Biology subjects

Siewert, K. M.

Publications and source records attributed to Siewert, K. M..

4 recordsLinked to original sources

bioRxiv2019

Assessing a causal relationship between circulating lipids and breast cancer risk via Mendelian randomization

ObjectiveTo assess a potential causal relationship between genetic variants associated with plasma lipid traits (high-density lipoprotein cholesterol, HDL; low-density lipoprotein cholesterol, LDL; triglycerides, TG) with risk for breast cancer.\n\nDesignMendelian randomization (MR) study.\n\nSetting and ParticipantsData from genome-wide association studies in up to 215,551 subjects from the Million Veterans Project were used to construct genetic instruments for plasma lipid traits. The effect of these instruments on breast cancer risk was evaluated using genetic data from the BCAC consortium based on 122,977 breast cancer cases and 105,974 controls.\n\nExposuresGenetically modified plasma levels of LDL, HDL, or TG.\n\nMain Outcomes and MeasuresOdds ratio (OR) for breast cancer risk per standard-deviation increase in HDL, LDL, or TG.\n\nResultsWe observed that a 1-SD genetically determined increase in HDL levels is associated with an increased risk for all breast cancers (HDL: OR=1.08, 95% CI=1.04-1.13, P=7.4x10-5).\n\nMultivariable MR analysis, which adjusted for the effects of LDL, TG, body mass index, and age at menarche, corroborated this observation for HDL (OR=1.06, 95% CI=1.03-1.10, P=4.9x10-4) and also a relationship between LDL and breast cancer 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 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 breast cancers (OR=1.11, 95% CI=1.06-1.16, P=1.5x10-6), including gene-specific associations at ABCA1, APOE-APOC1-APOC4-APOC2 and CETP. In addition, we find evidence that genetic variation at the ABO locus affects both lipid levels and breast cancer.\n\nConclusionsGenetically elevated plasma HDL levels appear to increase breast cancer risk. Future studies are required to understand the mechanism underlying this putative causal relationship, with the goal to develop potential therapeutic strategies aimed at altering the HDL-mediated effect on breast cancer risk.

genomics↗

bioRxiv2018

BetaScan2: Standardized statistics to detect balancing selection utilizing substitution data

The recently reported statistic {beta} detects balanced haplotypes without the need for genomic data from an outgroup species. Here we present an extension to the method that incorporates between-species substitution data into the {beta} statistic framework. We show that this approach outperforms existing summary statistics in simulations. We also present the variance of {beta} with and without substitution data, allowing calculation of a standardized score. Besides providing a measure of significance, this enables a proper comparison of {beta} values across varying underlying parameters, a feature lacking from some related methods.

evolutionary biology↗

bioRxiv2018

Bivariate GWAS scan identifies six novel loci associated with lipid levels and coronary artery disease

BackgroundPlasma lipid levels are heritable and genetically associated with risk of coronary artery disease (CAD). However, genome-wide association studies (GWAS) routinely analyze these traits independently of one another. Joint GWAS for two related phenotypes can lead to a higher-powered analysis to detect variants contributing to both traits.\n\nMethods and ResultsWe performed a bivariate GWAS to discover novel loci associated with heart disease, using a CAD Meta-Analysis (122,733 cases and 424,528 controls), and lipid traits, using data from the Global Lipid Genetics Consortium (188,577 subjects). We identified six previously unreported loci at genome-wide significance (P < 5 x 10-8), three which were associated with Triglycerides and CAD, two which were associated with LDL 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 one or more neighboring genes, indicating that these loci may be affecting disease risk through regulatory activity.\n\nConclusionsWe discovered six novel variants individually associated with both lipids and coronary artery disease.

genomics↗

bioRxiv2017

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, {beta}, which detects these clusters of alleles at similar frequencies. Simulation studies show that compared to existing summary statistics, our measure has improved power to detect balancing selection, and is reasonably powered in non-equilibrium demographic models or when recombination or mutation rate varies. We compute {beta} on 1000 Genomes Project data to identify lo ci 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.

evolutionary biology↗