Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 Jul 3;11(1):3353.
doi: 10.1038/s41467-020-16483-3.

Assessment of polygenic architecture and risk prediction based on common variants across fourteen cancers

Yan Dora Zhang  1   2 , Amber N Hurson  3   4 , Haoyu Zhang  3   5 , Parichoy Pal Choudhury  3 , Douglas F Easton  6   7 , Roger L Milne  8   9   10 , Jacques Simard  11 , Per Hall  12   13 , Kyriaki Michailidou  7   14 , Joe Dennis  7 , Marjanka K Schmidt  15   16 , Jenny Chang-Claude  17   18 , Puya Gharahkhani  19 , David Whiteman  20 , Peter T Campbell  21 , Michael Hoffmeister  22 , Mark Jenkins  9 , Ulrike Peters  23 , Li Hsu  23 , Stephen B Gruber  24 , Graham Casey  25 , Stephanie L Schmit  26 , Tracy A O'Mara  27 , Amanda B Spurdle  27 , Deborah J Thompson  7 , Ian Tomlinson  28   29 , Immaculata De Vivo  30   31 , Maria Teresa Landi  3 , Matthew H Law  19 , Mark M Iles  32 , Florence Demenais  33 , Rajiv Kumar  34 , Stuart MacGregor  19 , D Timothy Bishop  35 , Sarah V Ward  36 , Melissa L Bondy  37 , Richard Houlston  38 , John K Wiencke  39 , Beatrice Melin  40 , Jill Barnholtz-Sloan  41 , Ben Kinnersley  38 , Margaret R Wrensch  39 , Christopher I Amos  42 , Rayjean J Hung  43 , Paul Brennan  44 , James McKay  44 , Neil E Caporaso  3 , Sonja I Berndt  3 , Brenda M Birmann  30 , Nicola J Camp  45 , Peter Kraft  46 , Nathaniel Rothman  3 , Susan L Slager  47 , Andrew Berchuck  48 , Paul D P Pharoah  6   7 , Thomas A Sellers  26 , Simon A Gayther  49 , Celeste L Pearce  24   50 , Ellen L Goode  51 , Joellen M Schildkraut  52 , Kirsten B Moysich  53 , Laufey T Amundadottir  54 , Eric J Jacobs  21 , Alison P Klein  55 , Gloria M Petersen  51 , Harvey A Risch  56 , Rachel Z Stolzenberg-Solomon  3 , Brian M Wolpin  57 , Donghui Li  58 , Rosalind A Eeles  59 , Christopher A Haiman  24 , Zsofia Kote-Jarai  59 , Fredrick R Schumacher  60 , Ali Amin Al Olama  61   62 , Mark P Purdue  3 , Ghislaine Scelo  44 , Marlene D Dalgaard  63   64 , Mark H Greene  65 , Tom Grotmol  66 , Peter A Kanetsky  26 , Katherine A McGlynn  3 , Katherine L Nathanson  67 , Clare Turnbull  38 , Fredrik Wiklund  12 ; Breast Cancer Association Consortium (BCAC); Barrett’s and Esophageal Adenocarcinoma Consortium (BEACON); Colon Cancer Family Registry (CCFR); Transdisciplinary Studies of Genetic Variation in Colorectal Cancer (CORECT); Endometrial Cancer Association Consortium (ECAC); Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO); Melanoma Genetics Consortium (GenoMEL); Glioma International Case-Control Study (GICC); International Lung Cancer Consortium (ILCCO); Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) Consortium; International Consortium of Investigators Working on Non-Hodgkin’s Lymphoma Epidemiologic Studies (InterLymph); Ovarian Cancer Association Consortium (OCAC); Oral Cancer GWAS; Pancreatic Cancer Case-Control Consortium (PanC4); Pancreatic Cancer Cohort Consortium (PanScan); Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL); Renal Cancer GWAS; Testicular Cancer Consortium (TECAC); Stephen J Chanock  3 , Nilanjan Chatterjee #  68   69 , Montserrat Garcia-Closas #  3
Collaborators, Affiliations

Assessment of polygenic architecture and risk prediction based on common variants across fourteen cancers

Yan Dora Zhang et al. Nat Commun. .

Abstract

Genome-wide association studies (GWAS) have led to the identification of hundreds of susceptibility loci across cancers, but the impact of further studies remains uncertain. Here we analyse summary-level data from GWAS of European ancestry across fourteen cancer sites to estimate the number of common susceptibility variants (polygenicity) and underlying effect-size distribution. All cancers show a high degree of polygenicity, involving at a minimum of thousands of loci. We project that sample sizes required to explain 80% of GWAS heritability vary from 60,000 cases for testicular to over 1,000,000 cases for lung cancer. The maximum relative risk achievable for subjects at the 99th risk percentile of underlying polygenic risk scores (PRS), compared to average risk, ranges from 12 for testicular to 2.5 for ovarian cancer. We show that PRS have potential for risk stratification for cancers of breast, colon and prostate, but less so for others because of modest heritability and lower incidence.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Estimated effect-size distributions for susceptibility SNPs across 14 cancer sites.
Effect-size distribution of susceptibility SNPs is modeled using a two-component normal mixture model for all sites, except esophageal and oropharyngeal cancers. For these sites, effect sizes are modeled using a single normal distribution that provided similar fit as the two-component normal mixture model (see Supplementary Figs. 1 and 2). SNPs with extremely large effects are excluded for effect-size distribution estimation (see “Methods”). Plots are stratified by sample size of the GWAS for comparability. Distributions with fatter tails imply the underlying traits have relatively greater number of susceptibility SNPs with larger effects. Note here that the effect-size distribution is plotted on the log scale of odds ratio (x-axis). CLL chronic lymphocytic leukemia.
Fig. 2
Fig. 2. Projections of percentage of GWAS heritability explained by SNPs as sample size for GWAS increases.
Results are shown for projections including SNPs at the optimized p value threshold (solid curve) and at genome-wide significance (p < 5×10−8) level (dashed curve). Colored dots correspond to sample size for the largest published GWAS and those for doubled and quadruped sizes. For oropharyngeal cancer, the projections at the “current sample size” are based on a sample size of 25K cases and 25K controls. For breast and esophageal cancer, the projections at the “current sample size” are based on the current largest GWAS sample sizes: 123K cases and 106K controls and 10K cases and 17K controls, respectively. For all other cancer sites, the projections at the “current sample size” are based on the GWAS sample sizes in Supplementary Table 1. CLL chronic lymphocytic leukemia.
Fig. 3
Fig. 3. Projections of area under the curve (AUC) characterizing predictive performance of PRS as sample size for GWAS increases.
Results are shown for PRS including SNPs at the optimized p value threshold. The dotted horizontal red line indicates the maximum AUC achievable according to the estimate of GWAS heritability. Colored dots correspond to sample size for largest published GWAS and those for doubled and quadruped sizes. For oropharyngeal cancer, the projections at the “current sample size” are based on a sample size of 25K cases and 25K controls. For breast and esophageal cancer, the projections at the “current sample size” are based on the current largest GWAS sample sizes: 123K cases and 106K controls and 10K cases and 17K controls, respectively. For all other cancer sites, the projections at the “current sample size” are based on the GWAS sample sizes in Supplementary Table 1. CLL chronic lymphocytic leukemia.
Fig. 4
Fig. 4. Projections of relative risks for individuals at or higher than 99th percentile of PRS as sample size for GWAS increases.
Results are shown where PRS is built based on SNPs at optimized p value threshold. The dotted horizontal red line indicates the maximum relative risk achievable according to estimate of GWAS heritability. Colored dots correspond to sample size for the largest published GWAS and those for doubled and quadruped sizes. y-Axis is presented in log10 scale. For oropharyngeal cancer, the projections at the “current sample size” are based on a sample size of 25K cases and 25K controls. For breast and esophageal cancer, the projections at the “current sample size” are based on the current largest GWAS sample sizes: 123K cases and 106K controls and 10K cases and 17K controls, respectively. For all other cancer sites, the projections at the “current sample size” are based on the GWAS sample sizes in Supplementary Table 1. CLL chronic lymphocytic leukemia.
Fig. 5
Fig. 5. Projected distribution of average residual lifetime risk in the US population of non-Hispanic whites aged 30–75 years.
The risk is obtained according to variation of polygenic risk scores. The projections are shown for PRS built based on GWAS with current, doubled and quadrupled sample sizes and the best PRS that corresponds to limits defined by heritability. The projections are obtained by combining information on projected population variance of PRS, age-specific population incidence rate, competing risk of mortality and current distribution of age according to US 2016 census. For oropharyngeal cancer, the projections at the “current sample size” are based on a sample size of 25K cases and 25K controls. For breast and esophageal cancer, the projections at the “current sample size” are based on the current largest GWAS sample sizes: 123K cases and 106K controls and 10K cases and 17K controls, respectively. For all other cancer sites, the projections at the “current sample size” are based on the GWAS sample sizes in Supplementary Table 1. CLL chronic lymphocytic leukemia.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Sud A, Kinnersley B, Houlston RS. Genome-wide association studies of cancer: current insights and future perspectives. Nat. Rev. Cancer. 2017;17:692–704. - PubMed
    1. Tam, V. et al. Benefits and limitations of genome-wide association studies. Nat. Rev. Genet. 20, 467–484 (2019). - PubMed
    1. Law PJ, et al. Genome-wide association analysis implicates dysregulation of immunity genes in chronic lymphocytic leukaemia. Nat. Commun. 2017;8:14175. - PMC - PubMed
    1. Litchfield K, et al. Identification of 19 new risk loci and potential regulatory mechanisms influencing susceptibility to testicular germ cell tumor. Nat. Genet. 2017;49:1133–1140. - PMC - PubMed
    1. Mucci LA, et al. Familial risk and heritability of cancer among twins in Nordic countries. JAMA. 2016;315:68–76. - PMC - PubMed

Publication types