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[Preprint]. 2024 Dec 19:2024.12.16.628610.

doi: 10.1101/2024.12.16.628610.

Pathway Polygenic Risk Scores (pPRS) for the Analysis of Gene-environment Interaction

W James Gauderman¹, Yubo Fu¹, Bryan Queme², Eric Kawaguchi¹, Yinqiao Wang¹, John Morrison¹, Hermann Brenner^{3

4}, Andrew Chan^{5

6}, Stephen B Gruber⁷, Temitope Keku⁸, Li Li⁹, Victor Moreno^{10

11

12

13}, Andrew J Pellatt¹⁴, Ulrike Peters^{15

16}, N Jewel Samadder¹⁷, Stephanie L Schmit^{18

19}, Cornelia M Ulrich^{20

21}, Caroline Um²², Anna Wu²³, Juan Pablo Lewinger¹, David A Drew^{5

6}, Huaiyu Mi²

Affiliations

¹ Division of Biostatistics, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.
² Division of Bioinformatics, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.
³ Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.
⁴ German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
⁵ Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
⁶ Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
⁷ Center for Precision Medicine and Department of Medical Oncology, City of Hope National Medical Center, Duarte, CA, USA.
⁸ University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
⁹ Department of Family Medicine, UVA Comprehensive Cancer Center, UVA School of Medicine, Charlottesville, VA, USA.
¹⁰ Oncology Data Analytics Program, Catalan Institute of Oncology (ICO), L'Hospitalet de Llobregat, 08908 Barcelona, Spain.
¹¹ Colorectal Cancer Group, ONCOBELL Program, Institut d'Investigació Biomèdica de Bellvitge (IDIBELL), L'Hospitalet de Llobregat, 08908 Barcelona, Spain.
¹² Department of Clinical Sciences, Faculty of Medicine and Health Sciences and Universitat de Barcelona Institute of Complex Systems (UBICS), University of Barcelona (UB), L'Hospitalet de Llobregat, 08908 Barcelona, Spain.
¹³ Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain.
¹⁴ Intermountain Health, Salt Lake City, UT, USA.
¹⁵ Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA.
¹⁶ Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA.
¹⁷ Mayo Clinic Comprehensive Cancer Center, Phoenix, AZ, USA.
¹⁸ Genomic Medicine Institute, Cleveland Clinic, Cleveland, OH, USA.
¹⁹ Population and Cancer Prevention Program, Case Comprehensive Cancer Center, Cleveland, OH, USA.
²⁰ Huntsman Cancer Institute, Salt Lake City, UT, USA.
²¹ Department of Population Sciences, University of Utah, USA.
²² Department of Population Science, American Cancer Society, Atlanta, GA, USA.
²³ Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.

PMID: 39763728
PMCID: PMC11702571
DOI: 10.1101/2024.12.16.628610

Pathway Polygenic Risk Scores (pPRS) for the Analysis of Gene-environment Interaction

W James Gauderman et al. bioRxiv. 2024.

[Preprint]. 2024 Dec 19:2024.12.16.628610.

doi: 10.1101/2024.12.16.628610.

Authors

Affiliations

¹ Division of Biostatistics, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.
² Division of Bioinformatics, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.
³ Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.
⁴ German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
⁵ Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
⁶ Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
⁷ Center for Precision Medicine and Department of Medical Oncology, City of Hope National Medical Center, Duarte, CA, USA.
⁸ University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
⁹ Department of Family Medicine, UVA Comprehensive Cancer Center, UVA School of Medicine, Charlottesville, VA, USA.
¹⁰ Oncology Data Analytics Program, Catalan Institute of Oncology (ICO), L'Hospitalet de Llobregat, 08908 Barcelona, Spain.
¹¹ Colorectal Cancer Group, ONCOBELL Program, Institut d'Investigació Biomèdica de Bellvitge (IDIBELL), L'Hospitalet de Llobregat, 08908 Barcelona, Spain.
¹² Department of Clinical Sciences, Faculty of Medicine and Health Sciences and Universitat de Barcelona Institute of Complex Systems (UBICS), University of Barcelona (UB), L'Hospitalet de Llobregat, 08908 Barcelona, Spain.
¹³ Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain.
¹⁴ Intermountain Health, Salt Lake City, UT, USA.
¹⁵ Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA.
¹⁶ Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA.
¹⁷ Mayo Clinic Comprehensive Cancer Center, Phoenix, AZ, USA.
¹⁸ Genomic Medicine Institute, Cleveland Clinic, Cleveland, OH, USA.
¹⁹ Population and Cancer Prevention Program, Case Comprehensive Cancer Center, Cleveland, OH, USA.
²⁰ Huntsman Cancer Institute, Salt Lake City, UT, USA.
²¹ Department of Population Sciences, University of Utah, USA.
²² Department of Population Science, American Cancer Society, Atlanta, GA, USA.
²³ Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.

PMID: 39763728
PMCID: PMC11702571
DOI: 10.1101/2024.12.16.628610

Update in

Pathway polygenic risk scores (pPRS) for the analysis of gene-environment interaction.
Gauderman WJ, Fu Y, Queme B, Kawaguchi E, Wang Y, Morrison J, Brenner H, Chan A, Gruber SB, Keku T, Li L, Moreno V, Pellatt AJ, Peters U, Samadder NJ, Schmit SL, Ulrich CM, Um C, Wu A, Lewinger JP, Drew DA, Mi H. Gauderman WJ, et al. PLoS Genet. 2025 Aug 5;21(8):e1011543. doi: 10.1371/journal.pgen.1011543. eCollection 2025 Aug. PLoS Genet. 2025. PMID: 40763299 Free PMC article.

Abstract

A polygenic risk score (PRS) is used to quantify the combined disease risk of many genetic variants. For complex human traits there is interest in determining whether the PRS modifies, i.e. interacts with, important environmental (E) risk factors. Detection of a PRS by environment (PRS × E) interaction may provide clues to underlying biology and can be useful in developing targeted prevention strategies for modifiable risk factors. The standard PRS may include a subset of variants that interact with E but a much larger subset of variants that affect disease without regard to E. This latter subset will 'water down' the underlying signal in former subset, leading to reduced power to detect PRS × E interaction. We explore the use of pathway-defined PRS (pPRS) scores, using state of the art tools to annotate subsets of variants to genomic pathways. We demonstrate via simulation that testing targeted pPRS × E interaction can yield substantially greater power than testing overall PRS × E interaction. We also analyze a large study (N=78,253) of colorectal cancer (CRC) where E = non-steroidal anti-inflammatory drugs (NSAIDs), a well-established protective exposure. While no evidence of overall PRS × NSAIDs interaction (p=0.41) is observed, a significant pPRS × NSAIDs interaction (p=0.0003) is identified based on SNPs within the TGF-β / gonadotropin releasing hormone receptor (GRHR) pathway. NSAIDS is protective (OR=0.84) for those at the 5^th percentile of the TGF-β/GRHR pPRS (low genetic risk, OR), but significantly more protective (OR=0.70) for those at the 95^th percentile (high genetic risk). From a biological perspective, this suggests that NSAIDs may act to reduce CRC risk specifically through genes in these pathways. From a population health perspective, our result suggests that focusing on genes within these pathways may be effective at identifying those for whom NSAIDs-based CRC-prevention efforts may be most effective.

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Figures

**Figure 1:**
Number of genes and pathways represented among the 204 CRC-associated SNPs. Arrows indicate the four pathways with significant over-representation compared to expected (Table 2).

**Figure 2:**
A. Subset of 204 CRC-associated SNPs annotated to genes within the TGF-β and/or the Gonadotropin releasing hormone receptor (GRHR) pathways. B. Subset of 204 CRC-associated SNPs annotated to genes within the Cadherin signaling (CADH) and/or the Alzheimer’s disease-presenilin (ALZ) pathways.

**Figure 3:**
NSAIDs odds ratio for CRC by quantile of the joint TGF-β / GRHR pPRS quantile.

See this image and copyright information in PMC

References

1. McAllister K. et al. Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases. Am J Epidemiol 186, 753–761 (2017). - PMC - PubMed
1. Gauderman W.J. et al. Update on the State of the Science for Analytical Methods for Gene-Environment Interactions. Am J Epidemiol 186, 762–770 (2017). - PMC - PubMed
1. Khera A.V. et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet 50, 1219–1224 (2018). - PMC - PubMed
1. Zhang X. et al. Circulating 25-hydroxyvitamin D and survival outcomes of colorectal cancer: evidence from population-based prospective cohorts and Mendelian randomisation. Br J Cancer 130, 1585–1591 (2024). - PMC - PubMed
1. Zhang P. et al. Association of smoking and polygenic risk with the incidence of lung cancer: a prospective cohort study. Br J Cancer 126, 1637–1646 (2022). - PMC - PubMed

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This is a preprint.

Pathway Polygenic Risk Scores (pPRS) for the Analysis of Gene-environment Interaction

Affiliations

Pathway Polygenic Risk Scores (pPRS) for the Analysis of Gene-environment Interaction

Authors

Affiliations

Update in

Abstract

Figures

References

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