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Wang S, Li T, Zhao B, Dai W, Yao Y, Li C, Li T, Zhu H, Zhang H. Identification and validation of supervariants reveal novel loci associated with human white matter microstructure. Genome Res 2024; 34:20-33. [PMID: 38190638 PMCID: PMC10904010 DOI: 10.1101/gr.277905.123] [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: 03/18/2023] [Accepted: 12/05/2023] [Indexed: 01/10/2024]
Abstract
As an essential part of the central nervous system, white matter coordinates communications between different brain regions and is related to a wide range of neurodegenerative and neuropsychiatric disorders. Previous genome-wide association studies (GWASs) have uncovered loci associated with white matter microstructure. However, GWASs suffer from limited reproducibility and difficulties in detecting multi-single-nucleotide polymorphism (multi-SNP) and epistatic effects. In this study, we adopt the concept of supervariants, a combination of alleles in multiple loci, to account for potential multi-SNP effects. We perform supervariant identification and validation to identify loci associated with 22 white matter fractional anisotropy phenotypes derived from diffusion tensor imaging. To increase reproducibility, we use United Kingdom (UK) Biobank White British (n = 30,842) data for discovery and internal validation, and UK Biobank White but non-British (n = 1927) data, Europeans from the Adolescent Brain Cognitive Development study (n = 4399) data, and Europeans from the Human Connectome Project (n = 319) data for external validation. We identify 23 novel loci on the discovery set that have not been reported in the previous GWASs on white matter microstructure. Among them, three supervariants on genomic regions 5q35.1, 8p21.2, and 19q13.32 have P-values lower than 0.05 in the meta-analysis of the three independent validation data sets. These supervariants contain genetic variants located in genes that have been related to brain structures, cognitive functions, and neuropsychiatric diseases. Our findings provide a better understanding of the genetic architecture underlying white matter microstructure.
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Affiliation(s)
- Shiying Wang
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06510, USA
| | - Ting Li
- Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104-1686, USA
| | - Wei Dai
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06510, USA
| | - Yisha Yao
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06510, USA
| | - Cai Li
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, USA
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
| | - Heping Zhang
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06510, USA;
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Song Y, Lee D, Choi J, Lee JW, Hong K. Genome-wide association and replication studies for handedness in a Korean community-based cohort. Brain Behav 2023; 13:e3121. [PMID: 37337823 PMCID: PMC10498080 DOI: 10.1002/brb3.3121] [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: 09/23/2022] [Revised: 05/30/2023] [Accepted: 06/01/2023] [Indexed: 06/21/2023] Open
Abstract
INTRODUCTION Handedness is a conspicuous characteristic in human behavior, with a worldwide proportion of approximately 90% of people preferring to use the right hand for many tasks. In the Korean population, the proportion of left-handedness is relatively low at approximately 7%-10%, similar to that in other East-Asian cultures in which the use of the left hand for writing and other public activities has historically been oppressed. METHODS In this study, we conducted two genome-wide association studies (GWASs) between right-handedness and left-handedness, and between right-handedness and ambidexterity using logistic regression analyses using a Korean community-based cohort. We also performed association analyses with previously reported variants and our findings. RESULTS A total of 8806 participants were included for analysis, and the results identified 28 left-handedness-associated and 15 ambidexterity-associated loci; of these, two left-handedness loci (NEIL3 [rs11726465] and SVOPL [rs117495448]) and one ambidexterity locus (PDE8B/WDR41 [rs118077080]) showed near genome-wide significance. Association analyses with previously reported variants replicated ANKS1B (rs7132513) in left-handedness and ANKIB1 (rs2040498) in ambidexterity. CONCLUSION The variants and positional candidate genes identified and replicated in this study were largely associated with brain development, cerebral asymmetry, neurological processes, and neuropsychiatric diseases in line with previous findings. As the first East-Asian GWAS related to handedness, these results may provide an intriguing reference for further human neurologic research in the future.
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Affiliation(s)
- Youhyun Song
- Department of Family MedicineGangnam Severance HospitalYonsei University College of MedicineSeoulSouth Korea
- Healthcare Research Team, Health Promotion CenterGangnam Severance HospitalYonsei University College of MedicineSeoulSouth Korea
| | - Dasom Lee
- Theragen Bio Co. Ltd.Gyeonggi‐doSouth Korea
| | | | - Ji Won Lee
- Department of Family MedicineSeverance HospitalYonsei University College of MedicineSeoulSouth Korea
- Institute for Innovation in Digital HealthcareYonsei UniversitySeoulSouth Korea
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Liu Z, Dai W, Wang S, Yao Y, Zhang H. Deep learning identified genetic variants for COVID-19-related mortality among 28,097 affected cases in UK Biobank. Genet Epidemiol 2023; 47:215-230. [PMID: 36691909 PMCID: PMC10006374 DOI: 10.1002/gepi.22515] [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: 06/13/2022] [Revised: 10/19/2022] [Accepted: 01/11/2023] [Indexed: 01/25/2023]
Abstract
Analysis of host genetic components provides insights into the susceptibility and response to viral infection such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19). To reveal genetic determinants of susceptibility to COVID-19 related mortality, we train a deep learning model to identify groups of genetic variants and their interactions that contribute to the COVID-19 related mortality risk using the UK Biobank data (28,097 affected cases and 1656 deaths). We refer to such groups of variants as super variants. We identify 15 super variants with various levels of significance as susceptibility loci for COVID-19 mortality. Specifically, we identify a super variant (odds ratio [OR] = 1.594, p = 5.47 × 10-9 ) on Chromosome 7 that consists of the minor allele of rs76398985, rs6943608, rs2052130, 7:150989011_CT_C, rs118033050, and rs12540488. We also discover a super variant (OR = 1.353, p = 2.87 × 10-8 ) on Chromosome 5 that contains rs12517344, rs72733036, rs190052994, rs34723029, rs72734818, 5:9305797_GTA_G, and rs180899355.
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Affiliation(s)
- Zihuan Liu
- Department of Biostatistics, Yale University, 300 George Street, Ste 523, New Haven, CT, 06511
| | - Wei Dai
- Department of Biostatistics, Yale University, 300 George Street, Ste 523, New Haven, CT, 06511
| | - Shiying Wang
- Department of Biostatistics, Yale University, 300 George Street, Ste 523, New Haven, CT, 06511
| | - Yisha Yao
- Department of Biostatistics, Yale University, 300 George Street, Ste 523, New Haven, CT, 06511
| | - Heping Zhang
- Department of Biostatistics, Yale University, 300 George Street, Ste 523, New Haven, CT, 06511
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Zhao Y, Chang C, Zhang J, Zhang Z. Genetic underpinnings of brain structural connectome for young adults. J Am Stat Assoc 2023; 118:1473-1487. [PMID: 37982009 PMCID: PMC10655950 DOI: 10.1080/01621459.2022.2156349] [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: 03/08/2021] [Accepted: 11/29/2022] [Indexed: 12/13/2022]
Abstract
With distinct advantages in power over behavioral phenotypes, brain imaging traits have become emerging endophenotypes to dissect molecular contributions to behaviors and neuropsychiatric illnesses. Among different imaging features, brain structural connectivity (i.e., structural connectome) which summarizes the anatomical connections between different brain regions is one of the most cutting edge while under-investigated traits; and the genetic influence on the structural connectome variation remains highly elusive. Relying on a landmark imaging genetics study for young adults, we develop a biologically plausible brain network response shrinkage model to comprehensively characterize the relationship between high dimensional genetic variants and the structural connectome phenotype. Under a unified Bayesian framework, we accommodate the topology of brain network and biological architecture within the genome; and eventually establish a mechanistic mapping between genetic biomarkers and the associated brain sub-network units. An efficient expectation-maximization algorithm is developed to estimate the model and ensure computing feasibility. In the application to the Human Connectome Project Young Adult (HCP-YA) data, we establish the genetic underpinnings which are highly interpretable under functional annotation and brain tissue eQTL analysis, for the brain white matter tracts connecting the hippocampus and two cerebral hemispheres. We also show the superiority of our method in extensive simulations.
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Affiliation(s)
- Yize Zhao
- Department of Biostatistics, Yale University
| | - Changgee Chang
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania
| | - Jingwen Zhang
- Department of Biostatistics, Boston University, Boston, MA
| | - Zhengwu Zhang
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill
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Dai W, Li C, Li T, Hu J, Zhang H. Super-taxon in human microbiome are identified to be associated with colorectal cancer. BMC Bioinformatics 2022; 23:243. [PMID: 35729515 PMCID: PMC9215102 DOI: 10.1186/s12859-022-04786-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 06/06/2022] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Microbial communities in the human body, also known as human microbiota, impact human health, such as colorectal cancer (CRC). However, the different roles that microbial communities play in healthy and disease hosts remain largely unknown. The microbial communities are typically recorded through the taxa counts of operational taxonomic units (OTUs). The sparsity and high correlations among OTUs pose major challenges for understanding the microbiota-disease relation. Furthermore, the taxa data are structured in the sense that OTUs are related evolutionarily by a hierarchical structure. RESULTS In this study, we borrow the idea of super-variant from statistical genetics, and propose a new concept called super-taxon to exploit hierarchical structure of taxa for microbiome studies, which is essentially a combination of taxonomic units. Specifically, we model a genus which consists of a set of OTUs at low hierarchy and is designed to reflect both marginal and joint effects of OTUs associated with the risk of CRC to address these issues. We first demonstrate the power of super-taxon in detecting highly correlated OTUs. Then, we identify CRC-associated OTUs in two publicly available datasets via a discovery-validation procedure. Specifically, four species of two genera are found to be associated with CRC: Parvimonas micra, Parvimonas sp., Peptostreptococcus stomatis, and Peptostreptococcus anaerobius. More importantly, for the first time, we report the joint effect of Parvimonas micra and Parvimonas sp. (p = 0.0084) as well as that of Peptostrepto-coccus stomatis and Peptostreptococcus anaerobius (p = 8.21e-06) on CRC. The proposed approach provides a novel and useful tool for identifying disease-related microbes by taking the hierarchical structure of taxa into account and further sheds new lights on their potential joint effects as a community in disease development. CONCLUSIONS Our work shows that proposed approaches are effective to study the microbiota-disease relation taking into account for the sparsity, hierarchical and correlated structure among microbes.
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Affiliation(s)
- Wei Dai
- Department of Biostatistics, Yale University School of Public Health, 300 George Street, Ste 523, New Haven, CT, 06511, USA
| | - Cai Li
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Ting Li
- Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Jianchang Hu
- Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
| | - Heping Zhang
- Department of Biostatistics, Yale University School of Public Health, 300 George Street, Ste 523, New Haven, CT, 06511, USA.
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Li T, Hu J, Wang S, Zhang H. Super-variants identification for brain connectivity. Hum Brain Mapp 2020; 42:1304-1312. [PMID: 33236465 PMCID: PMC7927294 DOI: 10.1002/hbm.25294] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 10/25/2020] [Accepted: 11/12/2020] [Indexed: 12/17/2022] Open
Abstract
Identifying genetic biomarkers for brain connectivity helps us understand genetic effects on brain function. The unique and important challenge in detecting associations between brain connectivity and genetic variants is that the phenotype is a matrix rather than a scalar. We study a new concept of super‐variant for genetic association detection. Similar to but different from the classic concept of gene, a super‐variant is a combination of alleles in multiple loci but contributing loci can be anywhere in the genome. We hypothesize that the super‐variants are easier to detect and more reliable to reproduce in their associations with brain connectivity. By applying a novel ranking and aggregation method to the UK Biobank databases, we discovered and verified several replicable super‐variants. Specifically, we investigate a discovery set with 16,421 subjects and a verification set with 2,882 subjects, where they are formed according to release date, and the verification set is used to validate the genetic associations from the discovery phase. We identified 12 replicable super‐variants on Chromosomes 1, 3, 7, 8, 9, 10, 12, 15, 16, 18, and 19. These verified super‐variants contain single nucleotide polymorphisms that locate in 14 genes which have been reported to have association with brain structure and function, and/or neurodevelopmental and neurodegenerative disorders in the literature. We also identified novel loci in genes RSPO2 and TMEM74 which may be upregulated in brain issues. These findings demonstrate the validity of the super‐variants and its capability of unifying existing results as well as discovering novel and replicable results.
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Affiliation(s)
- Ting Li
- Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
| | - Jianchang Hu
- Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
| | - Shiying Wang
- Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
| | - Heping Zhang
- Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
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