Publications by authors named "Bogdan Pasaniuc"

104 Publications

Large-scale cross-cancer fine-mapping of the 5p15.33 region reveals multiple independent signals.

HGG Adv 2021 Jul 12;2(3). Epub 2021 Jun 12.

Department of Laboratory Medicine and Pathology, Mayo Clinic Comprehensive Cancer Center, Mayo Clinic, Rochester, MN, USA.

Genome-wide association studies (GWASs) have identified thousands of cancer risk loci revealing many risk regions shared across multiple cancers. Characterizing the cross-cancer shared genetic basis can increase our understanding of global mechanisms of cancer development. In this study, we collected GWAS summary statistics based on up to 375,468 cancer cases and 530,521 controls for fourteen types of cancer, including breast (overall, estrogen receptor [ER]-positive, and ER-negative), colorectal, endometrial, esophageal, glioma, head/neck, lung, melanoma, ovarian, pancreatic, prostate, and renal cancer, to characterize the shared genetic basis of cancer risk. We identified thirteen pairs of cancers with statistically significant local genetic correlations across eight distinct genomic regions. Specifically, the 5p15.33 region, harboring the and genes, showed statistically significant local genetic correlations for multiple cancer pairs. We conducted a cross-cancer fine-mapping of the 5p15.33 region based on eight cancers that showed genome-wide significant associations in this region (ER-negative breast, colorectal, glioma, lung, melanoma, ovarian, pancreatic, and prostate cancer). We used an iterative analysis pipeline implementing a subset-based meta-analysis approach based on cancer-specific conditional analyses and identified ten independent cross-cancer associations within this region. For each signal, we conducted cross-cancer fine-mapping to prioritize the most plausible causal variants. Our findings provide a more in-depth understanding of the shared inherited basis across human cancers and expand our knowledge of the 5p15.33 region in carcinogenesis.
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http://dx.doi.org/10.1016/j.xhgg.2021.100041DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8336922PMC
July 2021

Integrative genomic analyses identify susceptibility genes underlying COVID-19 hospitalization.

Nat Commun 2021 07 27;12(1):4569. Epub 2021 Jul 27.

Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.

Despite rapid progress in characterizing the role of host genetics in SARS-Cov-2 infection, there is limited understanding of genes and pathways that contribute to COVID-19. Here, we integrate a genome-wide association study of COVID-19 hospitalization (7,885 cases and 961,804 controls from COVID-19 Host Genetics Initiative) with mRNA expression, splicing, and protein levels (n = 18,502). We identify 27 genes related to inflammation and coagulation pathways whose genetically predicted expression was associated with COVID-19 hospitalization. We functionally characterize the 27 genes using phenome- and laboratory-wide association scans in Vanderbilt Biobank (n = 85,460) and identified coagulation-related clinical symptoms, immunologic, and blood-cell-related biomarkers. We replicate these findings across trans-ethnic studies and observed consistent effects in individuals of diverse ancestral backgrounds in Vanderbilt Biobank, pan-UK Biobank, and Biobank Japan. Our study highlights and reconfirms putative causal genes impacting COVID-19 severity and symptomology through the host inflammatory response.
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http://dx.doi.org/10.1038/s41467-021-24824-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8316582PMC
July 2021

Multitrait transcriptome-wide association study (TWAS) tests.

Genet Epidemiol 2021 Sep 3;45(6):563-576. Epub 2021 Jun 3.

Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.

Multitrait tests can improve power to detect associations between individual single-nucleotide polymorphisms (SNPs) and several related traits. Here, we develop methods for multi-SNP transcriptome-wide association (TWAS) tests to test the association between predicted gene expression levels and multiple phenotypes. We show that the correlation in TWAS test statistics for multiple phenotypes has the same form as multitrait statistics for the single-SNP setting. Thus, established methods for combining single-SNP test statistics across multiple traits can be extended directly to the TWAS setting. We performed an extensive evaluation across eight multitrait methods in simulations that varied gene-phenotype effect sizes in addition to the underlying covariance structure among the phenotypes. We found that all multitrait TWAS tests have well-calibrated Type I error (except ASSET, which can have a slightly elevated or depressed Type I error rate). Our results show that multitrait TWAS can improve statistical power compared with multiple single-trait TWAS followed by Bonferroni correction. To illustrate our approach to real data, we conducted a multitrait TWAS of four circulating lipid traits from the Global Lipids Genetics Consortium. We found that our multitrait Wald TWAS approach identified 506 genes associated with lipid levels compared with 87 identified through Bonferroni-corrected single-trait TWAS. Overall, we find that our proposed multitrait TWAS framework outperforms single-trait approaches to identify new genetic associations, especially for functionally correlated phenotypes and phenotypes with overlapping genome-wide association studies samples, leading to insights into the genetic architecture of multiple phenotypes.
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http://dx.doi.org/10.1002/gepi.22391DOI Listing
September 2021

Leveraging eQTLs to identify individual-level tissue of interest for a complex trait.

PLoS Comput Biol 2021 05 21;17(5):e1008915. Epub 2021 May 21.

Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, California, United States of America.

Genetic predisposition for complex traits often acts through multiple tissues at different time points during development. As a simple example, the genetic predisposition for obesity could be manifested either through inherited variants that control metabolism through regulation of genes expressed in the brain, or that control fat storage through dysregulation of genes expressed in adipose tissue, or both. Here we describe a statistical approach that leverages tissue-specific expression quantitative trait loci (eQTLs) corresponding to tissue-specific genes to prioritize a relevant tissue underlying the genetic predisposition of a given individual for a complex trait. Unlike existing approaches that prioritize relevant tissues for the trait in the population, our approach probabilistically quantifies the tissue-wise genetic contribution to the trait for a given individual. We hypothesize that for a subgroup of individuals the genetic contribution to the trait can be mediated primarily through a specific tissue. Through simulations using the UK Biobank, we show that our approach can predict the relevant tissue accurately and can cluster individuals according to their tissue-specific genetic architecture. We analyze body mass index (BMI) and waist to hip ratio adjusted for BMI (WHRadjBMI) in the UK Biobank to identify subgroups of individuals whose genetic predisposition act primarily through brain versus adipose tissue, and adipose versus muscle tissue, respectively. Notably, we find that these individuals have specific phenotypic features beyond BMI and WHRadjBMI that distinguish them from random individuals in the data, suggesting biological effects of tissue-specific genetic contribution for these traits.
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http://dx.doi.org/10.1371/journal.pcbi.1008915DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8174686PMC
May 2021

CDK4/6 inhibition reprograms the breast cancer enhancer landscape by stimulating AP-1 transcriptional activity.

Nat Cancer 2021 Jan 9;2(1):34-48. Epub 2020 Nov 9.

Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.

Pharmacologic inhibitors of cyclin-dependent kinases 4 and 6 (CDK4/6) were designed to induce cancer cell cycle arrest. Recent studies have suggested that these agents also exert other effects, influencing cancer cell immunogenicity, apoptotic responses, and differentiation. Using cell-based and mouse models of breast cancer together with clinical specimens, we show that CDK4/6 inhibitors induce remodeling of cancer cell chromatin characterized by widespread enhancer activation, and that this explains many of these effects. The newly activated enhancers include classical super-enhancers that drive luminal differentiation and apoptotic evasion, as well as a set of enhancers overlying endogenous retroviral elements that is enriched for proximity to interferon-driven genes. Mechanistically, CDK4/6 inhibition increases the level of several Activator Protein-1 (AP-1) transcription factor proteins, which are in turn implicated in the activity of many of the new enhancers. Our findings offer insights into CDK4/6 pathway biology and should inform the future development of CDK4/6 inhibitors.
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http://dx.doi.org/10.1038/s43018-020-00135-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8115221PMC
January 2021

Leveraging expression from multiple tissues using sparse canonical correlation analysis and aggregate tests improves the power of transcriptome-wide association studies.

PLoS Genet 2021 04 8;17(4):e1008973. Epub 2021 Apr 8.

Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.

Transcriptome-wide association studies (TWAS) test the association between traits and genetically predicted gene expression levels. The power of a TWAS depends in part on the strength of the correlation between a genetic predictor of gene expression and the causally relevant gene expression values. Consequently, TWAS power can be low when expression quantitative trait locus (eQTL) data used to train the genetic predictors have small sample sizes, or when data from causally relevant tissues are not available. Here, we propose to address these issues by integrating multiple tissues in the TWAS using sparse canonical correlation analysis (sCCA). We show that sCCA-TWAS combined with single-tissue TWAS using an aggregate Cauchy association test (ACAT) outperforms traditional single-tissue TWAS. In empirically motivated simulations, the sCCA+ACAT approach yielded the highest power to detect a gene associated with phenotype, even when expression in the causal tissue was not directly measured, while controlling the Type I error when there is no association between gene expression and phenotype. For example, when gene expression explains 2% of the variability in outcome, and the GWAS sample size is 20,000, the average power difference between the ACAT combined test of sCCA features and single-tissue, versus single-tissue combined with Generalized Berk-Jones (GBJ) method, single-tissue combined with S-MultiXcan, UTMOST, or summarizing cross-tissue expression patterns using Principal Component Analysis (PCA) approaches was 5%, 8%, 5% and 38%, respectively. The gain in power is likely due to sCCA cross-tissue features being more likely to be detectably heritable. When applied to publicly available summary statistics from 10 complex traits, the sCCA+ACAT test was able to increase the number of testable genes and identify on average an additional 400 additional gene-trait associations that single-trait TWAS missed. Our results suggest that aggregating eQTL data across multiple tissues using sCCA can improve the sensitivity of TWAS while controlling for the false positive rate.
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http://dx.doi.org/10.1371/journal.pgen.1008973DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8057593PMC
April 2021

Quantifying the contribution of dominance deviation effects to complex trait variation in biobank-scale data.

Am J Hum Genet 2021 05 2;108(5):799-808. Epub 2021 Apr 2.

Department of Computer Science, UCLA, Los Angeles, CA 90095, USA; Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA 90095, USA; Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA 90095, USA. Electronic address:

The proportion of variation in complex traits that can be attributed to non-additive genetic effects has been a topic of intense debate. The availability of biobank-scale datasets of genotype and trait data from unrelated individuals opens up the possibility of obtaining precise estimates of the contribution of non-additive genetic effects. We present an efficient method to estimate the variation in a complex trait that can be attributed to additive (additive heritability) and dominance deviation (dominance heritability) effects across all genotyped SNPs in a large collection of unrelated individuals. Over a wide range of genetic architectures, our method yields unbiased estimates of additive and dominance heritability. We applied our method, in turn, to array genotypes as well as imputed genotypes (at common SNPs with minor allele frequency [MAF] > 1%) and 50 quantitative traits measured in 291,273 unrelated white British individuals in the UK Biobank. Averaged across these 50 traits, we find that additive heritability on array SNPs is 21.86% while dominance heritability is 0.13% (about 0.48% of the additive heritability) with qualitatively similar results for imputed genotypes. We find no statistically significant evidence for dominance heritability (p<0.05/50 accounting for the number of traits tested) and estimate that dominance heritability is unlikely to exceed 1% for the traits analyzed. Our analyses indicate a limited contribution of dominance heritability to complex trait variation.
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http://dx.doi.org/10.1016/j.ajhg.2021.03.018DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8206203PMC
May 2021

Reprogramming of the FOXA1 cistrome in treatment-emergent neuroendocrine prostate cancer.

Nat Commun 2021 03 30;12(1):1979. Epub 2021 Mar 30.

Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.

Lineage plasticity, the ability of a cell to alter its identity, is an increasingly common mechanism of adaptive resistance to targeted therapy in cancer. An archetypal example is the development of neuroendocrine prostate cancer (NEPC) after treatment of prostate adenocarcinoma (PRAD) with inhibitors of androgen signaling. NEPC is an aggressive variant of prostate cancer that aberrantly expresses genes characteristic of neuroendocrine (NE) tissues and no longer depends on androgens. Here, we investigate the epigenomic basis of this resistance mechanism by profiling histone modifications in NEPC and PRAD patient-derived xenografts (PDXs) using chromatin immunoprecipitation and sequencing (ChIP-seq). We identify a vast network of cis-regulatory elements (N~15,000) that are recurrently activated in NEPC. The FOXA1 transcription factor (TF), which pioneers androgen receptor (AR) chromatin binding in the prostate epithelium, is reprogrammed to NE-specific regulatory elements in NEPC. Despite loss of dependence upon AR, NEPC maintains FOXA1 expression and requires FOXA1 for proliferation and expression of NE lineage-defining genes. Ectopic expression of the NE lineage TFs ASCL1 and NKX2-1 in PRAD cells reprograms FOXA1 to bind to NE regulatory elements and induces enhancer activity as evidenced by histone modifications at these sites. Our data establish the importance of FOXA1 in NEPC and provide a principled approach to identifying cancer dependencies through epigenomic profiling.
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http://dx.doi.org/10.1038/s41467-021-22139-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8010057PMC
March 2021

Gene Expression Imputation Across Multiple Tissue Types Provides Insight Into the Genetic Architecture of Frontotemporal Dementia and Its Clinical Subtypes.

Biol Psychiatry 2021 04 9;89(8):825-835. Epub 2021 Jan 9.

Department of Psychiatry, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, California; Center for Neurobehavioral Genetics, University of California, Los Angeles, Los Angeles, California.

Background: The etiology of frontotemporal dementia (FTD) is poorly understood. To identify genes with predicted expression levels associated with FTD, we integrated summary statistics with external reference gene expression data using a transcriptome-wide association study approach.

Methods: FUSION software was used to leverage FTD summary statistics (all FTD: n = 2154 cases, n = 4308 controls; behavioral variant FTD: n = 1337 cases, n = 2754 controls; semantic dementia: n = 308 cases, n = 616 controls; progressive nonfluent aphasia: n = 269 cases, n = 538 controls; FTD with motor neuron disease: n = 200 cases, n = 400 controls) from the International FTD-Genomics Consortium with 53 expression quantitative loci tissue type panels (n = 12,205; 5 consortia). Significance was assessed using a 5% false discovery rate threshold.

Results: We identified 73 significant gene-tissue associations for FTD, representing 44 unique genes in 34 tissue types. Most significant findings were derived from dorsolateral prefrontal cortex splicing data (n = 19 genes, 26%). The 17q21.31 inversion locus contained 23 significant associations, representing 6 unique genes. Other top hits included SEC22B (a gene involved in vesicle trafficking), TRGV5, and ZNF302. A single gene finding (RAB38) was observed for behavioral variant FTD. For other clinical subtypes, no significant associations were observed.

Conclusions: We identified novel candidate genes (e.g., SEC22B) and previously reported risk regions (e.g., 17q21.31) for FTD. Most significant associations were observed in dorsolateral prefrontal cortex splicing data despite the modest sample size of this reference panel. This suggests that our findings are specific to FTD and are likely to be biologically relevant highlights of genes at different FTD risk loci that are contributing to the disease pathology.
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http://dx.doi.org/10.1016/j.biopsych.2020.12.023DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8415425PMC
April 2021

Pre-existing conditions in Hispanics/Latinxs that are COVID-19 risk factors.

iScience 2021 Mar 12;24(3):102188. Epub 2021 Feb 12.

Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA.

Coronavirus disease 2019 (COVID-19) has exposed health care disparities in minority groups including Hispanics/Latinxs (HL). Studies of COVID-19 risk factors for HL have relied on county-level data. We investigated COVID-19 risk factors in HL using individual-level, electronic health records in a Los Angeles health system between March 9, 2020, and August 31, 2020. Of 9,287 HL tested for SARS-CoV-2, 562 were positive. HL constituted an increasing percentage of all COVID-19 positive individuals as disease severity escalated. Multiple risk factors identified in Non-Hispanic/Latinx whites (NHL-W), like renal disease, also conveyed risk in HL. Pre-existing nonrheumatic mitral valve disorder was a risk factor for HL hospitalization but not for NHL-W COVID-19 or HL influenza hospitalization, suggesting it may be a specific HL COVID-19 risk. Admission laboratory values also suggested that HL presented with a greater inflammatory response. COVID-19 risk factors for HL can help guide equitable government policies and identify at-risk populations.
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http://dx.doi.org/10.1016/j.isci.2021.102188DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7879099PMC
March 2021

A two-step approach to testing overall effect of gene-environment interaction for multiple phenotypes.

Bioinformatics 2021 Jan 16. Epub 2021 Jan 16.

Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA.

Motivation: While gene-environment (GxE) interactions contribute importantly to many different phenotypes, detecting such interactions requires well-powered studies and has proven difficult. To address this, we combine two approaches to improve GxE power: simultaneously evaluating multiple phenotypes and using a two-step analysis approach. Previous work shows that the power to identify a main genetic effect can be improved by simultaneously analyzing multiple related phenotypes. For a univariate phenotype, two-step methods produce higher power for detecting a GxE interaction compared to single step analysis. Therefore, we propose a two-step approach to test for an overall GxE effect for multiple phenotypes.

Results: Using simulations we demonstrate that, when more than one phenotype has GxE effect (i.e., GxE pleiotropy), our approach offers substantial gain in power (18%-43%) to detect an aggregate-level GxE effect for a multivariate phenotype compared to an analogous two-step method to identify GxE effect for a univariate phenotype. We applied the proposed approach to simultaneously analyze three lipids, LDL, HDL and Triglyceride with the frequency of alcohol consumption as environmental factor in the UK Biobank. The method identified two loci with an overall GxE effect on the vector of lipids, one of which was missed by the competing approaches.

Availability: We provide an R package MPGE implementing the proposed approach which is available from CRAN: https://cran.r-project.org/web/packages/MPGE/index.html.
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http://dx.doi.org/10.1093/bioinformatics/btaa1083DOI Listing
January 2021

PLEIO: a method to map and interpret pleiotropic loci with GWAS summary statistics.

Am J Hum Genet 2021 01 21;108(1):36-48. Epub 2020 Dec 21.

Department of Biomedical Sciences, BK21 Plus Biomedical Science Project, Seoul National University College of Medicine, Seoul 03080, Republic of Korea; Interdisciplinary Program in Bioengineering, Seoul National University, Seoul 03080, Republic of Korea. Electronic address:

Identifying and interpreting pleiotropic loci is essential to understanding the shared etiology among diseases and complex traits. A common approach to mapping pleiotropic loci is to meta-analyze GWAS summary statistics across multiple traits. However, this strategy does not account for the complex genetic architectures of traits, such as genetic correlations and heritabilities. Furthermore, the interpretation is challenging because phenotypes often have different characteristics and units. We propose PLEIO (Pleiotropic Locus Exploration and Interpretation using Optimal test), a summary-statistic-based framework to map and interpret pleiotropic loci in a joint analysis of multiple diseases and complex traits. Our method maximizes power by systematically accounting for genetic correlations and heritabilities of the traits in the association test. Any set of related phenotypes, binary or quantitative traits with different units, can be combined seamlessly. In addition, our framework offers interpretation and visualization tools to help downstream analyses. Using our method, we combined 18 traits related to cardiovascular disease and identified 13 pleiotropic loci, which showed four different patterns of associations.
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http://dx.doi.org/10.1016/j.ajhg.2020.11.017DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7820744PMC
January 2021

Credible learning of hydroxychloroquine and dexamethasone effects on COVID-19 mortality outside of randomized trials.

medRxiv 2020 Dec 8. Epub 2020 Dec 8.

Objectives: To investigate the effectiveness of hydroxychloroquine and dexamethasone on coronavirus disease (COVID-19) mortality using patient data outside of randomized trials.

Design: Phenotypes derived from electronic health records were analyzed using the stability-controlled quasi-experiment (SCQE) to provide a range of possible causal effects of hydroxy-chloroquine and dexamethasone on COVID-19 mortality.

Setting And Participants: Data from 2,007 COVID-19 positive patients hospitalized at a large university hospital system over the course of 200 days and not enrolled in randomized trials were analyzed using SCQE. For hyrdoxychloroquine, we examine a high-use cohort (n=766, days 1 to 43) and a later, low-use cohort (n=548, days 44 to 82). For dexamethasone, we examine a low-use cohort (n=614, days 44 to 101) and high-use cohort (n=622, days 102 to 200).

Outcome Measure: 14-day mortality, with a secondary outcome of 28-day mortality.

Results: Hydroxycholoroquine could only have been significantly (p<0.05) beneficial if baseline mortality was at least 6.4 percentage points (55%) lower among patients in the later (low-use) than the earlier (high-use) cohort. Hydroxychloroquine instead proves significantly harmful if baseline mortality rose from one cohort to the next by just 0.3 percentage points. Dexamethasone significantly reduced mortality risk if baseline mortality in the later (high-use) cohort (days 102-200) was higher than, the same as, or up to 1.5 percentage points lower than that in the earlier (low-use) cohort (days 44-101). It could only prove significantly harmful if mortality improved from one cohort to the next by 6.8 percentage points due to other causes-an assumption implying an unlikely 84% reduction in mortality due to other causes, leaving an in-hospital mortality rate of just 1.3%.

Conclusions: The assumptions required for a beneficial effect of hydroxychloroquine on 14 day mortality are difficult to sustain, while the assumptions required for hydroxychloroquine to be harmful are difficult to reject with confidence. Dexamethasone, by contrast, was beneficial under a wide range of plausible assumptions, and was only harmful if a nearly impossible assumption is met. More broadly, the SCQE reveals what inferences can be credibly supported by evidence from non-randomized uses of experimental therapies, making it a useful tool when randomized trials have not yet produced clear evidence or to provide corroborative evidence from different populations.
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http://dx.doi.org/10.1101/2020.12.06.20244798DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7743100PMC
December 2020

Integrative analyses identify susceptibility genes underlying COVID-19 hospitalization.

medRxiv 2020 Dec 8. Epub 2020 Dec 8.

Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA USA.

Despite rapid progress in characterizing the role of host genetics in SARS-Cov-2 infection, there is limited understanding of genes and pathways that contribute to COVID-19. Here, we integrated a genome-wide association study of COVID-19 hospitalization (7,885 cases and 961,804 controls from COVID-19 Host Genetics Initiative) with mRNA expression, splicing, and protein levels (n=18,502). We identified 27 genes related to inflammation and coagulation pathways whose genetically predicted expression was associated with COVID-19 hospitalization. We functionally characterized the 27 genes using phenome- and laboratory-wide association scans in Vanderbilt Biobank (BioVU; n=85,460) and identified coagulation-related clinical symptoms, immunologic, and blood-cell-related biomarkers. We replicated these findings across trans-ethnic studies and observed consistent effects in individuals of diverse ancestral backgrounds in BioVU, pan-UK Biobank, and Biobank Japan. Our study highlights putative causal genes impacting COVID-19 severity and symptomology through the host inflammatory response.
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http://dx.doi.org/10.1101/2020.12.07.20245308DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7743085PMC
December 2020

Optimized design of single-cell RNA sequencing experiments for cell-type-specific eQTL analysis.

Nat Commun 2020 10 30;11(1):5504. Epub 2020 Oct 30.

Department of Computer Science, University of California Los Angeles, 404 Westwood Plaza, Los Angeles, CA, 90095, USA.

Single-cell RNA-sequencing (scRNA-Seq) is a compelling approach to directly and simultaneously measure cellular composition and state, which can otherwise only be estimated by applying deconvolution methods to bulk RNA-Seq estimates. However, it has not yet become a widely used tool in population-scale analyses, due to its prohibitively high cost. Here we show that given the same budget, the statistical power of cell-type-specific expression quantitative trait loci (eQTL) mapping can be increased through low-coverage per-cell sequencing of more samples rather than high-coverage sequencing of fewer samples. We use simulations starting from one of the largest available real single-cell RNA-Seq data from 120 individuals to also show that multiple experimental designs with different numbers of samples, cells per sample and reads per cell could have similar statistical power, and choosing an appropriate design can yield large cost savings especially when multiplexed workflows are considered. Finally, we provide a practical approach on selecting cost-effective designs for maximizing cell-type-specific eQTL power which is available in the form of a web tool.
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http://dx.doi.org/10.1038/s41467-020-19365-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7599215PMC
October 2020

Transcriptomic Insight Into the Polygenic Mechanisms Underlying Psychiatric Disorders.

Biol Psychiatry 2021 01 12;89(1):54-64. Epub 2020 Jun 12.

Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California; Intellectual and Developmental Disabilities Research Center, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California; Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California. Electronic address:

Over the past decade, large-scale genetic studies have successfully identified hundreds of genetic variants robustly associated with risk for psychiatric disorders. However, mechanistic insight and clinical translation continue to lag the pace of risk variant identification, hindered by the sheer number of targets and their predominant noncoding localization, as well as pervasive pleiotropy and incomplete penetrance. Successful next steps require identification of "causal" genetic variants and their proximal biological consequences; placing variants within biologically defined functional contexts, reflecting specific molecular pathways, cell types, circuits, and developmental windows; and characterizing the downstream, convergent neurobiological impact of polygenicity within an individual. Here, we discuss opportunities and challenges of high-throughput transcriptomic profiling in the human brain, and how transcriptomic approaches can help pinpoint mechanisms underlying genetic risk for psychiatric disorders at a scale necessary to tackle daunting levels of polygenicity. These include transcriptome-wide association studies for risk gene prioritization through integration of genome-wide association studies with expression quantitative trait loci. We outline transcriptomic results that inform our understanding of the brain-level molecular pathology of psychiatric disorders, including autism spectrum disorder, bipolar disorder, major depressive disorder, and schizophrenia. Finally, we discuss systems-level approaches for integration of distinct genetic, genomic, and phenotypic levels, including combining spatially resolved gene expression and human neuroimaging maps. Results highlight the importance of understanding gene expression (dys)regulation across human brain development as a major contributor to psychiatric disease pathogenesis, from common variants acting as expression quantitative trait loci to rare variants enriched for gene expression regulatory pathways.
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http://dx.doi.org/10.1016/j.biopsych.2020.06.005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7718368PMC
January 2021

Efficient variance components analysis across millions of genomes.

Nat Commun 2020 08 11;11(1):4020. Epub 2020 Aug 11.

Department of Computer Science, UCLA, Los Angeles, CA, 90095, USA.

While variance components analysis has emerged as a powerful tool in complex trait genetics, existing methods for fitting variance components do not scale well to large-scale datasets of genetic variation. Here, we present a method for variance components analysis that is accurate and efficient: capable of estimating one hundred variance components on a million individuals genotyped at a million SNPs in a few hours. We illustrate the utility of our method in estimating and partitioning variation in a trait explained by genotyped SNPs (SNP-heritability). Analyzing 22 traits with genotypes from 300,000 individuals across about 8 million common and low frequency SNPs, we observe that per-allele squared effect size increases with decreasing minor allele frequency (MAF) and linkage disequilibrium (LD) consistent with the action of negative selection. Partitioning heritability across 28 functional annotations, we observe enrichment of heritability in FANTOM5 enhancers in asthma, eczema, thyroid and autoimmune disorders.
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http://dx.doi.org/10.1038/s41467-020-17576-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7419517PMC
August 2020

Prostate cancer reactivates developmental epigenomic programs during metastatic progression.

Nat Genet 2020 08 20;52(8):790-799. Epub 2020 Jul 20.

Center for Bioinformatics and Functional Genomics, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.

Epigenetic processes govern prostate cancer (PCa) biology, as evidenced by the dependency of PCa cells on the androgen receptor (AR), a prostate master transcription factor. We generated 268 epigenomic datasets spanning two state transitions-from normal prostate epithelium to localized PCa to metastases-in specimens derived from human tissue. We discovered that reprogrammed AR sites in metastatic PCa are not created de novo; rather, they are prepopulated by the transcription factors FOXA1 and HOXB13 in normal prostate epithelium. Reprogrammed regulatory elements commissioned in metastatic disease hijack latent developmental programs, accessing sites that are implicated in prostate organogenesis. Analysis of reactivated regulatory elements enabled the identification and functional validation of previously unknown metastasis-specific enhancers at HOXB13, FOXA1 and NKX3-1. Finally, we observed that prostate lineage-specific regulatory elements were strongly associated with PCa risk heritability and somatic mutation density. Examining prostate biology through an epigenomic lens is fundamental for understanding the mechanisms underlying tumor progression.
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http://dx.doi.org/10.1038/s41588-020-0664-8DOI Listing
August 2020

Prior diagnoses and medications as risk factors for COVID-19 in a Los Angeles Health System.

medRxiv 2020 Jul 9. Epub 2020 Jul 9.

Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA.

With the continuing coronavirus disease 2019 (COVID-19) pandemic coupled with phased reopening, it is critical to identify risk factors associated with susceptibility and severity of disease in a diverse population to help shape government policies, guide clinical decision making, and prioritize future COVID-19 research. In this retrospective case-control study, we used de-identified electronic health records (EHR) from the University of California Los Angeles (UCLA) Health System between March 9, 2020 and June 14, 2020 to identify risk factors for COVID-19 susceptibility (severe acute respiratory distress syndrome coronavirus 2 (SARS-CoV-2) PCR test positive), inpatient admission, and severe outcomes (treatment in an intensive care unit or intubation). Of the 26,602 individuals tested by PCR for SARS-CoV-2, 992 were COVID-19 positive (3.7% of Tested), 220 were admitted in the hospital (22% of COVID-19 positive), and 77 had a severe outcome (35% of Inpatient). Consistent with previous studies, males and individuals older than 65 years old had increased risk of inpatient admission. Notably, individuals self-identifying as Hispanic or Latino constituted an increasing percentage of COVID-19 patients as disease severity escalated, comprising 24% of those testing positive, but 40% of those with a severe outcome, a disparity that remained after correcting for medical comorbidities. Cardiovascular disease, hypertension, and renal disease were premorbid risk factors present before SARS-CoV-2 PCR testing associated with COVID-19 susceptibility. Less well-established risk factors for COVID-19 susceptibility included pre-existing dementia (odds ratio (OR) 5.2 [3.2-8.3], p=2.6 x 10), mental health conditions (depression OR 2.1 [1.6-2.8], p=1.1 x 10) and vitamin D deficiency (OR 1.8 [1.4-2.2], p=5.7 x 10). Renal diseases including end-stage renal disease and anemia due to chronic renal disease were the predominant premorbid risk factors for COVID-19 inpatient admission. Other less established risk factors for COVID-19 inpatient admission included previous renal transplant (OR 9.7 [2.8-39], p=3.2x10) and disorders of the immune system (OR 6.0 [2.3, 16], p=2.7x10). Prior use of oral steroid medications was associated with decreased COVID-19 positive testing risk (OR 0.61 [0.45, 0.81], p=4.3x10), but increased inpatient admission risk (OR 4.5 [2.3, 8.9], p=1.8x10). We did not observe that prior use of angiotensin converting enzyme inhibitors or angiotensin receptor blockers increased the risk of testing positive for SARS-CoV-2, being admitted to the hospital, or having a severe outcome. This study involving direct EHR extraction identified known and less well-established demographics, and prior diagnoses and medications as risk factors for COVID-19 susceptibility and inpatient admission. Knowledge of these risk factors including marked ethnic disparities observed in disease severity should guide government policies, identify at-risk populations, inform clinical decision making, and prioritize future COVID-19 research.
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http://dx.doi.org/10.1101/2020.07.03.20145581DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7340203PMC
July 2020

Massively parallel reporter assays of melanoma risk variants identify MX2 as a gene promoting melanoma.

Nat Commun 2020 06 1;11(1):2718. Epub 2020 Jun 1.

Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, 20892, USA.

Genome-wide association studies (GWAS) have identified ~20 melanoma susceptibility loci, most of which are not functionally characterized. Here we report an approach integrating massively-parallel reporter assays (MPRA) with cell-type-specific epigenome and expression quantitative trait loci (eQTL) to identify susceptibility genes/variants from multiple GWAS loci. From 832 high-LD variants, we identify 39 candidate functional variants from 14 loci displaying allelic transcriptional activity, a subset of which corroborates four colocalizing melanocyte cis-eQTL genes. Among these, we further characterize the locus encompassing the HIV-1 restriction gene, MX2 (Chr21q22.3), and validate a functional intronic variant, rs398206. rs398206 mediates the binding of the transcription factor, YY1, to increase MX2 levels, consistent with the cis-eQTL of MX2 in primary human melanocytes. Melanocyte-specific expression of human MX2 in a zebrafish model demonstrates accelerated melanoma formation in a BRAF background. Our integrative approach streamlines GWAS follow-up studies and highlights a pleiotropic function of MX2 in melanoma susceptibility.
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http://dx.doi.org/10.1038/s41467-020-16590-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7264232PMC
June 2020

Localizing Components of Shared Transethnic Genetic Architecture of Complex Traits from GWAS Summary Data.

Am J Hum Genet 2020 06 21;106(6):805-817. Epub 2020 May 21.

Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.

Despite strong transethnic genetic correlations reported in the literature for many complex traits, the non-transferability of polygenic risk scores across populations suggests the presence of population-specific components of genetic architecture. We propose an approach that models GWAS summary data for one trait in two populations to estimate genome-wide proportions of population-specific/shared causal SNPs. In simulations across various genetic architectures, we show that our approach yields approximately unbiased estimates with in-sample LD and slight upward-bias with out-of-sample LD. We analyze nine complex traits in individuals of East Asian and European ancestry, restricting to common SNPs (MAF > 5%), and find that most common causal SNPs are shared by both populations. Using the genome-wide estimates as priors in an empirical Bayes framework, we perform fine-mapping and observe that high-posterior SNPs (for both the population-specific and shared causal configurations) have highly correlated effects in East Asians and Europeans. In population-specific GWAS risk regions, we observe a 2.8× enrichment of shared high-posterior SNPs, suggesting that population-specific GWAS risk regions harbor shared causal SNPs that are undetected in the other GWASs due to differences in LD, allele frequencies, and/or sample size. Finally, we report enrichments of shared high-posterior SNPs in 53 tissue-specific functional categories and find evidence that SNP-heritability enrichments are driven largely by many low-effect common SNPs.
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http://dx.doi.org/10.1016/j.ajhg.2020.04.012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7273527PMC
June 2020

Transcriptome-wide association study of breast cancer risk by estrogen-receptor status.

Genet Epidemiol 2020 07 1;44(5):442-468. Epub 2020 Mar 1.

Department of Radiation Oncology, Hannover Medical School, Hannover, Germany.

Previous transcriptome-wide association studies (TWAS) have identified breast cancer risk genes by integrating data from expression quantitative loci and genome-wide association studies (GWAS), but analyses of breast cancer subtype-specific associations have been limited. In this study, we conducted a TWAS using gene expression data from GTEx and summary statistics from the hitherto largest GWAS meta-analysis conducted for breast cancer overall, and by estrogen receptor subtypes (ER+ and ER-). We further compared associations with ER+ and ER- subtypes, using a case-only TWAS approach. We also conducted multigene conditional analyses in regions with multiple TWAS associations. Two genes, STXBP4 and HIST2H2BA, were specifically associated with ER+ but not with ER- breast cancer. We further identified 30 TWAS-significant genes associated with overall breast cancer risk, including four that were not identified in previous studies. Conditional analyses identified single independent breast-cancer gene in three of six regions harboring multiple TWAS-significant genes. Our study provides new information on breast cancer genetics and biology, particularly about genomic differences between ER+ and ER- breast cancer.
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http://dx.doi.org/10.1002/gepi.22288DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7987299PMC
July 2020

Genetic Control of Expression and Splicing in Developing Human Brain Informs Disease Mechanisms.

Cell 2019 10;179(3):750-771.e22

Department of Neurology, Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, 695 Charles E. Young Drive South, Los Angeles, CA 90095, USA; Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Psychiatry, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, 695 Charles E. Young Drive South, Los Angeles, CA 90095, USA. Electronic address:

Tissue-specific regulatory regions harbor substantial genetic risk for disease. Because brain development is a critical epoch for neuropsychiatric disease susceptibility, we characterized the genetic control of the transcriptome in 201 mid-gestational human brains, identifying 7,962 expression quantitative trait loci (eQTL) and 4,635 spliceQTL (sQTL), including several thousand prenatal-specific regulatory regions. We show that significant genetic liability for neuropsychiatric disease lies within prenatal eQTL and sQTL. Integration of eQTL and sQTL with genome-wide association studies (GWAS) via transcriptome-wide association identified dozens of novel candidate risk genes, highlighting shared and stage-specific mechanisms in schizophrenia (SCZ). Gene network analysis revealed that SCZ and autism spectrum disorder (ASD) affect distinct developmental gene co-expression modules. Yet, in each disorder, common and rare genetic variation converges within modules, which in ASD implicates superficial cortical neurons. More broadly, these data, available as a web browser and our analyses, demonstrate the genetic mechanisms by which developmental events have a widespread influence on adult anatomical and behavioral phenotypes.
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http://dx.doi.org/10.1016/j.cell.2019.09.021DOI Listing
October 2019

Genome-wide germline correlates of the epigenetic landscape of prostate cancer.

Nat Med 2019 10 7;25(10):1615-1626. Epub 2019 Oct 7.

Ontario Institute for Cancer Research, Toronto, Ontario, Canada.

Oncogenesis is driven by germline, environmental and stochastic factors. It is unknown how these interact to produce the molecular phenotypes of tumors. We therefore quantified the influence of germline polymorphisms on the somatic epigenome of 589 localized prostate tumors. Predisposition risk loci influence a tumor's epigenome, uncovering a mechanism for cancer susceptibility. We identified and validated 1,178 loci associated with altered methylation in tumoral but not nonmalignant tissue. These tumor methylation quantitative trait loci influence chromatin structure, as well as RNA and protein abundance. One prominent tumor methylation quantitative trait locus is associated with AKT1 expression and is predictive of relapse after definitive local therapy in both discovery and validation cohorts. These data reveal intricate crosstalk between the germ line and the epigenome of primary tumors, which may help identify germline biomarkers of aggressive disease to aid patient triage and optimize the use of more invasive or expensive diagnostic assays.
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http://dx.doi.org/10.1038/s41591-019-0579-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7418214PMC
October 2019

Accurate estimation of SNP-heritability from biobank-scale data irrespective of genetic architecture.

Nat Genet 2019 08 29;51(8):1244-1251. Epub 2019 Jul 29.

Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.

SNP-heritability is a fundamental quantity in the study of complex traits. Recent studies have shown that existing methods to estimate genome-wide SNP-heritability can yield biases when their assumptions are violated. While various approaches have been proposed to account for frequency- and linkage disequilibrium (LD)-dependent genetic architectures, it remains unclear which estimates reported in the literature are reliable. Here we show that genome-wide SNP-heritability can be accurately estimated from biobank-scale data irrespective of genetic architecture, without specifying a heritability model or partitioning SNPs by allele frequency and/or LD. We show analytically and through extensive simulations starting from real genotypes (UK Biobank, N = 337 K) that, unlike existing methods, our closed-form estimator is robust across a wide range of architectures. We provide estimates of SNP-heritability for 22 complex traits in the UK Biobank and show that, consistent with our results in simulations, existing biobank-scale methods yield estimates up to 30% different from our theoretically-justified approach.
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http://dx.doi.org/10.1038/s41588-019-0465-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6686906PMC
August 2019

Genetic associations of breast and prostate cancer are enriched for regulatory elements identified in disease-related tissues.

Hum Genet 2019 Oct 22;138(10):1091-1104. Epub 2019 Jun 22.

Department of Epidemiology, University of Washington, Seattle, WA, USA.

Although genome-wide association studies (GWAS) have identified hundreds of risk loci for breast and prostate cancer, only a few studies have characterized the GWAS association signals across functional genomic annotations with a particular focus on single nucleotide polymorphisms (SNPs) located in DNA regulatory elements. In this study, we investigated the enrichment pattern of GWAS signals for breast and prostate cancer in genomic functional regions located in normal tissue and cancer cell lines. We quantified the overall enrichment of SNPs with breast and prostate cancer association p values < 1 × 10 across regulatory categories. We then obtained annotations for DNaseI hypersensitive sites (DHS), typical enhancers, and super enhancers across multiple tissue types, to assess if significant GWAS signals were selectively enriched in annotations found in disease-related tissue. Finally, we quantified the enrichment of breast and prostate cancer SNP heritability in regulatory regions, and compared the enrichment pattern of SNP heritability with GWAS signals. DHS, typical enhancers, and super enhancers identified in the breast cancer cell line MCF-7 were observed with the highest enrichment of genome-wide significant variants for breast cancer. For prostate cancer, GWAS signals were mostly enriched in DHS and typical enhancers identified in the prostate cancer cell line LNCaP. With progressively stringent GWAS p value thresholds, an increasing trend of enrichment was observed for both diseases in DHS, typical enhancers, and super enhancers located in disease-related tissue. Results from heritability enrichment analysis supported the selective enrichment pattern of functional genomic regions in disease-related cell lines for both breast and prostate cancer. Our results suggest the importance of studying functional annotations identified in disease-related tissues when characterizing GWAS results, and further demonstrate the role of germline DNA regulatory elements from disease-related tissue in breast and prostate carcinogenesis.
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http://dx.doi.org/10.1007/s00439-019-02041-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6745259PMC
October 2019
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