Dataset Information

Gene-Environment Interactions Relevant to Estrogen and Risk of Breast Cancer: Can Gene-Environment Interactions Be Detected Only among Candidate SNPs from Genome-Wide Association Studies?

ABSTRACT: In this study we aim to examine gene-environment interactions (GxEs) between genes involved with estrogen metabolism and environmental factors related to estrogen exposure. GxE analyses were conducted with 1970 Korean breast cancer cases and 2052 controls in the case-control study, the Seoul Breast Cancer Study (SEBCS). A total of 11,555 SNPs from the 137 candidate genes were included in the GxE analyses with eight established environmental factors. A replication test was conducted by using an independent population from the Breast Cancer Association Consortium (BCAC), with 62,485 Europeans and 9047 Asians. The GxE tests were performed by using two-step methods in GxEScan software. Two interactions were found in the SEBCS. The first interaction was shown between rs13035764 of NCOA1 and age at menarche in the GE|2df model (p-2df = 1.2 × 10^-3). The age at menarche before 14 years old was associated with the high risk of breast cancer, and the risk was higher when subjects had homozygous minor allele G. The second GxE was shown between rs851998 near ESR1 and height in the GE|2df model (p-2df = 1.1 × 10^-4). Height taller than 160 cm was associated with a high risk of breast cancer, and the risk increased when the minor allele was added. The findings were not replicated in the BCAC. These results would suggest specificity in Koreans for breast cancer risk.

SUBMITTER: Park J

PROVIDER: S-EPMC8156547 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

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Gene-Environment Interactions Relevant to Estrogen and Risk of Breast Cancer: Can Gene-Environment Interactions Be Detected Only among Candidate SNPs from Genome-Wide Association Studies?

Park JooYong J Choi Ji-Yeob JY Choi Jaesung J Chung Seokang S Song Nan N Park Sue K SK Han Wonshik W Noh Dong-Young DY Ahn Sei-Hyun SH Lee Jong Won JW Kim Mi Kyung MK Jee Sun Ha SH Wen Wanqing W Bolla Manjeet K MK Wang Qin Q Dennis Joe J Michailidou Kyriaki K Shah Mitul M Conroy Don M DM Harrington Patricia A PA Mayes Rebecca R Czene Kamila K Hall Per P Teras Lauren R LR Patel Alpa V AV Couch Fergus J FJ Olson Janet E JE Sawyer Elinor J EJ Roylance Rebecca R Bojesen Stig E SE Flyger Henrik H Lambrechts Diether D Baten Adinda A Matsuo Keitaro K Ito Hidemi H Guénel Pascal P Truong Thérèse T Keeman Renske R Schmidt Marjanka K MK Wu Anna H AH Tseng Chiu-Chen CC Cox Angela A Cross Simon S SS kConFab Investigators Andrulis Irene L IL Hopper John L JL Southey Melissa C MC Wu Pei-Ei PE Shen Chen-Yang CY Fasching Peter A PA Ekici Arif B AB Muir Kenneth K Lophatananon Artitaya A Brenner Hermann H Arndt Volker V Jones Michael E ME Swerdlow Anthony J AJ Hoppe Reiner R Ko Yon-Dschun YD Hartman Mikael M Li Jingmei J Mannermaa Arto A Hartikainen Jaana M JM Benitez Javier J González-Neira Anna A Haiman Christopher A CA Dörk Thilo T Bogdanova Natalia V NV Teo Soo Hwang SH Mohd Taib Nur Aishah NA Fletcher Olivia O Johnson Nichola N Grip Mervi M Winqvist Robert R Blomqvist Carl C Nevanlinna Heli H Lindblom Annika A Wendt Camilla C Kristensen Vessela N VN Nbcs Collaborators Tollenaar Rob A E M RAEM Heemskerk-Gerritsen Bernadette A M BAM Radice Paolo P Bonanni Bernardo B Hamann Ute U Manoochehri Mehdi M Lacey James V JV Martinez Maria Elena ME Dunning Alison M AM Pharoah Paul D P PDP Easton Douglas F DF Yoo Keun-Young KY Kang Daehee D

Cancers 20210514 10

In this study we aim to examine gene-environment interactions (GxEs) between genes involved with estrogen metabolism and environmental factors related to estrogen exposure. GxE analyses were conducted with 1970 Korean breast cancer cases and 2052 controls in the case-control study, the Seoul Breast Cancer Study (SEBCS). A total of 11,555 SNPs from the 137 candidate genes were included in the GxE analyses with eight established environmental factors. A replication test was conducted by using an i ...[more]

PMID: 34069208

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Project description:BackgroundThe abuse of psychogenic drugs can lead to multiple health-related problems. Genetic and environmental vulnerabilities are factors in the emergence of substance use disorders. Empirical evidence regarding the gene-environment interaction in substance use is mixed. Summaries of the latest findings from a candidate gene approach will be useful for revealing the significance of particular gene contributions. Thus, we aim to identify different gene-environment interactions in patterns of substance use and investigate whether any effects trend notably across different genders and races.MethodsWe reviewed published studies, until March 1, 2022, on substance use for candidate gene-environment interaction. Basic demographics of the included studies, target genes, environmental factors, main findings, patterns of gene-environment interaction, and other relevant information were collected and summarized.ResultsAmong a total of 44 studies, 38 demonstrated at least one significant interaction effect. About 61.5% of studies on the 5-HTTLPR gene, 100% on the MAOA gene, 42.9% on the DRD2 gene, 50% on the DRD4 gene, 50% on the DAT gene, 80% on the CRHR1 gene, 100% on the OPRM1 gene, 100% on the GABRA1 gene, and 50% on the CHRNA gene had a significant gene-environment interaction effect. The diathesis-stress model represents a dominant interaction pattern (89.5%) in the studies with a significant interaction effect; the remaining significant effect on substance use is found in the differential susceptibility model. The social push and swing model were not reported in the included studies.ConclusionThe gene-environment interaction research on substance use behavior is methodologically multidimensional, which causes difficulty in conducting pooled analysis, or stated differently-making it hard to identify single sources of significant influence over maladaptive patterns of drug taking. In decreasing the heterogeneity and facilitating future pooled analysis, researchers must (1) replicate the existing studies with consistent study designs and measures, (2) conduct power calculations to report gene-environment correlations, (3) control for covariates, and (4) generate theory-based hypotheses with factorial based experiments when designing future studies.

Dataset Information

Gene-Environment Interactions Relevant to Estrogen and Risk of Breast Cancer: Can Gene-Environment Interactions Be Detected Only among Candidate SNPs from Genome-Wide Association Studies?

Publications

Gene-Environment Interactions Relevant to Estrogen and Risk of Breast Cancer: Can Gene-Environment Interactions Be Detected Only among Candidate SNPs from Genome-Wide Association Studies?

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