1
|
Kachuri L, Mak ACY, Hu D, Eng C, Huntsman S, Elhawary JR, Gupta N, Gabriel S, Xiao S, Keys KL, Oni-Orisan A, Rodríguez-Santana JR, LeNoir MA, Borrell LN, Zaitlen NA, Williams LK, Gignoux CR, Burchard EG, Ziv E. Gene expression in African Americans, Puerto Ricans and Mexican Americans reveals ancestry-specific patterns of genetic architecture. Nat Genet 2023; 55:952-963. [PMID: 37231098 PMCID: PMC10260401 DOI: 10.1038/s41588-023-01377-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 03/21/2023] [Indexed: 05/27/2023]
Abstract
We explored ancestry-related differences in the genetic architecture of whole-blood gene expression using whole-genome and RNA sequencing data from 2,733 African Americans, Puerto Ricans and Mexican Americans. We found that heritability of gene expression significantly increased with greater proportions of African genetic ancestry and decreased with higher proportions of Indigenous American ancestry, reflecting the relationship between heterozygosity and genetic variance. Among heritable protein-coding genes, the prevalence of ancestry-specific expression quantitative trait loci (anc-eQTLs) was 30% in African ancestry and 8% for Indigenous American ancestry segments. Most anc-eQTLs (89%) were driven by population differences in allele frequency. Transcriptome-wide association analyses of multi-ancestry summary statistics for 28 traits identified 79% more gene-trait associations using transcriptome prediction models trained in our admixed population than models trained using data from the Genotype-Tissue Expression project. Our study highlights the importance of measuring gene expression across large and ancestrally diverse populations for enabling new discoveries and reducing disparities.
Collapse
Affiliation(s)
- Linda Kachuri
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA
| | - Angel C Y Mak
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Donglei Hu
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Celeste Eng
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Scott Huntsman
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Jennifer R Elhawary
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Namrata Gupta
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Shujie Xiao
- Center for Individualized and Genomic Medicine Research, Henry Ford Health System, Detroit, MI, USA
| | - Kevin L Keys
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Berkeley Institute for Data Science, University of California, Berkeley, Berkeley, CA, USA
| | - Akinyemi Oni-Orisan
- Department of Clinical Pharmacy, University of California, San Francisco, San Francisco, CA, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | | | | | - Luisa N Borrell
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York, New York, NY, USA
| | - Noah A Zaitlen
- Department of Neurology, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - L Keoki Williams
- Center for Individualized and Genomic Medicine Research, Henry Ford Health System, Detroit, MI, USA
- Department of Internal Medicine, Henry Ford Health System, Detroit, MI, USA
| | - Christopher R Gignoux
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
| | - Esteban González Burchard
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA.
| | - Elad Ziv
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA.
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.
| |
Collapse
|
2
|
Herrera-Luis E, Li A, Mak ACY, Perez-Garcia J, Elhawary JR, Oh SS, Hu D, Eng C, Keys KL, Huntsman S, Beckman KB, Borrell LN, Rodriguez-Santana J, Burchard EG, Pino-Yanes M. Epigenome-wide association study of lung function in Latino children and youth with asthma. Clin Epigenetics 2022; 14:9. [PMID: 35033200 PMCID: PMC8760660 DOI: 10.1186/s13148-022-01227-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 01/03/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Introduction
DNA methylation studies have associated methylation levels at different CpG sites or genomic regions with lung function. Moreover, genetic ancestry has been associated with lung function in Latinos. However, no epigenome-wide association study (EWAS) of lung function has been performed in this population. Here, we aimed to identify DNA methylation patterns associated with lung function in pediatric asthma among Latinos.
Results
We conducted an EWAS in whole blood from 250 Puerto Rican and 148 Mexican American children and young adults with asthma. A total of five CpGs exceeded the genome-wide significance threshold of p = 1.17 × 10−7 in the combined analyses from Puerto Ricans and Mexican Americans: cg06035600 (MAP3K6, p = 6.13 × 10−8) showed significant association with pre-bronchodilator Tiffeneau–Pinelli index, the probes cg00914963 (TBC1D16, p = 1.04 × 10−7), cg16405908 (MRGPRE, p = 2.05 × 10−8), and cg07428101 (MUC2, p = 5.02 × 10−9) were associated with post-bronchodilator forced vital capacity (FVC), and cg20515679 (KCNJ6) with post-bronchodilator Tiffeneau–Pinelli index (p = 1.13 × 10−8). However, these markers did not show significant associations in publicly available data from Europeans (p > 0.05). A methylation quantitative trait loci analysis revealed that methylation levels at these CpG sites were regulated by genetic variation in Latinos and the Biobank-based Integrative Omics Studies (BIOS) consortium. Additionally, two differentially methylated regions in REXOC and AURKC were associated with pre-bronchodilator Tiffeneau–Pinelli index (adjusted p < 0.05) in Puerto Ricans and Mexican Americans. Moreover, we replicated some of the previous differentially methylated signals associated with lung function in non-Latino populations.
Conclusions
We replicated previous associations of epigenetic markers with lung function in whole blood and identified novel population-specific associations shared among Latino subgroups.
Collapse
|
3
|
Jiang Y, Forno E, Han YY, Xu Z, Hu D, Boutaoui N, Eng C, Acosta-Pérez E, Huntsman S, Colón-Semidey A, Keys KL, Rodríguez-Santana JR, Alvarez M, Pino-Yanes M, Canino G, Chen W, Burchard EG, Celedón JC. A genome-wide study of DNA methylation in white blood cells and asthma in Latino children and youth. Epigenetics 2021; 16:577-585. [PMID: 32799603 PMCID: PMC8078676 DOI: 10.1080/15592294.2020.1809872] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 07/11/2020] [Accepted: 07/24/2020] [Indexed: 10/23/2022] Open
Abstract
Latinos are heavily affected with childhood asthma. Little is known about epigenetic mechanisms of asthma in Latino youth. We conducted a meta-analysis of two epigenome-wide association studies (EWAS) of asthma, using DNA from white blood cells (WBCs) from 1,136 Latino children and youth aged 6 to 20 years. Genes near the top CpG sites in this EWAS were examined in a pathway enrichment analysis, and we then assessed whether our results replicated those from publicly available data from three independent EWAS conducted in non-Latino populations. We found that DNA methylation profiles differed between subjects with and without asthma. After adjustment for covariates and multiple testing, two CpGs were differentially methylated at a false discovery rate (FDR)-adjusted P < 0.1, and 193 CpG sites were differentially methylated at FDR-adjusted P < 0.2. The two top CpGs are near genes relevant to inflammatory signalling, including CAMK1D (Calcium/Calmodulin Dependent Protein Kinase ID) and TIGIT (T Cell Immunoreceptor With Ig And ITIM Domains). Moreover, 25 genomic regions were differentially methylated between subjects with and without asthma, at Šidák-corrected P < 0.10. An enrichment analysis then identified the TGF-beta pathway as most relevant to asthma in our analysis, and we replicated some of the top signals from publicly available EWAS datasets in non-Hispanic populations. In conclusion, we have identified novel epigenetic markers of asthma in WBCs from Latino children and youth, while also replicating previous results from studies conducted in non-Latinos.
Collapse
Affiliation(s)
- Yale Jiang
- Division of Pulmonary Medicine, UPMC Children’s Hospital of Pittsburgh, Pittsburgh, PA, USA
- Department of Pediatrics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- School of Medicine, Tsinghua University, Beijing, China
| | - Erick Forno
- Division of Pulmonary Medicine, UPMC Children’s Hospital of Pittsburgh, Pittsburgh, PA, USA
- Department of Pediatrics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Yueh-Ying Han
- Division of Pulmonary Medicine, UPMC Children’s Hospital of Pittsburgh, Pittsburgh, PA, USA
- Department of Pediatrics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Zhongli Xu
- Division of Pulmonary Medicine, UPMC Children’s Hospital of Pittsburgh, Pittsburgh, PA, USA
- Department of Pediatrics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- School of Medicine, Tsinghua University, Beijing, China
| | - Donglei Hu
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Nadia Boutaoui
- Division of Pulmonary Medicine, UPMC Children’s Hospital of Pittsburgh, Pittsburgh, PA, USA
- Department of Pediatrics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Celeste Eng
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Edna Acosta-Pérez
- Behavioral Sciences Research Institute, Medical Science Campus, University of Puerto Rico, San Juan, PR
| | - Scott Huntsman
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Angel Colón-Semidey
- Department of Pediatrics, Medical Science Campus, University of Puerto Rico, San Juan, PR
| | - Kevin L. Keys
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
- Berkeley Institute for Data Science, University of California Berkeley, Berkeley, CA, USA
| | | | - María Alvarez
- Department of Pediatrics, Medical Science Campus, University of Puerto Rico, San Juan, PR
| | - Maria Pino-Yanes
- Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna, La Laguna, Santa Cruz de Tenerife, Spain
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
- Instituto de Tecnologías Biomédicas (ITB), Universidad de La Laguna, San Cristóbal de La Laguna, Santa Cruz de Tenerife, Spain
| | - Glorisa Canino
- Behavioral Sciences Research Institute, Medical Science Campus, University of Puerto Rico, San Juan, PR
| | - Wei Chen
- Division of Pulmonary Medicine, UPMC Children’s Hospital of Pittsburgh, Pittsburgh, PA, USA
- Department of Pediatrics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Esteban G. Burchard
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Juan C. Celedón
- Division of Pulmonary Medicine, UPMC Children’s Hospital of Pittsburgh, Pittsburgh, PA, USA
- Department of Pediatrics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| |
Collapse
|
4
|
Herrera-Luis E, Espuela-Ortiz A, Lorenzo-Diaz F, Keys KL, Mak ACY, Eng C, Huntsman S, Villar J, Rodriguez-Santana JR, Burchard EG, Pino-Yanes M. Genome-wide association study reveals a novel locus for asthma with severe exacerbations in diverse populations. Pediatr Allergy Immunol 2021; 32:106-115. [PMID: 32841424 PMCID: PMC7886969 DOI: 10.1111/pai.13337] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 07/08/2020] [Accepted: 08/11/2020] [Indexed: 12/18/2022]
Abstract
BACKGROUND Severe asthma exacerbations are a major cause of asthma morbidity and increased healthcare costs. Several studies have shown racial and ethnic differences in asthma exacerbation rates. We aimed to identify genetic variants associated with severe exacerbations in two high-risk populations for asthma. METHODS A genome-wide association study of asthma in children and youth with severe exacerbations was performed in 1283 exacerbators and 2027 controls without asthma of Latino ancestry. Independent suggestive variants (P ≤ 5 × 10-6 ) were selected for replication in 448 African Americans exacerbators and 595 controls. Case-only analyses were performed comparing the exacerbators with additional 898 Latinos and 524 African Americans asthma patients without exacerbations, while adjusting by treatment category as a proxy of asthma severity. We analyzed the functionality of associated variants with in silico methods and by correlating genotypes with methylation levels in whole blood in a subset of 473 Latinos. RESULTS We identified two genome-wide significant associations for susceptibility to asthma with severe exacerbations, including a novel locus located at chromosome 2p21 (rs4952375, odds ratio = 1.39, P = 3.8 × 10-8 ), which was also associated with asthma exacerbations in a case-only analysis (odds ratio = 1.25, P = 1.95 × 10-3 ). This polymorphism is an expression quantitative trait locus of the long intergenic non-protein coding RNA 1913 (LINC01913) in lung tissues (P = 1.3 × 10-7 ) and influences methylation levels of the protein kinase domain-containing cytoplasmic (PKDCC) gene in whole-blood cells (P = 9.8 × 10-5 ). CONCLUSION We identified a novel susceptibility locus for severe asthma exacerbations in Hispanic/Latino and African American youths with functional effects in gene expression and methylation status of neighboring genes.
Collapse
Affiliation(s)
- Esther Herrera-Luis
- Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna, San Cristóbal de La Laguna, Tenerife, Spain
| | - Antonio Espuela-Ortiz
- Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna, San Cristóbal de La Laguna, Tenerife, Spain
| | - Fabian Lorenzo-Diaz
- Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna, San Cristóbal de La Laguna, Tenerife, Spain
| | - Kevin L Keys
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA.,Berkeley Institute for Data Science, University of California Berkeley, Berkeley, CA, USA
| | - Angel C Y Mak
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Celeste Eng
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Scott Huntsman
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Jesús Villar
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain.,Multidisciplinary Organ Dysfunction Evaluation Research Network, Research Unit, Hospital Universitario Dr Negrín, Las Palmas de Gran Canaria, Spain.,Keenan Research Center for Biomedical Science at the Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, ON, Canada
| | | | - Esteban G Burchard
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA.,Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Maria Pino-Yanes
- Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna, San Cristóbal de La Laguna, Tenerife, Spain.,CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain.,Instituto de Tecnologías Biomédicas (ITB), Universidad de La Laguna, San Cristóbal de La Laguna Santa Cruz de Tenerife, Spain
| |
Collapse
|
5
|
Lee EY, Mak ACY, Hu D, Sajuthi S, White MJ, Keys KL, Eckalbar W, Bonser L, Huntsman S, Urbanek C, Eng C, Jain D, Abecasis G, Kang HM, Germer S, Zody MC, Nickerson DA, Erle D, Ziv E, Rodriguez-Santana J, Seibold MA, Burchard EG. Whole-Genome Sequencing Identifies Novel Functional Loci Associated with Lung Function in Puerto Rican Youth. Am J Respir Crit Care Med 2020; 202:962-972. [PMID: 32459537 DOI: 10.1164/rccm.202002-0351oc] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Rationale: Puerto Ricans have the highest childhood asthma prevalence in the United States (23.6%); however, the etiology is uncertain.Objectives: In this study, we sought to uncover the genetic architecture of lung function in Puerto Rican youth with and without asthma who were recruited from the island (n = 836).Methods: We used admixture-mapping and whole-genome sequencing data to discover genomic regions associated with lung function. Functional roles of the prioritized candidate SNPs were examined with chromatin immunoprecipitation sequencing, RNA sequencing, and expression quantitative trait loci data.Measurements and Main Results: We discovered a genomic region at 1q32 that was significantly associated with a 0.12-L decrease in the lung volume of exhaled air (95% confidence interval, -0.17 to -0.07; P = 6.62 × 10-8) with each allele of African ancestry. Within this region, two SNPs were expression quantitative trait loci of TMEM9 in nasal airway epithelial cells and MROH3P in esophagus mucosa. The minor alleles of these SNPs were associated with significantly decreased lung function and decreased TMEM9 gene expression. Another admixture-mapping peak was observed on chromosome 5q35.1, indicating that each Native American ancestry allele was associated with a 0.15-L increase in lung function (95% confidence interval, 0.08-0.21; P = 5.03 × 10-6). The region-based association tests identified four suggestive windows that harbored candidate rare variants associated with lung function.Conclusions: We identified common and rare genetic variants that may play a critical role in lung function among Puerto Rican youth. We independently validated an inflammatory pathway that could potentially be used to develop more targeted treatments and interventions for patients with asthma.
Collapse
Affiliation(s)
- Eunice Y Lee
- Department of Bioengineering and Therapeutic Sciences and.,Department of Medicine, University of California, San Francisco, San Francisco, California
| | - Angel C Y Mak
- Department of Medicine, University of California, San Francisco, San Francisco, California
| | - Donglei Hu
- Department of Medicine, University of California, San Francisco, San Francisco, California
| | - Satria Sajuthi
- Department of Pediatrics, Center for Genes, Environment, and Health, and
| | - Marquitta J White
- Department of Medicine, University of California, San Francisco, San Francisco, California
| | - Kevin L Keys
- Department of Medicine, University of California, San Francisco, San Francisco, California
| | | | - Luke Bonser
- Department of Medicine, University of California, San Francisco, San Francisco, California
| | - Scott Huntsman
- Department of Medicine, University of California, San Francisco, San Francisco, California
| | - Cydney Urbanek
- Department of Pediatrics, Center for Genes, Environment, and Health, and
| | - Celeste Eng
- Department of Medicine, University of California, San Francisco, San Francisco, California
| | | | - Gonçalo Abecasis
- Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan.,Regeneron Pharmaceuticals, Tarrytown, New York
| | - Hyun M Kang
- Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan
| | | | | | - Deborah A Nickerson
- Department of Genome Sciences, University of Washington, Seattle, Washington.,Northwest Genomics Center, Seattle, Washington.,Brotman Baty Institute, Seattle, Washington
| | - David Erle
- Department of Medicine, University of California, San Francisco, San Francisco, California
| | - Elad Ziv
- Department of Medicine, University of California, San Francisco, San Francisco, California
| | | | - Max A Seibold
- Department of Pediatrics, Center for Genes, Environment, and Health, and.,Department of Pediatrics, National Jewish Health, Denver, Colorado.,Division of Pulmonary Sciences and Critical Care Medicine, Department of Medicine, University of Colorado-Anschutz Medical Campus, Aurora, Colorado
| | - Esteban G Burchard
- Department of Bioengineering and Therapeutic Sciences and.,Department of Medicine, University of California, San Francisco, San Francisco, California
| |
Collapse
|
6
|
Goddard PC, Keys KL, Mak ACY, Lee EY, Liu AK, Samedy-Bates LA, Risse-Adams O, Contreras MG, Elhawary JR, Hu D, Huntsman S, Oh SS, Salazar S, Eng C, Himes BE, White MJ, Burchard EG. Integrative genomic analysis in African American children with asthma finds three novel loci associated with lung function. Genet Epidemiol 2020; 45:190-208. [PMID: 32989782 DOI: 10.1002/gepi.22365] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 08/21/2020] [Accepted: 09/14/2020] [Indexed: 11/06/2022]
Abstract
Bronchodilator (BD) drugs are commonly prescribed for treatment and management of obstructive lung function present with diseases such as asthma. Administration of BD medication can partially or fully restore lung function as measured by pulmonary function tests. The genetics of baseline lung function measures taken before BD medication have been extensively studied, and the genetics of the BD response itself have received some attention. However, few studies have focused on the genetics of post-BD lung function. To address this gap, we analyzed lung function phenotypes in 1103 subjects from the Study of African Americans, Asthma, Genes, and Environment, a pediatric asthma case-control cohort, using an integrative genomic analysis approach that combined genotype, locus-specific genetic ancestry, and functional annotation information. We integrated genome-wide association study (GWAS) results with an admixture mapping scan of three pulmonary function tests (forced expiratory volume in 1 s [FEV1 ], forced vital capacity [FVC], and FEV1 /FVC) taken before and after albuterol BD administration on the same subjects, yielding six traits. We identified 18 GWAS loci, and five additional loci from admixture mapping, spanning several known and novel lung function candidate genes. Most loci identified via admixture mapping exhibited wide variation in minor allele frequency across genotyped global populations. Functional fine-mapping revealed an enrichment of epigenetic annotations from peripheral blood mononuclear cells, fetal lung tissue, and lung fibroblasts. Our results point to three novel potential genetic drivers of pre- and post-BD lung function: ADAMTS1, RAD54B, and EGLN3.
Collapse
Affiliation(s)
- Pagé C Goddard
- Department of Genetics, Stanford University, Stanford, California, USA.,Department of Medicine, University of California, San Francisco, California, USA
| | - Kevin L Keys
- Department of Medicine, University of California, San Francisco, California, USA.,Berkeley Institute for Data Science, University of California, Berkeley, California, USA
| | - Angel C Y Mak
- Department of Medicine, University of California, San Francisco, California, USA
| | - Eunice Y Lee
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA
| | - Amy K Liu
- Department of Neurology, University of California, San Francisco, California, USA
| | - Lesly-Anne Samedy-Bates
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA
| | - Oona Risse-Adams
- Department of Medicine, University of California, San Francisco, California, USA.,Department of Biology, University of California, Santa Cruz, California, USA
| | - María G Contreras
- Department of Medicine, University of California, San Francisco, California, USA.,Department of Biology, San Francisco State University, San Francisco, California, USA
| | - Jennifer R Elhawary
- Department of Medicine, University of California, San Francisco, California, USA
| | - Donglei Hu
- Department of Medicine, University of California, San Francisco, California, USA
| | - Scott Huntsman
- Department of Medicine, University of California, San Francisco, California, USA
| | - Sam S Oh
- Department of Medicine, University of California, San Francisco, California, USA
| | - Sandra Salazar
- Department of Medicine, University of California, San Francisco, California, USA
| | - Celeste Eng
- Department of Medicine, University of California, San Francisco, California, USA
| | - Blanca E Himes
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Marquitta J White
- Department of Medicine, University of California, San Francisco, California, USA
| | - Esteban G Burchard
- Department of Medicine, University of California, San Francisco, California, USA.,Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA
| |
Collapse
|
7
|
Quick C, Anugu P, Musani S, Weiss ST, Burchard EG, White MJ, Keys KL, Cucca F, Sidore C, Boehnke M, Fuchsberger C. Sequencing and imputation in GWAS: Cost-effective strategies to increase power and genomic coverage across diverse populations. Genet Epidemiol 2020; 44:537-549. [PMID: 32519380 PMCID: PMC7449570 DOI: 10.1002/gepi.22326] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 04/02/2020] [Accepted: 05/22/2020] [Indexed: 01/03/2023]
Abstract
A key aim for current genome-wide association studies (GWAS) is to interrogate the full spectrum of genetic variation underlying human traits, including rare variants, across populations. Deep whole-genome sequencing is the gold standard to fully capture genetic variation, but remains prohibitively expensive for large sample sizes. Array genotyping interrogates a sparser set of variants, which can be used as a scaffold for genotype imputation to capture a wider set of variants. However, imputation quality depends crucially on reference panel size and genetic distance from the target population. Here, we consider sequencing a subset of GWAS participants and imputing the rest using a reference panel that includes both sequenced GWAS participants and an external reference panel. We investigate how imputation quality and GWAS power are affected by the number of participants sequenced for admixed populations (African and Latino Americans) and European population isolates (Sardinians and Finns), and identify powerful, cost-effective GWAS designs given current sequencing and array costs. For populations that are well-represented in existing reference panels, we find that array genotyping alone is cost-effective and well-powered to detect common- and rare-variant associations. For poorly represented populations, sequencing a subset of participants is often most cost-effective, and can substantially increase imputation quality and GWAS power.
Collapse
Affiliation(s)
- Corbin Quick
- Department of Biostatistics and Center for Statistical GeneticsUniversity of Michigan School of Public HealthAnn ArborMichigan
| | - Pramod Anugu
- University of Mississippi Medical CenterJacksonMississippi
| | - Solomon Musani
- University of Mississippi Medical CenterJacksonMississippi
| | - Scott T. Weiss
- Harvard Medical SchoolBostonMassachusetts
- Channing Department of Network MedicineBrigham and Women's HospitalBostonCalifornia
- Partners HealthCare Personalized MedicineBostonMassachusetts
| | - Esteban G. Burchard
- Department of MedicineUniversity of California San FranciscoSan FranciscoCalifornia
- Department of Bioengineering and Therapeutic SciencesUniversity of California San FranciscoSan FranciscoCalifornia
| | - Marquitta J. White
- Department of MedicineUniversity of California San FranciscoSan FranciscoCalifornia
| | - Kevin L. Keys
- Department of MedicineUniversity of California San FranciscoSan FranciscoCalifornia
| | - Francesco Cucca
- Istituto di Ricerca Genetica e Biomedica (IRGB), CNRMonserratoItaly
- Dipartimento di Scienze BiomedicheUniversità di SassariSassariItaly
| | - Carlo Sidore
- Istituto di Ricerca Genetica e Biomedica (IRGB), CNRMonserratoItaly
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical GeneticsUniversity of Michigan School of Public HealthAnn ArborMichigan
| | - Christian Fuchsberger
- Department of Biostatistics and Center for Statistical GeneticsUniversity of Michigan School of Public HealthAnn ArborMichigan
- Department of Genetics and Pharmacology, Institute of Genetic EpidemiologyMedical University of InnsbruckInnsbruckAustria
- Institute for Biomedicine, Eurac ResearchAffiliated Institute of the University of LübeckBolzanoItaly
| |
Collapse
|
8
|
Keys KL, Mak ACY, White MJ, Eckalbar WL, Dahl AW, Mefford J, Mikhaylova AV, Contreras MG, Elhawary JR, Eng C, Hu D, Huntsman S, Oh SS, Salazar S, Lenoir MA, Ye JC, Thornton TA, Zaitlen N, Burchard EG, Gignoux CR. On the cross-population generalizability of gene expression prediction models. PLoS Genet 2020; 16:e1008927. [PMID: 32797036 PMCID: PMC7449671 DOI: 10.1371/journal.pgen.1008927] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 08/26/2020] [Accepted: 06/10/2020] [Indexed: 11/21/2022] Open
Abstract
The genetic control of gene expression is a core component of human physiology. For the past several years, transcriptome-wide association studies have leveraged large datasets of linked genotype and RNA sequencing information to create a powerful gene-based test of association that has been used in dozens of studies. While numerous discoveries have been made, the populations in the training data are overwhelmingly of European descent, and little is known about the generalizability of these models to other populations. Here, we test for cross-population generalizability of gene expression prediction models using a dataset of African American individuals with RNA-Seq data in whole blood. We find that the default models trained in large datasets such as GTEx and DGN fare poorly in African Americans, with a notable reduction in prediction accuracy when compared to European Americans. We replicate these limitations in cross-population generalizability using the five populations in the GEUVADIS dataset. Via realistic simulations of both populations and gene expression, we show that accurate cross-population generalizability of transcriptome prediction only arises when eQTL architecture is substantially shared across populations. In contrast, models with non-identical eQTLs showed patterns similar to real-world data. Therefore, generating RNA-Seq data in diverse populations is a critical step towards multi-ethnic utility of gene expression prediction.
Collapse
Affiliation(s)
- Kevin L. Keys
- Department of Medicine, University of California, San Francisco, California, United States of America
- Berkeley Institute for Data Science, University of California, Berkeley, California, United States of America
| | - Angel C. Y. Mak
- Department of Medicine, University of California, San Francisco, California, United States of America
| | - Marquitta J. White
- Department of Medicine, University of California, San Francisco, California, United States of America
| | - Walter L. Eckalbar
- Department of Medicine, University of California, San Francisco, California, United States of America
| | - Andrew W. Dahl
- Department of Medicine, University of California, San Francisco, California, United States of America
| | - Joel Mefford
- Department of Medicine, University of California, San Francisco, California, United States of America
| | - Anna V. Mikhaylova
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
| | - María G. Contreras
- Department of Medicine, University of California, San Francisco, California, United States of America
- San Francisco State University, San Francisco, California, United States of America
| | - Jennifer R. Elhawary
- Department of Medicine, University of California, San Francisco, California, United States of America
| | - Celeste Eng
- Department of Medicine, University of California, San Francisco, California, United States of America
| | - Donglei Hu
- Department of Medicine, University of California, San Francisco, California, United States of America
| | - Scott Huntsman
- Department of Medicine, University of California, San Francisco, California, United States of America
| | - Sam S. Oh
- Department of Medicine, University of California, San Francisco, California, United States of America
| | - Sandra Salazar
- Department of Medicine, University of California, San Francisco, California, United States of America
| | | | - Jimmie C. Ye
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, United States of America
- Department of Bioengineering and Therapeutic Biosciences, University of California, San Francisco, California, United States of America
| | - Timothy A. Thornton
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
| | - Noah Zaitlen
- Department of Neurology, University of California, Los Angeles, California, United States of America
| | - Esteban G. Burchard
- Department of Medicine, University of California, San Francisco, California, United States of America
- Department of Bioengineering and Therapeutic Biosciences, University of California, San Francisco, California, United States of America
| | - Christopher R. Gignoux
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
- Department of Biostatistics and Informatics, School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| |
Collapse
|
9
|
Mak ACY, Sajuthi S, Joo J, Xiao S, Sleiman PM, White MJ, Lee EY, Saef B, Hu D, Gui H, Keys KL, Lurmann F, Jain D, Abecasis G, Kang HM, Nickerson DA, Germer S, Zody MC, Winterkorn L, Reeves C, Huntsman S, Eng C, Salazar S, Oh SS, Gilliland FD, Chen Z, Kumar R, Martínez FD, Wu AC, Ziv E, Hakonarson H, Himes BE, Williams LK, Seibold MA, Burchard EG. Lung Function in African American Children with Asthma Is Associated with Novel Regulatory Variants of the KIT Ligand KITLG/SCF and Gene-By-Air-Pollution Interaction. Genetics 2020; 215:869-886. [PMID: 32327564 PMCID: PMC7337089 DOI: 10.1534/genetics.120.303231] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Accepted: 04/22/2020] [Indexed: 01/12/2023] Open
Abstract
Baseline lung function, quantified as forced expiratory volume in the first second of exhalation (FEV1), is a standard diagnostic criterion used by clinicians to identify and classify lung diseases. Using whole-genome sequencing data from the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine project, we identified a novel genetic association with FEV1 on chromosome 12 in 867 African American children with asthma (P = 1.26 × 10-8, β = 0.302). Conditional analysis within 1 Mb of the tag signal (rs73429450) yielded one major and two other weaker independent signals within this peak. We explored statistical and functional evidence for all variants in linkage disequilibrium with the three independent signals and yielded nine variants as the most likely candidates responsible for the association with FEV1 Hi-C data and expression QTL analysis demonstrated that these variants physically interacted with KITLG (KIT ligand, also known as SCF), and their minor alleles were associated with increased expression of the KITLG gene in nasal epithelial cells. Gene-by-air-pollution interaction analysis found that the candidate variant rs58475486 interacted with past-year ambient sulfur dioxide exposure (P = 0.003, β = 0.32). This study identified a novel protective genetic association with FEV1, possibly mediated through KITLG, in African American children with asthma. This is the first study that has identified a genetic association between lung function and KITLG, which has established a role in orchestrating allergic inflammation in asthma.
Collapse
Affiliation(s)
- Angel C Y Mak
- Department of Medicine, University of California, San Francisco, California 94143
| | - Satria Sajuthi
- Center for Genes, Environment, and Health, National Jewish Health, Denver, Colorado 80206
| | - Jaehyun Joo
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Shujie Xiao
- Center for Individualized and Genomic Medicine Research, Department of Internal Medicine, Henry Ford Health System, Detroit, Michigan 48202
| | - Patrick M Sleiman
- Center for Applied Genomics, Children's Hospital of Philadelphia, Pennsylvania, 19104
- Division of Human Genetics, Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Marquitta J White
- Department of Medicine, University of California, San Francisco, California 94143
| | - Eunice Y Lee
- Department of Medicine, University of California, San Francisco, California 94143
| | - Benjamin Saef
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Donglei Hu
- Department of Medicine, University of California, San Francisco, California 94143
| | - Hongsheng Gui
- Center for Individualized and Genomic Medicine Research, Department of Internal Medicine, Henry Ford Health System, Detroit, Michigan 48202
| | - Kevin L Keys
- Department of Medicine, University of California, San Francisco, California 94143
- Berkeley Institute for Data Science, University of California, Berkeley, California 94720
| | | | - Deepti Jain
- Department of Biostatistics, University of Washington, Seattle, Washington 98195
| | - Gonçalo Abecasis
- Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan 48109
| | - Hyun Min Kang
- Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan 48109
| | - Deborah A Nickerson
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195
- Northwest Genomics Center, Seattle, Washington, 98195
- Brotman Baty Institute for Precision Medicine, Seattle, Washington, 98195
| | | | | | | | | | - Scott Huntsman
- Department of Medicine, University of California, San Francisco, California 94143
| | - Celeste Eng
- Department of Medicine, University of California, San Francisco, California 94143
| | - Sandra Salazar
- Department of Medicine, University of California, San Francisco, California 94143
| | - Sam S Oh
- Department of Medicine, University of California, San Francisco, California 94143
| | - Frank D Gilliland
- Department of Preventive Medicine, Division of Environmental Health, Keck School of Medicine, University of Southern California, Los Angeles, California 90033
| | - Zhanghua Chen
- Department of Preventive Medicine, Division of Environmental Health, Keck School of Medicine, University of Southern California, Los Angeles, California 90033
| | - Rajesh Kumar
- Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois 60611
| | - Fernando D Martínez
- Asthma and Airway Disease Research Center, University of Arizona, Tucson, Arizona 85721
| | - Ann Chen Wu
- Precision Medicine Translational Research (PRoMoTeR) Center, Department of Population Medicine, Harvard Medical School and Pilgrim Health Care Institute, Boston, Massachusetts 02215
| | - Elad Ziv
- Department of Medicine, University of California, San Francisco, California 94143
| | - Hakon Hakonarson
- Center for Applied Genomics, Children's Hospital of Philadelphia, Pennsylvania, 19104
- Division of Human Genetics, Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Blanca E Himes
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - L Keoki Williams
- Center for Individualized and Genomic Medicine Research, Department of Internal Medicine, Henry Ford Health System, Detroit, Michigan 48202
| | - Max A Seibold
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Esteban G Burchard
- Department of Medicine, University of California, San Francisco, California 94143
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California 94143
| |
Collapse
|
10
|
Chu BB, Keys KL, German CA, Zhou H, Zhou JJ, Sobel EM, Sinsheimer JS, Lange K. Iterative hard thresholding in genome-wide association studies: Generalized linear models, prior weights, and double sparsity. Gigascience 2020; 9:giaa044. [PMID: 32491161 PMCID: PMC7268817 DOI: 10.1093/gigascience/giaa044] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 02/27/2020] [Accepted: 04/14/2020] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Consecutive testing of single nucleotide polymorphisms (SNPs) is usually employed to identify genetic variants associated with complex traits. Ideally one should model all covariates in unison, but most existing analysis methods for genome-wide association studies (GWAS) perform only univariate regression. RESULTS We extend and efficiently implement iterative hard thresholding (IHT) for multiple regression, treating all SNPs simultaneously. Our extensions accommodate generalized linear models, prior information on genetic variants, and grouping of variants. In our simulations, IHT recovers up to 30% more true predictors than SNP-by-SNP association testing and exhibits a 2-3 orders of magnitude decrease in false-positive rates compared with lasso regression. We also test IHT on the UK Biobank hypertension phenotypes and the Northern Finland Birth Cohort of 1966 cardiovascular phenotypes. We find that IHT scales to the large datasets of contemporary human genetics and recovers the plausible genetic variants identified by previous studies. CONCLUSIONS Our real data analysis and simulation studies suggest that IHT can (i) recover highly correlated predictors, (ii) avoid over-fitting, (iii) deliver better true-positive and false-positive rates than either marginal testing or lasso regression, (iv) recover unbiased regression coefficients, (v) exploit prior information and group-sparsity, and (vi) be used with biobank-sized datasets. Although these advances are studied for genome-wide association studies inference, our extensions are pertinent to other regression problems with large numbers of predictors.
Collapse
Affiliation(s)
- Benjamin B Chu
- Department of Computational Medicine, University of California, Los Angeles, 621 Charles E Young Dr S, Los Angeles, CA, 90095, USA
| | - Kevin L Keys
- Department of Medicine, University of California, San Francisco, 1701 Divisadero St, San Francisco, CA, 94115, USA
- Berkeley Institute of Data Science, University of California, Berkeley, 190 Doe Library, Berkeley, CA 94720, USA
| | - Christopher A German
- Department of Biostatistics, University of California, Los Angeles, 650 Charles E Young Dr S, Los Angeles, CA, 90095, USA
| | - Hua Zhou
- Department of Biostatistics, University of California, Los Angeles, 650 Charles E Young Dr S, Los Angeles, CA, 90095, USA
| | - Jin J Zhou
- Division of Epidemiology and Biostatistics, University of Arizona, 1295 N. Martin Ave. Tucson, AZ, 85724, USA
| | - Eric M Sobel
- Department of Computational Medicine, University of California, Los Angeles, 621 Charles E Young Dr S, Los Angeles, CA, 90095, USA
- Department of Human Genetics, University of California, Los Angeles, 695 Charles E Young Dr S, Los Angeles, CA, 90095 USA
| | - Janet S Sinsheimer
- Department of Computational Medicine, University of California, Los Angeles, 621 Charles E Young Dr S, Los Angeles, CA, 90095, USA
- Department of Biostatistics, University of California, Los Angeles, 650 Charles E Young Dr S, Los Angeles, CA, 90095, USA
- Department of Human Genetics, University of California, Los Angeles, 695 Charles E Young Dr S, Los Angeles, CA, 90095 USA
| | - Kenneth Lange
- Department of Computational Medicine, University of California, Los Angeles, 621 Charles E Young Dr S, Los Angeles, CA, 90095, USA
- Department of Human Genetics, University of California, Los Angeles, 695 Charles E Young Dr S, Los Angeles, CA, 90095 USA
| |
Collapse
|
11
|
Lee EY, Oh SS, White MJ, Eng CS, Elhawary JR, Borrell LN, Nuckton TJ, Zeiger AM, Keys KL, Mak ACY, Hu D, Huntsman S, Contreras MG, Samedy LA, Goddard PC, Salazar SL, Brigino-Buenaventura EN, Davis A, Meade KE, LeNoir MA, Lurmann FW, Burchard EG, Eisen EA, Balmes JR. Ambient air pollution, asthma drug response, and telomere length in African American youth. J Allergy Clin Immunol 2019; 144:839-845.e10. [PMID: 31247265 PMCID: PMC6938647 DOI: 10.1016/j.jaci.2019.06.009] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Revised: 06/07/2019] [Accepted: 06/14/2019] [Indexed: 01/01/2023]
Abstract
BACKGROUND Telomere length (TL) can serve as a potential biomarker for conditions associated with chronic oxidative stress and inflammation, such as asthma. Air pollution can induce oxidative stress. Understanding the relationship between TL, asthma, and air pollution is important for identifying risk factors contributing to unhealthy aging in children. OBJECTIVES We sought to investigate associations between exposures to ambient air pollutants and TL in African American children and adolescents and to examine whether African ancestry, asthma status, and steroid medication use alter the association. METHODS Linear regression was used to examine associations between absolute telomere length (aTL) and estimated annual average residential ozone (O3) and fine particulate matter with a diameter of 2.5 μm or less (PM2.5) exposures in a cross-sectional analysis of 1072 children in an existing asthma case-control study. African ancestry, asthma status, and use of steroid medications were examined as effect modifiers. RESULTS Participants' aTLs were measured by using quantitative PCR. A 1-ppb and 1 μg/m3 increase in annual average exposure to O3 and PM2.5 were associated with a decrease in aTL of 37.1 kilo-base pair (kb; 95% CI, -66.7 to -7.4 kb) and 57.1 kb (95% CI, -118.1 to 3.9 kb), respectively. African ancestry and asthma were not effect modifiers; however, exposure to steroid medications modified the relationships between TL and pollutants. Past-year exposure to O3 and PM2.5 was associated with shorter TLs in patients without steroid use. CONCLUSION Exposure to air pollution was associated with shorter TLs in nonasthmatic children and adolescents. This was not the case for asthmatic children as a group, but those receiving steroid medication had less shortening than those not using steroids. Reduced exposure to air pollution in childhood might help to preserve TL.
Collapse
Affiliation(s)
- Eunice Y Lee
- Department of Bioengineering & Therapeutic Sciences, University of California, San Francisco, Calif; Department of Medicine, University of California, San Francisco, Calif.
| | - Sam S Oh
- Department of Medicine, University of California, San Francisco, Calif
| | - Marquitta J White
- Department of Medicine, University of California, San Francisco, Calif
| | - Celeste S Eng
- Department of Medicine, University of California, San Francisco, Calif
| | | | - Luisa N Borrell
- Graduate School of Public Health & Health Policy, City University of New York, New York, NY
| | - Thomas J Nuckton
- Department of Medicine, University of California, San Francisco, Calif
| | - Andrew M Zeiger
- Department of Medicine, University of California, San Francisco, Calif
| | - Kevin L Keys
- Department of Medicine, University of California, San Francisco, Calif
| | - Angel C Y Mak
- Department of Medicine, University of California, San Francisco, Calif
| | - Donglei Hu
- Department of Medicine, University of California, San Francisco, Calif
| | - Scott Huntsman
- Department of Medicine, University of California, San Francisco, Calif
| | - Maria G Contreras
- Department of Medicine, University of California, San Francisco, Calif; San Francisco State University, San Francisco, Calif
| | - Lesly-Anne Samedy
- Department of Bioengineering & Therapeutic Sciences, University of California, San Francisco, Calif
| | - Pagé C Goddard
- Department of Medicine, University of California, San Francisco, Calif
| | - Sandra L Salazar
- Department of Medicine, University of California, San Francisco, Calif
| | | | - Adam Davis
- Children's Hospital and Research Center, Oakland, Calif
| | | | | | | | - Esteban G Burchard
- Department of Bioengineering & Therapeutic Sciences, University of California, San Francisco, Calif; Department of Medicine, University of California, San Francisco, Calif.
| | - Ellen A Eisen
- Environmental Health Sciences Division, School of Public Health, University of California, Berkeley, Calif.
| | - John R Balmes
- Department of Medicine, University of California, San Francisco, Calif; Environmental Health Sciences Division, School of Public Health, University of California, Berkeley, Calif.
| |
Collapse
|
12
|
Keys KL, Zhou H, Lange K. Proximal Distance Algorithms: Theory and Practice. J Mach Learn Res 2019; 20:66. [PMID: 31649491 PMCID: PMC6812563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Proximal distance algorithms combine the classical penalty method of constrained minimization with distance majorization. If f(x) is the loss function, and C is the constraint set in a constrained minimization problem, then the proximal distance principle mandates minimizing the penalized loss f ( x ) + ρ 2 dist ( x , C ) 2 and following the solution x ρ to its limit as ρ tends to ∞. At each iteration the squared Euclidean distance dist(x,C)2 is majorized by the spherical quadratic ‖x- P C (x k )‖2, where P C (x k ) denotes the projection of the current iterate x k onto C. The minimum of the surrogate function f ( x ) + ρ 2 ‖ x - P C ( x k ) ‖ 2 is given by the proximal map prox ρ -1f [P C (x k )]. The next iterate x k+1 automatically decreases the original penalized loss for fixed ρ. Since many explicit projections and proximal maps are known, it is straightforward to derive and implement novel optimization algorithms in this setting. These algorithms can take hundreds if not thousands of iterations to converge, but the simple nature of each iteration makes proximal distance algorithms competitive with traditional algorithms. For convex problems, proximal distance algorithms reduce to proximal gradient algorithms and therefore enjoy well understood convergence properties. For nonconvex problems, one can attack convergence by invoking Zangwill's theorem. Our numerical examples demonstrate the utility of proximal distance algorithms in various high-dimensional settings, including a) linear programming, b) constrained least squares, c) projection to the closest kinship matrix, d) projection onto a second-order cone constraint, e) calculation of Horn's copositive matrix index, f) linear complementarity programming, and g) sparse principal components analysis. The proximal distance algorithm in each case is competitive or superior in speed to traditional methods such as the interior point method and the alternating direction method of multipliers (ADMM). Source code for the numerical examples can be found at https://github.com/klkeys/proxdist.
Collapse
Affiliation(s)
- Kevin L Keys
- Department of Medicine, University of California, San Francisco, CA 94158, USA
| | - Hua Zhou
- Department of Biostatistics, University of California, Los Angeles, CA 90095-1772, USA
| | - Kenneth Lange
- Departments of Biomathematics, Human Genetics, and Statistics, University of California, Los Angeles, CA 90095-1766, USA
| |
Collapse
|
13
|
Zhou H, Sinsheimer JS, Bates DM, Chu BB, German CA, Ji SS, Keys KL, Kim J, Ko S, Mosher GD, Papp JC, Sobel EM, Zhai J, Zhou JJ, Lange K. OPENMENDEL: a cooperative programming project for statistical genetics. Hum Genet 2019; 139:61-71. [PMID: 30915546 DOI: 10.1007/s00439-019-02001-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2018] [Accepted: 03/15/2019] [Indexed: 01/06/2023]
Abstract
Statistical methods for genome-wide association studies (GWAS) continue to improve. However, the increasing volume and variety of genetic and genomic data make computational speed and ease of data manipulation mandatory in future software. In our view, a collaborative effort of statistical geneticists is required to develop open source software targeted to genetic epidemiology. Our attempt to meet this need is called the OPENMENDEL project (https://openmendel.github.io). It aims to (1) enable interactive and reproducible analyses with informative intermediate results, (2) scale to big data analytics, (3) embrace parallel and distributed computing, (4) adapt to rapid hardware evolution, (5) allow cloud computing, (6) allow integration of varied genetic data types, and (7) foster easy communication between clinicians, geneticists, statisticians, and computer scientists. This article reviews and makes recommendations to the genetic epidemiology community in the context of the OPENMENDEL project.
Collapse
Affiliation(s)
- Hua Zhou
- Department of Biostatistics, UCLA Fielding School of Public Health, Los Angeles, USA.
| | - Janet S Sinsheimer
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, USA.
| | - Douglas M Bates
- Department of Statistics, University of Wisconsin, Madison, USA
| | - Benjamin B Chu
- Department of Biomathematics, David Geffen School of Medicine at UCLA, Los Angeles, USA
| | - Christopher A German
- Department of Biostatistics, UCLA Fielding School of Public Health, Los Angeles, USA
| | - Sarah S Ji
- Department of Biostatistics, UCLA Fielding School of Public Health, Los Angeles, USA
| | - Kevin L Keys
- Department of Medicine, University of California, San Francisco, USA
| | - Juhyun Kim
- Department of Biostatistics, UCLA Fielding School of Public Health, Los Angeles, USA
| | - Seyoon Ko
- Department of Statistics, Seoul National University, Seoul, South Korea
| | - Gordon D Mosher
- Departments of Statistics and Computer Science, University of California, Riverside, USA
| | - Jeanette C Papp
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, USA
| | - Eric M Sobel
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, USA
| | - Jing Zhai
- Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, USA
| | - Jin J Zhou
- Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, USA
| | - Kenneth Lange
- Department of Biomathematics, David Geffen School of Medicine at UCLA, Los Angeles, USA.
| |
Collapse
|
14
|
Landeros A, Stutz T, Keys KL, Alekseyenko A, Sinsheimer JS, Lange K, Sehl ME. BioSimulator.jl: Stochastic simulation in Julia. Comput Methods Programs Biomed 2018; 167:23-35. [PMID: 30501857 PMCID: PMC6388686 DOI: 10.1016/j.cmpb.2018.09.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Revised: 09/11/2018] [Accepted: 09/26/2018] [Indexed: 06/09/2023]
Abstract
BACKGROUND AND OBJECTIVES Biological systems with intertwined feedback loops pose a challenge to mathematical modeling efforts. Moreover, rare events, such as mutation and extinction, complicate system dynamics. Stochastic simulation algorithms are useful in generating time-evolution trajectories for these systems because they can adequately capture the influence of random fluctuations and quantify rare events. We present a simple and flexible package, BioSimulator.jl, for implementing the Gillespie algorithm, τ-leaping, and related stochastic simulation algorithms. The objective of this work is to provide scientists across domains with fast, user-friendly simulation tools. METHODS We used the high-performance programming language Julia because of its emphasis on scientific computing. Our software package implements a suite of stochastic simulation algorithms based on Markov chain theory. We provide the ability to (a) diagram Petri Nets describing interactions, (b) plot average trajectories and attached standard deviations of each participating species over time, and (c) generate frequency distributions of each species at a specified time. RESULTS BioSimulator.jl's interface allows users to build models programmatically within Julia. A model is then passed to the simulate routine to generate simulation data. The built-in tools allow one to visualize results and compute summary statistics. Our examples highlight the broad applicability of our software to systems of varying complexity from ecology, systems biology, chemistry, and genetics. CONCLUSION The user-friendly nature of BioSimulator.jl encourages the use of stochastic simulation, minimizes tedious programming efforts, and reduces errors during model specification.
Collapse
Affiliation(s)
- Alfonso Landeros
- Department of Biomathematics, David Geffen School of Medicine at UCLA, USA.
| | - Timothy Stutz
- Department of Biomathematics, David Geffen School of Medicine at UCLA, USA.
| | - Kevin L Keys
- Department of Medicine, University of California, San Francisco, CA, USA.
| | | | - Janet S Sinsheimer
- Department of Human Genetics, David Geffen School of Medicine at UCLA, USA.
| | - Kenneth Lange
- Department of Biomathematics, David Geffen School of Medicine at UCLA, USA.
| | - Mary E Sehl
- Department of Biomathematics, David Geffen School of Medicine at UCLA, USA.
| |
Collapse
|
15
|
Mak ACY, White MJ, Eckalbar WL, Szpiech ZA, Oh SS, Pino-Yanes M, Hu D, Goddard P, Huntsman S, Galanter J, Wu AC, Himes BE, Germer S, Vogel JM, Bunting KL, Eng C, Salazar S, Keys KL, Liberto J, Nuckton TJ, Nguyen TA, Torgerson DG, Kwok PY, Levin AM, Celedón JC, Forno E, Hakonarson H, Sleiman PM, Dahlin A, Tantisira KG, Weiss ST, Serebrisky D, Brigino-Buenaventura E, Farber HJ, Meade K, Lenoir MA, Avila PC, Sen S, Thyne SM, Rodriguez-Cintron W, Winkler CA, Moreno-Estrada A, Sandoval K, Rodriguez-Santana JR, Kumar R, Williams LK, Ahituv N, Ziv E, Seibold MA, Darnell RB, Zaitlen N, Hernandez RD. Whole-Genome Sequencing of Pharmacogenetic Drug Response in Racially Diverse Children with Asthma. Am J Respir Crit Care Med 2018; 197:1552-1564. [PMID: 29509491 PMCID: PMC6006403 DOI: 10.1164/rccm.201712-2529oc] [Citation(s) in RCA: 77] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Accepted: 03/05/2018] [Indexed: 12/25/2022] Open
Abstract
RATIONALE Albuterol, a bronchodilator medication, is the first-line therapy for asthma worldwide. There are significant racial/ethnic differences in albuterol drug response. OBJECTIVES To identify genetic variants important for bronchodilator drug response (BDR) in racially diverse children. METHODS We performed the first whole-genome sequencing pharmacogenetics study from 1,441 children with asthma from the tails of the BDR distribution to identify genetic association with BDR. MEASUREMENTS AND MAIN RESULTS We identified population-specific and shared genetic variants associated with BDR, including genome-wide significant (P < 3.53 × 10-7) and suggestive (P < 7.06 × 10-6) loci near genes previously associated with lung capacity (DNAH5), immunity (NFKB1 and PLCB1), and β-adrenergic signaling (ADAMTS3 and COX18). Functional analyses of the BDR-associated SNP in NFKB1 revealed potential regulatory function in bronchial smooth muscle cells. The SNP is also an expression quantitative trait locus for a neighboring gene, SLC39A8. The lack of other asthma study populations with BDR and whole-genome sequencing data on minority children makes it impossible to perform replication of our rare variant associations. Minority underrepresentation also poses significant challenges to identify age-matched and population-matched cohorts of sufficient sample size for replication of our common variant findings. CONCLUSIONS The lack of minority data, despite a collaboration of eight universities and 13 individual laboratories, highlights the urgent need for a dedicated national effort to prioritize diversity in research. Our study expands the understanding of pharmacogenetic analyses in racially/ethnically diverse populations and advances the foundation for precision medicine in at-risk and understudied minority populations.
Collapse
Affiliation(s)
| | | | | | | | | | - Maria Pino-Yanes
- Research Unit, Hospital Universitario N. S. de Candelaria, Universidad de La Laguna, Santa Cruz de Tenerife, Spain
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
| | | | | | | | | | - Ann Chen Wu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
- Precision Medicine Translational Research (PRoMoTeR) Center, Department of Population Medicine, Harvard Medical School and Pilgrim Health Care Institute, Boston, Massachusetts
| | - Blanca E. Himes
- Department of Biostatistics, Epidemiology and Informatics and
| | | | | | | | | | | | | | | | | | | | | | - Pui-Yan Kwok
- Cardiovascular Research Institute
- Institute for Human Genetics, and
| | | | - Juan C. Celedón
- Division of Pediatric Pulmonary Medicine, Allergy and Immunology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Erick Forno
- Division of Pediatric Pulmonary Medicine, Allergy and Immunology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Hakon Hakonarson
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Center for Applied Genomics, The Children’s Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania
| | - Patrick M. Sleiman
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Center for Applied Genomics, The Children’s Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania
| | - Amber Dahlin
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Kelan G. Tantisira
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Scott T. Weiss
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Denise Serebrisky
- Pediatric Pulmonary Division, Jacobi Medical Center, Bronx, New York
| | | | - Harold J. Farber
- Department of Pediatrics, Baylor College of Medicine and Texas Children’s Hospital, Houston, Texas
| | - Kelley Meade
- Children’s Hospital and Research Center, Oakland, California
| | | | - Pedro C. Avila
- Department of Medicine, Northwestern University, Chicago, Illinois
| | | | - Shannon M. Thyne
- Department of Pediatrics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | | | - Cheryl A. Winkler
- Basic Science Laboratory, Center for Cancer Research, National Cancer Institute, Leidos Biomedical Research, Frederick National Laboratory, Frederick, Maryland
| | - Andrés Moreno-Estrada
- National Laboratory of Genomics for Biodiversity (UGA-LANGEBIO), CINVESTAV, Irapuato, Guanajuato, Mexico
| | - Karla Sandoval
- National Laboratory of Genomics for Biodiversity (UGA-LANGEBIO), CINVESTAV, Irapuato, Guanajuato, Mexico
| | | | - Rajesh Kumar
- Division of Allergy and Immunology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
- Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois
| | - L. Keoki Williams
- Department of Internal Medicine, and
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit, Michigan
| | - Nadav Ahituv
- Department of Bioengineering and Therapeutic Sciences
- Institute for Human Genetics, and
| | | | - Max A. Seibold
- Center for Genes, Environment and Health, Department of Pediatrics, National Jewish Health, Denver, Colorado; and
| | - Robert B. Darnell
- New York Genome Center, New York, New York
- Laboratory of Molecular Neuro-Oncology and
- Howard Hughes Medical Institute, The Rockefeller University, New York, New York
| | | | - Ryan D. Hernandez
- Department of Bioengineering and Therapeutic Sciences
- Cardiovascular Research Institute
- Quantitative Biosciences Institute, University of California San Francisco, San Francisco, California
| | - on behalf of the NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium
- Department of Medicine
- Department of Bioengineering and Therapeutic Sciences
- Department of Pediatrics
- Cardiovascular Research Institute
- Institute for Human Genetics, and
- Quantitative Biosciences Institute, University of California San Francisco, San Francisco, California
- Research Unit, Hospital Universitario N. S. de Candelaria, Universidad de La Laguna, Santa Cruz de Tenerife, Spain
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
- Precision Medicine Translational Research (PRoMoTeR) Center, Department of Population Medicine, Harvard Medical School and Pilgrim Health Care Institute, Boston, Massachusetts
- Department of Biostatistics, Epidemiology and Informatics and
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- New York Genome Center, New York, New York
- Department of Public Health Sciences
- Department of Internal Medicine, and
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit, Michigan
- Division of Pediatric Pulmonary Medicine, Allergy and Immunology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Center for Applied Genomics, The Children’s Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania
- Pediatric Pulmonary Division, Jacobi Medical Center, Bronx, New York
- Department of Allergy and Immunology, Kaiser Permanente Vallejo Medical Center, Vallejo, California
- Department of Pediatrics, Baylor College of Medicine and Texas Children’s Hospital, Houston, Texas
- Children’s Hospital and Research Center, Oakland, California
- Bay Area Pediatrics, Oakland, California
- Department of Medicine, Northwestern University, Chicago, Illinois
- Department of Pediatrics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
- Veterans Caribbean Health Care System, San Juan, Puerto Rico
- Basic Science Laboratory, Center for Cancer Research, National Cancer Institute, Leidos Biomedical Research, Frederick National Laboratory, Frederick, Maryland
- National Laboratory of Genomics for Biodiversity (UGA-LANGEBIO), CINVESTAV, Irapuato, Guanajuato, Mexico
- Centro de Neumologia Pediatrica, San Juan, Puerto Rico
- Division of Allergy and Immunology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
- Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois
- Center for Genes, Environment and Health, Department of Pediatrics, National Jewish Health, Denver, Colorado; and
- Laboratory of Molecular Neuro-Oncology and
- Howard Hughes Medical Institute, The Rockefeller University, New York, New York
| |
Collapse
|
16
|
Keys KL, Chen GK, Lange K. Iterative hard thresholding for model selection in genome-wide association studies. Genet Epidemiol 2017; 41:756-768. [PMID: 28875524 DOI: 10.1002/gepi.22068] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2017] [Revised: 07/13/2017] [Accepted: 08/02/2017] [Indexed: 11/05/2022]
Abstract
A genome-wide association study (GWAS) correlates marker and trait variation in a study sample. Each subject is genotyped at a multitude of SNPs (single nucleotide polymorphisms) spanning the genome. Here, we assume that subjects are randomly collected unrelateds and that trait values are normally distributed or can be transformed to normality. Over the past decade, geneticists have been remarkably successful in applying GWAS analysis to hundreds of traits. The massive amount of data produced in these studies present unique computational challenges. Penalized regression with the ℓ1 penalty (LASSO) or minimax concave penalty (MCP) penalties is capable of selecting a handful of associated SNPs from millions of potential SNPs. Unfortunately, model selection can be corrupted by false positives and false negatives, obscuring the genetic underpinning of a trait. Here, we compare LASSO and MCP penalized regression to iterative hard thresholding (IHT). On GWAS regression data, IHT is better at model selection and comparable in speed to both methods of penalized regression. This conclusion holds for both simulated and real GWAS data. IHT fosters parallelization and scales well in problems with large numbers of causal markers. Our parallel implementation of IHT accommodates SNP genotype compression and exploits multiple CPU cores and graphics processing units (GPUs). This allows statistical geneticists to leverage commodity desktop computers in GWAS analysis and to avoid supercomputing. AVAILABILITY Source code is freely available at https://github.com/klkeys/IHT.jl.
Collapse
Affiliation(s)
- Kevin L Keys
- Department of Medicine, University of California, San Francisco, San Francisco, California, United States of America
| | - Gary K Chen
- Division of Biostatistics, University of Southern California, Los Angeles, California, United States of America
| | - Kenneth Lange
- Departments of Biomathematics, Human Genetics, and Statistics, University of California, Los Angeles, California, United States of America
| |
Collapse
|
17
|
Dall'Olio GM, Marino J, Schubert M, Keys KL, Stefan MI, Gillespie CS, Poulain P, Shameer K, Sugar R, Invergo BM, Jensen LJ, Bertranpetit J, Laayouni H. Ten simple rules for getting help from online scientific communities. PLoS Comput Biol 2011; 7:e1002202. [PMID: 21980280 PMCID: PMC3182872 DOI: 10.1371/journal.pcbi.1002202] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Affiliation(s)
- Giovanni M. Dall'Olio
- Institute of Evolutionary Biology (UPF-CSIC), Departament de Ciències Experimentals i de la Salut, Barcelona, Spain
- * E-mail:
| | - Jacopo Marino
- Institute of Organic Chemistry Universität Zurich, Zurich, Switzerland
| | | | - Kevin L. Keys
- Institute of Evolutionary Biology (UPF-CSIC), Departament de Ciències Experimentals i de la Salut, Barcelona, Spain
| | - Melanie I. Stefan
- California Institute of Technology, Biology Division, Pasadena, California, United States of America
| | - Colin S. Gillespie
- School of Mathematics & Statistics, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Pierre Poulain
- DSIMB, INSERM, U665, Paris, France
- Univ Paris Diderot, Sorbonne Paris Cité, UMR-S665, Paris, France
- Institut National de la Transfusion Sanguine, Paris, France
| | - Khader Shameer
- Division of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota, United States of America
- National Centre for Biological Sciences (TIFR), GKVK Campus, Bangalore, India
| | - Robert Sugar
- EMBL-EBI, Wellcome-Trust Genome Campus, Hinxton, United Kingdom
| | - Brandon M. Invergo
- Institute of Evolutionary Biology (UPF-CSIC), Departament de Ciències Experimentals i de la Salut, Barcelona, Spain
| | - Lars J. Jensen
- NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jaume Bertranpetit
- Institute of Evolutionary Biology (UPF-CSIC), Departament de Ciències Experimentals i de la Salut, Barcelona, Spain
| | - Hafid Laayouni
- Institute of Evolutionary Biology (UPF-CSIC), Departament de Ciències Experimentals i de la Salut, Barcelona, Spain
| |
Collapse
|
18
|
Anderson GD, Keys KL, De Ciechi PA, Masferrer JL. Combination therapies that inhibit cyclooxygenase-2 and leukotriene synthesis prevent disease in murine collagen induced arthritis. Inflamm Res 2009; 58:109-17. [PMID: 19184362 DOI: 10.1007/s00011-009-8149-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022] Open
Abstract
OBJECTIVE AND DESIGN To determine the effect of combinations of cyclooxygenase (COX) inhibitors and inhibitors of leukotriene (LT) syntheses on collagen induced arthritis (CIA) in mice. METHODS The CIA model was evaluated for the presence of eicosanoids in the paw tissue. Several selective cyclooxygenase 2 (COX-2) inhibitors or non-selective non-steroidal anti inflammatory drugs (NSAIDs) were evaluated alone or in combination with leukotriene (LT) synthesis inhibitors in the CIA model. RESULTS Arthritic paw tissue showed increased levels of prostaglandins and leukotrienes in comparison to normal paws. Analysis of mRNA levels indicated the inducible form of the COX-2 enzyme to be the source of prostaglandins. NSAIDs, COX-2 or leukotriene synthesis inhibitors administered alone in CIA decreased severity but had little effect on disease incidence. However, the combination of selective COX-2 inhibitors with leukotriene synthesis inhibitors produced significant decreases in both incidence and severity, suggesting an additive or synergistic effect. This effect was reversible with removal of drug. Little decrease in incidence was observed with the NSAID/5-LO inhibitor combinations. CONCLUSIONS These results suggest that the induction of the disease in CIA is mediated by products of the COX-2 enzyme and LTB4 production, and that blockade of both pathways is required to prevent CIA.
Collapse
|