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.

Serum calcium and risk of migraine: a Mendelian randomization study.

Migraine affects ∼14% of the world's population, though not all predisposing causal risk factors are known. We used electronic health records, genetic co-heritability analysis, and a two-sample Mendelian Randomization (MR) design to determine if elevated serum calcium levels were associated with risk of migraine headache. Co-morbidity was evaluated using electronic health records obtained from the PennOmics database comprising >1 million patient entries. Genetic co-heritability and causality via MR was assessed using data from the International Headache Consortium (23,285 cases, 95,425 controls) and circulating serum calcium levels (39,400 subjects). We observed co-occurrence of migraine and hypercalcaemia ICD-9 diagnoses (OR = 1.58, P = 4 × 10-13), even after inclusion of additional risk factors for migraine (OR = 1.23, P = 2 × 10-3). Second, we observed co-heritability (rg = 0.191, P = 0.03) between serum calcium and migraine headache, indicating that these traits have a genetic basis in common. Finally, we found that elevation of serum calcium levels by 1 mg/dl resulting from our genetic score was associated with an increase in risk of migraine (OR = 1.80, 95% CI: 1.31-2.46, P = 2.5 × 10-4), evidence supporting a causal hypothesis. We also present multiple MR sensitivity analyses in support of this central finding. Our results provide evidence that hypercalcaemia is comorbid with migraine headache diagnoses, and that genetically elevated serum calcium over lifetime appears to increase risk for migraine. Further studies will be required to understand the biological mechanism, pathways, and clinical implication for risk management.

Detecting Long-Term Balancing Selection Using Allele Frequency Correlation.

Balancing selection occurs when multiple alleles are maintained in a population, which can result in their preservation over long evolutionary time periods. A characteristic signature of this long-term balancing selection is an excess number of intermediate frequency polymorphisms near the balanced variant. However, the expected distribution of allele frequencies at these loci has not been extensively detailed, and therefore existing summary statistic methods do not explicitly take it into account. Using simulations, we show that new mutations which arise in close proximity to a site targeted by balancing selection accumulate at frequencies nearly identical to that of the balanced allele. In order to scan the genome for balancing selection, we propose a new summary statistic, ß, which detects these clusters of alleles at similar frequencies. Simulation studies show that compared with existing summary statistics, our measure has improved power to detect balancing selection, and is reasonably powered in non-equilibrium demographic models and under a range of recombination and mutation rates. We compute ß on 1000 Genomes Project data to identify loci potentially subjected to long-term balancing selection in humans. We report two balanced haplotypes-localized to the genes WFS1 and CADM2-that are strongly linked to association signals for complex traits. Our approach is computationally efficient and applicable to species that lack appropriate outgroup sequences, allowing for well-powered analysis of selection in the wide variety of species for which population data are rapidly being generated.

Population-specific causal disease effect sizes in functionally important regions impacted by selection.

Many diseases exhibit population-specific causal effect sizes with trans-ethnic genetic correlations significantly less than 1, limiting trans-ethnic polygenic risk prediction. We develop a new method, S-LDXR, for stratifying squared trans-ethnic genetic correlation across genomic annotations, and apply S-LDXR to genome-wide summary statistics for 31 diseases and complex traits in East Asians (average N = 90K) and Europeans (average N = 267K) with an average trans-ethnic genetic correlation of 0.85. We determine that squared trans-ethnic genetic correlation is 0.82× (s.e. 0.01) depleted in the top quintile of background selection statistic, implying more population-specific causal effect sizes. Accordingly, causal effect sizes are more population-specific in functionally important regions, including conserved and regulatory regions. In regions surrounding specifically expressed genes, causal effect sizes are most population-specific for skin and immune genes, and least population-specific for brain genes. Our results could potentially be explained by stronger gene-environment interaction at loci impacted by selection, particularly positive selection.

Multi-trait association studies discover pleiotropic loci between Alzheimer's disease and cardiometabolic traits.

BACKGROUND: Identification of genetic risk factors that are shared between Alzheimer's disease (AD) and other traits, i.e., pleiotropy, can help improve our understanding of the etiology of AD and potentially detect new therapeutic targets. Previous epidemiological correlations observed between cardiometabolic traits and AD led us to assess the pleiotropy between these traits. METHODS: We performed a set of bivariate genome-wide association studies coupled with colocalization analysis to identify loci that are shared between AD and eleven cardiometabolic traits. For each of these loci, we performed colocalization with Genotype-Tissue Expression (GTEx) project expression quantitative trait loci (eQTL) to identify candidate causal genes. RESULTS: We identified three previously unreported pleiotropic trait associations at known AD loci as well as four novel pleiotropic loci. One associated locus was tagged by a low-frequency coding variant in the gene DOCK4 and is potentially implicated in its alternative splicing. Colocalization with GTEx eQTL data identified additional candidate genes for the loci we detected, including ACE, the target of the hypertensive drug class of ACE inhibitors. We found that the allele associated with decreased ACE expression in brain tissue was also associated with increased risk of AD, providing human genetic evidence of a potential increase in AD risk from use of an established anti-hypertensive therapeutic. CONCLUSION: Our results support a complex genetic relationship between AD and these cardiometabolic traits, and the candidate causal genes identified suggest that blood pressure and immune response play a role in the pleiotropy between these traits.

BetaScan2: Standardized Statistics to Detect Balancing Selection Utilizing Substitution Data.

Long-term balancing selection results in a build-up of alleles at similar frequencies and a deficit of substitutions when compared with an outgroup at a locus. The previously published ß(1) statistics detect balancing selection using only polymorphism data. We now propose the ß(2) statistic which detects balancing selection using both polymorphism and substitution data. In addition, we derive the variance of all ß statistics, allowing for their standardization and thereby reducing the influence of parameters which can confound other selection tests. The standardized ß statistics outperform existing summary statistics in simulations, indicating ß is a well-powered and widely applicable approach for detecting balancing selection. We apply the ß(2) statistic to 1000 Genomes data and report two missense mutations with high ß scores in the ACSBG2 gene. An implementation of all ß statistics and their standardization are available in the BetaScan2 software package at https://github.com/ksiewert/BetaScan.

Cross-trait analyses with migraine reveal widespread pleiotropy and suggest a vascular component to migraine headache.

BACKGROUND: Nearly a fifth of the world's population suffer from migraine headache, yet risk factors for this disease are poorly characterized. METHODS: To further elucidate these factors, we conducted a genetic correlation analysis using cross-trait linkage disequilibrium (LD) score regression between migraine headache and 47 traits from the UK Biobank. We then tested for possible causality between these phenotypes and migraine, using Mendelian randomization. In addition, we attempted replication of our findings in an independent genome-wide association study (GWAS) when available. RESULTS: We report multiple phenotypes with genetic correlation (P < 1.06 × 10-3) with migraine, including heart disease, type 2 diabetes, lipid levels, blood pressure, autoimmune and psychiatric phenotypes. In particular, we find evidence that blood pressure directly contributes to migraine and explains a previously suggested causal relationship between calcium and migraine. CONCLUSIONS: This is the largest genetic correlation analysis of migraine headache to date, both in terms of migraine GWAS sample size and the number of phenotypes tested. We find that migraine has a shared genetic basis with a large number of traits, indicating pervasive pleiotropy at migraine-associated loci.

Bivariate Genome-Wide Association Scan Identifies 6 Novel Loci Associated With Lipid Levels and Coronary Artery Disease.

BACKGROUND: Plasma lipid levels are heritable and genetically associated with risk of coronary artery disease (CAD). However, genome-wide association studies (GWAS) routinely analyze these traits independent of one another. Joint GWAS for two related phenotypes can lead to a higher powered analysis to detect variants contributing to both traits. METHODS: We performed a bivariate GWAS to discover novel loci associated with both heart disease, using a CAD meta-analysis (122 733 cases and 424 528 controls), and lipid traits, using results from the Global Lipid Genetics Consortium (188 577 subjects). RESULTS: We identified six previously unreported loci at genome-wide significance ( P<5×10-8), three which were associated with triglycerides and CAD, two which were associated with LDL (low-density lipoprotein) cholesterol and CAD, and one associated with total cholesterol and CAD. At several of our loci, the GWAS signals jointly localize with genetic variants associated with expression level changes for more than one neighboring genes, indicating that these loci may be affecting disease risk through regulatory activity. CONCLUSIONS: We discovered six novel variants individually associated with both lipids and CAD.