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Grant Details

Grant Number: 2R01CA194393-05A1 Interpret this number
Primary Investigator: Lindstroem, Sara
Organization: University Of Washington
Project Title: Leveraging Cross-Cancer Shared Heritability to Better Understand the Genetic Architecture of Cancer
Fiscal Year: 2020


Abstract

ABSTRACT Genome-wide association studies (GWAS) and transcriptome-wide association studies (TWAS) have identified hundreds of common, modest-effect alleles and genes associated with cancer risk, but much of cancer heritability remains unexplained. To date, most epidemiological studies of cancer focus on individual cancer types. We propose to leverage the shared heritability across cancers to conduct the largest cross-cancer GWAS and TWAS to date. To achieve our goal, we will use individual and summary GWAS data from 12 solid cancers (breast, colorectal, endometrial, esophageal, glioma, head and neck, lung, melanoma, ovarian, pancreatic, prostate and renal) based on more than 400,000 cases and 900,000 controls expanding our prior work with six new cancer sites and more than 100,000 new cancer cases. We will conduct overall and subset-based cross-cancer GWAS meta-analysis to identify novel cancer risk alleles (Aim 1a). We will also develop statistical methods that explicitly test for pleiotropic effects using summary statistics only and apply these to both known and novel cancer SNPs (Aim 1b). We will develop and apply methods for cross-cancer TWAS, leveraging the genetic regulation of gene expression in both tumor (TCGA) and normal (GTEx) tissue (Aim 2). Finally, we will use novel methods that leverage both GWAS summary statistics and individual-level data from dbGaP and UK Biobank, as well as functional annotation data from the ENCODE and the RoadMap Epigenomics projects to conduct in-depth heritability analysis of cancer. Specifically, we will model the relative effect sizes of risk alleles as a function of allele frequency and genomic annotation (Aim 3a), and for the first time assess the presence of dominance effects across multiple cancers (Aim 3b). The proposed Aims build on our previous success in using large GWAS summary statistics to establish and quantify the shared genetic contribution to multiple cancers. They also build on our proven track record for developing and applying statistical methods to conduct multi-phenotype association studies and heritability estimation. Our application is in response to PA-17-239: “Secondary Analysis and Integration of Existing Data to Elucidate the Genetic Architecture of Cancer Risk and Related Outcomes”. We have brought together investigators from 12 different cancer GWAS consortia, creating an unprecedented opportunity to identify novel cancer susceptibility loci. As part of the proposed research, we will develop a series of new statistical methods that can be broadly applied to other disease groups with a shared genetic basis. Completion of our Aims will lead to discovery of novel cancer risk alleles and identify shared pathways involved in tumor development across cancers. It will also inform the design and analysis of future sequencing studies to identify low- frequency and rare variants associated with cancer risk, by providing guidance on plausible effect sizes, required sample sizes and the genomic features most likely to harbor large-effect low-frequency variants.



Publications

BATMAN: Fast and Accurate Integration of Single-Cell RNA-Seq Datasets via Minimum-Weight Matching.
Authors: Mandric I. , Hill B.L. , Freund M.K. , Thompson M. , Halperin E. .
Source: iScience, 2020-06-26; 23(6), p. 101185.
EPub date: 2020-05-20.
PMID: 32504875
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Localizing Components of Shared Transethnic Genetic Architecture of Complex Traits from GWAS Summary Data.
Authors: Shi H. , Burch K.S. , Johnson R. , Freund M.K. , Kichaev G. , Mancuso N. , Manuel A.M. , Dong N. , Pasaniuc B. .
Source: American journal of human genetics, 2020-06-04; 106(6), p. 805-817.
EPub date: 2020-05-21.
PMID: 32442408
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Genetic associations of breast and prostate cancer are enriched for regulatory elements identified in disease-related tissues.
Authors: Chen H. , Kichaev G. , Bien S.A. , MacDonald J.W. , Wang L. , Bammler T.K. , Auer P. , Pasaniuc B. , Lindström S. .
Source: Human genetics, 2019 Oct; 138(10), p. 1091-1104.
EPub date: 2019-06-22.
PMID: 31230194
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Integrative analysis of Dupuytren's disease identifies novel risk locus and reveals a shared genetic etiology with BMI.
Authors: Major M. , Freund M.K. , Burch K.S. , Mancuso N. , Ng M. , Furniss D. , Pasaniuc B. , Ophoff R.A. .
Source: Genetic epidemiology, 2019 09; 43(6), p. 629-645.
EPub date: 2019-05-13.
PMID: 31087417
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Accurate estimation of SNP-heritability from biobank-scale data irrespective of genetic architecture.
Authors: Hou K. , Burch K.S. , Majumdar A. , Shi H. , Mancuso N. , Wu Y. , Sankararaman S. , Pasaniuc B. .
Source: Nature genetics, 2019 08; 51(8), p. 1244-1251.
EPub date: 2019-07-29.
PMID: 31358995
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Elevated Platelet Count Appears to Be Causally Associated with Increased Risk of Lung Cancer: A Mendelian Randomization Analysis.
Authors: Zhu Y. , Wei Y. , Zhang R. , Dong X. , Shen S. , Zhao Y. , Bai J. , Albanes D. , Caporaso N.E. , Landi M.T. , et al. .
Source: Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2019 05; 28(5), p. 935-942.
EPub date: 2019-01-30.
PMID: 30700444
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Probabilistic fine-mapping of transcriptome-wide association studies.
Authors: Mancuso N. , Freund M.K. , Johnson R. , Shi H. , Kichaev G. , Gusev A. , Pasaniuc B. .
Source: Nature genetics, 2019 04; 51(4), p. 675-682.
EPub date: 2019-03-29.
PMID: 30926970
Related Citations

Shared heritability and functional enrichment across six solid cancers.
Authors: Jiang X. , Finucane H.K. , Schumacher F.R. , Schmit S.L. , Tyrer J.P. , Han Y. , Michailidou K. , Lesseur C. , Kuchenbaecker K.B. , Dennis J. , et al. .
Source: Nature communications, 2019-01-25; 10(1), p. 431.
EPub date: 2019-01-25.
PMID: 30683880
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Author Correction: Large-scale transcriptome-wide association study identifies new prostate cancer risk regions.
Authors: Mancuso N. , Gayther S. , Gusev A. , Zheng W. , Penney K.L. , PRACTICAL consortium , Kote-Jarai Z. , Eeles R. , Freedman M. , Haiman C. , et al. .
Source: Nature communications, 2019-01-08; 10(1), p. 171.
EPub date: 2019-01-08.
PMID: 30622272
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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; 9(1), p. 5141.
EPub date: 2018-12-03.
PMID: 30510157
Related Citations

Distinguishing genetic correlation from causation across 52 diseases and complex traits.
Authors: O'Connor L.J. , Price A.L. .
Source: Nature genetics, 2018 12; 50(12), p. 1728-1734.
EPub date: 2018-10-29.
PMID: 30374074
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; 103(4), p. 535-552.
PMID: 30290150
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; 9(1), p. 4079.
EPub date: 2018-10-04.
PMID: 30287866
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; 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; 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 04; 50(4), p. 621-629.
EPub date: 2018-04-09.
PMID: 29632380
Related Citations

Placenta and appetite genes GDF15 and IGFBP7 are associated with hyperemesis gravidarum.
Authors: Fejzo M.S. , Sazonova O.V. , Sathirapongsasuti J.F. , Hallgrímsdóttir I.B. , Vacic V. , MacGibbon K.W. , Schoenberg F.P. , Mancuso N. , Slamon D.J. , Mullin P.M. , et al. .
Source: Nature communications, 2018-03-21; 9(1), p. 1178.
EPub date: 2018-03-21.
PMID: 29563502
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.
PMID: 29481575
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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. , Lindström S. , Hui S. , Lemaçon A. , Soucy P. , Dennis J. , et al. .
Source: Nature genetics, 2017 Dec; 49(12), p. 1767-1778.
EPub date: 2017-10-23.
PMID: 29058716
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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; 101(5), p. 737-751.
PMID: 29100087
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Association analysis identifies 65 new breast cancer risk loci.
Authors: Michailidou K. , Lindström S. , Dennis J. , Beesley J. , Hui S. , Kar S. , Lemaçon A. , Soucy P. , Glubb D. , Rostamianfar A. , et al. .
Source: Nature, 2017-11-02; 551(7678), p. 92-94.
EPub date: 2017-10-23.
PMID: 29059683
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Quantifying the Genetic Correlation between Multiple Cancer Types.
Authors: Lindström 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 09; 26(9), p. 1427-1435.
EPub date: 2017-06-21.
PMID: 28637796
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Improved methods for multi-trait fine mapping of pleiotropic risk loci.
Authors: Kichaev G. , Roytman M. , Johnson R. , Eskin E. , Lindström S. , Kraft P. , Pasaniuc B. .
Source: Bioinformatics (Oxford, England), 2017-01-15; 33(2), p. 248-255.
EPub date: 2016-09-22.
PMID: 27663501
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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; 7, p. 10979.
EPub date: 2016-04-07.
PMID: 27052111
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