T2D Nature Paper 2016

Type 2 diabetes is a major global health threat ─ 1 in 10 people either has type 2 diabetes, or is likely to develop it during their life. But we are yet to fully understand what causes the disease and what puts people at risk – limiting the effectiveness of prevention and treatment strategies. We know that lifestyle has a large impact, but our genes also play their part. In a massive genetics study involving 120,000 DNA samples and 300 scientists in 22 countries, we’ve found that in the majority of cases, type 2 diabetes isn’t caused by a single faulty gene unique to an individual and their family. Instead, there are common changes in several genes throughout the population, which combine to lead to disease development.

Human genetics is one of the most powerful ways of understanding the mechanisms that underpin diabetes. By comparing DNA from people with type 2 diabetes and those without, we can highlight fundamental biological differences that have relevance for disease risk. As we currently lack effective preventative strategies and the available treatment options cannot tackle the root cause of disease, these genetic discoveries can point the way towards the development of new approaches. Importantly, such discoveries can help to target those approaches more effectively to the individuals in whom they are most likely to provide benefit, and least likely to cause harm.

New drug discovery avenues

Any pair of individuals will share 99.9% of their DNA. Those 0.1% of differences are what makes each of us different. Some of the differences in the code have no obvious impact on how our body works or what we look like. But other changes alter the way proteins work. We’ve found variations in over a dozen genes that cause changes in proteins that are associated with diabetes. Several of these provide new and important clues about the mechanisms underlying type 2 diabetes. Our results also highlight that diabetes risk is not the same in all countries. PAX4 is a gene involved in the development of the insulin-producing cells in the human pancreas. There’s a really powerful association between a variation in this gene and risk of diabetes, but only in individuals from East Asia (including Korea, China, Singapore). These findings matter because they provide many important new insights into the biology of diabetes: some of the genes and pathways implicated may represent novel avenues for drug development.

“Every unhappy family is unhappy in its own way” or have we all got the same problems to deal with?

The symptoms and consequences of diabetes are complex and the genetics underpinning the disease are no less so. It’s not as simple as ‘THE gene for diabetes found’. There’s a lot of debate about whether most of the genetic differences that influence individual’s predisposition to diabetes are ones that are widely shared within populations, or whether they are more often rare events, specific to an individual and their family. In this study we analysed both rare and common variations in DNA code and we showed that the genetic contribution to diabetes risk lies predominantly at shared sites. This matters because it has implications for the ways in which we will be able to use genetic data to support personalised medicine.

Rare changes can tell us a lot about the causes of diabetes

Although common variations seem to be the major contributor to diabetes, rare genetic changes still reveal huge clues to the processes that lead to diabetes. Here, we show that there is a strong link between rare variations in about 30 genes and type 2 diabetes. Interestingly, these genes are already known to be involved in some rare familial forms of diabetes that mostly start in early life. This matters because it shows that the same genes can harbour DNA sequence differences that result in very different types of diabetes. It also highlights the need for careful interpretation when DNA sequence changes are detected in genes of medical significance, since it will not always be obvious what the impact of the variation is likely to be.

Collaborating to accelerate progress

Because we believe it is important that all researchers can benefit from the data we have generated in this project, data and discoveries are available to researchers and to the wider world through a variety of means. For example, much of the data from this and other studies is available on the freely-accessible T2D genetics portal developed as part of the Accelerating Medicines Partnership (www.type2diabetesgenetics.org).

This study highlights the complexities of diabetes, but also the opportunities. We’ve shown that a genetic predisposition to diabetes is controlled by an unfavourable combination of genetic changes that are common in the general population, rather than each person with diabetes having their own faulty gene. By getting a better handle on these genetic variations and their biological impact, we’ll be able to create a range of treatments that target the causes of diabetes – not just the symptoms – and prescribe them based on genetic profile of disease.

Emma is the Public Engagement and Communications Officer for the Radcliffe Department of Medicine, University of Oxford

Our recent paper in Nature (Fuchsberger et al, published 11 July 2016) is a testament to the power of collaborative research. The samples that were examined came from individuals from 16 countries, with ancestral origins in Europe, Asia, Africa and the Americas. The author list includes over 300 scientists working in 22 countries. Funding to enable the research came from over 60 different sources – governments, charities, institutions – located in 12 countries (plus the EU).

Such collaboration is increasingly the norm in science these days.

A decade or two ago, “big science” on this scale was typical in physics and astronomy, but – outside of the Human Genome Project and a few high-profile endeavors – not in the life sciences. One of the biggest changes in science in my area (broadly genomics and translational medicine) over the past 15 years has been the transformation from research conducted at the level of the single lab, to research which is truly global. Fifteen years ago, there were probably 100 labs around the world chipping away (often in splendid isolation) at the task of uncovering genetic variants influencing type 2 diabetes. Those efforts were fragmented and inefficient; progress was glacial.

Now, almost all of the groups with interests in the discovery of diabetes risk variants have come together to pool resources and data and expertise, and make common cause via consortia such as DIAGRAM. (DIAGRAM is part of a complex ecosystem of related, overlapping and interdigitating consortia that also includes MEDIA, DIAMANTE, SAT2D, AGEN, GoT2D, T2D-GENES, AMP-T2D and others). They have come together because science moves faster this way, and the discoveries we make and the results that we publish are way more robust. Together, we have the power to tell confident stories. Together, we can have far more of the critical discussion, and undertake more substantive validation and replication of findings before publication rather than after.

The risk variants we uncover are “common goods” available to all scientists. They can access those findings through publications, through data sharing [www.diagram-consortium.org], and through web portals [www.type2diabetesgenetics.org]. If they want, they can pull down much of the raw data (from repositories such as dbGAP or EGA). Even when we pool our efforts for discovery, there’s plenty of room for individual groups to express their own identity and interests in the follow-up of these variants, understanding what they can tell us about biology, and how we might use this information clinically.

Increasingly, these consortia are extending beyond the academic domain. Biotech and big pharma has seen the advantages, and now wants to contribute data and funding and expertise to perform joint collaborative research in the precompetitive space (for example, through the Innovative Medicines Initiative in Europe; and the Accelerating Medicines Partnership in the States).

Everyone benefits.

But to make this work, we need the infrastructure to support such international collaboration. A global disease like diabetes needs a global response:  and, from both a scientific and a moral perspective, it’s vital that we ensure that the research that is done is relevant to all those with diabetes, or at risk of it, whether they live in Atlanta or Ahmedabad; in Kobenhavn or Kinshasa.

There are few countries, if any, that can sustain science on this scale on their own. Much science funding comes with a national focus, and, with honourable exceptions (such as the US NIH and the Wellcome Trust) does not easily encourage transnational collaboration.

For those of us in Europe, funding through the European Commission (through vehicles like Framework Programmes, the European Research Council, and the Innovative Medicines Initiative) has been an essential component of that mix of funding. It is one on which we have increasingly come to rely to support the building and implementation of international research, and the free exchange of ideas, expertise, and people across the continent. My own institution (Oxford) receives around 15% of its medical research income from EU grants. Members of my current or recent research group in Oxford have come from over 20 countries including representatives of around half the member states of the EU (see map). The freedom of movement within the EU, and the ability to recruit the brightest and best from wherever, has been essential for the science that we do.

Map of the countries of current or recent McCarthy Group member

Map of the countries of current and recent McCarthy Group members

To give one example. Between 2008 and 2012, I participated and co-led research program called ENGAGE (European Network for Genetic and Genomic Epidemiology). Funded by the EU via Framework VII, and led by my friend and colleague Leena Peltonen, till her untimely death, this project united researchers across European with interests in the genetics of complex diseases like diabetes. The grant started just as the first wave of genome wide association studies (GWAS) had been published and the field was starting to feel its way towards the need for further advances to be based around the aggregation of data through meta-analysis. We weren’t sure how well that was going to work, and there was a lot to be done in developing methods, and harmonising data, as well as dealing with the more sociological aspects (running large distributed consortia, allocating credit). ENGAGE provided the funding (for researchers, for meetings, for research) and the infrastructure that allowed like-minded researchers to come together and solve these challenges. It helped to set the agenda of joint research in this field in Europe, and has spawned a family of additional and subsequent collaborations. In the narrow metrics of publication, ENGAGE contributed to over 150 publications, of which more than half were in the top journals. There’s no doubt that without ENGAGE, we would have been far less able to capitalise on the opportunities available, and that science would have proceeded more slowly.

Much of this is at risk as a result of recent events. For those of us in the UK, there’s now a serious threat to future funding. We may lose direct funding from the EU, and at the same time the economic downturn resulting from Brexit, will likely compromise the funding available through the research councils and UK-based charities. We may lose the chance to engage with large collaborative European programs. Researchers from overseas already in the UK, or those thinking of coming, will be wondering whether the UK is really the kind of place where they and their families can flourish professionally, or personally.

We are already seeing the impact. Despite reassurances that nothing will change till the UK actually leaves the EU (in 2019, maybe), there are already stories about UK researchers sidelined from future EU grant applications (why take the chance of complicating approval of a successful grant?). Potential recruits to the UK are rightly reconsidering their options.

Lest you regard all of this as special pleading on behalf of one section of society, remember how much of our current and future national prosperity is invested in, and predicated upon, the ability to continue to generate advances through science. You don’t build “a knowledge economy” by putting obstacles in the way of the acquisition of knowledge. Nor by making it difficult for the incredibly mobile, outward looking pool of the most talented researchers to see their future in the UK.

Perhaps, wise counsel will prevail. Perhaps – somehow – free movement of labour within the EU, together with low barriers to the recruitment of skilled researchers from beyond, will be maintained, and with it, participation in European funding schemes. Perhaps a government interested in ensuring that science and innovation remains at the heart of future wealth generation will find the motivation to step back from the brink, so that the UK can continue to be the research driver for the continent. Perhaps.

Robert Turner Professor of Diabetic Medicine, Group Head, Wellcome Trust Centre for Human Genetics, Group Head / PI, Grant Holding Senior Scientist, Consultant Physician.

An interview with Mark McCarthy, by Dr Emma O’Brian, Public Engagement and Communications Officer at the Radcliffe Department of Medicine.

Professor McCarthy reflects about the insights revealed by this latest genetic research into Type 2 diabetes, the increasing understanding of role of genetic risk in complex disease, and how this will influence the development of future treatments.

Robert Turner Professor of Diabetic Medicine, Group Head, Wellcome Trust Centre for Human Genetics, Group Head / PI, Grant Holding Senior Scientist, Consultant Physician.

One of the main questions we sought to address in the paper which we recently published in Nature [1] relates to the relative contribution of common (technically speaking, those with an allele frequency over 5%) as opposed to lower-frequency alleles with respect to  predisposition to type 2 diabetes (T2D). We concluded that the evidence is increasingly stacking up in favour of the view that most of the genetic risk of T2D can be attributed to common alleles that are widely shared within, and between, human populations.

In this blog, I try to explain what that evidence is, why this question matters, and what the answer tells us about the forces that might underlie the exploding prevalence of this condition?

Two contrasting views of genetic architecture

This question — whether the genetic architecture of common, complex diseases like type 2 diabetes is best described in terms of the joint effects of a large number of shared variants of small effect, or alternatively as a jumble of genetically-distinct (quasi-)Mendelian syndromes — has been the subject of much debate over the years, and has surfaced in many different forms.

It can be traced back to the biometrician vs Mendelian debate over a century ago which featured different, apparently irreconcilable, views regarding the basis for common, continuous, inherited traits. Bateson and others in the Mendelian camp were focused on the discrete effects of segregating variation that could be shown to be consistent with Mendel’s “laws”. The biometricians (led by Pearson, Weldon and others) were struggling to see how those rules could be applied to continuous traits like height. Ultimately, these opposing views were reconciled by Fisher and colleagues, who demonstrated how the combined effects of multiple small variants, each of them observing the rules of Mendelian segregation could underpin continuous traits. This was termed the “infinitesimal” model. The concept of a continuous scale of liability (with disease defined when some threshold on that scale is exceeded), means that this model is equally applicable to apparently discrete traits such as diabetes or schizophrenia.

Large scale genetic data arrives

The advent of genome-wide association analyses provided empirical data to reinvigorate this debate.

Some were convinced that common diseases were largely the consequence of common variation along the lines of the infinitesimal model. This “common disease, common variant” argument was based on an important (but underappreciated) fact about human variation. Whilst the number of rare variant sites massively exceeds that of common variant sites (if we sequence enough humans we should find rare alleles at all sites where heterozygosity is compatible with life), most of the genetic differences between two individuals are to be found at common sites. If this sounds counterintuitive, try thinking about it this way. Whilst there are many, many rare allele sites across the genome, each of us is boringly wild-type (ie carries two copies of the “normal” allele) at virtually all of those sites, and they contribute little to interindividual variation. The question about the frequency spectrum of the variants influencing a common disease like diabetes then becomes: are the set of variants that influence diabetes risk somehow different from the broad swath of overall variation?

The contrary view (sometimes referred to as “common disease, rare variant”) holds that diseases like diabetes are really a collection of discrete syndromes in which risk is dominated by rare, large effect variants private to an individual and his or her relatives. After all, we already know some such syndromes (the various forms of maturity onset diabetes of the young are often hard to distinguish clinically from common forms of T2D): perhaps there are just way more of those waiting to be found? Mary-Claire King in a recent review, invoked Tolstoy to illustrate this concept: “Every unhappy family is unhappy in its own way” [2]. Others have used the term “clan genomics” to describe the view that the genetic contribution to common phenotypic variation would be dominated by rare alleles of recent origin and large effect that cluster within closely-related individuals [3]. The concept of synthetic association (the idea that common variant signals detected by GWAS could be driven by multiple rare causal variants) comes from the same well-spring of Mendelian focus [4]. One often hears at meetings the (apparently) accepted “wisdom” that “type 2 diabetes is really a collection of diseases”, the implication being that we will, at some point in the future, be able to shatter this diagnostic monolith into its constituency of discrete diseases (type 2A, type 2B …, type 2Z).

High Throughput Sequencing with an Illumina HiSeq X Sequencing System

Does it matter?

This may seem an abstract, almost Jesuitical, argument (“how many variants dance on the head of a pin?”). But the answer matters a great deal more than appears at first glance. It matters in terms of defining the best strategies for uncovering risk variants and using them for mechanistic insight. And it matters with regard to the most effective ways for using genetic information to develop more personalised (precision, individualised) strategies for treatment and prevention.

The case for common variants

The genome wide association (GWAS) approaches of the past decade have, for technical reasons, been biased towards the detection of association signals emanating from common variants. It’s no surprise therefore that almost all of the 100 or so genome wide significant signals for T2D appear to be driven by common, shared variants. At the same time, those 100 signals explain only around 10% of the overall genetic contribution to T2D [5].

Is this “missing” heritability down to a long tail of common variant effects, or does it represent a “hidden iceberg” of rare variant effects that have been invisible to common variant-biased discovery efforts?

The transition from array-based GWAS to sequence-based GWAS makes it possible to address this question, since it brings variants of all frequencies into view. In the paper just published in Nature, we have been able to start to pursue this strategy, and, for the first time, make a fair comparison of the contribution of those different types of allele to T2D risk.

I won’t go into the detail of what we discovered, but instead summarise the various lines of evidence that we and others have collected, all of which appear to be pointing towards the same conclusion:

  • In sequence based studies, we and others (notably our colleagues at Decode in Iceland [6]) have found only a handful of rare or low frequency alleles of large effect. This is true for analysis conducted at the single variant level but also for efforts to improve power through gene-level aggregation of rare alleles;
  • In conventional GWAS, the inclusion of ever larger sample sizes, and ever better imputation reference panels (both of which offer more traction for low frequency variants at least) has failed to deliver large numbers of lower frequency signals [7][9];
  • In exome array studies, which have made a subset of rare and low frequency variants of high biological pertinence available for high volume genotyping, discoveries have been limited to common variant signals; in the present study, we estimate that coding variants in the 0.1% to 5% frequency range, though far more numerous than those in the 5-50% range, make a considerably smaller contribution to individual risk of diabetes;
  • In fine mapping studies, there are few, if any, robust examples where focused genotyping has resolved the original common variant signal to a lower-frequency causal variant of larger effect [8]; in the present study, we extend this study to rare variants and again fail to find any evidence for the synthetic association model;
  • In trans-ethnic association studies, it is remarkable how many of the GWAS loci identified in one ethnic group can be detected in others: this would not be expected if these were driven by rare alleles [9];
  • In GWAS studies, we can show that a long tail of signals below genome wide significance makes a substantial contribution to overall diabetes risk [10], mirroring similar work for other traits performed by Peter Visscher and colleagues using the GCTA approach [11];
  • In simulation studies, where one models the allele frequency spectrum expected of T2D risk alleles under different assumptions regarding selective pressure, the distribution of association signals observed empirically is most obviously consistent with a model of limited selection pressure, and domination by common risk-alleles [12].

What this means for evolutionary selection

The conclusions seem clear. For T2D, genetic risk is predominantly driven by common risk variants that are widely shared both within and between populations. In other words, the allele frequency spectrum of diabetes risk variants (in terms of their contribution to interindividual variation in diabetes risk) mirrors the distribution of genetic variation at large. This in turn indicates that the variants that influence T2D risk have been under limited selection in human prehistory. (If a set of risk alleles were under very strong negative selection, any that arose through mutation would have limited longevity, and would constantly be eradicated from the population: the only risk alleles you would see would be those that arose recently, and they would be rare and private and unique to a lineage. Of course that’s exactly what you see with Mendelian alleles).

On one level that’s not a surprise. After all, T2D appears to be a relatively recent arrival as a major global contributor to disease burden, and its negative impact on health is mostly (though not exclusively) experienced in post-reproductive years. Both of those should limit the extent to which diabetes itself could have resulted in selective pressure.  Diabetes risk-alleles might, of course, have been the subject of adverse selection on the basis of some other, pleiotropic, impact, but the evidence points against that. It has been suggested that T2D risk alleles have been advantageous in human prehistory (the “thrifty genotype” concept) but efforts to detect the resonance of balancing selection in large-scale genetic data have not been successful [13].

B0009538 DNA double helix, illustration Credit: Anna Tanczos. Wellcome Images images@wellcome.ac.uk http://wellcomeimages.org Illustration of the DNA double helix structure first discovered by Watson and Crick in 1953. The DNA fragment depicted here is slightly distorted. The sugar-phosphate backbone is visible on complementary nucleotide strands with paired bases represented as rungs on a ladder. Digital artwork/Computer graphic 2014 Published: - Copyrighted work available under Creative Commons by-nc-nd 4.0, see http://wellcomeimages.org/indexplus/page/Prices.html

DNA double helix, illustration, http://wellcomeimages.org

What our findings do NOT say.

Let me finish by pointing out some of the (hopefully obvious) caveats.

First, what we find for T2D may or may not be relevant to other complex traits. Certainly, one would expect that rarer alleles would have a proportionately larger impact for complex diseases of earlier onset, with more profound effects on morbidity, mortality and fecundity (such as autism or schizophrenia).

Second, we have only just started our exploration of the rare variant space. The numbers of subjects we have been able to examine through sequencing remains far lower than those for whom we have common variant GWAS data. Our studies are thus far from comprehensive, and so far we provide a far more systematic exploration of the contribution of lower-frequency variants than the really rare ones.

Third, just because low-frequency and rare alleles don’t dominate the spectrum of risk for T2D does not mean that we should shut down the sequencers. Just as Mendelian alleles have provided powerful new insights into the biology of health and disease, the identification of high impact alleles influencing T2D risk (whether common or rare, though most of them will, of course, be the latter) will highlight key processes involved in the maintenance of metabolic homeostasis. Such pathways provide opportunities for the design of novel preventative and therapeutic approaches. The impact of protein truncating variants in the SLC30A8 gene provides an excellent example [14]. Many more such examples are likely to flow as more individuals, of more diverse ethnic origin, are sequenced.

Fourth, whilst it’s easier to write from an oppositional perspective (common vs rare, nature vs nurture, complex vs mendelian), the truth of course is far more inclusive. The genetic contribution to individual risk of T2D is influenced by common variants, by low-frequency variants, and by rare variants. T2D predisposition is also subject to the effects of events, exposures and experiences in early life, childhood, adolescence, adulthood and beyond. Somatic mutation almost certainly plays a role. Transgenerational epigenetic inheritance may be involved. To understand individual risk, and to define the mechanistic basis of T2D, we will first need to examine, dissect, characterise and quantify the contribution of each. Only once we have done that will we be able to generate a comprehensive model of T2D pathogenesis.

 

Citations

1 Fuchsberger C et al The genetic architecture of type 2 diabetes Nature 11 July 2016
2 McClellan J, King MC Genetic heterogeneity in human disease. Cell 2010;141:201
3 Lupski JR et al Clan Genomics and the Complex Architecture of Human Disease Cell 2011, 147, 32
4 Dickson SP et al Rare variants create synthetic genome-wide associations PLoS Biol 2010;8:e1000294
5 Morris AP et al Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes Nature Genetics 2012;44:981-990
6 Steinthorsdottir V et al Identification of low-frequency and rare sequence variants associated with elevated or reduced risk of type 2 diabetes Nature Genetics 2014;46:294
7 Morris AP et al Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes Nature Genetics 2012;44:981-990
8 Gaulton KJ et al Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci Nat Genet. 2015;47:1415-25
9 Mahajan A et al Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility Nat Genet. 2014;46:234-244
10 Morris AP et al Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes Nature Genetics 2012;44:981-990
11 Yang J Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index Nature Genetics 2015;47:1114
12 Agarwala V Evaluating empirical bounds on complex disease genetic architecture Nature Genetics 2013;45:1418
13 Ayub Q et al Revisiting the thrifty gene hypothesis via 65 loci associated with susceptibility to type 2 diabetes American Journal of Human Genetics 2014;94,1
14 Flannick J Loss-of-function mutations in SLC30A8 protectagainst type 2 diabetes Nat Genet. 2014;46:357

Robert Turner Professor of Diabetic Medicine, Group Head, Wellcome Trust Centre for Human Genetics, Group Head / PI, Grant Holding Senior Scientist, Consultant Physician.

Type 2 diabetes is one of the major threats to global health. Around 10% of the world’s population either has type 2 diabetes, or is likely to develop it during their lives. Both genes and environment contribute to an individual’s risk of developing this disease. Preventative strategies currently available (for example, sustainable changes to diet or levels of exercise) are, in most people, of limited effectiveness. Our available treatment options, though they can improve health and reduce the risk of complications, rarely result in the restoration of a normal metabolism.

As a result, there is a pressing need for an improved understanding of the mechanisms involved in T2D and its complications. Human genetics is one of the most powerful ways of delivering such an understanding, because, when successful, it highlights fundamental biological differences that play directly into differences in risk of disease. Discoveries obtained through human genetics can therefore point the way towards the development of novel preventative and therapeutic approaches. Not only that, such discoveries can help to target those approaches more effectively to the individuals in whom they are most likely to provide benefit, and least likely to cause harm.

Manhattan plot from T2D Paper showing single-variant analysis.

Manhattan plot from T2D Paper showing single-variant analysis.

The paper in Nature, which has just been published, reports on a major international effort (involving over 300 scientists in 22 countries) to use human genetics to define mechanisms underlying the development of type 2 diabetes. The main technical and scientific advance deployed in this particular study has been to move discovery of DNA sequence differences influencing type 2 diabetes risk beyond the common (shared) variants examined in previous genome-wide association studies. Instead, this study used newly available DNA sequencing technologies to explore the full inventory of DNA sequence changes (shared and unique) in multiple individuals. We were then able to compare how those DNA sequence differences are distributed between individuals who have type 2 diabetes and those who do not.

This study of the genetics of type 2 diabetes is unprecedented in both scale and scope. It involves data generated from over 120,000 individuals (some with diabetes, some without) from a wide range of ethnic groups. It includes individuals with ancestral origins in Europe, South and East Asia, the Americas and Africa. In some of these individuals, the entire genome was sequenced. In others, there was a specific focus on variation within the parts of the genome (the “exome”) that codes directly for proteins: these changes in protein-coding sequence are likely to be especially informative from a biological perspective.

There are three main findings from this research.

First, we find over a dozen genes that contain T2D associated DNA variants which change amino acid sequence. Several of these provide new and important clues about the mechanisms underlying type 2 diabetes. For example, we find a DNA sequence variant in a gene called PAX4 which is powerfully associated with diabetes, but only in individuals from East Asia (including Korea, China, Singapore). PAX4 is involved in the development of the insulin-producing beta-cells in the human pancreas. We also implicate another gene, TM6SF2, already known to be involved in the development of hepatic steatosis (“fatty liver”) and which we show here also influences T2D risk. These finding matters because they provide many important new insights into the biology of diabetes: some of the genes and pathways implicated may represent novel avenues for drug development.

Second, there has been a longstanding debate (going back over a century) as to whether most of the genetic differences that influence individual predisposition to common diseases such as diabetes are ones that are widely shared within populations, or whether they are more often rare or unique events, specific to an individual and their family. Because, in this study, we were able to analyse both rare and shared variants, we have been able to show that the genetic contribution to diabetes risk lies predominantly at shared sites. This matters because it has implications for the ways in which we will be able to use genetic data to support personalised medicine.

Third, although most of the association signals we detect involve common, shared variants, we do find some rare (private, unique) variants that influence risk of diabetes, and show that these can also provide valuable insights into disease biology. We had already shown, a couple of years ago, using some of the same data, that rare variants that abrogate function at the SLC30A8 gene, are protective against type 2 diabetes. Here, we show that there is an excess of T2D association amongst rare coding variants in a set of around 30 genes that are already known to be involved in some rare familial forms of diabetes that mostly start in early life. This matters because it demonstrates that the same genes can harbour DNA sequence differences which result in very different types of diabetes: it highlights the need for careful interpretation when DNA sequence changes are detected in genes of medical significance, since it will not always be obvious what the impact of that variant is likely to be.

Because, we believe it is important that all researchers can benefit from the data we have generated in this project, data and discoveries are available to researchers and to the wider world through a variety of means. For example, much of the data from this and other studies is available on the freely-accessible T2D genetics portal developed as part of the Accelerating Medicines Partnership (www.type2diabetesgenetics.org).

Robert Turner Professor of Diabetic Medicine, Group Head, Wellcome Trust Centre for Human Genetics, Group Head / PI, Grant Holding Senior Scientist, Consultant Physician.