1
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Mackay TFC, Anholt RRH. Pleiotropy, epistasis and the genetic architecture of quantitative traits. Nat Rev Genet 2024; 25:639-657. [PMID: 38565962 DOI: 10.1038/s41576-024-00711-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/14/2024] [Indexed: 04/04/2024]
Abstract
Pleiotropy (whereby one genetic polymorphism affects multiple traits) and epistasis (whereby non-linear interactions between genetic polymorphisms affect the same trait) are fundamental aspects of the genetic architecture of quantitative traits. Recent advances in the ability to characterize the effects of polymorphic variants on molecular and organismal phenotypes in human and model organism populations have revealed the prevalence of pleiotropy and unexpected shared molecular genetic bases among quantitative traits, including diseases. By contrast, epistasis is common between polymorphic loci associated with quantitative traits in model organisms, such that alleles at one locus have different effects in different genetic backgrounds, but is rarely observed for human quantitative traits and common diseases. Here, we review the concepts and recent inferences about pleiotropy and epistasis, and discuss factors that contribute to similarities and differences between the genetic architecture of quantitative traits in model organisms and humans.
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Affiliation(s)
- Trudy F C Mackay
- Center for Human Genetics, Clemson University, Greenwood, SC, USA.
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, USA.
| | - Robert R H Anholt
- Center for Human Genetics, Clemson University, Greenwood, SC, USA.
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, USA.
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2
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Qi G, Chhetri SB, Ray D, Dutta D, Battle A, Bhattacharjee S, Chatterjee N. Genome-wide large-scale multi-trait analysis characterizes global patterns of pleiotropy and unique trait-specific variants. Nat Commun 2024; 15:6985. [PMID: 39143063 PMCID: PMC11324957 DOI: 10.1038/s41467-024-51075-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Accepted: 07/29/2024] [Indexed: 08/16/2024] Open
Abstract
Genome-wide association studies (GWAS) have found widespread evidence of pleiotropy, but characterization of global patterns of pleiotropy remain highly incomplete due to insufficient power of current approaches. We develop fastASSET, a method that allows efficient detection of variant-level pleiotropic association across many traits. We analyze GWAS summary statistics of 116 complex traits of diverse types collected from the GRASP repository and large GWAS Consortia. We identify 2293 independent loci and find that the lead variants in nearly all these loci (~99%) to be associated with ≥ 2 traits (median = 6). We observe that degree of pleiotropy estimated from our study predicts that observed in the UK Biobank for a much larger number of traits (K = 4114) (correlation = 0.43, p-value < 2.2 × 10 - 16 ). Follow-up analyzes of 21 trait-specific variants indicate their link to the expression in trait-related tissues for a small number of genes involved in relevant biological processes. Our findings provide deeper insight into the nature of pleiotropy and leads to identification of highly trait-specific susceptibility variants.
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Affiliation(s)
- Guanghao Qi
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
| | - Surya B Chhetri
- Department of Biomedical Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Debashree Ray
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Diptavo Dutta
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Alexis Battle
- Department of Biomedical Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Genetic Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Samsiddhi Bhattacharjee
- Biotechnology Research and Innovation Council-National Institute of Biomedical Genomics (BRIC-NIBMG), Kalyani, India.
| | - Nilanjan Chatterjee
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
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3
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Hu X, Cai M, Xiao J, Wan X, Wang Z, Zhao H, Yang C. Benchmarking Mendelian randomization methods for causal inference using genome-wide association study summary statistics. Am J Hum Genet 2024; 111:1717-1735. [PMID: 39059387 DOI: 10.1016/j.ajhg.2024.06.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 06/26/2024] [Accepted: 06/26/2024] [Indexed: 07/28/2024] Open
Abstract
Mendelian randomization (MR), which utilizes genetic variants as instrumental variables (IVs), has gained popularity as a method for causal inference between phenotypes using genetic data. While efforts have been made to relax IV assumptions and develop new methods for causal inference in the presence of invalid IVs due to confounding, the reliability of MR methods in real-world applications remains uncertain. Instead of using simulated datasets, we conducted a benchmark study evaluating 16 two-sample summary-level MR methods using real-world genetic datasets to provide guidelines for the best practices. Our study focused on the following crucial aspects: type I error control in the presence of various confounding scenarios (e.g., population stratification, pleiotropy, and family-level confounders like assortative mating), the accuracy of causal effect estimates, replicability, and power. By comprehensively evaluating the performance of compared methods over one thousand exposure-outcome trait pairs, our study not only provides valuable insights into the performance and limitations of the compared methods but also offers practical guidance for researchers to choose appropriate MR methods for causal inference.
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Affiliation(s)
- Xianghong Hu
- School of Mathematical Sciences, Institute of Statistical Sciences, Shenzhen University, Shenzhen 518060, China; Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong, China; Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China
| | - Mingxuan Cai
- Department of Biostatistics, City University of Hong Kong, Hong Kong, China
| | - Jiashun Xiao
- Shenzhen Research Institute of Big Data, Shenzhen 518172, China
| | - Xiaomeng Wan
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong, China; Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China
| | - Zhiwei Wang
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong, China; Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06520, USA.
| | - Can Yang
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong, China; Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China; Big Data Bio-Intelligence Lab, The Hong Kong University of Science and Technology, Hong Kong SAR, China.
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4
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Bridges EC, Torsney C, Bates TC, Luciano M. Childhood Reading Ability and Pain in Childhood Through to Midlife. THE JOURNAL OF PAIN 2024; 25:104518. [PMID: 38580099 DOI: 10.1016/j.jpain.2024.03.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 03/21/2024] [Accepted: 03/25/2024] [Indexed: 04/07/2024]
Abstract
Dyslexia and pain have recently been shown to correlate on a genetic level, but there has been little exploration of this association on the phenotypic level despite reports of increased pain in Attention Deficit Hyperactivity Disorder, which commonly co-occurs with dyslexia. In this study we test for an association between reading ability, which is the primary feature of dyslexia, and pain both in childhood and adulthood. Logistic regression modeling was used to test associations between reading ability in childhood and pain from childhood to midlife in a large UK birth cohort; the 1958 National Child Development Study. Associations were found between poor childhood reading ability and increased headache and abdominal pain in childhood, and between poor childhood reading ability and headache, eye pain, back pain, and rheumatism in adulthood. Mediation analyses indicated that socioeconomic status (defined by employment) fully mediated the association between poor reading ability in childhood and back pain at age 42. By contrast, the association between reading ability and eye pain acted independently of socioeconomic status. Different mechanisms were thus indicated for the association of reading with different pain types, including manual labor and a potential shared biological pathway. PERSPECTIVE: This study found a relationship between poor reading ability in childhood and pain in childhood and adulthood. Those with reading difficulties should be monitored for pain symptoms. Future research may uncover shared biological mechanisms, increasing our understanding of pain and potential treatments.
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Affiliation(s)
- Elinor C Bridges
- School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh, UK
| | - Carole Torsney
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, UK; Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh, UK
| | - Timothy C Bates
- School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh, UK
| | - Michelle Luciano
- School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh, UK
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5
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Qin Y, Lei C, Lin T, Han X, Wang D. Identification of Potential Drug Targets for Myopia Through Mendelian Randomization. Invest Ophthalmol Vis Sci 2024; 65:13. [PMID: 39110588 PMCID: PMC11314700 DOI: 10.1167/iovs.65.10.13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 06/18/2024] [Indexed: 08/11/2024] Open
Abstract
Purpose The purpose of this study was to identify potential drug targets for myopia and explore underlying mechanisms. Methods Mendelian randomization (MR) was implemented to assess the effect of 2684 pharmacologically targetable genes in the blood and retina on the risk of myopia from a genomewide association study (GWAS) for age-at-onset of spectacle wearing-inferred mean spherical equivalent (MSE; discovery cohort, N = 287,448, European), which was further validated in a GWAS for autorefraction-measured MSE (replication cohort, N = 95,619, European). The reliability of the identified significant potential targets was strengthened by colocalization analysis. Additionally, enrichment analysis, protein-protein interaction network, and molecular docking were performed to explore the functional roles and the druggability of these targets. Results This systematic drug target identification has unveiled 6 putative genetically causal targets for myopia-CD34, CD55, Wnt3, LCAT, BTN3A1, and TSSK6-each backed by colocalization evidence in adult blood eQTL datasets. Functional analysis found that dopaminergic neuron differentiation, cell adhesion, Wnt signaling pathway, and plasma lipoprotein-associated pathways may be involved in myopia pathogenesis. Finally, drug prediction and molecular docking corroborated the pharmacological value of these targets with LCAT demonstrating the strongest binding affinity. Conclusions Our study not only opens new avenues for the development of therapeutic interventions for myopia but may also help to understand the underlying mechanisms of myopia.
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Affiliation(s)
- Yimin Qin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Chengcheng Lei
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Tianfeng Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Xiaotong Han
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Decai Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
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6
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Li X, Guo Y, Liang H, Wang J, Qi L. Genome-wide association analysis of hypertension and epigenetic aging reveals shared genetic architecture and identifies novel risk loci. Sci Rep 2024; 14:17792. [PMID: 39090212 PMCID: PMC11294447 DOI: 10.1038/s41598-024-68751-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Accepted: 07/26/2024] [Indexed: 08/04/2024] Open
Abstract
Hypertension is a disease associated with epigenetic aging. However, the pathogenic mechanism underlying this relationship remains unclear. We aimed to characterize the shared genetic architecture of hypertension and epigenetic aging, and identify novel risk loci. Leveraging genome-wide association studies (GWAS) summary statistics of hypertension (129,909 cases and 354,689 controls) and four epigenetic clocks (N = 34,710), we investigated genetic architectures and genetic overlap using bivariate casual mixture model and conditional/conjunctional false discovery rate methods. Functional gene-sets pathway analyses were performed by functional mapping and gene annotation (FUMA) protocol. Hypertension was polygenic with 2.8 K trait-influencing genetic variants. We observed cross-trait genetic enrichment and genetic overlap between hypertension and all four measures of epigenetic aging. Further, we identified 32 distinct genomic loci jointly associated with hypertension and epigenetic aging. Notably, rs1849209 was shared between hypertension and three epigenetic clocks (HannumAge, IEAA, and PhenoAge). The shared loci exhibited a combination of concordant and discordant allelic effects. Functional gene-set analyses revealed significant enrichment in biological pathways related to sensory perception of smell and nervous system processes. We observed genetic overlaps with mixed effect directions between hypertension and all four epigenetic aging measures, and identified 32 shared distinct loci with mixed effect directions, 25 of which were novel for hypertension. Shared genes enriched in biological pathways related to olfaction.
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Affiliation(s)
- Xin Li
- The Sino-Russian Medical Research Center of Jinan University, The Institute of Chronic Disease of Jinan University, The First Affiliated Hospital of Jinan University, Guangzhou, 511436, China
| | - Yu Guo
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150086, China
| | - Haihai Liang
- The Sino-Russian Medical Research Center of Jinan University, The Institute of Chronic Disease of Jinan University, The First Affiliated Hospital of Jinan University, Guangzhou, 511436, China.
- Department of Pharmacology (State-Province Key Laboratories of Biomedicine-Pharmaceutics of China, Key Laboratory of Cardiovascular Research, Ministry of Education), College of Pharmacy, Harbin Medical University, Harbin, 150086, China.
| | - Jinghao Wang
- Department of Pharmacy, the First Affiliated Hospital, Jinan University, Guangzhou, 510630, Guangdong, China.
- The Guangzhou Key Laboratory of Basic and Translational Research on Chronic Diseases, Jinan University, Guangzhou, 510630, Guangdong, China.
| | - Lishuang Qi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
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7
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Lai D, Zhang M, Green N, Abreu M, Schwantes-An TH, Parker C, Zhang S, Jin F, Sun A, Zhang P, Edenberg H, Liu Y, Foroud T. Genome-wide meta-analyses of cross substance use disorders in European, African, and Latino ancestry populations. RESEARCH SQUARE 2024:rs.3.rs-3955955. [PMID: 39070649 PMCID: PMC11275984 DOI: 10.21203/rs.3.rs-3955955/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Genetic risks for substance use disorders (SUDs) are due to both SUD-specific and SUD-shared genes. We performed the largest multivariate analyses to date to search for SUD-shared genes using samples of European (EA), African (AA), and Latino (LA) ancestries. By focusing on variants having cross-SUD and cross-ancestry concordant effects, we identified 45 loci. Through gene-based analyses, gene mapping, and gene prioritization, we identified 250 SUD-shared genes. These genes are highly expressed in amygdala, cortex, hippocampus, hypothalamus, and thalamus, primarily in neuronal cells. Cross-SUD concordant variants explained ~ 50% of the heritability of each SUD in EA. The top 5% individuals having the highest polygenic scores were approximately twice as likely to have SUDs as others in EA and LA. Polygenic scores had higher predictability in females than in males in EA. Using real-world data, we identified five drugs targeting identified SUD-shared genes that may be repurposed to treat SUDs.
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Affiliation(s)
- Dongbing Lai
- Department of Medical and Molecular Genetics, Indiana University School of Medicine
| | | | | | | | - Tae-Hwi Schwantes-An
- Department of Medical and Molecular Genetics, Indiana University School of Medicine
| | | | | | | | - Anna Sun
- Indiana University School of Medicine
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8
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Lu Q, Zhou Y, Qian Q, Chen Z, Tan Q, Chen H, Yin F, Wang Y, Liu Z, Tian P, Sun D. Whole-exome sequencing identifies high-confidence genes for tic disorders in a Chinese Han population. Clin Chim Acta 2024; 561:119759. [PMID: 38880274 DOI: 10.1016/j.cca.2024.119759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 05/23/2024] [Accepted: 06/02/2024] [Indexed: 06/18/2024]
Abstract
BACKGROUND Tic disorder (TD) is a polygenic neurodevelopmental disorder with high susceptibility. However, identifying high-confidence risk genes has been challenging due to poor replication across multiple studies. METHODS Whole-exome sequencing was performed on 390 TD patients and 372 unaffected individuals in a Chinese Han population. Analysis of variance, burden analysis and in silico prediction were used to identify candidate genes for TD. To facilitate data analysis and to focus on high-confidence genes, we defined a panel of 160 genes as known causal or candidate TD genes from previous studies. Gene enrichment and protein-protein interaction analysis were utilized to detect potential novel TD risk genes. RESULTS Totally, 14 variants across 12 known TD candidate genes were considered potential susceptibility variants. Ten variants across 10 known TD candidate genes were identified as potential disease-causing variants. Burden analysis identified variants of 28 known genes were significantly excess in TD patients. In addition, 354 previously unproven TD genes are over-represented in patients. Genes enriched in the PI3K-Akt signaling, sphingolipid metabolism and serotonergic synaptic pathways, as well as those interacting with FN1, were considered potential new candidate genes for TD. CONCLUSIONS This is the largest WES study focusing on TD patients in a Chinese Han population. Several variants recurring in our cohort were identified as high-confidence risk loci for TD. Moreover, we provided potential new risk genes that may be prioritized for further investigation.
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Affiliation(s)
- Qing Lu
- Department of Neurology, Wuhan Children's Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China
| | - Yong Zhou
- Puluo (Wuhan) Medical Biotechnology Co., LTD, Wuhan 430070, China; Department of Marketing, Wuhan Kindstar Clinical Diagnostic Institute Co., Ltd., Wuhan 430000, China
| | - Qiaoqiao Qian
- Department of Neurology, Wuhan Children's Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China
| | - Zhigang Chen
- Puluo (Wuhan) Medical Biotechnology Co., LTD, Wuhan 430070, China
| | - Qianqian Tan
- Puluo (Wuhan) Medical Biotechnology Co., LTD, Wuhan 430070, China; Department of Marketing, Wuhan Kindstar Clinical Diagnostic Institute Co., Ltd., Wuhan 430000, China
| | - Haiyun Chen
- Puluo (Wuhan) Medical Biotechnology Co., LTD, Wuhan 430070, China; Department of Marketing, Wuhan Kindstar Clinical Diagnostic Institute Co., Ltd., Wuhan 430000, China
| | - Fan Yin
- Puluo (Wuhan) Medical Biotechnology Co., LTD, Wuhan 430070, China; Department of Marketing, Wuhan Kindstar Clinical Diagnostic Institute Co., Ltd., Wuhan 430000, China
| | - Yue Wang
- Department of Pediatrics, First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Zhisheng Liu
- Department of Neurology, Wuhan Children's Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China
| | - Peichao Tian
- Department of Pediatrics, First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China.
| | - Dan Sun
- Department of Neurology, Wuhan Children's Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China.
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9
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Zhao C. Genome wide association study gateway-transitioning variants from association to causality in complex diseases. Sleep 2024; 47:zsae116. [PMID: 38752386 DOI: 10.1093/sleep/zsae116] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/12/2024] Open
Affiliation(s)
- Chen Zhao
- Institute of Human Genetics, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
- Neurogenetic Systems Analysis Group, Institute of Neurogenomics, Helmholtz Munich, Germany
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10
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Danieli PP, Hoang N, Selvanayagam T, Yang A, Breetvelt E, Tabbers M, Cohen C, Aelvoet AS, Trost B, Ward T, Semotiuk K, Durno C, Aronson M, Cohen Z, Dekker E, Vorstman J. Autistic traits in youth with familial adenomatous polyposis: A Dutch-Canadian case-control study. Am J Med Genet B Neuropsychiatr Genet 2024:e32999. [PMID: 38967411 DOI: 10.1002/ajmg.b.32999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 05/25/2024] [Accepted: 06/10/2024] [Indexed: 07/06/2024]
Abstract
This study investigated the neurodevelopmental impact of pathogenic adenomatous polyposis coli (APC) gene variants in patients with familial adenomatous polyposis (FAP), a cancer predisposition syndrome. We hypothesized that certain pathogenic APC variants result in behavioral-cognitive challenges. We compared 66 FAP patients (cases) and 34 unaffected siblings (controls) to explore associations between APC variants and behavioral and cognitive challenges. Our findings indicate that FAP patients exhibited higher Social Responsiveness Scale (SRS) scores, suggesting a greater prevalence of autistic traits when compared to unaffected siblings (mean 53.8 vs. 47.4, Wilcoxon p = 0.018). The distribution of SRS scores in cases suggested a bimodal pattern, potentially linked to the location of the APC variant, with scores increasing from the 5' to 3' end of the gene (Pearson's r = 0.33, p = 0.022). While we observed a trend toward lower educational attainment in cases, this difference was not statistically significant. This study is the first to explore the connection between APC variant location and neurodevelopmental traits in FAP, expanding our understanding of the genotype-phenotype correlation. Our results emphasize the importance of clinical assessment for autistic traits in FAP patients, shedding light on the potential role of APC gene variants in these behavioral and cognitive challenges.
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Affiliation(s)
- Polina Perlman Danieli
- Department of Psychiatry, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Ny Hoang
- Department of Genetic Counselling, The Hospital for Sick Children, Toronto, Ontario, Canada
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Thanuja Selvanayagam
- Department of Genetic Counselling, The Hospital for Sick Children, Toronto, Ontario, Canada
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Alvin Yang
- Department of Psychiatry, The Hospital for Sick Children, Toronto, Ontario, Canada
- The Familial Gastrointestinal Cancer Registry at the Zane Cohen Centre for Digestive Diseases, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Elemi Breetvelt
- Department of Psychiatry, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Merit Tabbers
- Department of Pediatrics, Emma Children's Hospital, Amsterdam, The Netherlands
- Department of Pediatric Gastroenterology, Hepatology and Nutrition, Emma Children's Hospital, Amsterdam Reproduction and Development and Amsterdam Gastroenterology Endocrinology Metabolism Research Institutes, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Christine Cohen
- Department of Gastroenterology and Hepatology, Amsterdam UMC Location University of Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Amsterdam, The Netherlands
- Department of Gastroenterology & Hepatology, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Arthur S Aelvoet
- Department of Gastroenterology and Hepatology, Amsterdam UMC Location University of Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Amsterdam, The Netherlands
- Department of Gastroenterology & Hepatology, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Brett Trost
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Thomas Ward
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- The Familial Gastrointestinal Cancer Registry at the Zane Cohen Centre for Digestive Diseases, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Kara Semotiuk
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- The Familial Gastrointestinal Cancer Registry at the Zane Cohen Centre for Digestive Diseases, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Carol Durno
- The Familial Gastrointestinal Cancer Registry at the Zane Cohen Centre for Digestive Diseases, Mount Sinai Hospital, Toronto, Ontario, Canada
- Division of Gastroenterology/Hepatology & Nutrition, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Paediatrics, University of Toronto, Toronto, Ontario, Canada
| | - Melyssa Aronson
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- The Familial Gastrointestinal Cancer Registry at the Zane Cohen Centre for Digestive Diseases, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Zane Cohen
- The Familial Gastrointestinal Cancer Registry at the Zane Cohen Centre for Digestive Diseases, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Evelien Dekker
- Department of Gastroenterology and Hepatology, Amsterdam UMC Location University of Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Amsterdam, The Netherlands
- Department of Gastroenterology & Hepatology, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Jacob Vorstman
- Department of Psychiatry, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
- Developmental Psychopathology, The Hospital for Sick Children, Toronto, Ontario, Canada
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11
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Gao Z, Zhao Q, Hastie T. PathGPS: discover shared genetic architecture using GWAS summary data. Biometrics 2024; 80:ujae060. [PMID: 39005072 PMCID: PMC11247175 DOI: 10.1093/biomtc/ujae060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 12/28/2023] [Accepted: 07/05/2024] [Indexed: 07/16/2024]
Abstract
The increasing availability and scale of biobanks and "omic" datasets bring new horizons for understanding biological mechanisms. PathGPS is an exploratory data analysis tool to discover genetic architectures using Genome Wide Association Studies (GWAS) summary data. PathGPS is based on a linear structural equation model where traits are regulated by both genetic and environmental pathways. PathGPS decouples the genetic and environmental components by contrasting the GWAS associations of "signal" genes with those of "noise" genes. From the estimated genetic component, PathGPS then extracts genetic pathways via principal component and factor analysis, leveraging the low-rank and sparse properties. In addition, we provide a bootstrap aggregating ("bagging") algorithm to improve stability under data perturbation and hyperparameter tuning. When applied to a metabolomics dataset and the UK Biobank, PathGPS confirms several known gene-trait clusters and suggests multiple new hypotheses for future investigations.
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Affiliation(s)
- Zijun Gao
- Marshall Business School, University of Southern California, Los Angeles CA, 90089, United States
| | - Qingyuan Zhao
- Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, Cambridge, CB3 0WB, United Kingdom
| | - Trevor Hastie
- Department of Statistics and Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, United States
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12
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St John ME, Dunker JC, Richards EJ, Romero S, Martin CH. Parallel evolution of integrated craniofacial traits in trophic specialist pupfishes. Ecol Evol 2024; 14:e11640. [PMID: 38979003 PMCID: PMC11228360 DOI: 10.1002/ece3.11640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 05/14/2024] [Accepted: 06/13/2024] [Indexed: 07/10/2024] Open
Abstract
Populations may adapt to similar environments via parallel or non-parallel genetic changes, but the frequency of these alternative mechanisms and underlying contributing factors are still poorly understood outside model systems. We used QTL mapping to investigate the genetic basis of highly divergent craniofacial traits between the scale-eater (Cyprinodon desquamator) and molluscivore (C. brontotheroides) pupfish adapting to two different hypersaline lake environments on San Salvador Island, Bahamas. We lab-reared F2 scale-eater x molluscivore intercrosses from two different lake populations, estimated linkage maps, scanned for significant QTL for 29 skeletal and craniofacial traits, female mate preference, and sex. We compared the location of QTL between lakes to quantify parallel and non-parallel genetic changes. We detected significant QTL for six craniofacial traits in at least one lake. However, nearly all shared QTL loci were associated with a different craniofacial trait within each lake. Therefore, our estimate of parallel evolution of craniofacial genetic architecture could range from one out of six identical trait QTL (low parallelism) to five out of six integrated trait QTL (high parallelism). We suggest that pleiotropy and trait integration can affect estimates of parallel evolution, particularly within rapid radiations. We also observed increased adaptive introgression in shared QTL regions, suggesting that gene flow contributed to parallel evolution. Overall, our results suggest that the same genomic regions may contribute to parallel adaptation across integrated suites of craniofacial traits, rather than specific traits, and highlight the need for a more expansive definition of parallel evolution.
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Affiliation(s)
| | - Julia C Dunker
- Department of Integrative Biology University of California Berkeley California USA
| | - Emilie J Richards
- Department of Ecology, Evolution and Behavior University of Minnesota Minneapolis Minnesota USA
| | - Stephanie Romero
- Department of Evolution and Ecology University of California Davis California USA
| | - Christopher H Martin
- Department of Integrative Biology University of California Berkeley California USA
- Museum of Vertebrate Zoology University of California Berkeley California USA
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13
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Chen F, Dong X, Yu Z, Zhang Y, Shi Y. The brain-heart axis: Integrative analysis of the shared genetic etiology between neuropsychiatric disorders and cardiovascular disease. J Affect Disord 2024; 355:147-156. [PMID: 38518856 DOI: 10.1016/j.jad.2024.03.098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 03/16/2024] [Accepted: 03/19/2024] [Indexed: 03/24/2024]
Abstract
BACKGROUND Multiple observational studies have reported substantial comorbidity between neuropsychiatric disorders and cardiovascular disease (CVD), but the underlying mechanisms remain largely unknown. METHODS Using GWAS summary datasets of 8 neuropsychiatric disorders and 6 cardiovascular diseases, an integrative analysis incorporating linkage-disequilibrium-score-regression (LDSC), Mendelian randomization (MR), functional mapping and annotation (FUMA), and functional enrichment analysis, was conducted to investigate shared genetic etiology of the brain-heart axis from the whole genome level, single-nucleotide polymorphism (SNP) level, gene level, and biological pathway level. RESULTS In LDSC analysis, 18 pairwise traits between neuropsychiatric disorders and CVD were identified with significant genetic overlaps, revealing extensive genome-wide genetic correlations. In bidirectional MR analysis, 19 pairwise traits were identified with significant causal relationships. Genetic liabilities to neuropsychiatric disorders, particularly attention-deficit hyperactivity disorder and major depressive disorder, conferred extensive significant causal effects on the risk of CVD, while hypertension seemed to be a risk factor for multiple neuropsychiatric disorders, with no significant heterogeneity or pleiotropy. In FUMA analysis, 13 shared independent significant SNPs and 887 overlapping protein-coding genes were detected between neuropsychiatric disorders and CVD. With GO and KEEG functional enrichment analysis, biological pathways of the brain-heart axis were highly concentrated in neurotransmitter synaptic transmission, lipid metabolism, aldosterone synthesis and secretion, glutathione metabolism, and MAPK signaling pathway. CONCLUSION Extensive genetic correlations and genetic overlaps between neuropsychiatric disorders and CVD were identified in this study, which might provide some new insights into the brain-heart axis and the therapeutic targets in clinical practice.
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Affiliation(s)
- Feifan Chen
- Department of Neonatology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Rare Diseases in Infection and Immunity, Chongqing 400014, China.
| | - Xinyu Dong
- Department of Neurosurgery, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, Chongqing Key Laboratory of Pediatrics, Children's Hospital of Chongqing Medical University, Chongqing 400014, China.
| | - Zhiwei Yu
- Department of Neurology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing 400014, China.
| | - Yihan Zhang
- Department of Neonatology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Rare Diseases in Infection and Immunity, Chongqing 400014, China.
| | - Yuan Shi
- Department of Neonatology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Rare Diseases in Infection and Immunity, Chongqing 400014, China.
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14
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Cao G, Luo Q, Wu Y, Chen G. Inflammatory bowel disease and rheumatoid arthritis share a common genetic structure. Front Immunol 2024; 15:1359857. [PMID: 38938570 PMCID: PMC11208460 DOI: 10.3389/fimmu.2024.1359857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 05/31/2024] [Indexed: 06/29/2024] Open
Abstract
Background The comorbidity rate of inflammatory bowel disease (IBD) and rheumatoid arthritis (RA) is high; nevertheless, the reasons behind this high rate remain unclear. Their similar genetic makeup probably contributes to this comorbidity. Methods Based on data obtained from the genome-wide association study of IBD and RA, we first assessed an overall genetic association by performing the linkage disequilibrium score regression (LDSC) analysis. Further, a local correlation analysis was performed by estimating the heritability in summary statistics. Next, the causality between the two diseases was analyzed by two-sample Mendelian randomization (MR). A genetic overlap was analyzed by the conditional/conjoint false discovery rate (cond/conjFDR) method.LDSC with specific expression of gene analysis was performed to identify related tissues between the two diseases. Finally, GWAS multi-trait analysis (MTAG) was also carried out. Results IBD and RA are correlated at the genomic level, both overall and locally. The MR results suggested that IBD induced RA. We identified 20 shared loci between IBD and RA on the basis of a conjFDR of <0.01. Additionally, we identified two tissues, namely spleen and small intestine terminal ileum, which were commonly associated with both IBD and RA. Conclusion Herein, we proved the presence of a polygenic overlap between the genetic makeup of IBD and RA and provided new insights into the genetic architecture and mechanisms underlying the high comorbidity between these two diseases.
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Affiliation(s)
- Guoling Cao
- Department of Anorectal Surgery, The People’s Hospital of Cangnan, Wenzhou, China
| | - Qinghua Luo
- Clinical Medical College, Jiangxi University of Chinese Medicine, Nanchang, China
| | - Yunxiang Wu
- Department of Anorectal Surgery, Affiliated Hospital of Jiangxi University of Chinese Medicine, Nanchang, China
| | - Guanghua Chen
- Department of Anorectal Surgery, Affiliated Hospital of Jiangxi University of Chinese Medicine, Nanchang, China
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15
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Ren Y, Wu C, Zhou H, Hu X, Miao Z. Dual-extraction modeling: A multi-modal deep-learning architecture for phenotypic prediction and functional gene mining of complex traits. PLANT COMMUNICATIONS 2024:101002. [PMID: 38872306 DOI: 10.1016/j.xplc.2024.101002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 05/27/2024] [Accepted: 06/11/2024] [Indexed: 06/15/2024]
Abstract
Despite considerable advances in extracting crucial insights from bio-omics data to unravel the intricate mechanisms underlying complex traits, the absence of a universal multi-modal computational tool with robust interpretability for accurate phenotype prediction and identification of trait-associated genes remains a challenge. This study introduces the dual-extraction modeling (DEM) approach, a multi-modal deep-learning architecture designed to extract representative features from heterogeneous omics datasets, enabling the prediction of complex trait phenotypes. Through comprehensive benchmarking experiments, we demonstrate the efficacy of DEM in classification and regression prediction of complex traits. DEM consistently exhibits superior accuracy, robustness, generalizability, and flexibility. Notably, we establish its effectiveness in predicting pleiotropic genes that influence both flowering time and rosette leaf number, underscoring its commendable interpretability. In addition, we have developed user-friendly software to facilitate seamless utilization of DEM's functions. In summary, this study presents a state-of-the-art approach with the ability to effectively predict qualitative and quantitative traits and identify functional genes, confirming its potential as a valuable tool for exploring the genetic basis of complex traits.
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Affiliation(s)
- Yanlin Ren
- State Key Laboratory for Crop Stress Resistance and High-Efficiency Production, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Chenhua Wu
- State Key Laboratory for Crop Stress Resistance and High-Efficiency Production, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - He Zhou
- State Key Laboratory for Crop Stress Resistance and High-Efficiency Production, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Xiaona Hu
- College of Chemistry & Pharmacy, Northwest A&F University, Yangling, Shaanxi 712100, China.
| | - Zhenyan Miao
- State Key Laboratory for Crop Stress Resistance and High-Efficiency Production, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi 712100, China; Key Laboratory of Biology and Genetics Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture, Northwest A&F University, Yangling, Shaanxi 712100, China.
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16
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Chen LG, Tubbs JD, Liu Z, Thach TQ, Sham PC. Mendelian randomization: causal inference leveraging genetic data. Psychol Med 2024; 54:1461-1474. [PMID: 38639006 DOI: 10.1017/s0033291724000321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/20/2024]
Abstract
Mendelian randomization (MR) leverages genetic information to examine the causal relationship between phenotypes allowing for the presence of unmeasured confounders. MR has been widely applied to unresolved questions in epidemiology, making use of summary statistics from genome-wide association studies on an increasing number of human traits. However, an understanding of essential concepts is necessary for the appropriate application and interpretation of MR. This review aims to provide a non-technical overview of MR and demonstrate its relevance to psychiatric research. We begin with the origins of MR and the reasons for its recent expansion, followed by an overview of its statistical methodology. We then describe the limitations of MR, and how these are being addressed by recent methodological advances. We showcase the practical use of MR in psychiatry through three illustrative examples - the connection between cannabis use and psychosis, the link between intelligence and schizophrenia, and the search for modifiable risk factors for depression. The review concludes with a discussion of the prospects of MR, focusing on the integration of multi-omics data and its extension to delineating complex causal networks.
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Affiliation(s)
- Lane G Chen
- Department of Psychiatry, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Justin D Tubbs
- Department of Psychiatry, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Zipeng Liu
- Department of Psychiatry, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Thuan-Quoc Thach
- Department of Psychiatry, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Pak C Sham
- Department of Psychiatry, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Centre for PanorOmic Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China
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17
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Trejo S. Exploring the Fetal Origins Hypothesis Using Genetic Data. SOCIAL FORCES; A SCIENTIFIC MEDIUM OF SOCIAL STUDY AND INTERPRETATION 2024; 102:1555-1581. [PMID: 38638179 PMCID: PMC11021852 DOI: 10.1093/sf/soae018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 10/12/2023] [Accepted: 11/23/2023] [Indexed: 04/20/2024]
Abstract
Birth weight is a robust predictor of valued life course outcomes, emphasizing the importance of prenatal development. But does birth weight act as a proxy for environmental conditions in utero, or do biological processes surrounding birth weight themselves play a role in healthy development? To answer this question, we leverage variation in birth weight that is, within families, orthogonal to prenatal environmental conditions: one's genes. We construct polygenic scores in two longitudinal studies (Born in Bradford, N = 2008; Wisconsin Longitudinal Study, N = 8488) to empirically explore the molecular genetic correlates of birth weight. A 1 standard deviation increase in the polygenic score is associated with an ~100-grams increase in birth weight and a 1.4 pp (22 percent) decrease in low birth weight probability. Sibling comparisons illustrate that this association largely represents a causal effect. The polygenic score-birth weight association is increased for children who spend longer in the womb and whose mothers have higher body mass index, though we find no differences across maternal socioeconomic status. Finally, the polygenic score affects social and cognitive outcomes, suggesting that birth weight is itself related to healthy prenatal development.
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Affiliation(s)
- Sam Trejo
- Princeton University, Department of Sociology and Office of Population Research, United States
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18
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Patel A, DiTraglia FJ, Zuber V, Burgess S. Selecting Invalid Instruments to Improve Mendelian Randomization with Two-Sample Summary Data. Ann Appl Stat 2024; 18:23-aoas1856. [PMID: 38737575 PMCID: PMC7615940 DOI: 10.1214/23-aoas1856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2024]
Abstract
Mendelian randomization (MR) is a widely-used method to estimate the causal relationship between a risk factor and disease. A fundamental part of any MR analysis is to choose appropriate genetic variants as instrumental variables. Genome-wide association studies often reveal that hundreds of genetic variants may be robustly associated with a risk factor, but in some situations investigators may have greater confidence in the instrument validity of only a smaller subset of variants. Nevertheless, the use of additional instruments may be optimal from the perspective of mean squared error even if they are slightly invalid; a small bias in estimation may be a price worth paying for a larger reduction in variance. For this purpose, we consider a method for "focused" instrument selection whereby genetic variants are selected to minimise the estimated asymptotic mean squared error of causal effect estimates. In a setting of many weak and locally invalid instruments, we propose a novel strategy to construct confidence intervals for post-selection focused estimators that guards against the worst case loss in asymptotic coverage. In empirical applications to: (i) validate lipid drug targets; and (ii) investigate vitamin D effects on a wide range of outcomes, our findings suggest that the optimal selection of instruments does not involve only a small number of biologically-justified instruments, but also many potentially invalid instruments.
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Affiliation(s)
| | | | - Verena Zuber
- Department of Biostatistics and Epidemiology, Imperial College London
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge
- Cardiovascular Epidemiology Unit, University of Cambridge
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19
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Deng Q, Song C, Lin S. An adaptive and robust method for multi-trait analysis of genome-wide association studies using summary statistics. Eur J Hum Genet 2024; 32:681-690. [PMID: 37237036 PMCID: PMC11153499 DOI: 10.1038/s41431-023-01389-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 05/01/2023] [Accepted: 05/10/2023] [Indexed: 05/28/2023] Open
Abstract
Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with human traits or diseases in the past decade. Nevertheless, much of the heritability of many traits is still unaccounted for. Commonly used single-trait analysis methods are conservative, while multi-trait methods improve statistical power by integrating association evidence across multiple traits. In contrast to individual-level data, GWAS summary statistics are usually publicly available, and thus methods using only summary statistics have greater usage. Although many methods have been developed for joint analysis of multiple traits using summary statistics, there are many issues, including inconsistent performance, computational inefficiency, and numerical problems when considering lots of traits. To address these challenges, we propose a multi-trait adaptive Fisher method for summary statistics (MTAFS), a computationally efficient method with robust power performance. We applied MTAFS to two sets of brain imaging derived phenotypes (IDPs) from the UK Biobank, including a set of 58 Volumetric IDPs and a set of 212 Area IDPs. Through annotation analysis, the underlying genes of the SNPs identified by MTAFS were found to exhibit higher expression and are significantly enriched in brain-related tissues. Together with results from a simulation study, MTAFS shows its advantage over existing multi-trait methods, with robust performance across a range of underlying settings. It controls type 1 error well and can efficiently handle a large number of traits.
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Affiliation(s)
- Qiaolan Deng
- Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH, USA
- Department of Statistics, College of Arts and Sciences, The Ohio State University, Columbus, OH, USA
| | - Chi Song
- Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH, USA
| | - Shili Lin
- Department of Statistics, College of Arts and Sciences, The Ohio State University, Columbus, OH, USA.
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20
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Peña E, Mas-Bermejo P, Lecube A, Ciudin A, Arenas C, Simó R, Rigla M, Caixàs A, Rosa A. Use of polygenic risk scores to assess weight loss after bariatric surgery: a 5-year follow-up study. J Gastrointest Surg 2024:S1091-255X(24)00485-2. [PMID: 38821212 DOI: 10.1016/j.gassur.2024.05.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 05/19/2024] [Accepted: 05/27/2024] [Indexed: 06/02/2024]
Abstract
BACKGROUND Bariatric surgery (BS) is currently the most effective long-term treatment of severe obesity. However, the interindividual variability observed in surgical outcomes suggests a moderating effect of several factors, including individual genetic background. This study aimed to investigate the contribution of the genetic architecture of body mass index (BMI) to the variability in weight loss outcomes after BS. METHODS A total of 106 patients with severe obesity who underwent Roux-en-Y gastric bypass (RYGB) or sleeve gastrectomy were followed up for 5 years. Changes in BMI (BMIchange) and percentage of total weight loss (%TWL) were evaluated during the postoperative period. Polygenic risk scores (PRSs), including 50 genetic variants, were calculated for each participant to determine their genetic risk of high BMI based on a previous genome-wide association study. Generalized estimating equation models were used to study the role of the individual's polygenic score and other factors on BMIchange and %TWL in the long term after surgery. RESULTS This study found an effect of the polygenic score on %TWL and BMIchange, in which patients with lower scores had better outcomes after surgery than those with higher scores. Furthermore, when analyzing only patients who underwent RYGB, the results were replicated, showing greater weight loss after surgery for patients with lower polygenic scores. DISCUSSION Our results indicate that genetic background assessed with PRSs, along with other individual factors, such as biological sex, age, and preoperative BMI, has an effect on BS outcomes and could represent a useful tool for estimating surgical outcomes in advance.
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Affiliation(s)
- Elionora Peña
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark; Secció de Zoologia i Antropologia Biòlogica, Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain
| | - Patricia Mas-Bermejo
- Secció de Zoologia i Antropologia Biòlogica, Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain; Institut de Biomedicina de la Universitat de Barcelona, Barcelona, Spain
| | - Albert Lecube
- Department of Endocrinology and Nutrition, Arnau de Vilanova University Hospital, Institut de Recerca Biomèdica de Lleida, Universitat de Lleida, Lleida, Spain
| | - Andreea Ciudin
- Diabetes and Metabolism Research Unit, Department of Endocrinology and Nutrition, Hospital Universitari Vall d'Hebron, Vall d'Hebron Institut de Recerca, Universitat Autònoma de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, Madrid, Spain
| | - Concepción Arenas
- Statistics Section of the Department of Genetics, Microbiology and Statistics, Universitat de Barcelona, Barcelona, Spain
| | - Rafael Simó
- Diabetes and Metabolism Research Unit, Department of Endocrinology and Nutrition, Hospital Universitari Vall d'Hebron, Vall d'Hebron Institut de Recerca, Universitat Autònoma de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, Madrid, Spain
| | - Mercedes Rigla
- Department of Endocrinology and Nutrition, Institut d'Investigació i Innovació, Parc Taulí Hospital Universitari, Sabadell, Spain; Departament de Medicina, Universitat Autònoma de Barcelona, Sabadell, Spain
| | - Assumpta Caixàs
- Department of Endocrinology and Nutrition, Institut d'Investigació i Innovació, Parc Taulí Hospital Universitari, Sabadell, Spain; Departament de Medicina, Universitat Autònoma de Barcelona, Sabadell, Spain
| | - Araceli Rosa
- Secció de Zoologia i Antropologia Biòlogica, Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain; Institut de Biomedicina de la Universitat de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica En Red de Salud Mental, Instituto de Salud Carlos III, Madrid, Spain.
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21
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Lai EY, Huang YT. Identifying pleiotropic genes via the composite test amidst the complexity of polygenic traits. Brief Bioinform 2024; 25:bbae327. [PMID: 39007593 PMCID: PMC11247409 DOI: 10.1093/bib/bbae327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 05/29/2024] [Accepted: 06/24/2024] [Indexed: 07/16/2024] Open
Abstract
Identifying the causal relationship between genotype and phenotype is essential to expanding our understanding of the gene regulatory network spanning the molecular level to perceptible traits. A pleiotropic gene can act as a central hub in the network, influencing multiple outcomes. Identifying such a gene involves testing under a composite null hypothesis where the gene is associated with, at most, one trait. Traditional methods such as meta-analyses of top-hit $P$-values and sequential testing of multiple traits have been proposed, but these methods fail to consider the background of genome-wide signals. Since Huang's composite test produces uniformly distributed $P$-values for genome-wide variants under the composite null, we propose a gene-level pleiotropy test that entails combining the aforementioned method with the aggregated Cauchy association test. A polygenic trait involves multiple genes with different functions to co-regulate mechanisms. We show that polygenicity should be considered when identifying pleiotropic genes; otherwise, the associations polygenic traits initiate will give rise to false positives. In this study, we constructed gene-trait functional modules using the results of the proposed pleiotropy tests. Our analysis suite was implemented as an R package PGCtest. We demonstrated the proposed method with an application study of the Taiwan Biobank database and identified functional modules comprising specific genes and their co-regulated traits.
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Affiliation(s)
- En-Yu Lai
- Institute of Statistical Science, Academia Sinica, No.128, Academia Road, Section 2, Nankang, Taipei 11529, Taiwan
| | - Yen-Tsung Huang
- Institute of Statistical Science, Academia Sinica, No.128, Academia Road, Section 2, Nankang, Taipei 11529, Taiwan
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22
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Liu Y, Yuan M, He J, Cai L, Leng A. The Impact of Non-alcohol Fatty Liver Disease on Bone Mineral Density is Mediated by Sclerostin by Mendelian Randomization Study. Calcif Tissue Int 2024; 114:502-512. [PMID: 38555554 DOI: 10.1007/s00223-024-01204-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 02/19/2024] [Indexed: 04/02/2024]
Abstract
Non-alcoholic fatty liver disease (NAFLD) has been found to be associated with osteoporosis (OP) in observational studies. However, the precise causal relationship between NAFLD and OP remains unclear. Here, we used Mendelian randomization (MR) to explore the causal relationship. We selected NAFLD-related single-nucleotide polymorphisms from a genome-wide meta-analysis (8434 cases and 434,770 controls) as instrumental variants. We used inverse variance weighted analysis for the primary MR analysis. Furthermore, we used similar methodologies in parallel investigations of other chronic liver diseases (CLDs). We performed sensitivity analyses to ensure the reliability of the results. We observed a causality between NAFLD and forearm bone mineral density (FABMD) (beta-estimate [β]: - 0.212; p-value: 0.034). We also found that sclerostin can act as a mediator to influence the NAFLD and FABMD pathways to form a mediated MR network (mediated proportion = 8.8%). We also identified indications of causal relationships between other CLDs and OP. However, we were unable to establish any associated mediators. Notably, our analyses did not yield any evidence of pleiotropy. Our findings have implications in the development of preventive and interventional measures aimed at managing low bone mineral density in patients with NAFLD.
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Affiliation(s)
- Yuan Liu
- Department of Gastroenterology, Xiangya Hospital, Central South University, No.88 Xiangya Road, Kaifu District, Changsha, 410000, Hunan Province, China
| | - Mengqin Yuan
- Department of Infectious Diseases, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jian He
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Longjiao Cai
- Department of Gastroenterology, Xiangya Hospital, Central South University, No.88 Xiangya Road, Kaifu District, Changsha, 410000, Hunan Province, China
| | - Aimin Leng
- Department of Gastroenterology, Xiangya Hospital, Central South University, No.88 Xiangya Road, Kaifu District, Changsha, 410000, Hunan Province, China.
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23
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Cao X, Zhang S, Sha Q. A novel method for multiple phenotype association studies based on genotype and phenotype network. PLoS Genet 2024; 20:e1011245. [PMID: 38728360 PMCID: PMC11111089 DOI: 10.1371/journal.pgen.1011245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 05/22/2024] [Accepted: 03/29/2024] [Indexed: 05/12/2024] Open
Abstract
Joint analysis of multiple correlated phenotypes for genome-wide association studies (GWAS) can identify and interpret pleiotropic loci which are essential to understand pleiotropy in diseases and complex traits. Meanwhile, constructing a network based on associations between phenotypes and genotypes provides a new insight to analyze multiple phenotypes, which can explore whether phenotypes and genotypes might be related to each other at a higher level of cellular and organismal organization. In this paper, we first develop a bipartite signed network by linking phenotypes and genotypes into a Genotype and Phenotype Network (GPN). The GPN can be constructed by a mixture of quantitative and qualitative phenotypes and is applicable to binary phenotypes with extremely unbalanced case-control ratios in large-scale biobank datasets. We then apply a powerful community detection method to partition phenotypes into disjoint network modules based on GPN. Finally, we jointly test the association between multiple phenotypes in a network module and a single nucleotide polymorphism (SNP). Simulations and analyses of 72 complex traits in the UK Biobank show that multiple phenotype association tests based on network modules detected by GPN are much more powerful than those without considering network modules. The newly proposed GPN provides a new insight to investigate the genetic architecture among different types of phenotypes. Multiple phenotypes association studies based on GPN are improved by incorporating the genetic information into the phenotype clustering. Notably, it might broaden the understanding of genetic architecture that exists between diagnoses, genes, and pleiotropy.
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Affiliation(s)
- Xuewei Cao
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, United States of America
| | - Shuanglin Zhang
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, United States of America
| | - Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, United States of America
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24
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Fang Z, Jia S, Mou X, Li Z, Hu T, Tu Y, Zhao J, Zhang T, Lin W, Lu Y, Feng C, Xia S. Shared genetic architecture and causal relationship between liver and heart disease. iScience 2024; 27:109431. [PMID: 38523778 PMCID: PMC10959668 DOI: 10.1016/j.isci.2024.109431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 01/08/2024] [Accepted: 03/04/2024] [Indexed: 03/26/2024] Open
Abstract
This study investigates the relationship and genetic mechanisms of liver and heart diseases, focusing on the liver-heart axis (LHA) as a fundamental biological basis. Through genome-wide association study analysis, we explore shared genes and pathways related to LHA. Shared genetic factors are found in 8 out of 20 pairs, indicating genetic correlations. The analysis reveals 53 loci with pleiotropic effects, including 8 loci exhibiting shared causality across multiple traits. Based on SNP-p level tissue-specific multi-marker analysis of genomic annotation (MAGMA) analysis demonstrates significant enrichment of pleiotropy in liver and heart diseases within different cardiovascular tissues and female reproductive appendages. Gene-specific MAGMA analysis identifies 343 pleiotropic genes associated with various traits; these genes show tissue-specific enrichment primarily in the liver, cardiovascular system, and other tissues. Shared risk loci between immune cells and both liver and cardiovascular diseases are also discovered. Mendelian randomization analyses provide support for causal relationships among the investigated trait pairs.
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Affiliation(s)
- Ziyi Fang
- Department of Gastroenterology, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu 322000, China
| | - Sixiang Jia
- Department of Cardiology, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu 322000, China
| | - Xuanting Mou
- Department of Cardiology, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu 322000, China
| | - Zhe Li
- Department of Cardiology, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu 322000, China
| | - Tianli Hu
- Department of Cardiology, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu 322000, China
| | - Yiting Tu
- Department of Orthopedics, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jianqiang Zhao
- Department of Cardiology, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu 322000, China
| | - Tianlong Zhang
- Department of Critical Care Medicine, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu 322000, China
| | - Wenting Lin
- Department of Cardiology, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu 322000, China
| | - Yile Lu
- Department of Cardiology, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu 322000, China
| | - Chao Feng
- Department of Cardiology, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu 322000, China
| | - Shudong Xia
- Department of Cardiology, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu 322000, China
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Luo L, Mehrotra DV, Shen J, Tang ZZ. Multi-trait analysis of gene-by-environment interactions in large-scale genetic studies. Biostatistics 2024; 25:504-520. [PMID: 36897773 DOI: 10.1093/biostatistics/kxad004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 02/15/2023] [Accepted: 02/22/2023] [Indexed: 03/11/2023] Open
Abstract
Identifying genotype-by-environment interaction (GEI) is challenging because the GEI analysis generally has low power. Large-scale consortium-based studies are ultimately needed to achieve adequate power for identifying GEI. We introduce Multi-Trait Analysis of Gene-Environment Interactions (MTAGEI), a powerful, robust, and computationally efficient framework to test gene-environment interactions on multiple traits in large data sets, such as the UK Biobank (UKB). To facilitate the meta-analysis of GEI studies in a consortium, MTAGEI efficiently generates summary statistics of genetic associations for multiple traits under different environmental conditions and integrates the summary statistics for GEI analysis. MTAGEI enhances the power of GEI analysis by aggregating GEI signals across multiple traits and variants that would otherwise be difficult to detect individually. MTAGEI achieves robustness by combining complementary tests under a wide spectrum of genetic architectures. We demonstrate the advantages of MTAGEI over existing single-trait-based GEI tests through extensive simulation studies and the analysis of the whole exome sequencing data from the UKB.
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Affiliation(s)
- Lan Luo
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, NJ 07065, USA
| | - Devan V Mehrotra
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., North Wales, PA 19454, USA
| | - Judong Shen
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, NJ 07065, USA
| | - Zheng-Zheng Tang
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, 330 N Orchard St, Madison, WI 53715, USA
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26
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Liu R, Shang X, Fu Y, Wang Y, Wang P, Yan S. Shared genetic architecture between hypothyroidism and rheumatoid arthritis: A large-scale cross-trait analysis. Mol Immunol 2024; 168:17-24. [PMID: 38368726 DOI: 10.1016/j.molimm.2024.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 01/10/2024] [Accepted: 02/02/2024] [Indexed: 02/20/2024]
Abstract
BACKGROUND In recent years, mounting evidence has indicated a co-morbid relationship between hypothyroidism and rheumatoid arthritis (RA), however, the shared genetic factors underlying this association remain unclear. This study aims to investigate the common genetic architecture between hypothyroidism and RA. METHODS Genome-wide association study (GWAS) summary statistics from recently published studies were utilized to examine the genetic correlation, shared genetic loci, and potential causal relationship between hypothyroidism and RA. Statistical methods included linkage disequilibrium score regression (LDSC), high-definition likelihood (HDL), cross-trait meta-analyses, colocalization analysis, multi-marker analysis of genomic annotation (MAGMA), tissue-specific enrichment analysis (TSEA), functional enrichment analysis, and latent causal variable method (LCV). RESULTS Our study demonstrated a significant genetic correlation between hypothyroidism and RA(LDSC:rg=0.3803,p=7.23e-11;HDL:rg=0.3849,p=1.02e-21). Through cross-trait meta-analysis, we identified 1035 loci, including 43 novel genetic loci. By integrating colocalization analysis and the MAGMA algorithm, we found a substantial number of genes, such as PTPN22, TYK2, and CTLA-4, shared between the two diseases, which showed significant enrichment across 14 tissues. These genes were primarily associated with the regulation of alpha-beta T cell proliferation, positive regulation of T cell activation, positive regulation of leukocyte cell-cell adhesion, T cell receptor signaling pathway, and JAK-STAT signaling pathway. However, our study did not reveal a significant causal association between the two diseases using the LCV approach. CONCLUSION Based on these findings, there is a significant genetic correlation between hypothyroidism and RA, suggesting a shared genetic basis for these conditions.
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Affiliation(s)
- Ruiyan Liu
- Endocrine Ward II, The First Affiliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou, China
| | - Xin Shang
- Endocrine Ward II, The First Affiliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou, China
| | - Yu Fu
- Endocrine Ward II, The First Affiliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou, China
| | - Ying Wang
- Department of Geriatrics, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
| | - Ping Wang
- Endocrine Ward II, The First Affiliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou, China.
| | - Shuxun Yan
- Endocrine Ward II, The First Affiliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou, China.
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27
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Tissink EP, Shadrin AA, van der Meer D, Parker N, Hindley G, Roelfs D, Frei O, Fan CC, Nagel M, Nærland T, Budisteanu M, Djurovic S, Westlye LT, van den Heuvel MP, Posthuma D, Kaufmann T, Dale AM, Andreassen OA. Abundant pleiotropy across neuroimaging modalities identified through a multivariate genome-wide association study. Nat Commun 2024; 15:2655. [PMID: 38531894 DOI: 10.1038/s41467-024-46817-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 03/12/2024] [Indexed: 03/28/2024] Open
Abstract
Genetic pleiotropy is abundant across spatially distributed brain characteristics derived from one neuroimaging modality (e.g. structural, functional or diffusion magnetic resonance imaging [MRI]). A better understanding of pleiotropy across modalities could inform us on the integration of brain function, micro- and macrostructure. Here we show extensive genetic overlap across neuroimaging modalities at a locus and gene level in the UK Biobank (N = 34,029) and ABCD Study (N = 8607). When jointly analysing phenotypes derived from structural, functional and diffusion MRI in a genome-wide association study (GWAS) with the Multivariate Omnibus Statistical Test (MOSTest), we boost the discovery of loci and genes beyond previously identified effects for each modality individually. Cross-modality genes are involved in fundamental biological processes and predominantly expressed during prenatal brain development. We additionally boost prediction of psychiatric disorders by conditioning independent GWAS on our multimodal multivariate GWAS. These findings shed light on the shared genetic mechanisms underlying variation in brain morphology, functional connectivity, and tissue composition.
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Affiliation(s)
- E P Tissink
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, The Netherlands.
- Department of Sleep and Cognition, Netherlands Institute for Neuroscience, an institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands.
| | - A A Shadrin
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Building 48, Oslo, Norway
| | - D van der Meer
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Building 48, Oslo, Norway
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - N Parker
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Building 48, Oslo, Norway
| | - G Hindley
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Building 48, Oslo, Norway
- Psychosis Studies, Institute of Psychiatry, Psychology and Neurosciences, King's College London, 16 De Crespigny Park, London, SE5 8AB, United Kingdom
| | - D Roelfs
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Building 48, Oslo, Norway
| | - O Frei
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Building 48, Oslo, Norway
| | - C C Fan
- Laureate Institute for Brain Research, Tulsa, OK, USA
- Department of Radiology, University of California San Diego, La Jolla, CA, 92037, USA
| | - M Nagel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, The Netherlands
| | - T Nærland
- K.G. Jebsen Centre for Neurodevelopmental disorders, Division of Paediatric Medicine, Institute of Clinical Medicine, University of Oslo, Building 31, Oslo, Norway
| | - M Budisteanu
- Prof. Dr. Alex Obregia Clinical Hospital of Psychiatry, Bucharest, Romania
- "Victor Babes" National Institute of Pathology, Bucharest, Romania
| | - S Djurovic
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Building 48, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental disorders, Division of Paediatric Medicine, Institute of Clinical Medicine, University of Oslo, Building 31, Oslo, Norway
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
| | - L T Westlye
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Building 48, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental disorders, Division of Paediatric Medicine, Institute of Clinical Medicine, University of Oslo, Building 31, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - M P van den Heuvel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, The Netherlands
- Department of Child and Adolescent Psychology and Psychiatry, section Complex Trait Genetics, Amsterdam Neuroscience, VU University Medical Centre, Amsterdam, The Netherlands
| | - D Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, The Netherlands
- Department of Child and Adolescent Psychology and Psychiatry, section Complex Trait Genetics, Amsterdam Neuroscience, VU University Medical Centre, Amsterdam, The Netherlands
| | - T Kaufmann
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Building 48, Oslo, Norway
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany
| | - A M Dale
- Department of Radiology, University of California San Diego, La Jolla, CA, 92037, USA
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, 92037, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, 92037, USA
| | - O A Andreassen
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Building 48, Oslo, Norway.
- K.G. Jebsen Centre for Neurodevelopmental disorders, Division of Paediatric Medicine, Institute of Clinical Medicine, University of Oslo, Building 31, Oslo, Norway.
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Mota LFM, Arikawa LM, Santos SWB, Fernandes Júnior GA, Alves AAC, Rosa GJM, Mercadante MEZ, Cyrillo JNSG, Carvalheiro R, Albuquerque LG. Benchmarking machine learning and parametric methods for genomic prediction of feed efficiency-related traits in Nellore cattle. Sci Rep 2024; 14:6404. [PMID: 38493207 PMCID: PMC10944497 DOI: 10.1038/s41598-024-57234-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 03/15/2024] [Indexed: 03/18/2024] Open
Abstract
Genomic selection (GS) offers a promising opportunity for selecting more efficient animals to use consumed energy for maintenance and growth functions, impacting profitability and environmental sustainability. Here, we compared the prediction accuracy of multi-layer neural network (MLNN) and support vector regression (SVR) against single-trait (STGBLUP), multi-trait genomic best linear unbiased prediction (MTGBLUP), and Bayesian regression (BayesA, BayesB, BayesC, BRR, and BLasso) for feed efficiency (FE) traits. FE-related traits were measured in 1156 Nellore cattle from an experimental breeding program genotyped for ~ 300 K markers after quality control. Prediction accuracy (Acc) was evaluated using a forward validation splitting the dataset based on birth year, considering the phenotypes adjusted for the fixed effects and covariates as pseudo-phenotypes. The MLNN and SVR approaches were trained by randomly splitting the training population into fivefold to select the best hyperparameters. The results show that the machine learning methods (MLNN and SVR) and MTGBLUP outperformed STGBLUP and the Bayesian regression approaches, increasing the Acc by approximately 8.9%, 14.6%, and 13.7% using MLNN, SVR, and MTGBLUP, respectively. Acc for SVR and MTGBLUP were slightly different, ranging from 0.62 to 0.69 and 0.62 to 0.68, respectively, with empirically unbiased for both models (0.97 and 1.09). Our results indicated that SVR and MTGBLUBP approaches were more accurate in predicting FE-related traits than Bayesian regression and STGBLUP and seemed competitive for GS of complex phenotypes with various degrees of inheritance.
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Affiliation(s)
- Lucio F M Mota
- School of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal, SP, 14884-900, Brazil.
| | - Leonardo M Arikawa
- School of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal, SP, 14884-900, Brazil
| | - Samuel W B Santos
- School of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal, SP, 14884-900, Brazil
| | - Gerardo A Fernandes Júnior
- School of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal, SP, 14884-900, Brazil
| | - Anderson A C Alves
- School of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal, SP, 14884-900, Brazil
| | - Guilherme J M Rosa
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison, WI, 53706, USA
| | - Maria E Z Mercadante
- Institute of Animal Science, Beef Cattle Research Center, Sertãozinho, SP, 14174-000, Brazil
- National Council for Science and Technological Development, Brasilia, DF, 71605-001, Brazil
| | - Joslaine N S G Cyrillo
- Institute of Animal Science, Beef Cattle Research Center, Sertãozinho, SP, 14174-000, Brazil
| | - Roberto Carvalheiro
- School of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal, SP, 14884-900, Brazil
- National Council for Science and Technological Development, Brasilia, DF, 71605-001, Brazil
| | - Lucia G Albuquerque
- School of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal, SP, 14884-900, Brazil.
- National Council for Science and Technological Development, Brasilia, DF, 71605-001, Brazil.
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29
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Guo J, Luo Q, Li C, Liang H, Cao Q, Li Z, Chen G, Yu X. Evidence for the gut-skin axis: Common genetic structures in inflammatory bowel disease and psoriasis. Skin Res Technol 2024; 30:e13611. [PMID: 38348734 PMCID: PMC10862160 DOI: 10.1111/srt.13611] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 01/26/2024] [Indexed: 02/15/2024]
Abstract
BACKGROUND Inflammatory bowel disease (IBD) and psoriasis (Ps) are common immune-mediated diseases that exhibit clinical comorbidity, possibly due to a common genetic structure. However, the exact mechanism remains unknown. METHODS The study population consisted of IBD and Ps genome-wide association study (GWAS) data. Genetic correlations were first evaluated. Then, the overall evaluation employed LD score regression (LDSC), while the local assessment utilized heritability estimation from summary statistics (HESS). Causality assessment was conducted through two-sample Mendelian randomization (2SMR), and genetic overlap analysis utilized the conditional false discovery rate/conjunctional FDR (cond/conjFDR) method. Finally, LDSC applied to specifically expressed genes (LDSC-SEG) was performed at the tissue level. For IBD and Ps-specific expressed genes, genetic correlation, causality, shared genetics, and trait-specific associated tissues were methodically examined. RESULTS At the genomic level, both overall and local genetic correlations were found between IBD and Ps. MR analysis indicated a positive causal relationship between Ps and IBD. The conjFDR analysis with a threshold of < 0.01 identified 43 loci shared between IBD and Ps. Subsequent investigations into disease-associated tissues indicated a close association of IBD and Ps with whole blood, lung, spleen, and EBV-transformed lymphocytes. CONCLUSION The current research offers a novel perspective on the association between IBD and Ps. It contributes to an enhanced comprehension of the genetic structure and mechanisms of comorbidities in both diseases.
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Affiliation(s)
- Jinyan Guo
- Department of Anorectal SurgeryJiangmen Wuyi Hospital of Traditional Chinese MedicineJiangmenChina
| | - Qinghua Luo
- Clinical Medical CollegeJiangxi University of Chinese MedicineNanchangChina
| | - Chunsheng Li
- Department of Anorectal SurgeryJiangmen Wuyi Hospital of Traditional Chinese MedicineJiangmenChina
| | - Hong Liang
- Department of Anorectal SurgeryJiangmen Wuyi Hospital of Traditional Chinese MedicineJiangmenChina
| | - Qiurui Cao
- Department of Anorectal SurgeryJiangmen Wuyi Hospital of Traditional Chinese MedicineJiangmenChina
| | - Zihao Li
- Department of Anorectal SurgeryJiangmen Wuyi Hospital of Traditional Chinese MedicineJiangmenChina
| | - Guanghua Chen
- Department of Anorectal SurgeryAffiliated Hospital of Jiangxi University of Chinese MedicineNanchangChina
| | - Xuchao Yu
- Department of Anorectal SurgeryAffiliated Hospital of Jiangxi University of Chinese MedicineNanchangChina
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30
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Li LJ, Zhong XX, Tan GZ, Song MX, Li P, Liu ZX, Xiong SC, Yang DQ, Liang ZJ. Investigation of causal relationships between cortical structure and osteoporosis using two-sample Mendelian randomization. Cereb Cortex 2024; 34:bhad529. [PMID: 38216542 DOI: 10.1093/cercor/bhad529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 12/03/2023] [Accepted: 12/16/2023] [Indexed: 01/14/2024] Open
Abstract
The mutual interaction between bone characteristics and brain had been reported previously, yet whether the cortical structure has any relevance to osteoporosis is questionable. Therefore, we applied a two-sample bidirectional Mendelian randomization analysis to investigate this relationship. We utilized the bone mineral density measurements of femoral neck (n = 32,735) and lumbar spine (n = 28,498) and data on osteoporosis (7300 cases and 358,014 controls). The global surficial area and thickness and 34 specific functional regions of 51,665 patients were screened by magnetic resonance imaging. For the primary estimate, we utilized the inverse-variance weighted method. The Mendelian randomization-Egger intercept test, MR-PRESSO, Cochran's Q test, and "leave-one-out" sensitivity analysis were conducted to assess heterogeneity and pleiotropy. We observed suggestive associations between decreased thickness in the precentral region (OR = 0.034, P = 0.003) and increased chance of having osteoporosis. The results also revealed suggestive causality of decreased bone mineral density in femoral neck to declined total cortical surface area (β = 1400.230 mm2, P = 0.003), as well as the vulnerability to osteoporosis and reduced thickness in the Parstriangularis region (β = -0.006 mm, P = 0.002). Our study supports that the brain and skeleton exhibit bidirectional crosstalk, indicating the presence of a mutual brain-bone interaction.
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Affiliation(s)
- Long-Jun Li
- The Third Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou 510006, PR China
| | - Xian-Xing Zhong
- Guangdong Research Institute for Orthopedics and Traumatology of Chinese Medicine, The Third Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510378, PR China
| | - Guo-Zhi Tan
- The Third Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou 510006, PR China
| | - Ming-Xi Song
- Department of Education and Research, The Third Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510378, PR China
| | - Pian Li
- The Third Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou 510006, PR China
| | - Zhen-Xin Liu
- The Third Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou 510006, PR China
| | - Si-Cheng Xiong
- The Third Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou 510006, PR China
| | - Da-Qi Yang
- The Third Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou 510006, PR China
| | - Zu-Jian Liang
- Department of Preventive Medicine, The Third Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510378, PR China
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31
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Gong H, Zhou Z, Bu C, Zhang D, Fang Q, Zhang XY, Song Y. Computational dissection of genetic variation modulating the response of multiple photosynthetic phenotypes to the light environment. BMC Genomics 2024; 25:81. [PMID: 38243219 PMCID: PMC10799405 DOI: 10.1186/s12864-024-09968-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 01/03/2024] [Indexed: 01/21/2024] Open
Abstract
BACKGROUND The expression of biological traits is modulated by genetics as well as the environment, and the level of influence exerted by the latter may vary across characteristics. Photosynthetic traits in plants are complex quantitative traits that are regulated by both endogenous genetic factors and external environmental factors such as light intensity and CO2 concentration. The specific processes impacted occur dynamically and continuously as the growth of plants changes. Although studies have been conducted to explore the genetic regulatory mechanisms of individual photosynthetic traits or to evaluate the effects of certain environmental variables on photosynthetic traits, the systematic impact of environmental variables on the dynamic process of integrated plant growth and development has not been fully elucidated. RESULTS In this paper, we proposed a research framework to investigate the genetic mechanism of high-dimensional complex photosynthetic traits in response to the light environment at the genome level. We established a set of high-dimensional equations incorporating environmental regulators to integrate functional mapping and dynamic screening of gene‒environment complex systems to elucidate the process and pattern of intrinsic genetic regulatory mechanisms of three types of photosynthetic phenotypes of Populus simonii that varied with light intensity. Furthermore, a network structure was established to elucidate the crosstalk among significant QTLs that regulate photosynthetic phenotypic systems. Additionally, the detection of key QTLs governing the response of multiple phenotypes to the light environment, coupled with the intrinsic differences in genotype expression, provides valuable insights into the regulatory mechanisms that drive the transition of photosynthetic activity and photoprotection in the face of varying light intensity gradients. CONCLUSIONS This paper offers a comprehensive approach to unraveling the genetic architecture of multidimensional variations in photosynthetic phenotypes, considering the combined impact of integrated environmental factors from multiple perspectives.
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Affiliation(s)
- Huiying Gong
- College of Science, Beijing Forestry University, No. 35, Qinghua East Road, Beijing, 100083, P. R. China
| | - Ziyang Zhou
- College of Science, Beijing Forestry University, No. 35, Qinghua East Road, Beijing, 100083, P. R. China
| | - Chenhao Bu
- College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing, 100083, P. R. China
| | - Deqiang Zhang
- College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing, 100083, P. R. China
| | - Qing Fang
- Faculty of Science, Yamagata University, Yamagata, 990, Japan
| | - Xiao-Yu Zhang
- College of Science, Beijing Forestry University, No. 35, Qinghua East Road, Beijing, 100083, P. R. China.
| | - Yuepeng Song
- College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing, 100083, P. R. China.
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32
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Li R, Benz L, Duan R, Denny JC, Hakonarson H, Mosley JD, Smoller JW, Wei WQ, Ritchie MD, Moore JH, Chen Y. mixWAS: An efficient distributed algorithm for mixed-outcomes genome-wide association studies. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.09.24301073. [PMID: 38260403 PMCID: PMC10802662 DOI: 10.1101/2024.01.09.24301073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Genome-wide association studies (GWAS) have been instrumental in identifying genetic associations for various diseases and traits. However, uncovering genetic underpinnings among traits beyond univariate phenotype associations remains a challenge. Multi-phenotype associations (MPA), or genetic pleiotropy, offer important insights into shared genes and pathways among traits, enhancing our understanding of genetic architectures of complex diseases. GWAS of biobank-linked electronic health record (EHR) data are increasingly being utilized to identify MPA among various traits and diseases. However, methodologies that can efficiently take advantage of distributed EHR to detect MPA are still lacking. Here, we introduce mixWAS, a novel algorithm that efficiently and losslessly integrates multiple EHRs via summary statistics, allowing the detection of MPA among mixed phenotypes while accounting for heterogeneities across EHRs. Simulations demonstrate that mixWAS outperforms the widely used MPA detection method, Phenome-wide association study (PheWAS), across diverse scenarios. Applying mixWAS to data from seven EHRs in the US, we identified 4,534 MPA among blood lipids, BMI, and circulatory diseases. Validation in an independent EHR data from UK confirmed 97.7% of the associations. mixWAS fundamentally improves the detection of MPA and is available as a free, open-source software.
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Affiliation(s)
- Ruowang Li
- Department of Computational Biomedicine, Cedars-Sinai Medical Center
| | - Luke Benz
- Department of Biostatistics, Harvard T.H. Chan School of Public Health
| | - Rui Duan
- Department of Biostatistics, Harvard T.H. Chan School of Public Health
| | - Joshua C Denny
- National Human Genome Research Institute, National Institutes of Health
| | - Hakon Hakonarson
- Division of Human Genetics, Children's Hospital of Philadelphia
- Center for Applied Genomics, Children's Hospital of Philadelphia
- Department of Pediatrics, University of Pennsylvania, Perelman School of Medicine
| | - Jonathan D Mosley
- Department of Medicine, Vanderbilt University Medical Center
- Department of Biomedical Informatics, Vanderbilt University Medical Center
| | - Jordan W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center
| | - Marylyn D Ritchie
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania
| | - Jason H Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center
| | - Yong Chen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania
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Wang M, Li X, Wang C, Zou M, Yang J, Li XD, Guo B. Asymmetric and parallel subgenome selection co-shape common carp domestication. BMC Biol 2024; 22:4. [PMID: 38166816 PMCID: PMC10762839 DOI: 10.1186/s12915-023-01806-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 12/18/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND The common carp (Cyprinus carpio) might best represent the domesticated allopolyploid animals. Although subgenome divergence which is well-known to be a key to allopolyploid domestication has been comprehensively characterized in common carps, the link between genetic architecture underlying agronomic traits and subgenome divergence is unknown in the selective breeding of common carps globally. RESULTS We utilized a comprehensive SNP dataset in 13 representative common carp strains worldwide to detect genome-wide genetic variations associated with scale reduction, vibrant skin color, and high growth rate in common carp domestication. We identified numerous novel candidate genes underlie the three agronomically most desirable traits in domesticated common carps, providing potential molecular targets for future genetic improvement in the selective breeding of common carps. We found that independently selective breeding of the same agronomic trait (e.g., fast growing) in common carp domestication could result from completely different genetic variations, indicating the potential advantage of allopolyploid in domestication. We observed that candidate genes associated with scale reduction, vibrant skin color, and/or high growth rate are repeatedly enriched in the immune system, suggesting that domestication of common carps was often accompanied by the disease resistance improvement. CONCLUSIONS In common carp domestication, asymmetric subgenome selection is prevalent, while parallel subgenome selection occurs in selective breeding of common carps. This observation is not due to asymmetric gene retention/loss between subgenomes but might be better explained by reduced pleiotropy through transposable element-mediated expression divergence between ohnologs. Our results demonstrate that domestication benefits from polyploidy not only in plants but also in animals.
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Affiliation(s)
- Min Wang
- Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xinxin Li
- Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Chongnv Wang
- Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Ming Zou
- Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Jing Yang
- Institute of Chinese Sturgeon, China Three Gorges Corporation, Yichang, 443100, Hubei, China
- Hubei Key Laboratory of Three Gorges Project for Conservation of Fishes, Institute of Chinese Sturgeon, China Three Gorges Corporation, Yichang, 443100, Hubei, China
| | - Xiang-Dong Li
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Integrated Management of Insect Pests and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Baocheng Guo
- Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China.
- Academy of Plateau Science and Sustainability, Qinghai Normal University, Xining, 810008, China.
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Roy DG, Singh L, Chaturvedi HK, Chinnaswamy S. Gender-dependent multiple cross-phenotype association of interferon lambda genetic variants with peripheral blood profiles in healthy individuals. Mol Genet Genomic Med 2024; 12:e2292. [PMID: 37795763 PMCID: PMC10767428 DOI: 10.1002/mgg3.2292] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 09/08/2023] [Accepted: 09/19/2023] [Indexed: 10/06/2023] Open
Abstract
BACKGROUND Type III interferons (IFN), also called as lambda IFNs (IFN-λs), are antiviral and immunomodulatory cytokines that are evolutionarily important in humans. Given their central roles in innate immunity, they could be influencing other aspects of human biology. This study aimed to examine the association of genetic variants that control the expression and/or activity of IFN-λ3 and IFN-λ4 with multiple phenotypes in blood profiles of healthy individuals. METHODS In a cohort of about 550 self-declared healthy individuals, after applying several exclusion criteria to determine their health status, we measured 30 blood parameters, including cellular, biochemical, and metabolic profiles. We genotyped them at rs12979860 and rs28416813 using competitive allele-specific PCR assays and tested their association with the blood profiles under dominant and recessive models for the minor allele. IFN-λ4 variants rs368234815 and rs117648444 were also genotyped or inferred. RESULTS We saw no association in the combined cohort under either of the models for any of the phenotypes. When we stratified the cohort based on gender, we saw a significant association only in males with monocyte (p = 1 × 10-3 ) and SGOT (p = 7 × 10-3 ) levels under the dominant model and with uric acid levels (p = 0.01) under the recessive model. When we tested the IFN-λ4 activity modifying variant within groupings based on absence or presence of one or two copies of IFN-λ4 and on different activity levels of IFN-λ4, we found significant (p < 0.05) association with several phenotypes like monocyte, triglyceride, VLDL, ALP, and uric acid levels, only in males. All the above significant associations did not show any confounding when we tested for the same with up to ten different demographic and lifestyle variables. CONCLUSIONS These results show that lambda interferons can have pleiotropic effects. However, gender seems to be an effect modifier, with males being more sensitive than females to the effect.
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Affiliation(s)
- Debarati Guha Roy
- Infectious Disease GeneticsNational Institute of Biomedical GenomicsKalyaniIndia
- Regional Centre for BiotechnologyFaridabadIndia
| | - Lucky Singh
- ICMR‐National Institute of Medical StatisticsNew DelhiIndia
| | | | - Sreedhar Chinnaswamy
- Infectious Disease GeneticsNational Institute of Biomedical GenomicsKalyaniIndia
- Regional Centre for BiotechnologyFaridabadIndia
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Xie H, Cao X, Zhang S, Sha Q. Joint analysis of multiple phenotypes for extremely unbalanced case-control association studies using multi-layer network. Bioinformatics 2023; 39:btad707. [PMID: 37991852 PMCID: PMC10697735 DOI: 10.1093/bioinformatics/btad707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 09/29/2023] [Accepted: 11/21/2023] [Indexed: 11/24/2023] Open
Abstract
MOTIVATION Genome-wide association studies is an essential tool for analyzing associations between phenotypes and single nucleotide polymorphisms (SNPs). Most of binary phenotypes in large biobanks are extremely unbalanced, which leads to inflated type I error rates for many widely used association tests for joint analysis of multiple phenotypes. In this article, we first propose a novel method to construct a Multi-Layer Network (MLN) using individuals with at least one case status among all phenotypes. Then, we introduce a computationally efficient community detection method to group phenotypes into disjoint clusters based on the MLN. Finally, we propose a novel approach, MLN with Omnibus (MLN-O), to jointly analyse the association between phenotypes and a SNP. MLN-O uses the score test to test the association of each merged phenotype in a cluster and a SNP, then uses the Omnibus test to obtain an overall test statistic to test the association between all phenotypes and a SNP. RESULTS We conduct extensive simulation studies to reveal that the proposed approach can control type I error rates and is more powerful than some existing methods. Meanwhile, we apply the proposed method to a real data set in the UK Biobank. Using phenotypes in Chapter XIII (Diseases of the musculoskeletal system and connective tissue) in the UK Biobank, we find that MLN-O identifies more significant SNPs than other methods we compare with. AVAILABILITY AND IMPLEMENTATION https://github.com/Hongjing-Xie/Multi-Layer-Network-with-Omnibus-MLN-O.
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Affiliation(s)
- Hongjing Xie
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI 49931, United States
| | - Xuewei Cao
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI 49931, United States
| | - Shuanglin Zhang
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI 49931, United States
| | - Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI 49931, United States
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36
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Ma Z, Chang Y, Brito LF, Li Y, Yang T, Wang Y, Yang N. Multitrait meta-analyses identify potential candidate genes for growth-related traits in Holstein heifers. J Dairy Sci 2023; 106:9055-9070. [PMID: 37641329 DOI: 10.3168/jds.2023-23462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 06/20/2023] [Indexed: 08/31/2023]
Abstract
Understanding the underlying pleiotropic relationships among growth and body size traits is important for refining breeding strategies in dairy cattle for optimal body size and growth rate. Therefore, we performed single-trait GWAS for monthly-recorded body weight (BW), hip height, body length, and chest girth from birth to 12 mo of age in Holstein animals, followed by stepwise multiple regression of independent or lowly-linked markers from GWAS loci using conditional and joint association analyses (COJO). Subsequently, we conducted a multitrait meta-analysis to detect pleiotropic markers. Based on the single-trait GWAS, we identified 170 significant SNPs, in which 59 of them remained significant after the COJO analyses. The most significant SNP, located at BTA7:3,676,741, explained 2.93% of the total phenotypic variance for BW6 (BW at 6 mo of age). We identified 17 SNPs with potential pleiotropic effects based on the multitrait meta-analyses, which resulted in 3 additional SNPs in comparison to those detected based on the single-trait GWAS. The identified quantitative trait loci regions overlap with genes known to influence human growth-related traits. According to positional and functional analyses, we proposed HMGA2, HNF4G, MED13L, BHLHE40, FRZB, DMP1, TRIB3, and GATAD2A as important candidate genes influencing the studied traits. The combination of single-trait GWAS and meta-analyses of GWAS results improved the efficiency of detecting associated SNPs, and provided new insights into the genetic mechanisms of growth and development in Holstein cattle.
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Affiliation(s)
- Z Ma
- Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture of China, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, 100193, Beijing, China; Beijing Sunlon Livestock Development Co. Ltd., 100029, Beijing, China
| | - Y Chang
- Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture of China, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, 100193, Beijing, China
| | - Luiz F Brito
- Department of Animal Sciences, Purdue University, West Lafayette, IN 47907
| | - Y Li
- Beijing Sunlon Livestock Development Co. Ltd., 100029, Beijing, China
| | - T Yang
- Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture of China, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, 100193, Beijing, China
| | - Y Wang
- Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture of China, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, 100193, Beijing, China.
| | - N Yang
- Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture of China, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, 100193, Beijing, China.
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Jaholkowski P, Hindley GFL, Shadrin AA, Tesfaye M, Bahrami S, Nerhus M, Rahman Z, O’Connell KS, Holen B, Parker N, Cheng W, Lin A, Rødevand L, Karadag N, Frei O, Djurovic S, Dale AM, Smeland OB, Andreassen OA. Genome-wide Association Analysis of Schizophrenia and Vitamin D Levels Shows Shared Genetic Architecture and Identifies Novel Risk Loci. Schizophr Bull 2023; 49:1654-1664. [PMID: 37163672 PMCID: PMC10686370 DOI: 10.1093/schbul/sbad063] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Low vitamin D (vitD) levels have been consistently reported in schizophrenia (SCZ) suggesting a role in the etiopathology. However, little is known about the role of underlying shared genetic mechanisms. We applied a conditional/conjunctional false discovery rate approach (FDR) on large, nonoverlapping genome-wide association studies for SCZ (N cases = 53 386, N controls = 77 258) and vitD serum concentration (N = 417 580) to evaluate shared common genetic variants. The identified genomic loci were characterized using functional analyses and biological repositories. We observed cross-trait SNP enrichment in SCZ conditioned on vitD and vice versa, demonstrating shared genetic architecture. Applying the conjunctional FDR approach, we identified 72 loci jointly associated with SCZ and vitD at conjunctional FDR < 0.05. Among the 72 shared loci, 40 loci have not previously been reported for vitD, and 9 were novel for SCZ. Further, 64% had discordant effects on SCZ-risk and vitD levels. A mixture of shared variants with concordant and discordant effects with a predominance of discordant effects was in line with weak negative genetic correlation (rg = -0.085). Our results displayed shared genetic architecture between SCZ and vitD with mixed effect directions, suggesting overlapping biological pathways. Shared genetic variants with complex overlapping mechanisms may contribute to the coexistence of SCZ and vitD deficiency and influence the clinical picture.
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Affiliation(s)
- Piotr Jaholkowski
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health
and Addiction, Oslo University Hospital, and Institute of Clinical Medicine,
University of Oslo, Oslo, Norway
| | - Guy F L Hindley
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health
and Addiction, Oslo University Hospital, and Institute of Clinical Medicine,
University of Oslo, Oslo, Norway
- Institute of Psychiatry, Psychology and Neuroscience, King’s College
London, London, UK
| | - Alexey A Shadrin
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health
and Addiction, Oslo University Hospital, and Institute of Clinical Medicine,
University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and
Oslo University Hospital, Oslo, Norway
| | - Markos Tesfaye
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health
and Addiction, Oslo University Hospital, and Institute of Clinical Medicine,
University of Oslo, Oslo, Norway
- Department of Psychiatry, St. Paul’s Hospital Millennium Medical
College, Addis Ababa, Ethiopia
| | - Shahram Bahrami
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health
and Addiction, Oslo University Hospital, and Institute of Clinical Medicine,
University of Oslo, Oslo, Norway
| | - Mari Nerhus
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health
and Addiction, Oslo University Hospital, and Institute of Clinical Medicine,
University of Oslo, Oslo, Norway
- Department of Special Psychiatry, Akershus University
Hospital, Lørenskog, Norway
- Division of Health Services Research and Psychiatry,
Institute of Clinical Medicine, Campus Ahus, University of Oslo,
Oslo, Norway
| | - Zillur Rahman
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health
and Addiction, Oslo University Hospital, and Institute of Clinical Medicine,
University of Oslo, Oslo, Norway
| | - Kevin S O’Connell
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health
and Addiction, Oslo University Hospital, and Institute of Clinical Medicine,
University of Oslo, Oslo, Norway
| | - Børge Holen
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health
and Addiction, Oslo University Hospital, and Institute of Clinical Medicine,
University of Oslo, Oslo, Norway
| | - Nadine Parker
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health
and Addiction, Oslo University Hospital, and Institute of Clinical Medicine,
University of Oslo, Oslo, Norway
| | - Weiqiu Cheng
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health
and Addiction, Oslo University Hospital, and Institute of Clinical Medicine,
University of Oslo, Oslo, Norway
| | - Aihua Lin
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health
and Addiction, Oslo University Hospital, and Institute of Clinical Medicine,
University of Oslo, Oslo, Norway
| | - Linn Rødevand
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health
and Addiction, Oslo University Hospital, and Institute of Clinical Medicine,
University of Oslo, Oslo, Norway
| | - Naz Karadag
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health
and Addiction, Oslo University Hospital, and Institute of Clinical Medicine,
University of Oslo, Oslo, Norway
| | - Oleksandr Frei
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health
and Addiction, Oslo University Hospital, and Institute of Clinical Medicine,
University of Oslo, Oslo, Norway
- Center for Bioinformatics, Department of Informatics, University of
Oslo, Oslo, Norway
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital,
Oslo, Norway
- NORMENT Centre, Department of Clinical Science, University of
Bergen, Bergen, Norway
| | - Anders M Dale
- Department of Radiology, University of California, San Diego,
La Jolla, CA
- Multimodal Imaging Laboratory, University of California San
Diego, La Jolla, CA
- Department of Psychiatry, University of California, San
Diego, La Jolla, CA
- Department of Neurosciences, University of California San
Diego, La Jolla, CA
| | - Olav B Smeland
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health
and Addiction, Oslo University Hospital, and Institute of Clinical Medicine,
University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health
and Addiction, Oslo University Hospital, and Institute of Clinical Medicine,
University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and
Oslo University Hospital, Oslo, Norway
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Wang S, Peng H, Chen F, Liu C, Zheng Q, Wang M, Wang J, Yu H, Xue E, Chen X, Wang X, Fan M, Qin X, Wu Y, Li J, Ye Y, Chen D, Hu Y, Wu T. Identification of genetic loci jointly influencing COVID-19 and coronary heart diseases. Hum Genomics 2023; 17:101. [PMID: 37964352 PMCID: PMC10647050 DOI: 10.1186/s40246-023-00547-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 10/29/2023] [Indexed: 11/16/2023] Open
Abstract
BACKGROUND Comorbidities of coronavirus disease 2019 (COVID-19)/coronary heart disease (CHD) pose great threats to disease outcomes, yet little is known about their shared pathology. The study aimed to examine whether comorbidities of COVID-19/CHD involved shared genetic pathology, as well as to clarify the shared genetic variants predisposing risks common to COVID-19 severity and CHD risks. METHODS By leveraging publicly available summary statistics, we assessed the genetically determined causality between COVID-19 and CHD with bidirectional Mendelian randomization. To further quantify the causality contributed by shared genetic variants, we interrogated their genetic correlation with the linkage disequilibrium score regression method. Bayesian colocalization analysis coupled with conditional/conjunctional false discovery rate analysis was applied to decipher the shared causal single nucleotide polymorphisms (SNPs). FINDINGS Briefly, we observed that the incident CHD risks post COVID-19 infection were partially determined by shared genetic variants. The shared genetic variants contributed to the causality at a proportion of 0.18 (95% CI 0.18-0.19) to 0.23 (95% CI 0.23-0.24). The SNP (rs10490770) located near LZTFL1 suggested direct causality (SNPs → COVID-19 → CHD), and SNPs in ABO (rs579459, rs495828), ILRUN(rs2744961), and CACFD1(rs4962153, rs3094379) may simultaneously influence COVID-19 severity and CHD risks. INTERPRETATION Five SNPs located near LZTFL1 (rs10490770), ABO (rs579459, rs495828), ILRUN (rs2744961), and CACFD1 (rs4962153, rs3094379) may simultaneously influence their risks. The current study suggested that there may be shared mechanisms predisposing to both COVID-19 severity and CHD risks. Genetic predisposition to COVID-19 is a causal risk factor for CHD, supporting that reducing the COVID-19 infection risk or alleviating COVID-19 severity among those with specific genotypes might reduce their subsequent CHD adverse outcomes. Meanwhile, the shared genetic variants identified may be of clinical implications for identifying the target population who are more vulnerable to adverse CHD outcomes post COVID-19 and may also advance treatments of 'Long COVID-19.'
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Affiliation(s)
- Siyue Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Hexiang Peng
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Feng Chen
- Department of Intensive Care Unit, PLA Rocket Force Characteristic Medical Center, Beijing, 100088, China
| | - Chunfang Liu
- School of Public Health, Baotou Medical College, Baotou, 014040, China
| | - Qiwen Zheng
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
- China National Center for Bioinformation, Beijing, 100101, China
| | - Mengying Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Jiating Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Huan Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Enci Xue
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Xi Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Xueheng Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Meng Fan
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Xueying Qin
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Yiqun Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Jin Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Ying Ye
- Department of Local Diseases Control and Prevention, Fujian Provincial Center for Disease Control and Prevention, Fuzhou, 350001, China
| | - Dafang Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Yonghua Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China.
| | - Tao Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China.
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Liang Q, Jiang Y, Shieh AW, Zhou D, Chen R, Wang F, Xu M, Niu M, Wang X, Pinto D, Wang Y, Cheng L, Vadukapuram R, Zhang C, Grennan K, Giase G, White KP, Peng J, Li B, Liu C, Chen C, Wang SH. The impact of common variants on gene expression in the human brain: from RNA to protein to schizophrenia risk. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.04.543603. [PMID: 37873195 PMCID: PMC10592607 DOI: 10.1101/2023.06.04.543603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Background The impact of genetic variants on gene expression has been intensely studied at the transcription level, yielding in valuable insights into the association between genes and the risk of complex disorders, such as schizophrenia (SCZ). However, the downstream impact of these variants and the molecular mechanisms connecting transcription variation to disease risk are not well understood. Results We quantitated ribosome occupancy in prefrontal cortex samples of the BrainGVEX cohort. Together with transcriptomics and proteomics data from the same cohort, we performed cis-Quantitative Trait Locus (QTL) mapping and identified 3,253 expression QTLs (eQTLs), 1,344 ribosome occupancy QTLs (rQTLs), and 657 protein QTLs (pQTLs) out of 7,458 genes quantitated in all three omics types from 185 samples. Of the eQTLs identified, only 34% have their effects propagated to the protein level. Further analysis on the effect size of prefrontal cortex eQTLs identified from an independent dataset showed clear post-transcriptional attenuation of eQTL effects. To investigate the biological relevance of the attenuated eQTLs, we identified 70 expression-specific QTLs (esQTLs), 51 ribosome-occupancy-specific QTLs (rsQTLs), and 107 protein-specific QTLs (psQTLs). Five of these omics-specific QTLs showed strong colocalization with SCZ GWAS signals, three of them are esQTLs. The limited number of GWAS colocalization discoveries from omics-specific QTLs and the apparent prevalence of eQTL attenuation prompted us to take a complementary approach to investigate the functional relevance of attenuated eQTLs. Using S-PrediXcan we identified 74 SCZ risk genes, 34% of which were novel, and 67% of these risk genes were replicated in a MR-Egger test. Notably, 52 out of 74 risk genes were identified using eQTL data and 70% of these SCZ-risk-gene-driving eQTLs show little to no evidence of driving corresponding variations at the protein level. Conclusion The effect of eQTLs on gene expression in the prefrontal cortex is commonly attenuated post-transcriptionally. Many of the attenuated eQTLs still correlate with SCZ GWAS signal. Further investigation is needed to elucidate a mechanistic link between attenuated eQTLs and SCZ disease risk.
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Affiliation(s)
- Qiuman Liang
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410000, China
| | - Yi Jiang
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410000, China
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430000, China
| | - Annie W. Shieh
- Center for Human Genetics, The Brown foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Dan Zhou
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310058, China
- Department of Molecular Physiology and Biophysics, Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN 37232, USA
| | - Rui Chen
- Department of Molecular Physiology and Biophysics, Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN 37232, USA
| | - Feiran Wang
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410000, China
| | - Meng Xu
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410000, China
| | - Mingming Niu
- Department of Structural Biology, Department of Developmental Neurobiology, Center for Proteomics and Metabolomics, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
| | - Xusheng Wang
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Dalila Pinto
- Department of Psychiatry, and Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, and Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Yue Wang
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA
| | - Lijun Cheng
- Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA
| | - Ramu Vadukapuram
- Department of Psychiatry, The University of Texas Rio Grande Valley, Harlingen, TX 78550, USA
| | - Chunling Zhang
- Department of Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, NY 13210, USA
| | - Kay Grennan
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY 13210, USA
| | - Gina Giase
- The Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | | | - Kevin P White
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117596, Singapore
| | - Junmin Peng
- Department of Structural Biology, Department of Developmental Neurobiology, Center for Proteomics and Metabolomics, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
| | - Bingshan Li
- Department of Molecular Physiology and Biophysics, Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN 37232, USA
| | - Chunyu Liu
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410000, China
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY 13210, USA
- School of Psychology, Shaanxi Normal University, Xi’an, Shaanxi 710062, China
| | - Chao Chen
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410000, China
- Furong Laboratory, Changsha, Hunan 410000, China
- National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410000, China
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Central South University, Changsha, Hunan 410000, China
| | - Sidney H. Wang
- Center for Human Genetics, The Brown foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
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Li X, Chen H, Selvaraj MS, Van Buren E, Zhou H, Wang Y, Sun R, McCaw ZR, Yu Z, Arnett DK, Bis JC, Blangero J, Boerwinkle E, Bowden DW, Brody JA, Cade BE, Carson AP, Carlson JC, Chami N, Chen YDI, Curran JE, de Vries PS, Fornage M, Franceschini N, Freedman BI, Gu C, Heard-Costa NL, He J, Hou L, Hung YJ, Irvin MR, Kaplan RC, Kardia SL, Kelly T, Konigsberg I, Kooperberg C, Kral BG, Li C, Loos RJ, Mahaney MC, Martin LW, Mathias RA, Minster RL, Mitchell BD, Montasser ME, Morrison AC, Palmer ND, Peyser PA, Psaty BM, Raffield LM, Redline S, Reiner AP, Rich SS, Sitlani CM, Smith JA, Taylor KD, Tiwari H, Vasan RS, Wang Z, Yanek LR, Yu B, Rice KM, Rotter JI, Peloso GM, Natarajan P, Li Z, Liu Z, Lin X. A statistical framework for powerful multi-trait rare variant analysis in large-scale whole-genome sequencing studies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.30.564764. [PMID: 37961350 PMCID: PMC10634938 DOI: 10.1101/2023.10.30.564764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally-scalable analytical pipeline for functionally-informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits, and further empowers rare variant association analysis by incorporating multiple functional annotations. We applied MultiSTAAR to jointly analyze three lipid traits (low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides) in 61,861 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered new associations with lipid traits missed by single-trait analysis, including rare variants within an enhancer of NIPSNAP3A and an intergenic region on chromosome 1.
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Affiliation(s)
- Xihao Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 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
| | - Margaret Sunitha Selvaraj
- Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Eric Van Buren
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Hufeng Zhou
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Yuxuan Wang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Ryan Sun
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Zachary R. McCaw
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Zhi Yu
- Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Donna K. Arnett
- Provost Office, University of South Carolina, Columbia, SC, USA
| | - Joshua C. Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - John Blangero
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, The University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - Eric Boerwinkle
- 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
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Donald W. Bowden
- Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Jennifer A. Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Brian E. Cade
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - April P. Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Jenna C. Carlson
- Department of Human Genetics and Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Nathalie Chami
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Joanne E. Curran
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, The University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - Paul S. de Vries
- 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
| | - Myriam Fornage
- 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
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, the University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Nora Franceschini
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Barry I. Freedman
- Department of Internal Medicine, Nephrology, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Charles Gu
- Division of Biology & Biomedical Sciences, Washington University School of Medicine, St. Louis, MO, USA
| | - Nancy L. Heard-Costa
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
| | - Jiang He
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
- Tulane University Translational Science Institute, New Orleans, LA, USA
| | - Lifang Hou
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Yi-Jen Hung
- Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Marguerite R. Irvin
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Robert C. Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Sharon L.R. Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Tanika Kelly
- Department of Medicine, Division of Nephrology, University of Illinois Chicago, Chicago, IL, USA
| | - Iain Konigsberg
- Department of Biomedical Informatics, University of Colorado, Aurora, CO, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Brian G. Kral
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Changwei Li
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
- Tulane University Translational Science Institute, New Orleans, LA, USA
| | - Ruth J.F. Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Michael C. Mahaney
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, The University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - Lisa W. Martin
- George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Rasika A. Mathias
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ryan L. Minster
- Department of Human Genetics and Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Braxton D. Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - May E. Montasser
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Alanna C. Morrison
- 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
| | - Nicholette D. Palmer
- Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Patricia A. Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Bruce M. Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Departments of Epidemiology, University of Washington, Seattle, WA, USA
- Department of Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Laura M. Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Alexander P. Reiner
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Departments of Epidemiology, University of Washington, Seattle, WA, USA
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Colleen M. Sitlani
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Jennifer A. Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Kent D. Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Hemant Tiwari
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Ramachandran S. Vasan
- Framingham Heart Study, Framingham, MA, USA
- Department of Quantitative and Qualitative Health Sciences, UT Health San Antonio School of Public Health, San Antonia, TX, USA
| | - Zhe Wang
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lisa R. Yanek
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Bing Yu
- 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
| | | | - Kenneth M. Rice
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Gina M. Peloso
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Pradeep Natarajan
- Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Zilin Li
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Zhonghua Liu
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Xihong Lin
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Statistics, Harvard University, Cambridge, MA, USA
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41
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Zhang Z, Jung J, Kim A, Suboc N, Gazal S, Mancuso N. A scalable approach to characterize pleiotropy across thousands of human diseases and complex traits using GWAS summary statistics. Am J Hum Genet 2023; 110:1863-1874. [PMID: 37879338 PMCID: PMC10645558 DOI: 10.1016/j.ajhg.2023.09.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 09/26/2023] [Accepted: 09/27/2023] [Indexed: 10/27/2023] Open
Abstract
Genome-wide association studies (GWASs) across thousands of traits have revealed the pervasive pleiotropy of trait-associated genetic variants. While methods have been proposed to characterize pleiotropic components across groups of phenotypes, scaling these approaches to ultra-large-scale biobanks has been challenging. Here, we propose FactorGo, a scalable variational factor analysis model to identify and characterize pleiotropic components using biobank GWAS summary data. In extensive simulations, we observe that FactorGo outperforms the state-of-the-art (model-free) approach tSVD in capturing latent pleiotropic factors across phenotypes while maintaining a similar computational cost. We apply FactorGo to estimate 100 latent pleiotropic factors from GWAS summary data of 2,483 phenotypes measured in European-ancestry Pan-UK BioBank individuals (N = 420,531). Next, we find that factors from FactorGo are more enriched with relevant tissue-specific annotations than those identified by tSVD (p = 2.58E-10) and validate our approach by recapitulating brain-specific enrichment for BMI and the height-related connection between reproductive system and muscular-skeletal growth. Finally, our analyses suggest shared etiologies between rheumatoid arthritis and periodontal condition in addition to alkaline phosphatase as a candidate prognostic biomarker for prostate cancer. Overall, FactorGo improves our biological understanding of shared etiologies across thousands of GWASs.
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Affiliation(s)
- Zixuan Zhang
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
| | - Junghyun Jung
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Artem Kim
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Noah Suboc
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Steven Gazal
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA; Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Nicholas Mancuso
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA; Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
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42
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Schmengler H, Oldehinkel AJ, Vollebergh WAM, Pasman JA, Hartman CA, Stevens GWJM, Nolte IM, Peeters M. Disentangling the interplay between genes, cognitive skills, and educational level in adolescent and young adult smoking - The TRAILS study. Soc Sci Med 2023; 336:116254. [PMID: 37751630 DOI: 10.1016/j.socscimed.2023.116254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 08/17/2023] [Accepted: 09/15/2023] [Indexed: 09/28/2023]
Abstract
Recent studies suggest that smoking and lower educational attainment may have genetic influences in common. However, little is known about the mechanisms through which genetics contributes to educational inequalities in adolescent and young adult smoking. Common genetic liabilities may underlie cognitive skills associated with both smoking and education, such as IQ and effortful control, in line with indirect health-related selection explanations. Additionally, by affecting cognitive skills, genes may predict educational trajectories and hereby adolescents' social context, which may be associated with smoking, consistent with social causation explanations. Using data from the Dutch TRAILS Study (N = 1581), we estimated the extent to which polygenic scores (PGSs) for ever smoking regularly (PGSSMOK) and years of education (PGSEDU) predict IQ and effortful control, measured around age 11, and whether these cognitive skills then act as shared predictors of smoking and educational level around age 16, 19, 22, and 26. Second, we assessed if educational level mediated associations between PGSs and smoking. Both PGSs were associated with lower effortful control, and PGSEDU also with lower IQ. Lower IQ and effortful control, in turn, predicted having a lower educational level. However, neither of these cognitive skills were directly associated with smoking behaviour after controlling for covariates and PGSs. This suggests that IQ and effortful control are not shared predictors of smoking and education (i.e., no indirect health-related selection related to cognitive skills). Instead, PGSSMOK and PGSEDU, partly through their associations with lower cognitive skills, predicted selection into a lower educational track, which in turn was associated with more smoking, in line with social causation explanations. Our findings suggest that educational differences in the social context contribute to associations between genetic liabilities and educational inequalities in smoking.
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Affiliation(s)
- Heiko Schmengler
- Department of Interdisciplinary Social Science, Utrecht University, the Netherlands.
| | - Albertine J Oldehinkel
- Interdisciplinary Center Psychopathology and Emotion Regulation, Department of Psychiatry, University Medical Center of Groningen, University of Groningen, the Netherlands
| | - Wilma A M Vollebergh
- Department of Interdisciplinary Social Science, Utrecht University, the Netherlands
| | - Joëlle A Pasman
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Sweden
| | - Catharina A Hartman
- Interdisciplinary Center Psychopathology and Emotion Regulation, Department of Psychiatry, University Medical Center of Groningen, University of Groningen, the Netherlands
| | | | - Ilja M Nolte
- Department of Epidemiology, University Medical Center of Groningen, University of Groningen, the Netherlands
| | - Margot Peeters
- Department of Interdisciplinary Social Science, Utrecht University, the Netherlands
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43
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Qu Y, Xiao C, Wu X, Zhu J, Qin C, He L, Cui H, Zhang L, Zhang W, Yang C, Yao Y, Li J, Liu Z, Zhang B, Wang W, Jiang X. Genetic Correlation, Shared Loci, and Causal Association Between Sex Hormone-Binding Globulin and Bone Mineral Density: Insights From a Large-Scale Genomewide Cross-Trait Analysis. J Bone Miner Res 2023; 38:1635-1644. [PMID: 37615194 DOI: 10.1002/jbmr.4904] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 07/20/2023] [Accepted: 08/17/2023] [Indexed: 08/25/2023]
Abstract
Although the impact of sex hormones on bone metabolism is well-documented, effect of their primary modulator, sex hormone-binding globulin (SHBG), remains inconclusive. This study aims to elucidate the genetic overlap between SHBG and heel estimated bone mineral density (eBMD), a widely-accepted tool for osteoporosis management and fracture risk assessment. Using summary statistics from large-scale genomewide association studies conducted for SHBG (N = 370,125), SHBG adjusted for body mass index (SHBGa, N = 368,929), and eBMD (N = 426,824), a comprehensive genomewide cross-trait approach was performed to quantify global and local genetic correlations, identify pleiotropic loci, and infer causal associations. A significant overall inverse genetic correlation was found for SHBG and eBMD (rg = -0.11, p = 3.34 × 10-10 ), which was further supported by the significant local genetic correlations observed in 11 genomic regions. Cross-trait meta-analysis revealed 219 shared loci, of which seven were novel. Notably, four novel loci (rs6542680, rs8178616, rs147110934, and rs815625) were further demonstrated to colocalize. Mendelian randomization identified a robust causal effect of SHBG on eBMD (beta = -0.22, p = 3.04 × 10-13 ), with comparable effect sizes observed in both men (beta = -0.16, p = 1.99 × 10-6 ) and women (beta = -0.19, p = 2.73 × 10-9 ). Replacing SHBG with SHBGa, the observed genetic correlations, pleiotropic loci and causal associations did not change substantially. Our work reveals a shared genetic basis between SHBG and eBMD, substantiated by multiple pleiotropic loci and a robust causal relationship. Although SHBG has been implicated in preventing and screening aging-related diseases, our findings support its etiological role in osteoporosis. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).
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Affiliation(s)
- Yang Qu
- Department of Epidemiology and Biostatistics and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Changfeng Xiao
- Department of Epidemiology and Biostatistics and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xueyao Wu
- Department of Epidemiology and Biostatistics and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Jingwei Zhu
- Department of Epidemiology and Biostatistics and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Chenjiarui Qin
- Department of Nutrition and Food Hygiene, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Lin He
- Department of Epidemiology and Biostatistics and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Huijie Cui
- Department of Epidemiology and Biostatistics and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Li Zhang
- Department of Epidemiology and Biostatistics and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Wenqiang Zhang
- Department of Epidemiology and Biostatistics and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Chunxia Yang
- Department of Epidemiology and Biostatistics and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Yuqin Yao
- Department of Occupational and Environmental Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Jiayuan Li
- Department of Epidemiology and Biostatistics and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Zhenmi Liu
- Department of Maternal, Child and Adolescent Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Ben Zhang
- Department of Epidemiology and Biostatistics and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Wenzhi Wang
- Department of Osteoporosis/Rheumatology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xia Jiang
- Department of Epidemiology and Biostatistics and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Nutrition and Food Hygiene, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
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44
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St-Pierre J, Oualkacha K. A copula-based set-variant association test for bivariate continuous, binary or mixed phenotypes. Int J Biostat 2023; 19:369-387. [PMID: 36279152 PMCID: PMC10644254 DOI: 10.1515/ijb-2022-0010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 05/26/2022] [Accepted: 08/23/2022] [Indexed: 11/15/2022]
Abstract
In genome wide association studies (GWAS), researchers are often dealing with dichotomous and non-normally distributed traits, or a mixture of discrete-continuous traits. However, most of the current region-based methods rely on multivariate linear mixed models (mvLMMs) and assume a multivariate normal distribution for the phenotypes of interest. Hence, these methods are not applicable to disease or non-normally distributed traits. Therefore, there is a need to develop unified and flexible methods to study association between a set of (possibly rare) genetic variants and non-normal multivariate phenotypes. Copulas are multivariate distribution functions with uniform margins on the [0, 1] interval and they provide suitable models to deal with non-normality of errors in multivariate association studies. We propose a novel unified and flexible copula-based multivariate association test (CBMAT) for discovering association between a genetic region and a bivariate continuous, binary or mixed phenotype. We also derive a data-driven analytic p-value procedure of the proposed region-based score-type test. Through simulation studies, we demonstrate that CBMAT has well controlled type I error rates and higher power to detect associations compared with other existing methods, for discrete and non-normally distributed traits. At last, we apply CBMAT to detect the association between two genes located on chromosome 11 and several lipid levels measured on 1477 subjects from the ASLPAC study.
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Affiliation(s)
- Julien St-Pierre
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
| | - Karim Oualkacha
- Département de Mathématiques, Université du Québec à Montréal, Montreal, QC, Canada
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45
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Zuo H, Li MM. Ankylosing spondylitis and psychiatric disorders in European population: a Mendelian randomization study. Front Immunol 2023; 14:1277959. [PMID: 37954601 PMCID: PMC10637577 DOI: 10.3389/fimmu.2023.1277959] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 10/13/2023] [Indexed: 11/14/2023] Open
Abstract
Background Epidemiologic evidence has demonstrated a correlation between ankylosing spondylitis and psychiatric disorders. However, little is known about the common genetics and causality of this association. This study aimed to investigate the common genetics and causality between ankylosing spondylitis (AS) and psychiatric disorders. Methods A two-sample Mendelian Randomization (MR) analysis was carried out to confirm causal relationships between ankylosing spondylitis and five mental health conditions including major depressive disorder (MDD), anxiety disorder (AXD), schizophrenia (SCZ), bipolar disorder (BIP), and anorexia nervosa (AN). Genetic instrumental variables associated with exposures and outcomes were derived from the largest available summary statistics of genome-wide association studies (GWAS). Bidirectional causal estimation of MR was primarily obtained using the inverse variance weighting (IVW) method. Other MR methods include MR-Egger regression, Weighted Median Estimator (WME), Weighted Mode, Simple Mode, and Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO). Sensitivity analyses are conducted to estimate the robustness of MR results. Results The findings suggest that AS may be causally responsible for the risk of developing SCZ (OR = 1.18, 95% confidence interval = (1.06, 1.31), P = 2.58 × 10-3) and AN (OR = 1.32, 95% confidence interval = (1.07, 1.64), P = 9.43 × 10-3). In addition, MDD, AXD, SCZ, AN, and BIP were not inversely causally related to AS (all p > 0.05). Conclusion Our study provides fresh insights into the relationship between AS and psychiatric disorders (SCZ and AN). Furthermore, it may provide new clues for risk management and preventive interventions for mental disorders in patients with AS.
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Affiliation(s)
| | - Min-Min Li
- Department of Clinical Laboratory Medicine, The First Affiliated Hospital of Jinan University, Guangzhou, China
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46
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Vorstman JAS, Scherer SW. Contemplating syndromic autism. Genet Med 2023; 25:100919. [PMID: 37330697 DOI: 10.1016/j.gim.2023.100919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 06/07/2023] [Accepted: 06/08/2023] [Indexed: 06/19/2023] Open
Affiliation(s)
- Jacob A S Vorstman
- Department of Psychiatry, Hospital for Sick Children University of Toronto, Toronto, ON, Canada; Program in Genetics and Genome Biology, Hospital for Sick Children, Toronto, ON, Canada; The Centre for Applied Genomics, Hospital for Sick Children, Toronto, ON, Canada.
| | - Stephen W Scherer
- Program in Genetics and Genome Biology, Hospital for Sick Children, Toronto, ON, Canada; The Centre for Applied Genomics, Hospital for Sick Children, Toronto, ON, Canada; McLaughlin Centre and Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.
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47
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Jin X, Shi G. Cauchy combination methods for the detection of gene-environment interactions for rare variants related to quantitative phenotypes. Heredity (Edinb) 2023; 131:241-252. [PMID: 37481617 PMCID: PMC10539363 DOI: 10.1038/s41437-023-00640-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 07/09/2023] [Accepted: 07/12/2023] [Indexed: 07/24/2023] Open
Abstract
The characterization of gene-environment interactions (GEIs) can provide detailed insights into the biological mechanisms underlying complex diseases. Despite recent interest in GEIs for rare variants, published GEI tests are underpowered for an extremely small proportion of causal rare variants in a gene or a region. By extending the aggregated Cauchy association test (ACAT), we propose three GEI tests to address this issue: a Cauchy combination GEI test with fixed main effects (CCGEI-F), a Cauchy combination GEI test with random main effects (CCGEI-R), and an omnibus Cauchy combination GEI test (CCGEI-O). ACAT was applied to combine p values of single-variant GEI analyses to obtain CCGEI-F and CCGEI-R and p values of multiple GEI tests were combined in CCGEI-O. Through numerical simulations, for small numbers of causal variants, CCGEI-F, CCGEI-R and CCGEI-O provided approximately 5% higher power than the existing GEI tests INT-FIX and INT-RAN; however, they had slightly higher power than the existing GEI test TOW-GE. For large numbers of causal variants, although CCGEI-F and CCGEI-R exhibited comparable or slightly lower power values than the competing tests, the results were still satisfactory. Among all simulation conditions evaluated, CCGEI-O provided significantly higher power than that of competing GEI tests. We further applied our GEI tests in genome-wide analyses of systolic blood pressure or diastolic blood pressure to detect gene-body mass index (BMI) interactions, using whole-exome sequencing data from UK Biobank. At a suggestive significance level of 1.0 × 10-4, KCNC4, GAR1, FAM120AOS and NT5C3B showed interactions with BMI by our GEI tests.
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Affiliation(s)
- Xiaoqin Jin
- State Key Laboratory of Integrated Services Networks, Xidian University, 2 South Taibai Road, Xi'an, Shaanxi, 710071, China.
| | - Gang Shi
- State Key Laboratory of Integrated Services Networks, Xidian University, 2 South Taibai Road, Xi'an, Shaanxi, 710071, China
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48
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Liu Z, Ye T, Sun B, Schooling M, Tchetgen ET. Mendelian randomization mixed-scale treatment effect robust identification and estimation for causal inference. Biometrics 2023; 79:2208-2219. [PMID: 35950778 DOI: 10.1111/biom.13735] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 08/02/2022] [Indexed: 11/28/2022]
Abstract
Standard Mendelian randomization (MR) analysis can produce biased results if the genetic variant defining an instrumental variable (IV) is confounded and/or has a horizontal pleiotropic effect on the outcome of interest not mediated by the treatment variable. We provide novel identification conditions for the causal effect of a treatment in the presence of unmeasured confounding by leveraging a possibly invalid IV for which both the IV independence and exclusion restriction assumptions may be violated. The proposed Mendelian randomization mixed-scale treatment effect robust identification (MR MiSTERI) approach relies on (i) an assumption that the treatment effect does not vary with the possibly invalid IV on the additive scale; (ii) that the confounding bias does not vary with the possibly invalid IV on the odds ratio scale; and (iii) that the residual variance for the outcome is heteroskedastic with respect to the possibly invalid IV. Although assumptions (i) and (ii) have, respectively, appeared in the IV literature, assumption (iii) has not; we formally establish that their conjunction can identify a causal effect even with an invalid IV. MR MiSTERI is shown to be particularly advantageous in the presence of pervasive heterogeneity of pleiotropic effects on the additive scale. We propose a simple and consistent three-stage estimator that can be used as a preliminary estimator to a carefully constructed efficient one-step-update estimator. In order to incorporate multiple, possibly correlated, and weak invalid IVs, a common challenge in MR studies, we develop a MAny Weak Invalid Instruments (MR MaWII MiSTERI) approach for strengthened identification and improved estimation accuracy. Both simulation studies and UK Biobank data analysis results demonstrate the robustness of the proposed methods.
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Affiliation(s)
- Zhonghua Liu
- Department of Biostatistics, Columbia University, New York, New York, USA
| | - Ting Ye
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
| | - Baoluo Sun
- Department of Statistics and Data Science, National University of Singapore, Singapore
| | - Mary Schooling
- CUNY Graduate School of Public Health and Health Policy, New York, New York, USA
- School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong SAR, China
| | - Eric Tchetgen Tchetgen
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Asgari Y, Sugier PE, Baghfalaki T, Lucotte E, Karimi M, Sedki M, Ngo A, Liquet B, Truong T. GCPBayes pipeline: a tool for exploring pleiotropy at the gene level. NAR Genom Bioinform 2023; 5:lqad065. [PMID: 37416786 PMCID: PMC10320750 DOI: 10.1093/nargab/lqad065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 05/16/2023] [Accepted: 06/16/2023] [Indexed: 07/08/2023] Open
Abstract
Cross-phenotype association using gene-set analysis can help to detect pleiotropic genes and inform about common mechanisms between diseases. Although there are an increasing number of statistical methods for exploring pleiotropy, there is a lack of proper pipelines to apply gene-set analysis in this context and using genome-scale data in a reasonable running time. We designed a user-friendly pipeline to perform cross-phenotype gene-set analysis between two traits using GCPBayes, a method developed by our team. All analyses could be performed automatically by calling for different scripts in a simple way (using a Shiny app, Bash or R script). A Shiny application was also developed to create different plots to visualize outputs from GCPBayes. Finally, a comprehensive and step-by-step tutorial on how to use the pipeline is provided in our group's GitHub page. We illustrated the application on publicly available GWAS (genome-wide association studies) summary statistics data to identify breast cancer and ovarian cancer susceptibility genes. We have shown that the GCPBayes pipeline could extract pleiotropic genes previously mentioned in the literature, while it also provided new pleiotropic genes and regions that are worthwhile for further investigation. We have also provided some recommendations about parameter selection for decreasing computational time of GCPBayes on genome-scale data.
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Affiliation(s)
- Yazdan Asgari
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team Exposome and Heredity, 94807 Villejuif, France
| | - Pierre-Emmanuel Sugier
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team Exposome and Heredity, 94807 Villejuif, France
- Laboratoire de Mathématiques et de leurs Applications de Pau, Université de Pau et des Pays de l’Adour, UMR CNRS 5142, E2S-UPPA, 64000 Pau, France
| | | | - Elise Lucotte
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team Exposome and Heredity, 94807 Villejuif, France
| | - Mojgan Karimi
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team Exposome and Heredity, 94807 Villejuif, France
| | - Mohammed Sedki
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team Psychiatrie du développement et trajectoires, 94807 Villejuif, France
| | - Amélie Ngo
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team Exposome and Heredity, 94807 Villejuif, France
| | - Benoit Liquet
- Laboratoire de Mathématiques et de leurs Applications de Pau, Université de Pau et des Pays de l’Adour, UMR CNRS 5142, E2S-UPPA, 64000 Pau, France
- School of Mathematical and Physical Sciences, Macquarie University, Sydney, NSW 2109, Australia
| | - Thérèse Truong
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team Exposome and Heredity, 94807 Villejuif, France
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50
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Calderwood A, Siles L, Eastmond PJ, Kurup S, Morris RJ. A causal inference and Bayesian optimisation framework for modelling multi-trait relationships-Proof-of-concept using Brassica napus seed yield under controlled conditions. PLoS One 2023; 18:e0290429. [PMID: 37656702 PMCID: PMC10473526 DOI: 10.1371/journal.pone.0290429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 08/09/2023] [Indexed: 09/03/2023] Open
Abstract
The improvement of crop yield is a major breeding target and there is a long history of research that has focussed on unravelling the mechanisms and processes that contribute to yield. Quantitative prediction of the interplay between morphological traits, and the effects of these trait-trait relationships on seed production remains, however, a challenge. Consequently, the extent to which crop varieties optimise their morphology for a given environment is largely unknown. This work presents a new combination of existing methodologies by framing crop breeding as an optimisation problem and evaluates the extent to which existing varieties exhibit optimal morphologies under the test conditions. In this proof-of-concept study using spring and winter oilseed rape plants grown under greenhouse conditions, we employ causal inference to model the hierarchically structured effects of 27 morphological yield traits on each other. We perform Bayesian optimisation of seed yield, to identify and quantify the morphologies of ideotype plants, which are expected to be higher yielding than the varieties in the studied panels. Under the tested growth conditions, we find that existing spring varieties occupy the optimal regions of trait-space, but that potentially high yielding strategies are unexplored in extant winter varieties. The same approach can be used to evaluate trait (morphology) space for any environment.
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Affiliation(s)
- Alexander Calderwood
- Department of Computational and Systems Biology, John Innes Centre, Norwich, Norfolk, United Kingdom
| | - Laura Siles
- Plant Sciences and the Bioeconomy, Rothamsted Research, Harpenden, Hertfordshire, United Kingdom
| | - Peter J. Eastmond
- Plant Sciences and the Bioeconomy, Rothamsted Research, Harpenden, Hertfordshire, United Kingdom
| | - Smita Kurup
- Plant Sciences and the Bioeconomy, Rothamsted Research, Harpenden, Hertfordshire, United Kingdom
| | - Richard J. Morris
- Department of Computational and Systems Biology, John Innes Centre, Norwich, Norfolk, United Kingdom
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