenomics and person specific genetic characteristics may affect the choice of general anaesthesia.

A 37 year old woman (Patient X) was booked onto the emergency theatre list on a Sunday morning. The planned operation was incision and drainage of an infected haematoma in the cheek, an unusual complication which had developed quickly over 48 hours following the extraction of a wisdom tooth by her own dentist. By the time she was admitted to hospital, she had extensive facial swelling, not just of her gum, but also the whole of the left side of her face from her forehead to her neck. In addition, she had reduced jaw movement, as well as limited mouth opening of less than one finger breadth. She was also feeling quite unwell having vomited during the night and her blood tests showed raised markers of infection. She was in pain requiring several different types of analgesia.

This presentation in itself poses some difficulty. One of our jobs as anaesthetists involves administering drugs to cause unconsciousness which subsequently requires maintenance of a patent airway using either a mask, an airway device that sits above the vocal cords, or by a tube in the trachea. We usually then maintain unconsciousness using an inhaled volatile anaesthetic via the chosen device.

During this operation we knew we were going to need to share the airway with our maxillo-facial surgery colleagues performing the procedure. To ensure the optimal outcome for all (an anaesthetised patient for us and access to the mouth for the maxfax team), a tube in the trachea was the most ideal option. However, to get to the trachea, we have to get in the mouth and get a good view of the vocal cords and this is where the potential problem could arise.
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