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Blancon J, Buet C, Dubreuil P, Tixier MH, Baret F, Praud S. Maize green leaf area index dynamics: genetic basis of a new secondary trait for grain yield in optimal and drought conditions. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:68. [PMID: 38441678 PMCID: PMC10914915 DOI: 10.1007/s00122-024-04572-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 02/03/2024] [Indexed: 03/07/2024]
Abstract
KEY MESSAGE Green Leaf Area Index dynamics is a promising secondary trait for grain yield and drought tolerance. Multivariate GWAS is particularly well suited to identify the genetic determinants of the green leaf area index dynamics. Improvement of maize grain yield is impeded by important genotype-environment interactions, especially under drought conditions. The use of secondary traits, that are correlated with yield, more heritable and less prone to genotype-environment interactions, can increase breeding efficiency. Here, we studied the genetic basis of a new secondary trait: the green leaf area index (GLAI) dynamics over the maize life cycle. For this, we used an unmanned aerial vehicle to characterize the GLAI dynamics of a diverse panel in well-watered and water-deficient trials in two years. From the dynamics, we derived 24 traits (slopes, durations, areas under the curve), and showed that six of them were heritable traits representative of the panel diversity. To identify the genetic determinants of GLAI, we compared two genome-wide association approaches: a univariate (single-trait) method and a multivariate (multi-trait) method combining GLAI traits, grain yield, and precocity. The explicit modeling of correlation structure between secondary traits and grain yield in the multivariate mixed model led to 2.5 times more associations detected. A total of 475 quantitative trait loci (QTLs) were detected. The genetic architecture of GLAI traits appears less complex than that of yield with stronger-effect QTLs that are more stable between environments. We also showed that a subset of GLAI QTLs explains nearly one fifth of yield variability across a larger environmental network of 11 water-deficient trials. GLAI dynamics is a promising grain yield secondary trait in optimal and drought conditions, and the detected QTLs could help to increase breeding efficiency through a marker-assisted approach.
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Affiliation(s)
- Justin Blancon
- UMR GDEC, INRAE, Université Clermont Auvergne, 63000, Clermont-Ferrand, France.
- Biogemma, Centre de Recherche de Chappes, 63720, Chappes, France.
| | - Clément Buet
- Biogemma, Centre de Recherche de Chappes, 63720, Chappes, France
| | - Pierre Dubreuil
- Biogemma, Centre de Recherche de Chappes, 63720, Chappes, France
| | | | | | - Sébastien Praud
- Biogemma, Centre de Recherche de Chappes, 63720, Chappes, France
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Buchardt AS, Zhou X, Ekstrøm CT. Joint regression analysis of multiple traits based on genetic relationships. BIOINFORMATICS ADVANCES 2024; 4:vbad192. [PMID: 38264461 PMCID: PMC10805347 DOI: 10.1093/bioadv/vbad192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 12/11/2023] [Accepted: 12/31/2023] [Indexed: 01/25/2024]
Abstract
Motivation Polygenic scores (PGSs) are widely available and employed in genomic data analyses for predicting and understanding genetic architectures. Existing approaches either require information on SNP level, do not infer clusters of traits sharing genetic characteristic, or do not have any immediate predictive properties. Results Here, we present geneJAM, which is a novel clustering and estimation method using PGSs for inferring a genetic relationship among multiple, simultaneously measured and potentially correlated traits in a multivariate GWAS.Using graphical lasso, we estimate a sparse covariance matrix of the PGSs and obtain clusters of traits sharing genetic characteristics. We use the clusters to specify the structure of the error covariance matrix of a generalized least squares (GLS) model and use the feasible GLS estimator for estimating a linear regression model with a certain unknown degree of correlation between the residuals.The method suits many biology studies well with traits embedded in some genetic functioning groups and facilitates development of the PGS research. We compare the method with fully parametric techniques on simulated data and illustrate the utility of the methods by examining a heterogeneous stock mouse data set from the Wellcome Trust Centre for Human Genetics. We demonstrate that the method successfully identifies clusters of traits and increases precision, power, and computational efficiency. Availability and implementation GeneJAM is implemented in R and available at: https://github.com/abuchardt/geneJAM.
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Affiliation(s)
- Ann-Sophie Buchardt
- Department of Public Health, University of Copenhagen, 1014 Copenhagen, Denmark
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, United States
| | - Claus Thorn Ekstrøm
- Department of Public Health, University of Copenhagen, 1014 Copenhagen, Denmark
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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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Garrido-Martín D, Calvo M, Reverter F, Guigó R. A fast non-parametric test of association for multiple traits. Genome Biol 2023; 24:230. [PMID: 37828616 PMCID: PMC10571397 DOI: 10.1186/s13059-023-03076-8] [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: 04/25/2022] [Accepted: 09/27/2023] [Indexed: 10/14/2023] Open
Abstract
The increasing availability of multidimensional phenotypic data in large cohorts of genotyped individuals requires efficient methods to identify genetic effects on multiple traits. Permutational multivariate analysis of variance (PERMANOVA) offers a powerful non-parametric approach. However, it relies on permutations to assess significance, which hinders the analysis of large datasets. Here, we derive the limiting null distribution of the PERMANOVA test statistic, providing a framework for the fast computation of asymptotic p values. Our asymptotic test presents controlled type I error and high power, often outperforming parametric approaches. We illustrate its applicability in the context of QTL mapping and GWAS.
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Affiliation(s)
- Diego Garrido-Martín
- Department of Genetics, Microbiology and Statistics, Universitat de Barcelona (UB), Av. Diagonal 643, Barcelona, 08028, Spain.
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, 08003, Catalonia, Spain.
| | - Miquel Calvo
- Department of Genetics, Microbiology and Statistics, Universitat de Barcelona (UB), Av. Diagonal 643, Barcelona, 08028, Spain
| | - Ferran Reverter
- Department of Genetics, Microbiology and Statistics, Universitat de Barcelona (UB), Av. Diagonal 643, Barcelona, 08028, Spain
| | - Roderic Guigó
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, 08003, Catalonia, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain
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5
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Neumann A, Ohlei O, Küçükali F, Bos IJ, Timsina J, Vos S, Prokopenko D, Tijms BM, Andreasson U, Blennow K, Vandenberghe R, Scheltens P, Teunissen CE, Engelborghs S, Frisoni GB, Blin O, Richardson JC, Bordet R, Lleó A, Alcolea D, Popp J, Marsh TW, Gorijala P, Clark C, Peyratout G, Martinez-Lage P, Tainta M, Dobson RJB, Legido-Quigley C, Van Broeckhoven C, Tanzi RE, Ten Kate M, Lill CM, Barkhof F, Cruchaga C, Lovestone S, Streffer J, Zetterberg H, Visser PJ, Sleegers K, Bertram L. Multivariate GWAS of Alzheimer's disease CSF biomarker profiles implies GRIN2D in synaptic functioning. Genome Med 2023; 15:79. [PMID: 37794492 PMCID: PMC10548686 DOI: 10.1186/s13073-023-01233-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 09/12/2023] [Indexed: 10/06/2023] Open
Abstract
BACKGROUND Genome-wide association studies (GWAS) of Alzheimer's disease (AD) have identified several risk loci, but many remain unknown. Cerebrospinal fluid (CSF) biomarkers may aid in gene discovery and we previously demonstrated that six CSF biomarkers (β-amyloid, total/phosphorylated tau, NfL, YKL-40, and neurogranin) cluster into five principal components (PC), each representing statistically independent biological processes. Here, we aimed to (1) identify common genetic variants associated with these CSF profiles, (2) assess the role of associated variants in AD pathophysiology, and (3) explore potential sex differences. METHODS We performed GWAS for each of the five biomarker PCs in two multi-center studies (EMIF-AD and ADNI). In total, 973 participants (n = 205 controls, n = 546 mild cognitive impairment, n = 222 AD) were analyzed for 7,433,949 common SNPs and 19,511 protein-coding genes. Structural equation models tested whether biomarker PCs mediate genetic risk effects on AD, and stratified and interaction models probed for sex-specific effects. RESULTS Five loci showed genome-wide significant association with CSF profiles, two were novel (rs145791381 [inflammation] and GRIN2D [synaptic functioning]) and three were previously described (APOE, TMEM106B, and CHI3L1). Follow-up analyses of the two novel signals in independent datasets only supported the GRIN2D locus, which contains several functionally interesting candidate genes. Mediation tests indicated that variants in APOE are associated with AD status via processes related to amyloid and tau pathology, while markers in TMEM106B and CHI3L1 are associated with AD only via neuronal injury/inflammation. Additionally, seven loci showed sex-specific associations with AD biomarkers. CONCLUSIONS These results suggest that pathway and sex-specific analyses can improve our understanding of AD genetics and may contribute to precision medicine.
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Affiliation(s)
- Alexander Neumann
- Complex Genetics of Alzheimer's Disease Group, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Olena Ohlei
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Ratzeburger Allee 160, V50.2M, Lübeck, 23562, Germany
| | - Fahri Küçükali
- Complex Genetics of Alzheimer's Disease Group, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Isabelle J Bos
- Netherlands Institute for Health Services Research, Utrecht, Netherlands
| | - Jigyasha Timsina
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St Louis, MO, USA
| | - Stephanie Vos
- Alzheimer Centrum Limburg, Maastricht University, Maastricht, Netherlands
| | - Dmitry Prokopenko
- Genetics and Aging Unit and McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Betty M Tijms
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Ulf Andreasson
- Department of Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
- Neurology Service, University Hospital Leuven, Leuven, Belgium
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Charlotte E Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Sebastiaan Engelborghs
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Department of Neurology and Memory Clinic, Universitair Ziekenhuis Brussel (UZ Brussel) and Center for Neurosciences (C4N), Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Giovanni B Frisoni
- Memory Center, Department of Rehabilitation and Geriatrics, Geneva University and University Hospitals, Geneva, Switzerland
| | - Oliver Blin
- Clinical Pharmacology & Pharmacovigilance Department, Marseille University Hospital, Marseille, France
| | | | - Régis Bordet
- Neuroscience & Cognition, CHU de Lille, University of Lille, Inserm, France
| | - Alberto Lleó
- Memory Unit, Neurology Department, Hospital de Sant Pau, Barcelona, Spain
- Centro de Investigación Biomédica en Red en Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
| | - Daniel Alcolea
- Memory Unit, Neurology Department, Hospital de Sant Pau, Barcelona, Spain
- Centro de Investigación Biomédica en Red en Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
| | - Julius Popp
- Department of Psychiatry, Psychotherapy and Psychosomatics, University of Zürich, Zurich, Switzerland
- Department of Psychiatry, University Hospital of Lausanne, Lausanne, Switzerland
| | - Thomas W Marsh
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St Louis, MO, USA
- Division of Biology & Biomedical Sciences, Washington University in St. Louis, St Louis, MO, USA
| | - Priyanka Gorijala
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St Louis, MO, USA
| | - Christopher Clark
- Department of Psychiatry, Psychotherapy and Psychosomatics, University of Zürich, Zurich, Switzerland
| | - Gwendoline Peyratout
- Department of Psychiatry, University Hospital of Lausanne, Lausanne, Switzerland
| | - Pablo Martinez-Lage
- Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, San Sebastian, Spain
| | - Mikel Tainta
- Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, San Sebastian, Spain
- Zumarraga Hospital, Osakidetza, Integrated Health Organization (OSI) Goierri-Urola Garia, Basque Country, Spain
| | - Richard J B Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, Boston, UK
- NIHR BioResource Centre Maudsley, NIHR Maudsley Biomedical Research Centre (BRC) at South London and Maudsley NHS Foundation Trust (SLaM) & Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, London, UK
- Health Data Research UK London, University College London, London, UK
- Institute of Health Informatics, University College London, London, UK
- The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK
| | - Cristina Legido-Quigley
- Steno Diabetes Center, Copenhagen, Denmark
- Institute of Pharmaceutical Science, King's College London, London, UK
| | - Christine Van Broeckhoven
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Neurodegenerative Brain Diseases Group, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium
| | - Rudolph E Tanzi
- Genetics and Aging Unit and McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Mara Ten Kate
- Alzheimer Center and Department of Neurology, VU University Medical Center, Amsterdam, Netherlands
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, Netherlands
| | - Christina M Lill
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Ratzeburger Allee 160, V50.2M, Lübeck, 23562, Germany
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
- Ageing Epidemiology Research Unit, School of Public Health, Imperial College, London, UK
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit, Amsterdam, Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, UK
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St Louis, MO, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St Louis, MO, USA
| | - Simon Lovestone
- Janssen Medical Ltd, Wycombe, UK
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Johannes Streffer
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- AC Immune SA, Lausanne, Switzerland
- Janssen R&D, LLC, Beerse, Belgium
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
- UK Dementia Research Institute, University College London, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
| | - Pieter Jelle Visser
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Alzheimer Centrum Limburg, Maastricht University, Maastricht, Netherlands
- Alzheimer Center and Department of Neurology, VU University Medical Center, Amsterdam, Netherlands
| | - Kristel Sleegers
- Complex Genetics of Alzheimer's Disease Group, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Lars Bertram
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Ratzeburger Allee 160, V50.2M, Lübeck, 23562, Germany.
- Centre for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway.
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Roth K, Pröll-Cornelissen MJ, Henne H, Appel AK, Schellander K, Tholen E, Große-Brinkhaus C. Multivariate genome-wide associations for immune traits in two maternal pig lines. BMC Genomics 2023; 24:492. [PMID: 37641029 PMCID: PMC10463314 DOI: 10.1186/s12864-023-09594-w] [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: 02/01/2023] [Accepted: 08/16/2023] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND Immune traits are considered to serve as potential biomarkers for pig's health. Medium to high heritabilities have been observed for some of the immune traits suggesting genetic variability of these phenotypes. Consideration of previously established genetic correlations between immune traits can be used to identify pleiotropic genetic markers. Therefore, genome-wide association study (GWAS) approaches are required to explore the joint genetic foundation for health biomarkers. Usually, GWAS explores phenotypes in a univariate (uv), trait-by-trait manner. Besides two uv GWAS methods, four multivariate (mv) GWAS approaches were applied on combinations out of 22 immune traits for Landrace (LR) and Large White (LW) pig lines. RESULTS In total 433 (LR: 351, LW: 82) associations were identified with the uv approach implemented in PLINK and a Bayesian linear regression uv approach (BIMBAM) software. Single Nucleotide Polymorphisms (SNPs) that were identified with both uv approaches (n = 32) were mostly associated with immune traits such as haptoglobin, red blood cell characteristics and cytokines, and were located in protein-coding genes. Mv GWAS approaches detected 647 associations for different mv immune trait combinations which were summarized to 133 Quantitative Trait Loci (QTL). SNPs for different trait combinations (n = 66) were detected with more than one mv method. Most of these SNPs are associated with red blood cell related immune trait combinations. Functional annotation of these QTL revealed 453 immune-relevant protein-coding genes. With uv methods shared markers were not observed between the breeds, whereas mv approaches were able to detect two conjoint SNPs for LR and LW. Due to unmapped positions for these markers, their functional annotation was not clarified. CONCLUSIONS This study evaluated the joint genetic background of immune traits in LR and LW piglets through the application of various uv and mv GWAS approaches. In comparison to uv methods, mv methodologies identified more significant associations, which might reflect the pleiotropic background of the immune system more accurately. In genetic research of complex traits, the SNP effects are generally small. Furthermore, one genetic variant can affect several correlated immune traits at the same time, termed pleiotropy. As mv GWAS methods consider strong dependencies among traits, the power to detect SNPs can be boosted. Both methods revealed immune-relevant potential candidate genes. Our results indicate that one single test is not able to detect all the different types of genetic effects in the most powerful manner and therefore, the methods should be applied complementary.
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Affiliation(s)
- Katharina Roth
- Institute of Animal Science, University of Bonn, Endenicher Allee 15, 53115, Bonn, Germany
| | | | - Hubert Henne
- BHZP GmbH, An der Wassermühle 8, 21368, Dahlenburg-Ellringen, Germany
| | | | - Karl Schellander
- Institute of Animal Science, University of Bonn, Endenicher Allee 15, 53115, Bonn, Germany
| | - Ernst Tholen
- Institute of Animal Science, University of Bonn, Endenicher Allee 15, 53115, Bonn, Germany
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Ting BW, Wright FA, Zhou YH. Simultaneous modeling of multivariate heterogeneous responses and heteroskedasticity via a two-stage composite likelihood. Biom J 2023; 65:e2200029. [PMID: 37212427 PMCID: PMC10524370 DOI: 10.1002/bimj.202200029] [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: 01/31/2022] [Revised: 02/08/2023] [Accepted: 03/13/2023] [Indexed: 05/23/2023]
Abstract
Multivariate heterogeneous responses and heteroskedasticity have attracted increasing attention in recent years. In genome-wide association studies, effective simultaneous modeling of multiple phenotypes would improve statistical power and interpretability. However, a flexible common modeling system for heterogeneous data types can pose computational difficulties. Here we build upon a previous method for multivariate probit estimation using a two-stage composite likelihood that exhibits favorable computational time while retaining attractive parameter estimation properties. We extend this approach to incorporate multivariate responses of heterogeneous data types (binary and continuous), and possible heteroskedasticity. Although the approach has wide applications, it would be particularly useful for genomics, precision medicine, or individual biomedical prediction. Using a genomics example, we explore statistical power and confirm that the approach performs well for hypothesis testing and coverage percentages under a wide variety of settings. The approach has the potential to better leverage genomics data and provide interpretable inference for pleiotropy, in which a locus is associated with multiple traits.
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Affiliation(s)
- Bryan W. Ting
- Bioinformatics Research Center, North Carolina State University, NC, USA
| | - Fred A. Wright
- Bioinformatics Research Center, North Carolina State University, NC, USA
- Department of Statistics, North Carolina State University, NC, USA
- Department of Biological Sciences, North Carolina State University, NC, USA
| | - Yi-Hui Zhou
- Bioinformatics Research Center, North Carolina State University, NC, USA
- Department of Statistics, North Carolina State University, NC, USA
- Department of Biological Sciences, North Carolina State University, NC, USA
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8
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Lee S, Yang HK, Lee HJ, Park DJ, Kong SH, Park SK. Cross-phenotype association analysis of gastric cancer: in-silico functional annotation based on the disease-gene network. Gastric Cancer 2023; 26:517-527. [PMID: 36995485 DOI: 10.1007/s10120-023-01380-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 03/02/2023] [Indexed: 03/31/2023]
Abstract
BACKGROUND A gene or variant has pleiotropic effects, and genetic variant identification across multiple phenotypes can provide a comprehensive understanding of biological pathways shared among different diseases or phenotypes. Discovery of genetic loci associated with multiple diseases can simultaneously support general interventions. Several meta-analyses have shown genetic associations with gastric cancer (GC); however, no study has identified associations with other phenotypes using this approach. METHODS Here, we applied disease network analysis and gene-based analysis (GBA) to examine genetic variants linked to GC and simultaneously associated with other phenotypes. We conducted a single-nucleotide polymorphism (SNP) level meta-analysis and GBA through a systematic genome-wide association study (GWAS) linked to GC, to integrate published results for the SNP variants and group them into major GC-associated genes. We then performed disease network and expression quantitative trait loci (eQTL) analyses to evaluate cross-phenotype associations and expression levels of GC-related genes. RESULTS Seven genes (MTX1, GBAP1, MUC1, TRIM46, THBS3, PSCA, and ABO) were associated with GC as well as blood urea nitrogen (BUN), glomerular filtration rate (GFR), and uric acid (UA). In addition, 17 SNPs regulated the expression of genes located on 1q22, 24 SNPs regulated the expression of PSCA on 8q24.3, and rs7849820 regulated the expression of ABO on 9q34.2. Furthermore, rs1057941 and rs2294008 had the highest posterior causal probabilities of being a causal candidate SNP in 1q22, and 8q24.3, respectively. CONCLUSIONS These findings identified seven GC-associated genes exhibiting a cross-association with GFR, BUN, and UA.
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Affiliation(s)
- Sangjun Lee
- Department of Preventive Medicine, Seoul National University College of Medicine, 103 Daehak-Ro, Jongro-Gu, Seoul, 03080, Korea
- Cancer Research Institute, Seoul National University, Seoul, Korea
- Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, Korea
| | - Han-Kwang Yang
- Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Hyuk-Joon Lee
- Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Do Joong Park
- Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Seong-Ho Kong
- Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Sue K Park
- Department of Preventive Medicine, Seoul National University College of Medicine, 103 Daehak-Ro, Jongro-Gu, Seoul, 03080, Korea.
- Cancer Research Institute, Seoul National University, Seoul, Korea.
- Integrated Major in Innovative Medical Science, Seoul National University College of Medicine, Seoul, Korea.
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Macri M, Luigi-Sierra MG, Guan D, Delgado JV, Alvarez JF, Amills M, Martínez AM. Univariate and multivariate genome-wide association studies for hematological traits in Murciano-Granadina goats. Anim Genet 2023. [PMID: 37127297 DOI: 10.1111/age.13328] [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: 07/26/2022] [Revised: 04/17/2023] [Accepted: 04/19/2023] [Indexed: 05/03/2023]
Abstract
Hematological traits are important indicators of health status, and they are frequently used as criteria for clinical diagnosis. In humans, the genomic architecture of blood traits has been investigated in depth and thousands of associations with genetic variants have been found. In contrast, the association between marker genotypes and the variation of hematological traits has not been investigated in goats yet. Herewith, we have recorded 12 hematological parameters in 882 Murciano-Granadina goats that were also genotyped with the Goat SNP50 BeadChip (Illumina). Performance of a univariate genome-wide association study (GWAS) made it possible to detect one genomic region on goat chromosome (CHI) 21 (19.2-19.5 Mb) associated, at the genome-wide level of significance, with 4 red blood cell traits. The three markers displaying the highest significances were rs268272996 (CHI21: 19225290 bp), rs268273004 (CHI21: 19565629 bp) and rs268239059 (CHI13: 9615190 bp). Consistently, a multivariate GWAS indicated that the rs268273004 marker on chromosome 21 is associated with seven blood cell traits. Interestingly, this marker maps close to the FA Complementation Group I (FANCI) gene (CHI21: 20021947-20077025 bp), which is functionally related to Fanconi anemia, a syndrome characterized by bone marrow failure, aplastic anemia, and congenital disorders. We have also uncovered additional chromosome-wide significant associations between genetic markers and erythrocyte and leukocyte traits in the univariate GWAS. These findings evidence that the phenotypic variation of hematological traits in goats is regulated, at least to some extent, by polygenic determinants distributed in multiple chromosomes.
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Affiliation(s)
- Martina Macri
- Animal Breeding Consulting S.L. Parque Científico Tecnológico de Córdoba, Córdoba, Spain
- Departamento de Genética, Universidad de Córdoba, Campus Universitario de Rabanales, Córdoba, Spain
| | - Maria Gracia Luigi-Sierra
- Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Dailu Guan
- Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Juan Vicente Delgado
- Departamento de Genética, Universidad de Córdoba, Campus Universitario de Rabanales, Córdoba, Spain
| | - Javier Fernández Alvarez
- Asociación Nacional de Criadores de Caprino de Raza Murciano-Granadina (CAPRIGRAN), Granada, Spain
| | - Marcel Amills
- Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Universitat Autònoma de Barcelona, Bellaterra, Spain
- Departament de Ciència Animal i dels Aliments, Facultat de Veterinària, Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Amparo Martínez Martínez
- Departamento de Genética, Universidad de Córdoba, Campus Universitario de Rabanales, Córdoba, Spain
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10
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Nosková A, Mehrotra A, Kadri NK, Lloret-Villas A, Neuenschwander S, Hofer A, Pausch H. Comparison of two multi-trait association testing methods and sequence-based fine mapping of six additive QTL in Swiss Large White pigs. BMC Genomics 2023; 24:192. [PMID: 37038103 PMCID: PMC10084639 DOI: 10.1186/s12864-023-09295-4] [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] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 04/04/2023] [Indexed: 04/12/2023] Open
Abstract
BACKGROUND Genetic correlations between complex traits suggest that pleiotropic variants contribute to trait variation. Genome-wide association studies (GWAS) aim to uncover the genetic underpinnings of traits. Multivariate association testing and the meta-analysis of summary statistics from single-trait GWAS enable detecting variants associated with multiple phenotypes. In this study, we used array-derived genotypes and phenotypes for 24 reproduction, production, and conformation traits to explore differences between the two methods and used imputed sequence variant genotypes to fine-map six quantitative trait loci (QTL). RESULTS We considered genotypes at 44,733 SNPs for 5,753 pigs from the Swiss Large White breed that had deregressed breeding values for 24 traits. Single-trait association analyses revealed eleven QTL that affected 15 traits. Multi-trait association testing and the meta-analysis of the single-trait GWAS revealed between 3 and 6 QTL, respectively, in three groups of traits. The multi-trait methods revealed three loci that were not detected in the single-trait GWAS. Four QTL that were identified in the single-trait GWAS, remained undetected in the multi-trait analyses. To pinpoint candidate causal variants for the QTL, we imputed the array-derived genotypes to the sequence level using a sequenced reference panel consisting of 421 pigs. This approach provided genotypes at 16 million imputed sequence variants with a mean accuracy of imputation of 0.94. The fine-mapping of six QTL with imputed sequence variant genotypes revealed four previously proposed causal mutations among the top variants. CONCLUSIONS Our findings in a medium-size cohort of pigs suggest that multivariate association testing and the meta-analysis of summary statistics from single-trait GWAS provide very similar results. Although multi-trait association methods provide a useful overview of pleiotropic loci segregating in mapping populations, the investigation of single-trait association studies is still advised, as multi-trait methods may miss QTL that are uncovered in single-trait GWAS.
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Affiliation(s)
- A Nosková
- ETH Zürich, Universitätstrasse 2, 8092, Zürich, Switzerland.
| | - A Mehrotra
- ETH Zürich, Universitätstrasse 2, 8092, Zürich, Switzerland
| | - N K Kadri
- ETH Zürich, Universitätstrasse 2, 8092, Zürich, Switzerland
| | | | | | - A Hofer
- SUISAG, Allmend 10, 6204, Sempach, Switzerland
| | - H Pausch
- ETH Zürich, Universitätstrasse 2, 8092, Zürich, Switzerland
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11
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Chu BB, Ko S, Zhou JJ, Jensen A, Zhou H, Sinsheimer JS, Lange K. Multivariate genome-wide association analysis by iterative hard thresholding. Bioinformatics 2023; 39:btad193. [PMID: 37067496 PMCID: PMC10133532 DOI: 10.1093/bioinformatics/btad193] [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: 07/28/2022] [Revised: 04/07/2023] [Accepted: 04/13/2023] [Indexed: 04/18/2023] Open
Abstract
MOTIVATION In a genome-wide association study, analyzing multiple correlated traits simultaneously is potentially superior to analyzing the traits one by one. Standard methods for multivariate genome-wide association study operate marker-by-marker and are computationally intensive. RESULTS We present a sparsity constrained regression algorithm for multivariate genome-wide association study based on iterative hard thresholding and implement it in a convenient Julia package MendelIHT.jl. In simulation studies with up to 100 quantitative traits, iterative hard thresholding exhibits similar true positive rates, smaller false positive rates, and faster execution times than GEMMA's linear mixed models and mv-PLINK's canonical correlation analysis. On UK Biobank data with 470 228 variants, MendelIHT completed a three-trait joint analysis (n=185 656) in 20 h and an 18-trait joint analysis (n=104 264) in 53 h with an 80 GB memory footprint. In short, MendelIHT enables geneticists to fit a single regression model that simultaneously considers the effect of all SNPs and dozens of traits. AVAILABILITY AND IMPLEMENTATION Software, documentation, and scripts to reproduce our results are available from https://github.com/OpenMendel/MendelIHT.jl.
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Affiliation(s)
- Benjamin B Chu
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095-1554, United States
| | - Seyoon Ko
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095-1554, United States
- Department of Biostatistics, Fielding School of Public Health at UCLA, Los Angeles, CA 90095-1554, United States
| | - Jin J Zhou
- Department of Biostatistics, Fielding School of Public Health at UCLA, Los Angeles, CA 90095-1554, United States
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095-1554, United States
| | - Aubrey Jensen
- Department of Biostatistics, Fielding School of Public Health at UCLA, Los Angeles, CA 90095-1554, United States
| | - Hua Zhou
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095-1554, United States
- Department of Biostatistics, Fielding School of Public Health at UCLA, Los Angeles, CA 90095-1554, United States
| | - Janet S Sinsheimer
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095-1554, United States
- Department of Biostatistics, Fielding School of Public Health at UCLA, Los Angeles, CA 90095-1554, United States
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095-1554, United States
| | - Kenneth Lange
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095-1554, United States
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095-1554, United States
- Department of Statistics at UCLA, Los Angeles, CA 90095-1554, United States
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12
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Sajal IH, Biswas S. Bivariate quantitative Bayesian LASSO for detecting association of rare haplotypes with two correlated continuous phenotypes. Front Genet 2023; 14:1104727. [PMID: 36968609 PMCID: PMC10033866 DOI: 10.3389/fgene.2023.1104727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 02/21/2023] [Indexed: 03/12/2023] Open
Abstract
In genetic association studies, the multivariate analysis of correlated phenotypes offers statistical and biological advantages compared to analyzing one phenotype at a time. The joint analysis utilizes additional information contained in the correlation and avoids multiple testing. It also provides an opportunity to investigate and understand shared genetic mechanisms of multiple phenotypes. Bivariate logistic Bayesian LASSO (LBL) was proposed earlier to detect rare haplotypes associated with two binary phenotypes or one binary and one continuous phenotype jointly. There is currently no haplotype association test available that can handle multiple continuous phenotypes. In this study, by employing the framework of bivariate LBL, we propose bivariate quantitative Bayesian LASSO (QBL) to detect rare haplotypes associated with two continuous phenotypes. Bivariate QBL removes unassociated haplotypes by regularizing the regression coefficients and utilizing a latent variable to model correlation between two phenotypes. We carry out extensive simulations to investigate the performance of bivariate QBL and compare it with that of a standard (univariate) haplotype association test, Haplo.score (applied twice to two phenotypes individually). Bivariate QBL performs better than Haplo.score in all simulations with varying degrees of power gain. We analyze Genetic Analysis Workshop 19 exome sequencing data on systolic and diastolic blood pressures and detect several rare haplotypes associated with the two phenotypes.
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13
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Soremekun C, Machipisa T, Soremekun O, Pirie F, Oyekanmi N, Motala AA, Chikowore T, Fatumo S. Multivariate GWAS analysis reveals loci associated with liver functions in continental African populations. PLoS One 2023; 18:e0280344. [PMID: 36809439 PMCID: PMC9942994 DOI: 10.1371/journal.pone.0280344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 12/27/2022] [Indexed: 02/23/2023] Open
Abstract
BACKGROUND Liver disease is any condition that causes liver damage and inflammation and may likely affect the function of the liver. Vital biochemical screening tools that can be used to evaluate the health of the liver and help diagnose, prevent, monitor, and control the development of liver disease are known as liver function tests (LFT). LFTs are performed to estimate the level of liver biomarkers in the blood. Several factors are associated with differences in concentration levels of LFTs in individuals, such as genetic and environmental factors. The aim of our study was to identify genetic loci associated with liver biomarker levels with a shared genetic basis in continental Africans, using a multivariate genome-wide association study (GWAS) approach. METHODS We used two distinct African populations, the Ugandan Genome Resource (UGR = 6,407) and South African Zulu cohort (SZC = 2,598). The six LFTs used in our analysis were: aspartate transaminase (AST), alanine transaminase (ALT), alkaline phosphatase (ALP), gamma-glutamyl transferase (GGT), total bilirubin, and albumin. A multivariate GWAS of LFTs was conducted using the exact linear mixed model (mvLMM) approach implemented in GEMMA and the resulting P-values were presented in Manhattan and quantile-quantile (QQ) plots. First, we attempted to replicate the findings of the UGR cohort in SZC. Secondly, given that the genetic architecture of UGR is different from that of SZC, we further undertook similar analysis in the SZC and discussed the results separately. RESULTS A total of 59 SNPs reached genome-wide significance (P = 5x10-8) in the UGR cohort and with 13 SNPs successfully replicated in SZC. These included a novel lead SNP near the RHPN1 locus (lead SNP rs374279268, P-value = 4.79x10-9, Effect Allele Frequency (EAF) = 0.989) and a lead SNP at the RGS11 locus (lead SNP rs148110594, P-value = 2.34x10-8, EAF = 0.928). 17 SNPs were significant in the SZC, while all the SNPs fall within a signal on chromosome 2, rs1976391 mapped to UGT1A was identified as the lead SNP within this region. CONCLUSIONS Using multivariate GWAS method improves the power to detect novel genotype-phenotype associations for liver functions not found with the standard univariate GWAS in the same dataset.
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Affiliation(s)
- Chisom Soremekun
- The African Computational Genomics (TACG) Research Group, MRC/UVRI, and LSHTM, Entebbe, Uganda
- Department of Immunology and Molecular Biology, College of Health Science, Makerere University, Kampala, Uganda
- H3Africa Bioinformatics Network (H3ABioNet) Node, Centre for Genomics Research and Innovation, NABDA/FMST, Abuja, Nigeria
| | - Tafadzwa Machipisa
- Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
- Department of Medicine, Hatter Institute for Cardiovascular Diseases Research in Africa and Cape Heart Institute, University of Cape Town, Cape Town, South Africa
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, Ontario, Canada
| | - Opeyemi Soremekun
- The African Computational Genomics (TACG) Research Group, MRC/UVRI, and LSHTM, Entebbe, Uganda
- Molecular Bio-Computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban, South Africa
| | - Fraser Pirie
- Department of Diabetes and Endocrinology, University of KwaZulu-Natal, Durban, South Africa
| | - Nashiru Oyekanmi
- H3Africa Bioinformatics Network (H3ABioNet) Node, Centre for Genomics Research and Innovation, NABDA/FMST, Abuja, Nigeria
| | - Ayesha A. Motala
- Department of Diabetes and Endocrinology, University of KwaZulu-Natal, Durban, South Africa
| | - Tinashe Chikowore
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Department of Pediatrics, MRC/Wits Developmental Pathways for Health Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Segun Fatumo
- The African Computational Genomics (TACG) Research Group, MRC/UVRI, and LSHTM, Entebbe, Uganda
- H3Africa Bioinformatics Network (H3ABioNet) Node, Centre for Genomics Research and Innovation, NABDA/FMST, Abuja, Nigeria
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
- * E-mail:
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14
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Küçükali F, Neumann A, Van Dongen J, De Pooter T, Joris G, De Rijk P, Ohlei O, Dobricic V, Bos I, Vos SJB, Engelborghs S, De Roeck E, Vandenberghe R, Gabel S, Meersmans K, Tsolaki M, Verhey F, Martinez‐Lage P, Tainta M, Frisoni G, Blin O, Richardson JC, Bordet R, Scheltens P, Popp J, Peyratout G, Johannsen P, Frölich L, Freund‐Levi Y, Streffer J, Lovestone S, Legido‐Quigley C, Kate MT, Barkhof F, Zetterberg H, Bertram L, Strazisar M, Visser PJ, Van Broeckhoven C, Sleegers K. Whole‐exome rare‐variant analysis of Alzheimer's disease and related biomarker traits. Alzheimers Dement 2022. [DOI: 10.1002/alz.12842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 09/16/2022] [Accepted: 09/28/2022] [Indexed: 12/08/2022]
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15
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Loughnan RJ, Shadrin AA, Frei O, van der Meer D, Zhao W, Palmer CE, Thompson WK, Makowski C, Jernigan TL, Andreassen OA, Fan CC, Dale AM. Generalization of cortical MOSTest genome-wide associations within and across samples. Neuroimage 2022; 263:119632. [PMID: 36115590 PMCID: PMC10635842 DOI: 10.1016/j.neuroimage.2022.119632] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 08/25/2022] [Accepted: 09/13/2022] [Indexed: 12/12/2022] Open
Abstract
Genome-Wide Association studies have typically been limited to univariate analysis in which a single outcome measure is tested against millions of variants. Recent work demonstrates that a Multivariate Omnibus Statistic Test (MOSTest) is well powered to discover genomic effects distributed across multiple phenotypes. Applied to cortical brain MRI morphology measures, MOSTest has resulted in a drastic improvement in power to discover loci when compared to established approaches (min-P). One question that arises is how well these discovered loci replicate in independent data. Here we perform 10 times cross validation within 34,973 individuals from UK Biobank for imaging measures of cortical area, thickness and sulcal depth (>1,000 dimensionality for each). By deploying a replication method that aggregates discovered effects distributed across multiple phenotypes, termed PolyVertex Score (MOSTest-PVS), we demonstrate a higher replication yield and comparable replication rate of discovered loci for MOSTest (# replicated loci: 242-496, replication rate: 96-97%) in independent data when compared with the established min-P approach (# replicated loci: 26-55, replication rate: 91-93%). An out-of-sample replication of discovered loci was conducted with a sample of 4,069 individuals from the Adolescent Brain Cognitive Development® (ABCD) study, who are on average 50 years younger than UK Biobank individuals. We observe a higher replication yield and comparable replication rate of MOSTest-PVS compared to min-P. This finding underscores the importance of using well-powered multivariate techniques for both discovery and replication of high dimensional phenotypes in Genome-Wide Association studies.
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Affiliation(s)
- Robert J Loughnan
- Department of Cognitive Science, University of California, 9500 Gilman Drive, La Jolla, San Diego, CA 92093, USA; Population Neuroscience and Genetics, University of California, 9500 Gilman Drive, La Jolla, San Diego, CA 92161, USA; Center for Multimodal Imaging and Genetics, San Diego School of Medicine, University of California, 9444 Medical Center Dr, La Jolla, CA 92037, USA.
| | - Alexey A Shadrin
- Department of Informatics, Centre for Bioinformatics, University of Oslo, Oslo, Norway
| | - Oleksandr Frei
- Division of Mental Health and Addiction, NORMENT Centre, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Informatics, Centre for Bioinformatics, University of Oslo, Oslo, Norway
| | - Dennis van der Meer
- Division of Mental Health and Addiction, NORMENT Centre, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Faculty of Health, Medicine and Life Sciences, School of Mental Health and Neuroscience, Maastricht University, the Netherlands
| | - Weiqi Zhao
- Department of Cognitive Science, University of California, 9500 Gilman Drive, La Jolla, San Diego, CA 92093, USA
| | - Clare E Palmer
- Center for Human Development, University of California, 9500 Gilman Drive, La Jolla, San Diego, CA 92161, USA
| | - Wesley K Thompson
- Population Neuroscience and Genetics, University of California, 9500 Gilman Drive, La Jolla, San Diego, CA 92161, USA; Herbert Wertheim School of Public Health, University of California, La Jolla, San Diego, CA, USA; Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research, Tulsa, OK, USA
| | - Carolina Makowski
- Center for Multimodal Imaging and Genetics, San Diego School of Medicine, University of California, 9444 Medical Center Dr, La Jolla, CA 92037, USA; Department of Radiology, San Diego School of Medicine, University of California, 9500 Gilman Drive, La Jolla, CA 92037, USA
| | - Terry L Jernigan
- Department of Cognitive Science, University of California, 9500 Gilman Drive, La Jolla, San Diego, CA 92093, USA; Center for Human Development, University of California, 9500 Gilman Drive, La Jolla, San Diego, CA 92161, USA; Department of Radiology, San Diego School of Medicine, University of California, 9500 Gilman Drive, La Jolla, CA 92037, USA; Department of Psychiatry, San Diego School of Medicine, University of California, 9500 Gilman Drive, La Jolla, CA 92037, USA
| | - Ole A Andreassen
- Department of Informatics, Centre for Bioinformatics, University of Oslo, Oslo, Norway
| | - Chun Chieh Fan
- Population Neuroscience and Genetics, University of California, 9500 Gilman Drive, La Jolla, San Diego, CA 92161, USA; Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research, Tulsa, OK, USA; Department of Radiology, San Diego School of Medicine, University of California, 9500 Gilman Drive, La Jolla, CA 92037, USA; Department of Radiology, Schoold of Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92037, USA
| | - Anders M Dale
- Department of Cognitive Science, University of California, 9500 Gilman Drive, La Jolla, San Diego, CA 92093, USA; Center for Multimodal Imaging and Genetics, San Diego School of Medicine, University of California, 9444 Medical Center Dr, La Jolla, CA 92037, USA; Department of Radiology, San Diego School of Medicine, University of California, 9500 Gilman Drive, La Jolla, CA 92037, USA; Department of Neuroscience, San Diego School of Medicine, University of California, 9500 Gilman Drive, La Jolla, CA 92037, USA.
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16
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Svishcheva GR, Tiys ES, Elgaeva EE, Feoktistova SG, Timmers PRHJ, Sharapov SZ, Axenovich TI, Tsepilov YA. A Novel Framework for Analysis of the Shared Genetic Background of Correlated Traits. Genes (Basel) 2022; 13:1694. [PMID: 36292579 PMCID: PMC9602050 DOI: 10.3390/genes13101694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 09/12/2022] [Accepted: 09/16/2022] [Indexed: 11/16/2022] Open
Abstract
We propose a novel effective framework for the analysis of the shared genetic background for a set of genetically correlated traits using SNP-level GWAS summary statistics. This framework called SHAHER is based on the construction of a linear combination of traits by maximizing the proportion of its genetic variance explained by the shared genetic factors. SHAHER requires only full GWAS summary statistics and matrices of genetic and phenotypic correlations between traits as inputs. Our framework allows both shared and unshared genetic factors to be effectively analyzed. We tested our framework using simulation studies, compared it with previous developments, and assessed its performance using three real datasets: anthropometric traits, psychiatric conditions and lipid concentrations. SHAHER is versatile and applicable to summary statistics from GWASs with arbitrary sample sizes and sample overlaps, allows for the incorporation of different GWAS models (Cox, linear and logistic), and is computationally fast.
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Affiliation(s)
- Gulnara R. Svishcheva
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
- Vavilov Institute of General Genetics, Russian Academy of Sciences, 117971 Moscow, Russia
| | - Evgeny S. Tiys
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
| | - Elizaveta E. Elgaeva
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
- Novosibirsk State University, 630090 Novosibirsk, Russia
| | - Sofia G. Feoktistova
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
| | - Paul R. H. J. Timmers
- MRC Human Genetics Unit, MRC Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH8 9YL, UK
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh EH8 9YL, UK
| | - Sodbo Zh. Sharapov
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
- Novosibirsk State University, 630090 Novosibirsk, Russia
| | - Tatiana I. Axenovich
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
| | - Yakov A. Tsepilov
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
- Novosibirsk State University, 630090 Novosibirsk, Russia
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17
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Reinert S. Quantitative genetics of pleiotropy and its potential for plant sciences. JOURNAL OF PLANT PHYSIOLOGY 2022; 276:153784. [PMID: 35944292 DOI: 10.1016/j.jplph.2022.153784] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 07/14/2022] [Accepted: 07/18/2022] [Indexed: 06/15/2023]
Affiliation(s)
- Stephan Reinert
- Friedrich-Alexander-University Erlangen-Nürnberg, Department of Biology, Division of Biochemistry, Biocomputing Lab, Staudtstraße 5, 91058, Erlangen, Germany.
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18
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Tan VY, Timpson NJ. The UK Biobank: A Shining Example of Genome-Wide Association Study Science with the Power to Detect the Murky Complications of Real-World Epidemiology. Annu Rev Genomics Hum Genet 2022; 23:569-589. [PMID: 35508184 DOI: 10.1146/annurev-genom-121321-093606] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Genome-wide association studies (GWASs) have successfully identified thousands of genetic variants that are reliably associated with human traits. Although GWASs are restricted to certain variant frequencies, they have improved our understanding of the genetic architecture of complex traits and diseases. The UK Biobank (UKBB) has brought substantial analytical opportunity and performance to association studies. The dramatic expansion of many GWAS sample sizes afforded by the inclusion of UKBB data has improved the power of estimation of effect sizes but, critically, has done so in a context where phenotypic depth and precision enable outcome dissection and the application of epidemiological approaches. However, at the same time, the availability of such a large, well-curated, and deeply measured population-based collection has the capacity to increase our exposure to the many complications and inferential complexities associated with GWASs and other analyses. In this review, we discuss the impact that UKBB has had in the GWAS era, some of the opportunities that it brings, and exemplar challenges that illustrate the reality of using data from this world-leading resource.
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Affiliation(s)
- Vanessa Y Tan
- Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom;
- Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Nicholas J Timpson
- Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom;
- Bristol Medical School, University of Bristol, Bristol, United Kingdom
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Neumann A, Nolte IM, Pappa I, Ahluwalia TS, Pettersson E, Rodriguez A, Whitehouse A, van Beijsterveldt CEM, Benyamin B, Hammerschlag AR, Helmer Q, Karhunen V, Krapohl E, Lu Y, van der Most PJ, Palviainen T, St Pourcain B, Seppälä I, Suarez A, Vilor-Tejedor N, Tiesler CMT, Wang C, Wills A, Zhou A, Alemany S, Bisgaard H, Bønnelykke K, Davies GE, Hakulinen C, Henders AK, Hyppönen E, Stokholm J, Bartels M, Hottenga JJ, Heinrich J, Hewitt J, Keltikangas-Järvinen L, Korhonen T, Kaprio J, Lahti J, Lahti-Pulkkinen M, Lehtimäki T, Middeldorp CM, Najman JM, Pennell C, Power C, Oldehinkel AJ, Plomin R, Räikkönen K, Raitakari OT, Rimfeld K, Sass L, Snieder H, Standl M, Sunyer J, Williams GM, Bakermans-Kranenburg MJ, Boomsma DI, van IJzendoorn MH, Hartman CA, Tiemeier H. A genome-wide association study of total child psychiatric problems scores. PLoS One 2022; 17:e0273116. [PMID: 35994476 PMCID: PMC9394806 DOI: 10.1371/journal.pone.0273116] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 08/02/2022] [Indexed: 11/25/2022] Open
Abstract
Substantial genetic correlations have been reported across psychiatric disorders and numerous cross-disorder genetic variants have been detected. To identify the genetic variants underlying general psychopathology in childhood, we performed a genome-wide association study using a total psychiatric problem score. We analyzed 6,844,199 common SNPs in 38,418 school-aged children from 20 population-based cohorts participating in the EAGLE consortium. The SNP heritability of total psychiatric problems was 5.4% (SE = 0.01) and two loci reached genome-wide significance: rs10767094 and rs202005905. We also observed an association of SBF2, a gene associated with neuroticism in previous GWAS, with total psychiatric problems. The genetic effects underlying the total score were shared with common psychiatric disorders only (attention-deficit/hyperactivity disorder, anxiety, depression, insomnia) (rG > 0.49), but not with autism or the less common adult disorders (schizophrenia, bipolar disorder, or eating disorders) (rG < 0.01). Importantly, the total psychiatric problem score also showed at least a moderate genetic correlation with intelligence, educational attainment, wellbeing, smoking, and body fat (rG > 0.29). The results suggest that many common genetic variants are associated with childhood psychiatric symptoms and related phenotypes in general instead of with specific symptoms. Further research is needed to establish causality and pleiotropic mechanisms between related traits.
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Affiliation(s)
- Alexander Neumann
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada
| | - Ilja M. Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Irene Pappa
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Tarunveer S. Ahluwalia
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
| | - Erik Pettersson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Alina Rodriguez
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Andrew Whitehouse
- Telethon Kids Institute, University of Western Australia, Perth, Australia
| | | | - Beben Benyamin
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, School of Health Sciences, University of South Australia, Adelaide, Australia
- South Australian Health and Medical Research Institute, Adelaide, Australia
| | - Anke R. Hammerschlag
- Child Health Research Centre, University of Queensland, Brisbane, QLD, Australia
- Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Quinta Helmer
- Netherlands Twin Register, Dept Biological Psychology, Vrije Universiteit, Amsterdam, The Netherlands
| | - Ville Karhunen
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- Centre for Life-Course Health Research, University of Oulu, Oulu, Finland
| | - Eva Krapohl
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Yi Lu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Peter J. van der Most
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Teemu Palviainen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Beate St Pourcain
- MRC Integrative Epidemiology Unit, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherland
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Ilkka Seppälä
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center—Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Anna Suarez
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Natalia Vilor-Tejedor
- Center for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
- BarcelonaBeta Brain Research Center (BBRC)–Pasqual Maragall Foundation, Barcelona, Spain
- Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, The Netherlands
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Carla M. T. Tiesler
- Institute of Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- LMU–Ludwig-Maximilians-Universität Munich, Div. Metabolic and Nutritional Medicine, Dr. von Hauner Children’s Hospital, University of Munich Medical Center, Munich, Germany
| | - Carol Wang
- School of Medicine and Public Health, Faculty of Medicine and Health, The University of Newcastle, Newcastle, New South Wales, Australia
| | - Amanda Wills
- Division of Substance Dependence, Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, United States of America
| | - Ang Zhou
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, School of Health Sciences, University of South Australia, Adelaide, Australia
- South Australian Health and Medical Research Institute, Adelaide, Australia
| | - Silvia Alemany
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
- CIBER Epidemiology and Public Health (CIBERESP), Barcelona, Spain
| | - Hans Bisgaard
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Klaus Bønnelykke
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Gareth E. Davies
- Avera Institute for Human Genetics, Sioux Falls, South Dakota, United States of America
| | - Christian Hakulinen
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Anjali K. Henders
- Institute of Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Elina Hyppönen
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, School of Health Sciences, University of South Australia, Adelaide, Australia
- South Australian Health and Medical Research Institute, Adelaide, Australia
- Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health, London, United Kingdom
| | - Jakob Stokholm
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
- Department of Pediatrics, Naestved Hospital, Naestved, Denmark
| | - Meike Bartels
- Netherlands Twin Register, Dept Biological Psychology, Vrije Universiteit, Amsterdam, The Netherlands
| | - Jouke-Jan Hottenga
- Netherlands Twin Register, Dept Biological Psychology, Vrije Universiteit, Amsterdam, The Netherlands
| | - Joachim Heinrich
- LMU–Ludwig-Maximilians-Universität Munich, Div. Metabolic and Nutritional Medicine, Dr. von Hauner Children’s Hospital, University of Munich Medical Center, Munich, Germany
- Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, Munich, Germany
| | - John Hewitt
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, United States of America
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, United States of America
| | | | - Tellervo Korhonen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Jari Lahti
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Marius Lahti-Pulkkinen
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center—Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Christel M. Middeldorp
- Child Health Research Centre, University of Queensland, Brisbane, QLD, Australia
- Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Child and Youth Mental Health Service, Children’s Health Queensland Hospital and Health Service, Brisbane, QLD, Australia
| | - Jackob M. Najman
- Public Health, University of Queensland, Brisbane, QLD, Australia
| | - Craig Pennell
- School of Medicine and Public Health, Faculty of Medicine and Health, The University of Newcastle, Newcastle, New South Wales, Australia
| | - Chris Power
- Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health, London, United Kingdom
| | - Albertine J. Oldehinkel
- Interdisciplinary Center Psychopathology and Emotion Regulation (IPCE), University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Robert Plomin
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Katri Räikkönen
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Olli T. Raitakari
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
| | - Kaili Rimfeld
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Lærke Sass
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
- Department of Pediatrics, Naestved Hospital, Naestved, Denmark
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Marie Standl
- LMU–Ludwig-Maximilians-Universität Munich, Div. Metabolic and Nutritional Medicine, Dr. von Hauner Children’s Hospital, University of Munich Medical Center, Munich, Germany
| | - Jordi Sunyer
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
- CIBER Epidemiology and Public Health (CIBERESP), Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Gail M. Williams
- Child and Youth Mental Health Service, Children’s Health Queensland Hospital and Health Service, Brisbane, QLD, Australia
| | | | - Dorret I. Boomsma
- Netherlands Twin Register, Dept Biological Psychology, Vrije Universiteit, Amsterdam, The Netherlands
| | | | - Catharina A. Hartman
- Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Henning Tiemeier
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Social and Behavioral Science, Harvard TH Chan School of Public Health, Boston, MA, United States of America
- * E-mail:
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20
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Multivariate phenotype analysis enables genome-wide inference of mammalian gene function. PLoS Biol 2022; 20:e3001723. [PMID: 35944064 PMCID: PMC9391051 DOI: 10.1371/journal.pbio.3001723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Revised: 08/19/2022] [Accepted: 06/22/2022] [Indexed: 11/23/2022] Open
Abstract
The function of the majority of genes in the human and mouse genomes is unknown. Investigating and illuminating this dark genome is a major challenge for the biomedical sciences. The International Mouse Phenotyping Consortium (IMPC) is addressing this through the generation and broad-based phenotyping of a knockout (KO) mouse line for every protein-coding gene, producing a multidimensional data set that underlies a genome-wide annotation map from genes to phenotypes. Here, we develop a multivariate (MV) statistical approach and apply it to IMPC data comprising 148 phenotypes measured across 4,548 KO lines. There are 4,256 (1.4% of 302,997 observed data measurements) hits called by the univariate (UV) model analysing each phenotype separately, compared to 31,843 (10.5%) hits in the observed data results of the MV model, corresponding to an estimated 7.5-fold increase in power of the MV model relative to the UV model. One key property of the data set is its 55.0% rate of missingness, resulting from quality control filters and incomplete measurement of some KO lines. This raises the question of whether it is possible to infer perturbations at phenotype-gene pairs at which data are not available, i.e., to infer some in vivo effects using statistical analysis rather than experimentation. We demonstrate that, even at missing phenotypes, the MV model can detect perturbations with power comparable to the single-phenotype analysis, thereby filling in the complete gene-phenotype map with good sensitivity. A factor analysis of the MV model's fitted covariance structure identifies 20 clusters of phenotypes, with each cluster tending to be perturbed collectively. These factors cumulatively explain 75% of the KO-induced variation in the data and facilitate biological interpretation of perturbations. We also demonstrate that the MV approach strengthens the correspondence between IMPC phenotypes and existing gene annotation databases. Analysis of a subset of KO lines measured in replicate across multiple laboratories confirms that the MV model increases power with high replicability.
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21
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Neumann A, Küçükali F, Bos I, Vos SJB, Engelborghs S, De Pooter T, Joris G, De Rijk P, De Roeck E, Tsolaki M, Verhey F, Martinez-Lage P, Tainta M, Frisoni G, Blin O, Richardson J, Bordet R, Scheltens P, Popp J, Peyratout G, Johannsen P, Frölich L, Vandenberghe R, Freund-Levi Y, Streffer J, Lovestone S, Legido-Quigley C, Ten Kate M, Barkhof F, Strazisar M, Zetterberg H, Bertram L, Visser PJ, van Broeckhoven C, Sleegers K. Rare variants in IFFO1, DTNB, NLRC3 and SLC22A10 associate with Alzheimer's disease CSF profile of neuronal injury and inflammation. Mol Psychiatry 2022; 27:1990-1999. [PMID: 35173266 PMCID: PMC9126805 DOI: 10.1038/s41380-022-01437-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 11/04/2021] [Accepted: 01/05/2022] [Indexed: 11/30/2022]
Abstract
Alzheimer's disease (AD) biomarkers represent several neurodegenerative processes, such as synaptic dysfunction, neuronal inflammation and injury, as well as amyloid pathology. We performed an exome-wide rare variant analysis of six AD biomarkers (β-amyloid, total/phosphorylated tau, NfL, YKL-40, and Neurogranin) to discover genes associated with these markers. Genetic and biomarker information was available for 480 participants from two studies: EMIF-AD and ADNI. We applied a principal component (PC) analysis to derive biomarkers combinations, which represent statistically independent biological processes. We then tested whether rare variants in 9576 protein-coding genes associate with these PCs using a Meta-SKAT test. We also tested whether the PCs are intermediary to gene effects on AD symptoms with a SMUT test. One PC loaded on NfL and YKL-40, indicators of neuronal injury and inflammation. Four genes were associated with this PC: IFFO1, DTNB, NLRC3, and SLC22A10. Mediation tests suggest, that these genes also affect dementia symptoms via inflammation/injury. We also observed an association between a PC loading on Neurogranin, a marker for synaptic functioning, with GABBR2 and CASZ1, but no mediation effects. The results suggest that rare variants in IFFO1, DTNB, NLRC3, and SLC22A10 heighten susceptibility to neuronal injury and inflammation, potentially by altering cytoskeleton structure and immune activity disinhibition, resulting in an elevated dementia risk. GABBR2 and CASZ1 were associated with synaptic functioning, but mediation analyses suggest that the effect of these two genes on synaptic functioning is not consequential for AD development.
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Affiliation(s)
- Alexander Neumann
- Complex Genetics of Alzheimer's Disease Group, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium.
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium.
| | - Fahri Küçükali
- Complex Genetics of Alzheimer's Disease Group, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Isabelle Bos
- Netherlands Institute for Health Services Research, Utrecht, the Netherlands
| | - Stephanie J B Vos
- Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands
| | - Sebastiaan Engelborghs
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Department of Neurology and Memory Clinic, Universitair Ziekenhuis Brussel (UZ Brussel) and Center for Neurosciences (C4N), Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Tim De Pooter
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Neuromics Support Facility, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium
| | - Geert Joris
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Neuromics Support Facility, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium
| | - Peter De Rijk
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Neuromics Support Facility, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium
| | - Ellen De Roeck
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken, Antwerp, Belgium
| | - Magda Tsolaki
- 1st Department of Neurology, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Makedonia, Thessaloniki, Greece
| | - Frans Verhey
- Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands
- Department of Psychiatry and Neuropsychology, Maastricht University, Maastricht, the Netherlands
- School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Pablo Martinez-Lage
- Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, San Sebastian, Spain
| | - Mikel Tainta
- Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, San Sebastian, Spain
| | - Giovanni Frisoni
- Department of Psychiatry, Faculty of Medicine, Geneva University Hospitals, Geneva, Switzerland
- RCCS Instituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Oliver Blin
- Clinical Pharmacology & Pharmacovigilance Department, Marseille University Hospital, Marseille, France
| | - Jill Richardson
- Neurosciences Therapeutic Area, GlaxoSmithKline R&D, Stevanage, UK
| | - Régis Bordet
- Neuroscience & Cognition, CHU de Lille, University of Lille, Inserm, France
| | - Philip Scheltens
- Alzheimer Center and Department of Neurology, VU University Medical Center, Amsterdam, the Netherlands
| | - Julius Popp
- Department of Geriatric Psychiatry, University Hospital of Psychiatry Zürich, Zürich, Switzerland
- Old Age Psychiatry, Department of Psychiatry, University Hospital of Lausanne, Lausanne, Switzerland
| | - Gwendoline Peyratout
- Department of Psychiatry, University Hospital of Lausanne, Lausanne, Switzerland
| | - Peter Johannsen
- Clinical Drug Development, Novo Nordisk, Copenhagen, Denmark
| | - Lutz Frölich
- Department of Geriatric Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Yvonne Freund-Levi
- Center for Alzheimer Research, Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society Karolinska Institute Stockholm Sweden, Stockholm, Sweden
- School of Medical Sciences Örebro, University Örebro, Örebro, Sweden
| | - Johannes Streffer
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Simon Lovestone
- Department of Psychiatry, University of Oxford, Oxford, UK
- Janssen Medical Ltd, High Wycombe, UK
| | - Cristina Legido-Quigley
- Steno Diabetes Center, Copenhagen, Denmark
- Institute of Pharmaceutical Sciences, King's College London, London, UK
| | - Mara Ten Kate
- Alzheimer Center and Department of Neurology, VU University Medical Center, Amsterdam, the Netherlands
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, the Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, the Netherlands
- Institutes of Neurology and Healthcare Engineering, University College London, London, UK
| | - Mojca Strazisar
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Neuromics Support Facility, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden
- Department of Molecular Neuroscience, UCL Institute of Neurology, London, UK
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- UK Dementia Research Institute, University College London, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
| | - Lars Bertram
- Lübeck Interdisciplinary Platform for Genome Analytics, University of Lübeck, Lübeck, Germany
- Centre for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway
| | - Pieter Jelle Visser
- Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands
- Alzheimer Center and Department of Neurology, VU University Medical Center, Amsterdam, the Netherlands
| | - Christine van Broeckhoven
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Neurodegenerative Brain Diseases Group, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium
| | - Kristel Sleegers
- Complex Genetics of Alzheimer's Disease Group, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
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22
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Bielak LF, Peyser PA, Smith JA, Zhao W, Ruiz‐Narvaez EA, Kardia SLR, Harlow SD. Multivariate, region-based genetic analyses of facets of reproductive aging in White and Black women. Mol Genet Genomic Med 2022; 10:e1896. [PMID: 35179313 PMCID: PMC9000932 DOI: 10.1002/mgg3.1896] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 01/14/2022] [Accepted: 01/31/2022] [Indexed: 01/28/2023] Open
Abstract
BACKGROUND Age at final menstrual period (FMP) and the accompanying hormone trajectories across the menopause transition do not occur in isolation, but likely share molecular pathways. Understanding the genetics underlying the endocrinology of the menopause transition may be enhanced by jointly analyzing multiple interrelated traits. METHODS In a sample of 347 White and 164 Black women from the Study of Women's Health Across the Nation (SWAN), we investigated pleiotropic effects of 54 candidate genetic regions of interest (ROI) on 5 menopausal traits (age at FMP and premenopausal and postmenopausal levels of follicle stimulation hormone and estradiol) using multivariate kernel regression (Multi-SKAT). A backward elimination procedure was used to identify which subset of traits were most strongly associated with a specific ROI. RESULTS In White women, the 20 kb ROI around rs10734411 was significantly associated with the multivariate distribution of age at FMP, premenopausal estradiol, and postmenopausal estradiol (omnibus p-value = .00004). This association did not replicate in the smaller sample of Black women. CONCLUSION This study using a region-based, multiple-trait approach suggests a shared genetic basis among multiple facets of reproductive aging.
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Affiliation(s)
- Lawrence F. Bielak
- Department of Epidemiology, School of Public HealthUniversity of MichiganAnn ArborMichiganUSA
| | - Patricia A. Peyser
- Department of Epidemiology, School of Public HealthUniversity of MichiganAnn ArborMichiganUSA
| | - Jennifer A. Smith
- Department of Epidemiology, School of Public HealthUniversity of MichiganAnn ArborMichiganUSA,Survey Research Center, Institute for Social ResearchUniversity of MichiganAnn ArborMichiganUSA
| | - Wei Zhao
- Department of Epidemiology, School of Public HealthUniversity of MichiganAnn ArborMichiganUSA
| | - Edward A. Ruiz‐Narvaez
- Department of Nutritional Sciences, School of Public HealthUniversity of MichiganAnn ArborMichiganUSA
| | - Sharon L. R. Kardia
- Department of Epidemiology, School of Public HealthUniversity of MichiganAnn ArborMichiganUSA
| | - Sioban D. Harlow
- Department of Epidemiology, School of Public HealthUniversity of MichiganAnn ArborMichiganUSA
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23
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Alotaibi RN, Howe BJ, Moreno Uribe LM, Ramirez CV, Restrepo C, Deleyiannis FW, Padilla C, Orioli IM, Buxó CJ, Hecht JT, Wehby GL, Neiswanger K, Murray JC, Shaffer JR, Weinberg SM, Marazita ML. Multivariate GWAS of Structural Dental Anomalies and Dental Caries in a Multi-Ethnic Cohort. FRONTIERS IN DENTAL MEDICINE 2022; 2:771116. [PMID: 36267138 PMCID: PMC9581442 DOI: 10.3389/fdmed.2021.771116] [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: 12/20/2023] Open
Abstract
Odontogenesis is a complex process, where disruption can result in dental anomalies and/or increase the risk of developing dental caries. Based on previous studies, certain dental anomalies tend to co-occur in patients, suggesting that these traits may share common genetic and etiological components. The main goal of this study was to implement a multivariate genome-wide association study approach to identify genetic variants shared between correlated structural dental anomalies and dental caries. Our cohort (N = 3,579) was derived from the Pittsburgh Orofacial Clefts Study, where multiple dental traits were assessed in both the unaffected relatives of orofacial cleft (OFC) cases (n = 2,187) and unaffected controls (n = 1,392). We identified four multivariate patterns of correlated traits in this data: tooth agenesis, impaction, and rotation (AIR); enamel hypoplasia, displacement, and rotation (HDR); displacement, rotation, and mamelon (DRM); and dental caries, tooth agenesis and enamel hypoplasia (CAH). We analyzed each of these four models using genome-wide multivariate tests of association. No genome-wide statistically significant results were found, but we identified multiple suggestive association signals (P < 10-5) near genes with known biological roles during tooth development, including ADAMTS9 and PRICKLE2 associated with AIR; GLIS3, WDR72, and ROR2 associated with HDR and DRM; ROBO2 associated with DRM; BMP7 associated with HDR; and ROBO1, SMAD2, and MSX2 associated with CAH. This is the first study to investigate genetic associations for multivariate patterns of correlated dental anomalies and dental caries. Further studies are needed to replicate these results in independent cohorts.
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Affiliation(s)
- Rasha N. Alotaibi
- Dental Health Department, College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia
- Center for Craniofacial and Dental Genetics, Department of Oral and Craniofacial Sciences, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Brian J. Howe
- Department of Family Dentistry, College of Dentistry, University of Iowa, Iowa City, IA, USA
- The Iowa Center for Oral Health Research, College of Dentistry, University of Iowa, Iowa City, IA, USA
| | - Lina M. Moreno Uribe
- The Iowa Center for Oral Health Research, College of Dentistry, University of Iowa, Iowa City, IA, USA
- Department of Orthodontics, School of Dentistry, University of Iowa, Iowa City, IA, USA
| | | | | | | | - Carmencita Padilla
- Department of Pediatrics, College of Medicine, University of the Philippines, Manila
| | - Ieda M. Orioli
- Department of Genetics, Institute of Biology, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Carmen J. Buxó
- School of Dental Medicine, University of Puerto Rico, San Juan, Puerto Rico
| | - Jacqueline T. Hecht
- Department of Pediatrics, University of Texas Health Science Center at Houston, Houston, Texas, TX, USA
| | - George L. Wehby
- Department of Health Management and Policy, College of Public Health, University of Iowa, Iowa City, IA, USA
| | - Katherine Neiswanger
- Center for Craniofacial and Dental Genetics, Department of Oral and Craniofacial Sciences, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jeffery C. Murray
- Department of Pediatrics, Carver College of Medicine, University of Iowa, Iowa City, IA, United States
| | - John R. Shaffer
- Center for Craniofacial and Dental Genetics, Department of Oral and Craniofacial Sciences, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Seth M. Weinberg
- Center for Craniofacial and Dental Genetics, Department of Oral and Craniofacial Sciences, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Mary L. Marazita
- Center for Craniofacial and Dental Genetics, Department of Oral and Craniofacial Sciences, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
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24
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Liu W, Xu Y, Wang A, Huang T, Liu Z. The eigen higher criticism and eigen Berk–Jones tests for multiple trait association studies based on GWAS summary statistics. Genet Epidemiol 2021; 46:89-104. [DOI: 10.1002/gepi.22439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 09/10/2021] [Accepted: 10/21/2021] [Indexed: 11/11/2022]
Affiliation(s)
- Wei Liu
- Department of Statistics and Actuarial Science The University of Hong Kong Hong Kong SAR China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences Xi'an Jiaotong University Health Science Center Xi'an China
| | - Yuyang Xu
- Department of Statistics and Actuarial Science The University of Hong Kong Hong Kong SAR China
| | - Anqi Wang
- Department of Statistics and Actuarial Science The University of Hong Kong Hong Kong SAR China
| | - Tao Huang
- Department of Epidemiology and Biostatistics, School of Public Health Peking University Beijing China
- Institute for Artificial Intelligence, Center for Intelligent Public Health Peking University Beijing China
- Key Laboratory of Molecular Cardiovascular Diseases, Peking University Ministry of Education Beijing China
| | - Zhonghua Liu
- Department of Statistics and Actuarial Science The University of Hong Kong Hong Kong SAR China
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25
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Wang Y, Wu P, Tong X, Sun J. A weighted method for the exclusive hypothesis test with application to typhoon data. CAN J STAT 2021. [DOI: 10.1002/cjs.11618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Yi Wang
- School of Statistics Beijing Normal University Beijing China
| | - Peng Wu
- School of Statistics Beijing Normal University Beijing China
| | - Xingwei Tong
- School of Statistics Beijing Normal University Beijing China
| | - Jianguo Sun
- Department of Statistics University of Missouri Columbia MO U.S.A
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26
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Yoshimura K, Morita Y, Konomi K, Ishida S, Fujiwara D, Kobayashi K, Tanaka M. A web-based survey on various symptoms of computer vision syndrome and the genetic understanding based on a multi-trait genome-wide association study. Sci Rep 2021; 11:9446. [PMID: 33941792 PMCID: PMC8093242 DOI: 10.1038/s41598-021-88827-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 04/16/2021] [Indexed: 11/12/2022] Open
Abstract
A variety of eye-related symptoms due to the overuse of digital devices is collectively referred to as computer vision syndrome (CVS). In this study, a web-based survey about mind and body functions, including eye strain, was conducted on 1998 Japanese volunteers. To investigate the biological mechanisms behind CVS, a multi-trait genome-wide association study (GWAS), a multivariate analysis on individual-level multivariate data, was performed based on the structural equation modeling methodology assuming a causal pathway for a genetic variant to influence each symptom via a single common latent variable. Twelve loci containing lead variants with a suggestive level of significance were identified. Two loci showed relatively strong signals and were associated with TRABD2B relative to the Wnt signaling pathway and SDK1 having neuronal adhesion and immune functions, respectively. By utilizing publicly available eQTL data, colocalization between GWAS and eQTL signals for four loci was detected, and a locus on 2p25.3 showed a strong colocalization (PPH4 > 0.9) on retinal MYT1L, known to play an important role in neuronal differentiation. This study suggested that the use of multivariate questionnaire data and multi-trait GWAS can lead to biologically reasonable findings and enhance our genetic understanding of complex relationships among symptoms related to CVS.
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Affiliation(s)
| | - Yuji Morita
- Kirin Central Research Institute, Kirin Holdings Company, Limited, Yokohama, Japan.
| | - Kenji Konomi
- Clinical and Translational Research Center, Keio University Hospital, Tokyo, Japan
| | | | - Daisuke Fujiwara
- Health Science Department, Kirin Holdings Company, Limited, Tokyo, Japan
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27
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Liu F, Zhou Z, Cai M, Wen Y, Zhang J. AGNEP: An Agglomerative Nesting Clustering Algorithm for Phenotypic Dimension Reduction in Joint Analysis of Multiple Phenotypes. Front Genet 2021; 12:648831. [PMID: 33981331 PMCID: PMC8107386 DOI: 10.3389/fgene.2021.648831] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Accepted: 04/01/2021] [Indexed: 11/17/2022] Open
Abstract
Genome-wide association study (GWAS) has identified thousands of genetic variants associated with complex traits and diseases. Compared with analyzing a single phenotype at a time, the joint analysis of multiple phenotypes can improve statistical power by taking into account the information from phenotypes. However, most established joint algorithms ignore the different level of correlations between multiple phenotypes; instead of that, they simultaneously analyze all phenotypes in a genetic model. Thus, they may fail to capture the genetic structure of phenotypes and consequently reduce the statistical power. In this study, we develop a novel method agglomerative nesting clustering algorithm for phenotypic dimension reduction analysis (AGNEP) to jointly analyze multiple phenotypes for GWAS. First, AGNEP uses an agglomerative nesting clustering algorithm to group correlated phenotypes and then applies principal component analysis (PCA) to generate representative phenotypes for each group. Finally, multivariate analysis is employed to test associations between genetic variants and the representative phenotypes rather than all phenotypes. We perform three simulation experiments with various genetic structures and a real dataset analysis for 19 Arabidopsis phenotypes. Compared to established methods, AGNEP is more powerful in terms of statistical power, computing time, and the number of quantitative trait nucleotides (QTNs). The analysis of the Arabidopsis real dataset further illustrates the efficiency of AGNEP for detecting QTNs, which are confirmed by The Arabidopsis Information Resource gene bank.
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Affiliation(s)
- Fengrong Liu
- College of Science, Nanjing Agricultural University, Nanjing, China.,School of Data Science, University of Science and Technology of China, Hefei, China
| | - Ziyang Zhou
- College of Science, Nanjing Agricultural University, Nanjing, China
| | - Mingzhi Cai
- College of Science, Nanjing Agricultural University, Nanjing, China
| | - Yangjun Wen
- College of Science, Nanjing Agricultural University, Nanjing, China
| | - Jin Zhang
- College of Science, Nanjing Agricultural University, Nanjing, China.,Postdoctoral Research Station of Crop Science, Nanjing Agricultural University, Nanjing, China
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28
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Chen Y, Wu H, Yang W, Zhao W, Tong C. Multivariate linear mixed model enhanced the power of identifying genome-wide association to poplar tree heights in a randomized complete block design. G3-GENES GENOMES GENETICS 2021; 11:6064171. [PMID: 33604666 PMCID: PMC8022933 DOI: 10.1093/g3journal/jkaa053] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 11/01/2020] [Indexed: 01/09/2023]
Abstract
With the advances in high-throughput sequencing technologies, it is not difficult to extract tens of thousands of single-nucleotide polymorphisms (SNPs) across many individuals in a fast and cheap way, making it possible to perform genome-wide association studies (GWAS) of quantitative traits in outbred forest trees. It is very valuable to apply traditional breeding experiments in GWAS for identifying genome variants associated with ecologically and economically important traits in Populus. Here, we reported a GWAS of tree height measured at multiple time points from a randomized complete block design (RCBD), which was established with clones from an F1 hybrid population of Populus deltoides and Populus simonii. A total of 22,670 SNPs across 172 clones in the RCBD were obtained with restriction site-associated DNA sequencing (RADseq) technology. The multivariate mixed linear model was applied by incorporating the pedigree relationship matrix of individuals to test the association of each SNP to the tree heights over 8 time points. Consequently, 41 SNPs were identified significantly associated with the tree height under the P-value threshold determined by Bonferroni correction at the significant level of 0.01. These SNPs were distributed on all but two chromosomes (Chr02 and Chr18) and explained the phenotypic variance ranged from 0.26% to 2.64%, amounting to 63.68% in total. Comparison with previous mapping studies for poplar height as well as the candidate genes of these detected SNPs were also investigated. We therefore showed that the application of multivariate linear mixed model to the longitudinal phenotypic data from the traditional breeding experimental design facilitated to identify far more genome-wide variants for tree height in poplar. The significant SNPs identified in this study would enhance understanding of molecular mechanism for growth traits and would accelerate marker-assisted breeding programs in Populus.
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Affiliation(s)
- Yuhua Chen
- Key Laboratory of Forest Genetics & Biotechnology of Ministry of Education, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China.,School of Animal Science and Technology, Jingling Institute of Technology, Nanjing 210038, China
| | - Hainan Wu
- Key Laboratory of Forest Genetics & Biotechnology of Ministry of Education, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
| | - Wenguo Yang
- Key Laboratory of Forest Genetics & Biotechnology of Ministry of Education, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
| | - Wei Zhao
- Key Laboratory of Forest Genetics & Biotechnology of Ministry of Education, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
| | - Chunfa Tong
- Key Laboratory of Forest Genetics & Biotechnology of Ministry of Education, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
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29
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Zhang X, Li R, Ritchie MD. Statistical Impact of Sample Size and Imbalance on Multivariate Analysis in silico and A Case Study in the UK Biobank. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2021; 2020:1383-1391. [PMID: 33936514 PMCID: PMC8075427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Large-scale biobank cohorts coupled with electronic health records offer unprecedented opportunities to study genotype-phenotype relationships. Genome-wide association studies uncovered disease-associated loci through univariate methods, with the focus on one trait at a time. With genetic variants being identifiedfor thousands of traits, researchers found that 90% of human genetic loci are associated with more than one trait, highlighting the ubiquity of pleiotropy. Recently, multivariate methods have been proposed to effectively identify pleiotropy. However, the statistical performance in natural biomedical data, which often have unbalanced case-control sample sizes, is largely known. In this work, we designed 21 scenarios of real-data informed simulations to thoroughly evaluate the statistical characteristics of univariate and multivariate methods. Our results can serve as a reference guide for the application of multivariate methods. We also investigated potential pleiotropy across type II diabetes, Alzheimer's disease, atherosclerosis of arteries, depression, and atherosclerotic heart disease in the UK Biobank.
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Affiliation(s)
- Xinyuan Zhang
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Ruowang Li
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Marylyn D Ritchie
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
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30
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Majumdar A, Burch KS, Haldar T, Sankararaman S, Pasaniuc B, Gauderman WJ, Witte JS. A two-step approach to testing overall effect of gene-environment interaction for multiple phenotypes. Bioinformatics 2021; 36:5640-5648. [PMID: 33453114 DOI: 10.1093/bioinformatics/btaa1083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 12/09/2020] [Accepted: 12/17/2020] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION While gene-environment (GxE) interactions contribute importantly to many different phenotypes, detecting such interactions requires well-powered studies and has proven difficult. To address this, we combine two approaches to improve GxE power: simultaneously evaluating multiple phenotypes and using a two-step analysis approach. Previous work shows that the power to identify a main genetic effect can be improved by simultaneously analyzing multiple related phenotypes. For a univariate phenotype, two-step methods produce higher power for detecting a GxE interaction compared to single step analysis. Therefore, we propose a two-step approach to test for an overall GxE effect for multiple phenotypes. RESULTS Using simulations we demonstrate that, when more than one phenotype has GxE effect (i.e., GxE pleiotropy), our approach offers substantial gain in power (18%-43%) to detect an aggregate-level GxE effect for a multivariate phenotype compared to an analogous two-step method to identify GxE effect for a univariate phenotype. We applied the proposed approach to simultaneously analyze three lipids, LDL, HDL and Triglyceride with the frequency of alcohol consumption as environmental factor in the UK Biobank. The method identified two loci with an overall GxE effect on the vector of lipids, one of which was missed by the competing approaches. AVAILABILITY We provide an R package MPGE implementing the proposed approach which is available from CRAN: https://cran.r-project.org/web/packages/MPGE/index.html.
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Affiliation(s)
- Arunabha Majumdar
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.,Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Kathryn S Burch
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Sriram Sankararaman
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - Bogdan Pasaniuc
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.,Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA
| | - W James Gauderman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - John S Witte
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
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31
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Yuan X, Biswas S. Detecting rare haplotype association with two correlated phenotypes of binary and continuous types. Stat Med 2021; 40:1877-1900. [PMID: 33438281 DOI: 10.1002/sim.8877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 11/18/2020] [Accepted: 12/25/2020] [Indexed: 11/10/2022]
Abstract
Multiple correlated traits/phenotypes are often collected in genetic association studies and they may share a common genetic mechanism. Joint analysis of correlated phenotypes has well-known advantages over one-at-a-time analysis including gain in power and better understanding of genetic etiology. However, when the phenotypes are of discordant types such as binary and continuous, the joint modeling is more challenging. Another research area of current interest is discovery of rare genetic variants. Currently there is no method available for detecting association of rare (or common) haplotypes with multiple discordant phenotypes jointly. Our goal is to fill this gap specifically for two discordant phenotypes. We consider a rare haplotype association method for a binary phenotype, logistic Bayesian LASSO (univariate LBL) and its extension for two correlated binary phenotypes (bivariate LBL-2B). Under this framework, we propose a haplotype association test with binary and continuous phenotypes jointly (bivariate LBL-BC). Specifically, we use a latent variable to induce correlation between the two phenotypes. We carry out extensive simulations to investigate bivariate LBL-BC and compare it with univariate LBL and bivariate LBL-2B. In most settings, bivariate LBL-BC performs the best. In only two situations, bivariate LBL-BC has similar performance-when the two phenotypes are (1) weakly or not correlated and the target haplotype affects the binary phenotype only and (2) strongly positively correlated and the target haplotype affects both phenotypes in positive direction. Finally, we apply the method to a data set on lung cancer and nicotine dependence and detect several haplotypes including a rare one.
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Affiliation(s)
- Xiaochen Yuan
- Department of Mathematical Sciences, University of Texas at Dallas, Richardson, Texas, USA
| | - Swati Biswas
- Department of Mathematical Sciences, University of Texas at Dallas, Richardson, Texas, USA
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32
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Fernandes SB, Zhang KS, Jamann TM, Lipka AE. How Well Can Multivariate and Univariate GWAS Distinguish Between True and Spurious Pleiotropy? Front Genet 2021; 11:602526. [PMID: 33584799 PMCID: PMC7873880 DOI: 10.3389/fgene.2020.602526] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 12/11/2020] [Indexed: 11/13/2022] Open
Abstract
Quantification of the simultaneous contributions of loci to multiple traits, a phenomenon called pleiotropy, is facilitated by the increased availability of high-throughput genotypic and phenotypic data. To understand the prevalence and nature of pleiotropy, the ability of multivariate and univariate genome-wide association study (GWAS) models to distinguish between pleiotropic and non-pleiotropic loci in linkage disequilibrium (LD) first needs to be evaluated. Therefore, we used publicly available maize and soybean genotypic data to simulate multiple pairs of traits that were either (i) controlled by quantitative trait nucleotides (QTNs) on separate chromosomes, (ii) controlled by QTNs in various degrees of LD with each other, or (iii) controlled by a single pleiotropic QTN. We showed that multivariate GWAS could not distinguish between QTNs in LD and a single pleiotropic QTN. In contrast, a unique QTN detection rate pattern was observed for univariate GWAS whenever the simulated QTNs were in high LD or pleiotropic. Collectively, these results suggest that multivariate and univariate GWAS should both be used to infer whether or not causal mutations underlying peak GWAS associations are pleiotropic. Therefore, we recommend that future studies use a combination of multivariate and univariate GWAS models, as both models could be useful for identifying and narrowing down candidate loci with potential pleiotropic effects for downstream biological experiments.
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Affiliation(s)
- Samuel B. Fernandes
- Department of Crop Science, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | | | | | - Alexander E. Lipka
- Department of Crop Science, University of Illinois at Urbana-Champaign, Urbana, IL, United States
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33
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White JD, Indencleef K, Naqvi S, Eller RJ, Hoskens H, Roosenboom J, Lee MK, Li J, Mohammed J, Richmond S, Quillen EE, Norton HL, Feingold E, Swigut T, Marazita ML, Peeters H, Hens G, Shaffer JR, Wysocka J, Walsh S, Weinberg SM, Shriver MD, Claes P. Insights into the genetic architecture of the human face. Nat Genet 2021; 53:45-53. [PMID: 33288918 PMCID: PMC7796995 DOI: 10.1038/s41588-020-00741-7] [Citation(s) in RCA: 70] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Accepted: 10/23/2020] [Indexed: 01/28/2023]
Abstract
The human face is complex and multipartite, and characterization of its genetic architecture remains challenging. Using a multivariate genome-wide association study meta-analysis of 8,246 European individuals, we identified 203 genome-wide-significant signals (120 also study-wide significant) associated with normal-range facial variation. Follow-up analyses indicate that the regions surrounding these signals are enriched for enhancer activity in cranial neural crest cells and craniofacial tissues, several regions harbor multiple signals with associations to different facial phenotypes, and there is evidence for potential coordinated actions of variants. In summary, our analyses provide insights into the understanding of how complex morphological traits are shaped by both individual and coordinated genetic actions.
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Affiliation(s)
- Julie D White
- Department of Anthropology, Pennsylvania State University, State College, PA, USA.
| | - Karlijne Indencleef
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium.
- Medical Imaging Research Center, UZ Leuven, Leuven, Belgium.
| | - Sahin Naqvi
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Ryan J Eller
- Department of Biology, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA
| | - Hanne Hoskens
- Medical Imaging Research Center, UZ Leuven, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Jasmien Roosenboom
- Department of Oral Biology, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Myoung Keun Lee
- Department of Oral Biology, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jiarui Li
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
- Medical Imaging Research Center, UZ Leuven, Leuven, Belgium
| | - Jaaved Mohammed
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Stephen Richmond
- Applied Clinical Research and Public Health, School of Dentistry, Cardiff University, Cardiff, UK
| | - Ellen E Quillen
- Department of Internal Medicine, Section of Molecular Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Center for Precision Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Heather L Norton
- Department of Anthropology, University of Cincinnati, Cincinnati, OH, USA
| | - Eleanor Feingold
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Tomek Swigut
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Mary L Marazita
- Department of Oral Biology, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Hilde Peeters
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Greet Hens
- Department of Neurosciences, Experimental Oto-Rhino-Laryngology, KU Leuven, Leuven, Belgium
| | - John R Shaffer
- Department of Oral Biology, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Joanna Wysocka
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA, USA
- Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Susan Walsh
- Department of Biology, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA
| | - Seth M Weinberg
- Department of Oral Biology, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Anthropology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Mark D Shriver
- Department of Anthropology, Pennsylvania State University, State College, PA, USA
| | - Peter Claes
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium.
- Medical Imaging Research Center, UZ Leuven, Leuven, Belgium.
- Department of Human Genetics, KU Leuven, Leuven, Belgium.
- Murdoch Children's Research Institute, Melbourne, Victoria, Australia.
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34
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Merrick LF, Burke AB, Zhang Z, Carter AH. Comparison of Single-Trait and Multi-Trait Genome-Wide Association Models and Inclusion of Correlated Traits in the Dissection of the Genetic Architecture of a Complex Trait in a Breeding Program. FRONTIERS IN PLANT SCIENCE 2021; 12:772907. [PMID: 35154175 PMCID: PMC8831792 DOI: 10.3389/fpls.2021.772907] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 12/29/2021] [Indexed: 05/06/2023]
Abstract
Unknown genetic architecture makes it difficult to characterize the genetic basis of traits and associated molecular markers because of the complexity of small effect quantitative trait loci (QTLs), environmental effects, and difficulty in phenotyping. Seedling emergence of wheat (Triticum aestivum L.) from deep planting, has a poorly understood genetic architecture, is a vital factor affecting stand establishment and grain yield, and is historically correlated with coleoptile length. This study aimed to dissect the genetic architecture of seedling emergence while accounting for correlated traits using one multi-trait genome-wide association study (MT-GWAS) model and three single-trait GWAS (ST-GWAS) models. The ST-GWAS models included one single-locus model [mixed-linear model (MLM)] and two multi-locus models [fixed and random model circulating probability unification (FarmCPU) and Bayesian information and linkage-disequilibrium iteratively nested keyway (BLINK)]. We conducted GWAS using two populations. The first population consisted of 473 varieties from a diverse association mapping panel phenotyped from 2015 to 2019. The second population consisted of 279 breeding lines phenotyped in 2015 in Lind, WA, with 40,368 markers. We also compared the inclusion of coleoptile length and markers associated with reduced height as covariates in our ST-GWAS models. ST-GWAS found 107 significant markers across 19 chromosomes, while MT-GWAS found 82 significant markers across 14 chromosomes. The FarmCPU and BLINK models, including covariates, were able to identify many small effect markers while identifying large effect markers on chromosome 5A. By using multi-locus model breeding, programs can uncover the complex nature of traits to help identify candidate genes and the underlying architecture of a trait, such as seedling emergence.
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35
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Abstract
PURPOSE OF REVIEW We summarize recent evidence on the shared genetics within and outside the musculoskeletal system (mostly related to bone density and osteoporosis). RECENT FINDINGS Osteoporosis is determined by an interplay between multiple genetic and environmental factors. Significant progress has been made regarding its genetic background revealing a number of robustly validated loci and respective pathways. However, pleiotropic factors affecting bone and other tissues are not well understood. The analytical methods proposed to test for potential associations between genetic variants and multiple phenotypes can be applied to bone-related data. A number of recent genetic studies have shown evidence of pleiotropy between bone density and other different phenotypes (traits, conditions, or diseases), within and outside the musculoskeletal system. Power benefits of combining correlated phenotypes, as well as unbiased discovery, make these studies promising. Studies in humans are supported by evidence from animal models. Drug development and repurposing should benefit from the pleiotropic approach. We believe that future studies should take into account shared genetics between the bone and related traits.
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Affiliation(s)
- M A Christou
- Clinical and Molecular Epidemiology Unit, Department of Hygiene and Epidemiology, School of Medicine, University of Ioannina, Ioannina, Greece
| | - E E Ntzani
- Clinical and Molecular Epidemiology Unit, Department of Hygiene and Epidemiology, School of Medicine, University of Ioannina, Ioannina, Greece
- Center for Research Synthesis in Health, Department of Health Services, Policy and Practice, School of Public Health, Brown University, Providence, RI, USA
| | - D Karasik
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, MA, USA.
- Azrieli Faculty of Medicine, Bar Ilan University, Safed, Israel.
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36
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Hutchinson A, Asimit J, Wallace C. Fine-mapping genetic associations. Hum Mol Genet 2020; 29:R81-R88. [PMID: 32744321 PMCID: PMC7733401 DOI: 10.1093/hmg/ddaa148] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 06/04/2020] [Accepted: 07/09/2020] [Indexed: 02/07/2023] Open
Abstract
Whilst thousands of genetic variants have been associated with human traits, identifying the subset of those variants that are causal requires a further 'fine-mapping' step. We review the basic fine-mapping approach, which is computationally fast and requires only summary data, but depends on an assumption of a single causal variant per associated region which is recognized as biologically unrealistic. We discuss different ways that the approach has been built upon to accommodate multiple causal variants in a region and to incorporate additional layers of functional annotation data. We further review methods for simultaneous fine-mapping of multiple datasets, either exploiting different linkage disequilibrium (LD) structures across ancestries or borrowing information between distinct but related traits. Finally, we look to the future and the opportunities that will be offered by increasingly accurate maps of causal variants for a multitude of human traits.
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Affiliation(s)
- Anna Hutchinson
- MRC Biostatistics Unit, Cambridge Biomedical Campus, Cambridge Institute of Public Health, Cambridge CB2 0SR, UK
| | - Jennifer Asimit
- MRC Biostatistics Unit, Cambridge Biomedical Campus, Cambridge Institute of Public Health, Cambridge CB2 0SR, UK
| | - Chris Wallace
- MRC Biostatistics Unit, Cambridge Biomedical Campus, Cambridge Institute of Public Health, Cambridge CB2 0SR, UK
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Cambridge, CB2 0AW, UK
- Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 2QQ, UK
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37
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Ravindran SP, Tams V, Cordellier M. Transcriptome‐wide genotype–phenotype associations in
Daphnia
in a predation risk environment. J Evol Biol 2020; 34:879-892. [DOI: 10.1111/jeb.13699] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 08/03/2020] [Accepted: 08/29/2020] [Indexed: 12/30/2022]
Affiliation(s)
- Suda Parimala Ravindran
- Department of Marine Sciences Tjärnö Marine Laboratory University of Gothenburg Strömstad Sweden
| | - Verena Tams
- Institute of Marine Ecosystem and Fishery Science Universität Hamburg Hamburg Germany
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38
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Zhao Y, Ning Y, Zhang F, Ding M, Wen Y, Shi L, Wang K, Lu M, Sun J, Wu M, Cheng B, Ma M, Zhang L, Cheng S, Shen H, Tian Q, Guo X, Deng HW. PCA-based GRS analysis enhances the effectiveness for genetic correlation detection. Brief Bioinform 2020; 20:2291-2298. [PMID: 30169568 DOI: 10.1093/bib/bby075] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Revised: 07/30/2018] [Accepted: 08/01/2018] [Indexed: 01/10/2023] Open
Abstract
Genetic risk score (GRS, also known as polygenic risk score) analysis is an increasingly popular method for exploring genetic architectures and relationships of complex diseases. However, complex diseases are usually measured by multiple correlated phenotypes. Analyzing each disease phenotype individually is likely to reduce statistical power due to multiple testing correction. In order to conquer the disadvantage, we proposed a principal component analysis (PCA)-based GRS analysis approach. Extensive simulation studies were conducted to compare the performance of PCA-based GRS analysis and traditional GRS analysis approach. Simulation results observed significantly improved performance of PCA-based GRS analysis compared to traditional GRS analysis under various scenarios. For the sake of verification, we also applied both PCA-based GRS analysis and traditional GRS analysis to a real Caucasian genome-wide association study (GWAS) data of bone geometry. Real data analysis results further confirmed the improved performance of PCA-based GRS analysis. Given that GWAS have flourished in the past decades, our approach may help researchers to explore the genetic architectures and relationships of complex diseases or traits.
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Affiliation(s)
- Yan Zhao
- School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Yujie Ning
- School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China.,Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Feng Zhang
- School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Miao Ding
- School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Yan Wen
- School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Liang Shi
- Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Kunpeng Wang
- Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Mengnan Lu
- Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Jingyan Sun
- Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Menglu Wu
- Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Bolun Cheng
- School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Mei Ma
- School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Lu Zhang
- School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Shiqiang Cheng
- School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Hui Shen
- Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, China
| | - Qing Tian
- Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, China
| | - Xiong Guo
- School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Hong-Wen Deng
- Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, China
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39
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Rice BR, Fernandes SB, Lipka AE. Multi-Trait Genome-Wide Association Studies Reveal Loci Associated with Maize Inflorescence and Leaf Architecture. PLANT & CELL PHYSIOLOGY 2020; 61:1427-1437. [PMID: 32186727 DOI: 10.1093/pcp/pcaa039] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Accepted: 03/17/2020] [Indexed: 05/23/2023]
Abstract
Maize inflorescence is a complex phenotype that involves the physical and developmental interplay of multiple traits. Given the evidence that genes could pleiotropically contribute to several of these traits, we used publicly available maize data to assess the ability of multivariate genome-wide association study (GWAS) approaches to identify pleiotropic quantitative trait loci (pQTL). Our analysis of 23 publicly available inflorescence and leaf-related traits in a diversity panel of n = 281 maize lines genotyped with 376,336 markers revealed that the two multivariate GWAS approaches we tested were capable of identifying pQTL in genomic regions coinciding with similar associations found in previous studies. We then conducted a parallel simulation study on the same individuals, where it was shown that multivariate GWAS approaches yielded a higher true-positive quantitative trait nucleotide (QTN) detection rate than comparable univariate approaches for all evaluated simulation settings except for when the correlated simulated traits had a heritability of 0.9. We therefore conclude that the implementation of state-of-the-art multivariate GWAS approaches is a useful tool for dissecting pleiotropy and their more widespread implementation could facilitate the discovery of genes and other biological mechanisms underlying maize inflorescence.
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Affiliation(s)
- Brian R Rice
- Department of Crop Sciences, University of Illinois, Urbana, IL, USA
| | | | - Alexander E Lipka
- Department of Crop Sciences, University of Illinois, Urbana, IL, USA
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40
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Wu Y, Cao H, Baranova A, Huang H, Li S, Cai L, Rao S, Dai M, Xie M, Dou Y, Hao Q, Zhu L, Zhang X, Yao Y, Zhang F, Xu M, Wang Q. Multi-trait analysis for genome-wide association study of five psychiatric disorders. Transl Psychiatry 2020; 10:209. [PMID: 32606422 PMCID: PMC7326916 DOI: 10.1038/s41398-020-00902-6] [Citation(s) in RCA: 112] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Revised: 06/06/2020] [Accepted: 06/16/2020] [Indexed: 02/05/2023] Open
Abstract
We conducted a cross-trait meta-analysis of genome-wide association study on schizophrenia (SCZ) (n = 65,967), bipolar disorder (BD) (n = 41,653), autism spectrum disorder (ASD) (n = 46,350), attention deficit hyperactivity disorder (ADHD) (n = 55,374), and depression (DEP) (n = 688,809). After the meta-analysis, the number of genomic loci increased from 14 to 19 in ADHD, from 3 to 10 in ASD, from 45 to 57 in DEP, from 8 to 54 in BD, and from 64 to 87 in SCZ. We observed significant enrichment of overlapping genes among different disorders and identified a panel of cross-disorder genes. A total of seven genes were found being commonly associated with four out of five psychiatric conditions, namely GABBR1, GLT8D1, HIST1H1B, HIST1H2BN, HIST1H4L, KCNB1, and DCC. The SORCS3 gene was highlighted due to the fact that it was involved in all the five conditions of study. Analysis of correlations unveiled the existence of two clusters of related psychiatric conditions, SCZ and BD that were separate from the other three traits, and formed another group. Our results may provide a new insight for genetic basis of the five psychiatric disorders.
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Affiliation(s)
- Yulu Wu
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Hongbao Cao
- Department of Psychiatry, First Clinical Medical College/First Hospital of Shanxi Medical University, Taiyuan, China
| | - Ancha Baranova
- School of Systems Biology, George Mason University (GMU), Fairfax, VA, USA
- Research Centre for Medical Genetics, Moscow, Russia
| | - Hailiang Huang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Sheng Li
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiaotong University, 1954 Huashan Road, Xuhui, 200030, Shanghai, China
| | - Lei Cai
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiaotong University, 1954 Huashan Road, Xuhui, 200030, Shanghai, China
| | - Shuquan Rao
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, China
| | - Minhan Dai
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Min Xie
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Yikai Dou
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Qinjian Hao
- The Center of Gerontology and Geriatrics, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Ling Zhu
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Xiangrong Zhang
- Department of Geriatric Psychiatry, Nanjing Brain Hospital, Affiliated to Nanjing Medical University, Nanjing, China
| | - Yin Yao
- Collaborative Innovation Center of Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China
| | - Fuquan Zhang
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing, China.
| | - Mingqing Xu
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiaotong University, 1954 Huashan Road, Xuhui, 200030, Shanghai, China.
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 South Wanping Road, Xuhui, 200030, Shanghai, China.
| | - Qiang Wang
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
- West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
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41
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Palombo V, Conte G, Mele M, Macciotta NPP, Stefanon B, Marsan PA, D'Andrea M. Use of multivariate factor analysis of detailed milk fatty acid profile to perform a genome-wide association study in Italian Simmental and Italian Holstein. J Appl Genet 2020; 61:451-463. [PMID: 32578141 DOI: 10.1007/s13353-020-00568-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 01/20/2020] [Accepted: 06/09/2020] [Indexed: 10/24/2022]
Abstract
Milk fatty acid (FA) profile is a clear example of complex and multiple correlated traits whose genetic basis is difficult to assess. Although genome-wide association (GWA) studies have been successful in the identification of significant genetic variants for complex traits, when correlated phenotypes are analysed separately, the outcomes are difficult to compare and interpret in a metabolic context. Here, we performed a multivariate factor analysis (MFA) on Italian Simmental and Italian Holstein milk fat profiles to extract latent unobserved factors able to explain correlation structure and common metabolic function among different FAs. Individual factor scores obtained by MFA were used to perform a single-SNP based GWA. In both breeds, MFA was able to extract ten latent factors with specific biological meaning, notably: de novo synthesis, desaturation activity and biohydrogenation. The GWA result confirmed the increased power of joint association analysis on multiple correlated traits and allowed us to identify major candidate genes with well-documented function consistent with the metabolic classification of factors obtained, such as DGAT1, FASN and SCD.
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Affiliation(s)
- Valentino Palombo
- Dipartimento Agricoltura, Ambiente e Alimenti, Università degli Studi del Molise, via De Sanctis snc, 86100, Campobasso, Italy
| | - Giuseppe Conte
- Dipartimento di Scienze Agrarie, Alimentari e Agro-ambientali, Università di Pisa, Via del Borghetto 80, 56124, Pisa, Italy
| | - Marcello Mele
- Dipartimento di Scienze Agrarie, Alimentari e Agro-ambientali, Università di Pisa, Via del Borghetto 80, 56124, Pisa, Italy
| | - Nicolò Pietro Paolo Macciotta
- Dipartimento di Agraria, Sezione Scienze Zootecniche, Università degli Studi di Sassari, Viale Italia 39, 07100, Sassari, Italy
| | - Bruno Stefanon
- Dipartimento di Scienze Agroambientali, Alimentari e Animali, Università di Udine, via delle Scienze, 208, 33100, Udine, Italy
| | - Paolo Ajmone Marsan
- Dipartimento di Scienze Animali degli Alimenti e della Nutrizione - DIANA e Centro di Ricerca Nutrigenomica e Proteomica - PRONUTRIGEN, Università Cattolica del Sacro Cuore, via Emilia Parmense, 84, 29122, Piacenza, Italy
| | - Mariasilvia D'Andrea
- Dipartimento Agricoltura, Ambiente e Alimenti, Università degli Studi del Molise, via De Sanctis snc, 86100, Campobasso, Italy.
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42
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Guo B, Wu B. Integrate multiple traits to detect novel trait-gene association using GWAS summary data with an adaptive test approach. Bioinformatics 2020; 35:2251-2257. [PMID: 30476000 DOI: 10.1093/bioinformatics/bty961] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Revised: 10/30/2018] [Accepted: 11/22/2018] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Genetics hold great promise to precision medicine by tailoring treatment to the individual patient based on their genetic profiles. Toward this goal, many large-scale genome-wide association studies (GWAS) have been performed in the last decade to identify genetic variants associated with various traits and diseases. They have successfully identified tens of thousands of disease-related variants. However they have explained only a small proportion of the overall trait heritability for most traits and are of very limited clinical use. This is partly owing to the small effect sizes of most genetic variants, and the common practice of testing association between one trait and one genetic variant at a time in most GWAS, even when multiple related traits are often measured for each individual. Increasing evidence suggests that many genetic variants can influence multiple traits simultaneously, and we can gain more power by testing association of multiple traits simultaneously. It is appealing to develop novel multi-trait association test methods that need only GWAS summary data, since it is generally very hard to access the individual-level GWAS phenotype and genotype data. RESULTS Many existing GWAS summary data-based association test methods have relied on ad hoc approach or crude Monte Carlo approximation. In this article, we develop rigorous statistical methods for efficient and powerful multi-trait association test. We develop robust and efficient methods to accurately estimate the marginal trait correlation matrix using only GWAS summary data. We construct the principal component (PC)-based association test from the summary statistics. PC-based test has optimal power when the underlying multi-trait signal can be captured by the first PC, and otherwise it will have suboptimal performance. We develop an adaptive test by optimally weighting the PC-based test and the omnibus chi-square test to achieve robust performance under various scenarios. We develop efficient numerical algorithms to compute the analytical P-values for all the proposed tests without the need of Monte Carlo sampling. We illustrate the utility of proposed methods through application to the GWAS meta-analysis summary data for multiple lipids and glycemic traits. We identify multiple novel loci that were missed by individual trait-based association test. AVAILABILITY AND IMPLEMENTATION All the proposed methods are implemented in an R package available at http://www.github.com/baolinwu/MTAR. The developed R programs are extremely efficient: it takes less than 2 min to compute the list of genome-wide significant single nucleotide polymorphisms (SNPs) for all proposed multi-trait tests for the lipids GWAS summary data with 2.5 million SNPs on a single Linux desktop. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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43
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Nguyen TH, Dobbyn A, Brown RC, Riley BP, Buxbaum JD, Pinto D, Purcell SM, Sullivan PF, He X, Stahl EA. mTADA is a framework for identifying risk genes from de novo mutations in multiple traits. Nat Commun 2020; 11:2929. [PMID: 32522981 PMCID: PMC7287090 DOI: 10.1038/s41467-020-16487-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2018] [Accepted: 05/06/2020] [Indexed: 11/12/2022] Open
Abstract
Joint analysis of multiple traits can result in the identification of associations not found through the analysis of each trait in isolation. Studies of neuropsychiatric disorders and congenital heart disease (CHD) which use de novo mutations (DNMs) from parent-offspring trios have reported multiple putatively causal genes. However, a joint analysis method designed to integrate DNMs from multiple studies has yet to be implemented. We here introduce multiple-trait TADA (mTADA) which jointly analyzes two traits using DNMs from non-overlapping family samples. We first demonstrate that mTADA is able to leverage genetic overlaps to increase the statistical power of risk-gene identification. We then apply mTADA to large datasets of >13,000 trios for five neuropsychiatric disorders and CHD. We report additional risk genes for schizophrenia, epileptic encephalopathies and CHD. We outline some shared and specific biological information of intellectual disability and CHD by conducting systems biology analyses of genes prioritized by mTADA.
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Affiliation(s)
- Tan-Hoang Nguyen
- Division of Psychiatric Genomics, Department of Genetics and Genomic Sciences, Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Virginia Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA.
| | - Amanda Dobbyn
- Division of Psychiatric Genomics, Department of Genetics and Genomic Sciences, Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ruth C Brown
- Virginia Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Brien P Riley
- Virginia Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Joseph D Buxbaum
- Seaver Autism Center, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Dalila Pinto
- Seaver Autism Center, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Mindich Child Health & Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Shaun M Purcell
- Sleep Center, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Patrick F Sullivan
- Departments of Genetics and Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Xin He
- Department of Human Genetics, University of Chicago, Chicago, IL, USA.
- Grossman Institute for Neuroscience, Quantitative Biology and Human Behavior, University of Chicago, Chicago, IL, USA.
| | - Eli A Stahl
- Division of Psychiatric Genomics, Department of Genetics and Genomic Sciences, Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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44
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Palombo V, Gaspa G, Conte G, Pilla F, Macciotta N, Mele M, D'Andrea M. Combined multivariate factor analysis and GWAS for milk fatty acids trait in Comisana sheep breed. Anim Genet 2020; 51:630-631. [PMID: 32394441 DOI: 10.1111/age.12948] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/13/2020] [Indexed: 01/05/2023]
Affiliation(s)
- Valentino Palombo
- Dipartimento, Agricoltura, Ambiente e Alimenti, Università del Molise, Campobasso, 86100, Italy
| | - Giustino Gaspa
- Dipartimento di Scienze Agrarie, Forestali e Alimentari, Università di Torino, Grugliasco, 10095, Italy.,Dipartimento di Agraria, Sezione Scienze Zootecniche, Università degli Studi di Sassari, Sassari, 07100, Italy
| | - Giuseppe Conte
- Dipartimento di Scienze Agrarie, Alimentari e Agro-ambientali, Università di Pisa, Pisa, 56124, Italy
| | - Fabio Pilla
- Dipartimento, Agricoltura, Ambiente e Alimenti, Università del Molise, Campobasso, 86100, Italy
| | - Nicola Macciotta
- Dipartimento di Agraria, Sezione Scienze Zootecniche, Università degli Studi di Sassari, Sassari, 07100, Italy
| | - Marcello Mele
- Dipartimento di Scienze Agrarie, Alimentari e Agro-ambientali, Università di Pisa, Pisa, 56124, Italy
| | - Mariasilvia D'Andrea
- Dipartimento, Agricoltura, Ambiente e Alimenti, Università del Molise, Campobasso, 86100, Italy
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Gianola D, Fernando RL. A Multiple-Trait Bayesian Lasso for Genome-Enabled Analysis and Prediction of Complex Traits. Genetics 2020; 214:305-331. [PMID: 31879318 PMCID: PMC7017027 DOI: 10.1534/genetics.119.302934] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Accepted: 12/20/2019] [Indexed: 12/21/2022] Open
Abstract
A multiple-trait Bayesian LASSO (MBL) for genome-based analysis and prediction of quantitative traits is presented and applied to two real data sets. The data-generating model is a multivariate linear Bayesian regression on possibly a huge number of molecular markers, and with a Gaussian residual distribution posed. Each (one per marker) of the [Formula: see text] vectors of regression coefficients (T: number of traits) is assigned the same T-variate Laplace prior distribution, with a null mean vector and unknown scale matrix Σ. The multivariate prior reduces to that of the standard univariate Bayesian LASSO when [Formula: see text] The covariance matrix of the residual distribution is assigned a multivariate Jeffreys prior, and Σ is given an inverse-Wishart prior. The unknown quantities in the model are learned using a Markov chain Monte Carlo sampling scheme constructed using a scale-mixture of normal distributions representation. MBL is demonstrated in a bivariate context employing two publicly available data sets using a bivariate genomic best linear unbiased prediction model (GBLUP) for benchmarking results. The first data set is one where wheat grain yields in two different environments are treated as distinct traits. The second data set comes from genotyped Pinus trees, with each individual measured for two traits: rust bin and gall volume. In MBL, the bivariate marker effects are shrunk differentially, i.e., "short" vectors are more strongly shrunk toward the origin than in GBLUP; conversely, "long" vectors are shrunk less. A predictive comparison was carried out as well in wheat, where the comparators of MBL were bivariate GBLUP and bivariate Bayes Cπ-a variable selection procedure. A training-testing layout was used, with 100 random reconstructions of training and testing sets. For the wheat data, all methods produced similar predictions. In Pinus, MBL gave better predictions that either a Bayesian bivariate GBLUP or the single trait Bayesian LASSO. MBL has been implemented in the Julia language package JWAS, and is now available for the scientific community to explore with different traits, species, and environments. It is well known that there is no universally best prediction machine, and MBL represents a new resource in the armamentarium for genome-enabled analysis and prediction of complex traits.
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Affiliation(s)
- Daniel Gianola
- Department of Animal Sciences, University of Wisconsin-Madison, Wisconsin 53706
- Department of Dairy Science, University of Wisconsin-Madison, Wisconsin 53706
- Department of Animal Science, Iowa State University, Ames, Iowa 50011
- Department of Plant Sciences, Technical University of Munich (TUM), TUM School of Life Sciences, Freising, 85354 Germany
| | - Rohan L Fernando
- Department of Animal Science, Iowa State University, Ames, Iowa 50011
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46
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Dhanapal AP, York LM, Hames KA, Fritschi FB. Genome-Wide Association Study of Topsoil Root System Architecture in Field-Grown Soybean [ Glycine max (L.) Merr.]. FRONTIERS IN PLANT SCIENCE 2020; 11:590179. [PMID: 33643326 PMCID: PMC7902768 DOI: 10.3389/fpls.2020.590179] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 12/14/2020] [Indexed: 05/09/2023]
Abstract
Water and nutrient acquisition is a critical function of plant root systems. Root system architecture (RSA) traits are often complex and controlled by many genes. This is the first genome-wide association study reporting genetic loci for RSA traits for field-grown soybean (Glycine max). A collection of 289 soybean genotypes was grown in three environments, root crowns were excavated, and 12 RSA traits assessed. The first two components of a principal component analysis of these 12 traits were used as additional aggregate traits for a total of 14 traits. Marker-trait association for RSA traits were identified using 31,807 single-nucleotide polymorphisms (SNPs) by a genome-wide association analysis. In total, 283 (non-unique) SNPs were significantly associated with one or more of the 14 root traits. Of these, 246 were unique SNPs and 215 SNPs were associated with a single root trait, while 26, four, and one SNPs were associated with two, three, and four root traits, respectively. The 246 SNPs marked 67 loci associated with at least one of the 14 root traits. Seventeen loci on 13 chromosomes were identified by SNPs associated with more than one root trait. Several genes with annotation related to processes that could affect root architecture were identified near these 67 loci. Additional follow-up studies will be needed to confirm the markers and candidate genes identified for RSA traits and to examine the importance of the different root characteristics for soybean productivity under a range of soil and environmental conditions.
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Affiliation(s)
| | - Larry M. York
- Noble Research Institute, LLC, Ardmore, OK, United States
| | - Kasey A. Hames
- Division of Plant Sciences, University of Missouri, Columbia, MO, United States
| | - Felix B. Fritschi
- Division of Plant Sciences, University of Missouri, Columbia, MO, United States
- *Correspondence: Felix B. Fritschi
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Nath AP, Ritchie SC, Grinberg NF, Tang HHF, Huang QQ, Teo SM, Ahola-Olli AV, Würtz P, Havulinna AS, Santalahti K, Pitkänen N, Lehtimäki T, Kähönen M, Lyytikäinen LP, Raitoharju E, Seppälä I, Sarin AP, Ripatti S, Palotie A, Perola M, Viikari JS, Jalkanen S, Maksimow M, Salmi M, Wallace C, Raitakari OT, Salomaa V, Abraham G, Kettunen J, Inouye M. Multivariate Genome-wide Association Analysis of a Cytokine Network Reveals Variants with Widespread Immune, Haematological, and Cardiometabolic Pleiotropy. Am J Hum Genet 2019; 105:1076-1090. [PMID: 31679650 DOI: 10.1016/j.ajhg.2019.10.001] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Accepted: 09/30/2019] [Indexed: 01/18/2023] Open
Abstract
Cytokines are essential regulatory components of the immune system, and their aberrant levels have been linked to many disease states. Despite increasing evidence that cytokines operate in concert, many of the physiological interactions between cytokines, and the shared genetic architecture that underlies them, remain unknown. Here, we aimed to identify and characterize genetic variants with pleiotropic effects on cytokines. Using three population-based cohorts (n = 9,263), we performed multivariate genome-wide association studies (GWAS) for a correlation network of 11 circulating cytokines, then combined our results in meta-analysis. We identified a total of eight loci significantly associated with the cytokine network, of which two (PDGFRB and ABO) had not been detected previously. In addition, conditional analyses revealed a further four secondary signals at three known cytokine loci. Integration, through the use of Bayesian colocalization analysis, of publicly available GWAS summary statistics with the cytokine network associations revealed shared causal variants between the eight cytokine loci and other traits; in particular, cytokine network variants at the ABO, SERPINE2, and ZFPM2 loci showed pleiotropic effects on the production of immune-related proteins, on metabolic traits such as lipoprotein and lipid levels, on blood-cell-related traits such as platelet count, and on disease traits such as coronary artery disease and type 2 diabetes.
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Affiliation(s)
- Artika P Nath
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia; Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, United Kingdom; Department of Microbiology and Immunology, University of Melbourne, Parkville, Victoria 3010, Australia.
| | - Scott C Ritchie
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia; Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, United Kingdom
| | - Nastasiya F Grinberg
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, Department of Medicine, University of Cambridge, Cambridge CB2 0AW, United Kingdom
| | - Howard Ho-Fung Tang
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia
| | - Qin Qin Huang
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia; Department of Clinical Pathology, University of Melbourne, Parkville, Victoria 3010, Australia
| | - Shu Mei Teo
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia; Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, United Kingdom
| | - Ari V Ahola-Olli
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA; Institute for Molecular Medicine Finland, University of Helsinki, Helsinki 00014, Finland
| | - Peter Würtz
- Research Programs Unit, Diabetes and Obesity, University of Helsinki, Helsinki 00014, Finland; Nightingale Health Ltd., Helsinki 00300, Finland
| | - Aki S Havulinna
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki 00014, Finland; National Institute of Health and Welfare, Helsinki 00271, Finland
| | - Kristiina Santalahti
- Medicity Research Laboratory, Department of Medical Microbiology and Immunology, University of Turku, Turku 20520, Finland
| | - Niina Pitkänen
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku 20520, Finland
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere 33520, Finland; Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere 33520, Finland
| | - Mika Kähönen
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere 33520, Finland; Department of Clinical Physiology, Tampere University Hospital, Tampere 33521, Finland
| | - Leo-Pekka Lyytikäinen
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere 33520, Finland; Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere 33520, Finland
| | - Emma Raitoharju
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere 33520, Finland; Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere 33520, Finland
| | - Ilkka Seppälä
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere 33520, Finland; Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere 33520, Finland
| | - Antti-Pekka Sarin
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki 00014, Finland; National Institute of Health and Welfare, Helsinki 00271, Finland
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki 00014, Finland; Department of Public Health, University of Helsinki, Helsinki 00014, Finland; Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Aarno Palotie
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki 00014, Finland; Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts 02114, USA; Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts 02114, USA; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
| | - Markus Perola
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki 00014, Finland; National Institute of Health and Welfare, Helsinki 00271, Finland
| | - Jorma S Viikari
- Department of Medicine, University of Turku, Turku 20520, Finland; Division of Medicine, Turku University Hospital, Turku 20520, Finland
| | - Sirpa Jalkanen
- Medicity Research Laboratory, Department of Medical Microbiology and Immunology, University of Turku, Turku 20520, Finland
| | - Mikael Maksimow
- Medicity Research Laboratory, Department of Medical Microbiology and Immunology, University of Turku, Turku 20520, Finland
| | - Marko Salmi
- Medicity Research Laboratory and Institute of Biomedicine, University of Turku, Turku 20520, Finland
| | - Chris Wallace
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, Department of Medicine, University of Cambridge, Cambridge CB2 0AW, United Kingdom; MRC Biostatistics Unit, Institute of Public Health, Cambridge CB2 0SR, United Kingdom
| | - Olli T Raitakari
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku 20520, Finland; The Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku 20520, Finland
| | - Veikko Salomaa
- Medicity Research Laboratory, Department of Medical Microbiology and Immunology, University of Turku, Turku 20520, Finland
| | - Gad Abraham
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia; Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, United Kingdom; Department of Clinical Pathology, University of Melbourne, Parkville, Victoria 3010, Australia
| | - Johannes Kettunen
- Medicity Research Laboratory, Department of Medical Microbiology and Immunology, University of Turku, Turku 20520, Finland; Computational Medicine, Centre for Life Course Health Research, University of Oulu, Oulu 90014, Finland; NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio 70211, Finland; Biocenter Oulu, University of Oulu, Oulu 90014, Finland
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia; Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, United Kingdom; Department of Clinical Pathology, University of Melbourne, Parkville, Victoria 3010, Australia; The Alan Turing Institute, London, United Kingdom.
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Turchin MC, Stephens M. Bayesian multivariate reanalysis of large genetic studies identifies many new associations. PLoS Genet 2019; 15:e1008431. [PMID: 31596850 PMCID: PMC6802844 DOI: 10.1371/journal.pgen.1008431] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Revised: 10/21/2019] [Accepted: 09/17/2019] [Indexed: 01/08/2023] Open
Abstract
Genome-wide association studies (GWAS) have now been conducted for hundreds of phenotypes of relevance to human health. Many such GWAS involve multiple closely-related phenotypes collected on the same samples. However, the vast majority of these GWAS have been analyzed using simple univariate analyses, which consider one phenotype at a time. This is despite the fact that, at least in simulation experiments, multivariate analyses have been shown to be more powerful at detecting associations. Here, we conduct multivariate association analyses on 13 different publicly-available GWAS datasets that involve multiple closely-related phenotypes. These data include large studies of anthropometric traits (GIANT), plasma lipid traits (GlobalLipids), and red blood cell traits (HaemgenRBC). Our analyses identify many new associations (433 in total across the 13 studies), many of which replicate when follow-up samples are available. Overall, our results demonstrate that multivariate analyses can help make more effective use of data from both existing and future GWAS. Genome-wide association studies (GWAS) have become a common and powerful tool for identifying significant correlations between markers of genetic variation and physical traits of interest. Often these studies are conducted by comparing genetic variation against single traits one at a time (‘univariate’); however, it has previously been shown that it is possible to increase your power to detect significant associations by comparing genetic variation against multiple traits simultaneously (‘multivariate’). Despite this apparent increase in power though, researchers still rarely conduct multivariate GWAS, even when studies have multiple traits readily available. Here, we reanalyze 13 previously published GWAS using a multivariate method and find >400 additional associations. Our method makes use of univariate GWAS summary statistics and is available as a software package, thus making it accessible to other researchers interested in conducting the same analyses. We also show, using studies that have multiple releases, that our new associations have high rates of replication. Overall, we argue multivariate approaches in GWAS should no longer be overlooked and how, often, there is low-hanging fruit in the form of new associations by running these methods on data already collected.
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Affiliation(s)
- Michael C. Turchin
- Department of Human Genetics, The University of Chicago, Chicago, Illinois, United States of America
| | - Matthew Stephens
- Department of Human Genetics, The University of Chicago, Chicago, Illinois, United States of America
- Department of Statistics, The University of Chicago, Chicago, Illinois, United States of America
- * E-mail:
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49
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Yuan X, Biswas S. Bivariate logistic Bayesian LASSO for detecting rare haplotype association with two correlated phenotypes. Genet Epidemiol 2019; 43:996-1017. [PMID: 31544985 DOI: 10.1002/gepi.22258] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Revised: 07/31/2019] [Accepted: 08/09/2019] [Indexed: 11/08/2022]
Abstract
In genetic association studies, joint modeling of related traits/phenotypes can utilize the correlation between them and thereby provide more power and uncover additional information about genetic etiology. Moreover, detecting rare genetic variants are of current scientific interest as a key to missing heritability. Logistic Bayesian LASSO (LBL) has been proposed recently to detect rare haplotype variants using case-control data, that is, a single binary phenotype. As there is currently no haplotype association method that can handle multiple binary phenotypes, we extend LBL to fill this gap. We develop a bivariate model by using a latent variable to induce correlation between the two outcomes. We carry out extensive simulations to investigate the bivariate LBL and compare with the univariate LBL. The bivariate LBL performs better or similar to the univariate LBL in most settings. It has the highest gain in power when a haplotype is associated with both traits and it affects at least one trait in a direction opposite to the direction of the correlation between the traits. We analyze two data sets-Genetic Analysis Workshop 19 sequence data on systolic and diastolic blood pressures and a genome-wide association data set on lung cancer and smoking and detect several associated rare haplotypes.
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Affiliation(s)
- Xiaochen Yuan
- Department of Mathematical Sciences, University of Texas at Dallas, Richardson, Texas
| | - Swati Biswas
- Department of Mathematical Sciences, University of Texas at Dallas, Richardson, Texas
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50
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Schaid DJ, Tong X, Batzler A, Sinnwell JP, Qing J, Biernacka JM. Multivariate generalized linear model for genetic pleiotropy. Biostatistics 2019; 20:111-128. [PMID: 29267957 DOI: 10.1093/biostatistics/kxx067] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2017] [Accepted: 11/05/2017] [Indexed: 02/07/2023] Open
Abstract
When a single gene influences more than one trait, known as pleiotropy, it is important to detect pleiotropy to improve the biological understanding of a gene. This can lead to improved screening, diagnosis, and treatment of diseases. Yet, most current multivariate methods to evaluate pleiotropy test the null hypothesis that none of the traits are associated with a variant; departures from the null could be driven by just one associated trait. A formal test of pleiotropy should assume a null hypothesis that one or fewer traits are associated with a genetic variant. We recently developed statistical methods to analyze pleiotropy for quantitative traits having a multivariate normal distribution. We now extend this approach to traits that can be modeled by generalized linear models, such as analysis of binary, ordinal, or quantitative traits, or a mixture of these types of traits. Based on methods from estimating equations, we developed a new test for pleiotropy. We then extended the testing framework to a sequential approach to test the null hypothesis that $k+1$ traits are associated, given that the null of $k$ associated traits was rejected. This provides a testing framework to determine the number of traits associated with a genetic variant, as well as which traits, while accounting for correlations among the traits. By simulations, we illustrate the Type-I error rate and power of our new methods, describe how they are influenced by sample size, the number of traits, and the trait correlations, and apply the new methods to a genome-wide association study of multivariate traits measuring symptoms of major depression. Our new approach provides a quantitative assessment of pleiotropy, enhancing current analytic practice.
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Affiliation(s)
- Daniel J Schaid
- Department of Health Sciences Research, Mayo Clinic, Harwick 775, 200 First ST SW, Rochester, MN, USA
| | - Xingwei Tong
- School of Statistics, Beijing Normal University, Beijing, China
| | - Anthony Batzler
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Jason P Sinnwell
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Jiang Qing
- School of Statistics, Beijing Normal University, Beijing, China
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