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Genome-wide association studies of cancer: current insights and future perspectives

Nature Reviews Cancer volume 17, pages 692–704 (2017)Cite this article

Subjects

Key Points

  • The architecture of inherited genetic susceptibility to cancer is defined by a spectrum of predisposition alleles that have differing frequencies and impact.

  • Genome-wide association studies (GWAS) provide an agnostic approach to the identification of genetic variation influencing cancer risk. For most cancers, GWAS have been performed, and hundreds of risk alleles have been identified, most of which are common and individually confer a modest increase in risk.

  • Most cancer risk loci identified through GWAS locate to non-coding regions of the genome and influence gene expression through diverse mechanisms.

  • As well as improving our understanding of cancer, information from GWAS has direct clinical relevance in identifying nongenetic aetiological risk factors, optimising population screening, identifying therapeutic targets, drug repositioning and prognostication.

  • Although challenging, deciphering the biological basis of identified associations is necessary to fully realise the potential of GWAS.

Abstract

Genome-wide association studies (GWAS) provide an agnostic approach for investigating the genetic basis of complex diseases. In oncology, GWAS of nearly all common malignancies have been performed, and over 450 genetic variants associated with increased risks have been identified. As well as revealing novel pathways important in carcinogenesis, these studies have shown that common genetic variation contributes substantially to the heritable risk of many common cancers. The clinical application of GWAS is starting to provide opportunities for drug discovery and repositioning as well as for cancer prevention. However, deciphering the functional and biological basis of associations is challenging and is in part a barrier to fully unlocking the potential of GWAS.

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Figure 1: Genetic architecture of cancer risk.
Figure 2: Design of GWAS.
Figure 3: Regulatory interactions at the pleiotropic 8q24.21 risk locus.
Figure 4: Potential molecular mechanisms of risk SNPs identified by GWAS.
Figure 5: The clinical application of GWAS.
Figure 6: Population distribution of polygenic risk score for CRC ordered by RR.

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Acknowledgements

The authors are grateful to Cancer Research UK for support. A.S. is in receipt of a clinical training fellowship from Cancer Research UK.

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Authors and Affiliations

  1. Division of Genetics and Epidemiology, The Institute of Cancer Research,

    Amit Sud, Ben Kinnersley & Richard S. Houlston

  2. Division of Molecular Pathology, The Institute of Cancer Research, 15 Cotswold Road, London, SM2 5NG, Sutton, UK

    Richard S. Houlston

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  1. Amit Sud
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A.S., B.K. and R.S.H. researched data for the article, made substantial contributions to discussions of the content, wrote the article and reviewed and/or edited the manuscript before submission.

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Supplementary information

Supplementary Figure 1

Cancer susceptibility loci identified through genome-wide association studies. (DOC 804 kb)

Supplementary Table 1

Cancer GWAS loci identified as of March 2017 (XLSX 184 kb)

PowerPoint slides

Glossary

Heritability

An estimate of the proportion of variation in a trait in a given population that is due to genetic variation. In particular, narrow-sense heritability is the proportion of variance in a trait due to additive genetic factors, whereas broad-sense heritability is the proportion of variance in a trait due to all genetic factors (for example, including dominance and gene–gene interactions).

Relative risk

(RR). The ratio of disease occurrence in one group versus another (for example, cancer risk in patient relatives compared with in the general population). The RR estimate associated with common risk alleles identified through genome-wide association studies (GWAS) is usually a per-allele RR (as in the codominant log-additive genetic model).

Mendelian predisposition

A phenomenon that occurs when a germline mutation in a single gene (for example, a cancer susceptibility gene) is sufficient to cause cancer in a majority of patients (for example, female carriers of BRCA1 mutations have an ∼80% lifetime risk of developing breast cancer). These mutations can be dominant or recessive, which are caused by monoallelic and biallelic mutations, respectively.

Cancer susceptibility genes

(CSGs). Genes in which inherited mutations (commonly high-penetrance) predispose individuals to cancer.

Penetrance

The proportion of individuals carrying a particular allele (for example, in a cancer susceptibility gene) who go on to develop cancer. High-penetrance mutations confer a high risk of causing cancer, whereas low-penetrance polymorphisms confer a low risk.

Risk allele frequency

The frequency of a risk allele (B) in a given population at a biallelic site with a non-risk allele (A), derived from genotype counts through the following formula: (2 × BB + AB)/(2 × (AA + AB + BB))

Odds ratios

(ORs). The odds that an outcome will occur given a particular exposure compared with the absence of that exposure (for example, comparing variant site allele frequency in patients with cancer and in controls).

Linkage disequilibrium

(LD). The nonrandom association of alleles at different sites in a given population. Alleles in high LD are those where their shared frequency combinations are greater than those that would be expected if they were inherited independently. LD can be affected by factors such as natural selection and genetic drift as well as by rates of mutation and recombination.

Effect size

A quantitative measurement statistic of the strength of an association between two variables (for example, single nucleotide polymorphism (SNP) genotype and cancer risk).

Pleiotropy

A phenomenon that occurs when a risk locus is associated with multiple phenotypic traits. In some cases, the same variant is presumed to influence multiple traits, while in other cases, different traits map to distinct locations within the risk locus.

Population attributable risk

The number of cases of disease among exposed individuals that can be attributed to that exposure (for example, carriers of a particular risk single nucleotide polymorphism (SNP)).

Genome-wide complex trait analysis

(GCTA). A computational method by which the narrow-sense heritability of a trait can be estimated through case–control genotypes from genome-wide association studies (GWAS) and estimates of trait incidence.

Fine-mapping

A process of refining association signals from genome-wide association studies (GWAS) and prioritising likely causative variants (for example, through in silico annotations of putative functional effects).

Hi-C analysis

A form of chromosome conformation capture in which crosslinked DNA fragments are sequenced in order to infer the three-dimensional structure of the genome and to identify potential regulatory interactions.

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Sud, A., Kinnersley, B. & Houlston, R. Genome-wide association studies of cancer: current insights and future perspectives. Nat Rev Cancer 17, 692–704 (2017). https://doi.org/10.1038/nrc.2017.82

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