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

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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Photo of Max, who died aged four months from Ogden syndrome. Posted with permission from his family.

I have just published in Nature a commentary discussing the need to bring exome and genome sequencing into the clinical arena, so that these data are generated with the same rigorous clinical standards as for any other clinical test. This way, we can then easily return at least medically actionable results to research participants. In this day and age of consumer and patient empowerment, I can also see eventually returning all data, including the raw data, to any interested participants, as this can then promote crowd-sourcing for data analysis, with research participants controlling and promoting the relative privacy of and analysis of their own data.

As I described in my commentary, my thinking on this matter was prompted mainly by Max  (see picture) and his family. The obituary for Max can be found here, and that of his cousin, Sutter, here. We described their condition here, and we named this new disease Ogden Syndrome in honor of where the first family lives. I am now trying to think about and discuss the human aspects of and lessons from this story. My thinking has also been influenced somewhat by the late James Neel, who wrote a very thought-provoking book called Physician to the Gene Pool.

To me, it was deeply disconcerting that I could not officially return any results to this family (or to another family in a different project discussed here) even when the papers describing the genetic basis of their disease were published, as this was considered “research” and was not performed in a clinically appropriate (CLIA-certified) manner. This was all the more painful when one of the sisters in the Ogden family became pregnant and asked me what I knew. I cannot predict whether it would have helped or hurt this woman to learn during her pregnancy that she was indeed a carrier of the mutation, with the associated 50% risk of her baby boy having the disease. I also do not know if she would have undergone any genetic testing via amniocentesis of the fetus prior to birth (with the associated ~1% risk of miscarriage from the procedure), nor do I know what decisions she might have made prior to the birth even if she had undergone such testing. All in all, it was certainly an ethical and moral dilemma for me not to be able to return the research result to her, given that the results were not obtained in a CLIA-certified manner. It is still an issue, as there are even now financial and systematic barriers for getting all women in the family tested with a CLIA-certified gene test for NAA10 (which was developed over a six month period by ARUP Laboratories). It would have been so much better if we had just done the entire sequencing up front in a CLIA-certified manner.

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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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Dr Anna Middleton is an Ethics Researcher and Registered Genetic Counsellor, based at the Wellcome Trust Sanger Institute. She leads the ethics component of the Deciphering Developmental Disorders study, a collaborative project involving WTSI and the 23 National Health Service Regional Clinical Genetics Services in the UK. This project involves searching for the genetic cause of developmental disorders, using array-CGH, SNP genotyping and exome sequencing, in ~12,000 children in the UK who currently have no genetic diagnosis.

One of the issues raised by this, and many other research projects, is what should happen to ‘incidental’ findings, i.e. potentially interesting results from genomic analyses that are not directly related to the condition under study.  Here Anna discusses the research she is conducting on this topic as part of the DDD study, and provides a link to the DDD Genomethics survey where you can share your own views (I should also disclose here that both Caroline and I also work on the DDD study).[KIM]

Whole genome studies have the ability to produce enormous volumes of valuable data for individuals who take part in research. However, as a consequence of analysing all 20,000+ genes, whole genome studies unavoidably involve the discovery of health related information that may have actual clinical significance for the research participant.  Some of this will be considered a ‘pertinent finding’, i.e. directly related to the phenotype under study (e.g. the child’s developmental disorder); some of this will be considered an ‘incidental or secondary finding’ in that it is not directly linked to the phenotype under study or the research question that the genomic researchers are trying to answer.

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One of my research area is the diagnosis of rare genetic conditions and in that context I collaborate with David Kelsell at Queen Mary University of London. One of the interesting cases we have analysed recently is an extremely rare condition in which the patients suffer severe chronic skin and bowel inflammation. I was in charge of the analysis of the sequence data, so for readers interested in the technical aspects here is a short overview: this was a sequencing project of pooled DNA (from three samples, only one of them relevant to this study), using a capture array to enrich for regions of the genome showing evidence of being linked to the disease. I first came across a 4 bp deletion in the ADAM17 gene, and it was rapidly identified by David Kelsell and his team as a likely cause of the disorder. Further functional work confirmed that this variant is almost certainly the disease-causing mutation.

This study was published last year and an interesting aspect is that the individual in whom this mutation was first seen is now 19 years old, doing well, and entering his second year at the University of Cambridge. Daniel MacArthur and myself met with him to discuss his thoughts and feelings about the whole experience.
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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.

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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It’s a truism that resonates across science: Size matters when doing and interpreting the statistical (and practical) meaning of a study. But the size of what? Well, it’s quite a few things—all of which are very important in understanding what a study is ultimately telling us. One of the first numbers researchers focus on is the p-value. The p-value relies on a bit of counterintuitive logic: It represents the percentage of times you would get an effect as big as you got (or bigger) if there is really no effect in the general population. So we first assume that there is really no difference in some outcome between two groups across the general population (we call this the null hypothesis), and then we ask what are the chances of us finding the difference that we found (or bigger) given this assumption. If this percentage is low (many fields adopt a p = .05 standard, or a 5% chance that we’d get the effect we got or bigger if there is really no effect in the general population), then we can reject the initial idea that there is no difference in the general population. So what have we learned if the p-value is .05 or lower? That there is likely a difference in the general population—how big this difference is remains a mystery, however; the p-value never answers that question.

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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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