ACMG guidelines on IFs – responding to the response…

StethCollage1The ongoing debate about whether, what, when and how to feedback incidental findings (IFs) from whole genome sequencing continues to rage on both sides of the Atlantic following the American College of Medical Genetics and Genomics’ controversial recommendations on reporting IFs released last month. In an unexpected twist, the authors of the guidance have now written “a clarification” in response to the many criticisms that have been raised including here on GenomesUnzipped. The clarification covers five points – autonomy, children, labs, communication and interpretation.

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Identification of genomic regions shared between distant relatives

This is a guest post by Graham Coop and Peter Ralph, cross-posted from the Coop Lab website.

We’ve been addressing some of the FAQs on topics arising from our paper on the geography of recent genetic genealogy in Europe (PLOS Biology). We wanted to write one on shared genetic material in personal genomics data but it got a little long, and so we are posting it as its own blog post.

Personal genomics companies that type SNPs genome-wide can identify blocks of shared genetic material between people in their databases, offering the chance to identify distant relatives. Finding a connection to someone else who is an unknown relative is exciting, whether you do this through your family tree or through personal genomics (we’ve both pored over our 23&me results a bunch). However, given the fact that nearly everyone in Europe is related to nearly everyone else over the past 1000 years (see our recent paper and FAQs), and likely everyone in the world is related over the past ~3000 years, how should you interpret that genetic connection?

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Why predicting the phenotypic effect of mutations is hard

shutterstock_12830056By now, we’re probably all  familiar with Niels Bohr’s famous quote that “prediction is very difficult, especially about the future”. Although Bohr’s experience was largely in quantum physics, the same problem is true in human genetics. Despite a plethora of genetic variants associated with disease – with frequencies ranging from ultra-rare to commonplace, and effects ranging from protective to catastrophic – variants where we can accurately predict the severity, onset and clinical implications are still few and far between. Phenotypic heterogeneity is the norm even for many rare Mendelian variants, and despite the heritable nature of many common diseases, genomic prediction is rarely good enough to be clinically useful.

The breadth of genomic complexity was really brought home to me a few weeks ago while listening to a range of fascinating talks at the Genomic Disorders 2013 conference. Set against a policy backdrop that includes the recent ACMG guidelines recommending opportunistic screening of 57 genes, and ongoing rumblings in the UK about the 100,000 NHS genomes, the lack of predictability in genomic medicine is rather sobering. For certain genes and diseases, we can or will be able to make accurate and clinically useful predictions; but for many, we can’t and won’t. So what’s the problem? In short, context matters – genomic, environmental and phenotypic. Here are six reasons why genomic prediction is hard, all of which were covered by one or more speakers at Genomic Disorders (I recommend reading to the end – the last one on the list is rather surprising!):

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No choice for you

Guest Co-Author: Dr Anna Middleton is an Ethics Researcher and Registered Genetic Counsellor, based at the Wellcome Trust Sanger Institute, UK.

StethCollage1The American College of Medical Genetics (ACMG) has recently published recommendations for reporting incidental findings (IFs) in clinical exome and genome sequencing. These advocate actively searching for a set of specific IFs unrelated to the condition under study. For example, a two year old child may have her (and her parents’) exome sequenced to explore a diagnosis for intellectual disability and at the same time will be tested for a series of cancer and cardiac genetic variants. The ACMG feel it is unethical not to look for a series of incidental conditions while the genome is being interrogated, conditions that the patient or their family may be able to take steps to prevent. This flies in the face of multiple International guidelines that advise against testing children for adult onset conditions. The ACMG justify this as “a fiduciary duty to prevent harm by warning patients and their families”. They conclude that “this principle supersedes concerns about autonomy”, i.e. the duty of the clinician to perform opportunistic screening outweighs the patients right not to know about other genetic conditions and their right to be able to make autonomous decisions about testing.

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Henrietta Lacks’s genome sequence has been publicly available for years

Last week, scientists at the European Molecular Biology Laboratory reported that they had sequenced the genome of the Henrietta Lacks, or “HeLa”, cell line. This report was met with considerable consternation by those who (justifiably, in my opinion) wondered why scientists are still experimenting on a cell line obtained without consent in the 1950s [1]. In response to a bit of a backlash, the researchers removed the HeLa sequence from the public internet, and even the paper itself might disappear from the formal scientific literature.

However, it is unfair to treat the authors of this paper as scapegoats for the systematic failure of scientists to deal with issues surrounding genomic “privacy”. Consider this important piece of information: the genome sequence of the HeLa cell line has been publicly available for years (and remains so).
hela

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Do we have an obligation to look?

One of the major bioethical debates in clinical genetics and genomics research is the issue of what to do with incidental or secondary findings (IFs) unrelated to the original clinical or research question. Every genome contains thousands of rare variants, including a surprising number of loss of function variants, as well as hundreds of variants associated with common disease and dozens linked with recessive conditions. As whole genome or exome sequencing is used more routinely in non-anonymised cohorts – such as the 100,000 patient genomes to be sequenced by the UK NHS – these variants will be uncovered and linked to an increasing number of individuals. What should we do with them?

Robert Green of Brigham and Women’s Hospital in Boston, who co-chairs the American College of Medical Genetics (ACMG) working group on secondary findings, was quoted in a Nature blog last year saying, “we don’t think it’s going to be a sustainable strategy for the evolving practice of genomic medicine to ignore secondary findings of medical importance”. But just saying it doesn’t make it so. There are still numerous questions that need to be addressed – you can be part of the debate by participating in the Sanger Institute’s Genomethics survey.

Questionnaire web

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Learning more from your 23andMe results with Imputation

PeterAndEliana This is a guest post by Peter Cheng and Eliana Hechter from the University of California, Berkeley.

Suppose that you’ve had your DNA genotyped by 23andMe or some other DTC genetic testing company. Then an article shows up in your morning newspaper or journal (like this one) and suddenly there’s an additional variant you want to know about. You check your raw genotypes file to see if the variant is present on the chip, but it isn’t! So what next? [Note: the most recent 23andMe chip does include this variant, although older versions of their chip do not.]

Genotype imputation is a process used for predicting, or “imputing”, genotypes that are not assayed by a genotyping chip. The process compares the genotyped data from a chip (e.g. your 23andMe results) with a reference panel of genomes (supplied by big genome projects like the 1000 Genomes or HapMap projects) in order to make predictions about variants that aren’t on the chip. If you want a technical review of imputation (and the program IMPUTE in particular), we recommend Marchini & Howie’s 2010 Nature Reviews Genetics article. However, the following figure provides an intuitive understanding of the process.

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£100M for whole patient genomes – an implementation challenge

The recent announcement that the UK Government has earmarked £100 million to “sequence 100,000 whole genomes of NHS patients at diagnostic quality over the next three to five years” raises a number of questions, with which the Department of Health are no doubt grappling as I write. I’ve previously discussed the thorny issue of using targeted versus whole genome sequencing to maximize diagnostic yield and benefit patients. However, one of the great achievements of next generation sequencing technologies is to make the assay – actually sequencing genome (or some portion of it) – one of the easier parts of clinical genomics. Although laboratories will have to be suitably equipped, staffed and flexibly managed to deal with high sample throughput and ever changing scientific specifications, the biggest challenge will be to implement genomic knowledge in the clinic.

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£100M for whole patient genomes – revolutionising genetic diagnostics or squandering NHS cash?

On 10th December 2012, UK Prime Minister David Cameron launched a Report on the Strategy for UK Life Sciences One Year On by announcing that the Government has earmarked £100 million to “sequence 100,000 whole genomes of NHS patients at diagnostic quality over the next three to five years”. This ambitious initiative – which will focus initially on cancer, rare diseases and infectious diseases – aims to train a new generation of genetic scientists, stimulate the UK life sciences industry and “revolutionise” patient care.

There is no doubt that this investment offers a major opportunity for the UK to firmly establish itself as a world-leader in medical genomics. However, deciding how best to use the £100M to maximise patient benefit will be a challenge. There are numerous implementation issues, outlined in the PHG Foundation’s response to the announcement. Not least of these is the urgent need for informatics provision to facilitate storage, processing, annotation, interpretation and secure access to both genomic and phenotypic data. This will involve determining appropriate ethical and operational standards across a broad range of questions.

But there is one particularly crucial question that needs to be answered early on: what is the most appropriate assay to use for clinical implementation? All the literature released by the Government, and quoted extensively by the press, states quite categorically that the money will be used for “sequencing whole genomes”. Surely this can’t really be true? (I certainly hope it’s just coincidence that if you multiply a £1000 genome by 100,000 patients you reach the magic figure of £100 million…) If it is the case, there are several major problems.

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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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