Genetically Predicted Body Mass Index and Breast Cancer Risk: Mendelian Randomization Analyses of Data from 145,000 Women of European Descent

Disentangling the relationships of body mass index and circulating sex hormone concentrations in mammographic density using Mendelian randomization

PURPOSE

Mammographic density phenotypes, adjusted for age and body mass index (BMI), are strong predictors of breast cancer risk. BMI is associated with mammographic density measures, but the role of circulating sex hormone concentrations is less clear. We investigated the relationship between BMI, circulating sex hormone concentrations, and mammographic density phenotypes using Mendelian randomization (MR).

METHODS

We applied two-sample MR approaches to assess the association between genetically predicted circulating concentrations of sex hormones [estradiol, testosterone, sex hormone-binding globulin (SHBG)], BMI, and mammographic density phenotypes (dense and non-dense area). We created instrumental variables from large European ancestry-based genome-wide association studies and applied estimates to mammographic density phenotypes in up to 14,000 women of European ancestry. We performed analyses overall and by menopausal status.

RESULTS

Genetically predicted BMI was positively associated with non-dense area (IVW: β = 1.79; 95% CI = 1.58, 2.00; p = 9.57 × 10-63) and inversely associated with dense area (IVW: β = - 0.37; 95% CI = - 0.51,- 0.23; p = 4.7 × 10-7). We observed weak evidence for an association of circulating sex hormone concentrations with mammographic density phenotypes, specifically inverse associations between genetically predicted testosterone concentration and dense area (β = - 0.22; 95% CI = - 0.38, - 0.053; p = 0.009) and between genetically predicted estradiol concentration and non-dense area (β =  - 3.32; 95% CI = - 5.83, - 0.82; p = 0.009), although results were not consistent across a range of MR approaches.

CONCLUSION

Our findings support a positive causal association between BMI and mammographic non-dense area and an inverse association between BMI and dense area. Evidence was weaker and inconsistent for a causal effect of circulating sex hormone concentrations on mammographic density phenotypes. Based on our findings, associations between circulating sex hormone concentrations and mammographic density phenotypes are weak at best.

Obesity-Dependent Association of the rs10454142 PPP1R21 with Breast Cancer

The purpose of this work was to find a link between the breast cancer (BC)-risk effects of sex hormone-binding globulin (SHBG)-associated polymorphisms and obesity. The study was conducted on a sample of 1498 women (358 BC; 1140 controls) who, depending on the presence/absence of obesity, were divided into two groups: obese (119 BC; 253 controls) and non-obese (239 BC; 887 controls). Genotyping of nine SHBG-associated single nucleotide polymorphisms (SNP)-rs17496332 PRMT6, rs780093 GCKR, rs10454142 PPP1R21, rs3779195 BAIAP2L1, rs440837 ZBTB10, rs7910927 JMJD1C, rs4149056 SLCO1B1, rs8023580 NR2F2, and rs12150660 SHBG-was executed, and the BC-risk impact of these loci was analyzed by logistic regression separately in each group of obese/non-obese women. We found that the BC-risk effect correlated by GWAS with the SHBG-level polymorphism rs10454142 PPP1R21 depends on the presence/absence of obesity. The SHBG-lowering allele C rs10454142 PPP1R21 has a risk value for BC in obese women (allelic model: CvsT, OR = 1.52, 95%CI = 1.10-2.11, and pperm = 0.013; additive model: CCvsTCvsTT, OR = 1.71, 95%CI = 1.15-2.62, and pperm = 0.011; dominant model: CC + TCvsTT, OR = 1.95, 95%CI = 1.13-3.37, and pperm = 0.017) and is not associated with the disease in women without obesity. SNP rs10454142 PPP1R21 and 10 proxy SNPs have adipose-specific regulatory effects (epigenetic modifications of promoters/enhancers, DNA interaction with 51 transcription factors, eQTL/sQTL effects on five genes (PPP1R21, RP11-460M2.1, GTF2A1L, STON1-GTF2A1L, and STON1), etc.), can be "likely cancer driver" SNPs, and are involved in cancer-significant pathways. In conclusion, our study detected an obesity-dependent association of the rs10454142 PPP1R21 with BC in women.

Adiposity and cancer: meta-analysis, mechanisms, and future perspectives

Obesity is a recognised risk factor for many cancers and with rising global prevalence, has become a leading cause of cancer. Here we summarise the current evidence from both population-based epidemiologic investigations and experimental studies on the role of obesity in cancer development. This review presents a new meta-analysis using data from 40 million individuals and reports positive associations with 19 cancer types. Utilising major new data from East Asia, the meta-analysis also shows that the strength of obesity and cancer associations varies regionally, with stronger relative risks for several cancers in East Asia. This review also presents current evidence on the mechanisms linking obesity and cancer and identifies promising future research directions. These include the use of new imaging data to circumvent the methodological issues involved with body mass index and the use of omics technologies to resolve biologic mechanisms with greater precision and clarity.

Relationship between oily fish intake and breast cancer based on estrogen receptor status: a Mendelian randomization study

OBJECTIVE

We used a Mendelian randomization (MR) method in our research to examine the relationship between genetically determined oily fish intake and breast cancer (BC) incidence.

METHODS

The summary data pertaining to the oily fish intake were acquired from the UK Biobank, which consisted of a sample size of 460,443 people. Information on BC was received from the Breast Cancer Association Consortium (BCAC). We analyzed the causal connection between oily fish intake and BC incidence using various methods, including inverse variance weighting (IVW). Heterogeneity was investigated using Cochran's Q test. IVW, MR-Egger, and MR-PRESSO methods were used for sensitivity analysis. In addition, a multivariate MR adjusted for body mass index (BMI) and weight was used for further research.

RESULTS

Two-sample MR results showed that oily fish intake was negatively associated with total breast cancer (odds ratio (OR) 0.58, 95% confidence interval (CI) 0.39-0.87, IVW method), estrogen receptor-positive (ER +) breast cancer (OR 0.44, 95% CI 0.21-0.93, IVW method), and estrogen receptor-negative (ER-) breast cancer (OR 0.53, 95% CI 0.30-0.93, IVW method). The sensitivity analysis did not observe the presence of heterogeneity and horizontal pleiotropy. In multivariate MR analysis, the negative association between oily fish intake and total breast cancer (P = 0.03) and ER- breast cancer (P = 0.04) risk persisted after adjusting for BMI and body weight. However, no correlation was found in ER + breast cancer (P = 0.30).

CONCLUSION

The oily fish intake has a negatively correlated with the incidence of total breast cancer, particularly in the cases of ER- breast cancer. There is a lack of substantial evidence supporting a link between the oily fish intake and the incidence of ER + breast cancer.

Causal associations between schizophrenia and cancers risk: a Mendelian randomization study

Background

Previous observational studies have reported inconsistent findings regarding the incidence of cancer in patients with schizophrenia compared to the general population. The causal relationship between schizophrenia and cancer remains unclear and requires further investigation.

Objective

To investigate the causal relationship between schizophrenia and cancer.

Methods

In this study, a two-sample Mendelian randomization (MR) analysis was conducted using publicly available genome-wide association studies to determine the causal relationship. The effect estimates were calculated using the random-effects inverse-variance-weighted method.

Results

We determined a causal relationship between genetic predisposition to schizophrenia and cancer, with schizophrenia increasing lung cancer (odds ratio (OR) = 1.0007; 95% confidence interval (CI), 1.0001-1.0013; p = 0.0192), thyroid cancer (OR = 1.5482; CI, 1.1112-2.1569; p =0.0098),colorectal cancer (OR = 1.0009; CI, 1.0001-1.0018; p = 0.0344), ovarian cancer (OR = 1.0770; CI, 1.0352-1.1203; p = 0.0002), breast cancer (OR = 1.0011; CI, 1.0001- 1.0022; p =0.0352) and reduced the risk of malignant neoplasm of the stomach (OR = 0.8502; CI, 0.7230-0.9998; p = 0.0496).

Conclusions

This study conducted a two-sample MR analysis and discovered a positive causal relationship between schizophrenia and breast, ovarian, thyroid, lung, and colorectal cancers. On the other hand, an inverse causal relationship was found between schizophrenia and malignant neoplasm of the stomach.

Association between angiotensin-converting enzyme inhibitor-induced cough and the risk of lung cancer: a Mendelian randomization study

Background: Observational studies and meta-analyses have demonstrated a positive correlation between the use of angiotensin-converting enzyme inhibitors (ACEIs) and lung cancer. However, the findings remain controversial; furthermore, the relationship between ACEI-induced cough and lung cancer development remains unknown. We used Mendelian randomization (MR) to verify the association between ACEI use, ACEI-induced cough, and the risk of lung cancer. Methods: We performed a two-sample MR analysis to determine the unconfounded relationships between ACE inhibition, which mimics the effects of ACEIs, and genetic proxies for ACEI-induced cough and lung cancer. Single nucleotide polymorphisms that imitate ACE receptors and ACEI-induced cough were collected and integrated into a meta-analysis of existing genome-wide association studies for various lung cancers. The relationship was quantified using inverse variance weighting, weighted median, and MR-Egger methods. Results: A statistically significant association was observed between ACE inhibition and the risk of small cell lung cancer for Europeans (excluding rs118121655/rs80311894). Associations were identified between ACEI-induced cough and the risk of lung cancer for Europeans, although not for Asians, and between ACEI-induced cough and lung adenocarcinoma (excluding rs360206). Conclusion: Our findings reveal a relationship between ACE inhibition and lung cancer development, as well as a significant association between ACEI-induced cough and a higher risk of lung cancer for Europeans. Patients with hypertension who experience dry cough as a side effect of ACEI use should consider switching to an alternative antihypertensive treatment.

Cardiovascular variability, sociodemographics, and biomarkers of disease: the MIDUS study

Introduction: Like heart rate, blood pressure (BP) is not steady but varies over intervals as long as months to as short as consecutive cardiac cycles. This blood pressure variability (BPV) consists of regularly occurring oscillations as well as less well-organized changes and typically is computed as the standard deviation of multiple clinic visit-to-visit (VVV-BP) measures or from 24-h ambulatory BP recordings (ABPV). BP also varies on a beat-to-beat basis, quantified by methods that parse variation into discrete bins, e.g., low frequency (0.04-0.15 Hz, LF). However, beat-to-beat BPV requires continuous recordings that are not easily acquired. As a result, we know little about the relationship between LF-BPV and basic sociodemographic characteristics such as age, sex, and race and clinical conditions. Methods: We computed LF-BPV during an 11-min resting period in 2,118 participants in the Midlife in the US (MIDUS) study. Results: LF-BPV was negatively associated with age, greater in men than women, and unrelated to race or socioeconomic status. It was greater in participants with hypertension but unrelated to hyperlipidemia, hypertriglyceridemia, diabetes, elevated CRP, or obesity. LF-diastolic BPV (DBPV), but not-systolic BPV (SBPV), was negatively correlated with IL-6 and s-ICAM and positively correlated with urinary epinephrine and cortisol. Finally, LF-DBPV was negatively associated with mortality, an effect was rendered nonsignificant by adjustment by age but not other sociodemographic characteristics. Discussion: These findings, the first from a large, national sample, suggest that LF-BPV differs significantly from VVV-BP and ABPV. Confirming its relationship to sociodemographic risk factors and clinical outcomes requires further study with large and representative samples.

An exploration of the correlations between seven psychiatric disorders and the risks of breast cancer, breast benign tumors and breast inflammatory diseases: Mendelian randomization analyses

Background

Previous observational studies have showed that certain psychiatric disorders may be linked to breast cancer risk, there is, however, little understanding of relationships between mental disorders and a variety of breast diseases. This study aims to investigate if mental disorders influence the risks of overall breast cancer, the two subtypes of breast cancer (ER+ and ER-), breast benign tumors and breast inflammatory diseases.

Methods

During our research, genome-wide association study (GWAS) data for seven psychiatric disorders (schizophrenia, major depressive disorder, bipolar disorder, post-traumatic stress disorder, panic disorder, obsessive-compulsive disorder and anorexia nervosa) from the Psychiatric Genomics Consortium (PGC) and the UK Biobank were selected, and single-nucleotide polymorphisms (SNPs) significantly linked to these mental disorders were identified as instrumental variables. GWAS data for breast diseases came from the Breast Cancer Association Consortium (BCAC) as well as the FinnGen consortium. We performed two-sample Mendelian randomization (MR) analyses and multivariable MR analyses to assess these SNPs' effects on various breast diseases. Both heterogeneity and pleiotropy were evaluated by sensitivity analyses.

Results

When the GWAS data of psychiatric disorders were derived from the PGC, our research found that schizophrenia significantly increased the risks of overall breast cancer (two-sample MR: OR 1.05, 95%CI [1.03-1.07], p = 3.84 × 10^-6; multivariable MR: OR 1.06, 95%CI [1.04-1.09], p = 2.34 × 10^-6), ER+ (OR 1.05, 95%CI [1.02-1.07], p = 5.94 × 10^-5) and ER- (two-sample MR: OR 1.04, 95%CI [1.01-1.07], p = 0.006; multivariable MR: OR 1.06, 95%CI [1.02-1.10], p = 0.001) breast cancer. Nevertheless, major depressive disorder only showed significant positive association with overall breast cancer (OR 1.12, 95%CI [1.04-1.20], p = 0.003) according to the two-sample MR analysis, but not in the multivariable MR analysis. In regards to the remainder of the mental illnesses and breast diseases, there were no significant correlations. While as for the data from the UK Biobank, schizophrenia did not significantly increase the risk of breast cancer.

Conclusions

The correlation between schizophrenia and breast cancer found in this study may be false positive results caused by underlying horizontal pleiotropy, rather than a true cause-and-effect relationship. More prospective studies are still needed to be carried out to determine the definitive links between mental illnesses and breast diseases.

Genetically proxied impaired GIPR signaling and risk of 6 cancers

Preclinical and genetic studies suggest that impaired glucose-dependent insulinotropic polypeptide receptor (GIPR) signaling worsens glycemic control. The relationship between GIPR signaling and the risk of cancers influenced by impaired glucose homeostasis is unclear. We examined the association of a variant in GIPR, rs1800437 (E354Q), shown to impair long-term GIPR signaling and lower circulating glucose-dependent insulinotropic peptide concentrations, with risk of 6 cancers influenced by impaired glucose homeostasis (breast, colorectal, endometrial, lung, pancreatic, and renal) in up to 235,698 cases and 333,932 controls. Each copy of E354Q was associated with a higher risk of overall and luminal A-like breast cancer and this association was consistent in replication and colocalization analyses. E354Q was also associated with higher postprandial glucose concentrations but diminished insulin secretion and lower testosterone concentrations. Our human genetics analysis suggests an adverse effect of the GIPR E354Q variant on breast cancer risk, supporting further evaluation of GIPR signaling in breast cancer prevention.

Genetic association of mosaic loss of chromosome Y with prostate cancer in men of European and East Asian ancestries: a Mendelian randomization study

Background: Genomic instability is a significant hallmark of aging and has a major impact on aging biology. Mosaic loss of chromosome Y (mLOY) in blood cells is a common chromosomal abnormality in aging men and is considered an indicator of genomic instability. Previous studies have indicated a connection between mLOY and prostate cancer risk, but the causal relationship has not been fully established. Methods: To determine the causal effect of mLOY on prostate cancer, we conducted a Mendelian Randomization (MR) study in two ancestral groups. We utilized 125 and 42 mLOY-associated variants as instrumental variables (IVs) in European and East Asian GWAS of prostate cancer, respectively. Summary-level data on prostate cancer was obtained from the PRACTICAL consortium (79,148 cases and 61,106 controls of European ancestry) and the Biobank Japan consortium (5,408 cases and 103,939 controls of East Asian ancestry). A single population was used to assess the causal relationship in East Asian ancestry. Our main method for obtaining MR results was inverse-variance weighted (IVW), and we conducted sensitivity analyses to confirm the robustness of our results. Finally, we combined the estimates from both sources using a fixed-effects meta-analysis. Results: Our MR analysis using the IVW method showed that a one-unit increase in genetically predicted mLOY was associated with an increased risk of prostate cancer in the PRACTICAL consortium (OR = 1.09%, 95% CI: 1.05-1.13, p = 1.2 × 10^-5), but not in the Biobank Japan consortium (OR = 1.13%, 95% CI: 0.88-1.45, p = 0.34). Sensitivity analyses robustly indicated increased odds ratios for prostate cancer with every one-unit increase in genetically predicted mLOY for the PRACTICAL consortium. Furthermore, mLOY was found to be associated with prostate cancer risk in a meta-analysis of both sources (OR = 1.09%, 95% CI: 1.05-1.13, p = 8.0 × 10^-6). Conclusion: Our MR study provides strong evidence that higher mLOY increases the risk of prostate cancer. Preventing mLOY may be a means of reducing the risk of developing prostate cancer.

Body Shape Phenotypes and Breast Cancer Risk: A Mendelian Randomization Analysis

Observational and genetic studies have linked different anthropometric traits to breast cancer (BC) risk, with inconsistent results. We aimed to investigate the association between body shape defined by a principal component (PC) analysis of anthropometric traits (body mass index [BMI], height, weight, waist-to-hip ratio [WHR], and waist and hip circumference) and overall BC risk and by tumor sub-type (luminal A, luminal B, HER2+, triple negative, and luminal B/HER2 negative). We performed two-sample Mendelian randomization analyses to assess the association between 188 genetic variants robustly linked to the first three PCs and BC (133,384 cases/113,789 controls from the Breast Cancer Association Consortium (BCAC)). PC1 (general adiposity) was inversely associated with overall BC risk (0.89 per 1 SD [95% CI: 0.81-0.98]; p-value = 0.016). PC2 (tall women with low WHR) was weakly positively associated with overall BC risk (1.05 [95% CI: 0.98-1.12]; p-value = 0.135), but with a confidence interval including the null. PC3 (tall women with large WHR) was not associated with overall BC risk. Some of these associations differed by BC sub-types. For instance, PC2 was positively associated with a risk of luminal A BC sub-type (1.09 [95% CI: 1.01-1.18]; p-value = 0.02). To clarify the inverse association of PC1 with breast cancer risk, future studies should examine independent risk associations of this body shape during childhood/adolescence and adulthood.

Using genetic association data to guide drug discovery and development: Review of methods and applications

Evidence on the validity of drug targets from randomized trials is reliable but typically expensive and slow to obtain. In contrast, evidence from conventional observational epidemiological studies is less reliable because of the potential for bias from confounding and reverse causation. Mendelian randomization is a quasi-experimental approach analogous to a randomized trial that exploits naturally occurring randomization in the transmission of genetic variants. In Mendelian randomization, genetic variants that can be regarded as proxies for an intervention on the proposed drug target are leveraged as instrumental variables to investigate potential effects on biomarkers and disease outcomes in large-scale observational datasets. This approach can be implemented rapidly for a range of drug targets to provide evidence on their effects and thus inform on their priority for further investigation. In this review, we present statistical methods and their applications to showcase the diverse opportunities for applying Mendelian randomization in guiding clinical development efforts, thus enabling interventions to target the right mechanism in the right population group at the right time. These methods can inform investigators on the mechanisms underlying drug effects, their related biomarkers, implications for the timing of interventions, and the population subgroups that stand to gain the most benefit. Most methods can be implemented with publicly available data on summarized genetic associations with traits and diseases, meaning that the only major limitations to their usage are the availability of appropriately powered studies for the exposure and outcome and the existence of a suitable genetic proxy for the proposed intervention.

Disentangling the aetiological pathways between body mass index and site-specific cancer risk using tissue-partitioned Mendelian randomisation

BACKGROUND

Body mass index (BMI) is known to influence the risk of various site-specific cancers, however, dissecting which subcomponents of this heterogenous risk factor are predominantly responsible for driving disease effects has proven difficult to establish. We have leveraged tissue-specific gene expression to separate the effects of distinct phenotypes underlying BMI on the risk of seven site-specific cancers.

METHODS

SNP-exposure estimates were weighted in a multivariable Mendelian randomisation analysis by their evidence for colocalization with subcutaneous adipose- and brain-tissue-derived gene expression using a recently developed methodology.

RESULTS

Our results provide evidence that brain-tissue-derived BMI variants are predominantly responsible for driving the genetically predicted effect of BMI on lung cancer (OR: 1.17; 95% CI: 1.01-1.36; P = 0.03). Similar findings were identified when analysing cigarettes per day as an outcome (Beta = 0.44; 95% CI: 0.26-0.61; P = 1.62 × 10-6), highlighting a possible shared aetiology or mediator effect between brain-tissue BMI, smoking and lung cancer. Our results additionally suggest that adipose-tissue-derived BMI variants may predominantly drive the effect of BMI and increased risk for endometrial cancer (OR: 1.71; 95% CI: 1.07-2.74; P = 0.02), highlighting a putatively important role in the aetiology of endometrial cancer.

CONCLUSIONS

The study provides valuable insight into the divergent underlying pathways between BMI and the risk of site-specific cancers.

Identifying and ranking causal biochemical biomarkers for breast cancer: a Mendelian randomisation study

BACKGROUND

Only a few of the 34 biochemical biomarkers measured in the UK Biobank (UKB) have been associated with breast cancer, with many associations suffering from possible confounding and reverse causation. This study aimed to screen and rank all UKB biochemical biomarkers for possible causal relationships with breast cancer.

METHODS

We conducted two-sample Mendelian randomisation (MR) analyses on ~420,000 women by leveraging summary-level genetic exposure associations from the UKB study (n = 194,174) and summary-level genetic outcome associations from the Breast Cancer Association Consortium (n = 228,951). Our exposures included all 34 biochemical biomarkers in the UKB, and our outcomes were overall, oestrogen-positive, and oestrogen-negative breast cancer. We performed inverse-variance weighted MR, weighted median MR, MR-Egger, and MR-PRESSO for 30 biomarkers for which we found multiple instrumental variables. We additionally performed multivariable MR to adjust for known risk factors, bidirectional MR to investigate reverse causation, and MR Bayesian model averaging to rank the significant biomarkers by their genetic evidence.

RESULTS

Increased genetic liability to overall breast cancer was robustly associated with the following biomarkers by decreasing importance: testosterone (odds ratio (OR): 1.12, 95% confidence interval (CI): 1.04-1.21), high-density lipoprotein (HDL) cholesterol (OR: 1.08, 95% CI: 1.04-1.13), insulin-like growth factor 1 (OR: 1.08, 95% CI: 1.02-1.13), and alkaline phosphatase (ALP) (OR: 0.93, 95% CI: 0.89-0.98).

CONCLUSIONS

Our findings support a likely causal role of genetically predicted levels of testosterone, HDL cholesterol, and IGF-1, as well as a novel potential role of ALP in breast cancer aetiology. Further studies are needed to understand full disease pathways that may inform breast cancer prevention.

The Modifying Effect of Obesity on the Association of Matrix Metalloproteinase Gene Polymorphisms with Breast Cancer Risk

Objective: We investigated the possible modifying effect of obesity on the association of matrix metalloproteinase (MMP) gene polymorphisms with breast cancer (BC) risk. Methods: A total of 1104 women divided into two groups according to their body mass index (BMI): BMI ≥ 30 (119 BC, and 190 control) and BMI < 30 (239 BC, and 556 control) were genotyped for specially selected (according to their association with BC in the previous study) 10 single-nucleotide polymorphisms (SNP) of MMP1, 2, 3, 8, and 9 genes. Logistic regression association analysis was performed in each studied group of women (with/without obesity). Functional annotation of BC-correlated MMP polymorphic variants was analyzed by in silico bioinformatics. Results: We observed significant differences in the involvement of MMP SNPs in BC in obese and non-obese women. Polymorphic loci MMP9 (c.836 A > G (rs17576) and c. 1721 C > G (rs2250889)) were BC-protective factors in obese women (OR 0.71, allelic model, and OR 0.55, additive model, respectively). Genotypes TT MMP2 (c.-1306 C > T,rs243865) and AA MMP9 (c. 1331-163 G > A,rs3787268) determined BC susceptibility in non-obese women (OR 0.31, and OR 2.36, respectively). We found in silico substantial multidirectional influences on gene expression in adipose tissue BC-related polymorphic loci: BC risk allele A-rs3787268 in non-obese women is associated with low expression NEURL2, PLTP, RP3-337O18.9, SPATA25, and ZSWIM1, whereas BC risk allele A-rs17576 in obese women is associated with high expression in the same genes in visceral and/or subcutaneous adipose. Conclusions: our study indicated that obesity has a significant modifying effect on the association of MMP genes with BC risk in postmenopausal women.

ExPRSweb: An online repository with polygenic risk scores for common health-related exposures

Complex traits are influenced by genetic risk factors, lifestyle, and environmental variables, so-called exposures. Some exposures, e.g., smoking or lipid levels, have common genetic modifiers identified in genome-wide association studies. Because measurements are often unfeasible, exposure polygenic risk scores (ExPRSs) offer an alternative to study the influence of exposures on various phenotypes. Here, we collected publicly available summary statistics for 28 exposures and applied four common PRS methods to generate ExPRSs in two large biobanks: the Michigan Genomics Initiative and the UK Biobank. We established ExPRSs for 27 exposures and demonstrated their applicability in phenome-wide association studies and as predictors for common chronic conditions. Especially the addition of multiple ExPRSs showed, for several chronic conditions, an improvement compared to prediction models that only included traditional, disease-focused PRSs. To facilitate follow-up studies, we share all ExPRS constructs and generated results via an online repository called ExPRSweb.

The influence of age, menstrual state and body mass index on the relation between osteopenia and osteoporosis associated with breast cancer

Osteoporosis and body mass index (BMI) have been reported to be associated with breast cancer. The correlation between other types of bone loss and breast cancer requires further illustrative evidence. Effect of age and menstrual state on the relationship of osteoporosis, osteopenia and BMI with breast cancer was evaluated in this study. Two hundred breast cancer patients and 200 breast cancer-free patients were included in a case-control study. Bone mineral density (BMD) and BMI were determined in all subjects. Postmenopause was the most frequent menstrual state. osteopenia was found to be significant in breast cancer patients and osteoporosis was significant in another group of women. There was no significant difference between breast cancer patients and control women in terms of age and menstrual status. Obesity was common in all subjects. In conclusion, bone loss rates are significantly low in breast cancer patients relative to healthy women. There was no significant impact of age, menstrual status or BMI on the development of osteopenia or osteoporosis in breast cancer patients. In addition, BMD analytical results may identify the appropriate treatment for bone loss. A routine check-up of osteopenia or osteoporosis is highly recommended for breast cancer patients.Impact StatementWhat is already known on this subject? Breast cancer may be associated with a wide variety of human body parameters. These parameters can include age, body mass index (BMI), obesity and menstrual status. There is also a correlation between osteoporosis and breast cancer.What do the results of this study add? Osteopenia is the most frequent form of bone loss among women with breast cancer. In contrast, osteoporosis as the worst stage of bone loss is unusual in these patients. Factors such as age, menstruation and BMI have no effect on the development of breast cancer.What are the implications of these findings for clinical practice and/or further research? Identification of the type of bone loss in women with breast cancer is essential to determine the appropriate type of therapy.

Mendelian Randomization and GWAS Meta Analysis Revealed the Risk-Increasing Effect of Schizophrenia on Cancers

The causal relationship between cancer and Schizophrenia (SCZ) remains controversial. Some researchers have found that SCZ is a cancer-preventive factor in cohort studies or meta-analyses, whereas others have found the opposite. To understand more about this issue, we used two-sample Mendelian randomization (2SMR) on available GWAS summary results to evaluate potential genetic connections between SCZ and 13 cancers. We discovered that the genetic susceptibility to schizophrenia lead to an increasing risk of breast cancer (odds ratio [OR] per log-odds increase in schizophrenia risk: 1.049, 95% confidence interval [CI]:1.023–1.075; p = 0.00012; FDR = 0.0017), ovarian cancer (OR, 1.326; 95% CI, 1.267–1.387; p = 0.0007; FDR = 0.0045), and thyroid cancer (OR, 1.575; 95% CI, 1.048–2.365; p = 0.0285; FDR = 0.123). Secondly, we performed a meta-analysis based on the GWAS summary statistics of SCZ and the three significant cancers. Next, we associated genetic variants to genes using two gene mapping strategies: (a) positional mapping based on genomic proximity and (b) expression quantitative trait loci (eQTL) mapping based on gene expression linkage across multiple tissues. As a result, we identified 114 shared loci and 437 shared genes in three groups, respectively. Functional enrichment analysis shows that the most enriched biological pathways are related to epigenetic modification. In addition, we noticed that SCZ would affect the level of thyroid-stimulating hormone (OR, 1.095; 95% CI, 1.006–1.191; p = 0.0354; FDR = 0.177), which may further affect the level of estrogen and the risk of the above three cancers. In conclusion, our findings under the 2SMR assumption provide crucial insights into the risk-increasing effect of SCZ on three cancers’ risk. Furthermore, these results may provide insights into understanding the genetic predisposition and underlying biological pathways of comorbid SCZ and cancers.

Mendelian randomization analyses of 23 known and suspected risk factors and biomarkers for breast cancer overall and by molecular subtypes

Many risk factors have been identified for breast cancer. The potential causality for some of them remains uncertain, and few studies have comprehensively investigated these associations by molecular subtypes. We performed a two-sample Mendelian randomization (MR) study to evaluate potential causal associations of 23 known and suspected risk factors and biomarkers with breast cancer risk overall and by molecular subtypes using data from the Breast Cancer Association Consortium. The inverse-variance weighted method was used to estimate odds ratios (OR) and 95% confidence interval (CI) for association of each trait with breast cancer risk. Significant associations with breast cancer risk were found for 15 traits, including age at menarche, age at menopause, body mass index, waist-to-hip ratio, height, physical activity, cigarette smoking, sleep duration, and morning-preference chronotype, and six blood biomarkers (estrogens, insulin-like growth factor-1, sex hormone-binding globulin [SHBG], telomere length, HDL-cholesterol and fasting insulin). Noticeably, an increased circulating SHBG was associated with a reduced risk of estrogen receptor (ER)-positive cancer (OR = 0.83, 95% CI: 0.73-0.94), but an elevated risk of ER-negative (OR = 1.12, 95% CI: 0.93-1.36) and triple negative cancer (OR = 1.19, 95% CI: 0.92-1.54) (P_{heterogeneity}  = 0.01). Fasting insulin was most strongly associated with an increased risk of HER2-negative cancer (OR = 1.94, 95% CI: 1.18-3.20), but a reduced risk of HER2-enriched cancer (OR = 0.46, 95% CI: 0.26-0.81) (P_{heterogeneity}  = 0.006). Results from sensitivity analyses using MR-Egger and MR-PRESSO were generally consistent. Our study provides strong evidence supporting potential causal associations of several risk factors for breast cancer and suggests potential heterogeneous associations of SHBG and fasting insulin levels with subtypes of breast cancer.

The timing of adiposity and changes in the life course on the risk of cancer

Excess body weight has been established as a risk factor for at least twelve cancer sites, though questions remain as to the timing of associations for adiposity and cancer risk throughout the life course. We conducted a narrative review summarizing existing evidence to provide insights into the complex timing relationship between adiposity and risk of seven common obesity-related cancers. We considered five types of studies, including traditional epidemiologic studies examining adiposity at different time points, studies examining weight gain in specific life phases, studies examining weight loss over a period including from bariatric surgery, life course trajectory analysis, and Mendelian randomization studies. The results showed that lifetime excess body weight is associated with increased risk of cancers of endometrium, colorectum, liver, kidney, and pancreas. Early life obesity is one of the strongest risk factors for pancreatic cancer but less directly important than adult obesity for endometrial and kidney cancer. Interestingly, heavy weight during childhood, adolescence, and early adulthood is protective against pre- and postmenopausal breast cancer and possibly advanced prostate cancer. It is apparent that preventing weight gain later in adulthood would likely reduce risk of many cancers, including postmenopausal breast cancer, endometrial cancer, colorectal cancer (especially in men), liver cancer, kidney cancer, and probably advanced prostate cancer. Furthermore, weight loss even late in life may confer benefits for cancers of breast, endometrium, colorectum, and liver among patients with obesity, as mostly demonstrated by studies of bariatric surgery. Overall, maintaining a healthy weight throughout the life course will help prevent a large number of cancers.

Lipid peroxidation biomarkers associated with height and obesity measures in the opposite direction in women

OBJECTIVE

This study aimed to investigate whether and how lipid peroxidation markers are associated with height and obesity measures.

METHODS

In two independent samples of women (Study 1: n = 1,005; Study 2: n = 1,158), systemic levels of lipid peroxidation were assessed by urinary markers F₂ -isoprostanes (F₂ -IsoPs) and its major metabolite (F₂ -IsoP-M), with gas chromatography/negative ion chemical ionization mass spectrometry assays. Anthropometric parameters were directly measured and genetically estimated, and they were used in the primary analysis and in a Mendelian randomization analysis in relation to lipid peroxidation, respectively, with general linear models.

RESULTS

After adjusting for potential confounders, it was found that measured adult height was inversely associated with levels of F₂ -IsoPs (β = -0.89, p < 0.001) and F₂ -IsoP-M (β = -0.71, p = 0.003), whereas obesity measures were positively associated with F₂ -IsoP-M (β = 1.81, p < 0.001 for BMI; and β = 0.77, p < 0.001 for waist circumference). Results were consistent between the two study samples. The opposite associations were further replicated when using genetically determined measures of height and obesity in the Mendelian randomization analysis. Moreover, analyses mutually adjusted for height and obesity measures suggested that these associations were independent of one another.

CONCLUSIONS

This study, for the first time, to our knowledge, reveals that a shared biological process (lipid peroxidation) is associated with both height and obesity measures but in the opposite direction.

Body mass index, breast density, and the risk of breast cancer development in relation to the menopausal status; results from a population-based screening program in a native African-Arab country

Background

Risk factors are traits or behaviors that have an influence on the development of breast cancer (BC). Awareness of the prevalent risk factors can guide in developing prevention interventions.

Purpose

To evaluate the correlation between the breast density, body mass index, and the risk of breast cancer development in relation to the menopausal status in a native African-Arab population.

Material and methods

The study included 30,443 screened females who were classified into cancer and non-cancer groups and each group was further sub-classified into pre- and postmenopausal groups. The breast density (BD) was reported and subjectively classified according to the 2013 ACR BI-RADS breast density classification. The weight and height were measured, and the body mass index (BMI) was calculated and classified according to the WHO BMI classification.

Results

A statistically significant difference was calculated between the mean BMI in the cancer and non-cancer groups (p: .027) as well as between the pre- and postmenopausal groups (p < .001). A positive statistically insignificant correlation was calculated between the breast density and the risk of breast cancer in the premenopausal group (OR: 1.062, p: .919) and a negative highly significant correlation was calculated in the postmenopausal group (OR: 0.234, p < .001).

Conclusion

BMI and BD are inversely associated with each other. The current studied population presented unique ethnic characteristics, where a decreased BD and an increased BMI were found to be independent risk factors for developing breast cancer.

Deciphering how early life adiposity influences breast cancer risk using Mendelian randomization

Studies suggest that adiposity in childhood may reduce the risk of breast cancer in later life. The biological mechanism underlying this effect is unclear but is likely to be independent of body size in adulthood. Using a Mendelian randomization framework, we investigate 18 hypothesised mediators of the protective effect of childhood adiposity on later-life breast cancer, including hormonal, reproductive, physical, and glycaemic traits. Our results indicate that, while most of the hypothesised mediators are affected by childhood adiposity, only IGF-1 (OR: 1.08 [1.03: 1.15]), testosterone (total/free/bioavailable ~ OR: 1.12 [1.05: 1.20]), age at menopause (OR: 1.05 [1.03: 1.07]), and age at menarche (OR: 0.92 [0.86: 0.99], direct effect) influence breast cancer risk. However, multivariable Mendelian randomization analysis shows that the protective effect of childhood body size remains unaffected when accounting for these traits (ORs: 0.59-0.67). This suggests that none of the investigated potential mediators strongly contribute to the protective effect of childhood adiposity on breast cancer risk individually. It is plausible, however, that several related traits could collectively mediate the effect when analysed together, and this work provides a compelling foundation for investigating other mediating pathways in future studies.

Body size in early life and the risk of postmenopausal breast cancer

BACKGROUND

Greater early life adiposity has been reported to reduce postmenopausal breast cancer risk but it is unclear whether this association varies by tumour characteristics. We aimed to assess associations of early life body size with postmenopausal breast cancer and its subtypes, allowing for body size at other ages.

METHODS

A total of 342,079 postmenopausal UK women who reported their body size at age 10, clothes size at age 20, and body mass index (BMI) at baseline (around age 60) were followed by record linkage to national databases for cancers and deaths. Cox regression yielded adjusted relative risks (RRs) of breast cancer, overall and by tumour subtype, in relation to body size at different ages.

RESULTS

During an average follow-up of 14 years, 15,506 breast cancers were diagnosed. After adjustment for 15 potential confounders, greater BMI at age 60 was associated with an increased risk of postmenopausal breast cancer (RR per 5 kg/m2=1.20, 95%CI 1.18-1.22) whereas greater adiposity in childhood and, to a lesser extent, early adulthood, was associated with a reduced risk (0.70, 0.66-0.74, and 0.92, 0.89-0.96, respectively). Additional adjustment for midlife BMI strengthened associations with BMI at both age 10 (0.63, 0.60-0.68) and at age 20 (0.78, 0.75-0.81). The association with midlife adiposity was confined to hormone sensitive subtypes but early life adiposity had a similar impact on the risk of all subtypes.

CONCLUSION

Early life and midlife adiposity have opposite effects on postmenopausal breast cancer risk and the biological mechanisms underlying these associations are likely to differ.

GTQC: Automated Genotyping Array Quality Control and Report

Genotyping array is the most economical approach for conducting large-scale genome-wide genetic association studies. Thorough quality control is key to generating high integrity genotyping data and robust results. Quality control of genotyping array is generally a complicated process, as it requires intensive manual labor in implementing the established protocols and curating a comprehensive quality report. There is an urgent need to reduce manual intervention via an automated quality control process. Based on previously established protocols and strategies, we developed an R package GTQC (GenoTyping Quality Control) to automate a majority of the quality control steps for general array genotyping data. GTQC covers a comprehensive spectrum of genotype data quality metrics and produces a detailed HTML report comprising tables and figures. Here, we describe the concepts underpinning GTQC and demonstrate its effectiveness using a real genotyping dataset. R package GTQC streamlines a majority of the quality control steps and produces a detailed HTML report on a plethora of quality control metrics, thus enabling a swift and rigorous data quality inspection prior to downstream GWAS and related analyses. By significantly cutting down on the time on genotyping quality control procedures, GTQC ensures maximum utilization of available resources and minimizes waste and inefficient allocation of manual efforts. GTQC tool can be accessed at https://github.com/slzhao/GTQC.

Associations of genetically proxied inhibition of HMG-CoA reductase, NPC1L1, and PCSK9 with breast cancer and prostate cancer

BACKGROUND

Preclinical and epidemiological studies indicate a potential chemopreventive role of low-density lipoprotein cholesterol (LDL-C) -lowering drugs in the risks of breast cancer and prostate cancer, but the causality remains unclear. We aimed to evaluate the association of genetically proxied inhibition of 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase, Niemann-Pick C1-Like 1 (NPC1L1), and proprotein convertase subtilisin/kexin type 9 (PCSK9) with risks of breast cancer and prostate cancer using a two-sample Mendelian randomization (MR) method.

METHODS

Single-nucleotide polymorphisms (SNPs) in HMGCR, NPC1L1, and PCSK9 associated with LDL-C in a genome-wide association study (GWAS) meta-analysis from the Global Lipids Genetics Consortium (GLGC; up to 188,577 European individuals) were used to proxy inhibition of HMG-CoA reductase, NPC1L1, and PCSK9. Summary statistics with outcomes were obtained from a GWAS meta-analysis of the Breast Cancer Association Consortium (BCAC; 228,951 European females) and a Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL; 140,254 European males) consortium. SNPs were combined into multiallelic models and MR estimates representing lifelong inhibition of targets were generated using the inverse-variance weighted method.

RESULTS

Genetically proxied inhibition of HMG-CoA reductase (OR: 0.84; 95% CI 0.74-0.95; P = 0.005) and NPC1L1 (OR: 0.72; 95% CI 0.58-0.90; P = 0.005) equivalent to a 1-mmol/L (38.7 mg/dL) reduction in LDL-C was associated with reduced breast cancer risk. There were no significant associations of genetically proxied inhibition of PCSK9 with breast cancer. In contrast, genetically proxied inhibition of PCSK9 (OR: 0.81; 95% CI 0.73-0.90; P < 0.001) but not HMG-CoA reductase and NPC1L1 was negatively associated with prostate cancer. In the secondary analysis, genetically proxied inhibition of HMG-CoA reductase (OR: 0.82; 95% CI 0.71-0.95; P = 0.008) and NPC1L1 (OR: 0.66; 95% CI 0.50-0.86; P = 0.002) was associated with estrogen receptor-positive breast cancer, whereas there was no association of HMG-CoA reductase and NPC1L1 with estrogen receptor-negative breast cancer.

CONCLUSIONS

Genetically proxied inhibition of HMG-CoA reductase and NPC1L1 was significantly associated with lower odds of breast cancer, while genetically proxied inhibition of PCSK9 was associated with reduced risk of prostate cancer. Further randomized controlled trials are needed to confirm the respective roles of these LDL-C-lowering drugs in breast cancer and prostate cancer.

Mendelian Randomization: A Review of Methods for the Prevention, Assessment, and Discussion of Pleiotropy in Studies Using the Fat Mass and Obesity-Associated Gene as an Instrument for Adiposity

Pleiotropy assessment is critical for the validity of Mendelian randomization (MR) analyses, and its management remains a challenging task for researchers. This review examines how the authors of MR studies address bias due to pleiotropy in practice. We reviewed Pubmed, Medline, Embase and Web of Science for MR studies published before 21 May 2020 that used at least one single-nucleotide polymorphism (SNP) in the fat mass and obesity-associated (FTO) gene as instrumental variable (IV) for body mass index, irrespective of the outcome. We reviewed: 1) the approaches used to prevent pleiotropy, 2) the methods cited to detect or control the independence or the exclusion restriction assumption highlighting whether pleiotropy assessment was explicitly stated to justify the use of these methods, and 3) the discussion of findings related to pleiotropy. We included 128 studies, of which thirty-three reported one approach to prevent pleiotropy, such as the use of multiple (independent) SNPs combined in a genetic risk score as IVs. One hundred and twenty studies cited at least one method to detect or account for pleiotropy, including robust and other IV estimation methods (n = 70), methods for detection of heterogeneity between estimated causal effects across IVs (n = 72), methods to detect or account associations between IV and outcome outside thought the exposure (n = 85), and other methods (n = 5). Twenty-one studies suspected IV invalidity, of which 16 explicitly referred to pleiotropy, and six incriminating FTO SNPs. Most reviewed MR studies have cited methods to prevent or to detect or control bias due to pleiotropy. These methods are heterogeneous, their triangulation should increase the reliability of causal inference.

Systematic review of Mendelian randomization studies on risk of cancer

BACKGROUND

We aimed to map and describe the current state of Mendelian randomization (MR) literature on cancer risk and to identify associations supported by robust evidence.

METHODS

We searched PubMed and Scopus up to 06/10/2020 for MR studies investigating the association of any genetically predicted risk factor with cancer risk. We categorized the reported associations based on a priori designed levels of evidence supporting a causal association into four categories, namely robust, probable, suggestive, and insufficient, based on the significance and concordance of the main MR analysis results and at least one of the MR-Egger, weighed median, MRPRESSO, and multivariable MR analyses. Associations not presenting any of the aforementioned sensitivity analyses were not graded.

RESULTS

We included 190 publications reporting on 4667 MR analyses. Most analyses (3200; 68.6%) were not accompanied by any of the assessed sensitivity analyses. Of the 1467 evaluable analyses, 87 (5.9%) were supported by robust, 275 (18.7%) by probable, and 89 (6.1%) by suggestive evidence. The most prominent robust associations were observed for anthropometric indices with risk of breast, kidney, and endometrial cancers; circulating telomere length with risk of kidney, lung, osteosarcoma, skin, thyroid, and hematological cancers; sex steroid hormones and risk of breast and endometrial cancer; and lipids with risk of breast, endometrial, and ovarian cancer.

CONCLUSIONS

Despite the large amount of research on genetically predicted risk factors for cancer risk, limited associations are supported by robust evidence for causality. Most associations did not present a MR sensitivity analysis and were thus non-evaluable. Future research should focus on more thorough assessment of sensitivity MR analyses and on more transparent reporting.

Disease consequences of higher adiposity uncoupled from its adverse metabolic effects using Mendelian randomisation

Background

Some individuals living with obesity may be relatively metabolically healthy, whilst others suffer from multiple conditions that may be linked to adverse metabolic effects or other factors. The extent to which the adverse metabolic component of obesity contributes to disease compared to the non-metabolic components is often uncertain. We aimed to use Mendelian randomisation (MR) and specific genetic variants to separately test the causal roles of higher adiposity with and without its adverse metabolic effects on diseases.

Methods

We selected 37 chronic diseases associated with obesity and genetic variants associated with different aspects of excess weight. These genetic variants included those associated with metabolically 'favourable adiposity' (FA) and 'unfavourable adiposity' (UFA) that are both associated with higher adiposity but with opposite effects on metabolic risk. We used these variants and two sample MR to test the effects on the chronic diseases.

Results

MR identified two sets of diseases. First, 11 conditions where the metabolic effect of higher adiposity is the likely primary cause of the disease. Here, MR with the FA and UFA genetics showed opposing effects on risk of disease: coronary artery disease, peripheral artery disease, hypertension, stroke, type 2 diabetes, polycystic ovary syndrome, heart failure, atrial fibrillation, chronic kidney disease, renal cancer, and gout. Second, 9 conditions where the non-metabolic effects of excess weight (e.g. mechanical effect) are likely a cause. Here, MR with the FA genetics, despite leading to lower metabolic risk, and MR with the UFA genetics, both indicated higher disease risk: osteoarthritis, rheumatoid arthritis, osteoporosis, gastro-oesophageal reflux disease, gallstones, adult-onset asthma, psoriasis, deep vein thrombosis, and venous thromboembolism.

Conclusions

Our results assist in understanding the consequences of higher adiposity uncoupled from its adverse metabolic effects, including the risks to individuals with high body mass index who may be relatively metabolically healthy.

Funding

Diabetes UK, UK Medical Research Council, World Cancer Research Fund, National Cancer Institute.

Mendelian randomisation analyses of UK Biobank and published data suggest that increased adiposity lowers risk of breast and prostate cancer

Breast (BCa) and prostate (PrCa) cancer are the first and second most common types of cancer in women and men, respectively. We aimed to explore the causal effect of adiposity on BCa and PrCa risk in the UK Biobank and published data. We used Mendelian randomisation (MR) to assess the causal effect of body mass index (BMI), body fat percentage (BFP), waist circumference (WC), hip circumference (HC), and waist-to-hip ratio (WHR) on BCa and PrCa risk. We found that increased BMI, WC and HC decreased the risk of breast cancer (OR 0.70 per 5.14 kg/m² [0.59-0.85, p = 2.1 × 10^-4], 0.76 per 12.49 cm [60-0.97, p = 0.028] and 0.73 per 10.31 cm [0.59-0.90, p = 3.7 × 10^-3], respectively) and increased WC and BMI decreased the risk of prostate cancer (0.68 per 11.32 cm [0.50-0.91, p = 0.01] and 0.76 per 10.23 kg/m² [0.61-0.95, p = 0.015], respectively) in UK Biobank participants. We confirmed our results with a two-sample-MR of published data. In conclusion, our results suggest a protective effect of adiposity on the risk of BCa and PrCa highlighting the need to re-evaluate the role of adiposity as cancer risk factor.

Adiposity and cancer: a Mendelian randomization analysis in the UK biobank

BACKGROUND

Observational and Mendelian randomization (MR) studies link obesity and cancer, but it remains unclear whether these depend upon related metabolic abnormalities.

METHODS

We used information from 321,472 participants in the UK biobank, including 30,561 cases of obesity-related cancer. We constructed three genetic instruments reflecting higher adiposity together with either "unfavourable" (82 SNPs), "favourable" (24 SNPs) or "neutral" metabolic profile (25 SNPs). We looked at associations with 14 types of cancer, previously suggested to be associated with obesity.

RESULTS

All genetic instruments had a strong association with BMI (p < 1 × 10^-300 for all). The instrument reflecting unfavourable adiposity was also associated with higher CRP, HbA1c and adverse lipid profile, while instrument reflecting metabolically favourable adiposity was associated with lower HbA1c and a favourable lipid profile. In MR-inverse-variance weighted analysis unfavourable adiposity was associated with an increased risk of non-hormonal cancers (OR = 1.22, 95% confidence interval [CI]:1.08, 1.38), but a lower risk of hormonal cancers (OR = 0.80, 95%CI: 0.72, 0.89). From individual cancers, MR analyses suggested causal increases in the risk of multiple myeloma (OR = 1.36, 95%CI: 1.09, 1.70) and endometrial cancer (OR = 1.77, 95%CI: 1.16, 2.68) by greater genetically instrumented unfavourable adiposity but lower risks of breast and prostate cancer (OR = 0.72, 95%CI: 0.61, 0.83 and OR = 0.81, 95%CI: 0.68, 0.97, respectively). Favourable or neutral adiposity were not associated with the odds of any individual cancer.

CONCLUSIONS

Higher adiposity associated with a higher risk of non-hormonal cancer but a lower risk of some hormone related cancers. Presence of metabolic abnormalities might aggravate the adverse effects of higher adiposity on cancer. Further studies are warranted to investigate whether interventions on adverse metabolic health may help to alleviate obesity-related cancer risk.

Body Fat Distribution and Risk of Breast, Endometrial, and Ovarian Cancer: A Two-Sample Mendelian Randomization Study

Simple Summary

The causal impact of body fat distribution on female-specific cancers is largely unknown. For the first time we used a two-sample multivariable Mendelian randomization (MR) approach to elucidate the role and causal relations of body composition assessed by segmental bioelectrical impedance analysis on the risks of breast, endometrial and ovarian cancers and their subtypes. We found that abdominal fat content increases the risk for ovarian cancer and its endometrioid and clear cell subtypes independent of overall fat content. General adiposity has a protective effect on risk of breast cancer and its ER- and ER+ subtypes but increases the risk for endometrial cancer, ovarian cancer, and the endometrioid ovarian cancer subtype. This study extends the literature by addressing specifically the causal role of visceral fat on female-specific cancers.

Abstract

Background: Mounting evidence shows that adiposity increases female-specific cancer risk, but the role of body fat distribution is less clear. We used a two-sample Mendelian randomization (MR) approach to elucidate causal relations of body fat distribution to the risks of breast, endometrial and ovarian cancers and their subtypes. Methods: Body composition was assessed using segmental bioelectrical impedance analysis, yielding trunk, arm, and leg fat ratios (TFR, AFR, LFR) and BMI including 195,043 and 434,794 European women, respectively. The sample sizes for the outcomes ranged between 58,396 and 228,951. Causal effects were estimated per one standard deviation increment in the respective exposure within the radial regression framework. Robust sensitivity analyses were performed to verify MR assumptions. In a multivariable MR setting, the proportion of risk attributable to overall and abdominal fat content was assessed. Results: TFR, which represents abdominal fat content, was associated with ovarian cancer and its clear cell and endometrioid histotypes independent of overall fat content. BMI was inversely associated with breast cancer and its ER− and ER+ subtypes, but positively with endometrial cancer and ovarian cancer, including its endometrioid histotype. These estimates were confirmed using AFR as proxy for overall body fat. Conclusions: Visceral adiposity seems to be a driver of elevated ovarian cancer risk, particularly of the endometrioid and clear cell ovarian cancer histotypes. General adiposity decreases the risk of breast cancer but increases the risk of endometrial and ovarian cancer.

Serum iron status and the risk of breast cancer in the European population: a two-sample Mendelian randomisation study

BACKGROUND

Previous observational studies have provided conflicting results on the association between serum iron status and the risk of breast cancer. Considering the relevance of this relationship to breast cancer prevention, its elucidation is warranted.

OBJECT

We used a two-sample Mendelian randomisation (MR) study to explore the causal relationship between serum iron status and the risk of breast cancer.

METHOD

To select single nucleotide polymorphisms (SNPs) that could be used as instrumental variables for iron status, we used the Genetics of Iron Status consortium, which includes 11 discovery and 8 replication cohorts, encompassing 48,972 individuals of European descent. Moreover, we used the OncoArray network to select SNPs that could be considered instrumental variables for the outcome of interest (breast cancer); this dataset included 122,977 individuals of European descent with breast cancer and 105,974 peers without breast cancer. Both conservative (SNPs associated with overall iron status markers) and liberal (SNPs associated with the levels of at least one iron status marker) approaches were used as part of the MR analysis. For the former, we used an inverse-variance weighted (IVW) method, whereas for the latter, we used the IVW, MR-Egger regression, weighted median and simple mode methods.

RESULTS

When the conservative approach was used, iron status showed no significant association with the risk of breast cancer or any of its subtypes. However, when the liberal approach was used, transferrin levels were found to be positively associated with the risk of ER-negative breast cancer based on the simple mode method (OR for MR, 1.225; 95% CI, 1.064, 1.410; P = 0.030). Nevertheless, the levels of the other iron status markers showed no association with the risk of breast cancer or its subtypes (P > 0.05).

CONCLUSION

In our MR study, the liberal approach suggested that changes in the concentration of transferrin could increase the risk of ER-negative breast cancer, although the levels of other iron status markers had no effect on the risk of breast cancer or its subtypes. This should be verified in future studies.

Body size and composition and risk of site-specific cancers in the UK Biobank and large international consortia: A mendelian randomisation study

BACKGROUND

Evidence for the impact of body size and composition on cancer risk is limited. This mendelian randomisation (MR) study investigates evidence supporting causal relationships of body mass index (BMI), fat mass index (FMI), fat-free mass index (FFMI), and height with cancer risk.

METHODS AND FINDINGS

Single nucleotide polymorphisms (SNPs) were used as instrumental variables for BMI (312 SNPs), FMI (577 SNPs), FFMI (577 SNPs), and height (293 SNPs). Associations of the genetic variants with 22 site-specific cancers and overall cancer were estimated in 367,561 individuals from the UK Biobank (UKBB) and with lung, breast, ovarian, uterine, and prostate cancer in large international consortia. In the UKBB, genetically predicted BMI was positively associated with overall cancer (odds ratio [OR] per 1 kg/m2 increase 1.01, 95% confidence interval [CI] 1.00-1.02; p = 0.043); several digestive system cancers: stomach (OR 1.13, 95% CI 1.06-1.21; p < 0.001), esophagus (OR 1.10, 95% CI 1.03, 1.17; p = 0.003), liver (OR 1.13, 95% CI 1.03-1.25; p = 0.012), and pancreas (OR 1.06, 95% CI 1.01-1.12; p = 0.016); and lung cancer (OR 1.08, 95% CI 1.04-1.12; p < 0.001). For sex-specific cancers, genetically predicted elevated BMI was associated with an increased risk of uterine cancer (OR 1.10, 95% CI 1.05-1.15; p < 0.001) and with a lower risk of prostate cancer (OR 0.97, 95% CI 0.94-0.99; p = 0.009). When dividing cancers into digestive system versus non-digestive system, genetically predicted BMI was positively associated with digestive system cancers (OR 1.04, 95% CI 1.02-1.06; p < 0.001) but not with non-digestive system cancers (OR 1.01, 95% CI 0.99-1.02; p = 0.369). Genetically predicted FMI was positively associated with liver, pancreatic, and lung cancer and inversely associated with melanoma and prostate cancer. Genetically predicted FFMI was positively associated with non-Hodgkin lymphoma and melanoma. Genetically predicted height was associated with increased risk of overall cancer (OR per 1 standard deviation increase 1.09; 95% CI 1.05-1.12; p < 0.001) and multiple site-specific cancers. Similar results were observed in analyses using the weighted median and MR-Egger methods. Results based on consortium data confirmed the positive associations between BMI and lung and uterine cancer risk as well as the inverse association between BMI and prostate cancer, and, additionally, showed an inverse association between genetically predicted BMI and breast cancer. The main limitations are the assumption that genetic associations with cancer outcomes are mediated via the proposed risk factors and that estimates for some lower frequency cancer types are subject to low precision.

CONCLUSIONS

Our results show that the evidence for BMI as a causal risk factor for cancer is mixed. We find that BMI has a consistent causal role in increasing risk of digestive system cancers and a role for sex-specific cancers with inconsistent directions of effect. In contrast, increased height appears to have a consistent risk-increasing effect on overall and site-specific cancers.

An iterative approach to detect pleiotropy and perform Mendelian Randomization analysis using GWAS summary statistics

MOTIVATION

The overall association evidence of a genetic variant with multiple traits can be evaluated by cross-phenotype association analysis using summary statistics from genome-wide association studies. Further dissecting the association pathways from a variant to multiple traits is important to understand the biological causal relationships among complex traits.

RESULTS

Here, we introduce a flexible and computationally efficient Iterative Mendelian Randomization and Pleiotropy (IMRP) approach to simultaneously search for horizontal pleiotropic variants and estimate causal effect. Extensive simulations and real data applications suggest that IMRP has similar or better performance than existing Mendelian Randomization methods for both causal effect estimation and pleiotropic variant detection. The developed pleiotropy test is further extended to detect colocalization for multiple variants at a locus. IMRP will greatly facilitate our understanding of causal relationships underlying complex traits, in particular, when a large number of genetic instrumental variables are used for evaluating multiple traits.

AVAILABILITY AND IMPLEMENTATION

The software IMRP is available at https://github.com/XiaofengZhuCase/IMRP. The simulation codes can be downloaded at http://hal.case.edu/∼xxz10/zhu-web/ under the link: MR Simulations software.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The Role of Mendelian Randomization Studies in Deciphering the Effect of Obesity on Cancer

Associations of obesity have been established for at least 11 cancer sites in observational studies, though some questions remain as to causality, strength of associations, and timing of associations throughout the life course. In recent years, Mendelian randomization (MR) has provided complementary information to traditional approaches, but the validity requires that the genetic instrumental variables be causally related to cancers only mediated by the exposure. We summarize and evaluate existing evidence from MR studies in comparison with conventional observational studies to provide insights into the complex relationship between obesity and multiple cancers. MR studies further establish the causality of adult obesity with esophageal adenocarcinoma, cancers of the colorectum, endometrium, ovary, kidney, and pancreas, as well as the inverse association of early life obesity with breast cancer. MR studies, which might account for lifelong adiposity, suggest that the associations in observational studies typically based on single measurement may underestimate the magnitude of the association. For lung cancer, MR studies find a positive association with obesity, supporting that the inverse association observed in some conventional observational studies likely reflects reverse causality (loss of lean body mass before diagnosis) and confounding by smoking. However, MR studies have not had sufficient power for gallbladder cancer, gastric cardia cancer, and multiple myeloma. In addition, more MR studies are needed to explore the effect of obesity at different time points on postmenopausal breast cancer and aggressive prostate cancer.

Genetically determined elevated C-reactive protein associated with primary colorectal cancer risk: Mendelian randomization with lifestyle interactions

Systemic inflammation-related etiologic pathways via inflammatory cytokines in the development of colorectal cancer (CRC) have not been convincingly determined and may be confounded by lifestyle factors or reverse causality. We investigated the genetically predicted C-reactive protein (CRP) phenotype in the potential causal pathway of primary CRC risk in postmenopausal women in a Mendelian randomization (MR) framework. We employed individual-level data of the Women's Health Initiative Database for Genotypes and Phenotypes Study, which consists of 5 genome-wide association (GWA) studies, including 10,142 women, 737 of whom developed primary CRC. We examined 61 GWA single-nucleotide polymorphisms (SNPs) associated with CRP by using weighted/penalized MR weighted-medians and MR gene-environment interactions that allow some relaxation of the strict variable requirements and attenuate the heterogeneous estimates of outlying SNPs. In lifestyle-stratification analyses, genetically determined CRP exhibited its effects on the decreased CRC risk in non-viscerally obese and high-fat diet subgroups. In contrast, genetically driven CRP was associated with an increased risk for CRC in women who smoked ≥ 15 cigarettes/day, with significant interaction of the gene-smoking relationship. Further, a substantially increased risk of CRC induced by CRP was observed in relatively short-term users (< 5 years) of estrogen (E)-only and also longer-term users (5 to > 10 years) of E plus progestin. Our findings may provide novel evidence on immune-related etiologic pathways connected to CRC risk and suggest the possible use of CRP as a CRC-predictive biomarker in women with particular behaviors and CRP marker-informed interventions to reduce CRC risk.

Genetically Predicted C-Reactive Protein Associated With Postmenopausal Breast Cancer Risk: Interrelation With Estrogen and Cancer Molecular Subtypes Using Mendelian Randomization

Background

Immune-related etiologic pathways that influence breast cancer risk are incompletely understood and may be confounded by lifestyles or reverse causality. Using a Mendelian randomization (MR) approach, we investigated the potential causal relationship between genetically elevated C-reactive protein (CRP) concentrations and primary invasive breast cancer risk in postmenopausal women.

Methods

We used individual-level data obtained from 10,179 women, including 537 who developed breast cancer, from the Women’s Health Initiative Database for Genotypes and Phenotypes Study, which consists of five genome-wide association (GWA) studies. We examined 61 GWA single-nucleotide polymorphisms (SNPs) previously associated with CRP. We employed weighted/penalized weighted–medians and MR gene–environment interactions that allow instruments’ invalidity to some extent and attenuate the heterogeneous estimates of outlying SNPs.

Results

In lifestyle-stratification analyses, genetically elevated CRP decreased risk for breast cancer in exogenous estrogen-only, estrogen + progestin, and past oral contraceptive (OC) users, but only among relatively short-term users (<5 years). Estrogen-only users for ≥5 years had more profound CRP-decreased breast cancer risk in dose–response fashion, whereas past OC users for ≥5 years had CRP-increased cancer risk. Also, genetically predicted CRP was strongly associated with increased risk for hormone-receptor positive or human epidermal growth factor receptor-2 negative breast cancer.

Conclusions

Our findings may provide novel evidence on the immune-related molecular pathways linking to breast cancer risk and suggest potential clinical use of CRP to predict the specific cancer subtypes. Our findings suggest potential interventions targeting CRP–inflammatory markers to reduce breast cancer risk.

Combined association of general and central obesity with incidence and mortality of cancers in 22 sites

BACKGROUND

Body mass index (BMI) and waist circumference (WC) are measures of general and central obesity, respectively, and both have been shown to be associated with cancer. However, there is insufficient evidence of their combined association with the risk of cancer.

OBJECTIVES

This study aimed to investigate the associations of combinations of BMI and WC with cancer at 22 sites.

METHODS

A total of 386,101 (54.5% women) UK Biobank participants aged from 37 to 73 y were included. The outcomes were incidence of and mortality from cancer at 22 sites. Participants were categorized as normal weight (BMI 18.5-24.9) or overweight (including obese, BMI ≥ 25) and as normal WC or centrally obese (WC ≥ 94 cm for men and ≥80 cm for women). Four mutually exclusive groups were derived: 1) normal weight without central obesity, 2) normal weight with central obesity, 3) overweight without central obesity, and 4) overweight with central obesity. We used Cox proportional hazards models to estimate HRs and 95% CIs.

RESULTS

The mean follow-up period was 8.8 y. Compared with participants with normal weight and WC, men who were overweight and centrally obese had higher cancer incidence risk at 3 sites [stomach (HR: 1.75; 95% CI: 1.33, 2.32; Padj = 0.002), kidney (HR: 1.45; 95% CI: 1.17, 1.81; Padj = 0.016), and colorectal (HR: 1.31; 95% CI: 1.17, 1.47; Padj < 0.001) cancer]. Similar associations were found at 4 sites in women [endometrial (HR: 2.48; 95% CI: 2.06, 2.98; Padj < 0.001), uterine (HR: 2.23; 95% CI: 1.89, 2.64; Padj < 0.001), kidney (HR: 1.84; 95% CI: 1.37, 2.46; Padj = 0.001), and breast (HR: 1.24; 95% CI: 1.16, 1.32; Padj < 0.001) cancer] and for all-cause cancer (HR: 1.07; 95% CI: 1.03, 1.10; Padj = 0.003). Only endometrial cancer mortality (HR: 3.28; 95% CI: 1.77, 6.07; Padj = 0.004) was significantly associated with being overweight and centrally obese.

CONCLUSIONS

The combination of general and central obesity was associated with a higher risk at several cancer sites and some associations were sex-specific.

Associations of six adiposity-related markers with incidence and mortality from 24 cancers-findings from the UK Biobank prospective cohort study

BACKGROUND

Adiposity is a strong risk factor for cancer incidence and mortality. However, most of the evidence available has focused on body mass index (BMI) as a marker of adiposity. There is limited evidence on relationships of cancer with other adiposity markers, and if these associations are linear or not. The aim of this study was to investigate the associations of six adiposity markers with incidence and mortality from 24 cancers by accounting for potential non-linear associations.

METHODS

A total of 437,393 participants (53.8% women; mean age 56.3 years) from the UK Biobank prospective cohort study were included in this study. The median follow-up was 8.8 years (interquartile range 7.9 to 9.6) for mortality and 9.3 years (IQR 8.6 to 9.9) for cancer incidence. Adiposity-related exposures were BMI, body fat percentage, waist-hip ratio, waist-height ratio, and waist and hip circumference. Incidence and mortality of 24 cancers sites were the outcomes. Cox proportional hazard models were used with each of the exposure variables fitted separately on penalised cubic splines.

RESULTS

During follow-up, 47,882 individuals developed cancer and 11,265 died due to cancer during the follow-up period. All adiposity markers had similar associations with overall cancer incidence. BMI was associated with a higher incidence of 10 cancers (stomach cardia (hazard ratio per 1 SD increment 1.35, (95% CI 1.23; 1.47)), gallbladder (1.33 (1.12; 1.58)), liver (1.27 (1.19; 1.36)), kidney (1.26 (1.20; 1.33)), pancreas (1.12 (1.06; 1.19)), bladder (1.09 (1.04; 1.14)), colorectal (1.10 (1.06; 1.13)), endometrial (1.73 (1.65; 1.82)), uterine (1.68 (1.60; 1.75)), and breast cancer (1.08 (1.05; 1.11))) and overall cancer (1.03 (1.02; 1.04)). All these associations were linear except for breast cancer in postmenopausal women. Similar results were observed when other markers of central and overall adiposity were used. For mortality, nine cancer sites were linearly associated with BMI and eight with waist circumference and body fat percentage.

CONCLUSION

Adiposity, regardless of the marker used, was associated with an increased risk in 10 cancer sites.

Genetic Variation and Hot Flashes: A Systematic Review

CONTEXT

Approximately 70% of women report experiencing vasomotor symptoms (VMS, hot flashes and/or night sweats). The etiology of VMS is not clearly understood but may include genetic factors.

EVIDENCE ACQUISITION

We searched PubMed and Embase in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidance. We included studies on associations between genetic variation and VMS. We excluded studies focused on medication interventions or prevention or treatment of breast cancer.

EVIDENCE SYNTHESIS

Of 202 unique citations, 18 citations met the inclusion criteria. Study sample sizes ranged from 51 to 17 695. Eleven of the 18 studies had fewer than 500 participants; 2 studies had 1000 or more. Overall, statistically significant associations with VMS were found for variants in 14 of the 26 genes assessed in candidate gene studies. The cytochrome P450 family 1 subfamily A member 1 (CYP1B1) gene was the focus of the largest number (n = 7) of studies, but strength and statistical significance of associations of CYP1B1 variants with VMS were inconsistent. A genome-wide association study reported statistically significant associations between 14 single-nucleotide variants in the tachykinin receptor 3 gene and VMS. Heterogeneity across trials regarding VMS measurement methods and effect measures precluded quantitative meta-analysis; there were few studies of each specific genetic variant.

CONCLUSIONS

Genetic variants are associated with VMS. The associations are not limited to variations in sex-steroid metabolism genes. However, studies were few and future studies are needed to confirm and extend these findings.

Alternative Applications of Genotyping Array Data Using Multivariant Methods

One of the forerunners that pioneered the revolution of high-throughput genomic technologies is the genotyping microarray technology, which can genotype millions of single-nucleotide variants simultaneously. Owing to apparent benefits, such as high speed, low cost, and high throughput, the genotyping array has gained lasting applications in genome-wide association studies (GWAS) and thus accumulated an enormous amount of data. Empowered by continuous manufactural upgrades and analytical innovation, unconventional applications of genotyping array data have emerged to address more diverse genetic problems, holding promise of boosting genetic research into human diseases through the re-mining of the rich accumulated data. Here, we review several unconventional genotyping array analysis techniques that have been built on the idea of large-scale multivariant analysis and provide empirical application examples. These unconventional outcomes of genotyping arrays include polygenic score, runs of homozygosity (ROH)/heterozygosity ratio, distant pedigree computation, and mitochondrial DNA (mtDNA) copy number inference.

Assessing the Relationship Between Leukocyte Telomere Length and Cancer Risk/Mortality in UK Biobank and TCGA Datasets With the Genetic Risk Score and Mendelian Randomization Approaches

Background

Telomere length is an important indicator of tumor progression and survival for cancer patients. Previous work investigated the associations between genetically predicted telomere length and cancers; however, the types of cancers investigated in those studies were relatively limited or the telomere length-associated genetic variants employed often came from genome-wide association studies (GWASs) with small sample sizes.

Methods

We constructed the genetic risk score (GRS) for leukocyte telomere length based on 17 associated genetic variants available from the largest telomere length GWAS up to 78,592 individuals. Then, a comprehensive analysis was undertaken to evaluate the association between the constructed GRS and the risk or mortality of a wide range of cancers [i.e., 37 cancers in the UK Biobank and 33 cancers in The Cancer Genome Atlas (TCGA)]. We further applied the two-sample Mendelian randomization (MR) to estimate the causal effect of leukocyte telomere length on UK Biobank cancers via summary statistics.

Results

In the UK Biobank dataset, we found that the GRS of leukocyte telomere length was associated with a decreased risk of nine types of cancer (i.e., significant association with multiple myeloma, chronic lymphocytic leukemia, kidney/renal cell cancer, bladder cancer, malignant melanoma, basal cell carcinoma, and prostate cancer and suggestive association with sarcoma/fibrosarcoma and Hodgkin’s lymphoma/Hodgkin’s disease). In addition, we found that the GRS was suggestively associated with an increased risk of leukemia. In the TCGA dataset, we observed suggestive evidence that the GRS was associated with a high death hazard of rectum adenocarcinoma (READ), sarcoma (SARC), and skin cutaneous melanoma (SKCM), while the GRS was associated with a low death hazard of kidney renal papillary cell carcinoma (KIRP). The results of MR further supported the association for leukocyte telomere length on the risk of malignant melanoma, Hodgkin’s lymphoma/Hodgkin’s disease, chronic lymphocytic leukemia and multiple myeloma.

Conclusion

Our study reveals that telomere played diverse roles in different types of cancers. However, further validations in large-scale prospective studies and deeper investigations of the biologic mechanisms are warranted.

Using genetics to decipher the link between type 2 diabetes and cancer: shared aetiology or downstream consequence?

Recent developments in the field of genetics have accelerated our understanding of the aetiology of complex diseases. Type 2 diabetes mellitus and cancer are no exception, with large-scale genome-wide association studies (GWAS) facilitating exploration of the underlying pathology. Here, we discuss how genetics studies can be used to investigate the relationship between these complex diseases. Observational epidemiological studies consistently report that people with type 2 diabetes have a higher risk of several types of cancer. Indeed, type 2 diabetes and cancer share many common risk factors, such as obesity, ageing, poor diet and low levels of physical activity. However, questions remain regarding the biological mechanisms that link these two diseases. Large-scale GWAS of type 2 diabetes and cancer allow us to consider the evidence for shared genetic architecture. Several shared susceptibility genes have been identified, yet tissue specificity and direction of effect must be taken into account when considering common genetic aetiology. We also consider how GWAS, and associated techniques such as Mendelian randomisation, allow us to dissect the link between the two diseases and address questions such as 'Does type 2 diabetes cause cancer or is the increased risk observed driven by higher adiposity or another associated metabolic feature?' Graphical abstract.

The relationship between circulating lipids and breast cancer risk: A Mendelian randomization study

BACKGROUND

A number of epidemiological and genetic studies have attempted to determine whether levels of circulating lipids are associated with risks of various cancers, including breast cancer (BC). However, it remains unclear whether a causal relationship exists between lipids and BC. If alteration of lipid levels also reduced risk of BC, this could present a target for disease prevention. This study aimed to assess a potential causal relationship between genetic variants associated with plasma lipid traits (high-density lipoprotein, HDL; low-density lipoprotein, LDL; triglycerides, TGs) with risk for BC using Mendelian randomization (MR).

METHODS AND FINDINGS

Data from genome-wide association studies in up to 215,551 participants from the Million Veteran Program (MVP) were used to construct genetic instruments for plasma lipid traits. The effect of these instruments on BC risk was evaluated using genetic data from the BCAC (Breast Cancer Association Consortium) based on 122,977 BC cases and 105,974 controls. Using MR, we observed that a 1-standard-deviation genetically determined increase in HDL levels is associated with an increased risk for all BCs (HDL: OR [odds ratio] = 1.08, 95% confidence interval [CI] = 1.04-1.13, P < 0.001). Multivariable MR analysis, which adjusted for the effects of LDL, TGs, body mass index (BMI), and age at menarche, corroborated this observation for HDL (OR = 1.06, 95% CI = 1.03-1.10, P = 4.9 × 10-4) and also found a relationship between LDL and BC risk (OR = 1.03, 95% CI = 1.01-1.07, P = 0.02). We did not observe a difference in these relationships when stratified by breast tumor estrogen receptor (ER) status. We repeated this analysis using genetic variants independent of the leading association at core HDL pathway genes and found that these variants were also associated with risk for BCs (OR = 1.11, 95% CI = 1.06-1.16, P = 1.5 × 10-6), including locus-specific associations at ABCA1 (ATP Binding Cassette Subfamily A Member 1), APOE-APOC1-APOC4-APOC2 (Apolipoproteins E, C1, C4, and C2), and CETP (Cholesteryl Ester Transfer Protein). In addition, we found evidence that genetic variation at the ABO locus is associated with both lipid levels and BC. Through multiple statistical approaches, we minimized and tested for the confounding effects of pleiotropy and population stratification on our analysis; however, the possible existence of residual pleiotropy and stratification remains a limitation of this study.

CONCLUSIONS

We observed that genetically elevated plasma HDL and LDL levels appear to be associated with increased BC risk. Future studies are required to understand the mechanism underlying this putative causal relationship, with the goal of developing potential therapeutic strategies aimed at altering the cholesterol-mediated effect on BC risk.

Using off-target data from whole-exome sequencing to improve genotyping accuracy, association analysis and polygenic risk prediction

Whole-exome sequencing (WES) has been widely used to study the role of protein-coding variants in genetic diseases. Non-coding regions, typically covered by sparse off-target data, are often discarded by conventional WES analyses. Here, we develop a genotype calling pipeline named WEScall to analyse both target and off-target data. We leverage linkage disequilibrium shared within study samples and from an external reference panel to improve genotyping accuracy. In an application to WES of 2527 Chinese and Malays, WEScall can reduce the genotype discordance rate from 0.26% (SE= 6.4 × 10-6) to 0.08% (SE = 3.6 × 10-6) across 1.1 million single nucleotide polymorphisms (SNPs) in the deeply sequenced target regions. Furthermore, we obtain genotypes at 0.70% (SE = 3.0 × 10-6) discordance rate across 5.2 million off-target SNPs, which had ~1.2× mean sequencing depth. Using this dataset, we perform genome-wide association studies of 10 metabolic traits. Despite of our small sample size, we identify 10 loci at genome-wide significance (P < 5 × 10-8), including eight well-established loci. The two novel loci, both associated with glycated haemoglobin levels, are GPATCH8-SLC4A1 (rs369762319, P = 2.56 × 10-12) and ROR2 (rs1201042, P = 3.24 × 10-8). Finally, using summary statistics from UK Biobank and Biobank Japan, we show that polygenic risk prediction can be significantly improved for six out of nine traits by incorporating off-target data (P < 0.01). These results demonstrate WEScall as a useful tool to facilitate WES studies with decent amounts of off-target data.

The Use Of Genetic Correlation And Mendelian Randomization Studies To Increase Our Understanding of Relationships Between Complex Traits

PURPOSE OF THE REVIEW

Increasing access to large-scale genetic datasets in population-based studies allows for genetic association studies as a means to examine previously known and novel relationships among complex traits. In this review, we discuss two widely used approaches to leverage genetic data to study the links between traits: Genome-wide genetic correlation and Mendelian Randomization (MR) studies.

RECENT FINDINGS

Both genetic correlation and MR studies have provided important novel insights. However, although they are less sensitive to many sources of bias present in traditional, observational epidemiology, they still rely on assumptions that in practice might be difficult to assess. To overcome this, development of novel methods less sensitive to these assumptions is an active area of research.

SUMMARY

We believe that as population-based genetic datasets grow larger and novel methods allowing for weaker forms of current assumptions become available, genetic correlation and MR studies will become an integral part of genetic epidemiology studies.

Use of genetic variation to separate the effects of early and later life adiposity on disease risk: mendelian randomisation study

OBJECTIVE

To evaluate whether body size in early life has an independent effect on risk of disease in later life or whether its influence is mediated by body size in adulthood.

DESIGN

Two sample univariable and multivariable mendelian randomisation.

SETTING

The UK Biobank prospective cohort study and four large scale genome-wide association studies (GWAS) consortiums.

PARTICIPANTS

453 169 participants enrolled in UK Biobank and a combined total of more than 700 000 people from different GWAS consortiums.

EXPOSURES

Measured body mass index during adulthood (mean age 56.5) and self-reported perceived body size at age 10.

MAIN OUTCOME MEASURES

Coronary artery disease, type 2 diabetes, breast cancer, and prostate cancer.

RESULTS

Having a larger genetically predicted body size in early life was associated with an increased odds of coronary artery disease (odds ratio 1.49 for each change in body size category unless stated otherwise, 95% confidence interval 1.33 to 1.68) and type 2 diabetes (2.32, 1.76 to 3.05) based on univariable mendelian randomisation analyses. However, little evidence was found of a direct effect (ie, not through adult body size) based on multivariable mendelian randomisation estimates (coronary artery disease: 1.02, 0.86 to 1.22; type 2 diabetes:1.16, 0.74 to 1.82). In the multivariable mendelian randomisation analysis of breast cancer risk, strong evidence was found of a protective direct effect for larger body size in early life (0.59, 0.50 to 0.71), with less evidence of a direct effect of adult body size on this outcome (1.08, 0.93 to 1.27). Including age at menarche as an additional exposure provided weak evidence of a total causal effect (univariable mendelian randomisation odds ratio 0.98, 95% confidence interval 0.91 to 1.06) but strong evidence of a direct causal effect, independent of early life and adult body size (multivariable mendelian randomisation odds ratio 0.90, 0.85 to 0.95). No strong evidence was found of a causal effect of either early or later life measures on prostate cancer (early life body size odds ratio 1.06, 95% confidence interval 0.81 to 1.40; adult body size 0.87, 0.70 to 1.08).

CONCLUSIONS

The findings suggest that the positive association between body size in childhood and risk of coronary artery disease and type 2 diabetes in adulthood can be attributed to individuals remaining large into later life. However, having a smaller body size during childhood might increase the risk of breast cancer regardless of body size in adulthood, with timing of puberty also putatively playing a role.

Correlation between breast cancer and lifestyle within the Gulf Cooperation Council countries: A systematic review

BACKGROUND

In the six Gulf Cooperation Council countries (GCCCs), Bahrain, Saudi Arabia, Kuwait, Oman, Qatar and the United Arab Emirates, breast cancer (BC) is the greatest cause of cancer incidence and mortality. Obesity and physical inactivity are established risk factors for BC globally and appear to be more of a problem in high income countries like the GCCCs.

AIM

To determine whether obesity and physical inactivity are associated with BC incidence in the GCCCs using the United Kingdom as a comparator.

METHODS

This systematic review was carried out according to PRISMA guidelines. A cancer registry and a statistical data search was done to identify the BC incidence over the past two decades and the prevalence of obesity and physical inactivity in the GCCCs. Additionally, a systematic search of the databases, MEDLINE, Web of Science, and PubMed between 1999 and 2019 was performed to determine whether obesity and physical inactivity are risk factors for BC in the GCCCs. All papers were critically appraised according to their research methods and were assessed for quality and risk of bias.

RESULTS

BC was the top malignancy in each GCC country. Women tended to be diagnosed with BC at a younger age than women in the United Kingdom. The greatest 10-year increase in BC incidence was seen in Saudi Arabia (54.2%), approximately seven times the rate of increase seen in the United Kingdom (7.6%). The prevalence of obesity and physical inactivity was greater in all the GCCCs in comparison to the United Kingdom. A total of 155 full studies were reviewed of which 17 were included. Of those, eight looked at the prevalence of obesity and physical inactivity in the Gulf States and nine looked at these as risk factors for BC. Only one study found an association between BC and obesity (odds ratio = 2.29). No studies looked solely at the link between physical inactivity and BC.

CONCLUSION

The prevalence of obesity and physical inactivity was high within the GCCCs, but the majority of the included studies found no positive correlation between obesity or physical inactivity and BC. A high proportion of women in this study were pre-menopausal which could contribute to the negative findings.