Genomics England and the 100,000 genomes

Genomics-England-logo21The UK’s ambitious plan to sequence 100,000 whole genomes of NHS patients over the next 3-5 years, announced by the UK Prime Minister in December last year, sparked interest and curiosity throughout the UK genetics community. Undeterred by the enormity of the task, a new company, Genomics England Limited (GeL), was set up in June of this year by the Department of Health, tasked with delivering the UK100K genome project. Yesterday, they held what I’m sure will be the first of many ‘Town Hall’ engagement events, to inform and consult clinicians, scientists, patients and the public on their nascent plans.

So what did we learn? First, let’s be clear on the aims. GeL’s remit is to deliver 100,000 whole genome sequences of NHS patients by the end of 2017. No fewer patients, no less sequence. At its peak, GeL will produce 30,000 whole genome sequences per year. There’s no getting away from the fact that this is an extremely ambitious plan! But fortunately, the key people at GeL are under no illusions about the fact that theirs is a near impossible task. Continue reading ‘Genomics England and the 100,000 genomes’

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Uncovering functional variation in humans by genome and transcriptome sequencing

Tuuli_chamonix2_croppedDr. Tuuli Lappalainen is a postdoctoral researcher at Stanford University, where she works on functional genetic variation in human populations and specializes in population-scale RNA-sequencing. She kindly agreed to write a guest post on her recent publication in Nature, “Uncovering functional variation in humans by genome and transcriptome sequencing”, which describes work done while she was at the University of Geneva. -DM

In a paper published online today in Nature we describe results of the largest RNA-sequencing study of multiple human populations to date, and provide a comprehensive map of how genetic variation affects the transcriptome. This was achieved by RNA-sequencing of individuals that are part of the 1000 Genomes sample set, thus adding a functional dimension to the most important catalogue of human genomes. In this blog post, I will discuss how our findings shed light on genetic associations to disease.

As genome-wide studies are providing an increasingly comprehensive catalog of genetic variants that predispose to various diseases, we are faced with a huge challenge: what do these variants actually do in the cell? Understanding the biological mechanisms underlying diseases is essential to develop interventions, but traditional molecular biology follow-up is not really feasible for the thousands of discovered GWAS loci. Thus, we need high-throughput approaches for measuring genetic effects at the cellular level, which is an intermediate between the genome and the disease. The cellular trait most amenable for such analysis is the transcriptome, which we can now measure reliably and robustly by RNA-sequencing (as shown by our companion paper in Nature Biotechnology).

In this project, several European institutes of the Geuvadis Consortium sequenced mRNA and small RNA from lymphoblast cell lines from 465 individuals that are in the 1000 Genomes sample set. The idea of gene expression analysis of genetic reference samples is not new (see e.g. papers by Stranger et al., Pickrell et al. and Montgomery et al.), but the bigger scale and better quality enables discovery of exciting new biology.
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Further reflections on genomic screening

Scales_of_justiceNow the dust has settled, I’ve been reflecting on the controversial recommendation from the American College of Medical Genetics and Genomics (ACMG) that all clinical genomes should be screened for a specific set of conditions. Following the release of the guidelines, the European Society of Human Genetics (ESHG) published its more conservative recommendations, and vigorous debate has continued internationally regarding the wisdom of introducing genomic screening. While I still have some major reservations about the policy (outlined in previous posts), upon reflection there are certainly things some aspects that make a lot of sense…

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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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