51
|
Shi G. Genome-wide variance quantitative trait locus analysis suggests small interaction effects in blood pressure traits. Sci Rep 2022; 12:12649. [PMID: 35879408 PMCID: PMC9314370 DOI: 10.1038/s41598-022-16908-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 07/18/2022] [Indexed: 11/09/2022] Open
Abstract
Genome-wide variance quantitative trait loci (vQTL) analysis complements genome-wide association study (GWAS) and has the potential to identify novel variants associated with the trait, explain additional trait variance and lead to the identification of factors that modulate the genetic effects. I conducted genome-wide analysis of the UK Biobank data and identified 27 vQTLs associated with systolic blood pressure (SBP), diastolic blood pressure (DBP) and pulse pressure (PP). The top single-nucleotide polymorphisms (SNPs) are enriched for expression QTLs (eQTLs) or splicing QTLs (sQTLs) annotated by GTEx, suggesting their regulatory roles in mediating the associations with blood pressure (BP). Of the 27 vQTLs, 14 are known BP-associated QTLs discovered by GWASs. The heteroscedasticity effects of the 13 novel vQTLs are larger than their genetic main effects, which were not detected by existing GWASs. The total R-squared of the 27 top SNPs due to variance heteroscedasticity is 0.28%, compared with 0.50% owing to their main effects. The overall effect size of the variance heteroscedasticity is small in GWAS SNPs compared with their main effects. For the 411, 384 and 285 GWAS SNPs associated with SBP, DBP and PP, respectively, their heteroscedasticity effects were 0.52%, 0.43%, and 0.16%, and their main effects were 5.13%, 5.61%, and 3.75%, respectively. The number and effects of the vQTLs are small, which suggests that the effects of gene-environment and gene-gene interactions are small. The main effects of the SNPs remain the major source of genetic variance for BP, which would probably be true for other complex traits as well.
Collapse
Affiliation(s)
- Gang Shi
- School of Telecommunications Engineering, Xidian University, 2 South Taibai Road, Xi'an, 710071, Shaanxi, China.
| |
Collapse
|
52
|
Wright KM, Deighan AG, Di Francesco A, Freund A, Jojic V, Churchill GA, Raj A. Age and diet shape the genetic architecture of body weight in diversity outbred mice. eLife 2022; 11:64329. [PMID: 35838135 PMCID: PMC9286741 DOI: 10.7554/elife.64329] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Accepted: 05/20/2022] [Indexed: 12/26/2022] Open
Abstract
Understanding how genetic variation shapes a complex trait relies on accurately quantifying both the additive genetic and genotype–environment interaction effects in an age-dependent manner. We used a linear mixed model to quantify diet-dependent genetic contributions to body weight measured through adulthood in diversity outbred female mice under five diets. We observed that heritability of body weight declined with age under all diets, except the 40% calorie restriction diet. We identified 14 loci with age-dependent associations and 19 loci with age- and diet-dependent associations, with many diet-dependent loci previously linked to neurological function and behavior in mice or humans. We found their allelic effects to be dynamic with respect to genomic background, age, and diet, identifying several loci where distinct alleles affect body weight at different ages. These results enable us to more fully understand and predict the effectiveness of dietary intervention on overall health throughout age in distinct genetic backgrounds. Body weight is one trait influenced by genes, age and environmental factors. Both internal and external environmental pressures are known to affect genetic variation over time. However, it is largely unknown how all factors – including age – interact to shape metabolism and bodyweight. Wright et al. set out to quantify the interactions between genes and diet in ageing mice and found that the effect of genetics on mouse body weight changes with age. In the experiments, Wright et al. weighed 960 female mice with diverse genetic backgrounds, starting at two months of age into adulthood. The animals were randomized to different diets at six months of age. Some mice had unlimited food access, others received 20% or 40% less calories than a typical mouse diet, and some fasted one or two days per week. Variations in their genetic background explained about 80% of differences in mice’s weight, but the influence of genetics relative to non-genetic factors decreased as they aged. Mice on the 40% calorie restriction diet were an exception to this rule and genetics accounted for 80% of their weight throughout adulthood, likely due to reduced influence from diet and reduced interactions between diet and genes. Several genes involved in metabolism, neurological function, or behavior, were associated with mouse weight. The experiments highlight the importance of considering interactions between genetics, environment, and age in determining complex traits like body weight. The results and the approaches used by Wright et al. may help other scientists learn more about how the genetic predisposition to disease changes with environmental stimuli and age.
Collapse
Affiliation(s)
- Kevin M Wright
- Calico Life Sciences LLC, South San Francisco, United States
| | | | | | - Adam Freund
- Calico Life Sciences LLC, South San Francisco, United States
| | - Vladimir Jojic
- Calico Life Sciences LLC, South San Francisco, United States
| | | | - Anil Raj
- Calico Life Sciences LLC, South San Francisco, United States
| |
Collapse
|
53
|
Westerman KE, Majarian TD, Giulianini F, Jang DK, Miao J, Florez JC, Chen H, Chasman DI, Udler MS, Manning AK, Cole JB. Variance-quantitative trait loci enable systematic discovery of gene-environment interactions for cardiometabolic serum biomarkers. Nat Commun 2022; 13:3993. [PMID: 35810165 PMCID: PMC9271055 DOI: 10.1038/s41467-022-31625-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 06/24/2022] [Indexed: 11/29/2022] Open
Abstract
Gene-environment interactions represent the modification of genetic effects by environmental exposures and are critical for understanding disease and informing personalized medicine. These often induce differential phenotypic variance across genotypes; these variance-quantitative trait loci can be prioritized in a two-stage interaction detection strategy to greatly reduce the computational and statistical burden and enable testing of a broader range of exposures. We perform genome-wide variance-quantitative trait locus analysis for 20 serum cardiometabolic biomarkers by multi-ancestry meta-analysis of 350,016 unrelated participants in the UK Biobank, identifying 182 independent locus-biomarker pairs (p < 4.5×10-9). Most are concentrated in a small subset (4%) of loci with genome-wide significant main effects, and 44% replicate (p < 0.05) in the Women's Genome Health Study (N = 23,294). Next, we test each locus-biomarker pair for interaction across 2380 exposures, identifying 847 significant interactions (p < 2.4×10-7), of which 132 are independent (p < 0.05) after accounting for correlation between exposures. Specific examples demonstrate interaction of triglyceride-associated variants with distinct body mass- versus body fat-related exposures as well as genotype-specific associations between alcohol consumption and liver stress at the ADH1B gene. Our catalog of variance-quantitative trait loci and gene-environment interactions is publicly available in an online portal.
Collapse
Affiliation(s)
- Kenneth E Westerman
- Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA, USA.
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
| | - Timothy D Majarian
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Franco Giulianini
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Dong-Keun Jang
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Jenkai Miao
- Division of Endocrinology, Boston Children's Hospital, Boston, MA, USA
| | - Jose C Florez
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Han Chen
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Brigham and Women's Hospital, Boston, MA, 02115, USA
- Medical and Population Genetics Program, Broad Institute, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Miriam S Udler
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Alisa K Manning
- Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Joanne B Cole
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Division of Endocrinology, Boston Children's Hospital, Boston, MA, USA.
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
| |
Collapse
|
54
|
Xiao J, Cai M, Yu X, Hu X, Chen G, Wan X, Yang C. Leveraging the local genetic structure for trans-ancestry association mapping. Am J Hum Genet 2022; 109:1317-1337. [PMID: 35714612 PMCID: PMC9300880 DOI: 10.1016/j.ajhg.2022.05.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 05/23/2022] [Indexed: 01/09/2023] Open
Abstract
Over the past two decades, genome-wide association studies (GWASs) have successfully advanced our understanding of the genetic basis of complex traits. Despite the fruitful discovery of GWASs, most GWAS samples are collected from European populations, and these GWASs are often criticized for their lack of ancestry diversity. Trans-ancestry association mapping (TRAM) offers an exciting opportunity to fill the gap of disparities in genetic studies between non-Europeans and Europeans. Here, we propose a statistical method, LOG-TRAM, to leverage the local genetic architecture for TRAM. By using biobank-scale datasets, we showed that LOG-TRAM can greatly improve the statistical power of identifying risk variants in under-represented populations while producing well-calibrated p values. We applied LOG-TRAM to the GWAS summary statistics of various complex traits/diseases from BioBank Japan, UK Biobank, and African populations. We obtained substantial gains in power and achieved effective correction of confounding biases in TRAM. Finally, we showed that LOG-TRAM can be successfully applied to identify ancestry-specific loci and the LOG-TRAM output can be further used for construction of more accurate polygenic risk scores in under-represented populations.
Collapse
Affiliation(s)
- Jiashun Xiao
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China; Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Mingxuan Cai
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China; Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Xinyi Yu
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China; Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Xianghong Hu
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China; Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Gang Chen
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Xiang Wan
- Shenzhen Research Institute of Big Data, Shenzhen 518172, China; Pazhou Lab, Guangzhou 510330, China.
| | - Can Yang
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China; Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China.
| |
Collapse
|
55
|
Johnson R, Sotoudeh R, Conley D. Polygenic Scores for Plasticity: A New Tool for Studying Gene-Environment Interplay. Demography 2022; 59:1045-1070. [PMID: 35553650 DOI: 10.1215/00703370-9957418] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Fertility, health, education, and other outcomes of interest to demographers are the product of an individual's genetic makeup and their social environment. Yet, gene × environment (G×E) research deploys a limited toolkit on the genetic side to study the gene-environment interplay, relying on polygenic scores (PGSs) that reflect the influence of genetics on levels of an outcome. In this article, we develop a genetic summary measure better suited for G×E research: variance polygenic scores (vPGSs), which are PGSs that reflect genetic contributions to plasticity in outcomes. First, we use the UK Biobank (N ∼ 408,000 in the analytic sample) and the Health and Retirement Study (N ∼ 5,700 in the analytic sample) to compare four approaches to constructing PGSs for plasticity. The results show that widely used methods for discovering which genetic variants affect outcome variability fail to serve as distinctive new tools for G×E. Second, using the PGSs that do capture distinctive genetic contributions to plasticity, we analyze heterogeneous effects of a UK education reform on health and educational attainment. The results show the properties of a useful new tool for population scientists studying the interplay of nature and nurture and for population-based studies that are releasing PGSs to applied researchers.
Collapse
Affiliation(s)
- Rebecca Johnson
- McCourt School of Public Policy, Georgetown University, Washington, DC, USA
| | | | - Dalton Conley
- Department of Sociology and Office of Population Research, Princeton University, Princeton, NJ, USA
| |
Collapse
|
56
|
Li M, Zhang YW, Zhang ZC, Xiang Y, Liu MH, Zhou YH, Zuo JF, Zhang HQ, Chen Y, Zhang YM. A compressed variance component mixed model for detecting QTNs and QTN-by-environment and QTN-by-QTN interactions in genome-wide association studies. MOLECULAR PLANT 2022; 15:630-650. [PMID: 35202864 DOI: 10.1016/j.molp.2022.02.012] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Revised: 01/26/2022] [Accepted: 02/19/2022] [Indexed: 05/25/2023]
Abstract
Although genome-wide association studies are widely used to mine genes for quantitative traits, the effects to be estimated are confounded, and the methodologies for detecting interactions are imperfect. To address these issues, the mixed model proposed here first estimates the genotypic effects for AA, Aa, and aa, and the genotypic polygenic background replaces additive and dominance polygenic backgrounds. Then, the estimated genotypic effects are partitioned into additive and dominance effects using a one-way analysis of variance model. This strategy was further expanded to cover QTN-by-environment interactions (QEIs) and QTN-by-QTN interactions (QQIs) using the same mixed-model framework. Thus, a three-variance-component mixed model was integrated with our multi-locus random-SNP-effect mixed linear model (mrMLM) method to establish a new methodological framework, 3VmrMLM, that detects all types of loci and estimates their effects. In Monte Carlo studies, 3VmrMLM correctly detected all types of loci and almost unbiasedly estimated their effects, with high powers and accuracies and a low false positive rate. In re-analyses of 10 traits in 1439 rice hybrids, detection of 269 known genes, 45 known gene-by-environment interactions, and 20 known gene-by-gene interactions strongly validated 3VmrMLM. Further analyses of known genes showed more small (67.49%), minor-allele-frequency (35.52%), and pleiotropic (30.54%) genes, with higher repeatability across datasets (54.36%) and more dominance loci. In addition, a heteroscedasticity mixed model in multiple environments and dimension reduction methods in quite a number of environments were developed to detect QEIs, and variable selection under a polygenic background was proposed for QQI detection. This study provides a new approach for revealing the genetic architecture of quantitative traits.
Collapse
Affiliation(s)
- Mei Li
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Ya-Wen Zhang
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China; State Key Laboratory of Cotton Biology, Anyang 455000, China
| | - Ze-Chang Zhang
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Yu Xiang
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Ming-Hui Liu
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Ya-Hui Zhou
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Jian-Fang Zuo
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Han-Qing Zhang
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Ying Chen
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Yuan-Ming Zhang
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.
| |
Collapse
|
57
|
Staley JR, Windmeijer F, Suderman M, Lyon MS, Davey Smith G, Tilling K. A robust mean and variance test with application to high-dimensional phenotypes. Eur J Epidemiol 2022; 37:377-387. [PMID: 34651232 PMCID: PMC9187575 DOI: 10.1007/s10654-021-00805-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 09/06/2021] [Indexed: 12/01/2022]
Abstract
Most studies of continuous health-related outcomes examine differences in mean levels (location) of the outcome by exposure. However, identifying effects on the variability (scale) of an outcome, and combining tests of mean and variability (location-and-scale), could provide additional insights into biological mechanisms. A joint test could improve power for studies of high-dimensional phenotypes, such as epigenome-wide association studies of DNA methylation at CpG sites. One possible cause of heterogeneity of variance is a variable interacting with exposure in its effect on outcome, so a joint test of mean and variability could help in the identification of effect modifiers. Here, we review a scale test, based on the Brown-Forsythe test, for analysing variability of a continuous outcome with respect to both categorical and continuous exposures, and develop a novel joint location-and-scale score (JLSsc) test. These tests were compared to alternatives in simulations and used to test associations of mean and variability of DNA methylation with gender and gestational age using data from the Accessible Resource for Integrated Epigenomics Studies (ARIES). In simulations, the Brown-Forsythe and JLSsc tests retained correct type I error rates when the outcome was not normally distributed in contrast to the other approaches tested which all had inflated type I error rates. These tests also identified > 7500 CpG sites for which either mean or variability in cord blood methylation differed according to gender or gestational age. The Brown-Forsythe test and JLSsc are robust tests that can be used to detect associations not solely driven by a mean effect.
Collapse
Affiliation(s)
- James R Staley
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Frank Windmeijer
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- Department of Statistics and Nuffield College, University of Oxford, Oxford, UK
| | - Matthew Suderman
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Matthew S Lyon
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- National Institute for Health Research Bristol Biomedical Research Centre, University of Bristol, Oakfield House, Bristol, BS8 2BN, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Kate Tilling
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK.
| |
Collapse
|
58
|
Tang Y, You D, Yi H, Yang S, Zhao Y. IPRS: Leveraging Gene-Environment Interaction to Reconstruct Polygenic Risk Score. Front Genet 2022; 13:801397. [PMID: 35401709 PMCID: PMC8989431 DOI: 10.3389/fgene.2022.801397] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 02/08/2022] [Indexed: 12/30/2022] Open
Abstract
Background: Polygenic risk score (PRS) is widely regarded as a predictor of genetic susceptibility to disease, applied to individuals to predict the risk of disease occurrence. When the gene-environment (G×E) interaction is considered, the traditional PRS prediction model directly uses PRS to interact with the environment without considering the interactions between each variant and environment, which may lead to prediction performance and risk stratification of complex diseases are not promising. Methods: We developed a method called interaction PRS (iPRS), reconstructing PRS by leveraging G×E interactions. Two extensive simulations evaluated prediction performance, risk stratification, and calibration performance of the iPRS prediction model, and compared it with the traditional PRS prediction model. Real data analysis was performed using existing data from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial study to predict genetic susceptibility, pack-years of smoking history, and G×E interactions in patients with lung cancer. Results: Two extensive simulations indicated iPRS prediction model could improve the prediction performance of disease risk, the accuracy of risk stratification, and clinical calibration performance compared with the traditional PRS prediction model, especially when antagonism accounted for the majority of the interaction. PLCO real data analysis also suggested that the iPRS prediction model was superior to the PRS prediction model in predictive effect (p = 0.0205). Conclusion: IPRS prediction model could have a good application prospect in predicting disease risk, optimizing the screening of high-risk populations, and improving the clinical benefits of preventive interventions among populations.
Collapse
Affiliation(s)
- Yingdan Tang
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Dongfang You
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Honggang Yi
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Sheng Yang
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China
- *Correspondence: Sheng Yang, ; Yang Zhao,
| | - Yang Zhao
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China
- Center of Biomedical Big Data and the Laboratory of Biomedical Big Data, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
- *Correspondence: Sheng Yang, ; Yang Zhao,
| |
Collapse
|
59
|
Ko S, German CA, Jensen A, Shen J, Wang A, Mehrotra DV, Sun YV, Sinsheimer JS, Zhou H, Zhou JJ. GWAS of longitudinal trajectories at biobank scale. Am J Hum Genet 2022; 109:433-445. [PMID: 35196515 PMCID: PMC8948167 DOI: 10.1016/j.ajhg.2022.01.018] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 01/25/2022] [Indexed: 12/12/2022] Open
Abstract
Biobanks linked to massive, longitudinal electronic health record (EHR) data make numerous new genetic research questions feasible. One among these is the study of biomarker trajectories. For example, high blood pressure measurements over visits strongly predict stroke onset, and consistently high fasting glucose and Hb1Ac levels define diabetes. Recent research reveals that not only the mean level of biomarker trajectories but also their fluctuations, or within-subject (WS) variability, are risk factors for many diseases. Glycemic variation, for instance, is recently considered an important clinical metric in diabetes management. It is crucial to identify the genetic factors that shift the mean or alter the WS variability of a biomarker trajectory. Compared to traditional cross-sectional studies, trajectory analysis utilizes more data points and captures a complete picture of the impact of time-varying factors, including medication history and lifestyle. Currently, there are no efficient tools for genome-wide association studies (GWASs) of biomarker trajectories at the biobank scale, even for just mean effects. We propose TrajGWAS, a linear mixed effect model-based method for testing genetic effects that shift the mean or alter the WS variability of a biomarker trajectory. It is scalable to biobank data with 100,000 to 1,000,000 individuals and many longitudinal measurements and robust to distributional assumptions. Simulation studies corroborate that TrajGWAS controls the type I error rate and is powerful. Analysis of eleven biomarkers measured longitudinally and extracted from UK Biobank primary care data for more than 150,000 participants with 1,800,000 observations reveals loci that significantly alter the mean or WS variability.
Collapse
Affiliation(s)
- Seyoon Ko
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA,Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Christopher A. German
- Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Aubrey Jensen
- Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Judong Shen
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Kenilworth, NJ 07033, USA
| | - Anran Wang
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Kenilworth, NJ 07033, USA
| | - Devan V. Mehrotra
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Kenilworth, NJ 07033, USA
| | - Yan V. Sun
- Department of Epidemiology, Emory University, Atlanta, GA 30322, USA
| | - Janet S. Sinsheimer
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA,Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA 90095, USA,Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Hua Zhou
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA 90095, USA.
| | - Jin J. Zhou
- Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA 90095, USA,Department of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA,Department of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ 85721, USA,Corresponding author
| |
Collapse
|
60
|
Ye C, Xie X, Lv W, Huang K, Yang AJ, Jiang S, Liu X, Zhu D, Qiu X, Tong M, Zhou T, Hsu CH, Chang G, Lin H, Li P, Yang K, Wang Z, Jiang T, Renshaw Wang X. Nonreciprocal Transport in a Bilayer of MnBi 2Te 4 and Pt. NANO LETTERS 2022; 22:1366-1373. [PMID: 35073094 DOI: 10.1021/acs.nanolett.1c04756] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
MnBi2Te4 (MBT) is the first intrinsic magnetic topological insulator with the interaction of spin-momentum locked surface electrons and intrinsic magnetism, and it exhibits novel magnetic and topological phenomena. Recent studies suggested that the interaction of electrons and magnetism can be affected by the Mn-doped Bi2Te3 phase at the surface due to inevitable structural defects. Here, we report an observation of nonreciprocal transport, that is, current-direction-dependent resistance, in a bilayer composed of antiferromagnetic MBT and nonmagnetic Pt. The emergence of the nonreciprocal response below the Néel temperature confirms a correlation between nonreciprocity and intrinsic magnetism in the surface state of MBT. The angular dependence of the nonreciprocal transport indicates that nonreciprocal response originates from the asymmetry scattering of electrons at the surface of MBT mediated by magnon. Our work provides an insight into nonreciprocity arising from the correlation between magnetism and Dirac surface electrons in intrinsic magnetic topological insulators.
Collapse
Affiliation(s)
- Chen Ye
- Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link 637371, Singapore
| | - Xiangnan Xie
- State Key Laboratory of High Performance Computing, College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, P.R. China
| | - Wenxing Lv
- Physics Laboratory, Industrial Training Center, Shenzhen Polytechnic, Shenzhen, Guangdong 518055, P.R. China
| | - Ke Huang
- Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link 637371, Singapore
| | - Allen Jian Yang
- Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link 637371, Singapore
| | - Sicong Jiang
- Department of NanoEngineering and Program of Chemical Engineering, University of California San Diego, La Jolla, California 92093-0448, United States
- Program of Materials Science and Engineering, University of California San Diego, La Jolla, California 92093-0418, United States
| | - Xue Liu
- Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link 637371, Singapore
- Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, P.R. China
| | - Dapeng Zhu
- Fert Beijing Institute, MIIT Key Laboratory of Spintronics, School of Integrated Circuit Science and Engineering, Beihang University, Beijing 100191, P.R. China
- Beihang-Goertek Joint Microelectronics Institute, Qingdao Research Institute, Beihang University, Qingdao 266000, P.R. China
| | - Xuepeng Qiu
- Shanghai Key Laboratory of Special Artificial Microstructure Materials and Technology and School of Physics Science and Engineering, Tongji University, Shanghai 200092, P.R. China
| | - Mingyu Tong
- College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, P.R. China
| | - Tong Zhou
- State Key Laboratory of High Performance Computing, College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, P.R. China
| | - Chuang-Han Hsu
- Insitute of Physics, Academia Sinica, Taipei 11529, Taiwan
| | - Guoqing Chang
- Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link 637371, Singapore
| | - Hsin Lin
- Insitute of Physics, Academia Sinica, Taipei 11529, Taiwan
| | - Peisen Li
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, P.R. China
| | - Kesong Yang
- Department of NanoEngineering and Program of Chemical Engineering, University of California San Diego, La Jolla, California 92093-0448, United States
- Program of Materials Science and Engineering, University of California San Diego, La Jolla, California 92093-0418, United States
| | - Zhenyu Wang
- State Key Laboratory of High Performance Computing, College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, P.R. China
- National Innovation Institute of Defense Technology, Academy of Military Sciences PLA China, Beijing 100010, P.R. China
- Beijing Academy of Quantum Information Sciences, Beijing 100084, P.R. China
| | - Tian Jiang
- State Key Laboratory of High Performance Computing, College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, P.R. China
- Beijing Institute for Advanced Study, National University of Defense Technology, Changsha 410073, P.R. China
| | - Xiao Renshaw Wang
- Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link 637371, Singapore
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore
| |
Collapse
|
61
|
Kim C, Kim YJ, Choi W, Jang HM, Hwang MY, Jung S, Lim H, Hong SB, Yoon K, Kim BJ, Park HY, Han B. Phenome-wide association study of the major histocompatibility complex region in the Korean population identifies novel association signals. Hum Mol Genet 2022; 31:2655-2667. [PMID: 35043955 DOI: 10.1093/hmg/ddac016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 11/11/2021] [Accepted: 01/11/2022] [Indexed: 11/13/2022] Open
Abstract
Human leukocyte antigen (HLA) gene variants in the major histocompatibility complex (MHC) region are associated with numerous complex human diseases and quantitative traits. Previous phenome-wide association studies (PheWAS) for this region demonstrated that HLA association patterns to the phenome have both population-specific and population-shared components. We performed MHC PheWAS in the Korean population by analyzing associations between phenotypes and genetic variants in the MHC region using the Korea Biobank Array project data samples from the Korean Genome and Epidemiology Study (KoGES) cohorts. Using this single-population dataset, we curated and analyzed 82 phenotypes for 125 673 Korean individuals after imputing HLA using CookHLA, a recently developed imputation framework. More than one-third of these phenotypes showed significant associations, confirming 56 known associations and discovering 13 novel association signals that were not reported previously. In addition, we analyzed heritability explained by the variants in the MHC region and genetic correlations among phenotypes based on the MHC variants.
Collapse
Affiliation(s)
- Chanwoo Kim
- Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Republic of Korea
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA
| | - Young Jin Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Chungcheongbuk-do 28159,, Republic of Korea
| | - Wanson Choi
- Department of Biomedical Sciences, BK21 Plus Biomedical Science Project, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| | - Hye-Mi Jang
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Chungcheongbuk-do 28159,, Republic of Korea
| | - Mi Yeong Hwang
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Chungcheongbuk-do 28159,, Republic of Korea
| | - Sunwoo Jung
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul 08826, Republic of Korea
| | - Hyunjoon Lim
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul 08826, Republic of Korea
| | - Sang Bin Hong
- Department of Neurology, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| | - Kyungheon Yoon
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Chungcheongbuk-do 28159,, Republic of Korea
| | - Bong-Jo Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Chungcheongbuk-do 28159,, Republic of Korea
| | - Hyun-Young Park
- Department of Precision Medicine, National Institute of Health, Cheongju-si, Chungcheongbuk-do 28159, Republic of Korea
| | - Buhm Han
- 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 08826, Republic of Korea
| |
Collapse
|
62
|
Martínez-Gil N, Ugartondo N, Grinberg D, Balcells S. Wnt Pathway Extracellular Components and Their Essential Roles in Bone Homeostasis. Genes (Basel) 2022; 13:genes13010138. [PMID: 35052478 PMCID: PMC8775112 DOI: 10.3390/genes13010138] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 01/10/2022] [Accepted: 01/11/2022] [Indexed: 12/11/2022] Open
Abstract
The Wnt pathway is involved in several processes essential for bone development and homeostasis. For proper functioning, the Wnt pathway is tightly regulated by numerous extracellular elements that act by both activating and inhibiting the pathway at different moments. This review aims to describe, summarize and update the findings regarding the extracellular modulators of the Wnt pathway, including co-receptors, ligands and inhibitors, in relation to bone homeostasis, with an emphasis on the animal models generated, the diseases associated with each gene and the bone processes in which each member is involved. The precise knowledge of all these elements will help us to identify possible targets that can be used as a therapeutic target for the treatment of bone diseases such as osteoporosis.
Collapse
|
63
|
Differential effects of steroid hormones on levels of broad-sense heritability in a wild bird: possible mechanism of environment × genetic variance interaction? Heredity (Edinb) 2022; 128:63-76. [PMID: 34921237 PMCID: PMC8733014 DOI: 10.1038/s41437-021-00490-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Revised: 11/27/2021] [Accepted: 11/30/2021] [Indexed: 01/03/2023] Open
Abstract
Genetic variation is one of the key concepts in evolutionary biology and an important prerequisite of evolutionary change. However, we know very little about processes that modulate its levels in wild populations. In particular, we still are to understand why genetic variances often depend on environmental conditions. One of possible environment-sensitive modulators of observed levels of genetic variance are maternal effects. In this study we attempt to experimentally test the hypothesis that maternally transmitted agents (e.g. hormones) may influence the expression of genetic variance in quantitative traits in the offspring. We manipulated the levels of steroid hormones (testosterone and corticosterone) in eggs laid by blue tits in a wild population. Our experimental setup allowed for full crossing of genetic and rearing effects with the experimental manipulation. We observed that birds treated with corticosterone exhibited a significant decrease in broad-sense genetic variance of tarsus length, and an increase in this component in body mass on the 2nd day post-hatching. Our study indicates, that maternally transmitted substances such as hormones may have measurable impact on the levels of genetic variance and hence, on the evolutionary potential of quantitative traits.
Collapse
|
64
|
Vandermeulen MD, Cullen PJ. Gene by Environment Interactions reveal new regulatory aspects of signaling network plasticity. PLoS Genet 2022; 18:e1009988. [PMID: 34982769 PMCID: PMC8759647 DOI: 10.1371/journal.pgen.1009988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Revised: 01/14/2022] [Accepted: 12/09/2021] [Indexed: 11/18/2022] Open
Abstract
Phenotypes can change during exposure to different environments through the regulation of signaling pathways that operate in integrated networks. How signaling networks produce different phenotypes in different settings is not fully understood. Here, Gene by Environment Interactions (GEIs) were used to explore the regulatory network that controls filamentous/invasive growth in the yeast Saccharomyces cerevisiae. GEI analysis revealed that the regulation of invasive growth is decentralized and varies extensively across environments. Different regulatory pathways were critical or dispensable depending on the environment, microenvironment, or time point tested, and the pathway that made the strongest contribution changed depending on the environment. Some regulators even showed conditional role reversals. Ranking pathways' roles across environments revealed an under-appreciated pathway (OPI1) as the single strongest regulator among the major pathways tested (RAS, RIM101, and MAPK). One mechanism that may explain the high degree of regulatory plasticity observed was conditional pathway interactions, such as conditional redundancy and conditional cross-pathway regulation. Another mechanism was that different pathways conditionally and differentially regulated gene expression, such as target genes that control separate cell adhesion mechanisms (FLO11 and SFG1). An exception to decentralized regulation of invasive growth was that morphogenetic changes (cell elongation and budding pattern) were primarily regulated by one pathway (MAPK). GEI analysis also uncovered a round-cell invasion phenotype. Our work suggests that GEI analysis is a simple and powerful approach to define the regulatory basis of complex phenotypes and may be applicable to many systems.
Collapse
Affiliation(s)
- Matthew D. Vandermeulen
- Department of Biological Sciences, University at Buffalo, Buffalo, New York, United States of America
| | - Paul J. Cullen
- Department of Biological Sciences, University at Buffalo, Buffalo, New York, United States of America
| |
Collapse
|
65
|
Ding Y, Hou K, Burch KS, Lapinska S, Privé F, Vilhjálmsson B, Sankararaman S, Pasaniuc B. Large uncertainty in individual polygenic risk score estimation impacts PRS-based risk stratification. Nat Genet 2022; 54:30-39. [PMID: 34931067 PMCID: PMC8758557 DOI: 10.1038/s41588-021-00961-5] [Citation(s) in RCA: 54] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Accepted: 09/29/2021] [Indexed: 01/05/2023]
Abstract
Although the cohort-level accuracy of polygenic risk scores (PRSs)-estimates of genetic value at the individual level-has been widely assessed, uncertainty in PRSs remains underexplored. In the present study, we show that Bayesian PRS methods can estimate the variance of an individual's PRS and can yield well-calibrated credible intervals via posterior sampling. For 13 real traits in the UK Biobank (n = 291,273 unrelated 'white British'), we observe large variances in individual PRS estimates which impact interpretation of PRS-based stratification; averaging across traits, only 0.8% (s.d. = 1.6%) of individuals with PRS point estimates in the top decile have corresponding 95% credible intervals fully contained in the top decile. We provide an analytical estimator for the expectation of individual PRS variance as a function of SNP heritability, number of causal SNPs and sample size. Our results showcase the importance of incorporating uncertainty in individual PRS estimates into subsequent analyses.
Collapse
Affiliation(s)
- Yi Ding
- Bioinformatics Interdepartmental Program, University of California, Los Angeles (UCLA), Los Angeles, CA, USA.
| | - Kangcheng Hou
- Bioinformatics Interdepartmental Program, University of California, Los Angeles (UCLA), Los Angeles, CA, USA.
| | - Kathryn S Burch
- Bioinformatics Interdepartmental Program, University of California, Los Angeles (UCLA), Los Angeles, CA, USA
| | - Sandra Lapinska
- Bioinformatics Interdepartmental Program, University of California, Los Angeles (UCLA), Los Angeles, CA, USA
| | - Florian Privé
- Department of Economics and Business Economics, National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
| | - Bjarni Vilhjálmsson
- Department of Economics and Business Economics, National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
| | - Sriram Sankararaman
- Bioinformatics Interdepartmental Program, University of California, Los Angeles (UCLA), Los Angeles, CA, USA
- Department of Computer Science, UCLA, Los Angeles, CA, USA
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, University of California, Los Angeles (UCLA), Los Angeles, CA, USA.
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
| |
Collapse
|
66
|
San-Cristobal R, de Toro-Martín J, Vohl MC. Appraisal of Gene-Environment Interactions in GWAS for Evidence-Based Precision Nutrition Implementation. Curr Nutr Rep 2022; 11:563-573. [PMID: 35948824 PMCID: PMC9750926 DOI: 10.1007/s13668-022-00430-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/15/2022] [Indexed: 01/31/2023]
Abstract
PURPOSE OF REVIEW This review aims to analyse the currently reported gene-environment (G × E) interactions in genome-wide association studies (GWAS), involving environmental factors such as lifestyle and dietary habits related to metabolic syndrome phenotypes. For this purpose, the present manuscript reviews the available GWAS registered on the GWAS Catalog reporting the interaction between environmental factors and metabolic syndrome traits. RECENT FINDINGS Advances in omics-related analytical and computational approaches in recent years have led to a better understanding of the biological processes underlying these G × E interactions. A total of 42 GWAS were analysed, reporting over 300 loci interacting with environmental factors. Alcohol consumption, sleep time, smoking habit and physical activity were the most studied environmental factors with significant G × E interactions. The implementation of more comprehensive GWAS will provide a better understanding of the metabolic processes that determine individual responses to environmental exposures and their association with the development of chronic diseases such as obesity and the metabolic syndrome. This will facilitate the development of precision approaches for better prevention, management and treatment of these diseases.
Collapse
Affiliation(s)
- Rodrigo San-Cristobal
- grid.23856.3a0000 0004 1936 8390Centre Nutrition, Santé Et Société (NUTRISS), Institut Sur La Nutrition Et Les Aliments Fonctionnels (INAF), Université Laval, Québec, QC Canada ,grid.23856.3a0000 0004 1936 8390School of Nutrition, Université Laval, Quebec, QC G1V 0A6 Canada
| | - Juan de Toro-Martín
- grid.23856.3a0000 0004 1936 8390Centre Nutrition, Santé Et Société (NUTRISS), Institut Sur La Nutrition Et Les Aliments Fonctionnels (INAF), Université Laval, Québec, QC Canada ,grid.23856.3a0000 0004 1936 8390School of Nutrition, Université Laval, Quebec, QC G1V 0A6 Canada
| | - Marie-Claude Vohl
- grid.23856.3a0000 0004 1936 8390Centre Nutrition, Santé Et Société (NUTRISS), Institut Sur La Nutrition Et Les Aliments Fonctionnels (INAF), Université Laval, Québec, QC Canada ,grid.23856.3a0000 0004 1936 8390School of Nutrition, Université Laval, Quebec, QC G1V 0A6 Canada
| |
Collapse
|
67
|
Abstract
Aims Deciphering the genetic relationships between major depressive disorder (MDD) and osteoarthritis (OA) may facilitate an understanding of their biological mechanisms, as well as inform more effective treatment regimens. We aim to investigate the mechanisms underlying relationships between MDD and OA in the context of common genetic variations. Methods Linkage disequilibrium score regression was used to test the genetic correlation between MDD and OA. Polygenic analysis was performed to estimate shared genetic variations between the two diseases. Two-sample bidirectional Mendelian randomization analysis was used to investigate causal relationships between MDD and OA. Genomic loci shared between MDD and OA were identified using cross-trait meta-analysis. Fine-mapping of transcriptome-wide associations was used to prioritize putatively causal genes for the two diseases. Results MDD has a significant genetic correlation with OA (rg = 0.29) and the two diseases share a considerable proportion of causal variants. Mendelian randomization analysis indicates that genetic liability to MDD has a causal effect on OA (bxy = 0.24) and genetic liability to OA conferred a causal effect on MDD (bxy = 0.20). Cross-trait meta-analyses identified 29 shared genomic loci between MDD and OA. Together with fine-mapping of transcriptome-wide association signals, our results suggest that Estrogen Receptor 1 (ESR1), SRY-Box Transcription Factor 5 (SOX5), and Glutathione Peroxidase 1 (GPX1) may have therapeutic implications for both MDD and OA. Conclusion The study reveals substantial shared genetic liability between MDD and OA, which may confer risk for one another. Our findings provide a novel insight into phenotypic relationships between MDD and OA. Cite this article: Bone Joint Res 2022;11(1):12–22.
Collapse
Affiliation(s)
- Fuquan Zhang
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Shuquan Rao
- State Key Laboratory of Experimental Hematology, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
| | - Ancha Baranova
- School of Systems Biology, George Mason University, Fairfax, Virginia, USA.,Research Centre for Medical Genetics, Moscow, Russia
| |
Collapse
|
68
|
Dong A, Feng L, Yang D, Wu S, Zhao J, Wang J, Wu R. FunGraph: A statistical protocol to reconstruct omnigenic multilayer interactome networks for complex traits. STAR Protoc 2021; 2:100985. [PMID: 34927094 PMCID: PMC8649398 DOI: 10.1016/j.xpro.2021.100985] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
We describe a statistical protocol of how to reconstruct and dissect functional omnigenic multilayer interactome networks that mediate complex dynamic traits in a genome-wide association study (GWAS). This protocol, named FunGraph, can analyze how each locus affects phenotypic variation through its own direct effect and a complete set of indirect effects due to regulation by other loci co-existing in large-scale networks. FunGraph is applicable to any GWAS aimed to characterize the genetic architecture of dynamic phenotypic traits. For complete details on the use and execution of this protocol, please refer to Wang et al. (2021). TurboID enabled biotin-based proximity labeling protocol for C. elegans Experimental design guidelines for proximity labeling in C. elegans A step-by-step TurboID protocol from transgene design to protein identification
Collapse
Affiliation(s)
- Ang Dong
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Li Feng
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Dengcheng Yang
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Shuang Wu
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Jinshuai Zhao
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Jing Wang
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Rongling Wu
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China.,Center for Statistical Genetics, Departments of Public Health Sciences and Statistics, The Pennsylvania State University, Hershey, PA 17033, USA
| |
Collapse
|
69
|
Domingue BW, Kanopka K, Mallard TT, Trejo S, Tucker-Drob EM. Modeling Interaction and Dispersion Effects in the Analysis of Gene-by-Environment Interaction. Behav Genet 2021; 52:56-64. [PMID: 34855050 DOI: 10.1007/s10519-021-10090-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 10/28/2021] [Indexed: 11/25/2022]
Abstract
Genotype-by-environment interaction (GxE) studies probe heterogeneity in response to risk factors or interventions. Popular methods for estimation of GxE examine multiplicative interactions between individual genetic and environmental measures. However, risk factors and interventions may modulate the total variance of an epidemiological outcome that itself represents the aggregation of many other etiological components. We expand the traditional GxE model to directly model genetic and environmental moderation of the dispersion of the outcome. We derive a test statistic, [Formula: see text], for inferring whether an interaction identified between individual genetic and environmental measures represents a more general pattern of moderation of the total variance in the phenotype by either the genetic or the environmental measure. We validate our method via extensive simulation, and apply it to investigate genotype-by-birth year interactions for Body Mass Index (BMI) with polygenic scores in the Health and Retirement Study (N = 11,586) and individual genetic variants in the UK Biobank (N = 380,605). We find that changes in the penetrance of a genome-wide polygenic score for BMI across birth year are partly representative of a more general pattern of expanding BMI variation across generations. Three individual variants found to be more strongly associated with BMI among later born individuals, were also associated with the magnitude of variability in BMI itself within any given birth year, suggesting that they may confer general sensitivity of BMI to a range of unmeasured factors beyond those captured by birth year. We introduce an expanded GxE regression model that explicitly models genetic and environmental moderation of the dispersion of the outcome under study. This approach can determine whether GxE interactions identified are specific to the measured predictors or represent a more general pattern of moderation of the total variance in the outcome by the genetic and environmental measures.
Collapse
Affiliation(s)
- Benjamin W Domingue
- Graduate School of Education, Stanford University and Center for Population Health Sciences, Stanford Medicine, Stanford, USA.
| | - Klint Kanopka
- Graduate School of Education, Stanford University, Stanford, USA
| | - Travis T Mallard
- Department of Psychology, University of Texas at Austin, Austin, USA
| | - Sam Trejo
- Department of Sociology and Office of Population Research, Princeton University, Princeton, USA
| | - Elliot M Tucker-Drob
- Department of Psychology and Population Research Center, University of Texas at Austin, Austin, USA.
| |
Collapse
|
70
|
Mills HL, Higgins JP, Morris RW, Kessler D, Heron J, Wiles N, Davey Smith G, Tilling K. Detecting Heterogeneity of Intervention Effects Using Analysis and Meta-analysis of Differences in Variance Between Trial Arms. Epidemiology 2021; 32:846-854. [PMID: 34432720 PMCID: PMC8478324 DOI: 10.1097/ede.0000000000001401] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 07/12/2021] [Indexed: 11/25/2022]
Abstract
BACKGROUND Randomized controlled trials (RCTs) with continuous outcomes usually only examine mean differences in response between trial arms. If the intervention has heterogeneous effects, then outcome variances will also differ between arms. Power of an individual trial to assess heterogeneity is lower than the power to detect the same size of main effect. METHODS We describe several methods for assessing differences in variance in trial arms and apply them to a single trial with individual patient data and to meta-analyses using summary data. Where individual data are available, we use regression-based methods to examine the effects of covariates on variation. We present an additional method to meta-analyze differences in variances with summary data. RESULTS In the single trial, there was agreement between methods, and the difference in variance was largely due to differences in prevalence of depression at baseline. In two meta-analyses, most individual trials did not show strong evidence of a difference in variance between arms, with wide confidence intervals. However, both meta-analyses showed evidence of greater variance in the control arm, and in one example, this was perhaps because mean outcome in the control arm was higher. CONCLUSIONS Using meta-analysis, we overcame low power of individual trials to examine differences in variance using meta-analysis. Evidence of differences in variance should be followed up to identify potential effect modifiers and explore other possible causes such as varying compliance.
Collapse
Affiliation(s)
- Harriet L. Mills
- From the Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Julian P.T. Higgins
- From the Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- National Institute for Health Research Bristol Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, Bristol, United Kingdom
| | - Richard W. Morris
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - David Kessler
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- National Institute for Health Research Bristol Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, Bristol, United Kingdom
| | - Jon Heron
- From the Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Nicola Wiles
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- National Institute for Health Research Bristol Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, Bristol, United Kingdom
| | - George Davey Smith
- From the Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Kate Tilling
- From the Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- National Institute for Health Research Bristol Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, Bristol, United Kingdom
| |
Collapse
|
71
|
Abstract
PURPOSE OF REVIEW We provide an overview of recent findings with respect to gene-environment (GxE) interactions for cardiovascular disease (CVD) risk and discuss future opportunities for advancing the field. RECENT FINDINGS Over the last several years, GxE interactions for CVD have mostly been identified for smoking and coronary artery disease (CAD) or related risk factors. By comparison, there is more limited evidence for GxE interactions between CVD outcomes and other exposures, such as physical activity, air pollution, diet, and sex. The establishment of large consortia and population-based cohorts, in combination with new computational tools and mouse genetics platforms, can potentially overcome some of the limitations that have hindered human GxE interaction studies and reveal additional association signals for CVD-related traits. The identification of novel GxE interactions is likely to provide a better understanding of the pathogenesis and genetic liability of CVD, with significant implications for healthy lifestyles and therapeutic strategies.
Collapse
|
72
|
Abstract
Causation has multiple distinct meanings in genetics. One reason for this is meaning slippage between two concepts of the gene: Mendelian and molecular. Another reason is that a variety of genetic methods address different kinds of causal relationships. Some genetic studies address causes of traits in individuals, which can only be assessed when single genes follow predictable inheritance patterns that reliably cause a trait. A second sense concerns the causes of trait differences within a population. Whereas some single genes can be said to cause population-level differences, most often these claims concern the effects of many genes. Polygenic traits can be understood using heritability estimates, which estimate the relative influences of genetic and environmental differences to trait differences within a population. Attempts to understand the molecular mechanisms underlying polygenic traits have been developed, although causal inference based on these results remains controversial. Genetic variation has also recently been leveraged as a randomizing factor to identify environmental causes of trait differences. This technique-Mendelian randomization-offers some solutions to traditional epidemiological challenges, although it is limited to the study of environments with known genetic influences.
Collapse
Affiliation(s)
- Kate E Lynch
- Department of Philosophy, The University of Sydney, Sydney, New South Wales 2006, Australia
| |
Collapse
|
73
|
Liu D, Ban HJ, El Sergani AM, Lee MK, Hecht JT, Wehby GL, Moreno LM, Feingold E, Marazita ML, Cha S, Szabo-Rogers HL, Weinberg SM, Shaffer JR. PRICKLE1 × FOCAD Interaction Revealed by Genome-Wide vQTL Analysis of Human Facial Traits. Front Genet 2021; 12:674642. [PMID: 34434215 PMCID: PMC8381734 DOI: 10.3389/fgene.2021.674642] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 06/03/2021] [Indexed: 12/14/2022] Open
Abstract
The human face is a highly complex and variable structure resulting from the intricate coordination of numerous genetic and non-genetic factors. Hundreds of genomic loci impacting quantitative facial features have been identified. While these associations have been shown to influence morphology by altering the mean size and shape of facial measures, their effect on trait variance remains unclear. We conducted a genome-wide association analysis for the variance of 20 quantitative facial measurements in 2,447 European individuals and identified several suggestive variance quantitative trait loci (vQTLs). These vQTLs guided us to conduct an efficient search for gene-by-gene (G × G) interactions, which uncovered an interaction between PRICKLE1 and FOCAD affecting cranial base width. We replicated this G × G interaction signal at the locus level in an additional 5,128 Korean individuals. We used the hypomorphic Prickle1 Beetlejuice (Prickle1 Bj ) mouse line to directly test the function of Prickle1 on the cranial base and observed wider cranial bases in Prickle1 Bj/Bj . Importantly, we observed that the Prickle1 and Focadhesin proteins co-localize in murine cranial base chondrocytes, and this co-localization is abnormal in the Prickle1 Bj/Bj mutants. Taken together, our findings uncovered a novel G × G interaction effect in humans with strong support from both epidemiological and molecular studies. These results highlight the potential of studying measures of phenotypic variability in gene mapping studies of facial morphology.
Collapse
Affiliation(s)
- Dongjing Liu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Hyo-Jeong Ban
- Future Medicine Division, Korea Institute of Oriental Medicine, Daejeon, South Korea
| | - Ahmed M. El Sergani
- Center for Craniofacial and Dental Genetics, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Oral and Craniofacial Sciences, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Myoung Keun Lee
- Center for Craniofacial and Dental Genetics, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Jacqueline T. Hecht
- Department of Pediatrics, McGovern Medical Center, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - George L. Wehby
- Department of Health Management and Policy, The University of Iowa, Iowa City, IA, United States
| | - Lina M. Moreno
- Department of Orthodontics, The University of Iowa, Iowa City, IA, United States
| | - Eleanor Feingold
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States
| | - Mary L. Marazita
- Center for Craniofacial and Dental Genetics, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Oral and Craniofacial Sciences, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Psychiatry, Clinical and Translational Science Institute, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Seongwon Cha
- Future Medicine Division, Korea Institute of Oriental Medicine, Daejeon, South Korea
| | - Heather L. Szabo-Rogers
- Department of Oral and Craniofacial Sciences, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Developmental Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- Regenerative Medicine at the McGowan Institute, University of Pittsburgh, Pittsburgh, PA, United States
- Center for Craniofacial Regeneration, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Seth M. Weinberg
- Center for Craniofacial and Dental Genetics, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Oral and Craniofacial Sciences, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States
| | - John R. Shaffer
- Center for Craniofacial and Dental Genetics, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Oral and Craniofacial Sciences, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States
| |
Collapse
|
74
|
Dinneen TJ, Ghrálaigh FN, Walsh R, Lopez LM, Gallagher L. How does genetic variation modify ND-CNV phenotypes? Trends Genet 2021; 38:140-151. [PMID: 34364706 DOI: 10.1016/j.tig.2021.07.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 06/30/2021] [Accepted: 07/06/2021] [Indexed: 02/05/2023]
Abstract
Rare copy-number variants (CNVs) associated with neurodevelopmental disorders (NDDs), i.e., ND-CNVs, provide an insight into the neurobiology of NDDs and, potentially, a link between biology and clinical outcomes. However, ND-CNVs are characterised by incomplete penetrance resulting in heterogeneous carrier phenotypes, ranging from non-affected to multimorbid psychiatric, neurological, and physical phenotypes. Recent evidence indicates that other variants in the genome, or 'other hits', may partially explain the variable expressivity of ND-CNVs. These may be other rare variants or the aggregated effects of common variants that modify NDD risk. Here we discuss the recent findings, current questions, and future challenges relating to other hits research in the context of ND-CNVs and their potential for improved clinical diagnostics and therapeutics for ND-CNV carriers.
Collapse
Affiliation(s)
- Thomas J Dinneen
- Department of Psychiatry, Trinity College Dublin, Dublin, Ireland.
| | - Fiana Ní Ghrálaigh
- Department of Psychiatry, Trinity College Dublin, Dublin, Ireland; Department of Biology, National University of Ireland Maynooth, Maynooth, Ireland
| | - Ruth Walsh
- Department of Psychiatry, Trinity College Dublin, Dublin, Ireland
| | - Lorna M Lopez
- Department of Psychiatry, Trinity College Dublin, Dublin, Ireland; Department of Biology, National University of Ireland Maynooth, Maynooth, Ireland
| | - Louise Gallagher
- Department of Psychiatry, Trinity College Dublin, Dublin, Ireland.
| |
Collapse
|
75
|
Lin WY, Chan CC, Liu YL, Yang AC, Tsai SJ, Kuo PH. Sex-specific autosomal genetic effects across 26 human complex traits. Hum Mol Genet 2021; 29:1218-1228. [PMID: 32160288 DOI: 10.1093/hmg/ddaa040] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 12/26/2019] [Accepted: 03/05/2020] [Indexed: 12/28/2022] Open
Abstract
Previous studies have shown that men and women have different genetic architectures across many traits. However, except waist-to-hip ratio (WHR) and waist circumference (WC), it remains unknown whether the genetic effects of a certain trait are weaker or stronger on men/women. With ~18 000 Taiwan Biobank subjects, we comprehensively investigate sexual heterogeneity in autosomal genetic effects, for traits regarding cardiovascular health, diabetes, kidney, liver, anthropometric profiles, blood, etc. 'Gene-by-sex interactions' (G $\times$ S) were detected in 18 out of 26 traits, each with an interaction P-value (${{P}}_{{INT}}$) less than $0.05/104={0.00048}$, where 104 is the number of tests conducted in this study. The most significant evidence of G $\times$ S was found in WHR (${{P}}_{{INT}}$ = 3.2 $\times{{10}}^{-{55}}$) and WC (${{P}}_{{INT}}$ = 2.3$\times{{10}}^{-{41}}$). As a novel G$\times$S investigation for other traits, we here find that the autosomal genetic effects are weaker on women than on men, for low-density lipoprotein cholesterol (LDL-C), uric acid (UA) and diabetes-related traits such as fasting glucose and glycated hemoglobin. For LDL-C and UA, the evidence of G$\times$S is especially notable in subjects aged less than 50 years, where estrogen can play a role in attenuating the autosomal genetic effects of these two traits. Men and women have systematically distinct environmental contexts caused by hormonal milieu and their specific society roles, which may trigger diverse gene expressions despite the same DNA materials. As many environmental exposures are difficult to collect and quantify, sex can serve as a good surrogate for these factors.
Collapse
Affiliation(s)
- Wan-Yu Lin
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.,Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Chang-Chuan Chan
- Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan.,Institute of Environmental and Occupational Health Sciences, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Yu-Li Liu
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan
| | - Albert C Yang
- Division of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, MA, USA.,Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan
| | - Shih-Jen Tsai
- Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan.,Division of Psychiatry, National Yang-Ming University, Taipei, Taiwan.,Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Po-Hsiu Kuo
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.,Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan
| |
Collapse
|
76
|
Heilbron K, Mozaffari SV, Vacic V, Yue P, Wang W, Shi J, Jubb AM, Pitts SJ, Wang X. Advancing drug discovery using the power of the human genome. J Pathol 2021; 254:418-429. [PMID: 33748968 PMCID: PMC8251523 DOI: 10.1002/path.5664] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2020] [Revised: 03/11/2021] [Accepted: 03/16/2021] [Indexed: 12/31/2022]
Abstract
Human genetics plays an increasingly important role in drug development and population health. Here we review the history of human genetics in the context of accelerating the discovery of therapies, present examples of how human genetics evidence supports successful drug targets, and discuss how polygenic risk scores could be beneficial in various clinical settings. We highlight the value of direct-to-consumer platforms in the era of fast-paced big data biotechnology, and how diverse genetic and health data can benefit society. © 2021 23andMe, Inc. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.
Collapse
|
77
|
Braz CU, Rowan TN, Schnabel RD, Decker JE. Genome-wide association analyses identify genotype-by-environment interactions of growth traits in Simmental cattle. Sci Rep 2021; 11:13335. [PMID: 34172761 PMCID: PMC8233360 DOI: 10.1038/s41598-021-92455-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 06/07/2021] [Indexed: 02/06/2023] Open
Abstract
Understanding genotype-by-environment interactions (G × E) is crucial to understand environmental adaptation in mammals and improve the sustainability of agricultural production. Here, we present an extensive study investigating the interaction of genome-wide SNP markers with a vast assortment of environmental variables and searching for SNPs controlling phenotypic variance (vQTL) using a large beef cattle dataset. We showed that G × E contribute 10.1%, 3.8%, and 2.8% of the phenotypic variance of birth weight, weaning weight, and yearling weight, respectively. G × E genome-wide association analysis (GWAA) detected a large number of G × E loci affecting growth traits, which the traditional GWAA did not detect, showing that functional loci may have non-additive genetic effects regardless of differences in genotypic means. Further, variance-heterogeneity GWAA detected loci enriched with G × E effects without requiring prior knowledge of the interacting environmental factors. Functional annotation and pathway analysis of G × E genes revealed biological mechanisms by which cattle respond to changes in their environment, such as neurotransmitter activity, hypoxia-induced processes, keratinization, hormone, thermogenic and immune pathways. We unraveled the relevance and complexity of the genetic basis of G × E underlying growth traits, providing new insights into how different environmental conditions interact with specific genes influencing adaptation and productivity in beef cattle and potentially across mammals.
Collapse
Affiliation(s)
- Camila U Braz
- Division of Animal Sciences, University of Missouri, Columbia, MO, 65211, USA
| | - Troy N Rowan
- Division of Animal Sciences, University of Missouri, Columbia, MO, 65211, USA
- Genetics Area Program, University of Missouri, Columbia, MO, 65211, USA
| | - Robert D Schnabel
- Division of Animal Sciences, University of Missouri, Columbia, MO, 65211, USA
- Genetics Area Program, University of Missouri, Columbia, MO, 65211, USA
- Informatics Institute, University of Missouri, Columbia, MO, 65211, USA
| | - Jared E Decker
- Division of Animal Sciences, University of Missouri, Columbia, MO, 65211, USA.
- Genetics Area Program, University of Missouri, Columbia, MO, 65211, USA.
- Informatics Institute, University of Missouri, Columbia, MO, 65211, USA.
| |
Collapse
|
78
|
Lozovsky ER, Daniels RF, Heffernan GD, Jacobus DP, Hartl DL. Relevance of Higher-Order Epistasis in Drug Resistance. Mol Biol Evol 2021; 38:142-151. [PMID: 32745183 PMCID: PMC7782864 DOI: 10.1093/molbev/msaa196] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
We studied five chemically distinct but related 1,3,5-triazine antifolates with regard to their effects on growth of a set of mutants in dihydrofolate reductase. The mutants comprise a combinatorially complete data set of all 16 possible combinations of four amino acid replacements associated with resistance to pyrimethamine in the malaria parasite Plasmodium falciparum. Pyrimethamine was a mainstay medication for malaria for many years, and it is still in use in intermittent treatment during pregnancy or as a partner drug in artemisinin combination therapy. Our goal was to investigate the extent to which the alleles yield similar adaptive topographies and patterns of epistasis across chemically related drugs. We find that the adaptive topographies are indeed similar with the same or closely related alleles being fixed in computer simulations of stepwise evolution. For all but one of the drugs the topography features at least one suboptimal fitness peak. Our data are consistent with earlier results indicating that third order and higher epistatic interactions appear to contribute only modestly to the overall adaptive topography, and they are largely conserved. In regard to drug development, our data suggest that higher-order interactions are likely to be of little value as an advisory tool in the choice of lead compounds.
Collapse
Affiliation(s)
- Elena R Lozovsky
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA
| | - Rachel F Daniels
- Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA
| | | | | | - Daniel L Hartl
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA
| |
Collapse
|
79
|
Lu H, Wang T, Zhang J, Zhang S, Huang S, Zeng P. Evaluating marginal genetic correlation of associated loci for complex diseases and traits between European and East Asian populations. Hum Genet 2021; 140:1285-1297. [PMID: 34091770 DOI: 10.1007/s00439-021-02299-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 05/31/2021] [Indexed: 12/14/2022]
Abstract
Genome-wide association studies (GWASs) have successfully identified a large amount of single-nucleotide polymorphisms associated with many complex phenotypes in diverse populations. However, a comprehensive understanding of the genetic correlation of associated loci of phenotypes across populations remains lacking and the extent to which associations discovered in one population can be generalized to other populations or can be utilized for trans-ethnic genetic prediction is also unclear. By leveraging summary statistics, we proposed MAGIC to evaluate the trans-ethnic marginal genetic correlation (rm) of per-allele effect sizes for associated SNPs (P < 5E-8) under the framework of measurement error models. We confirmed the methodological advantage of MAGIC over general approaches through simulations and demonstrated its utility by analyzing 34 GWAS summary statistics of phenotypes from the East Asian (Nmax = 254,373) and European (Nmax = 1,220,901) populations. Among these phenotypes, rm was estimated to range from 0.584 (se = 0.140) for breast cancer to 0.949 (se = 0.035) for age of menarche, with an average of 0.835 (se = 0.045). We also uncovered that the trans-ethnic genetic prediction accuracy for phenotypes in the target population would substantially become low when using associated SNPs identified in non-target populations, indicating that associations discovered in the one population cannot be simply generalized to another population and that the accuracy of trans-ethnic phenotype prediction is generally dissatisfactory. Overall, our study provides in-depth insight into trans-ethnic genetic correlation and prediction for complex phenotypes across diverse populations.
Collapse
Affiliation(s)
- Haojie Lu
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Ting Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Jinhui Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Shuo Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Shuiping Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.,Center for Medical Statistics and Data Analysis, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Ping Zeng
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China. .,Center for Medical Statistics and Data Analysis, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
| |
Collapse
|
80
|
Hall MA, Wallace J, Lucas AM, Bradford Y, Verma SS, Müller-Myhsok B, Passero K, Zhou J, McGuigan J, Jiang B, Pendergrass SA, Zhang Y, Peissig P, Brilliant M, Sleiman P, Hakonarson H, Harley JB, Kiryluk K, Van Steen K, Moore JH, Ritchie MD. Novel EDGE encoding method enhances ability to identify genetic interactions. PLoS Genet 2021; 17:e1009534. [PMID: 34086673 PMCID: PMC8208534 DOI: 10.1371/journal.pgen.1009534] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 06/16/2021] [Accepted: 04/06/2021] [Indexed: 11/26/2022] Open
Abstract
Assumptions are made about the genetic model of single nucleotide polymorphisms (SNPs) when choosing a traditional genetic encoding: additive, dominant, and recessive. Furthermore, SNPs across the genome are unlikely to demonstrate identical genetic models. However, running SNP-SNP interaction analyses with every combination of encodings raises the multiple testing burden. Here, we present a novel and flexible encoding for genetic interactions, the elastic data-driven genetic encoding (EDGE), in which SNPs are assigned a heterozygous value based on the genetic model they demonstrate in a dataset prior to interaction testing. We assessed the power of EDGE to detect genetic interactions using 29 combinations of simulated genetic models and found it outperformed the traditional encoding methods across 10%, 30%, and 50% minor allele frequencies (MAFs). Further, EDGE maintained a low false-positive rate, while additive and dominant encodings demonstrated inflation. We evaluated EDGE and the traditional encodings with genetic data from the Electronic Medical Records and Genomics (eMERGE) Network for five phenotypes: age-related macular degeneration (AMD), age-related cataract, glaucoma, type 2 diabetes (T2D), and resistant hypertension. A multi-encoding genome-wide association study (GWAS) for each phenotype was performed using the traditional encodings, and the top results of the multi-encoding GWAS were considered for SNP-SNP interaction using the traditional encodings and EDGE. EDGE identified a novel SNP-SNP interaction for age-related cataract that no other method identified: rs7787286 (MAF: 0.041; intergenic region of chromosome 7)–rs4695885 (MAF: 0.34; intergenic region of chromosome 4) with a Bonferroni LRT p of 0.018. A SNP-SNP interaction was found in data from the UK Biobank within 25 kb of these SNPs using the recessive encoding: rs60374751 (MAF: 0.030) and rs6843594 (MAF: 0.34) (Bonferroni LRT p: 0.026). We recommend using EDGE to flexibly detect interactions between SNPs exhibiting diverse action. Although traditional genetic encodings are widely implemented in genetics research, including in genome-wide association studies (GWAS) and epistasis, each method makes assumptions that may not reflect the underlying etiology. Here, we introduce a novel encoding method that estimates and assigns an individualized data-driven encoding for each single nucleotide polymorphism (SNP): the elastic data-driven genetic encoding (EDGE). With simulations, we demonstrate that this novel method is more accurate and robust than traditional encoding methods in estimating heterozygous genotype values, reducing the type I error, and detecting SNP-SNP interactions. We further applied the traditional encodings and EDGE to biomedical data from the Electronic Medical Records and Genomics (eMERGE) Network for five phenotypes, and EDGE identified a novel interaction for age-related cataract not detected by traditional methods, which replicated in data from the UK Biobank. EDGE provides an alternative approach to understanding and modeling diverse SNP models and is recommended for studying complex genetics in common human phenotypes.
Collapse
Affiliation(s)
- Molly A. Hall
- Department of Veterinary and Biomedical Sciences, College of Agricultural Sciences, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- Penn State Cancer Institute, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- * E-mail:
| | - John Wallace
- Department of Veterinary and Biomedical Sciences, College of Agricultural Sciences, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Anastasia M. Lucas
- Department of Genetics, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Yuki Bradford
- Department of Genetics, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Shefali S. Verma
- Department of Genetics, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Bertram Müller-Myhsok
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom
| | - Kristin Passero
- Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Jiayan Zhou
- Department of Veterinary and Biomedical Sciences, College of Agricultural Sciences, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - John McGuigan
- Department of Veterinary and Biomedical Sciences, College of Agricultural Sciences, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Beibei Jiang
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom
| | | | - Yanfei Zhang
- Genomic Medicine Institute, Geisinger Health System, Danville, Pennsylvania, United States of America
| | - Peggy Peissig
- Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, Wisconsin, United States of America
| | - Murray Brilliant
- Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, Wisconsin, United States of America
| | - Patrick Sleiman
- Department of Pediatrics, Center for Applied Genomics, Children’s Hospital of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Hakon Hakonarson
- Department of Pediatrics, Center for Applied Genomics, Children’s Hospital of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - John B. Harley
- Center for Autoimmune Genomics and Etiology (CAGE), Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America
- United States Department of Veterans Affairs Medical Center, Cincinnati, Ohio, United States of America
| | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, New York, United States of America
| | - Kristel Van Steen
- WELBIO, GIGA-R Medical Genomics-BIO3, University of Liège, Liège, Belgium
- Department of Human Genetics, University of Leuven, Leuven, Belgium
| | - Jason H. Moore
- Department of Genetics, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Marylyn D. Ritchie
- Department of Genetics, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| |
Collapse
|
81
|
Lin M, Park DS, Zaitlen NA, Henn BM, Gignoux CR. Admixed Populations Improve Power for Variant Discovery and Portability in Genome-Wide Association Studies. Front Genet 2021; 12:673167. [PMID: 34108994 PMCID: PMC8181458 DOI: 10.3389/fgene.2021.673167] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 04/27/2021] [Indexed: 11/13/2022] Open
Abstract
Genome-wide association studies (GWAS) are primarily conducted in single-ancestry settings. The low transferability of results has limited our understanding of human genetic architecture across a range of complex traits. In contrast to homogeneous populations, admixed populations provide an opportunity to capture genetic architecture contributed from multiple source populations and thus improve statistical power. Here, we provide a mechanistic simulation framework to investigate the statistical power and transferability of GWAS under directional polygenic selection or varying divergence. We focus on a two-way admixed population and show that GWAS in admixed populations can be enriched for power in discovery by up to 2-fold compared to the ancestral populations under similar sample size. Moreover, higher accuracy of cross-population polygenic score estimates is also observed if variants and weights are trained in the admixed group rather than in the ancestral groups. Common variant associations are also more likely to replicate if first discovered in the admixed group and then transferred to an ancestral population, than the other way around (across 50 iterations with 1,000 causal SNPs, training on 10,000 individuals, testing on 1,000 in each population, p = 3.78e-6, 6.19e-101, ∼0 for FST = 0.2, 0.5, 0.8, respectively). While some of these FST values may appear extreme, we demonstrate that they are found across the entire phenome in the GWAS catalog. This framework demonstrates that investigation of admixed populations harbors significant advantages over GWAS in single-ancestry cohorts for uncovering the genetic architecture of traits and will improve downstream applications such as personalized medicine across diverse populations.
Collapse
Affiliation(s)
- Meng Lin
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Danny S Park
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, San Francisco, CA, United States
| | - Noah A Zaitlen
- Department of Neurology and Computational Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Brenna M Henn
- Department of Anthropology, Center for Population Biology and the Genome Center, University of California, Davis, Davis, CA, United States
| | - Christopher R Gignoux
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| |
Collapse
|
82
|
Abdulkareem M, Petersen SE. The Promise of AI in Detection, Diagnosis, and Epidemiology for Combating COVID-19: Beyond the Hype. Front Artif Intell 2021; 4:652669. [PMID: 34056579 PMCID: PMC8160471 DOI: 10.3389/frai.2021.652669] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 04/13/2021] [Indexed: 12/24/2022] Open
Abstract
COVID-19 has created enormous suffering, affecting lives, and causing deaths. The ease with which this type of coronavirus can spread has exposed weaknesses of many healthcare systems around the world. Since its emergence, many governments, research communities, commercial enterprises, and other institutions and stakeholders around the world have been fighting in various ways to curb the spread of the disease. Science and technology have helped in the implementation of policies of many governments that are directed toward mitigating the impacts of the pandemic and in diagnosing and providing care for the disease. Recent technological tools, artificial intelligence (AI) tools in particular, have also been explored to track the spread of the coronavirus, identify patients with high mortality risk and diagnose patients for the disease. In this paper, areas where AI techniques are being used in the detection, diagnosis and epidemiological predictions, forecasting and social control for combating COVID-19 are discussed, highlighting areas of successful applications and underscoring issues that need to be addressed to achieve significant progress in battling COVID-19 and future pandemics. Several AI systems have been developed for diagnosing COVID-19 using medical imaging modalities such as chest CT and X-ray images. These AI systems mainly differ in their choices of the algorithms for image segmentation, classification and disease diagnosis. Other AI-based systems have focused on predicting mortality rate, long-term patient hospitalization and patient outcomes for COVID-19. AI has huge potential in the battle against the COVID-19 pandemic but successful practical deployments of these AI-based tools have so far been limited due to challenges such as limited data accessibility, the need for external evaluation of AI models, the lack of awareness of AI experts of the regulatory landscape governing the deployment of AI tools in healthcare, the need for clinicians and other experts to work with AI experts in a multidisciplinary context and the need to address public concerns over data collection, privacy, and protection. Having a dedicated team with expertise in medical data collection, privacy, access and sharing, using federated learning whereby AI scientists hand over training algorithms to the healthcare institutions to train models locally, and taking full advantage of biomedical data stored in biobanks can alleviate some of problems posed by these challenges. Addressing these challenges will ultimately accelerate the translation of AI research into practical and useful solutions for combating pandemics.
Collapse
Affiliation(s)
- Musa Abdulkareem
- Barts Heart Centre, Barts Health National Health Service (NHS) Trust, London, United Kingdom
- National Institute for Health Research (NIHR) Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
- Health Data Research UK, London, United Kingdom
| | - Steffen E. Petersen
- Barts Heart Centre, Barts Health National Health Service (NHS) Trust, London, United Kingdom
- National Institute for Health Research (NIHR) Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
- Health Data Research UK, London, United Kingdom
- The Alan Turing Institute, London, United Kingdom
| |
Collapse
|
83
|
Wang H, Ye M, Fu Y, Dong A, Zhang M, Feng L, Zhu X, Bo W, Jiang L, Griffin CH, Liang D, Wu R. Modeling genome-wide by environment interactions through omnigenic interactome networks. Cell Rep 2021; 35:109114. [PMID: 33979624 DOI: 10.1016/j.celrep.2021.109114] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 03/11/2021] [Accepted: 04/21/2021] [Indexed: 10/21/2022] Open
Abstract
How genes interact with the environment to shape phenotypic variation and evolution is a fundamental question intriguing to biologists from various fields. Existing linear models built on single genes are inadequate to reveal the complexity of genotype-environment (G-E) interactions. Here, we develop a conceptual model for mechanistically dissecting G-E interplay by integrating previously disconnected theories and methods. Under this integration, evolutionary game theory, developmental modularity theory, and a variable selection method allow us to reconstruct environment-induced, maximally informative, sparse, and casual multilayer genetic networks. We design and conduct two mapping experiments by using a desert-adapted tree species to validate the biological application of the model proposed. The model identifies previously uncharacterized molecular mechanisms that mediate trees' response to saline stress. Our model provides a tool to comprehend the genetic architecture of trait variation and evolution and trace the information flow of each gene toward phenotypes within omnigenic networks.
Collapse
Affiliation(s)
- Haojie Wang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Meixia Ye
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Yaru Fu
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Ang Dong
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Miaomiao Zhang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Li Feng
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Xuli Zhu
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Wenhao Bo
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Libo Jiang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Christopher H Griffin
- Applied Research Laboratory, The Pennsylvania State University, University Park, PA 16802, USA
| | - Dan Liang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Rongling Wu
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China; Center for Statistical Genetics, Departments of Public Health Sciences and Statistics, The Pennsylvania State University, Hershey, PA 17033, USA.
| |
Collapse
|
84
|
Villicaña S, Bell JT. Genetic impacts on DNA methylation: research findings and future perspectives. Genome Biol 2021; 22:127. [PMID: 33931130 PMCID: PMC8086086 DOI: 10.1186/s13059-021-02347-6] [Citation(s) in RCA: 83] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 04/09/2021] [Indexed: 12/17/2022] Open
Abstract
Multiple recent studies highlight that genetic variants can have strong impacts on a significant proportion of the human DNA methylome. Methylation quantitative trait loci, or meQTLs, allow for the exploration of biological mechanisms that underlie complex human phenotypes, with potential insights for human disease onset and progression. In this review, we summarize recent milestones in characterizing the human genetic basis of DNA methylation variation over the last decade, including heritability findings and genome-wide identification of meQTLs. We also discuss challenges in this field and future areas of research geared to generate insights into molecular processes underlying human complex traits.
Collapse
Affiliation(s)
- Sergio Villicaña
- Department of Twin Research and Genetic Epidemiology, St. Thomas’ Hospital, King’s College London, 3rd Floor, South Wing, Block D, London, SE1 7EH UK
| | - Jordana T. Bell
- Department of Twin Research and Genetic Epidemiology, St. Thomas’ Hospital, King’s College London, 3rd Floor, South Wing, Block D, London, SE1 7EH UK
| |
Collapse
|
85
|
Schmitz LL, Goodwin J, Miao J, Lu Q, Conley D. The impact of late-career job loss and genetic risk on body mass index: Evidence from variance polygenic scores. Sci Rep 2021; 11:7647. [PMID: 33828129 PMCID: PMC8027610 DOI: 10.1038/s41598-021-86716-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 03/16/2021] [Indexed: 02/02/2023] Open
Abstract
Unemployment shocks from the COVID-19 pandemic have reignited concerns over the long-term effects of job loss on population health. Past research has highlighted the corrosive effects of unemployment on health and health behaviors. This study examines whether the effects of job loss on changes in body mass index (BMI) are moderated by genetic predisposition using data from the U.S. Health and Retirement Study (HRS). To improve detection of gene-by-environment (G × E) interplay, we interacted layoffs from business closures-a plausibly exogenous environmental exposure-with whole-genome polygenic scores (PGSs) that capture genetic contributions to both the population mean (mPGS) and variance (vPGS) of BMI. Results show evidence of genetic moderation using a vPGS (as opposed to an mPGS) and indicate genome-wide summary measures of phenotypic plasticity may further our understanding of how environmental stimuli modify the distribution of complex traits in a population.
Collapse
Affiliation(s)
- Lauren L Schmitz
- Robert M. La Follette School of Public Affairs, University of Wisconsin-Madison, 1225 Observatory Drive, Madison, WI, 53706, USA.
| | - Julia Goodwin
- Department of Sociology, University of Wisconsin-Madison, Madison, WI, USA
| | - Jiacheng Miao
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
| | - Qiongshi Lu
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
- Department of Statistics, University of Wisconsin-Madison, Madison, WI, USA
| | - Dalton Conley
- Department of Sociology, Princeton University & NBER, Princeton, NJ, USA
| |
Collapse
|
86
|
Secolin R, Gonsales MC, Rocha CS, Naslavsky M, De Marco L, Bicalho MAC, Vazquez VL, Zatz M, Silva WA, Lopes-Cendes I. Exploring a Region on Chromosome 8p23.1 Displaying Positive Selection Signals in Brazilian Admixed Populations: Additional Insights Into Predisposition to Obesity and Related Disorders. Front Genet 2021; 12:636542. [PMID: 33841501 PMCID: PMC8027303 DOI: 10.3389/fgene.2021.636542] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 03/04/2021] [Indexed: 11/13/2022] Open
Abstract
We recently reported a deviation of local ancestry on the chromosome (ch) 8p23.1, which led to positive selection signals in a Brazilian population sample. The deviation suggested that the genetic variability of candidate genes located on ch 8p23.1 may have been evolutionarily advantageous in the early stages of the admixture process. In the present work, we aim to extend the previous work by studying additional Brazilian admixed individuals and examining DNA sequencing data from the ch 8p23.1 candidate region. Thus, we inferred the local ancestry of 125 exomes from individuals born in five towns within the Southeast region of Brazil (São Paulo, Campinas, Barretos, and Ribeirão Preto located in the state of São Paulo and Belo Horizonte, the capital of the state of Minas Gerais), and compared to data from two public Brazilian reference genomic databases, BIPMed and ABraOM, and with information from the 1000 Genomes Project phase 3 and gnomAD databases. Our results revealed that ancestry is similar among individuals born in the five Brazilian towns assessed; however, an increased proportion of sub-Saharan African ancestry was observed in individuals from Belo Horizonte. In addition, individuals from the five towns considered, as well as those from the ABRAOM dataset, had the same overrepresentation of Native-American ancestry on the ch 8p23.1 locus that was previously reported for the BIPMed reference sample. Sequencing analysis of ch 8p23.1 revealed the presence of 442 non-synonymous variants, including frameshift, inframe deletion, start loss, stop gain, stop loss, and splicing site variants, which occurred in 24 genes. Among these genes, 13 were associated with obesity, type II diabetes, lipid levels, and waist circumference (PRAG1, MFHAS1, PPP1R3B, TNKS, MSRA, PRSS55, RP1L1, PINX1, MTMR9, FAM167A, BLK, GATA4, and CTSB). These results strengthen the hypothesis that a set of variants located on ch 8p23.1 that result from positive selection during early admixture events may influence obesity-related disease predisposition in admixed individuals of the Brazilian population. Furthermore, we present evidence that the exploration of local ancestry deviation in admixed individuals may provide information with the potential to be translated into health care improvement.
Collapse
Affiliation(s)
- Rodrigo Secolin
- Department of Medical Genetics and Genomic Medicine, Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), University of Campinas - UNICAMP, Campinas, Brazil
| | - Marina C Gonsales
- Department of Medical Genetics and Genomic Medicine, Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), University of Campinas - UNICAMP, Campinas, Brazil
| | - Cristiane S Rocha
- Department of Medical Genetics and Genomic Medicine, Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), University of Campinas - UNICAMP, Campinas, Brazil
| | - Michel Naslavsky
- Departament of Genetics and Evolutive Biology, Human Genome and Stem Cell Research Center, Institute of Bioscience, University of São Paulo (USP), São Paulo, Brazil
| | - Luiz De Marco
- Department of Surgery, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Maria A C Bicalho
- Department of Clinical Medicine, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Vinicius L Vazquez
- Molecular Oncology Research Center (CPOM) - Barretos Cancer Hospital, Barretos, Brazil
| | - Mayana Zatz
- Departament of Genetics and Evolutive Biology, Human Genome and Stem Cell Research Center, Institute of Bioscience, University of São Paulo (USP), São Paulo, Brazil
| | - Wilson A Silva
- Department of Genetics, Ribeirão Preto Medical School, University of São Paulo at Ribeirão Preto (USP), Ribeirão Preto, Brazil
| | - Iscia Lopes-Cendes
- Department of Medical Genetics and Genomic Medicine, Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), University of Campinas - UNICAMP, Campinas, Brazil
| |
Collapse
|
87
|
Hannon E, Mansell G, Walker E, Nabais MF, Burrage J, Kepa A, Best-Lane J, Rose A, Heck S, Moffitt TE, Caspi A, Arseneault L, Mill J. Assessing the co-variability of DNA methylation across peripheral cells and tissues: Implications for the interpretation of findings in epigenetic epidemiology. PLoS Genet 2021; 17:e1009443. [PMID: 33739972 PMCID: PMC8011804 DOI: 10.1371/journal.pgen.1009443] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 03/31/2021] [Accepted: 02/23/2021] [Indexed: 02/01/2023] Open
Abstract
Most epigenome-wide association studies (EWAS) quantify DNA methylation (DNAm) in peripheral tissues such as whole blood to identify positions in the genome where variation is statistically associated with a trait or exposure. As whole blood comprises a mix of cell types, it is unclear whether trait-associated DNAm variation is specific to an individual cellular population. We collected three peripheral tissues (whole blood, buccal epithelial and nasal epithelial cells) from thirty individuals. Whole blood samples were subsequently processed using fluorescence-activated cell sorting (FACS) to purify five constituent cell-types (monocytes, granulocytes, CD4+ T cells, CD8+ T cells, and B cells). DNAm was profiled in all eight sample-types from each individual using the Illumina EPIC array. We identified significant differences in both the level and variability of DNAm between different sample types, and DNAm data-derived estimates of age and smoking were found to differ dramatically across sample types from the same individual. We found that for the majority of loci variation in DNAm in individual blood cell types was only weakly predictive of variance in DNAm measured in whole blood, although the proportion of variance explained was greater than that explained by either buccal or nasal epithelial samples. Covariation across sample types was much higher for DNAm sites influenced by genetic factors. Overall, we observe that DNAm variation in whole blood is additively influenced by a combination of the major blood cell types. For a subset of sites, however, variable DNAm detected in whole blood can be attributed to variation in a single blood cell type providing potential mechanistic insight about EWAS findings. Our results suggest that associations between whole blood DNAm and traits or exposures reflect differences in multiple cell types and our data will facilitate the interpretation of findings in epigenetic epidemiology. As epigenetic variation is cell-type specific, an ongoing challenge in epigenetic epidemiology is how to interpret studies performed using bulk tissue (for example, whole blood) which comprises a mix of different cell types. In this study, we identified major differences in DNA methylation (DNAm) across multiple peripheral tissues and different blood cell types, with each sample type being characterized by a unique signature across multiple genomic loci. We demonstrate how these differences influence commonly used prediction scores derived from DNAm data for age and tobacco smoking, with estimates for the same individual being highly variable across tissues and cell types. Our results enabled us to assess the extent to which variable DNAm in each individual blood cell type relates to variation measured in whole blood. We found that although individual blood cell types predict more of the variation in DNAm in whole blood compared to buccal and nasal epithelial cells, the actual proportion of variance explained is relatively small, except for at sites where DNAm is under genetic control. Our data indicate that for most sites variation in multiple blood cell types additively combines to drive variation in DNAm measured in whole blood. Of note, for a subset of sites, variation in DNAm detected in whole blood can be attributed to a specific blood cell type, potentially facilitating the interpretation of EWAS findings.
Collapse
Affiliation(s)
- Eilis Hannon
- University of Exeter Medical School, University of Exeter Medical School, University of Exeter, Exeter, United Kingdom
| | - Georgina Mansell
- University of Exeter Medical School, University of Exeter Medical School, University of Exeter, Exeter, United Kingdom
| | - Emma Walker
- University of Exeter Medical School, University of Exeter Medical School, University of Exeter, Exeter, United Kingdom
| | - Marta F Nabais
- University of Exeter Medical School, University of Exeter Medical School, University of Exeter, Exeter, United Kingdom
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Joe Burrage
- University of Exeter Medical School, University of Exeter Medical School, University of Exeter, Exeter, United Kingdom
| | - Agnieszka Kepa
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Janis Best-Lane
- Section of Anaesthetics, Pain Medicine and Intensive Care Medicine, Department of Surgery and Cancer, Imperial College London and Imperial College Healthcare NHS Trust, London, United Kingdom
- Imperial Clinical Trials Unit, Imperial College London, London, United Kingdom
| | - Anna Rose
- BRC Flow Cytometry Platform, NIHR GSTT/KCL Comprehensive Biomedical Research Centre, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Suzanne Heck
- Biomedical Research Centre at Guy's and St Thomas' Hospitals and King's College London, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Terrie E Moffitt
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Department of Psychology and Neuroscience, Duke University, Durham, United States of America
- Center for Genomic and Computational Biology, Duke University, Durham, United States of America
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, United States of America
| | - Avshalom Caspi
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Department of Psychology and Neuroscience, Duke University, Durham, United States of America
- Center for Genomic and Computational Biology, Duke University, Durham, United States of America
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, United States of America
| | - Louise Arseneault
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Jonathan Mill
- University of Exeter Medical School, University of Exeter Medical School, University of Exeter, Exeter, United Kingdom
| |
Collapse
|
88
|
Marderstein AR, Davenport ER, Kulm S, Van Hout CV, Elemento O, Clark AG. Leveraging phenotypic variability to identify genetic interactions in human phenotypes. Am J Hum Genet 2021; 108:49-67. [PMID: 33326753 DOI: 10.1016/j.ajhg.2020.11.016] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Accepted: 11/23/2020] [Indexed: 12/13/2022] Open
Abstract
Although thousands of loci have been associated with human phenotypes, the role of gene-environment (GxE) interactions in determining individual risk of human diseases remains unclear. This is partly because of the severe erosion of statistical power resulting from the massive number of statistical tests required to detect such interactions. Here, we focus on improving the power of GxE tests by developing a statistical framework for assessing quantitative trait loci (QTLs) associated with the trait means and/or trait variances. When applying this framework to body mass index (BMI), we find that GxE discovery and replication rates are significantly higher when prioritizing genetic variants associated with the variance of the phenotype (vQTLs) compared to when assessing all genetic variants. Moreover, we find that vQTLs are enriched for associations with other non-BMI phenotypes having strong environmental influences, such as diabetes or ulcerative colitis. We show that GxE effects first identified in quantitative traits such as BMI can be used for GxE discovery in disease phenotypes such as diabetes. A clear conclusion is that strong GxE interactions mediate the genetic contribution to body weight and diabetes risk.
Collapse
Affiliation(s)
- Andrew R Marderstein
- Tri-Institutional Program in Computational Biology & Medicine, Weill Cornell Medicine, New York, NY 10021, USA; Institute of Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA; Department of Computational Biology, Cornell University, Ithaca, NY 14850, USA
| | - Emily R Davenport
- Department of Biology, Huck Institutes of the Life Sciences, Institute for Computational and Data Sciences, Pennsylvania State University, University Park, PA 16802, USA
| | - Scott Kulm
- Institute of Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | | | - Olivier Elemento
- Institute of Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA.
| | - Andrew G Clark
- Department of Computational Biology, Cornell University, Ithaca, NY 14850, USA.
| |
Collapse
|
89
|
Koomar T, Thomas TR, Pottschmidt NR, Lutter M, Michaelson JJ. Estimating the Prevalence and Genetic Risk Mechanisms of ARFID in a Large Autism Cohort. Front Psychiatry 2021; 12:668297. [PMID: 34177659 PMCID: PMC8221394 DOI: 10.3389/fpsyt.2021.668297] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 05/07/2021] [Indexed: 12/27/2022] Open
Abstract
This study is the first genetically-informed investigation of avoidant/restrictive food intake disorder (ARFID), an eating disorder that profoundly impacts quality of life for those affected. ARFID is highly comorbid with autism, and we provide the first estimate of its prevalence in a large and phenotypically diverse autism cohort (a subsample of the SPARK study, N = 5,157 probands). This estimate, 21% (at a balanced accuracy 80%), is at the upper end of previous estimates from studies based on clinical samples, suggesting under-diagnosis and potentially lack of awareness among caretakers and clinicians. Although some studies suggest a decrease of disordered eating symptoms by age 6, our estimates indicate that up to 17% (at a balanced accuracy 87%) of parents of autistic children are also at heightened risk for ARFID, suggesting a lifelong risk for disordered eating. We were also able to provide the first estimates of narrow-sense heritability (h2) for ARFID risk, at 0.45. Genome-wide association revealed a single hit near ZSWIM6, a gene previously implicated in neurodevelopmental conditions. While, the current sample was not well-powered for GWAS, effect size and heritability estimates allowed us to project the sample sizes necessary to more robustly discover ARFID-linked loci via common variants. Further genetic analysis using polygenic risk scores (PRS) affirmed genetic links to autism as well as neuroticism and metabolic syndrome.
Collapse
Affiliation(s)
- Tanner Koomar
- Department of Psychiatry, The University of Iowa, Iowa City, IA, United States
| | - Taylor R Thomas
- Department of Psychiatry, The University of Iowa, Iowa City, IA, United States
| | - Natalie R Pottschmidt
- Department of Psychology, Pennsylvania State University, State College, PA, United States
| | - Michael Lutter
- Eating Recovery Center of San Antonio, San Antonio, TX, United States
| | - Jacob J Michaelson
- Department of Psychiatry, The University of Iowa, Iowa City, IA, United States
| |
Collapse
|
90
|
Subramaniam S, Nadeau J. The Mechanistic Metamorphosis. WIREs Mech Dis 2020; 13:e1517. [PMID: 33369203 DOI: 10.1002/wsbm.1517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Shankar Subramaniam
- Departments of Bioengineering, Computer Science & Engineering, and Cellular & Molecular Medicine, UC San Diego, La Jolla, CA, USA
| | - Joseph Nadeau
- Center for Molecular Medicine, Maine Medical Research Institute, Scarborough, ME, USA
| |
Collapse
|
91
|
How wide is the application of genetic big data in biomedicine. Biomed Pharmacother 2020; 133:111074. [PMID: 33378973 DOI: 10.1016/j.biopha.2020.111074] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 11/16/2020] [Accepted: 11/27/2020] [Indexed: 12/17/2022] Open
Abstract
In the era of big data, massive genetic data, as a new industry, has quickly swept almost all industries, especially the pharmaceutical industry. As countries around the world start to build their own gene banks, scientists study the data to explore the origins and migration of humans. Moreover, big data encourage the development of cancer therapy and bring good news to cancer patients. Big datum has been involved in the study of many diseases, and it has been found that analyzing diseases at the gene level can lead to more beneficial treatment options than ordinary treatments. This review will introduce the development of extensive data in medical research from the perspective of big data and tumor, neurological and psychiatric diseases, cardiovascular diseases, other applications and the development direction of big data in medicine.
Collapse
|
92
|
Grenn FP, Kim JJ, Makarious MB, Iwaki H, Illarionova A, Brolin K, Kluss JH, Schumacher‐Schuh AF, Leonard H, Faghri F, Billingsley K, Krohn L, Hall A, Diez‐Fairen M, Periñán MT, Foo JN, Sandor C, Webber C, Fiske BK, Gibbs JR, Nalls MA, Singleton AB, Bandres‐Ciga S, Reed X, Blauwendraat C. The Parkinson's Disease Genome-Wide Association Study Locus Browser. Mov Disord 2020; 35:2056-2067. [PMID: 32864809 PMCID: PMC7754106 DOI: 10.1002/mds.28197] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 05/27/2020] [Accepted: 06/10/2020] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Parkinson's disease (PD) is a neurodegenerative disease with an often complex component identifiable by genome-wide association studies. The most recent large-scale PD genome-wide association studies have identified more than 90 independent risk variants for PD risk and progression across more than 80 genomic regions. One major challenge in current genomics is the identification of the causal gene(s) and variant(s) at each genome-wide association study locus. The objective of the current study was to create a tool that would display data for relevant PD risk loci and provide guidance with the prioritization of causal genes and potential mechanisms at each locus. METHODS We included all significant genome-wide signals from multiple recent PD genome-wide association studies including themost recent PD risk genome-wide association study, age-at-onset genome-wide association study, progression genome-wide association study, and Asian population PD risk genome-wide association study. We gathered data for all genes 1 Mb up and downstream of each variant to allow users to assess which gene(s) are most associated with the variant of interest based on a set of self-ranked criteria. Multiple databases were queried for each gene to collect additional causal data. RESULTS We created a PD genome-wide association study browser tool (https://pdgenetics.shinyapps.io/GWASBrowser/) to assist the PD research community with the prioritization of genes for follow-up functional studies to identify potential therapeutic targets. CONCLUSIONS Our PD genome-wide association study browser tool provides users with a useful method of identifying potential causal genes at all known PD risk loci from large-scale PD genome-wide association studies. We plan to update this tool with new relevant data as sample sizes increase and new PD risk loci are discovered. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This article has been contributed to by US Government employees and their work is in the public domain in the USA.
Collapse
Affiliation(s)
- Francis P. Grenn
- Laboratory of NeurogeneticsNational Institute on Aging, National Institutes of HealthBethesdaMarylandUSA
| | - Jonggeol J. Kim
- Laboratory of NeurogeneticsNational Institute on Aging, National Institutes of HealthBethesdaMarylandUSA
| | - Mary B. Makarious
- Laboratory of NeurogeneticsNational Institute on Aging, National Institutes of HealthBethesdaMarylandUSA
| | - Hirotaka Iwaki
- Laboratory of NeurogeneticsNational Institute on Aging, National Institutes of HealthBethesdaMarylandUSA
- Data Tecnica InternationalGlen EchoMarylandUSA
| | | | - Kajsa Brolin
- Lund UniversityTranslational Neurogenetics Unit, Department of Experimental Medical ScienceLundSweden
| | - Jillian H. Kluss
- Laboratory of NeurogeneticsNational Institute on Aging, National Institutes of HealthBethesdaMarylandUSA
| | | | - Hampton Leonard
- Laboratory of NeurogeneticsNational Institute on Aging, National Institutes of HealthBethesdaMarylandUSA
- Data Tecnica InternationalGlen EchoMarylandUSA
| | - Faraz Faghri
- Laboratory of NeurogeneticsNational Institute on Aging, National Institutes of HealthBethesdaMarylandUSA
- Data Tecnica InternationalGlen EchoMarylandUSA
| | - Kimberley Billingsley
- Laboratory of NeurogeneticsNational Institute on Aging, National Institutes of HealthBethesdaMarylandUSA
| | - Lynne Krohn
- Department of Human GeneticsMcGill UniversityMontrealQuebecCanada
| | - Ashley Hall
- Department of Molecular and Clinical PharmacologyInstitute of Translational Medicine, University of LiverpoolLiverpoolUK
| | - Monica Diez‐Fairen
- Fundació Docència i Recerca Mútua Terrassa and Movement Disorders Unit, Department of NeurologyUniversity Hospital Mútua TerrassaBarcelonaSpain
| | - Maria Teresa Periñán
- Unidad de Trastornos del Movimiento, Servicio de Neurología y Neurofisiología Clínica, Instituto de Biomedicina de SevillaHospital Universitario Virgen del Rocío/CSIC/Universidad de SevillaSevilleSpain
| | - Jia Nee Foo
- Lee Kong Chian School of MedicineNanyang Technological University SingaporeSingaporeSingapore
- Human GeneticsGenome Institute of Singapore, A*STARSingaporeSingapore
| | - Cynthia Sandor
- UK Dementia Research Institute, Cardiff UniversityCardiffUK
| | - Caleb Webber
- UK Dementia Research Institute, Cardiff UniversityCardiffUK
| | - Brian K. Fiske
- The Michael J. Fox Foundation for Parkinson's Research, Grand Central StationNew YorkNYUSA
| | - J. Raphael Gibbs
- Laboratory of NeurogeneticsNational Institute on Aging, National Institutes of HealthBethesdaMarylandUSA
| | - Mike A. Nalls
- Laboratory of NeurogeneticsNational Institute on Aging, National Institutes of HealthBethesdaMarylandUSA
- Data Tecnica InternationalGlen EchoMarylandUSA
| | - Andrew B. Singleton
- Laboratory of NeurogeneticsNational Institute on Aging, National Institutes of HealthBethesdaMarylandUSA
| | - Sara Bandres‐Ciga
- Laboratory of NeurogeneticsNational Institute on Aging, National Institutes of HealthBethesdaMarylandUSA
| | - Xylena Reed
- Laboratory of NeurogeneticsNational Institute on Aging, National Institutes of HealthBethesdaMarylandUSA
| | - Cornelis Blauwendraat
- Laboratory of NeurogeneticsNational Institute on Aging, National Institutes of HealthBethesdaMarylandUSA
| | | |
Collapse
|
93
|
Nag A, McCarthy MI, Mahajan A. Large-Scale Analyses Provide No Evidence for Gene-Gene Interactions Influencing Type 2 Diabetes Risk. Diabetes 2020; 69:2518-2522. [PMID: 32826294 PMCID: PMC7576558 DOI: 10.2337/db20-0224] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 08/18/2020] [Indexed: 11/15/2022]
Abstract
A growing number of genetic loci have been shown to influence individual predisposition to type 2 diabetes (T2D). Despite longstanding interest in understanding whether nonlinear interactions between these risk variants additionally influence T2D risk, the ability to detect significant gene-gene interaction (GGI) effects has been limited to date. To increase power to detect GGI effects, we combined recent advances in the fine-mapping of causal T2D risk variants with the increased sample size available within UK Biobank (375,736 unrelated European participants, including 16,430 with T2D). In addition to conventional single variant-based analysis, we used a complementary polygenic score-based approach, which included partitioned T2D risk scores that capture biological processes relevant to T2D pathophysiology. Nevertheless, we found no evidence in support of GGI effects influencing T2D risk. The current study was powered to detect interactions between common variants with odds ratios >1.2, so these findings place limits on the contribution of GGIs to the overall heritability of T2D.
Collapse
Affiliation(s)
- Abhishek Nag
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, U.K
| | - Mark I McCarthy
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, U.K
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, U.K
- NIHR Oxford Biomedical Research Centre, Churchill Hospital, Oxford, U.K
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, U.K.
| |
Collapse
|
94
|
Sun C, Kovacs P, Guiu-Jurado E. Genetics of Obesity in East Asians. Front Genet 2020; 11:575049. [PMID: 33193685 PMCID: PMC7606890 DOI: 10.3389/fgene.2020.575049] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 09/17/2020] [Indexed: 12/31/2022] Open
Abstract
Obesity has become a public health problem worldwide. Compared with Europe, people in Asia tend to suffer from type 2 diabetes with a lower body mass index (BMI). Genome-wide association studies (GWASs) have identified over 750 loci associated with obesity. Although the majority of GWAS results were conducted in individuals of European ancestry, a recent GWAS in individuals of Asian ancestry has made a significant contribution to the identification of obesity susceptibility loci. Indeed, owing to the multifactorial character of obesity with a strong environmental component, the revealed loci may have distinct contributions in different ancestral genetic backgrounds and in different environments as presented through diet and exercise among other factors. Uncovering novel, yet unrevealed genes in non-European ancestries may further contribute to explaining the missing heritability for BMI. In this review, we aimed to summarize recent advances in obesity genetics in individuals of Asian ancestry. We therefore compared proposed mechanisms underlying susceptibility loci for obesity associated with individuals of European and Asian ancestries and discussed whether known genetic variants might explain ethnic differences in obesity risk. We further acknowledged that GWAS implemented in individuals of Asian ancestries have not only validated the potential role of previously specified obesity susceptibility loci but also exposed novel ones, which have been missed in the initial genetic studies in individuals of European ancestries. Thus, multi-ethnic studies have a great potential not only to contribute to a better understanding of the complex etiology of human obesity but also potentially of ethnic differences in the prevalence of obesity, which may ultimately pave new avenues in more targeted and personalized obesity treatments.
Collapse
Affiliation(s)
| | - Peter Kovacs
- Medical Department III – Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
| | | |
Collapse
|
95
|
Xu F, Fu Y, Sun TY, Jiang Z, Miao Z, Shuai M, Gou W, Ling CW, Yang J, Wang J, Chen YM, Zheng JS. The interplay between host genetics and the gut microbiome reveals common and distinct microbiome features for complex human diseases. MICROBIOME 2020; 8:145. [PMID: 33032658 PMCID: PMC7545574 DOI: 10.1186/s40168-020-00923-9] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 09/13/2020] [Indexed: 05/11/2023]
Abstract
BACKGROUND Interest in the interplay between host genetics and the gut microbiome in complex human diseases is increasing, with prior evidence mainly being derived from animal models. In addition, the shared and distinct microbiome features among complex human diseases remain largely unclear. RESULTS This analysis was based on a Chinese population with 1475 participants. We estimated the SNP-based heritability, which suggested that Desulfovibrionaceae and Odoribacter had significant heritability estimates (0.456 and 0.476, respectively). We performed a microbiome genome-wide association study to identify host genetic variants associated with the gut microbiome. We then conducted bidirectional Mendelian randomization analyses to examine the potential causal associations between the gut microbiome and complex human diseases. We found that Saccharibacteria could potentially decrease the concentration of serum creatinine and increase the estimated glomerular filtration rate. On the other hand, atrial fibrillation, chronic kidney disease and prostate cancer, as predicted by host genetics, had potential causal effects on the abundance of some specific gut microbiota. For example, atrial fibrillation increased the abundance of Burkholderiales and Alcaligenaceae and decreased the abundance of Lachnobacterium, Bacteroides coprophilus, Barnesiellaceae, an undefined genus in the family Veillonellaceae and Mitsuokella. Further disease-microbiome feature analysis suggested that systemic lupus erythematosus and chronic myeloid leukaemia shared common gut microbiome features. CONCLUSIONS These results suggest that different complex human diseases share common and distinct gut microbiome features, which may help reshape our understanding of disease aetiology in humans. Video Abstract.
Collapse
Affiliation(s)
- Fengzhe Xu
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
| | - Yuanqing Fu
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Ting-Yu Sun
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Zengliang Jiang
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Zelei Miao
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
| | - Menglei Shuai
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
| | - Wanglong Gou
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
| | - Chu-Wen Ling
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Jian Yang
- Institute for Molecular Bioscience, The University of Queensland, QLD, Brisbane, Australia
- Institute for Advanced Research, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
| | - Jun Wang
- CAS Key Laboratory for Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China.
| | - Yu-Ming Chen
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China.
| | - Ju-Sheng Zheng
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China.
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.
| |
Collapse
|
96
|
Kerin M, Marchini J. Inferring Gene-by-Environment Interactions with a Bayesian Whole-Genome Regression Model. Am J Hum Genet 2020; 107:698-713. [PMID: 32888427 PMCID: PMC7536582 DOI: 10.1016/j.ajhg.2020.08.009] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Accepted: 08/11/2020] [Indexed: 01/05/2023] Open
Abstract
The contribution of gene-by-environment (GxE) interactions for many human traits and diseases is poorly characterized. We propose a Bayesian whole-genome regression model for joint modeling of main genetic effects and GxE interactions in large-scale datasets, such as the UK Biobank, where many environmental variables have been measured. The method is called LEMMA (Linear Environment Mixed Model Analysis) and estimates a linear combination of environmental variables, called an environmental score (ES), that interacts with genetic markers throughout the genome. The ES provides a readily interpretable way to examine the combined effect of many environmental variables. The ES can be used both to estimate the proportion of phenotypic variance attributable to GxE effects and to test for GxE effects at genetic variants across the genome. GxE effects can induce heteroskedasticity in quantitative traits, and LEMMA accounts for this by using robust standard error estimates when testing for GxE effects. When applied to body mass index, systolic blood pressure, diastolic blood pressure, and pulse pressure in the UK Biobank, we estimate that 9.3%, 3.9%, 1.6%, and 12.5%, respectively, of phenotypic variance is explained by GxE interactions and that low-frequency variants explain most of this variance. We also identify three loci that interact with the estimated environmental scores (−log10p>7.3).
Collapse
|
97
|
Domingue BW, Trejo S, Armstrong-Carter E, Tucker-Drob EM. Interactions between Polygenic Scores and Environments: Methodological and Conceptual Challenges. SOCIOLOGICAL SCIENCE 2020; 7:465-486. [PMID: 36091972 PMCID: PMC9455807 DOI: 10.15195/v7.a19] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Interest in the study of gene-environment interaction has recently grown due to the sudden availability of molecular genetic data-in particular, polygenic scores-in many long-running longitudinal studies. Identifying and estimating statistical interactions comes with several analytic and inferential challenges; these challenges are heightened when used to integrate observational genomic and social science data. We articulate some of these key challenges, provide new perspectives on the study of gene-environment interactions, and end by offering some practical guidance for conducting research in this area. Given the sudden availability of well-powered polygenic scores, we anticipate a substantial increase in research testing for interaction between such scores and environments. The issues we discuss, if not properly addressed, may impact the enduring scientific value of gene-environment interaction studies.
Collapse
|
98
|
Szoke A, Pignon B, Boster S, Jamain S, Schürhoff F. Schizophrenia: Developmental Variability Interacts with Risk Factors to Cause the Disorder: Nonspecific Variability-Enhancing Factors Combine with Specific Risk Factors to Cause Schizophrenia. Bioessays 2020; 42:e2000038. [PMID: 32864753 DOI: 10.1002/bies.202000038] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 08/10/2020] [Indexed: 12/31/2022]
Abstract
A new etiological model is proposed for schizophrenia that combines variability-enhancing nonspecific factors acting during development with more specific risk factors. This model is better suited than the current etiological models of schizophrenia, based on the risk factors paradigm, for predicting and/or explaining several important findings about schizophrenia: high co-morbidity rates, low specificity of many risk factors, and persistence in the population of the associated genetic polymorphisms. Compared with similar models, e.g., de-canalization, common psychopathology factor, sexual-selection, or differential sensitivity to the environment, this proposal is more general and integrative. Recently developed research methods have proven the existence of genetic and environmental factors that enhance developmental variability. Applying such methods to newly collected or already available data can allow for testing the hypotheses upon which this model is built. If validated, this model may change the understanding of the etiology of schizophrenia, the research models, and preventionbrk paradigms.
Collapse
Affiliation(s)
- Andrei Szoke
- INSERM, U955, Translational NeuroPsychiatry Lab, Créteil, 94000, France.,AP-HP, DHU IMPACT, Pôle de Psychiatrie, Hôpitaux Universitaires Henri-Mondor, Créteil, 94000, France.,Fondation FondaMental, Créteil, 94000, France.,UPEC, Faculté de Médecine, Université Paris-Est Créteil, Créteil, 94000, France
| | - Baptiste Pignon
- INSERM, U955, Translational NeuroPsychiatry Lab, Créteil, 94000, France.,AP-HP, DHU IMPACT, Pôle de Psychiatrie, Hôpitaux Universitaires Henri-Mondor, Créteil, 94000, France.,Fondation FondaMental, Créteil, 94000, France.,UPEC, Faculté de Médecine, Université Paris-Est Créteil, Créteil, 94000, France
| | | | - Stéphane Jamain
- INSERM, U955, Translational NeuroPsychiatry Lab, Créteil, 94000, France.,UPEC, Faculté de Médecine, Université Paris-Est Créteil, Créteil, 94000, France
| | - Franck Schürhoff
- INSERM, U955, Translational NeuroPsychiatry Lab, Créteil, 94000, France.,AP-HP, DHU IMPACT, Pôle de Psychiatrie, Hôpitaux Universitaires Henri-Mondor, Créteil, 94000, France.,Fondation FondaMental, Créteil, 94000, France.,UPEC, Faculté de Médecine, Université Paris-Est Créteil, Créteil, 94000, France
| |
Collapse
|
99
|
Mellors T, Withers JB, Ameli A, Jones A, Wang M, Zhang L, Sanchez HN, Santolini M, Do Valle I, Sebek M, Cheng F, Pappas DA, Kremer JM, Curtis JR, Johnson KJ, Saleh A, Ghiassian SD, Akmaev VR. Clinical Validation of a Blood-Based Predictive Test for Stratification of Response to Tumor Necrosis Factor Inhibitor Therapies in Rheumatoid Arthritis Patients. NETWORK AND SYSTEMS MEDICINE 2020. [DOI: 10.1089/nsm.2020.0007] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Affiliation(s)
| | | | - Asher Ameli
- Scipher Medicine, Waltham, Massachusetts, USA
| | - Alex Jones
- Scipher Medicine, Waltham, Massachusetts, USA
| | | | - Lixia Zhang
- Scipher Medicine, Waltham, Massachusetts, USA
| | | | - Marc Santolini
- Center for Research and Interdisciplinarity (CRI), University Paris Descartes, Paris, France
| | - Italo Do Valle
- Center for Complex Network Research, Department of Physics, Northeastern University, Boston, Massachusetts, USA
| | - Michael Sebek
- Center for Complex Network Research, Department of Physics, Northeastern University, Boston, Massachusetts, USA
| | - Feixiong Cheng
- Center for Complex Network Research, Department of Physics, Northeastern University, Boston, Massachusetts, USA
| | - Dimitrios A. Pappas
- Division of Rheumatology, College of Physicians and Surgeons, Columbia University, New York, New York, USA
- CORRONA, LCC, Waltham, Massachusetts, USA
| | - Joel M. Kremer
- CORRONA, LCC, Waltham, Massachusetts, USA
- Albany Medical College, The Center for Rheumatology, Albany, New York, USA
| | - Jeffery R. Curtis
- Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | | | - Alif Saleh
- Scipher Medicine, Waltham, Massachusetts, USA
| | | | | |
Collapse
|
100
|
Carson C, Lawson HA. Genetic background and diet affect brown adipose gene coexpression networks associated with metabolic phenotypes. Physiol Genomics 2020; 52:223-233. [PMID: 32338175 PMCID: PMC7311675 DOI: 10.1152/physiolgenomics.00003.2020] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Revised: 04/01/2020] [Accepted: 04/22/2020] [Indexed: 01/10/2023] Open
Abstract
Adipose is a dynamic endocrine organ that is critical for regulating metabolism and is highly responsive to nutritional environment. Brown adipose tissue is an exciting potential therapeutic target; however, there are no systematic studies of gene-by-environment interactions affecting function of this organ. We leveraged a weighted gene coexpression network analysis to identify transcriptional networks in brown adipose tissue from LG/J and SM/J inbred mice fed high- or low-fat diets and correlate these networks with metabolic phenotypes. We identified eight primary gene network modules associated with variation in obesity and diabetes-related traits. Four modules were enriched for metabolically relevant processes such as immune and cytokine response, cell division, peroxisome functions, and organic molecule metabolic processes. The relative expression of genes in these modules is highly dependent on both genetic background and dietary environment. Genes in the immune/cytokine response and cell division modules are particularly highly expressed in high fat-fed SM/J mice, which show unique brown adipose-dependent remission of diabetes. The interconnectivity of genes in these modules is also heavily dependent on diet and strain, with most genes showing both higher expression and coexpression under the same context. We highlight several genes of interest, Col28a1, Cyp26b1, Bmp8b, and Ngef, that have distinct expression patterns among strain-by-diet contexts and fall under metabolic quantitative trait loci previously mapped in an F16 generation of an advanced intercross between LG/J and SM/J. Each of these genes have some connection to obesity and diabetes-related traits, but have not been studied in brown adipose tissue. Our results provide important insights into the relationship between brown adipose and systemic metabolism by being the first gene-by-environment study of brown adipose transcriptional networks.
Collapse
Affiliation(s)
- Caryn Carson
- Department of Genetics, Washington University School of Medicine, Saint Louis, Missouri
| | - Heather A Lawson
- Department of Genetics, Washington University School of Medicine, Saint Louis, Missouri
| |
Collapse
|