Biology subjects

Voight, B. F.

Publications and source records attributed to Voight, B. F..

At least 19 recordsLinked to original sources

bioRxiv2026

Utilizing single-cell data for per-cell type eQTL mapping in the human pancreas

Aims/hypothesis The human pancreas is a central organ for metabolic regulation that is comprised of diverse cell types that uniquely contribute to its function. Previous studies have performed expression quantitative trail loci (eQTL) discovery in either whole pancreas or in pancreatic islets, but due to differences between pancreatic cell types, this approach does not reveal cell type-specific effects. In this study, we sought to either implicate the cell type of action for known eQTLs or identify new eQTLs that may have been masked in bulk studies by performing eQTL discovery in individual pancreatic cell types. Methods We clustered 153,018 single-cell RNA sequencing (scRNA-seq) data from 71 pancreatic islet donors from the Human Pancreas Analysis Program (HPAP). We performed eQTL discovery in six pancreatic cell types using this resource directly. We further utilized this single cell resource as a reference to deconvolute bulk pancreatic RNA sequencing data from 305 Genotype Tissue Expression (GTEx) project donors and performed eQTL discovery in four pancreatic cell types. Finally, we performed fine-mapping and co-localization of pancreatic cell type eQTLs with metabolic GWAS to connect our findings to metabolic disease risk. Results From analyzing 71 individuals with single cell profiles, we identified 112 unique eGenes across six pancreatic cell types, 99 of which had been identified previously and 13 unique to this study. From the deconvoluted eQTLs, we identified 3,134 unique eGenes across four pancreatic cell types, 116 of which were unique to our study. Fine-mapping and co-localization of eQTLs with metabolic GWAS yielded key leads that warrant further investigation, such as the association of rs2168101 with LMO1 expression in alpha cells. Conclusions/interpretation We identified new signals that were previously not found in bulk pancreatic eQTL studies and potential cell type of action for several signals that were identified previously. Although there are limitations to the power, and therefore, discoverability of this study, it provides insights into how individual pancreatic cells differently contribute to metabolic disease.

genetics↗

bioRxiv2026

ARCHER-LD: Rapid Long-Range Linkage Disequilibrium Calculations at Biobank Scale using GPU Acceleration

Linkage disequilibrium (LD) information from one's own dataset is considered optimal for downstream analyses such as statistical fine-mapping. However, the computational complexity ({approx}N2 / 2 computations for N variants) lead most studies to use external reference panels, such as from 1000 Genomes. To capture LD across all variant pairs in biobank-scale whole-genome sequencing (WGS) datasets with hundreds of millions of variants, new computational strategies are essential. We present a novel approach that uses multi-GPU distributed computing to compute R2 for every variant pair in a dataset. On chromosome 22 (1.8 million variants) of the 30x WGS 1000 Genomes dataset, our method using eight consumer-level 12GB GPUs (NVIDIA RTX 2080TIs) takes <10 minutes, while the same calculation with PLINK using a high-end 64-threaded CPU (Intel Xeon Gold 6338) takes >80 minutes, corresponding to a {approx}8x speedup. We further demonstrate true biobank-scale performance in the Penn Medicine Biobank (PMBB; 57,170 samples), computing chromosome 1 LD ({approx}1.38 million variants) in 49 minutes versus 22.7 hours for PLINK, a {approx}28x speedup. With this method, we successfully computed on the aforementioned 30x WGS 1000 Genomes dataset ({approx}120 million variants and {approx}2500 samples) the entire LD matrix (>1e16, or 10 quadrillion elements) in under 6 hours using 512 NVIDIA 40GB A100 GPUs on the Department of Energy Argonne Leadership Computing Facility Polaris Supercomputer. We make this tool, coded in Python using CuPy, publicly available. Using this tool, researchers can leverage the full extent of their genomic data without relying on external LD reference panels and acquire more accurate, population-specific findings, particularly for groups underrepresented in existing databases.

bioinformatics↗

bioRxiv2026

PanKbase Integrated Single-Cell Map: A Comprehensive Atlas of Human Pancreatic Islets

Single-cell RNA sequencing (scRNA-seq) of human pancreatic islet tissue is a powerful tool for investigating type 1 diabetes (T1D). However, individual datasets are limited in size and fragmented across donors, laboratories, and experimental conditions. To address this, we constructed a comprehensive, integrated scRNA-seq atlas of isolated human pancreatic islets by collating publicly available data generated from tissue provided by resources including the Human Pancreas Analysis Program, the Integrated Islet Distribution Program, and Prodo Labs. Systematic quality controls were implemented to select high-quality samples, reads, and cells. During integration, we accounted for important variables such as age, sex, body mass index, origin study, treatments, islet distribution resources, and sequencing chemistry. Our single-cell atlas comprises 191 high-quality samples from 140 donors (59 female, 81 male) across five phenotypic groups: no diabetes (controls, n=69), autoantibody positivity without diabetes (n=12), pre-diabetes (n=11), T1D (n=12), and type 2 diabetes (T2D) (n=36). In total, the atlas contains 448,935 cells, capturing 13 distinct populations, including alpha cells (43.3%) and beta cells (26.8%), as well as groups such as immune cells (0.6%). Publicly available at www.pankbase.org, this atlas provides a platform for hypothesis-driven investigation of diabetes pathophysiology and, given rigorous quality control, is well-suited for downstream machine-learning applications. Article HighlightsO_LICurrent scRNA-seq datasets of pancreatic islet tissue are limited in size and scattered across donors, laboratories, and experimental conditions, underscoring the need for a consolidated resource. C_LIO_LIWe harmonized datasets from multiple sources to build a comprehensive single-cell map of isolated human pancreatic islets. C_LIO_LIOur atlas captures 448,935 cells from 191 high-quality samples across 140 donors and multiple phenotypic groups, identifying 13 distinct cell populations. C_LIO_LIAvailable at www.pankbase.org, the atlas provides a scalable, rigorously curated platform to support hypothesis-driven diabetes research and can enable a broad range of downstream computational applications. C_LI

genomics↗

bioRxiv2026

A meta-analysis of chromatin-associated loci provides insights into mechanistic interpretations of trait heritability

The vast majority of trait-associated loci discovered through genome-wide association studies (GWAS) are non-coding, yet most lack statistical alignment with any discovered expression quantitative trait loci (eQTLs). In particular, eQTLs are depleted at gene-distal regions and at "functionally important" genes - those with strong selective constraint and complex regulatory landscapes - likely due to selective depletion of high-effect variants. Here, we investigate the role of variants with weaker effects on expression transmitted through distal regulatory elements, which are detectable as chromatin accessibility QTLs (caQTLs). We aggregated caQTL data from ten studies derived across different tissues, cell-types and lines, representing 104,024 lead caQTLs across 3,457 samples. We found that, across a range of gene properties, caQTLs are discovered at functionally important genes more often than eQTLs. These observations are consistent with a model in which many eQTLs and GWAS hits are mediated through genetic effects on regulatory elements, which may have weak or context-dependent effects on gene expression. Our results suggest that caQTL discovery is more sensitive than eQTL discovery in capturing the molecular consequences of GWAS hits, and can provide complimentary information to eQTLs by implicating functional mechanisms of additional disease-associated loci.

genetics↗

bioRxiv2026

Tonsillar expression quantitative trait loci verify and expand genetic contributors to childhood atopic diseases

The spectrum of causal variants, mechanisms, and immunologic gene networks that influence pediatric atopic traits are not completely understood. Human genetic variation associated with transcript abundance (eQTLs) can help to advance our understanding, yet prior work has focused on profiling immune cell populations collected from peripheral blood primarily in adult populations, leaving uncharacterized tissue-resident lymphocytes collected from children. Here, we paired genotyping with gene expression profiling across four populations of tonsil-derived immune cell types - including previously uncharacterized germinal center B cells - collected from 103 children across development (ages 1-18). Using these data, we report cell-type specific gene networks and identify 13,393 eGenes (1,793 eGenes not previously reported in similar datasets) influenced by 27,603 eQTLs (5,199 not previously reported). We link discovered eQTLs to associations identified in pediatric and adult asthma and atopy traits, nominating 78 eGenes like TRAF3, ZBTB10 and JAZF1 in disease relevant cell types. Our resource is freely available and exemplifies the importance of discovery in native tissues and across human development.

immunology↗

bioRxiv2025

Methylation-associated mutagenesis underlies variation in the mutation spectrum across eukaryotes

Mutation spectra vary across genetic and environmental contexts, leading to differences between and within species. Most research on mutation spectrum has focused on the trinucleotide (3-mer) mutation types in mammals, limiting the breadth and depth of variation surveyed. In this study, we use whole-genome resequencing data across 108 eukaryotic species - including mammals, fish, plants, and invertebrates - to characterize pentanucleotide (5-mer) non-coding mutation spectra using a Bayesian approach. Our findings reveal cytosine transition mutability at CpG and (among plants) at CHG sites as the main drivers of variation in mutation spectra across eukaryotes, correlating strongly with genomic CpG and CHG depletion. However, despite the influence of methylation on CpG mutability, genome-wide average CpG methylation levels do not predict CpG transition rates across species and CHG methylation does not predict CHG transition rate, indicating unknown genetic or environmental factors influencing mutation rates at methylated cytosines. Together, our results illustrate the pivotal role of mutagenesis in shaping genome composition across eukaryotes and highlight a gap in knowledge about the mechanisms governing mutation rates.

evolutionary biology↗

bioRxiv2024

Characterization of non-coding variants associated with transcription factor binding through ATAC-seq-defined footprint QTLs in liver

Non-coding variants discovered by genome-wide association studies (GWAS) are enriched in regulatory elements harboring transcription factor (TF) binding motifs, strongly suggesting a connection between disease association and the disruption of cis-regulatory sequences. Occupancy of a TF inside a region of open chromatin can be detected in ATAC-seq where bound TFs block the transposase Tn5, leaving a pattern of relatively depleted Tn5 insertions known as a "footprint". Here, we sought to identify variants associated with TF-binding, or "footprint quantitative trait loci" (fpQTLs) in ATAC-seq data generated from 170 human liver samples. We used computational tools to scan the ATAC-seq reads to quantify TF binding likelihood as "footprint scores" at variants derived from whole genome sequencing generated in the same samples. We tested for association between genotype and footprint score and observed 693 fpQTLs associated with footprint-inferred TF binding (FDR < 5%). Given that Tn5 insertion sites are measured with base-pair resolution, we show that fpQTLs can aid GWAS and QTL fine-mapping by precisely pinpointing TF activity within broad trait-associated loci where the underlying causal variant is unknown. Liver fpQTLs were strongly enriched across ChIP-seq peaks, liver expression QTLs (eQTLs), and liver-related GWAS loci, and their inferred effect on TF binding was concordant with their effect on underlying sequence motifs in 80% of cases. We conclude that fpQTLs can reveal causal GWAS variants, define the role of TF binding site disruption in disease and provide functional insights into non-coding variants, ultimately informing novel treatments for common diseases. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/614730v1_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@129928borg.highwire.dtl.DTLVardef@53fff8org.highwire.dtl.DTLVardef@c9f814org.highwire.dtl.DTLVardef@1bdf1a8_HPS_FORMAT_FIGEXP M_FIG C_FIG We leverage footprinting methods to infer transcription factor binding likelihood genome-wide across 170 liver ATAC-seq samples and implicate 693 SNPs with a genetic influence on binding. Unlike other comparable approaches, this analytical method is not limited in resolution by the constraints of linkage disequilibrium, and can prioritize likely causal variants at GWAS loci for subsequent experimental validation.

genetics↗

bioRxiv2024

Genotype inference from aggregated chromatin accessibility data reveals genetic regulatory mechanisms

BackgroundUnderstanding the genetic causes for variability in chromatin accessibility can shed light on the molecular mechanisms through which genetic variants may affect complex traits. Thousands of ATAC-seq samples have been collected that hold information about chromatin accessibility across diverse cell types and contexts, but most of these are not paired with genetic information and come from diverse distinct projects and laboratories. ResultsWe report here joint genotyping, chromatin accessibility peak calling, and discovery of quantitative trait loci which influence chromatin accessibility (caQTLs), demonstrating the capability of performing caQTL analysis on a large scale in a diverse sample set without pre-existing genotype information. Using 10,293 profiling samples representing 1,454 unique donor individuals across 653 studies from public databases, we catalog 23,381 caQTLs in total. After joint discovery analysis, we cluster samples based on accessible chromatin profiles to identify context-specific caQTLs. We find that caQTLs are strongly enriched for annotations of gene regulatory elements across diverse cell types and tissues and are often strongly linked with genetic variation associated with changes in expression (eQTLs), indicating that caQTLs can mediate genetic effects on gene expression. We demonstrate sharing of causal variants for chromatin accessibility and diverse complex human traits, enabling a more complete picture of the genetic mechanisms underlying complex human phenotypes. ConclusionsOur work provides a proof of principle for caQTL calling from previously ungenotyped samples, and represents one of the largest, most diverse caQTL resources currently available, informing mechanisms of genetic regulation of gene expression and contribution to disease.

genomics↗

bioRxiv2024

Accelerating Genome- and Phenome-Wide Association Studies using GPUs - A case study using data from the Million Veteran Program

The expansion of biobanks has significantly propelled genomic discoveries yet the sheer scale of data within these repositories poses formidable computational hurdles, particularly in handling extensive matrix operations required by prevailing statistical frameworks. In this work, we introduce computational optimizations to the SAIGE (Scalable and Accurate Implementation of Generalized Mixed Model) algorithm, notably employing a GPU-based distributed computing approach to tackle these challenges. We applied these optimizations to conduct a large-scale genome-wide association study (GWAS) across 2,068 phenotypes derived from electronic health records of 635,969 diverse participants from the Veterans Affairs (VA) Million Veteran Program (MVP). Our strategies enabled scaling up the analysis to over 6,000 nodes on the Department of Energy (DOE) Oak Ridge Leadership Computing Facility (OLCF) Summit High-Performance Computer (HPC), resulting in a 20-fold acceleration compared to the baseline model. We also provide a Docker container with our optimizations that was successfully used on multiple cloud infrastructures on UK Biobank and All of Us datasets where we showed significant time and cost benefits over the baseline SAIGE model.

genetics↗

bioRxiv2024

GWAS-informed data integration and non-coding CRISPRi screen illuminate genetic etiology of bone mineral density

Over 1,100 independent signals have been identified with genome-wide association studies (GWAS) for bone mineral density (BMD), a key risk factor for mortality-increasing fragility fractures; however, the effector gene(s) for most remain unknown. Informed by a variant-to-gene mapping strategy implicating 89 non-coding elements predicted to regulate osteoblast gene expression at BMD GWAS loci, we executed a single-cell CRISPRi screen in human fetal osteoblasts (hFOBs). The BMD relevance of hFOBs was supported by heritability enrichment from stratified LD-score regression involving 98 cell types grouped into 15 tissues. 23 genes showed perturbation in the screen, with four (ARID5B, CC2D1B, EIF4G2, and NCOA3) exhibiting consistent effects upon siRNA knockdown on three measures of osteoblast maturation and mineralization. Lastly, additional heritability enrichments, genetic correlations, and multi-trait fine-mapping revealed unexpectedly that many BMD GWAS signals are pleiotropic and likely mediate their effects via non-bone tissues. Extending our CRISPRi screening approach to these tissues could play a key role in fully elucidating the etiology of BMD.

genomics↗

bioRxiv2023

G6PC2 controls glucagon secretion by defining the setpoint for glucose in pancreatic α-cells

Impaired glucose suppression of glucagon secretion (GSGS) is a hallmark of type 2 diabetes. A critical role for -cell intrinsic mechanisms in regulating glucagon secretion was previously established through genetic manipulation of the glycolytic enzyme glucokinase (GCK) in mice. Genetic variation at the G6PC2 locus, encoding an enzyme that opposes GCK, has been reproducibly associated with fasting blood glucose and hemoglobin A1c levels. Here, we find that trait-associated variants in the G6PC2 promoter are located in open chromatin not just in {beta}- but also in -cells, and document allele-specific G6PC2 expression of linked variants in human - cells. Using -cell specific gene ablation of G6pc2 in mice, we show that this gene plays a critical role in controlling glucagon secretion independent of alterations in insulin output, islet hormone content, or islet morphology; findings we confirmed in primary human -cells. Collectively, our data demonstrate that G6PC2 impacts glycemic control via its action in -cells and suggest that G6PC2 inhibitors could help control blood glucose through a novel, bi-hormonal mechanism.

genetics↗

bioRxiv2022

Regularized sequence-context mutational trees capture variation in mutation rates across the human genome

Germline mutation is the mechanism by which genetic variation in a population is created. Inferences derived from mutation rate models are fundamental to many population genetics inference methods. Previous models have demonstrated that nucleotides flanking polymorphic sites - the local sequence context - explain variation in the probability that a site is polymorphic. However, limitations to these models exist as the size of the local sequence context window expands. These include a lack of robustness to data sparsity at typical sample sizes, lack of regularization to generate parsimonious models and lack of quantified uncertainty in estimated rates to facilitate comparison between models. To address these limitations, we developed Baymer, a regularized Bayesian hierarchical tree model that captures the heterogeneous effect of sequence contexts on polymorphism probabilities. Baymer implements an adaptive Metropolis-within-Gibbs Markov Chain Monte Carlo sampling scheme to estimate the posterior distributions of sequence-context based probabilities that a site is polymorphic. We show that Baymer accurately infers polymorphism probabilities and well-calibrated posterior distributions, robustly handles data sparsity, appropriately regularizes to return parsimonious models, and scales computationally at least up to 9-mer context windows. We demonstrate application of Baymer in three ways - first, identifying differences in polymorphism probabilities between continental populations in the 1000 Genomes Phase 3 dataset, second, in a sparse data setting to examine the use of polymorphism models as a proxy for de novo mutation probabilities as a function of variant age, sequence context window size, and demographic history, and third, comparing model concordance between different great ape species. We find a shared context-dependent mutation rate architecture underlying our models, enabling a transfer-learning inspired strategy for modeling germline mutations. In summary, Baymer is an accurate polymorphism probability estimation algorithm that automatically adapts to data sparsity at different sequence context levels, thereby making efficient use of the available data. AUTHOR SUMMARYMany biological questions rely on accurate estimates of where and how frequently mutations arise in populations. One factor that has been shown to predict the probability that a mutation occurs is the local DNA sequence surrounding a potential site for mutation. It has been shown that increasing the size of local DNA sequence immediately surrounding a site improves prediction of where, what type, and how frequently the site is mutated. However, current methods struggle to take full advantage of this trend as well as capturing how certain our estimates are, in practice. We have designed a model, implemented in software (named Baymer), that is able to use large windows of sequence context to accurately model mutation probabilities in a computationally efficient manner. We use Baymer to identify specific DNA sequences that have the biggest impacts on mutability and apply the model to find motifs that have potentially evolved mutability between different human populations. We also apply it to show that germline mutations observed as polymorphic sites in humans - those that have occurred in our recent evolutionary history - can model very young mutations (de novo mutations) as well as polymorphism observed in populations of closely related great ape species.

genomics↗

bioRxiv2022

Estimating the effect size of a hidden causal factor between SNPs and a continuous trait: a mediation model approach

BackgroundObservational studies and Mendelian randomization experiments have been used to identify many causal factors for complex traits in humans. Given a set of causal factors, it is important to understand the extent to which these causal factors explain some, all, or none of the genetic heritability, as measured by single-nucleotide polymorphisms (SNPs) that are associated with the trait. Using the mediation model framework with SNPs as the exposure, a trait of interest as the outcome, and the known causal factors as the mediators, we hypothesize that any unexplained association between the SNPs and the outcome trait is mediated by an additional unobserved, hidden causal factor. ResultsWe propose a method to infer the effect size of this hidden mediating causal factor on the outcome trait by utilizing the estimated associations between a continuous outcome trait, the known causal factors, and the SNPs. The proposed method consists of three steps and, in the end, implements Markov chain Monte Carlo to obtain a posterior distribution for the hidden mediators effect size. We evaluate our proposed method via extensive simulations and show that when the model assumptions hold, our method estimates the effect size of the hidden mediator well and controls type I error rate if the hidden mediator does not exist.. In addition, we apply the method to the UK Biobank data and find that a potential hidden mediator for waist-hip ratio exists in the European population, and the hidden mediator has a large effect size relatively to the effect size of the known mediator BMI. ConclusionsWe develop a framework to infer the effect of potential, hidden mediators influencing complex traits. This framework can begin to place boundaries on unexplained risk factors contributing to complex traits.

genetics↗

bioRxiv2021

Obesity and adiposity have opposing genetic impacts on human blood traits

Obesity, hyperlipidemia, and truncal adiposity concordantly elevate cardiovascular disease risks, but have unknown genetic effects on blood trait variation. Using Mendelian randomization, we define unexpectedly opposing roles for generalized obesity and truncal adiposity on blood traits. Elevated genetically determined body mass index (BMI) and lipid levels decreased hemoglobin and hematocrit levels, explaining consistent with clinical observations associating obesity and anemia. However, lipid-related effects were confined to erythroid traits, whereas BMI affected multiple blood lineages, indicating broad effects on hematopoiesis. BMI-related effects were unexpectedly opposed by truncal adipose distribution, which increased hemoglobin and blood cell counts across lineages. Conditional analyses indicated genes, pathways, and cell types responsible for these effects, including Leptin Receptor and other blood cell-extrinsic factors in adipocytes and endothelium, which regulate hematopoietic stem and progenitor cell biology. Our findings identify novel roles for obesity and adipose distribution on hematopoiesis and show that genetically determined adiposity plays a previously underappreciated role in determining blood cell formation and function.

genetics↗

bioRxiv2020

Cis-regulatory architecture of human ESC-derived hypothalamic neuron differentiation aids in variant-to-gene mapping of relevant common complex traits

SummaryThe hypothalamus regulates metabolic homeostasis by influencing behavior, energy utilization and endocrine systems. Given its role governing health-relevant traits, such as body weight and reproductive timing, understanding the genetic regulation of hypothalamic development and function should yield insights into these traits and diseases. However, given its inaccessibility, studying human hypothalamic gene regulation has proven challenging. To address this gap, we generated a chromatin architecture atlas of an established embryonic stem cell (ESC)-derived hypothalamic-like neuron (HN) model across three stages of in vitro differentiation. We profiled accessible chromatin and identified physically interacting contacts between gene promoters and their putative cis-regulatory elements (cREs) to characterize changes in the gene regulatory landscape during hypothalamic differentiation. Next, we integrated these data with GWAS loci for multiple traits and diseases enriched for heritability in these cells, identifying candidate effector genes and cREs impacting transcription factor binding. Our results reveal common target genes for these traits, potentially identifying core hypothalamic developmental pathways. Our atlas will enable future efforts to determine precise mechanisms underlying hypothalamic development with respect to specific disease pathogenesis.Competing Interest StatementThe authors have declared no competing interest.View Full Text

genomics↗

bioRxiv2020

Identifying non-identical-by-descent rare variants in population-scale whole genome sequencing data

The site frequency spectrum in human populations is not accurately modeled by an infinite sites model, which assumes that all mutations are unique. Despite the pervasiveness of recurrent mutations, we lack computational methods to identify these events at specific sites in population sequencing data. Rare alleles that are identical-by-descent (IBD) are expected to segregate on a long, shared haplotype background that descends from a common ancestor. However, alleles introduced by recurrent mutation or by non-crossover gene conversions are identical-by-state and will have a shorter expected shared haplotype background. We hypothesized that the expected difference in shared haplotype background length can distinguish IBD and non-IBD variants in population sequencing data without pedigree information. We implemented a Bayesian hierarchical model and used Gibbs sampling to estimate the posterior probability of IBD state for rare variants, using simulations to demonstrate that our approach accurately distinguishes rare IBD and non-IBD variants. Applying our method to whole genome sequencing data from 3,621 individuals in the UK10K consortium, we found that non-IBD variants correlated with higher local mutation rates and genomic features like replication timing. Using a heuristic to categorize non-IBD variants as gene conversions or recurrent mutations, we found that potential gene conversions had expected properties such as enriched local GC content. By identifying recurrent mutations, we can better understand the spectrum of recent mutations in human populations, a source of genetic variation driving evolution and a key factor in understanding recent demographic history.

genetics↗

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↗

bioRxiv2019

Genetic colocalization atlas points to common regulatory sites and genes for hematopoietic traits and hematopoietic contributions to disease phenotypes

BackgroundGenetic associations link hematopoietic traits and disease end-points, but most causal variants and genes underlying these relationships are unknown. Here, we used genetic colocalization to nominate loci and genes related to shared genetic signal for hematopoietic, cardiovascular, autoimmune, neuropsychiatric and cancer phenotypes. ResultsOur findings recapitulate developmental hematopoietic lineage relationships, identify loci associating traits with causal genetic relationships, and reveal novel associations. Out of 2706 loci with genome-wide significant signal for at least 1 blood trait, we identified 1779 unique sites (66%) with shared genetic signal for 2+ hematologic traits at a false discovery rate <5%. We could assign some sites to specific developmental cell types during hematopoiesis based on affected traits, including those likely to impact hematopoietic progenitor cells and/or megakaryocyte-erythroid progenitor cells. Through an expanded analysis of 70 human traits, we define 2+ colocalizing traits at 2007 loci from an analysis of 9852 sites (20%) containing genome-wide significant signal for at least 1 GWAS trait. In addition to variants and genes underlying shared genetic signal between blood traits and disease phenotypes that had been previously related through mendelian randomization studies, we define loci and related genes underlying shared signal between eosinophil count and eczema. We also identified colocalizing signals in a number of clinically relevant coding mutations, including in sites linking PTPN22 with Crohns disease, NIPA with coronary artery disease and platelet trait variation, and the hemochromatosis gene HFE with altered lipid levels. Finally, we anticipate potential off-target effects on blood traits related novel therapeutic targets, including TRAIL. ConclusionsOur findings provide a road map for gene validation experiments and novel therapeutics related to hematopoietic development, and offer a rationale for pleiotropic interactions between hematopoietic loci and disease end-points.

genetics↗