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Type 2 diabetes: genetic data sharing to advance complex disease research

Nature Reviews Genetics volume 17, pages 535–549 (2016)Cite this article

Subjects

Key Points

  • Type 2 diabetes mellitus (T2D) is a highly prevalent common metabolic disease, which exemplifies many of the challenges and approaches for other complex diseases.

  • Early genome-wide association studies (GWAS) were successful for T2D, aided in large part by collaboration and data sharing by the genetics research community.

  • The substantial portion of T2D heritability unexplained by GWAS triggered a debate on whether rare or common variants were dominant characteristics of T2D genetic architecture. More recent sequencing studies and GWAS have produced empirical results consistent with common variant models.

  • A substantial number of experimental approaches have been used to investigate disease mechanisms and biology for T2D, including deep phenotyping of variant carriers, higher-resolution 'fine mapping' studies, integration with epigenomic and transcriptomic data sets and functional studies in model systems.

  • Larger and larger genetic studies will be needed to identify or characterize additional T2D associations in the population. At the same time, open access to these data will be needed for a broad experimental community to produce biological insights that might be missed by the analyses of GWAS consortia.

  • An integrated T2D knowledge base and portal, recently embraced by the T2D genetics community, is one possible mechanism to maximize the global use of genetic data sets to be produced in coming years.

Abstract

As with other complex diseases, unbiased association studies followed by physiological and experimental characterization have for years formed a paradigm for identifying genes or processes of relevance to type 2 diabetes mellitus (T2D). Recent large-scale common and rare variant genome-wide association studies (GWAS) suggest that substantially larger association studies are needed to identify most T2D loci in the population. To hasten clinical translation of genetic discoveries, new paradigms are also required to aid specialized investigation of nascent hypotheses. We argue for an integrated T2D knowledgebase, designed for a worldwide community to access aggregated large-scale genetic data sets, as one paradigm to catalyse convergence of these efforts.

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Figure 1: The history of T2D GWAS.
Figure 2: Common resources, analyses and workflows for understanding T2D biology.
Figure 3: The evolution of human genetic knowledge bases.
Figure 4: An integrated knowledge base of T2D genetics.

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Acknowledgements

The authors thank B. Alexander for help with figure design and creation, as well as helpful discussions.

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Authors and Affiliations

  1. Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, 02142, Massachusetts, USA

    Jason Flannick & Jose C. Florez

  2. Department of Medicine, Center for Human Genetic Research, Massachusetts General Hospital, 185 Cambridge Street, Boston, 02062, Massachusetts, USA

    Jason Flannick & Jose C. Florez

  3. Department of Medicine, Diabetes Research Center, Diabetes Unit, Massachusetts General Hospital, 185 Cambridge Street, Boston, 02062, Massachusetts, USA

    Jose C. Florez

  4. Department of Medicine, Harvard Medical School, 25 Shattuck Street, Boston, 02115, Massachusetts, USA

    Jose C. Florez

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Correspondence to Jose C. Florez.

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Glossary

Heritability

The proportion of phenotypic variance in a population owing to genetic differences, as opposed to environmental differences.

Genome-wide association studies

(GWAS). An approach for genetic mapping that compares frequencies of variants across the genome between disease cases and matched controls. This is a paradigm for identifying genes or biological processes that are relevant to a phenotype by identifying correlations between polymorphic genetic markers and the presence of the phenotype.

Causal variants

Specific mutations underlying the molecular cascade that produces a phenotypic trait; by design, in most genetic mapping studies, the associated genetic marker is merely correlated with the underlying causal variant.

Effector transcripts

The specific RNA transcript (for example, mRNA transcribed from a gene) for which the function or expression is altered by the causal variant, leading to a phenotypic difference.

Genetic architecture

The number, frequencies and effects on disease of genetic variants in a population.

Common disease common variant hypothesis

(CDCV hypothesis). The hypothesis that, owing to historical human population growth, some disease loci for common diseases may harbour alleles common in the population.

Linkage disequilibrium

Correlations among nearby variants, owing to historical patterns of demography and recombination, exploited by genome-wide association studies to map common variant associations.

Glycaemic

Traits pertaining to the physiology of blood glucose regulation, usually involving measures of glucose, insulin or other related hormones.

Rare variant models

A model of genetic architecture in which rare variants (for example, those with a frequency < 1%) explain most of the heritability.

Synthetic associations

A hypothesis based on simulations that multiple causal rare variants of strong effects might cause a common variant statistical association.

Common variant models

Models of genetic architecture in which common variants (for example, those with a frequency > 1%) explain most of the heritability.

Imputation

A technique to infer the unknown genotype of a variant in an individual based on correlations with nearby genotyped variants.

Allelic series

A number of alleles of a gene or locus with a range of phenotypic and/or molecular effects that are of use to infer a genetic–phenotypic dose–response curve.

Polygenicity

An idealized model in which a phenotype is caused by a large number of variants, each with small and normally distributed phenotypic effects.

Transcriptomic

The study of the expression levels of all transcripts in a cell.

Epigenomic

The study of all epigenetic modifications of a cell, including DNA methylation and histone modifications, which are largely responsible for the genes expressed in a specific tissue at a given developmental stage or metabolic state.

Homeostasis model assessments

A method based on fasting measures of glucose and insulin levels that is used to estimate β-cell function or insulin resistance.

Mendelian randomization

A technique that uses genetic variation to infer causal relationships between correlated phenotypes.

Fine mapping

An approach to localize common variant association signals to potentially causal variants, using exhaustive candidate enumeration and genotyping in large case–control samples.

Protein-truncating variants

Variants, such as nonsense, frameshift, readthrough or splice site mutations, that lead to incomplete protein sequences and possibly non-functional proteins.

Expression quantitative trait loci

(eQTLs). Associations between a genetic marker and expression levels of a transcript.

cis-eQTLs

Expression quantitative trait loci (eQTLs) on the same chromosome and typically near the location of the gene that encodes the associated transcript.

trans-eQTLs

Expression quantitative trait loci (eQTLs) in a different chromosome from the gene encoding the associated transcript.

CRISPR–Cas9 editing

A technique for precise and efficient editing of genetic information within a cell.

Interactome

The study of all protein–protein interactions in a system.

Business intelligence

A term, commonly used in business, that denotes a set of techniques for transforming raw data into meaningful insights.

Big data

A term for data sets that are so large or complex that new paradigms are needed to extract meaningful insights from them.

Data warehouses

A system for carrying out integrated analyses across multiple initially disparate data sources.

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Flannick, J., Florez, J. Type 2 diabetes: genetic data sharing to advance complex disease research. Nat Rev Genet 17, 535–549 (2016). https://doi.org/10.1038/nrg.2016.56

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