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The genetic interplay between body mass index, breast size and breast cancer risk: a Mendelian randomization analysis.

ABSTRACT: BACKGROUND:Evidence linking breast size to breast cancer risk has been inconsistent, and its interpretation is often hampered by confounding factors such as body mass index (BMI). Here, we used linkage disequilibrium score regression and two-sample Mendelian randomization (MR) to examine the genetic associations between BMI, breast size and breast cancer risk. METHODS:Summary-level genotype data from 23andMe, Inc (breast size, n?=?33 790), the Breast Cancer Association Consortium (breast cancer risk, n?=?228 951) and the Genetic Investigation of ANthropometric Traits (BMI, n?=?183 507) were used for our analyses. In assessing causal relationships, four complementary MR techniques [inverse variance weighted (IVW), weighted median, weighted mode and MR-Egger regression] were used to test the robustness of the results. RESULTS:The genetic correlation (rg) estimated between BMI and breast size was high (rg?=?0.50, P?=?3.89x10-43). All MR methods provided consistent evidence that higher genetically predicted BMI was associated with larger breast size [odds ratio (ORIVW): 2.06 (1.80-2.35), P?=?1.38x10-26] and lower overall breast cancer risk [ORIVW: 0.81 (0.74-0.89), P?=?9.44x10-6]. No evidence of a relationship between genetically predicted breast size and breast cancer risk was found except when using the weighted median and weighted mode methods, and only with oestrogen receptor (ER)-negative risk. There was no evidence of reverse causality in any of the analyses conducted (P?>?0.050). CONCLUSION:Our findings indicate a potential positive causal association between BMI and breast size and a potential negative causal association between BMI and breast cancer risk. We found no clear evidence for a direct relationship between breast size and breast cancer risk.

SUBMITTER: Ooi BNS

PROVIDER: S-EPMC6659372 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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The genetic interplay between body mass index, breast size and breast cancer risk: a Mendelian randomization analysis.

Ooi Brandon Nick Sern BNS Loh Huiwen H Ho Peh Joo PJ Milne Roger L RL Giles Graham G Gao Chi C Kraft Peter P John Esther M EM Swerdlow Anthony A Brenner Hermann H Wu Anna H AH Haiman Christopher C Evans D Gareth DG Zheng Wei W Fasching Peter A PA Castelao Jose Esteban JE Kwong Ava A Shen Xia X Czene Kamila K Hall Per P Dunning Alison A Easton Douglas D Hartman Mikael M Li Jingmei J

International journal of epidemiology 20190601 3

<h4>Background</h4>Evidence linking breast size to breast cancer risk has been inconsistent, and its interpretation is often hampered by confounding factors such as body mass index (BMI). Here, we used linkage disequilibrium score regression and two-sample Mendelian randomization (MR) to examine the genetic associations between BMI, breast size and breast cancer risk.<h4>Methods</h4>Summary-level genotype data from 23andMe, Inc (breast size, n = 33 790), the Breast Cancer Association Consortium ...[more]

PMID: 31243447

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Project description:BackgroundRecent studies have reported associations between body mass index (BMI) and various autoimmune disorders. However, it is still uncertain whether there exists a direct cause-and-effect relationship between BMI and autoimmune thyroiditis (AIT). The aim of our study is to investigate the causal association between BMI and AIT.MethodsWe conducted a two-sample summary data Mendelian randomization (MR) analysis using genome-wide association studies (GWAS) summary statistics data related to BMI as exposure, and GWAS summary statistic data sets for AIT as the outcome. Robustly associated single-nucleotide polymorphisms (SNPs) for BMI were selected as instrumental variables (IVs). We used the inverse variance weighted (IVW) method as the primary method and performed other MR methods such as MR-Egger regression, weighted median, simple mode, and weighted mode analyses for further validation. The slope of MR-Egger regression was used to correct for pleiotropy and provide estimates of causality. The p-value for the intercept in MR-Egger was utilized to detect any directional pleiotropic effects. Heterogeneity and sensitivity analyses were performed to assess the robustness of our findings.ResultsSeventy-eight SNPs were selected from GWAS on BMI as the IVs. Our MR analysis using the IVW method showed a potential causal association between BMI and AIT (OR = 3.071, 95% CI 1.324-7.118). Findings from other MR methods are non-significant, although the direction of effect is consistent. There was no evidence that the result was affected by genetic pleiotropy (MR-Egger regression intercept = 0.01, SE = 0.00025, p = 0.719). Heterogeneity and sensitivity analyses revealed no significant heterogeneity among SNPs, and no single SNP drove the observed associations.ConclusionOur findings suggest a potential causal association between BMI and AIT, which may provide a basis for further investigation into the relationship between BMI and AIT. Further studies are required as only the IVW method shows significant results, and the case sample size is small.

Project description:BackgroundPsoriasis is a common inflammatory skin disease that has been reported to be associated with obesity. We aimed to investigate a possible causal relationship between body mass index (BMI) and psoriasis.Methods and findingsFollowing a review of published epidemiological evidence of the association between obesity and psoriasis, mendelian randomization (MR) was used to test for a causal relationship with BMI. We used a genetic instrument comprising 97 single-nucleotide polymorphisms (SNPs) associated with BMI as a proxy for BMI (expected to be much less confounded than measured BMI). One-sample MR was conducted using individual-level data (396,495 individuals) from the UK Biobank and the Nord-Trøndelag Health Study (HUNT), Norway. Two-sample MR was performed with summary-level data (356,926 individuals) from published BMI and psoriasis genome-wide association studies (GWASs). The one-sample and two-sample MR estimates were meta-analysed using a fixed-effect model. To test for a potential reverse causal effect, MR analysis with genetic instruments comprising variants from recent genome-wide analyses for psoriasis were used to test whether genetic risk for this skin disease has a causal effect on BMI. Published observational data showed an association of higher BMI with psoriasis. A mean difference in BMI of 1.26 kg/m2 (95% CI 1.02-1.51) between psoriasis cases and controls was observed in adults, while a 1.55 kg/m2 mean difference (95% CI 1.13-1.98) was observed in children. The observational association was confirmed in UK Biobank and HUNT data sets. Overall, a 1 kg/m2 increase in BMI was associated with 4% higher odds of psoriasis (meta-analysis odds ratio [OR] = 1.04; 95% CI 1.03-1.04; P = 1.73 × 10(-60)). MR analyses provided evidence that higher BMI causally increases the odds of psoriasis (by 9% per 1 unit increase in BMI; OR = 1.09 (1.06-1.12) per 1 kg/m2; P = 4.67 × 10(-9)). In contrast, MR estimates gave little support to a possible causal effect of psoriasis genetic risk on BMI (0.004 kg/m2 change in BMI per doubling odds of psoriasis (-0.003 to 0.011). Limitations of our study include possible misreporting of psoriasis by patients, as well as potential misdiagnosis by clinicians. In addition, there is also limited ethnic variation in the cohorts studied.ConclusionsOur study, using genetic variants as instrumental variables for BMI, provides evidence that higher BMI leads to a higher risk of psoriasis. This supports the prioritization of therapies and lifestyle interventions aimed at controlling weight for the prevention or treatment of this common skin disease. Mechanistic studies are required to improve understanding of this relationship.

Dataset Information

The genetic interplay between body mass index, breast size and breast cancer risk: a Mendelian randomization analysis.

Publications

The genetic interplay between body mass index, breast size and breast cancer risk: a Mendelian randomization analysis.

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