Skip to main content
Grant Details

Grant Number: 5U01CA194393-03 Interpret this number
Primary Investigator: Lindstroem, Sara
Organization: University Of Washington
Project Title: Quantifying and Characterizing the Shared Genetic Contribution to Common Cancers
Fiscal Year: 2017
Back to top


┐ DESCRIPTION (provided by applicant): Together, breast, colorectal, lung, ovarian and prostate cancer account for more than 850,000 cancer diagnoses and 290,000 deaths annually in the US. Family- and population-based studies have shown that these cancers have a heritable component, and multi-cancer susceptibility regions identified by genome-wide association studies (GWAS), suggests that this heritability is partly shared across cancers. Furthermore, GWAS have also revealed distinct susceptibility regions for specific tumor subtypes (e.g. estrogen receptor (ER)+ vs. ER- breast cancer), offering some insights to ethnic disparities in cancer incidence by tumor aggressiveness and mortality. This proposal will capitalize on the OncoArray data to study the genetic contribution to cancer risk and prognosis. The GAME-ON consortium recently launched the OncoArray initiative with the goal of identifying novel susceptibility loci for breast, colorectal, lung, ovarian and prostate cancer. In total, GWAS data on more than 350,000 individuals across these five GAME-ON cancers is being generated, creating unprecedented opportunities to jointly study multiple cancers in a homogenously derived dataset. We will quantify and functionally characterize the shared genetic contribution to GAME-ON cancers and use this information to fine-map multi-cancer GWAS regions. To accomplish our goals, we will leverage publically available databases (e.g. ENCODE) to functionally partition the shared genetic component, allowing us to assess the relative importance of functional categories (e.g. exonic, regulatory) for cancer development. For breast and prostate cancer, two cancers with ethnic disparities in cancer incidence and mortality, we will assess the genetic correlation between populations of European and African-American ancestry. Functionally characterizing the shared heritability between ethnicities will highlight biological mechanisms and provide insights into differences in disease aggressiveness between ethnicities. We will fine-map multi-cancer GWAS regions and estimate variant-specific posterior probabilities of causality. Our fine-mapping approach is unique in that it jointly models functiona annotations and the genotype-phenotype association while allowing for multiple causal SNPs within a region, greatly improving statistical power if more than one causal variant exist. Finally we will develop statistical methods to quantify the genetic contribution to variation in survival times and apply our methods on a prostate cancer dataset of 60,000 cases (9,000 deaths). Multi-generational studies show that a cancer prognosis is often "inherited" from parent to offspring suggesting a genetic component but lack of adequate statistical methods and empirical datasets have precluded formal assessment of such. This project capitalizes on the large-scale OncoArray initiative, giving us adequate statistical power to obtain precise heritability estimates Our results will elucidate cancer biology, identify novel connections between cancers and cancer subtypes and have long-standing impact on focus and design of futures studies aiming at understanding the mechanisms causing cancer.

Back to top


GWAS and colocalization analyses implicate carotid intima-media thickness and carotid plaque loci in cardiovascular outcomes.
Authors: Franceschini N. , Giambartolomei C. , de Vries P.S. , Finan C. , Bis J.C. , Huntley R.P. , Lovering R.C. , Tajuddin S.M. , Winkler T.W. , Graff M. , et al. .
Source: Nature Communications, 2018-12-03 00:00:00.0; 9(1), p. 5141.
EPub date: 2018-12-03 00:00:00.0.
PMID: 30510157
Related Citations

Large-scale transcriptome-wide association study identifies new prostate cancer risk regions.
Authors: Mancuso N. , Gayther S. , Gusev A. , Zheng W. , Penney K.L. , Kote-Jarai Z. , Eeles R. , Freedman M. , Haiman C. , Pasaniuc B. , et al. .
Source: Nature Communications, 2018-10-04 00:00:00.0; 9(1), p. 4079.
EPub date: 2018-10-04 00:00:00.0.
PMID: 30287866
Related Citations

Phenotype-Specific Enrichment of Mendelian Disorder Genes near GWAS Regions across 62 Complex Traits.
Authors: Freund M.K. , Burch K.S. , Shi H. , Mancuso N. , Kichaev G. , Garske K.M. , Pan D.Z. , Miao Z. , Mohlke K.L. , Laakso M. , et al. .
Source: American Journal Of Human Genetics, 2018-10-04 00:00:00.0; 103(4), p. 535-552.
PMID: 30290150
Related Citations

A Bayesian framework for multiple trait colocalization from summary association statistics.
Authors: Giambartolomei C. , Zhenli Liu J. , Zhang W. , Hauberg M. , Shi H. , Boocock J. , Pickrell J. , Jaffe A.E. , CommonMind Consortium , Pasaniuc B. , et al. .
Source: Bioinformatics (oxford, England), 2018-08-01 00:00:00.0; 34(15), p. 2538-2545.
PMID: 29579179
Related Citations

A unifying framework for joint trait analysis under a non-infinitesimal model.
Authors: Johnson R. , Shi H. , Pasaniuc B. , Sankararaman S. .
Source: Bioinformatics (oxford, England), 2018-07-01 00:00:00.0; 34(13), p. i195-i201.
PMID: 29949958
Related Citations

Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types.
Authors: Finucane H.K. , Reshef Y.A. , Anttila V. , Slowikowski K. , Gusev A. , Byrnes A. , Gazal S. , Loh P.R. , Lareau C. , Shoresh N. , et al. .
Source: Nature Genetics, 2018 Apr; 50(4), p. 621-629.
EPub date: 2018-04-09 00:00:00.0.
PMID: 29632380
Related Citations

Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer.
Authors: Milne R.L. , Kuchenbaecker K.B. , Michailidou K. , Beesley J. , Kar S. , Lindstr÷m S. , Hui S. , Lemašon A. , Soucy P. , Dennis J. , et al. .
Source: Nature Genetics, 2017 Dec; 49(12), p. 1767-1778.
EPub date: 2017-10-23 00:00:00.0.
PMID: 29058716
Related Citations

Association analysis identifies 65 new breast cancer risk loci.
Authors: Michailidou K. , Lindstr÷m S. , Dennis J. , Beesley J. , Hui S. , Kar S. , Lemašon A. , Soucy P. , Glubb D. , Rostamianfar A. , et al. .
Source: Nature, 2017-11-02 00:00:00.0; 551(7678), p. 92-94.
EPub date: 2017-10-23 00:00:00.0.
PMID: 29059683
Related Citations

Local Genetic Correlation Gives Insights into the Shared Genetic Architecture of Complex Traits.
Authors: Shi H. , Mancuso N. , Spendlove S. , Pasaniuc B. .
Source: American Journal Of Human Genetics, 2017-11-02 00:00:00.0; 101(5), p. 737-751.
PMID: 29100087
Related Citations

Quantifying the genetic correlation between multiple cancer types.
Authors: Lindstr÷m S. , Finucane H. , Bulik-Sullivan B. , Schumacher F.R. , Amos C.I. , Hung R.J. , Rand K. , Gruber S.B. , Conti D. , Permuth J.B. , et al. .
Source: Cancer Epidemiology, Biomarkers & Prevention : A Publication Of The American Association For Cancer Research, Cosponsored By The American Society Of Preventive Oncology, 2017-06-21 00:00:00.0; , .
EPub date: 2017-06-21 00:00:00.0.
PMID: 28637796
Related Citations

Improved methods for multi-trait fine mapping of pleiotropic risk loci.
Authors: Kichaev G. , Roytman M. , Johnson R. , Eskin E. , Lindstr÷m S. , Kraft P. , Pasaniuc B. .
Source: Bioinformatics (oxford, England), 2017-01-15 00:00:00.0; 33(2), p. 248-255.
EPub date: 2016-09-22 00:00:00.0.
PMID: 27663501
Related Citations

Atlas of prostate cancer heritability in European and African-American men pinpoints tissue-specific regulation.
Authors: Gusev A. , Shi H. , Kichaev G. , Pomerantz M. , Li F. , Long H.W. , Ingles S.A. , Kittles R.A. , Strom S.S. , Rybicki B.A. , et al. .
Source: Nature Communications, 2016-04-07 00:00:00.0; 7, p. 10979.
EPub date: 2016-04-07 00:00:00.0.
PMID: 27052111
Related Citations

Methods for fine-mapping with chromatin and expression data.
Authors: Roytman M. , Kichaev G. , Gusev A. , Pasaniuc B. .
Source: Plos Genetics, 2018 02; 14(2), p. e1007240.
EPub date: 2018-02-26 00:00:00.0.
PMID: 29481575
Related Citations

Back to Top